Technical Review

Number 9 

Closing the Quality Gap: 

A Critical Analysis of 

Quality Improvement Strategies 

Volume 2—Diabetes Mellitus Care 

Prepared for: 

Agency for Healthcare Research and Quality 

U.S. Department of Health and Human Services 

540 Gaither Road 

Rockville, MD 20850 

www.ahrq.gov 

Contract No. 290-02-0017 

Prepared by: 

Stanford University–UCSF Evidence-based Practice Center 

Kaveh G. Shojania, M.D. Kaveh G. Shojania, M.D. 

Kathryn M. McDonald, M.M. 

Robert M. Wachter, M.D. 

Douglas K. Owens, M.D., M.S. 

Series Editors 

Sumant R. Ranji, M.D. 

Lauren K. Shaw, B.A. 

Lisa N. Charo, B.A. 

Jennifer C. Lai, B.Sc. 

Robert J. Rushakoff, M.D. 

Kathryn M. McDonald, M.M. 

Douglas K. Owens, M.D., M.S. 

Investigators

Amy J. Markowitz, J.D. 

Managing Editor 

AHRQ Publication No. 04-0051-2 

September 2004 
This document is in the public domain and may be used and reprinted without permission except 

those copyrighted materials noted, for which further reproduction is prohibited without the 

specific permission of copyright holders. 

Suggested Citation: 

Shojania KG, Ranji SR, Shaw LK, Charo LN, Lai JC, Rushakoff RJ, McDonald KM, Owens

DK. Diabetes Mellitus Care. Vol. 2 of : Shojania KG, McDonald KM, Wachter RM, Owens DK. 

Closing The Quality Gap: A Critical Analysis of Quality Improvement Strategies. Technical 

Review 9 (Contract No. 290-02-0017 to the Stanford University–UCSF Evidence-based Practice 

Center). AHRQ Publication No. 04-0051-2. Rockville, MD: Agency for Healthcare Research 

and Quality. September 2004. 

ii
Preface

The Agency for Healthcare Research and Quality (AHRQ), through its Evidence-based 

Practice Centers (EPCs), sponsors the development of evidence reports and technology 

assessments to assist public- and private-sector organizations in their efforts to improve the 

quality of health care in the United States. The reports and assessments provide organizations 

with comprehensive, science-based information on common, costly medical conditions and new 

health care technologies. The EPCs systematically review the relevant scientific literature on 

topics assigned to them by AHRQ and conduct additional analyses when appropriate prior to 

developing their reports and assessments. 

To bring the broadest range of experts into the development of evidence reports and health 

technology assessments, AHRQ encourages the EPCs to form partnerships and enter into 

collaborations with other medical and research organizations. The EPCs work with these partner 

organizations to ensure that the evidence reports and technology assessments they produce will 

become building blocks for health care quality improvement projects throughout the Nation. The 

reports undergo peer review prior to their release. 

AHRQ expects that the EPC evidence reports and technology assessments will inform 

individual health plans, providers, and purchasers as well as the health care system as a whole by 

providing important information to help improve health care quality. 

We welcome comments on this evidence report. They may be sent by mail to the Task Order 

Officer named below at: Agency for Healthcare Research and Quality, 540 Gaither Road, 

Rockville, MD 20850, or by email to epc@ahrq.gov. 

Carolyn M. Clancy, M.D. Jean R. Slutsky, P.A., M.S.P.H. 

Director Director, Center for 

Agency for Healthcare Research and Quality Outcomes and Evidence 

 Agency for Healthcare Research and Quality 

 

 

Kenneth S. Fink, M.D., M.G.A., M.P.H. 

Director, EPC Program

Agency for Healthcare Research and Quality 

The authors of this report are responsible for its content. Statements in the report 

should not be construed as endorsement by the Agency for Healthcare Research and 

Quality or the U.S. Department of Health and Human Services of a particular drug, 

device, test, treatment, or other clinical service. 

Marian D. James, M.A., Ph.D. 

EPC Program Task Order Officer 

Agency for Healthcare Research and Quality 

iii
iv
Structured Abstract 

Objective: Care remains suboptimal for a substantial proportion of the more than 17 million 

patients in the United States with diabetes. This review examines strategies for improving the 

quality of care for adult type 2 diabetic patients, through changes in provider behavior and 

modifications to the organization of care. 

Search Strategy and Inclusion Criteria: The researchers searched the MEDLINE® database, 

the Cochrane Collaboration’s Effective Practice and Organisation of Care (EPOC) registry, 

article bibliographies, and relevant journals for experimental evaluations of quality improvement 

(QI) interventions involving outpatient care for adults with type 2 diabetes mellitus. The 

investigators included randomized or quasi-randomized controlled trials, controlled before–after 

studies, and interrupted time series in which at least one reported outcome involved changes in 

serum hemoglobin A1c or a measure of provider adherence to a recommended process of care. 

Data Collection and Analysis: Two reviewers independently abstracted relevant data, 

including classifying the components of each QI intervention as provider education, provider 

reminders, facilitated relay of clinical information, patient education, promotion of selfmanagement, patient reminders, audit and feedback, organizational change, or financial 

incentives. Certain categories were further subdivided into major subtypes (e.g., professional 

meetings for provider education and disease management for organizational change). The 

investigators also assessed the impact of clinical information systems as a mediator for 

interventions of all types. They compared different QI strategies in terms of the median effects 

achieved for glycemic control and for a generalized measure of clinician adherence. In addition, 

linear regression analyses were performed using methodologic features and QI types as 

predictors, taking into account baseline groups differences and study size. 

Main Results: Fifty-eight articles reporting a total of 66 trials met the established inclusion 

criteria. The most common interventions employed were organizational change in 40 trials, 

patient education in 28 trials, and provider education in 24 trials. Fifty-two trials involved 

interventions employing more than one QI strategy, with a median of 2 strategies per trial and a 

maximum of 5. The included trials reported a median absolute reduction in HbA1c of 0.48% 

(interquartile range: 0.20%, 1.38%), and a median improvement in clinician adherence of 4.9% 

(interquartile range: 3.8%, 15.0%). Trials in the lower 2 quartiles of sample size reported 

substantially larger effect sizes, as did non-randomized trials, strongly suggesting the presence of 

publication bias, with publication of smaller non-randomized trials occurring more often when 

reported improvements are large. Multifaceted trials reported a median reduction in HbA1c of 

0.60% (interquartile range: 0.30%, 1.40%), compared to a median reduction of 0.0% 

(interquartile range: -0.08%, 0.16%) for trials of a single intervention (p=0.01). The benefit of 

employing more than one QI strategy appeared to persist among larger, randomized trials, but the 

small numbers of studies limits the reliability of this impression. The investigators did not find 

any specific type of QI strategy to confer unambiguous benefit. Provider education and disease 

management were the only strategies to approach statistical significance, compared with 

interventions absent these strategies. 

v
Conclusion: The authors’ analysis of quality improvement strategies for diabetes care showed 

no particular type of QI to have an advantage over others, but suggested that employing at least 

two strategies provides a greater chance of success than single-faceted interventions, in terms of 

improving glycemic control or provider adherence. These conclusions are limited by probable 

publication bias favoring smaller trials and non-randomized trials, and the confounding presence 

of multiple QI strategies in a given intervention, as well as important patient and provider 

factors, and organizational characteristics. 

vi
Contents

Summary ...........................................................................................................................................1 

Chapter 1. Introduction .....................................................................................................................5 

 Background .................................................................................................................................5 

 The Quality Gap ..........................................................................................................................5 

Setting Goals for Clinical Care vs. Performance Measurement .................................................7 

 Key Questions .............................................................................................................................7 

Chapter 2. Methods ...........................................................................................................................9 

Types of Quality Improvement Strategies ..................................................................................9 

 Scope ...........................................................................................................................................11 

Inclusion and Exclusion Criteria ...........................................................................................11 

Included Trial Designs ..........................................................................................................12 

Terminology to Distinguish Studies, Interventions, and Comparisons .................................12 

Literature Search and Review Process ........................................................................................13 

 Publication Bias ....................................................................................................................13 

 Outcome Measures ......................................................................................................................14 

Formats for Reported Outcomes ...........................................................................................16 

 Analysis .......................................................................................................................................17 

The Median Reported Effect as a Summary Measure ..........................................................17 

Standardization of Direction of Effect ..................................................................................18 

Accounting for “Cluster” Effects ..........................................................................................18 

 Regression Analysis ..............................................................................................................19 

Multiple Comparisons and A Priori Hypotheses ..................................................................20 

Chapter 3. Results .............................................................................................................................23 

Search Yield and Results of Article Review Process .................................................................23 

Features of the Included Studies ...........................................................................................23

Analysis by Outcome Measures ..................................................................................................25 

Effect of QI Strategies on Glycemic Control ........................................................................26 

Effect of QI Strategies on Provider Adherence ....................................................................27 

Effect of the Number of QI Strategies Per Intervention .......................................................27 

Analysis by Type of QI Strategy ................................................................................................29 

 Provider Education ................................................................................................................29 

Patient Education, Promotion of Self-Management, and Patient Reminders .......................31 

Provider Reminders and Facilitated Relay of Clinical Data .................................................31 

Audit and Feedback ..............................................................................................................31 

Organizational Change ..........................................................................................................32 

Additional Analyses–Clinical Information Systems .............................................................33 

Effects of Study Setting and Methodologic Features .................................................................34 

 Study Setting .........................................................................................................................34 

Methodologic Features ..........................................................................................................34 

Chapter 4. Discussion .......................................................................................................................35 

Effectiveness of QI Strategies ....................................................................................................35 

Publication Bias .........................................................................................................................35 

vii
Benefit of Multifaceted Interventions ........................................................................................36 

Uncertain Benefit for Specific QI Strategies .............................................................................37 

Little Benefit from Existing Clinical Information Systems .......................................................37 

Comparison with Previous Review of this Topic ......................................................................38 

Limitations .................................................................................................................................39 

References .........................................................................................................................................41 

Figures 

Figure 1. Search strategy and article review process........................................................................49 

Tables 

Table 1. Summary features of included studies.................................................................................51 

Table 2a. Number and design of included studies for each quality improvement strategy ...............57 

Table 2b. Number of quality improvement strategies per study intervention ...................................58 

Table 3a. Association between type of quality improvement strategy and glycemic control 

stratified by study sample size .....................................................................................................59 

Table 3b. Association between improvements in provider adherence* and type of quality 

improvement strategy stratified by study sample size .................................................................60 

Table 4a. Associations between improvements in glycemic control and provider adherence 

stratified by trial design ...............................................................................................................61 

Table 4b. Impacts on glycemic control and provider adherence stratified by trial design and 

sample size ...................................................................................................................................62 

Table 5a. Association between improvement in glycemic control and number of quality 

improvement strategies stratified by study sample size...............................................................63 

Table 5b. Association between improvement in provider adherence and number of quality 

improvement strategies stratified by study sample size*.............................................................64 

Table 6. Associations between number of quality improvement strategies and improvements in 

glycemic control and provider adherence stratified by trial design.............................................65 

Table 7a. Regression results for impact of general study features on glycemic control and 

provider adherence.......................................................................................................................66 

Table 7b. Regression results for impacts of quality improvement strategies by strategy type and 

by the number of strategies per intervention................................................................................67 

Table 7c. Significance tests (Mann-Whitney) for median effects associated with selected 

methodologic features and QI strategies......................................................................................68

Table 8a. Association between improvements in glycemic control and specific substrategies of 

provider education stratified by study sample size ......................................................................69 

Table 8b. Association between improvements in glycemic control and specific substrategies of 

provider education stratified by study design ..............................................................................70 

Table 9a. Association between improvements in glycemic control and specific substrategies of 

patient education stratified by sample size ..................................................................................71 

Table 9b. Association between improvements in glycemic control and specific substrategies of 

patient education stratified by study design.................................................................................72

Table 10a. Association between improvements in glycemic control and specific substrategies of 

provider reminder stratified by sample size.................................................................................73 

viii
Table 10b. Association between improvements in glycemic control and specific substrategies of 

provider reminder stratified by trial design .................................................................................74

Table 11a. Association between improvements in glycemic control and specific substrategies of 

organizational change stratified by study sample size.................................................................75 

Table 11b. Association between improvements in glycemic control and specific substrategies of 

organizational change stratified by study design.........................................................................76 

Table 12a. Association between improvements in glycemic control and various roles for clinical 

information systems stratified by quartiles of sample size ..........................................................77 

Table 12b. Association between improvements in provider adherence and glycemic control for 

various roles for clinical information systems stratified by trial design......................................79 

Appendixes 

Appendix A. Comparison of recommended goals for clinical practice vs. targets for performance 

measurement 

Appendix B. MEDLINE® search for diabetes quality improvement articles 

Appendix C. Abstraction forms for screening and full-text review 

Appendix D. Articles excluded solely on the basis that intervention focused only on patient 

education or promotion of self-management 

Appendix E. Summary of results for each included study 

Appendix F. Calculation of effective sample sizes for trials with clustering

Appendix G. Articles excluded at the level of full-text review 

Appendix H. Additional tables for diabetes results and analysis 

Appendixes and Evidence Tables for this report are provided electronically at

http://www.ahrq.gov/clinic/tp/dbgap2tp.htm. 

ix
Summary 

Diabetes affects more than 17 million people in the United States alone. Taking into account 

undiagnosed cases and cases of impaired glucose tolerance, one in seven Americans either has 

diabetes or is at high risk for developing it. Despite a high-quality evidence base to aid providers 

in treating diabetes and screening for its complications, the quality of diabetes care remains less 

than optimal, with many patients not receiving established processes of care (such as eye and 

foot screening), or achieving optimal outcomes (such as controlled glycosylated hemoglobin 

levels). 

To bring data to bear on the quality improvement opportunities cited by the Institute of 

Medicine (IOM) in its 2003 report, Priority Areas for National Action: Transforming Health 

Care Quality, the Agency for Healthcare Research and Quality (AHRQ) engaged the Stanford–

UCSF Evidence-based Practice Center (EPC) to analyze the scientific literature on quality 

improvement strategies for some of the 20 disease and practice priorities named in the IOM 

Report. The resulting investigations focus on translating research into practice—identifying 

those activities that increase the rate at which practices known to be effective are applied to 

patient care in real world settings. In other words, the EPC research effort aims to help narrow 

the “quality gap” that is in large part responsible for suboptimal health care practices and 

outcomes. In addition to furthering the IOM’s quality agenda, this analysis also has been 

prepared in support of the National Healthcare Quality Report (NHQR) (also see the National 

Healthcare Disparities Report). 

In this, the second volume of the Closing the Quality Gap series, the authors focused on 

quality improvement in the management of patients with diabetes mellitus (Type 2 diabetes), 

which accounts for more than 90% of diabetes cases in the U.S. Quality improvement targets 

included measures of disease control (e.g., serum HbA1c, blood pressure) and provider adherence 

(e.g., serial monitoring of serum HbA1c, control of hypertension, and management of other 

cardiovascular risk factors, as well as monitoring for nephropathy, neuropathy, and retinopathy). 

The carefully designed methodology used to review the vast amount of existing quality 

literature on particular diseases is the result of collaborative efforts of the editorial team, in 

consultation with several of the undisputed experts in the field. To ensure consistency in the 

review, the editors developed a taxonomy of interventions that modifies several well-established 

classification systems, denominating the QI strategies as follows: 

1. Provider reminder systems 

2. Facilitated relay of clinical data to providers 

3. Audit and feedback 

4. Provider education 

5. Patient education 

6. Promotion of self-management 

7. Patient reminder systems 

8. Organizational change 

9. Financial, regulatory or legislative incentives 

1
For more information regarding the origins and details of the research framework developed 

for the Closing the Quality Gap series, the statistical tools and analytical processes used 

throughout, and the target audiences expected to benefit most from the series, please refer to 

Volume 1—Series Overview and Methodology (AHRQ Publication No. 04-0051-1). 

 From an original sample of 3,601 potentially relevant articles, a total of 529 articles were 

reviewed at the full text level. Of the 126 articles meeting criteria for complete abstraction, 

approximately half dealt solely with patient education or self-management; these are likely to be 

reviewed in a subsequent volume of the Closing the Quality Gap series addressing these patientfocused interventions as they apply to many chronic illnesses, and therefore were excluded from

this review. (Interventions involving patient education or self-management but including at least 

one QI strategy directed at providers or organizational change were included in the present 

review.) Thus, 58 articles, reporting a total of 66 comparisons, made up the final sample of 

studies included in the present review. 

Taken as a group, interventions in the included comparisons reported a median absolute 

reduction in HbA1c of 0.48% (inter-quartile (IQ) range: 0.20% to 1.38%) above any reductions 

observed in the control groups, and a median improvement in clinician adherence of 4.9% (IQ 

range: 3.8% to 15.0%). Studies utilizing multiple QI strategies did appear to exert stronger 

effects than single-intervention studies. The 32 multifaceted trials reported a median reduction in 

HbA1c of 0.60% (IQ range: 0.30%, 1.40%), compared to no change (-0.08%, 0.16%) in trials of 

single interventions (p=0.01). This finding does confirm one of the authors’ a priori hypotheses, 

but nevertheless should be interpreted with caution. Given the small number of studies involved, 

the lesser improvements associated with single-faceted interventions might relate to the specific 

strategies employed in these studies, rather than any intrinsic inferiority of the single-faceted QI 

interventions. 

The investigators did not find any individual QI strategies to be unambiguously beneficial in 

diabetes care. Provider education resulted in large median effects for both glycemic control and 

clinician adherence, but these findings were of only borderline significance even before 

correction for multiple comparisons. Interventions employing disease or case management 

strategies resulted in significantly greater median reductions in serum HbA1c, compared with 

interventions lacking any component of disease management (p=0.009), but this result would not 

retain statistical significance after correction for the 10-15 comparisons in the analysis. 

Moreover, the impact of disease management was not significant in the regression analysis, 

which, in contrast to the median effects analysis, adjusted for study size and baseline differences 

between the intervention and control groups. All other evaluated QI strategies (including the 

integration of a computerized clinical information system) failed to improve serum HbA1c levels 

or clinician adherence to an appreciable extent, when analyzed quantitatively. 

Even when particular QI strategies (or combinations of strategies) were associated with 

improved outcomes or processes, these effects exhibited a striking association with sample size 

and trial design. For example, among the 38 trials reporting changes in mean HbA1c, those 

falling in the lower two quartiles of sample size reported a median absolute reduction in serum

HbA1c of 1.30%, whereas those falling in the upper two quartiles reported a median absolute 

reduction in serum HbA1c of only 0.21%. A similar (though not statistically significant) 

relationship between sample size and study results was seen in studies of interventions targeting 

increased provider adherence. The reviewers also found a correlation between trial design 

(randomized vs. non-randomized) and the magnitude of the effect on QI targets such as provider 

adherence. For example, randomized trials of a variety of interventions reported a median 

2
absolute improvement in provider adherence of 4.5% (QI range: 3.5%, 5.4%) compared with 

18.0% (QI range: 17.2%, 21.0%) for non-randomized trials. Taken together, these findings 

support the presence of a substantial publication bias, manifested by a greater propensity of 

smaller studies with non-randomized designs to be published when they report large 

improvements, rather than small or no improvement. 

Also of interest was the discovery that studies reporting provider adherence exhibited a

significant association with study period, with more recent trials reporting smaller improvements 

(regression coefficient: -0.04; 95% CI: -0.07 to -0.21, p=0.004). This finding appears related to 

the fact that baseline adherence reported in both intervention and control groups improved over 

time, presumably reflecting the general impact of passive relevant knowledge dissemination, as 

well as more active QI initiatives. Whatever its etiology, this secular trend likely means that 

demonstrating QI impacts becomes more difficult over time, at least in the realm of diabetes 

care. 

In summary, this review (which expands upon previous work by virtue of its scope and the 

use of quantitative analysis of the effect of QI strategies on key outcomes) found that 

multifaceted interventions may be more likely to exert positive effects on glycemic control and 

(to a lesser extent) provider adherence than single interventions. The investigators were unable to 

identify any individual QI strategy as clearly more effective than any other. The review also 

uncovered probable publication bias, as well as a significant diminution of effect in more recent 

trials. 

3
4
Chapter 1. Introduction

Background 

In the past decade, diabetes mellitus (DM) has reached epidemic proportions in the United 

States. The disease now affects more than 17 million people nationwide.1

 Include undiagnosed 

cases and individuals with impaired glucose tolerance, and one in every seven Americans either 

has diabetes, or is at high risk for developing the disease. Equally disturbing, the prevalence of 

diabetes has increased by 60% since 1991,1

 and there is a direct correlation with the increase in 

obesity over the same period.2

 As a result, diabetes is likely to pose a major public health 

problem for decades to come.

3

The past decade also has played witness to major advances in diabetes care, beginning with 

the publication of the Diabetes Control and Complications Trial (DCCT) in 1993,4

 followed by 

the United Kingdom Prospective Diabetes Study (UKPDS) trials in the second half of the 

1990s.5, 6 These studies clearly demonstrated that aggressive management of hyperglycemia 

significantly improves quality of life, while reducing morbidity and mortality. Effective 

treatment strategies also have been developed for the control of diabetes’ principal comorbid 

conditions, hyperlipidemia and hypertension,7-9

 and for the screening and early treatment of 

complications such as retinopathy, neuropathy, nephropathy, and foot disease.10 Since diabetesrelated complications account for more than 200,000 deaths, 82,000 amputations, 38,000 new 

cases of end-stage renal disease, and 12,000 cases of blindness annually, the implementation of 

proven screening and treatment strategies could significantly reduce morbidity and mortality.1

Exact projections are difficult to establish, but evidence from the UKPDS trials shows that a 

mere one point reduction in the average hemoglobin A1c (HbA1c) could lead to a 25% decrease in 

diabetes-related deaths.11 Morevoer, the National Committee for Quality Assurance estimates 

that improved glycemic control could prevent 13,600 deaths annually in the U.S. alone.12

The Quality Gap 

Diabetes care in the U.S. consistently has failed to meet recommended quality standards. 

Data collected between 1988 and 1995 (derived from the Center for Disease Control’s 

population-based Behavioral Risk Factor Surveillance System [BRFSS], as well as the National 

Health and Nutrition Examination [NHANES] surveys) reveal significant quality gaps in the 

treatment of diabetes and in screening for diabetes-related complications. Nearly one in five 

diabetics has poor glycemic control (HbA1c level > 9.5%), more than one-third have elevated 

blood pressure (> 140/90 mmHg) and more than half of diabetes patients have elevated LDLcholesterol levels (even under the more liberal National Cholesterol Education Program [NCEPII] guidelines, with unacceptable levels defined as > 130 mg/dl.).13 Diabetics also do not receive 

appropriate screening measures: only 28% receive the recommended HbA1c measurements more

than once yearly, while just 55% obtain annual foot examinations, and 63% submit to an annual 

dilated eye examination.13 These data were collected before publication and widespread 

distribution of the DCCT and UKPDS results, but more recent data do not indicate significant 

improvement.14 Racial and ethnic disparities persist as well. African Americans and Hispanics 

are significantly more likely to die of diabetes-related complications than are Caucasians,1

 while 

5
Native Americans and other vulnerable populations suffer under a disproportionate burden of 

diabetes and diabetes-related morbidity and mortality.15

The National Committee on Quality Assurance (NCQA) used similar criteria to compile its 

Health Plan Employer Data Information Set (HEDIS®), the basis for its annual “State of Health 

Care Quality” report. The HEDIS data uses benchmarks established in 1997 by the Diabetes 

Quality Improvement Project, a consortium of 13 public and private organizations including the 

American Diabetes Association. In 2001, HEDIS data show that among patients with 

commercial health insurance, 37% had an HbA1c greater than 9.5%, 45% had a blood pressure of 

over 140/90 mmHg, and 50% had LDL-cholesterol of over 130 mg/dl.12 Process measures also 

revealed disconcerting lapses: 19% of diabetics did not receive HbA1c measurements, 48% did 

not receive retinal exams, and nephropathy screenings were not conducted for 54% of diabetic 

patients.12 Thus, despite the continuing advances in diabetes treatment and an increased focus on 

bridging quality gaps, many patients who should have adequate access to care are not receiving 

guideline-concordant care. 

Outpatient care for diabetes exemplifies the challenges of, and opportunities for, chronic 

disease management. Involving patients in their own care, particularly with regard to education 

and self-management, can improve health outcomes, as well as the diabetic patient’s quality of

life.10 However, the quality gaps that persist in the treatment and secondary prevention screening 

processes demonstrate the pressing need to improve medical providers’ adherence to standards of 

care in both of these areas. Management priorities have focused traditionally on glycemic 

control, but control of hypertension and hyperlipidemia is of equal importance, given the high 

morbidity and mortality from cardiovascular disease in diabetic patients. Regular monitoring of 

long-term glycemic control and hyperlipidemia also should accompany traditional secondary 

prevention measures such as screening for retinopathy (with dialation), neuropathy (with foot 

exams) and nephropathy (with urine microalbuminuria); smoking cessation; as well as influenza 

and pneumococcal vaccination. 

A prior systematic review of this topic area16 examined articles from MEDLINE®, the 

Cochrane Collaboration’s Effective Practice and Organisation of Care database (EPOC), and 

related databases. The comprehensive search strategy identified a total of 41 articles involving 

professional and/organizational interventions intended to improve diabetes management, though 

it should be noted the review has not been updated to include articles published since 1999. The 

results of the review suggest that multifaceted interventions and interventions involving

organizational change (i.e., those involving a change in the structure or delivery of health care) 

could have a positive effect on key processes of care, though analysis of the identified studies 

was purely descriptive. 

With the exception of a recent review of disease management strategies,17 prior systematic 

reviews have not included any quantitative synthesis of quality improvement (QI) strategies, 

presumably because of study heterogeneity along multiple dimensions (e.g., trial design, study, 

setting, variations in definitions of “provider education,” “audit and feedback,” “disease 

management,” and other labels for QI strategies), as well as relatively small numbers of trials. In 

this review, the authors have expanded upon the previous qualitative and systematic review of 

strategies to improve diabetes care16 with the inclusion of more recent studies, and through the 

use of quantitative analysis to better characterize the effectiveness of particular QI strategies. 

They have also analyzed more general conclusions such as the relative impact of multifaceted 

interventions or organizational interventions versus strategies targeting only behavioral change in 

providers. 

6
Settings Goals for Clinical Care vs. 

Performance Measurement 

Recognizing the urgent need to improve the quality of diabetes care, the National Diabetes 

Quality Improvement Alliance (NDQIA), a consortium of prominent organizations involved with 

diabetic patient care and overall health care quality improvement efforts,*

 released updated 

guidelines in May, 2003.18 The NDQIA report18 distinguishes between targets appropriate for 

guiding the treatment of individual patients versus targets appropriate for measuring the 

performance of a clinic or larger health care delivery system (Appendix A). For instance, the 

NDQIA endorses the HbA1c < 7.0% as the optimal target for clinical care but, for purposes of 

performance measurement, focuses on the percentage of patients with HbA1c below 9.0%. This 

focus on “poor control as it pertains to performance measurement” versus “good control as it 

pertains to individual patient care” reflects a recognition of various factors other than quality 

issues that can influence the percentage of patients achieving optimal control. Further examples 

of this awareness include variations in the degree to which local laboratories follow guidelines 

for assay selection,19 comorbid conditions, frequency and severity of hypoglycemia, and patient 

preferences, among other factors. 

Studies included in this review have a tendency to specify the targets found in clinical 

guidelines as their QI goals—although this was not part of the authors’ inclusion criteria. Rather, 

the outcomes have been structured to accommodate expected variations in QI targets. These 

outcomes are explained in Chapter 2, Methods. 

Key Questions 

In this review, the literature on quality improvement for diabetes care has been carefully 

synthesized in an effort to address three questions: 

1. Are there QI strategies that improve physicians’ treatment of diabetes and its 

comorbidities? 

• Can the control of hyperglycemia, hyperlipidemia, and hypertension be improved?

• Can microvascular and macrovascular complications be prevented?

2. Are there QI strategies that improve provider adherence to recommended monitoring? 

• Which interventions improve physicians’ adherence to long-term glucose monitoring, 

to screening for hyperlipidemia and hypertension, and to screening for complications 

such as retinopathy, neuropathy, and nephropathy?

3. Are there QI strategies that improve patients’ adherence to treatment and self-care 

measures? 

 

* Organizations include the Agency for Healthcare Research and Quality, American Academy of Family Physicians, American

Association of Clinical Endocrinologists, American College of Physicians, American Diabetes Association, American Medical 

Association, Centers for Disease Control and Prevention, Centers for Medicare & Medicaid Services, National Institute of 

Diabetes and Digestive and Kidney Diseases, and U.S. Department of Veterans Affairs, among others.

7
Although screening and prevention of diabetes have become increasingly important, these 

interventions lack a clear evidence base indicating a reduction in morbidity and mortality.10

Accordingly, the authors did not address questions related to screening for diabetes in this 

review. (By contrast, the impact of screening for elevated blood pressure is well established, so 

screening interventions are included in Volume 3, the hypertension review.)

8
Chapter 2. Methods 

Types of Quality Improvement Strategies 

A variety of interventions have been tested with the goal of improving the quality of care for 

common clinical conditions. From the literature, a conceptual framework has been developed for 

the classification of quality improvement strategies (See Chapters 1-3). These interventions can 

target organizations, providers, patient communities, or individual patients, and have been 

evaluated in a wide variety of formats. For each study, reviewers described key features of the 

intervention in free-text format and answered a series of questions designed to characterize the 

intervention in terms of its component QI strategies. The taxonomy of QI strategies is defined as 

follows: 

Provider reminders—Information tied to a specific clinical encounter, provided verbally, in 

writing, or by computer, that is intended to prompt the clinician to recall information (e.g., to 

make medication adjustments or order appropriate screening tests), or to consider performing a 

specific process of care. The phrase “tied to a specific clinical encounter” distinguishes reminder 

systems from the audit and feedback strategy, where clinicians are typically presented with 

summaries of their performance relative to a process or outcome of care over multiple 

encounters. 

Facilitated relay of clinical data to providers—Clinical information collected directly from

patients is relayed to the provider in situations where the data are not generally collected during a 

patient visit, or when collected using a means other than the existing local medical record system

(e.g., transmission of a patient’s home glucose level). The investigators expected there to be 

some overlap with the provider reminder systems strategy, but kept them separate at the 

abstraction stage. This was done to allow for the possibility that the data could be subsequently 

analyzed with and without collapsing the strategies. 

Audit and feedback—Any summary of a health care provider’s clinical performance or an 

institution’s clinical performance that is reported, either publicly or confidentially, to or about 

the clinician or institution (e.g., the percentage of a provider's patients who have achieved or 

have not achieved some clinical target). The practice of benchmarking refers to the distribution 

of performance data from institutions or providers regarded as leaders in the field. It is

considered a type of audit and feedback, so long as local data is provided in addition to the 

benchmark figures. 

 Provider education—Any intervention that includes one of the following three 

substrategies: educational workshops, meetings (e.g., traditional Continuing Medical Education 

[CME]), and lectures (live or computer-based); educational outreach visits (the use of a trained 

person who meets with providers in their practice settings to disseminate information intended to 

change the provider's practice); or the distribution of educational materials (published or printed 

recommendations for clinical care, including clinical practice guidelines, audio-visual materials 

and electronic publications). 

 Patient education—Live appearance patient education, for individuals or members of a 

patient group or community, or via the distribution of printed or audio-visual educational 

materials. Only those approaches that include patient education as part of a multifaceted strategy 

were evaluated. Those in which patient education was the sole approach were excluded. One of 

9
the upcoming volumes in the Closing the Quality Gap series may be used to review the topic of 

patient education with regard to its effect on a variety of chronic diseases, including diabetes. 

 Promotion of self-management—The distribution of materials (e.g., devices for glucose 

self-monitoring) or access to a resource that enhances the patients' ability to manage their 

condition, the communication of clinical test data back to the patient, or followup phone calls 

from the provider to the patient with recommended adjustments to care. The authors expected 

some overlap with the patient education and patient reminders strategies, but elected to separate 

the strategies at the abstraction stage. This was done to allow for the possibility that the data 

could be analyzed subsequently, with and without collapsing the strategies. 

 Patient reminders—Any effort directed toward patients that encourages them to keep 

appointments or adhere to other aspects of self-care. 

 Organizational change—Changes in the structure or delivery of care designed to improve 

the efficiency or breadth and depth of clinical care. These include the use of disease management 

or case management tactics (coordination of assessment, treatment, and arrangement for referrals 

by a person or multidisciplinary team in collaboration with or supplementary to the primary care 

provider); other personnel or team changes; the use of telemedicine (communication and case 

discussion between distant health care professionals); Total Quality Management (TQM) or 

Continuous Quality Improvement (CQI) approaches (quality problem cycles of measurement, 

intervention design, implementation, and re-measurement); and changes to medical records 

systems or hospital information systems. Three substrategies (disease/case management, 

team/staffing changes, and medical records changes) also were extracted for analysis in studies 

that identified organizational change as one of their multiple strategies. 

Financial, regulatory, or legislative incentives—Interventions with positive or negative 

financial incentives directed at providers (e.g., linked to adherence to some process of care or 

achievement of some target patient outcome). This strategy also included positive or negative 

financial incentives directed at patients, system-wide changes in reimbursement (e.g., capitation, 

prospective payment, or a shift from fee-for-service to salary pay structure), changes to provider 

licensure requirements, or changes to institutional accreditation requirements. 

In addition to the aforementioned QI strategies, the authors had planned initially to abstract

data on intervention features such as social influence (e.g., local opinion leaders74, 75), the 

involvement of top-level management, intervention designs based on a theory of behavior or 

organizational change,76-78 and other potential “mediators” of intervention success.73

Unfortunately, the identified studies rarely explored these and other potentially relevant features 

of intervention design.79 Moreover, few studies considered organizational context80 and local 

attitudes and beliefs,81 so questions targeting these potential predictors of intervention success or 

failure were eliminated from the abstraction forms and from the analysis. 

The one “mediator” that reviewers anticipated would be reported with sufficient frequency 

and detail was the use of clinical information systems, which was identified as a potential 

predictor of success in a prior review.16, 43 For each article, therefore, the involvement of a 

clinical information system in the design or implementation of the intervention was recorded 

(regardless of QI strategy type). The potential roles identified in structured form were: 

identification and/or group allocation of eligible patients or providers; reminders generated by 

existing clinical information system; and decision support at point of care. Additional potential 

roles include facilitated communication between providers (e.g., generation of e-mails between 

members of care team); and audit data gathered from clinical information systems to design QI 

strategy (e.g., audit and feedback, TQM, provider education, or financial incentives). 

10
Scope 

This report focuses on quality improvement strategies that target adult patients with type 2 

diabetes mellitus (DM). Type 2 diabetes accounts for 90-95% of diabetes cases in the United 

States. It is the sixth leading cause of death among the general U.S. population, and is the 

seventh leading cause of disability among adults.1

 Numerous trials in this patient population 

show that diabetes-related morbidity and mortality can be reduced with close adherence to 

treatment guidelines. Accordingly, quality improvement strategies have a tendency to focus on 

adult type 2 diabetics. The reviewers did not assess QI strategies for children with diabetes, due 

to the higher prevalence of type 1 DM (although cases of type 2 DM also are on the rise among 

children), as well as the unique challenges involved with the treatment of this patient population 

and their potential to limit the applicability of QI strategies tested in the adult population. Studies 

that focused exclusively on gestational diabetes also were excluded. 

A wide range of interventions targeting patient behavior, individual provider behavior, and 

systemic problems has been researched and implemented to address these quality gaps. As 

documented previously, the treatment goals for diabetic patients are clear and well supported by 

research. Providers can avail themselves of a variety of safe and effective treatments to help 

patients reach these goals. Thus, the focus of this review is interventions that seek to change the 

methods by which organizations or providers deliver care, with the goal of improving individual 

patient outcomes. Studies that focused exclusively on patient behavioral changes were not 

included in this review, but will likely be addressed in a future series report on patient education 

and self-management as QI strategies for chronic illness. 

Inclusion and Exclusion Criteria 

The general inclusion criteria were described in the chapter on Methods, in Volume 1 of this 

series. Briefly, included studies are required to: 

• Evaluate an intervention meeting the authors’ definition for quality improvement 

(definitions summarized below); 

• Use an experimental or quasi-experimental design – including patient or cluster 

randomized controlled trials (RCTs), quasi-randomized controlled trials (quasi-RCTs), 

controlled–before after studies (CBAs), and interrupted time series (ITS) (as defined 

below); and

• Report at least one measure of disease control, provider adherence, or patient compliance, 

(as defined below and in the abstraction forms in Appendix C), specifically related to 

diabetes. 

Many of the abstracted outcomes relate to blood pressure control and modification of 

important cardiovascular risk factors (e.g., smoking cessation, weight loss). When a study 

reported such outcomes but did not include any measures specifically related to glycemic control 

or the prevention of diabetic complications (e.g., retinopathy, neuropathy, nephropathy, skin 

ulcers), the investigators did not include it. For instance, a study of blood pressure control in 

diabetics would be excluded from the present review if the only outcomes reported were related 

11
to hypertension. The study would, however, be eligible for inclusion in the hypertension review 

(Volume 3 of this series). 

Studies also were excluded when their outcomes consisted solely of provider or patient 

understanding, satisfaction, or self-efficacy; or solely of costs and resource use (i.e., when these 

outcomes were not accompanied by at least one measure of disease control, provider adherence, 

or patient compliance). 

Included Trial Designs 

Randomized trials offer the best means of isolating the effects of a given intervention, as 

patients in different study groups generally differ only with respect to their exposure to the 

treatment (i.e., other known and unknown factors affecting relevant outcomes should be 

distributed equally between the groups). Patient randomized trials and cluster randomized trials 

have been included in the review for this reason. In the former, individual patients are assigned 

randomly to an intervention group or a control group. Patient randomized trials represent the 

gold standard for health care evaluations. For trials of QI interventions, however, the advantages 

of patient randomized trials must be weighed against the disadvantage of contamination. Because 

clinicians will care for patients in both study groups, the level of care received by the control 

group patients may improve over the course of the trial. This could lead to an apparent null 

result, despite the improvement in patient outcomes occuring as a result of the intervention.64 

 Cluster randomized trials seek to avoid this contamination by allocating the intervention 

at the level of clinicians as individuals or groups (e.g., clinics as the unit of allocation). This 

approach overcomes the problems of contamination at the cost of a decreased effective sample 

size. Since patients within a given “cluster” receive their care at the same participating study 

clinic, outcomes for these patients cannot be regarded as completely independent.65-72 The 

statistical correction for this violation of independence decreases the effective sample size and, 

as a result, the efficiency of the study. The choice of a clustered RCT versus patient RCT, 

therefore, depends on the magnitude of the contamination across patients that would occur under 

the latter design choice.68 The authors’ judgment regarding the appropriateness of the particular 

choice made in a given trial did not have a direct impact on their analysis, as the effective sample 

size was adjusted for cluster effects whenever the unit of analysis differed from the unit of 

treatment allocation. 

Terminology to Distinguish Studies, Interventions, 

and Comparisons 

Since the reviewed articles did not present their study data in a uniform fashion, the authors 

adopted the following terminology to better describe the quality improvement interventions 

reviewed for this volume: 

When a single study led to multiple publications (articles) describing different aspects of 

the study, (e.g., a methods article followed later by a results paper, or several results papers) 

each publication was identified separately and all articles emerging from the same study were 

reviewed together. 

A single study may include several different study arms (groups of subjects), with 

different QI interventions provided to the subjects in each study arm. These are often 

12
reported in a single published article. For purposes of analysis, the researchers regarded each 

intervention that was studied in contrast to a control group as a separate comparison. For 

example, a single study with one control group and three different arms receiving different 

QI interventions (e.g., provider education and organizational change in one arm, patient 

reminder and organizational change in another arm, audit and feedback in a third arm), was 

compared with the control group, and was regarded (and listed in the Tables section) as three 

comparisons. When an article reported several comparisons, the reviewers performed a

separate data abstraction for each comparison. 

The intervention described in a particular study may be multifaceted, that is, it may 

involve more than one QI strategy. For example, the intervention may consist of a 

combination of provider education and provider reminders. A multifaceted intervention that 

was applied to a single study arm and judged against the control group was treated as a single 

comparison. 

Literature Search and Review Process 

The search strategy began with a broad electronic search of the MEDLINE® database from

January 1966 to July 2003 (the specific search is shown in Appendix B). The reviewers 

augmented these results with a search of the Cochrane Collaboration’s (EPOC) database,20, 21

(which includes the results of extensive periodic searches of the EMBASE®, CINAHL®, and 

MEDLINE® databases), as well as hand searches of article bibliographies and specific journals.22

They also performed their own MEDLINE® review because of slight differences in scope (e.g., a 

desire to identify articles involving patient education or self-management only, even though they 

may be included in a subsequent volume of this series, rather than in the present review). This 

step was considered a means of enhancing the completeness of the EPOC database search, in an 

effort to be as thorough and meticulous as possible.* 17, 21, 23-47 

To meet the criteria for full abstraction, articles had to assess the effect of a quality 

improvement strategy on disease control, provider adherence, or patient adherence in adults. 

(Appendix C shows the structured abstraction forms used to guide these judgments.) A total of 

529 articles merited full-text reviews. These involved two independent reviewers, at least one of 

whom was a core investigator or senior methodologist (as opposed to a trained research 

assistant). At the full-text level, reviewers abstracted basic information on the study design, 

quality improvement strategy, and variety of outcomes. (The complete full-text abstraction form

also is shown in Appendix C.) All disagreements were resolved by consensus. 

Publication Bias 

Publication bias refers to an overestimation of effect size, and is due to the preferential 

publication of positive studies. Given the absence of a single, well-established analytic method 

for detecting or correcting the effects of publication bias,48-50 the preferred approach to 

preventing this source of error is a thorough search for unpublished research.51-53 Unfortunately, 

 

* The EPOC Web site reports a sensitivity of 92.4% for the registry’s search strategy and precision of 18.5%, but the gold

standard of hand searching included a fairly limited sample of journals and somewhat outdated time periods: Medical Care

(1969-95), BMJ (1992-94), and full text searching from the Ovid Biomedical Core Collection all original and miscellaneous 

articles from Annals of Internal Medicine, BMJ, JAMA and Lancet (1995-96).

13
the search for unpublished quality improvement trials is complicated by a paucity of welldefined and centralized information resources. Unlike research into topics of clinical care, (e.g., 

determining the best drug to use in the event of heart failure or the preferred test for pulmonary 

embolism), there are no relevant clinical trial registries for QI strategies or pharmaceutical 

companies to query for unpublished data. For disease-specific reviews, as opposed to reviews of 

general strategies (e.g., audit and feedback,54 provider education,55, 56 disease management,17

etc.), some conference proceedings related to the disease or specialty may exist. To that end, the 

conference proceedings of several prominent meetings in endocrinology and diabetes care were 

reviewed. 

Another problem that may exacerbate the impact of publication bias on reviews of quality 

improvement studies involves the research often conducted by personnel interested in pragmatic, 

local quality improvement. The results of such studies may be less likely to be submitted for 

publication, while investigators involved in clinical research trials generally have a stronger 

incentive to publish. And though publication bias may occur at the level of journal acceptance, it 

is unlikely that investigators would opt not to submit their work anywhere, simply because the 

trial had a negative finding. By contrast, some quality improvement studies may be undertaken 

by personnel for whom quality assurance activities are a part of their job descriptions. The 

emphasis often is placed on measures of success in such instances, rather than on research 

dissemination. In effect, the incentive to publish may be particularly low when the evaluation 

result is negative. 

One exception to the generalization regarding the dearth of sources for unpublished QI trials 

is the Health Care Quality Improvement Program (HCQIP) database, maintained by the U.S. 

Centers for Medicare & Medicaid Services (CMS).57 This database includes descriptions of 

research projects conducted by Medicare Peer Review Organizations (PRO), now called Quality 

Improvement Organizations (QIOs). Unfortunately, the relatively unstructured format of these 

project narratives makes for time-intensive searches. And while recent changes to the database

denied the reviewers access during the timeframe of this project, they plan to revisit the access 

issue for future topics in the series. 

The difficulty in obtaining unpublished QI trials and vulnerability to publication bias were 

incentives for analyzing the studies in terms of median effect sizes. As described in greater detail 

in the Methods section of Volume 1, the investigators summarized their findings for a given QI 

type or study feature by reporting the median effect size achieved by the studies (e.g., the median 

effect on HbA1c reported by studies sharing a specific QI strategy). Publication bias is more 

likely a factor in smaller, low quality studies.52, 58-63 When the results are similar to other studies, 

they will have little effect on the median. When the results report larger effects for a given 

intervention, they will not affect the median so long as their total number is small. 

Outcome Measures 

Investigators targeted three broad categories of study outcomes: measures of disease control, 

measures of provider adherence to recommended care, and patient adherence to prescribed 

medications and self-care recommendations. Measures of disease control included intermediate 

clinical outcomes such as HbA1c and blood pressure, as well as clinical endpoints such as 

mortality, cardiovascular events, vision loss, and amputation. Quality improvement studies 

generally have insufficient power to detect changes in morbidity and mortality, as such studies 

require large numbers of patients and lengthy observation periods.82 Accordingly, the focus 

14
shifted to intermediate clinical outcomes as measures of disease control, especially serum HbA1c 

and blood pressure. HbA1c levels have a well-established connection to glycemic control over 

time and to the prevention of important diabetic complications.

4-6, 11 Management of 

cardiovascular risk factors—especially control of hypertension—now is recognized for being as 

important to diabetic patient outcomes as glycemic control.7

 Similarly, studies that promoted 

provider adherence to measures shown to prevent diabetic complications also were made a 

priority. Serial monitoring of serum HbA1c, blood pressure, and cholesterol, as well as 

monitoring for nephropathy (with microalbuminuria), neuropathy (by foot examinations), and 

retinopathy (with dilated retinal examinations) are widely accepted care practices (see Appendix 

A). 

Guidelines for the care of diabetic patients continue to evolve, and significant changes have 

occurred within the last half-decade alone. While recommendations for optimal glycemic control 

have remained stable, new data have led to recommendations for tighter blood pressure and 

hyperlipidemia control.83, 84 The NCEP-III guidelines for cholesterol management were issued in 

2001, and the latest Joint National Committee on the Prevention, Detection, Evaluation, and 

Treatment of High Blood Pressure (JNC-VII) recommendations for hypertension management 

were announced in 2003. These guidelines support an LDL-cholesterol target of < 100 mg/dl and 

a blood pressure target of < 130/80 mmHg, for diabetic patients. But most of the quality 

improvement strategy trials reviewed for this study were undertaken in years when less 

restrictive guidelines were in place, and therefore contain targeted goals that would be 

considered suboptimal by current standards. Since the focus of this review was to identify 

effective strategies for implementation, the investigators opted to include studies designed to 

improve provider adherence with the targets deemed acceptable at the time of the original 

study—even in those instances where they no longer were consistent with present guidelines. 

Core outcomes were abstracted using a structured format, in an effort to permit quantitative 

analysis. For measures of disease control, the core outcome consisted of changes in serum HbA1c 

and blood pressure. Serum lipids, creatinine, and outcomes related to retinal disease and foot 

ulcers also were captured, with the expectation that such outcomes were reported in too many 

different formats across studies to permit their inclusion in the quantitative analysis. (As 

mentioned previously, investigators also included clinical endpoints such as death and

amputation, but expected studies to report these infrequently and to be underpowered to detect 

differences across study groups.) For measures of provider adherence, outcomes were captured 

as reported in the study, but all adherence outcomes were categorized in terms of broad 

categories: 

• Adherence to guideline targets for frequency of glycemic control assessment (e.g., 

measuring HbA1c at least once within a specified time period). 

• Adherence to recommended screening practices for ophthalmologic complications (e.g., 

performance of, or referral for, dilated retinal exam). 

• Adherence to recommended screening practices for renal complications (e.g., checking 

urine microalbumin). 

• Adherence to recommended screening practices for neuropathy or foot-related 

complications (e.g., performance of, or referral for, foot examination). 

• Adherence to treatment choices for achieving glycemic control (e.g., making specific 

medication choices). 

15
• Adherence to guideline targets for managing blood pressure or cardiovascular disease. 

• Adherence to recommendations for patient education or counseling regarding dietary 

regime, exercise, smoking, and other lifestyle factors. 

For measures of provider adherence, the investigators stipulated outcomes data sourced from

chart reviews, direct observations, and clinical information systems, but not from provider selfreporting. They did permit data taken from patient self-reporting, for patient adherence 

outcomes. 

Patient outcomes were collected in a similar fashion for two broad categories: adherence to 

prescribed medication regimens; and for adherence to self-care practices (e.g. self-monitoring of 

blood glucose, recommendations for diet and exercise, and keeping appointments). In accordance 

with a recent systematic review of strategies designed to increase adherence to prescribed 

medication regimens,46 studies were included only if they reported at least one treatment 

outcome in addition to measures of adherence. 

For outcomes involving medication adherence, investigators also made an effort to record the 

method of measurement, relative to the following categories: laboratory confirmation (e.g., the 

detection of a drug or metabolite in blood or urine, including biochemical assays for smoking 

cessation); pharmacy data (e.g., filled or refilled prescriptions); specially designed dispensers 

that record medication use, home medication counts, office medication counts (e.g., patients 

bring in bottles with unused pills); patient self-report (via interview or survey). While the 

number of studies reporting medication outcomes was anticipated to be too few to permit the 

incorporation of these measurement categories into the analysis, the data was captured in 

preparation for the final volume in this series and the possibility that sufficient studies may be 

found to examine their impact across the various priority topics. 

Formats for Reported Outcomes 

The investigators anticipated four basic formats for reporting outcomes. Results could be 

reported at the provider level (e.g., mean blood pressure for patients cared for by a particular 

physician or group of physicians), or patient level (e.g., mean HbA1c for individual patients in a 

particular clinic). These outcomes also could be reported as a summary measure for a group 

(e.g., mean blood pressure), or as the percentage of patients within a certain range (e.g., 

percentage of patients with HbA1c < 9.5%). Most studies were expected to report summary data 

at the patient level, as the clinical significance is more immediately evident. The full-text 

abstraction forms were devised to target the following measures: 

• Reductions in HbA1c , systolic blood pressure (SBP), and diastolic blood pressure (DBP) 

were reported as mean and standard deviation†

 for each study group before and after the 

intervention; 

• Changes in adherence to a process of care were reported as the percentage of patients in 

each group who received the process of care before and after the intervention; changes in 

patient adherence to a recommended process of self-care or a medication regimen was 

 

†

 Where standard error of the mean (SEM) was reported, we calculated the standard deviation (SD) based on SEM = 

SD/SQRT(N), for sample size N. 

16
defined similarly as the percentage of patient adherence in each group at baseline and 

following the intervention. 

Investigators also had planned to document disease control outcomes reported as a percentage of 

patients with HbA1c, SBP, or DBP decreases within a target range. The same was true for 

adherence outcomes, framed as summary measures applied to providers rather than patients (e.g., 

in trials where clinicians were randomized, the reviewers expected some studies would report 

summary scores for clinicians in each group rather than for all patients within each group). So 

few studies reported outcomes in this manner, however, that the researchers changed their 

abstraction form and analytic plan to focus on outcomes reported in the formats listed previously. 

Analysis 

The Median Reported Effect as a Summary Measure 

In addition to descriptive and qualitative analyses, two more forms of analysis had been 

planned. The first involved the calculation of a “median effect” among outcomes within a given 

category (e.g., all provider adherence outcomes reported by a given study) so that studies 

exhibiting the same features could be compared in terms of a common metric. Following the 

method employed in a recent systematic review of strategies for guideline implementation,85 the 

researchers identified in each study the adherence outcome that exhibited the median 

improvement attributable to the intervention. For example, if a study reported one outcome

involving adherence to a guideline for checking HbA1c, another related to screening for retinal 

disease, and a third outcome for delivery of patient education, investigators calculated the net 

improvement attributable to the intervention for each outcome. The net improvement in 

adherence was calculated as (Post-intervention adherence – Pre-intervention adherence)Study group

- (Post-intervention adherence –Pre-intervention adherence)Control group. Then the median value 

among values for the net improvement in adherence was calculated. This median value was 

regarded as the primary adherence outcome for that particular study. 

Outcomes were not combined for measures of disease control, so the net reduction in HbA1c, 

SBP, or DBP attributable to the intervention was reported.‡

 However, in analyzing the impact of 

a particular QI type or study feature (e.g., trial design), investigators did report a similar “median 

effect” across studies, such as the median reduction in HbA1c reported by RCTs versus CBAs, or 

the median reduction in HbA1c reported by all studies employing a given QI type. 

Focusing on median effects rather than average effects helped to eliminate skewing in the 

summary measure, based on one or two outliers with particularly large or small effect sizes. The 

reviewers regarded this effect as particularly important, given the aforementioned difficulty in 

accounting for publication bias. They considered the calculation of a weighted median, with 

weighting based on sample size, in order to avoid studies with equal weights, irregardless of size. 

But weighted medians are not as straight forward as weighted means, especially when the aim is 

to preserve the original significance of the effect size (e.g., the relation to a reduction in HbA1c or 

SBP in the units used for those outcomes). Therefore, instead of applying a variable weighting 

scheme, investigators examined the median effect sizes by different strata of study sample size 

 

‡

 The net change in a disease control measure such as HbA1c was calculated as (Post-intervention HbA1c – Pre-intervention

HbA1c)Study group – (Post-intervention HbA1c – Pre-intervention HbA1c)Control group. 

17
(e.g., comparing the median effect among studies with sample sizes in the lowest quartile against 

those in the highest quartile, or those in the lower half versus the upper half. 

A simple non-parametric assessment—the Mann-Whitney rank-sum test—was used when 

possible to ascertain differences in median effects.

86 Such comparisons were possible only for 

mutually exclusive categories (e.g., randomized versus non-randomized trials) or for all 

interventions with a particular QI strategy compared with all those lacking the same strategy. It 

was not possible, however, to compare one strategy with another because of the frequent overlap 

between the two groups, and the contribution of the same interventions to the two medians. 

Standardization of Direction of Effect 

To avoid confusion regarding the direction of effect, investigators standardized all outcomes 

so that a positive change would reflect improvement. Thus, all reported changes in HbA1c and 

blood pressure are reductions, and a positive change always can be interpreted as an 

improvement. For adherence outcomes, the adherence target was constructed in such a way that 

an increase in the outcome always corresponded to an improvement. Thus, if a study reported an 

outcome involving the percentage of clinicians who failed to perform some process of care, the 

complementary percentage of clinicians who succeeded in performing the targeted process of 

care was used. Similarly, if a study reported an outcome involving the delivery of an undesirable 

process of care (e.g., prescribing a medication regarded as harmful), the complementary 

percentage of clinicians who did not perform the adverse process of care was used, so that a 

positive change would better indicate improvement. 

Accounting for “Cluster” Effects 

“Clustering” was anticipated in a substantial number of studies for which the unit of analysis 

and unit of allocation differ (e.g., providers or clinics randomized, but patient level outcomes 

analyzed). Clustering is significant in that patients within a cluster are not independent (i.e., 

patients at one clinic have a greater resemblance to one another than to patients at other sites, or 

those cared for by other providers in the trial). Unit of analysis errors do not affect point 

estimates for effect sizes, but they may spuriously narrow the associated confidence interval, 

leading to potential false-positive trial results.65-72 To avoid the same inflation of precision in 

this analysis, investigators calculated an effective sample size for each study.§

 From the 

perspective of this analysis, the degree to which investigators acknowledged or accounted for 

cluster effects did not affect the analysis, forepart from the fact that investigators who did 

consider cluster effects in the design or analysis of the trial were more likely to report data such 

as the number of providers randomized, rather than just the total numbers of patients in each 

group. They also were more likely to provide more technical details, such as values for the intracluster coefficient (ICC). Because so few studies reported ICC values,87-89 the reviewers used 

values derived from published estimates.67 (Appendix F shows the calculation of effective 

sample sizes and presents sensitivity analyses based on the range of published ICC values.) 

 

§

 Effective N = (km) / (1 + (m-1)r) where k is the number of clusters and m is the number of observations per cluster and r is the 

intra-cluster coefficient. When r = 0, then N = km. When r = 1, then N = k.

65-72

18
Regression Analysis 

For the more involved quantitative analyses—meta-regression analysis of included studies—

investigators used a more conventional measure of effect size, defined as the difference between 

the means of the intervention and control arms divided by the pooled estimate of the within 

group standard deviation.** These formal effect sizes, and the above median effect measures, 

were constructed in such a way that a positive result always reflected improvement (e.g., a 

positive reduction in average HbA1c or a positive improvement in adherence). 

Specifically, regression models were constructed using the pre-intervention effect size (ESPre) 

as a predictor variable. Initially, each methodologic feature or QI strategy was modeled with 

ESPre to evaluate its effect on the post-intervention effect size (ESPost). Investigators subsequently 

developed multivariate models, using multiple components as an individual feature’s covariates, 

in order to independently assess the effect of an individual feature after adjustment for other 

components. This model assumed no important interactions between types of QI strategies, 

provider factors, or other potential predictors. This assumption may be overly simplistic, but it 

was considered reasonable in the context of this exploratory analysis. 

 Linear regression was carried out as Y = β0 + β1X1 + β2X2, with X1= ESpre and the dependent 

variable, Y, corresponding to the outcome of interest—a measure of disease control such as 

HbA1c or the “summary adherence” outcome described above, in which the value for each study 

corresponds to the adherence outcome with the median effect among the outcomes reported by 

the study. ESpre was retained as a predictorin all analyses because of expected baseline 

differences between the study and control groups and their role as important covariates, even 

when these differences did not meet conventional thresholds for statistical significance. This 

point is discussed in detail in the analysis of trials reporting changes in a target measurement 

rather than single endpoints (Volume 1, Chapter 2: Methods). 

Briefly, investigators often assume that if a study is randomized, baseline values of the 

outcome of interest (e.g., baseline HbA1c in the control and study groups) can be handled in the 

manner of basic demographic and clinical features reported typically as subject characteristics in 

a “Table 1.” Despite the common practice of reporting p-values in a “Table 1,” assessing 

baseline differences is philosophically unsound. If randomization has been carried out 

appropriately, any observed differences have, by definition, occurred on the basis of chance.90-94 

More importantly, even when baseline differences do not have p-values below 0.05, such 

differences may nevertheless exert significant effects on the observed results.90-94 If the 

correlation is low (less than approximately 0.3), using change score from the baseline value will 

add variation, so the followup score is more likely to show a significant result. Conversely, if the 

correlation is high (greater than approximately 0.6) using only the followup score will lose 

information and the change score is more likely to prove significant.95 

Only four studies96-99 (including one excluded at Stage 3) reported average change scores per 

patient (e.g., post-intervention HbA1c – pre-intervention HbA1c) accompanied by a value for the 

standard deviation of the change score. These four studies contributed two values each (one per 

 

** Effect size = (X

_

 I - X

_

 C) / Sp where X

_

 I is the mean for the intervention group, X

_

 C is the mean for the control group and Sp is the 

pooled within groups standard deviation, which is calculated from: 

Sp

2

 = ( (NI – 1) SI

2

 + (NC – 1) SC

2

) / (NI + NC – 2). NI and NC are the intervention and control sample sizes and SI and SC are 

the intervention and control standard deviations. 

19
study group), permitting estimation of the correlation between baseline and followup values 

according to the equation: SDx-y = SDx

2

 + SDy

2

 – 2*SDx*SDy*ρ, where ρ represents the 

correlation between the pre- and post-intervention measurements. The calculated values for ρ

had a median of 0.64 [inter-quartile (IQ) range: 0.50,0.72]. Given this range of values for 

correlation within groups, baseline differences between the study groups were considered a 

necessary predictor in the regression analyses. 

Multiple Comparisons and A Priori Hypotheses 

Meta-regression analysis amounts to studying the epidemiology of a population of trials. In 

other words, despite the experimental nature of the trial, this analysis is essentially observational. 

Moreover, the number of trials (in the range of 10 to 60, depending on the QI strategy and 

outcome being reported), while reasonable for a conventional meta-analysis addressing a focused 

question (Is Treatment A superior to Treatment B?) with one or two potential confounding 

variables, is relatively small for an analysis with at least 10 to 15 potential variables of interest. 

As outlined in Table 4 of Volume 1, Chapter 2, in this series, potential predictors of intervention 

success or failure include features of trial methodology, the setting in which the trial occurred, 

attitudes of clinicians towards the QI target, organizational support for the intervention, and 

many other potential predictors. Investigators were unable to adequately incorporate some of 

these into this analysis, while others proved too elusive to be abstracted in a consistent and 

accurate manner. 

Given the aforementioned considerations and the sheer number of comparisons considered in 

the analysis, any significant findings should be regarded as exploratory and solely for the 

purpose of generating hypotheses, regardless of adjustments to the threshold for statistical

significance. In this context, more important than the actual correction is the degree to which 

significant findings relate to an a priori hypothesis. In the case of this review, the only specific 

hypotheses were as follows: 

• Trial design may have a significant impact on results, with randomized trials 

expected to show lesser effect sizes 

Despite the epistemological superiority of randomized trials, the magnitude of bias 

introduced by well-conducted non-randomized trials and other observational research is 

surprisingly modest. Moreover, the direction and magnitude of bias do not appear to be 

consistent (i.e., observational studies may under-or over-estimate true effects to varying 

extents).100-102 It should be noted, however, that comparisons of results from randomized 

and non-randomized trials have been used for clinical efficacy research, not for quality 

improvement research. The significance of this distinction is that potential confounders 

are far better known for the former, based on the results of previous clinical research and 

a much greater understanding of the relevant pathophysiology, than exists for our 

understanding of behavioral change, organizational theory, etc. 

Thus, QI trials may be more susceptible to confounding by unknown variables, both at 

the level of trial design and analysis. For this reason, the investigators expected study 

design to exert an influence on the results of QI studies. They had no hypothesis about the 

direction of this effect, apart from the likelihood that publication bias would exert a 

greater influence on non-randomized trials, and that the positive trials would be over20
represented among the non-randomized trials to a greater degree than the randomized 

trials. 

• Study period may be an important predictor, but the authors had no hypothesis 

about the direction of effect 

There are three reasons for which the study period was considered a potential predictor or 

confounder: showing an effect might have become more difficult over time if care has 

improved in response to previous QI studies (higher baseline adherence, as might occur 

over time, was in fact associated with smaller effects in the recent update of the Cochrane 

review of audit and feedback54); study design has probably improved with time, as may 

be the case for intervention design (e.g., the type and number of QI strategies employed); 

regardless of baseline adherence, QI targets may have become harder to achieve because 

what counts as adherence has changed (e.g., guidelines recommend lower target levels for 

serum HbA1c, blood pressure, lipids) and the newer targets are generally more difficult to 

achieve. 

These factors could exert an influence opposite that of the effect, so the investigators had 

no hypothesis regarding the net impact of study period on QI trial results. 

• Number of QI strategies 

The number of QI strategies employed is at least as important as the choice of strategies 

(e.g., multifaceted interventions are more likely to succeed than interventions using a 

single QI strategy103). Two recent reviews—one of strategies to promote guideline 

implementation85 and one on audit and feedback as a QI strategy54—have called into 

question the widely held view that multifaceted strategies are superior to single-faceted

ones. The authors considered this hypothesis worthy of examination, in terms of its 

practical value to those engaged in QI work and research, and the likelihood that this 

review would involve sufficient data to address the question. (By contrast, the researchers 

expected relatively small numbers of studies for any particular QI type.) 

21
22
Chapter 3. Results 

Search Yield and Results of Article Review Process 

Figure 1 depicts the article search and review process, with the results at each step. The 

MEDLINE® search using PubMed® yielded a total of 3,601 citations. Searching the EPOC 

database produced an additional 104 articles deemed relevant for full abstraction, of which 12 

articles104-115 reporting 16 trials, met full inclusion criteria. (Conversely, the MEDLINE® search 

identified 10 articles82, 87, 89, 116-122 evaluating 11 trials, which were not indexed in the EPOC 

database.) The manual search yielded an additional 77 articles, though only two of these23, 24 met

the inclusion criteria for this review.†† 

A total of 529 articles merited full-text review. Of these, 139 were deemed “not a quality 

improvement or not an evaluation” and were excluded. This relatively large number of articles 

outside the scope of the review reflects the fact that 97 of the citations contained no abstract, and 

so they could not be screened out at Stages 1 or 2. Other reasons for exclusion after full text 

review included: excluded topic (22 articles); study design failed to meet the criteria for RCTs, 

quasi-RCTs, CBAs, or ITS (176 articles); no eligible outcomes (42 articles); duplicate or 

overlapping articles (14 articles); publication prior to 1980 (three articles); and other reasons (six 

articles), including one abstract not yet published or available as a manuscript from the 

authors.123 One published article could not be obtained.124, 125 (Figure 1 identifies all six of the 

articles excluded as “other,” and Appendix G lists all citations excluded after the full text review,

along with reasons for the exclusions.) 

As shown in Figure 1, a total of 126 articles merited full abstraction. The intervention in 68 

of these articles consisted solely of patient education or promotion of self-management. These 

articles are listed in Appendix D and their results will likely be analyzed in another volume of

this series that focuses on patient education and self-management. Those articles involving 

patient education in combination with other interventions were included in the present review. 

The study sample for the review consisted of 58 articles, reporting a total of 66 comparisons 

(Table 1). 

Features of the Included Studies 

 Demographics. Table 1 displays the included 66 trials, along with descriptions of the trials 

with regard to setting, design, and QI strategies. (Appendix E presents structured summaries of 

the results for each of these studies.) Roughly half (29 articles reporting 34 comparisons) were 

published in the 1990s, 88, 96, 97, 106, 108-110, 115, 116, 120, 122, 126-144

 while 24 articles (reporting 26 

comparisons) were published in 2000 or later.82, 87, 89, 98, 104, 107, 112-114, 117-119, 121, 145-155 Only five 

articles (reporting six comparisons) were published in the 1980s.105, 111, 156-158 Roughly half of 

the comparisons (36 studies; 55% overall) were conducted in the United States. Twelve studies 

82, 87, 104, 126, 129, 130, 134, 140, 142-144, 154 (involving 13 comparisons) selected patients with poor 

glycemic control, poor adherence to medications or clinic attendance, the presence of specific 

 

†† The relatively low yield of the manual search likely reflects the existing contribution of hand searching to the EPOC registry. 

Also, a substantial number of articles identified by the manual search involved patient education or self-management only. While 

they did not meet inclusion criteria for the present review, they will likely meet criteria for inclusion in a forthcoming volume of

this Series focusing on patient education and self-management. 

23
comorbid conditions (e.g., hypertension, hyperlipidemia, coronary artery disease, obesity, 

tobacco use), or advanced illness (e.g., specific and previously documented diabetic 

complications such as nephropathy, neuropathy, or retinopathy). 

Many studies omitted key data elements. For instance, among the 33 studies reporting mean 

reductions in HbA1c, 10 articles (reporting 12 comparisons) did not provide standard deviations 

or standard errors of the mean,88, 89, 112, 121, 137, 141, 143, 152, 153, 157 and four studies provided no 

baseline HbA1c values for either study group.88, 107, 120, 156 Similarly, among the 26 studies 

reporting at least one measure of provider adherence, nine articles (reporting 10 comparisons) 

included no baseline values for any of the adherence outcomes.110, 111, 119, 120, 122, 127, 130, 154, 155 

 Methodologic features. Forty seven (71%) of the included trials had a randomized 

design,82, 87-89, 97, 98, 104, 106, 107, 110, 112, 114-117, 121, 126-133, 135, 136, 139, 140, 143-150, 152-157

 while one used a 

quasi-randomized design,96 and another 18 were controlled before–after studies.105, 108, 109, 111, 113,

118-120, 122, 134, 137, 138, 141, 142, 151, 158 The investigators identified numerous uncontrolled before–

after studies, but none of these met the adopted EPOC criteria for inclusion as a time series, 

which mandates data from at least three time points in the pre- and post-intervention periods.159

Thirty-five of the included trials involved clustering (i.e., unit of analysis differed from unit of 

allocation‡‡).87-89, 96, 105, 107-110, 113-115, 119, 121, 122, 129, 132, 137, 138, 141, 145, 147-149, 151-153, 155, 157, 158

 The 

number of clustered units per trial ranged from a low of one clinic, team, or firm per study 

group96, 109, 114, 119, 137, 153

 to a high of 247 (with clinicians as the unit of allocation).149 Only three 

clustered trials reported ICC values.87-89 One of these studies87 reported ICC values for each of 

the seven adherence outcomes identified in the study. These values ranged from 0.02 to 0.33, and 

the researchers used the median (0.18) to calculate the effective sample size for the adherence 

outcomes in this study. The additional two studies reported ICC values for measures of disease 

control. In one case,88 the ICC = 0.045 and 0.047. The other89 used a value of 0.07 in performing 

the power calculation, though the rationale for choosing this number was unclear. Since the 

remaining studies did not report ICC values, the investigators assigned values based on data from

the literature.

67 

 Reported outcomes. The included studies reported a wide range of specific outcomes, with 

51 studies reporting at least one measure of disease control and 26 reporting at least one measure 

of provider adherence. Only nine studies meeting the inclusion criteria (which required studies to 

report at least one measure of disease control or provider adherence) reported patient adherence 

outcomes. Thirty eight studies reported changes in serum HbA1c in the format of mean and 

standard deviation for each study group, while 13 studies reported other measures related to 

changes in serum HbA1c (e.g., the percentage of patients with serum HbA1c falling within a 

certain range). Twenty-one studies reported an outcome involving blood pressure control, 

although only 15 reported the mean systolic or diastolic blood pressure, and just eight reported 

sufficient information to permit quantitative analysis (i.e., a standard deviation in at least one of 

the measurement periods for each study group and mean values from both periods). 

Because of the variety of adherence outcomes, the investigators focused the analysis on a 

summary measure of provider adherence. As described in the Methods section, they calculated 

for each study the net change attributable to the intervention for all adherence outcomes reported 

and then used the outcome with the median effect as the data contributed by that study for this 

 

‡‡ Cluster trials allocate participants at one level (e.g. providers randomized to intervention or control group), but collect and 

analyze data at the level of individual patients or clinical encounters. Analyzing such studies at the patient level produces socalled unit of analysis errors 160 unless investigators adjust for correlation within each cluster. As described in Methods and 

illustrated in Appendix F, we calculated an “effective sample size” to adjust for clustering effects whenever the unit of analysis 

and unit of study group allocation were not the same. 

24
summary adherence measure. (Although nine studies included outcomes related to patient 

adherence, they exhibited little overlap in terms of the type of adherence outcome reported or the 

QI strategies employed. Consequently, patient adherence outcomes were not included in the 

present analysis.) 

Provider adherence outcomes were reported in 26 studies, however only 17 had sufficient 

data to permit quantitative analysis. Adherence outcomes related to appropriate monitoring of 

serum HbA1c in 19 comparisons, management of hypertension or coronary artery disease in 14 

studies, and to monitoring of laboratory values other than serum HbA1c or glucose in 20 

comparisons (e.g., serum lipids, urine microalbumin). Thirteen studies reported adherence 

outcomes related to screening for diabetic complications of the foot, and 14 studies measured 

provider adherence in connection with screening for ophthalmologic complications. 

Among the disease control outcomes, the analysis focused on changes in serum HbA1c 

(results for studies reporting usable data on changes in blood pressure are shown in Appendix E, 

Table E2). All outcomes were standardized so that a positive number for any change (or for any 

coefficient in the regression analysis) corresponds to an improvement, while a negative value 

reflects an undesirable change. As described previously, all outcomes involving changes in mean 

serum HbA1c refer to the net reduction in serum HbA1c (i.e., the intervention was associated with 

a positive reduction in HbA1c, as desired). Adherence outcomes also were standardized so that 

adherence was measured in terms of the desired process in such a way that positive changes 

always reflect an improvement in care. 

Types and numbers of quality improvement strategies. As shown in Table 2a, the most 

common type of QI intervention fell into the broad category of organizational change (40 

comparisons including 24 RCTs), followed by patient education (28 comparisons, 23 RCTs), and 

provider education (24 comparisons including 16 RCTs). Apart from studies161-164

 of incentives 

directed at patients for the purpose of reinforcing patient education or self-care, only one study131

evaluated financial incentives. (These other studies161-164 will be included in the forthcoming 

volume of QI strategies focused on patient education and self-management.) The investigators 

reported results for the specific QI strategies, including analyses collapsing some of the 

categories with clear overlap (e.g., patient education, promotion of self-management, and patient 

reminders). 

Table 2b depicts the number of different QI strategies examined per study. Fourteen studies 

evaluated single-component interventions. Fifty-two trials involved interventions employing 

more than one of the nine QI strategies in the taxonomy, with five being the maximum number 

of strategies involved in any single intervention.117, 133, 148, 150, 157

 

The median number of QI strategies per intervention was two. Though not a QI strategy per 

se, the researchers also abstracted information on the role played by clinical information systems 

in the trials. 

Analysis by Outcome Measures 

Trial design was a significant negative predictor of effect size for both outcomes (i.e., trials 

with a randomized design reported smaller improvements in glycemic control, and for provider 

adherence). For studies reporting impacts on glycemic control, sample size also exhibited a 

significant inverse correlation with the magnitude of effect (i.e., the larger studies showed 

smaller reductions). This inverse correlation persisted among the randomized trials and 

suggested an independent effect of sample size, rather than confounding due to a tendency of 

25
larger trials to have a randomized design. Consequently, the tables showing the associations 

between QI strategies and targeted outcomes are stratified generally by sample size for glycemic 

control, and by study design for both outcomes. 

Publication bias, to be discussed below, provides the most likely explanation for the striking 

patterns shown in the tables stratifying reported results by sample size and by trial design. Thus, 

in addition to illustrating the magnitude of the relationships between effect sizes and study 

features, the tables also summarize the likely effect sizes of each QI type based on the subset of 

studies with the least apparent bias. 

Effect of QI Strategies on Glycemic Control 

Table 3a shows the median reductions in serum HbA1c achieved by interventions employing 

different QI strategies. Among the 38 comparisons with sufficient data regarding changes in 

mean serum HbA1c in the study and control groups, the median effect on serum HbA1c was an 

absolute reduction of 0.48% (IQ range: 0.20%, 1.38%). For specific QI strategies, trials that 

included provider education (alone or in combination with other QI strategies) had the highest 

median effect, with a median absolute decrease in serum HbA1c equal to 1.10% (IQ range: 

0.56%, 1.50%). Trials that included promotion of self-management showed the lowest median 

reduction in serum HbA1c (0.40%; IQ range: 0.20%, 0.60%). 

Stratifying the results by study sample size shows that the larger studies reported generally 

smaller effects. For instance, the 10 trials in the lowest quartile of sample size reported a median 

reduction in serum HbA1c of 1.35% (IQ range: 0.81%, 1.73%), while the 10 trials in the highest 

quartile reported a median reduction of only 0.10% (IQ range: 0.10%, 0.33%). Similarly, the 19 

trials falling in the lower two quartiles of sample size reported a median reduction in serum

HbA1c of 1.30% (IQ range: 0.41%, 1.49%), while the 19 trials in the upper two quartiles reported 

a median reduction of only 0.21% (IQ range: 0.10%, 0.55%). As shown in Table 3a, this pattern 

was consistent for all but one of the QI strategies. (Provider reminders represent the one 

exception, as reported reductions in serum HbA1c were approximately equivalent for studies in 

the upper and lower quartiles of sample size.) 

Table 7a confirms the inverse relationship suggested by visual inspection as statistically 

significant. Specifically, the Spearman rank correlation coefficient for the relationship between 

sample size and improvement in serum HbA1c was -0.46 (95% CI: –0.09, –0.72; p=0.02). This 

relationship does not appear to reflect confounding based on trial design, as the same correlation 

existed among randomized trials (Spearman correlation coefficient = –0.48; 95% CI: -0.03,-0.77; 

p=0.04). 

The relationship between sample size and reported effects strongly suggests publication bias, 

such that smaller studies reporting negative or less impressive results are not as likely to be 

published. For this reason, the authors have presented the results for impacts on glycemic control 

stratified by quartiles of sample size wherever possible. (The issue of publication bias is explored 

further in the Discussion section.) 

26
Effect of QI Strategies on Provider Adherence 

Table 3b exhibits the median improvements in the summary measure of provider adherence 

achieved by interventions employing the various QI strategies. Among the 17 trials with 

sufficient data regarding changes in provider adherence, the achieved median effect was an 

attributable 4.9% increase in adherence (IQ range: 3.8%, 15.0%). Self-management appears to 

have the largest median effect in Table 3b, but this result reflects a single study152 in which the 

intervention also included components of patient education and organizational change. (The 

issue of other interventions as a potential confounding presence is addressed in the analysis of 

specific QI types.) 

Apart from self-management, the largest median effects on provider adherence were 

associated with provider education and audit and feedback. Among the 11 trials with some

component of provider education, either alone or in combination with other QI strategies, the 

median increase in adherence was 5.6% (IQ range: 4.15%, 17.2%). Nine trials utilizing audit and 

feedback also achieved a 5.6% median increase in adherence (IQ range: 3.4%, 16.4%). 

Visual inspection of Table 3b suggests no striking variation in the effect sizes across quartiles 

of sample size, and Table 7a confirms the correlation to be smaller than for studies reporting 

changes in serum HbA1c and lacking in statistical significance (Spearman correlation coefficient 

= –0.22; 95% CI: 0.29, –0.63; p=0.4). However, trial design was a highly significant predictor in 

the regression analysis (p=0.0008; Table 7a). Based on the mean post-intervention differences 

between the study and control groups, the parameter estimate of –0.30 for the impact of trial 

design implies that randomization generally decreased the improvement in provider adherence 

associated with an intervention by 15.3% (see text accompanying Table 7a). 

Table 4a bears out this relationship by showing the median effects associated with 

randomized and non-randomized trials, both overall and for each QI strategy. For those studies 

detailing impacts on glycemic control, RCTs reported a median reduction in serum HbA1c of 

0.39% compared with 1.40% for non-randomized trials, and this difference was statistically 

significant (p=0.008 for Mann-Whitney test; Table 7c). This difference diminished only slightly 

with the restriction of the analysis to trials in the upper two quartiles of sample size (p=0.03; data 

not shown). Table 4b bears out the persistence of a relationship between sample size and 

magnitude of reductions in serum HbA1c, even among randomized trials. 

Effect of the Number of QI Strategies per Intervention

Trials utilizing combinations of QI strategies were more likely to exert a positive effect. The 

six trials involving single-faceted interventions had no overall effect on glycemic control (Table 

5a). The median reduction in serum HbA1c reported by these trials was 0.00 (IQ range: –0.08, 

0.16) (i.e., some studies reported increases in serum HbA1c). By contrast, the 32 trials involving 

interventions with at least two strategies reported a median absolute reduction in serum HbA1c of 

0.60% (IQ range: 0.30%, 1.40%). The Mann-Whitney test comparing these median effects was 

statistically significant at p=0.01 (Table 7c). The difference diminished only slightly when the 

analysis was restricted to trials in the upper two quartiles of sample size (p=0.03; data not 

shown). 

The relationship between number of QI strategies and magnitude of effect was slightly 

weaker for provider adherence (Table 5b). The 14 trials involving interventions with at least two 

QI strategies were associated with a median increase in provider adherence of 5.3% (IQ range: 

27
4.5%,16.1%) compared with the three single-faceted trials, which reported a median increase in 

adherence of 3.0% (IQ range: 2.0%,3.5%). The Mann-Whitney test for this comparison suggests 

rejection of the null hypothesis (p=0.04; Table 7c), that these two medians are the same. This pvalue would not withstand correction for multiple comparisons, but the beneficial impact of

multifaceted interventions was one of our a priori hypotheses (as described in the Methods 

section). 

For trials reporting impacts on glycemic control, the apparent superiority of interventions 

with more than one QI strategy persisted when the analysis was restricted to randomized trials 

(Table 6). The 23 randomized comparisons of interventions involving at least two distinct QI 

strategies reported a median reduction in serum HbA1c of 0.41% (IQ range: 0.25%, 0.94%) 

attributable to the intervention, as opposed to 0.00% (IQ range: −0.10%, 0.00%) for the five 

randomized comparisons involving single-faceted interventions. Even with the relatively small 

number of studies involved, these medians are unlikely to be drawn from the same population 

(p=0.008 for Mann-Whitney test). Among trials in the upper two quartiles of sample size, 

interventions with at least two strategies retained their association with greater median reduction 

in serum HbA1c, though the difference between the medians was less striking (p=0.01; Table 7c). 

The greater impact of interventions with at least two QI types persisted even when the analysis 

was restricted to interventions involving organizational change, which would generally be 

regarded as more complex and more intense than other QI types. In other words, even in studies 

of single-faceted interventions employing a form of organizational change as the sole QI 

strategy, the addition of at least one more strategy was found to increase the overall effect 

(0.71% vs. 0.05% median reduction in serum HbA1c; p=0.002). 

The investigators further explored the relationship between number of QI strategies and 

magnitude of effects using an alternate classification scheme in which important subtypes of 

provider education and organizational change were treated as their own category. Specifically, 

the broad category of provider education was replaced by three categories—workshops or 

meetings,55 distribution of educational materials,165 and educational outreach56—and 

organizational change was replaced by four strategies—disease or case management,17 changes 

to team structure or personnel, modification of medical records systems, and “other 

organizational change.” This alternate classification scheme resembles that used in the Cochrane 

review of QI interventions for diabetes care,16 and is more consistent with other reviews focusing 

on these specific strategies.17, 55, 56, 165

Under this alternate classification of the QI strategies, five studies still were catagorized as 

single-faceted,104, 112, 116, 121, 127 but the median number of strategies increased from two to three 

and the maximum number of strategies increased from five to six.153, 157 Appendix H (Tables 

H4a-c) presents the same relationships discussed above and shown in Tables 5a, 5b, and 6, but 

using the results of this alternate classification in which major substrategies are promoted to their 

own category of QI strategy (Tables H4a, H4b, and H4c). The relationship between increased 

numbers of QI strategies and magnitude of effects appears somewhat stronger for studies 

reporting impacts on glycemic control. Though the analysis is not shown, the Mann-Whitney test 

of the difference in median effects between single and multifaceted interventions using the 

alternate classification scheme had greater significance (p=0.005). 

28
Analysis by Type of QI Strategy 

Visual inspection of Tables 3a, 3b, and 4a suggests no striking differences among the various 

QI strategies. However, studies that included provider education or audit and feedback, alone or 

in combination with other strategies, were among those associated with the largest effects on 

both outcomes. The effects also exhibited less erosion with stratification by sample size or by 

trial design. 

Tables 8a through 11b show the median effects on glycemic control and provider adherence 

for specific QI strategies. These tables attempt to address several particular limitations inherent 

to this analysis: 

1. Several of the nine major QI strategies in our taxonomy may include substrategies that 

are sufficiently distinct to warrant their own category. To address this heterogeneity 

within some of the QI strategies, the tables of specific QI strategies compare specific 

substrategies to the overall set of QI strategies—not just other strategies within the same

category. For example, “disease management” is compared with all other interventions, 

and not just interventions designated as “organizational change.” Similarly, educational 

meetings are compared with all other strategies, not just those designated as having some

component of provider education. (Provider education and organizational change are 

analyzed in this manner in Tables 8a, 8b, and 11a, 11b, respectively.) 

2. Several of the categories currently defined as separate QI strategies may overlap with 

other QI strategies such that they might reasonably have been designated a substrategy 

within those categories (e.g., patient education might subsume promotion of selfmanagement or even patient reminders; provider reminders might subsume facilitated 

relay of clinical data to providers). Tables 9a and 9b address this issue for patient 

education by presenting median effects for various ways of collapsing patient education, 

self-management, and patient reminders. Tables 10a and 10b present similar analyses for 

provider reminders and the facilitated relay of clinical data. 

3. The apparent benefit of any particular strategy is confounded by the presence of other 

strategies in the same intervention. Comparing interventions with a particular strategy to 

those interventions with no such component provides some estimate of the attributable 

effect of a given strategy (e.g., the median effect of all studies with provider education 

versus the median effect of all interventions with no component of provider education). 

Nevertheless, no definitive statements can be made about the effects of individual QI 

strategies because most studies used more than one strategy. The researchers also 

performed linear regression as a means of assessing the relative benefits of a particular 

QI strategy. 

Provider Education 

Interventions with some component of provider education, alone or in combination with 

other QI strategies, produced significantly larger improvements in glycemic control. Such 

interventions had a median absolute reduction in serum HbA1c of 1.10% (IQ range: 

0.56%,1.50%), compared with 0.40% (IQ range: 0.10%, 1.08%) for interventions with no 

provider education component. The Mann-Whitney test suggests that these two medians are 

29
unlikely to be equivalent (p=0.02; Table 7c). But since the impact of provider education does not 

have a specific relation to any of the a priori hypotheses, this p-value would have to be adjusted 

for multiple hypothesis testing, in which case it would lose its significance (p= 0.2-0.3 if the 

number of comparisons were taken to be 10-15). 

Visual inspection of Table 8a gives the impression that the greater effects associated with 

interventions having some component of provider education, compared with those with no such 

component, persisted among larger studies (1.50% median reduction in serum HbA1c vs. 0.20%). 

This comparison loses its significance, however, even without adjusting for multiple hypothesis 

testing (p=0.30 for Mann-Whitney test). Restriction of the analysis to RCTs also results in loss of 

significance (p=0.06 before adjustment for multiple comparisons), despite the persistent 

appearance of a larger effect in Table 8b. 

 Interventions involving provider education also reported greater improvements in provider 

adherence than did interventions without any educational component for providers. But the 

relative difference was less striking than for glycemic control, and it diminished when the 

analysis was restricted to RCTs (Table 8b). Interestingly, provider education also was the only 

QI strategy to have even borderline significance in the regression analysis (Table 7b), with a 

coefficient of 0.25 (95% CI: 0.00, 0.51%; p = 0.05). At the same time, a specific benefit for 

provider education did not relate to any of the a priori hypotheses, so this p-value requires 

adjustment for multiple comparisons and would remove the appearance of a significant result. 

As acknowledged in the preceding section, the designation of “provider education” as a 

single category—including components as diverse as workshops and conferences, educational 

outreach, and distribution of printed materials—is somewhat arbitrary. These components were 

compared to the overall set of QI strategies, and not just to other strategies with some element of 

provider education, as if educational meetings and dissemination of educational materials were 

regarded as their own categories. Using this more general approach to the comparisons (Tables 

8a and 8b), interventions with educational meetings or workshops appeared more effective than 

interventions without them. This also is true of interventions involving the distribution of 

educational materials, compared with those lacking the distributed materials component (p=0.03 

and p=0.06, respectively). Only one study of educational outreach reported effect on glycemic 

control in a format compatible with this analysis, preventing meaningful comparisons with 

interventions lacking educational outreach. 

Putting aside the issue of multiple hypothesis testing, the small numbers of studies make 

confounding by the presence of other interventions a significant possibility. The one study of 

educational materials, for example, that fell in the upper two quartiles of sample size involved a 

fairly intensive case management intervention.109 There is no way to assess the impact 

attributable to the component involving educational materials. 

Confounding by the presence of other interventions is still quite probable, even with larger 

numbers of comparisons. Across all sample sizes, for instance, eight comparisons89, 96, 109, 115, 153,

157 evaluated interventions involving educational materials distributed to providers. These 

comparisons reported a median reduction in serum HbA1c of 0.91% (IQ range: 0.52%,1.48%) 

compared with 0.40% (IQ range: 0.1%, 1.25%) for the 30 interventions involving no distribution 

of educational materials. The eight comparisons with interventions including educational 

materials involved problem-based learning in one trial judged to involve no other QI strategies,96

a multifaceted intervention including components of audit and feedback and disease/case 

management in addition to provider education,153 and patient reminders and disease or case 

management in addition to provider education.109 Other comparisons included elements of 

30
patient education, patient reminders, and provider reminders in addition to provider education,157

a Web-based decision support tool,89 and an intensive, multifaceted intervention including 

benchmarking, computerized decision support, and frequent interaction with participating 

patients and providers.135 Thus, even among these eight interventions involving the distribution 

of educational materials and also reporting an effect on glycemic control, the apparent benefit of 

educational material actually might reflect the benefits of the other intervention components 

involved. 

Patient Education, Promotion of Self-management, 

and Patient Reminders 

Tables 9a and 9b present the median effects on glycemic control and provider adherence for 

patient education, promotion of self-management, and patient reminders, alone or in combination 

with other QI strategies. The 18 trials involving patient education achieved a median reduction in 

HbA1c of 0.70% (IQ range: 0.34%, 1.45%), compared with the median reduction of 0.39% (IQ 

range: 0.10%, 0.81%) seen in the 20 studies with no patient education component (MannWhitney p=0.08). The regression analysis detected no significant effect for patient education, in 

terms of glycemic control or the summary measure of provider adherence. 

The investigators were unable to detect any important effects in the analysis for selfmanagement or patient reminders. Collapsing the patient education, patient reminders and 

promotion of self-management strategies into a single, broad category added nine more studies 

but left the median effect relatively unchanged at 0.8% (IQ range: 0.33%, 1.44%). Moreover, 

self-management and patient reminders—separately or as a collapsed category—produced 

roughly the same median effects as interventions without any of these QI strategy components. 

Provider Reminders and Facilitated Relay 

of Clinical Data 

Among comparisons of all sizes, neither provider reminders nor facilitated relay of clinical 

data to providers achieved results substantially different from all other QI strategies, or all 

comparisons without any component of either of these strategies (Table 10a). Among larger 

studies, both strategies achieved marginally increased reductions in serum HbA1c compared to 

interventions without these strategies, but this benefit disappeared for RCTs (Table 10b). Neither 

strategy produced any apparent benefit for provider adherence beyond what was achieved by all 

other strategies, and by all interventions without components of either of these two strategies. 

Audit and Feedback 

The five trials utilizing audit and feedback137, 145, 153, 157 reported a median reduction in HbA1c

of 0.71% (IQ range: 0.41%, 1.40%), compared with 0.47% (IQ range: 0.20%, 1.30%) for trials 

absent audit and feedback—though these medians are unlikely be different (p=0.5 for MannWhitney test). The improvement in provider adherence seen with audit and feedback (median 

improvement of 5.6% [IQ range: 3.4%, 16.4%]) also was superior to that achieved by 

interventions without any audit and feedback component (median improvement of 4.5% [IQ 

range: 4.0%, 5.1%]), though this difference also was non-significant (p=0.4). 

31
The results for audit and feedback also may illustrate another form of publication bias related 

to quality of reporting, rather than sample size or trial design. Of the five comparisons (reported 

in four publications137, 145, 153, 157) evaluating the impact on glycemic control of an intervention 

involving audit and feedback, four comparisons137, 153, 157 reported no standard deviations for any 

of the reported serum HbA1c group means. (Consequently, the regression coefficient shown in 

Table 7b reflects the comparison of the post-intervention effect size for a single study of audit 

and feedback145 with the 26 other comparisons involving no component of audit and feedback). 

The single comparison that provided sufficient data to warrant inclusion in the regression 

analysis reported a net reduction in serum HbA1c attributable to the intervention of only 0.10% 

(from this single study), which appears significantly lower than the median reduction of 0.47% 

(95% CI: 0.24%, 0.99%) associated with all studies lacking any component of audit and 

feedback. 

The researchers were unable to adequately capture any objective measure of “intensity” for 

audit and feedback (or any QI strategy), and therefore did not adjust for any such measure in the 

analysis. Consequently, it is possible that the single trial of audit and feedback included in the 

regression analysis was a particularly low intensity form of this general strategy and/or the 26 

interventions with no audit and feedback component involved some high intensity versions of QI 

strategies other than feedback. The former possibility appears unlikely, as the aforementioned 

comparison of audit and feedback145 also involved a computerized decision support system used 

to guide physicians in matters of diagnostics, history recording, the physical exam, additional 

tests, and treatment, as well as providing recommendations for key management decisions. 

Nevertheless, any inferences regarding the relative benefit (or lack thereof) of audit and 

feedback, compared with the merits of other QI strategies, would be highly speculative given 

only one (or even five137, 145, 153, 157) trials as a basis for comparison. 

Organizational Change 

Organizational changes were present in 27 of the 38 comparisons reporting changes in mean 

serum HbA1c, but the investigators were able to calculate a value for the summary measure of 

provider adherence in only six of the 17 trials (Tables 11a and 11b). While organizational change 

as a broad category had little apparent impact on glycemic control, it was the disease or case 

management and changes to the existing medical record system (e.g., implementation of a 

specialized diabetes patient registry, or a more general electronic medical record) strategies that 

achieved median reductions in serum HbA1c notably greater than the interventions absent these 

strategies. This appeared to be the case across the entire sample of studies and in the subset of 

interventions that included some component of organizational change. For changes to the 

medical record (e.g., implementation of a clinical information system), the five studies classified 

as implementing this type of organizational change reported a median reduction in serum HbA1c

of 1.40% (IQ range: 1.40%, 1.90%), compared with a median reduction of 0.40% (IQ range: 

0.10%, 0.80%) for the 33 trials with no such component. This comparison was judged significant 

after the Mann-Whitney test was applied to the two medians (p=0.007; Table 7c), however the 

five trials involving this type of organizational change all appeared in the lower two quartiles of

sample size (Table 11a). 

The comparison of the median effects associated with interventions involving disease

management and those interventions without any such component appeared to have a significant 

impact on the median reduction in serum HbA1c (p=0.009; Table 7c). But this appearance of 

32
statistical significance would not endure an adjustment for multiple hypothesis testing (given the 

10-15 basic comparisons made). It should be noted that a trend towards a significant difference 

persisted among non-randomized trials in the upper two quartiles of sample size (p=0.003), 

although less so among randomized trials (p=0.06 without adjusting for multiple comparisons). 

Adding disease management to an intervention was associated with less substantial 

incremental improvement in provider adherence (Table 11b). Because of the inverse associations 

with trial design and study period (discussed below), the researchers repeated the regression 

analysis of the impact of disease management with inclusion of trial design and study year as 

predictors. This analysis, however, left the parameter estimate and associated confidence interval 

relatively unchanged (data not shown). That disease management had little impact on provider 

adherence is perhaps to be expected, given the focus on structured followup and patient 

management, rather than aspects of provider behavior. 

Only one trial examined changes to the medical record with provider adherence, and it 

reported less improvement than did studies without this intervention component. Changes to 

team personnel or structure produced unimpressive effects on glycemic control. Only two studies 

employed this intervention component and also reported provider adherence. The single RCT did 

achieve a larger improvement in generalized provider adherence than the 13 RCTs without 

changes to team structure or personnel, while the non-randomized trial did not (Table 11b). 

 

Additional Analyses–Clinical Information Systems 

Clinical information system is a broad term encompassing systems performing a wide variety 

of functions. A general feature that serves to distinguish clinical information systems from

administrative information systems is that the former require data entry or data retrieval by 

clinicians at the point of care.166 The researchers identified interventions using a clinical 

information system for any of the following purposes: trial participant identification or

enrollment, provider reminder delivery, clinical decision support, provider-to-provider 

communications enhancements, or clinical performance auditing. 

As shown in Tables 12a and 12b, 20 trials (30%) involved some role for a clinical 

information system, though this role was limited to identifying or enrolling eligible participants 

in 6 of the trials.121, 129, 133, 143, 152, 155 Interventions that used a clinical information system in at 

least one such capacity achieved greater reductions in glycemic control than did interventions in 

which this component played no role (Table 12a). The difference, however, was not statistically 

significant (p=0.10 for Mann-Whitney test; Table 7c), even without adjustment for multiple 

comparisons. Moreover, even the appearance of a benefit for interventions with some role for a 

clinical information system diminished substantially in larger studies and those with a 

randomized design (Tables 12a and 12b). 

The roles listed above clearly have the potential to differ widely in their effect (e.g., 

provision of decision support, versus mere identification of eligible participants in the 

intervention). At the same time, focusing on the specific roles for this intervention suggested no 

apparent benefit for decision support, auditing clinical performance, or any of the other roles 

examined (Tables 12a and 12b). 

33
Effects of Study Setting and Methodologic Features 

Study Setting 

Table 7a shows relationships between key study outcomes and country (e.g., U.S. versus 

non-U.S.), study period (i.e., the midpoint of the observation period for the trial), and patient 

selection. Patient selection refers to any explicit efforts to enrich the study population for more 

complex patients, defined in terms of comorbid conditions, presence of diabetic complications, 

problems with treatment adherence, or poor access to care (e.g., uninsured patients). 

The only statistically significant finding among these relationships is a negative correlation 

between study period and provider adherence, meaning that more recent trials had a tendency 

toward smaller improvements in adherence (p= 0.004). The relatively low p-value would retain 

conventional statistical significance, even with correction for as many as 15 comparisons. More 

important, this finding relates to one of the investigators’ a priori hypotheses. As stated in 

Methods section (Page 24), one reason for entertaining this hypothesis was that baseline 

adherence might have improved in response to past QI efforts, making further improvement 

more difficult. The mean baseline adherence in control and intervention groups across all studies 

was 51 ±18%. Although not depicted in Table 7a, baseline adherence did exhibit a substantial 

positive correlation with study period, with a Spearman rank correlation of 0.6 (p=0.006). 

Methodologic Features 

The most striking finding in Table 7a is the highly significant negative correlation between 

use of a randomized design and the generalized measure of provider adherence, with 

randomization reducing the effect size by roughly 30% (p=0.0007). Again, this p value would 

retain its statistical significance with adjustment for multiple comparisons. Moreover, this was 

another of the researchers’ a priori hypotheses. As there were only three non-randomized trials 

reporting impacts on provider adherence,105, 108, 113

 this statistical significance reflects a striking 

difference in effect size. As shown in Table 6, these three non-randomized trials reported a 

median improvement in provider adherence of 18% (IQ range: 17.2%, 21.0%), compared with a 

median of 4.5% (IQ range: 3.5%, 5.4%) for the 14 randomized comparisons. 

A substantial negative correlation also existed between sample size and effect size (i.e.,

larger studies tended to show smaller effects), further confirming the trend toward larger effects 

for smaller studies seen in Tables 3-12. Also, as shown in Table 7a, the rank correlation 

coefficients for this relationship were -0.46 (p=0.02) and -0.22 (p=0.4) for glycemic control and 

provider adherence, respectively. While of smaller magnitude, the correlation for provider 

adherence still is noteworthy. (The statistically non-significant result likely reflects the small 

number of studies reporting this outcome.) Also, though not shown in Table 7a, the inverse 

correlation between sample size and effect size changed very little among RCTs alone. Only six 

of the RCTs had a cluster design, so the p-value lost statistical significance, but the magnitude of 

the correlation increased overall (Spearman rank correlation coefficient = -0.54; p=0.3). 

34
Chapter 4. Discussion

Effectiveness of QI Strategies 

This investigation determined that QI interventions provided small-to-modest improvements 

in glycemic control and provider adherence. Taken as a whole, the interventions studied in the 66 

included comparisons reported a median absolute reduction in serum HbA1c of 0.48% (IQ range: 

0.20%, 1.38%) and median absolute increase in provider adherence of 4.9% (IQ range: 3.8%, 

15.0%) above any improvements observed from “usual care.” The researchers also found that 

interventions involving more than one QI strategy resulted in a greater benefit than did 

interventions using a single strategy. This difference achieved statistical significance, but 

nevertheless should be interpreted with caution, as the small number of single-faceted 

interventions in the review makes confounding by other factors (e.g., the intensity of these single 

interventions, as well as various patient, provider, and organizational characteristics) a 

substantial possibility. Disease management and changes to the existing medical record system

(e.g., implementation of a specialized patient registry, or a more generalized clinical information 

system) were associated with trend toward larger improvements in glycemic control, but these 

relationships were not pre-specified as hypotheses. Moreover, they would not withstand 

correction for multiple comparisons in this largely exploratory analysis, and were less

pronounced in RCTs. 

The findings should be interpreted cautiously for several reasons. First, the reviewers found 

that larger and more rigorously designed trials found a smaller benefit than did smaller or less 

rigorously designed trials. As discussed below, this finding strongly suggests the presence of 

publication bias. Second, most interventions involved multiple QI strategies, thus limiting 

assessments of the intrinsic benefit for any particular QI strategy. Finally, this review considered 

only QI studies regarding diabetes. QI studies related to other diseases are relevant in 

understanding the usefulness of specific QI strategies, as discussed in further detail below. 

Because of the importance of potential publication bias, the discussion will begin with this topic. 

Publication Bias 

The researchers found a significant inverse correlation between trial design and the 

magnitude of reported improvements in provider adherence, and, to a lesser extent, glycemic 

control (i.e., comparisons employing a randomized design reported significantly smaller 

improvements). Whereas non-randomized trials reported a median absolute improvement in 

provider adherence of 18.0% (IQ range: 17.2%, 21.0%), randomized trials reported a median 

improvement of only 4.5% (IQ range: 3.5%, 5.4%). The Spearman correlation coefficient was 

significant for both outcomes, but the investigators also tested the impact of trial design in a 

regression model adjusted for baseline differences between the control and intervention groups, 

as well as weighting by sample size, to ensure that this did not reflect baseline imbalances in 

study groups (which would occur more commonly in non-randomized trials). This analysis 

eliminated the significant relationship between trial design and glycemic control, but the 

relationship remained highly significant for provider adherence. On average, the improvement in 

provider adherence observed in randomized trials was 14.3% less than that observed in nonrandomized trials (p=0.001). 

35
For studies of glycemic control, the relationship to trial design was less clear-cut, but a 

striking inverse relationship existed with sample size. Among the 38 comparisons reporting 

changes in mean glycemic control, those falling in the lowest quartile of sample size reported a 

median reduction in serum HbA1c of 1.35% (IQ range: 0.81%, 1.73%), whereas those in the 

highest quartile reported a median reduction of only 0.10% (IQ range: 0.10%, 0.33%). This 

inverse relationship between sample size and observed impact on glycemic control was 

statistically significant (Spearman rank correlation coefficient = −0.39, 95% CI: −0.01, −0.67; p= 

0.04).§§ These findings strongly suggest substantial publication bias operating at the level of 

sample size and trial design, such that publication of smaller studies with non-randomized 

designs occurs more often when reported improvements are large, than when the improvements 

are small or negative. 

Correction for multiple hypothesis testing might give the appearance of chance association 

for some of these relationships. However, the results of these hypothesis tests also must be 

considered in the context of the prior probability or expectation that such associations might well 

exist. As discussed in the Methods section, publication bias is likely to affect this review, at least 

to the same extent that it exists for meta-analyses of clinical research—if not to a greater extent. 

Sample size and trial design are the two most often identified factors playing a role in publication 

bias. Thus, the detection of an inverse relationship between either study size or trial design and 

the magnitude of reported effect is more plausibly regarded as a confirmation of publication bias, 

than as a chance association due to multiple comparisons. 

Benefit of Multifaceted Interventions 

Despite the associations of effect size with sample size and trial design, certain findings 

appear to reflect more than just the effects of publication bias. In particular, interventions having 

at least two component QI strategies were associated with median effects significantly larger 

than were single-faceted interventions. The 32 comparisons involving interventions with at least 

two strategies reported a median reduction in serum HbA1c of 0.60% (95% CI: 0.30%, 1.40%) 

compared with a median reduction of 0.00% (IQ range: -0.08%, 0.16%). These medians are 

unlikely to be equivalent, given the Mann-Whitney test result of p=0.01. The significance of this 

difference further increased (p=0.005) when the investigators reclassified interventions using a 

scheme similar to other authors, in which the major substrategies of provider education and 

organizational change were treated as their own categories. These results might be considered of

borderline significance, given the multiple hypotheses explored in the analysis, except that this 

hypothesis differed from the others in its role as one of three a priori hypotheses. 

Nevertheless, this finding will require further exploration. Other reviews have reached 

conflicting conclusions regarding the relative impact of adding more QI strategies, irrespective 

of their content.16, 54, 85, 103 As with the analysis of specific QI types, the apparent impacts of a 

particular number of strategies (even the simple distinction between single- and multifaceted) is 

confounded by the distribution of the particular QI types across interventions. The authors cannot 

rule out the possibility that the one or two strategies with the largest, true underlying effects 

happen to be included in strategies that incorporated more QI components. Further confounding 

 

§§ This inverse correlation persisted with restriction among randomized trials and even among randomized trials without 

clustering effects (Spearman = 0.415; p=0.07). The loss of statistical significance for the second subset presumably reflects the

decreased number of studies. 

36
undoubtedly occurs as a result of non-random relationships between the adoption of more 

complex interventions and characteristics related to the local proponents of the intervention 

and/or the organizational milieu. For example, more complex interventions may occur more 

commonly in institutions with a greater commitment to quality improvement, which might affect 

support from senior management, availability of resources, and attitudes of participants, among 

other potential predictors of intervention success.

Uncertain Benefit for Specific QI Strategies 

Disease management was the only strategy to exhibit an impact on median effects on 

glycemic control that approached a level of significance such that it would withstand correction 

for multiple hypothesis testing. Even without such adjustment, however, this apparent effect was 

diminished somewhat by a focus on larger trials and diminished substantially by restricting the 

analysis to randomized trials. Moreover, the regression analysis adjusting for baseline group 

imbalances and weighting by sample size yielded a non-significant result for disease 

management as a predictor of improved glycemic control. 

A recent and systematic review of disease management strategies reported significant 

beneficial effects on measures of disease control such as the authors examined.17 This 

comprehensive and well-conducted review had the advantage of cutting across multiple 

conditions (in contrast to this review of diabetes, and another systematic review focused on 

disease management for heart failure patients167). The recent crosscutting review,17 however, did 

not take into account cluster effects.168 Nor did it adjust for baseline differences between 

intervention and control groups. As outlined in the Methods section and reviewed elsewhere at 

length, the adoption of a randomized design does not preclude the need to adjust for baseline 

differences likely to impact the outcome of interest, even when these baseline differences do not 

appear significant.90-94

Among other individual QI strategies, trials using provider education achieved the highest 

absolute reduction in HbA1c and had a significant Mann-Whitney comparison test, versus trials 

without provider education. Provider education also was the only strategy to emerge as a 

significant predictor for improved provider adherence in regression analysis. As outlined above, 

however, these results were found to lose their significance if adjusted for multiple comparison 

testing. 

Little Benefit from Existing Clinical 

Information Systems 

Apart from the implementation of a new clinical information system (which was treated as a 

type of organizational change), the investigators further assessed the potential impact of existing 

clinical information systems performing any of five specific roles. Thirty percent of the included 

interventions involved some role for a clinical information system, and these interventions 

reported greater median improvement in glycemic control than did interventions without any role 

for a clinical information system. This difference was not statistically significant, however, 

(p=0.10 for Mann-Whitney test even without adjustment for multiple comparisons) and shifting 

the focus to larger studies and those with a randomized design diminished substantially the 

appearance of a benefit for interventions with some role for a clinical information system. 

37
Clinical information systems also had no apparent additional effect on provider adherence, 

compared with interventions without any role for an information system. 

Focusing on specific roles for clinical information systems suggested no incremental benefit 

for any particular informatics function (e.g., decision support, auditing clinical performance, 

reminder systems). It should be noted that these findings reflect very small numbers of studies. 

The disappointing findings, however, also should be considered in light of likely confounding 

factors, which could inflate reported effects. For instance, the presence of sophisticated 

information systems is likely to be associated with the presence of other factors plausibly 

associated with successful interventions (e.g., greater financial resources, increased institutional 

investment in QI). Furthermore, while other reviews have found evidence supporting the impact 

of decision support systems,33, 169 it is noteworthy that the most recent and possibly best-designed 

study assessing the impact of a clinical information system in outpatient management of chronic 

illnesses showed no beneficial impact on processes of care or any patient outcome for asthma or 

chronic angina.170 The same investigators are likely to publish the results of a similar trial 

focused specifically on diabetes care in the near future,171 which will add substantially to the 

evidence addressing this topic. 

Of course, the absence of a demonstrable benefit does not prove a lack of benefit, and there 

are sound a priori reasons to believe that changes to existing medical record systems (e.g., a 

clinical information system deployment) might confer some benefit in diabetes care. In addition 

to the non-significance of this result, however, it is worth noting that evaluations of clinical 

information systems involve a special type of publication bias, insomuch as systems with 

failed172-174 or unsatisfactory175 implementations generally are excluded from evaluations of the 

of the intervention benefits, even though these implementations consume significant QI 

resources. 

Comparison with Previous Review of this Topic 

In the previous Cochrane review of this topic,16 all included trials were judged to have more 

than one QI strategy, permitting no direct comparison of single and multifaceted interventions. 

(The authors inferred a benefit from multifaceted interventions based on the general finding of 

positive effects for the various multifaceted interventions evaluated.) The researchers involved 

with this review regarded 14 trials as having a single QI strategy, nine of which were published 

after the last substantive update to the Cochrane review.89, 104, 112, 114, 116, 119, 121, 147, 155 The 

multiple QI strategies designation given to the remaining five studies by the Cochrane reviewers 

reflected differences in taxonomy in several instances. For instance, educational meetings and 

distribution of educational materials were considered separate strategies, rather than 

substrategies within the broader category of patient education. In other cases, however, there 

appears to have been a difference in judgment between the present reviewers and those involved 

with the previous study. Nevertheless, when the present investigators employed a taxonomy

more akin to that used in the Cochrane review (in which major substrategies of provider 

education and organizational change were treated as their own categories), multifaceted 

interventions showed a median reduction in serum HbA1c of significantly greater magnitude than 

that reported by single strategy interventions (0.58% vs. 0.05%; p=0.005). 

The Cochrane review also included a study176 as an interrupted time series that the present 

investigators regarded as a simple before–after study and therefore excluded it. Re-review of 

these studies might produce consensus, but this disagreement further reinforces the notion that 

38
catagorizing design attributes for trials can be challenging and requires interpretation based on 

limited descriptions. 

The Cochrane review16 of this topic suggested that multifaceted interventions carried benefit, 

and cited organizational changes (including computerized patient tracking systems and structured 

recall of patients) as particularly worthwhile interventions. The present review lends greater 

credence to the benefit of multifaceted interventions, as the Cochrane review included no single 

faceted interventions and performed no quantitative analysis. Thus, the benefit of multifaceted 

interventions was inferred simply from the qualitative impression of benefit derived from the 

included studies, without any comparison to single-faceted interventions. 

Limitations 

An important limitation of this review arises from the studies themselves, and underscores 

the need for more rigorously designed studies of quality improvement interventions. The 

limitation can be split into two categories: issues specifically related to the design and 

interpretation of research in quality improvement64, 79, 177, 178 and problems with respect to the 

optimal design and reporting of health care research in general. Examples of the second category 

include a failure to report baseline data for the outcomes of interest, data omissions such as 

sample sizes or standard deviations, and inappropriate choices of statistical tests. Examples of

the first category—problems more specifically related to quality improvement—highlight 

important gaps in the literature. They include limited descriptions of the interventions 

themselves, such that many interventions could not be categorized except in the most general 

terms (e.g., “provider education” or “disease management”), nor replicated by other 

investigators; omissions of important information regarding factors likely to affect intervention 

success or failure (e.g., the degree of institutional support, the availability of ancillary 

administrative resources, the attitudes of participants towards the intervention, or the perceived 

quality target). Another factor hampering quality improvement research is the lack of grounding 

in a theoretical understanding of how to effect change at the level of individual behavior or 

organizational culture and structure (see Chapter 3). For instance, the vast majority of studies 

provided no answer to the most basic question of why a particular QI strategy was selected to 

address a given problem (e.g., why provider education and not, say, audit and feedback—or vice 

versa). Choices regarding the format for delivering the selected QI strategy similarly received 

little to no attention. Some of this information is challenging to collect or quantify (e.g., degree 

of institutional support), but other types of data (e.g., attitudes of participants) could be collected 

and may prove helpful in determining why some QI interventions succeed while others fail. 

The lack of theoretic grounding for many QI interventions is in stark contrast to the clinical 

research (see Chapter 3). By the time a clinical intervention reaches the stage of evaluation in a 

randomized trial, a substantial body of research (both basic scientific and epidemiologic) 

generally exists and lends credence to the hypothesis that the intervention will benefit patients. as 

A number of theoretic models of behavioral or organizational change so exist, as noted in 

Chapter 2, but their usefulness and applicability to QI interventions in health care has not been 

well studied. Improving the state of evidence regarding QI interventions will require

commensurate improvements in the preliminary research leading up to the design or selection of 

a particular intervention. 

39
Despite the aforementioned general concerns regarding the methodology and underpinnings 

of many QI intervention evaluations, it is possible that beneficial interventions already exist and 

that our analysis has failed to identify their benefits relative to other interventions or usual care. 

It is worth noting in this regard that the investigators’ analysis suggested a modest but 

statistically significant correlation between study period and baseline provider adherence (i.e., 

more recent studies tended to report higher baseline adherence). This suggests quality

improvement has occured and, perhaps more importantly, that achieving the same rate of 

improvement may become more difficult with time. The decision to pursue further 

improvements with respect to any given target depends on a number of factors: the effectiveness 

of the targeted process of care (e.g., how well tight control works to prevent diabetic 

complications); the effectiveness of strategies for promoting further changes in patient or 

provider behavior; the costs associated with the targeted quality gap; and the costs of 

interventions attempting to narrow this gap.179 

 The small number of studies in certain areas also is likely to limit our ability to detect true 

benefits for QI strategies. Although this review is the largest study of this particular topic area to 

date, the number of studies for a particular QI strategy assessing specific outcomes was generally 

10 or fewer. When possible, the investigators performed regression analysis and also 

nonparametric tests to assess for differences between medians. Given the small number of 

studies in some groups, however, the possibility that small effects may have gone undetected 

cannot be ignored. 

In an attempt to increase the number of studies providing data for the quantitative analysis, 

the researchers focused on glycemic control (measured by serum HbA1c) as the sole measure of 

disease control. Thus, they were unable to capture the impacts of the interventions on other 

important aspects of diabetes-related morbidity, such as cardiovascular disease. A QI strategy 

producing even a modest impact on blood pressure control or hyperlipidemia could confer 

substantial benefits to patients. In fact, one of the most comprehensive studies in the sample82

demonstrated a reduction in cardiovascular mortality, while reporting no significant reduction in 

HbA1c for intervention patients compared with patients receiving usual care. Thus, in the absence 

of markedly positive effects for any single strategy, a crosscutting strategy that has modest 

individual effects on glycemic control, hypertension, and hyperlipidemia could result in a 

significant benefit for diabetic patients. 

40
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173. Massaro TA. Introducing physician order entry at a major academic medical center: I. Impact on organizational culture 

and behavior. Acad Med. 1993;68:20-25. 

174. Chin T. Doctors pull plug on paperless system: California's Cedars-Sinai turns off its computerized physician order 

entry system after physicians revolt, demonstrating that implementing new technology is easier said than done. 

American Medical news. Feb. 17, 2003: Available at: http://www.amaassn.org/amednews/2003/2002/2017/bil20217.htm. 

175. Ash JS, Gorman PN, Seshadri V, Hersh WR. Computerized physician order entry in U.S. hospitals: results of a 2002 

survey. J Am Med Inform Assoc. 2003. 

176. Rith-Najarian S, Branchaud C, Beaulieu O, Gohdes D, Simonson G, Mazze R. Reducing lower-extremity amputations 

due to diabetes. Application of the staged diabetes management approach in a primary care setting. J Fam Pract.

1998;47:127-132. 

177. Solberg LI. Guideline implementation: what the literature doesn't tell us. Jt Comm J Qual Improv. 2000;26:525-537. 

178. Grimshaw J, Campbell M, Eccles M, Steen N. Experimental and quasi-experimental designs for evaluating guideline 

implementation strategies. Fam Pract. 2000;17 Suppl 1:S11-16. 

179. Mason J, Freemantle N, Nazareth I, Eccles M, Haines A, Drummond M. When is it cost-effective to change the 

behavior of health professionals? JAMA. 2001;286:2988-2992. 

48
403 Exclusions

R1: 139

R2: 22 

R3: 177 

R4: 0 

R5: 42 

R6: 14 

R7: 3 

R8: 6 

MEDLINE®

Stage 1: title & abstract review by trained research 

assistants 

 All excluded articles then reviewed by a core

investigator (SRR, KGS), with 8 exclusions 

reversed and passed on to next stage of 

review

N = 1669 

articles

Exclusions 

R1: 2113 

3601 citations

1140 Exclusions

R1: 608 

R2: 254 

R3: 213 

R4: 65 

Stage 2: title & abstract review by a core investigator 

Stage 3: full text review by 2 independent reviewers (at 

least one core investigator)

N= 529 

articles 

Articles meeting criteria for full

abstraction

n=126 

Patient education only

n=68 

(to be included in 

subsequent review) 

Some component of 

provider or org change 

(± patient education) 

n=58 articles 

(reporting 66 

comparisons) included 

in current review

EPOC 

Hand Search

77 

citations 

104 

citations 

Figure 1: Search Strategy and article review process

49
Figure 1 Legend 

EPOC = Cochrane Effective Practice and Organization of Care database, described above, contains the results of extensive 

electronic searches of multiple large bibliographic databases, as well as hand searching of key journals. The 104 citations 

indicated above as contributed by EPOC do not include the 241 additional citations in EPOC already identified by the PubMed 

search. 

Hand searching conducted for this project involved scanning bibliographies of all articles included at Stage 4 and the 

bibliographies of all systematic reviews and meta-analyses related to QI strategies in diabetes. When no systematic review 

existed for a given topic, we searched the bibliographies of traditional (narrative) review articles, editorials, and news items that

appeared to describe QI studies involving outpatient diabetic care. 

Reasons for Exclusion

R1 = not QI or not an evaluation

R2 = excluded topic: interventions restricted to diabetes in pregnancy, Type I Diabetes Mellitus, diabetes in children/adolescents, 

screening for new diagnoses of diabetes, preventing diabetes in high risk patients, hospital care 

R3 = study design below Level 2 (i.e. does not meet criteria for RCT, quasi-RCT, CBA, or ITS)

R4 = unrelated to diabetes care (e.g., QI article retrieved by broad search but related to a different chronic illness) 

R5 = no eligible outcomes 

R6 = duplicate article (in some cases the article may have only partially overlapped with another report of the same study; in

general the earlier or smaller the two publications was excluded, but reviewed with the other article in case it contained any

additional information.) 

R7 = study published prior to 1980 

R8 = other; included one abstract not yet published or available as a manuscript from authors1

; one cross-over trial with an

inadequate wash out period and insufficient information to allow inclusion of the part of the trial by itself

2

; one study without 

clear documentation of the number of patients involved3

; one study with data that appeared to contain several errors relating to 

the eligible outcomes

4

; one publication we were unable to obtain5; and one which described a QI strategy for eye screening of 

diabetics in rural areas that did not fit into any of our analytic categories.6

 

Figure 1 References 

1. Blonde L, Guthrie R, Testa M, et al. Diabetes management by a team of diabetes nurse educators, endocrinologists and 

primary care physicians in a managed care setting [abstract]. Book/Association for Health Services Research.16:318-319. 

2. Shultz EK, Bauman A, Hayward M, Holzman R. Improved care of patients with diabetes through telecommunications. Ann 

N Y Acad Sci. 1992;670:141-145. 

3. Nilasena DS, Lincoln MJ. A computer-generated reminder system improves physician compliance with diabetes preventive 

care guidelines. Proc Annu Symp Comput Appl Med Care. 1995:640-645. 

4. Integrated care for diabetes: clinical, psychosocial, and economic evaluation. Diabetes Integrated Care Evaluation Team. 

BMJ. 1994;308:1208-1212. 

5. Thompson FJ, Veves A, Ashe H, Boulton AJM, et al. A team approach to diabetic foot care: the Manchester experience. 

Foot. 1991;1:75-82. 

6. Leese GP, Ahmed S, Newton RW, et al. Use of mobile screening unit for diabetic retinopathy in rural and urban areas. BMJ. 

1993;306:187-189. 

50
Table 1. Summary features of included studies 

Setting Study period

(duration) 

Study Design 

(Number of 

Patients) 

QI strategies 

employed* 

Outcomes 

reported†

2 endocrinology

clinics (Canada)1 --- 

(3 months) 

RCT

(42) 

Self-Mx, Facil 

Relay 

Dz: HbA1c, other 

2 clinics, 

(unspecified US 

city)

2

--- 

(6 months) 

CBA 

(117) 

Self-Mx, Facil 

relay, Org change 

Dz: HbA1c, other 

2 clinics 

(unspecified US 

city)

2

--- 

(6 months) 

CBA 

(87) 

Self-Mx, Facil 

relay, Org change 

Dz: HbA1c, other 

2 large clinics 

(Jacksonville, FL)

3 --- 

(1 year) 

RCT

(138) 

Patient Ed, Facil 

relay, Org change 

Dz: other 

Adhere: other 

2 clinics, Baystate 

Medical Center

(Springfield, MA)4

1995-1996 

(15 months) 

Quasi-RCT

(144, 2 firms) 

Prvdr Ed Dz: HbA1c 

6 group practices 

(United Kingdom)5

--- 

(2 years) 

CBA 

(242, 6 practices) 

Pt Remind, Prvdr 

Ed, Facil relay, Org 

change 

Dz: HbA1c 

Individual GP offices 

and a public hospital 

outpatient clinic

(Netherlands)6

1994 

(1 year) 

CBA 

(275, 32 providers) 

Facil relay, Org

change 

Dz: HbA1c 

Massachusetts 

General Hospital 

Diabetes Center 

(Boston, MA)7

--- 

(1 year) 

RCT

(201) 

Facil relay Dz: HbA1c 

Medical U. of South 

Carolina Adult 

Primary Care Center 

(Charleston, SC)

8

2001 

(6 months) 

RCT

(120) 

Patient Ed, Org 

change 

Adhere: other 

* Pt Remind – patient reminder, Prvdr Remind – provider reminder, Org change – organizational change, 

Self-Mx — self-management, Facil Relay — facilitated relay of clinical data to providers, Patient Ed — patient education, 

Prvdr Ed — provider education, Audit & Fdbck — audit and feedback to provider Financial — financial or regulatory

intervention 

†

 Dz: measure of disease control; hemoglobin A1c (HbA1c), blood pressure (BP); other. 

Adhere: measure of provideradherence to guideline or recommendation for measurement of HbA1c (labeled simply

HbA1c), management of hypertension or coronary artery disease (HTN/CAD); screening or referral for detection of

complications involving the foot or eye (foot/eye); other: patient education/counseling measurement of other lab value 

Pat comp: patient compliance

51
Table 1. Summary features of included studies (continued)

Diabetes outpatient

clinic 

(Australia)

9

1998-1999 

(1 year) 

RCT

(73) 

Org change Dz: HbA1c 

Medicaid program 

 (Hennepin County,

MN)10

--- 

(1 year) 

RCT

(96) 

Financial Pat comp

Diabetes foot clinic 

(Lithuania)11

1995-1997 

(2 years) 

CBA 

(145) 

Patient Ed, Org 

change 

Dz: other 

28 general practices 

(Netherlands)12

1989-1995 

(6 years) 

CBA 

(505, 28 providers) 

Patient Ed, SelfMx, Facil relay,

Org change 

Dz: HbA1c, BP, 

other 

6 primary care

centers 

(Florida)13

--- 

(2 years) 

CBA 

(1029, 6 practices) 

Prvdr Ed, Org 

change 

Adhere: HTN/CAD, 

Foot/Eye, other 

24 non-training

general practices 

(United Kingdom)14

1993 

(1 year) 

RCT

(24 practices) 

Prvdr Ed, Audit & 

Fdbck 

Adhere: HbA1c,

HTN/CAD, 

Foot/Eye, other 

Diabetes centers 

(Minnesota, Florida, 

Colorado)

15

1992-1993 

(6 months) 

RCT

(247) 

Patient Ed, SelfMx, Org change Dz: HbA1c 

124 general 

practices (The 

Netherlands)16

1996-1999 

(3 years) 

RCT

(1431, 124 

practices) 

Prvdr Ed, Audit & 

Fdbck 

Adhere: HTN/CAD, 

Foot/Eye, other 

Diabetes clinic 

(Denmark)17, 18†

1993-2001 

(9 years) 

RCT

(160) 

Patient Ed, Org 

change 

Dz: HbA1c, BP, 

other 

University-affiliated 

family practice clinic 

(unspecified US 

city)

19

--- 

(6 months) 

RCT

(67) 

Patient Ed, Prvdr 

Ed 

Dz: HbA1c 

2 primary care clinics 

(unspecified US 

city)

20

--- 

(3 months) 

RCT

(200) 

Patient Ed, SelfMx, Org change Dz: HbA1c, other 

Specialized diabetic 

service 

(Netherlands)21

--- 

(1 year) 

RCT

(246, 15 practices) 

Patient Ed, Facil 

relay, Org change 

Dz: HbA1c, BP, 

other 

52
Table 1. Summary features of included studies (continued)

10 primary care

practices 

(Germany)

22

1993 

(8 months) 

CBA 

(403, 17 providers) 

Prvdr Ed, Audit & 

Fdbck 

Adhere: HbA1c,

HTN/CAD, Foot/Eye, 

other 

Pat comp 

Hospital diabetic 

clinic and GP offices 

(United Kingdom)23

--- 

(5 years) 

RCT

(200) 

Prvdr Ed, Org 

change 

Dz: HbA1c 

29 general practices 

(Norway)

24

--- 

(21 months) 

RCT

(1034, 17 

practices) 

Prvdr Remind, 

Audit & Fdbck 

Dz: HbA1c, BP 

Adhere: HbA1c,

HTN/CAD, other 

U. of Washington 

Family Medical 

Center 

(Seattle, WA)

25

1998-1999 

(14 months) 

RCT

(109, 2 firms) 

Prvdr Ed, Audit & 

Fdbck, Org change 

Dz: HbA1c, BP 

Royal Prince Alfred 

Hospital Diabetes 

Clinic 

(Australia)

26

--- 

(1 year) 

RCT

(137) 

Org change Dz: HbA1c, BP, 

other 

Adhere: HbA1c,

HTN/CAD, other 

Pat comp 

Royal Prince Alfred 

Hospital Diabetes 

Clinic 

(Australia)

26

--- 

(1 year) 

RCT

(134) 

Self-Mx, Pt 

Remind, Prvdr 

Remind, Org 

change 

Dz: HbA1c, BP, 

other 

Adhere: HbA1c,

HTN/CAD, other 

Pat comp 

General practices 

(Great Britain)27

1988-1990 

(31 months) 

RCT

(181) 

Pt Remind, Facil 

relay

Dz: HbA1c, other 

University-affiliated 

outpatient clinic 

(unspecified US 

city)

28

--- 

(4 months) 

RCT

(45) 

Patient Ed, Org 

change 

Dz: HbA1c, other 

Ambulatory Care 

Quality Improvement 

Project 

(Alabama)29

1996-1998 

(25 months) 

RCT

(1931, 70 

providers) 

Audit & Fdbck Adhere: HbA1c,

Foot/Eye

Cedars Sinai 

Medical Center

(Los Angeles, CA)30

1994-1995 

(1 year) 

RCT

(360, 43 practice 

teams) 

Patient Ed, Prvdr 

Ed 

Dz: HbA1c, BP, 

other 

5 outpatient 

practices in a 

university-based

training program31

1998-1999 

(1 year) 

RCT

(497, 44 providers) 

Audit & Fdbck Adhere: other 

53
Table 1. Summary features of included studies (continued)

2 clinics 

(unspecified

California city)

32

--- 

(1 year) 

CBA 

(2 practices) 

Pt Remind, Prvdr 

Ed, Org change 

Dz: HbA1c, other 

14 independent

physician offices 

(unspecified

California city)

32

--- 

(1 year) 

CBA 

(2 practices) 

Pt Remind, Prvdr 

Ed, Org change 

Dz: HbA1c, other 

Diabetes education 

program 

(unspecified US 

city)

33

1998-1999 

(6 months) 

RCT

(150) 

Patient Ed, SelfMx, Pt Remind, 

Prvdr Remind, 

Facil relay 

Dz: HbA1c, BP, 

other 

Choa Chu Kang

polyclinic 

(Singapore)34

2000-2001 

(7 months) 

CBA 

(211) 

Patient Ed, Prvdr 

Remind, Org 

change 

Dz: HbA1c, BP 

Academic general 

medicine practice, 

Regenstrief Health 

Center 

(Indianapolis, 

Indiana)35

1989-1991 

(2 years) 

RCT

(396, 4 firms) 

Patient Ed, Pt 

Remind, Prvdr Ed, 

Prvdr Remind 

Dz: other, Pat 

comp 

Duke Family

Medicine Center 

(North Carolina)

36

1993-1994 

(6 months) 

RCT

(58 providers) 

Prvdr Remind Adhere: HbA1c,

HTN/CAD, 

Foot/Eye, other 

General medicine 

clinic, Indiana 

University Medical 

Center37

1978-1982 

(3 years) 

RCT

(260, 13 firms)

Prvdr Ed, Prvdr 

Remind, Audit & 

Fdbck 

Dz: HbA1c, BP, 

other 

General medicine 

clinic, Indiana 

University Medical 

Center37

1978-1982 

(3 years) 

RCT

(273, 14 firms)

Patient Ed, Pt 

Remind, Prvdr Ed, 

Prvdr Remind, 

Audit & Fdbck 

Dz: HbA1c, BP, 

other 

21 primary

healthcare sites 

(Australia)

38

1999-2000 

(13 months) 

RCT

(727, 21 practices) 

Pt Remind, Prvdr 

Ed, Prvdr Remind, 

Audit & Fdbck, Org 

change 

Dz: other 

Adhere: HbA1c,

HTN/CAD, 

Foot/Eye, other 

Hospital-based Adult 

Medicine Clinic

(unspecified US 

city)

39

1998-1999 

(1 year) 

RCT

(598, 2 firms) 

Prvdr Ed, Prvdr 

Remind

Dz: HbA1c, BP, 

other 

Adhere: HbA1c,

HTN/CAD, 

Foot/Eye, other 

2 clinics 

(unspecified Midwest 

US city)

40

1993-1995 

(21 months) 

CBA 

(267, 2 practices) 

Patient Ed, Audit & 

Fdbck, Org change 

Dz: HbA1c, other 

54
Table 1. Summary features of included studies (continued)

Endocrinology dept. 

tertiary care hospital 

 (South Korea)41

2000-2001 

(3 months) 

RCT

(50) 

Patient Ed, Facil 

relay

Dz: HbA1c, other 

311 general 

practices 

 (Denmark)42

1989-1995 

(6 years) 

RCT

(1263, 484 

providers) 

Patient Ed, Prvdr 

Ed, Prvdr Remind, 

Audit & Fdbck 

Dz: other 

2 primary healthcare centers 

(Sweden)43

1993-1994 

(1 year) 

CBA 

(408, 2 practices) 

Org change Adhere: HbA1c,

HTN/CAD, 

Foot/Eye, other 

Pat Comp 

16 teaching-hospital 

affiliated primary

care practices 

(Boston, 

Massachusetts)44

--- 

(30 months) 

CBA 

(16 practices) 

Audit & Fdbck, Org 

change 

Adhere: other 

2 general medicine 

clinics 

(unspecified US 

city)

45

--- 

(1 year) 

RCT

(148) 

Patient Ed, SelfMx, Facil relay,

Org change 

Dz: HbA1c, other 

Pat comp 

4 university-affiliated 

VA clinics 

(unspecified US 

city)

46

--- 

(1 year) 

RCT

(292) 

Patient Ed, SelfMx, Pt Remind, 

Facil relay, Org

change 

Dz: HbA1c, other 

Diabetes clinic, Vrije 

Universiteit Medical 

Center 

(The Netherlands)47

1997-1999 

(20 months) 

RCT

(400) 

Org change Dz: HbA1c 

9 primary health

centers 

(United Arab 

Emirates)48

--- 

(18 months) 

CBA 

(219, 9 practices) 

Patient Ed, Prvdr 

Ed, Facil relay, Org 

change 

Dz: BP, other 

General practices 

(The Netherlands)49

1992-1997 

(5 years) 

CBA 

(478, 27 providers) 

Prvdr Ed, Facil 

relay, Audit & 

Fdbck 

Dz: other 

Adhere: HbA1c,

HTN/CAD, other 

3 Turku area 

podiatry clinics 

(Finland)

50

--- 

(1 year) 

RCT

(530) 

Patient Ed, Org 

change 

Dz: other 

Pat comp 

Pleasanton facility,

Kaiser Permanente 

Medical Care

Program 

(N. California)51

1995-1997 

(6 months) 

RCT

(185) 

Patient Ed, Pt 

Remind, Org 

change 

Dz: HbA1c 

Pat comp 

55
Table 1. Summary features of included studies (continued)

Sub-specialty

diabetes clinic 

(unspecified US 

city)

52

1996 

(3 months) 

CBA 

(82) 

Org change Dz: HbA1c, BP 

Adhere: HbA1c,

Foot/Eye, other 

Division of 

Community Internal 

Medicine, Mayo 

Clinic 

(Minnesota)53

2000-2001 

(6 months) 

RCT

(727, 29 providers) 

Org change Dz: HbA1c, other 

Adhere: HbA1c,

other 

Division of 

Community Internal 

Medicine, Mayo 

Clinic 

(Minnesota)53

2000-2001 

(6 months) 

RCT

(752, 29 providers) 

Pt Remind, Org 

change 

Dz: HbA1c, other 

Adhere: HbA1c,

other 

Diabetes clinic 

(Canada)54

--- 

(6 months) 

RCT

(46) 

Patient Ed, Facil 

relay, Org change 

Dz: HbA1c 

General practices 

(England)55

--- 

(16 months) 

CBA 

(218) 

Org change Dz: other 

Adhere: HbA1c

35 primary care

practices 

(Seattle, 

Washington)56

--- 

(1 year) 

RCT

(707, 35 practices) 

Patient Ed, SelfMx, Org change Dz: HbA1c, other 

Adhere: Foot/Eye, 

other 

2 community health 

centers 

(unspecified

Northeast US city)

57

--- 

(1 year) 

RCT

(400, 2 practices) 

Prvdr Ed Adhere: HbA1c,

Foot/Eye, other 

2 community health 

centers 

(unspecified

Northeast US city)

57

--- 

(1 year) 

RCT

(400, 2 practices) 

Patient Ed, Prvdr 

Ed 

Adhere: HbA1c,

Foot/Eye, other 

Australia58 --- 

(6 months) 

RCT

(265, 160 

providers) 

Prvdr Ed, Audit & 

Fdbck 

Adhere: HbA1c,

HTN/CAD, 

Foot/Eye, other 

Australia58 --- 

(6 months) 

RCT

(256, 160 

providers) 

Prvdr Ed, Audit & 

Fdbck 

Adhere: HbA1c,

HTN/CAD, 

Foot/Eye, other 

General medical 

clinic, Durham Dept 

of VA Medical 

Center 

(North Carolina)

59

--- 

(1 year) 

RCT

(275) 

Patient Ed, SelfMx, Pt Remind, 

Facil relay, Org

change 

Dz: HbA1c, other 

56
Table 2a. Number and design of included studies for each quality improvement strategy 

QI strategy Randomized Controlled 

Trial 

Controlled Before-After

Study

Total

Provider education 14 publications 4, 14, 16, 19, 

23, 25, 30, 35, 37-39, 42, 57, 58 

 (16 comparisons) 

6 publications 5, 13, 22, 32, 

48, 49 

(8 comparisons) 

20 publications

(24 comparisons) 

Provider reminders 8 publications 24, 33, 35-39, 42

(10 comparisons) 

1 publication34

(1 comparison)

9 publications 

(11 comparisons) 

Facilitated Relay of 

clinical data 

11 publications 1, 3, 7, 21, 27, 

 33, 41, 45, 46, 54, 59

(11 comparisons)

6 publications 2, 5, 6, 12, 48, 49 

(6 comparisons)

17 publications

(17 comparisons) 

Patient education 20 publications 3, 8, 15, 17, 

19-21, 28, 30, 33, 35, 41, 42, 

45, 46, 50, 51, 54, 56, 59

(23 comparisons) 

5 publications 11, 12, 34, 40, 48 

(5 comparisons) 

25 publications

(28 comparisons) 

Promotion of selfManagement 

8 publications 1, 15, 20, 33, 

45, 46, 56, 59

(10 comparisons) 

2 publications 2, 12 

(3 comparisons) 

10 publications 

(13 comparisons) 

Patient reminders 8 publications 27, 33, 35, 38, 

45, 51, 59, 60

(10 comparisons)

2 publications15, 32

(3 comparisons) 

10 publications

(13 comparisons) 

Audit and feedback 10 publications 14, 16, 24, 25, 

29, 31, 37, 38, 42, 58

(11 comparisons) 

4 publications 22, 40, 44, 49 

(4 comparisons) 

14 publications 

(15 comparisons) 

Organizational change 21 publications 3, 8, 9, 15, 17, 

20, 21, 23, 25, 26, 28, 38, 

45-47, 50, 51, 53, 54, 56, 59 

(24 comparisons) 

14 publications 2, 5, 6, 11-13, 

32, 34, 40, 43, 44, 48, 52, 55 

(16 comparisons) 

35 publications

(40 comparisons) 

Financial Incentives 1 (publication)10

(1 comparison)

0 1 publication 

(1 comparison)

Total 42 publications

 (48 comparisons) 

16 publications

(18 comparisons) 

58 publications

(66 comparisons) 

57
Table 2b. Number of quality improvement strategies per study intervention 

Number of comparisons

Number of QI Citations types in 

intervention 

Randomized Non-Randomized Total

Single QI type 114, 7, 9, 10, 26, 29, 31, 36, 

47,53, 57

343, 52, 55 14 

Multiple (total) 371, 3, 8, 14-17, 19-21, 23-28, 

30, 33, 35, 37-39, 41, 

42,45, 46, 50, 51, 53, 

54, 56-59

152, 5, 6, 11-13, 22, 32, 34, 

40, 44, 48, 49

52 

2 QI strategies 181, 8, 14, 16, 17, 19, 23, 24, 

27, 28, 30, 39, 41, 50, 

53, 57, 58

56, 11, 13, 22, 44 23 

3 QI strategies 103, 15, 20, 21, 25, 37, 51, 

 54, 56

72, 5, 32, 34, 40, 49 17 

4 QI strategies 426, 35, 42, 46 315, 12, 48 7 

5 QI strategies 533, 37, 38, 45, 59 0 5

58
Table 3a. Association between type of quality improvement strategy and glycemic control stratified by study

sample size* 

Median Reduction in HbA1c 

[inter-quartile range]

†

N=Number of comparisons

All Comparisons Comparisons 

with sample size 

 in lowest 

 quartile 

Comparisons 

with sample size 

 in lower 2 

quartiles 

Comparisons 

with sample size 

 in upper 2 

quartiles

Comparisons 

with sample size 

in highest 

quartile

All QI types 0.48 

[0.20, 1.38] 

N=38 

1.35 

[0.81, 1.73] 

N=10 

1.30 

[0.41, 1.49] 

N=19 

0.21 

[0.10, 0.55] 

N=19 

0.10 

[0.10, 0.33] 

N=10 

Provider 

Education 

1.1 

[0.56, 1.5] 

N=9 

1.10 

[0.71, 1.50] 

N=5 

1.29 

[0.67, 1.60] 

N=8 

0.37 

- - - - 

N=1 

0.37 

- - - - 

N=1 

Provider 

Reminders 

0.41 

[0.34, 0.97] 

N=7 N=0 

0.41 

[0.36, 0.94] 

N=3 

0.42 

[0.3 0, 0.85] 

N=4

0.24 

[0.10, 0.40] 

N=2 

Facilitated 

relay

0.48 

[0.33, 1.38] 

N=14 

1.30 

[0.85, 1.55] 

N=3 

1.40 

[0.85, 1.60] 

N=7 

0.30 

[0.21, 0.48] 

N=7 

0.35 

[0.18, 0.53] 

N=4 

Patient 

Education 

0.70 

[0.34, 1.45] 

N=18 

1.50 

[1.40, 1.80] 

N=5 

1.49 

[1.42, 1.73] 

N=6 

0.48 

[0.20, 0.60] 

N=12 

0.35 

[0.17, 0.53] 

N=4 

Selfmanagement 

0.40 

[0.20, 0.60] 

N=13 

0.40 

- - - - 

N=1 

0.90 

[0.38, 1.40] 

N=4 

0.30 

[0.20, 0.50] 

N=9 

0.35 

[0.17, 0.53] 

N=4 

Patient 

reminders 

0.60 

[0.35, 1.29] 

N=11 

1.95 

[1.10, 2.80] 

N=2 

1.29 

[0.57, 1.79] 

N=6 

0.47 

[0.20, 0.60] 

N=5 

0.20 

[0.15, 0.40] 

N=3 

Audit & 

feedback 

0.71 

[0.41, 1.40] 

N=5 

1.06 

[0.70, 1.40] 

N=2 

1.06 

[0.63, 1.42] 

N=4 

0.10 

- - - - 

N=1 

0.10 

- - - - 

N=1 

Organizational 

Change

0.60 

[0.10, 1.35] 

N=27 

1.35 

[1.15, 2.00] 

N=6 

1.35 

[0.61, 1.53] 

N=12 

0.20 

[0.10, 0.60] 

N=15 

0.10 

[0.08, 0.27] 

N=8 

This table shows the median effect on serum HbA1c for all studies in which the intervention involved a given QI type. This was

calculated as follows. For each study, we calculated the net change in serum HbA1c attributable to the intervention as: 

Net ∆HbA1c (Post-intervention HbA1c – Pre-intervention HbA1c)Study group - (Post-intervention HbA1c –Pre-intervention 

HbA1c)Control group. 

The median value was then obtained from the values of Net ∆HbA1c for the studies involving a given QI type or quartile of sample 

size. 

* Sample size stratifications for HbA1c: 1st quartile=69, median=123, 3rd quartile=226 

†

 When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles 

59
Table 3b. Association between improvements in provider adherence* and type of quality improvement strategy

stratified by study sample size†

Median Improvement in provider adherence 

[inter-quartile range]

‡

N=Number of comparisons

All Comparisons Comparisons

with sample size 

in lowest

quartile

Comparisons

with sample size 

in lower 2 

quartiles 

Comparisons

with sample size 

in upper 2 

quartiles

Comparisons

with sample size 

in highest 

quartile

All QI types 

4.8 

[3.8, 15.0] 

N=17 

4.5 

[2.8, 11.4] 

N=3 

10.6 

[4.6, 17.6] 

N=6 

4.5 

[3.6, 5.8] 

N=11 

4.2 

[3.7, 5.2] 

N=8 

Provider 

Education 

5.6 

[4.15, 17.2] 

N=11 

4.5 

[2.8, 11.4] 

N=3 

16.4 

[4.5, 18.0] 

N=5 

5.3 

[4.1, 12.7] 

N=6 

5.3 

[4.7, 8.0] 

N=4 

Provider 

Reminders 

3.4 

[2.2, 3.6] 

N=3 

- - - - 

N=0 

- - - - 

N=0 

3.4 

[2.2, 3.6] 

N=3 

3.6 

[3.4, 3.8] 

N=2

Facilitated 

Relay

4.85 

- - - - 

N=1

- - - - 

N=0

4.85 

- - - - 

N=1

- - - - 

N=0 

- - - - 

N=0 

Patient 

Education 

4.9 

[4.7, 5.4] 

N=3 

4.5 

- - - - 

N=1 

4.7 

[4.5, 4.9] 

N=2 

6.0 

- - - - 

N=1 

- - - - 

N=0 

SelfManagement 

6.0 

- - - - 

N=1 

- - - - 

N=0 

- - - - 

N=0 

6.0 

- - - - 

N=1 

- - - - 

N=0 

Patient 

Reminders 

2.8 

[1.0, 4.5] 

N=2 N=0 N=0 

2.8 

[1.0, 4.5] 

N=2 

4.5 

- - - - 

N=1 

Audit & 

Feedback 

5.6 

[3.4, 16.4] 

N=9 

18.3 

- - - - 

N=1 

17.4 

[16.4, 18.3] 

N=2 

5.0 

[3.2, 10.3] 

N=7 

5.0 

[3.4, 5.6] 

N=5 

Organizational 

Change

4.7 

[4.1, 5.7] 

N=6 

4.9 

- - - - 

N=1 

11.4 

[4.9, 18.0] 

N=2 

4.3 

[3.3, 4.9] 

N=4 

4.3 

[4.0, 4.5] 

N=2 

* For each comparison, the general provider adherence outcome captured the adherence outcome with the median effect size

reported by that study. For example, if a study reported one adherence outcome involving checking HbA1c, another relating to

referral for screening for retinal disease and another for delivery of patient education, each of these outcomes would have an

effect size calculated and the one with the median effect size would contribute the generic adherence outcome for that study. 

†

 Sample size stratification for HbA1c: 1st quartile=69, median=123, 3rd quartile=226 

‡ When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

60
Table 4a. Associations between improvements in glycemic control and provider adherence stratified by 

trial design 

Median Reduction in HbA1c

[inter-quartile range]* 

N=Number of comparisons

Median Improvement in provider adherence 

[inter-quartile range]* 

N=Number of comparisons

All 

Comparisons 

RCT Non-RCT†

 All

Comparisons 

RCT Non-RCT†

 

All QI types 

0.48 

[0.20, 1.38] 

N=38 

0.39 

[0.10, 0.73] 

N=28 

1.40 

[0.70, 1.78] 

N=10 

4.9 

[3.8, 15.0] 

N=17 

4.5 

[3.5, 5.4] 

N=14 

18.0 

[17.2, 21.0] 

N=3 

Provider 

Education 

1.1 

[0.56, 1.5] 

N=9 

0.71 

[0.41, 1.47] 

N=5 

1.50 

[0.97, 2.13] 

N=4 

5.6 

[4.2, 17.2] 

N=11 

4.8 

[3.1, 8.0] 

N=8 

18.0 

[17.2, 21.0] 

N=3 

Provider 

Reminders 

0.41 

[0.34, 0.97] 

N=7 

0.39 

[0.32, 0.45] 

N=6 

1.99 

- - - - 

N=1 

3.4 

[2.2, 3.6] 

N=3 

3.4 

[2.2, 3.6] 

N=3 

- - - - 

 

N=0 

Facilitated 

relay

0.48 

[0.33, 1.38] 

N=14 

0.40 

[0.30, 0.60] 

N=9 

1.40 

[0.50, 1.40] 

N=5 

4.9 

- - - - 

N=1 

4.9 

- - - - 

N=1 

- - - - 

 

N=0 

Patient 

Education 

0.70 

[0.34, 1.45] 

N=18 

0.60 

[0.25, 1.39] 

N=15 

1.40 

[0.95, 1.70] 

N=3 

4.9 

[4.7, 5.4] 

N=3 

4.9 

[4.7, 5.4] 

N=3 

- - - - 

 

N=0 

Selfmanagement 

0.40 

[0.20, 0.60] 

N=13 

0.30 

[0.20, 0.45] 

N=10 

1.40 

[0.95, 1.40] 

N=3 

6.0 

- - - - 

N=1 

6.0 

- - - - 

N=1 

- - - - 

 

N=0 

Patient 

reminders 

0.60 

[0.35, 1.29] 

N=11 

0.43 

[0.28, 0.72] 

N=8 

1.90 

[1.50, 2.35] 

N=3 

2.8 

[1.0, 4.5] 

N=2 

2.8 

[1.0, 4.5] 

N=2 

- - - - 

 

N=0 

Audit &

feedback 

0.71 

[0.41, 1.40] 

N=5 

0.56 

[0.33, 0.90] 

N=4 

1.40 

- - - - 

N=1 

5.6 

[3.4, 16.4] 

N=9 

5.0 

[3.2, 10.3] 

N=7 

20.2 

[16.4, 23.9] 

N=2* 

Organizational 

Change

0.60 

[0.10, 1.35] 

N=27 

0.25 

[0.03, 0.68] 

N=18 

1.4 

[1.1, 1.9] 

N=9 

4.7 

[4.1, 5.7] 

N=6 

4.5 

[4.0, 4.9] 

N=5 

18.0 

- - - - 

N=1 

* When N=2, square brackets show the actual results of each study rather than interpolated inter-quartile range. 

† Non-RCT included 16 controlled before-after studies and 1 quasi-randomized trial. 

61
Table 4b. Impacts on glycemic control and provider adherence stratified by trial design and sample size* 

All sizes Comparisons

with sample 

size in lowest 

quartile

Comparisons

 with sample 

 size in lower 

 2 quartiles 

Comparisons 

with sample 

size in upper 

2 quartiles 

Comparisons

with sample 

size in 

highest 

quartile

All trial 

designs

0.48 

[0.20, 1.38] 

N=38 

1.35 

[0.81, 1.73] 

N=10 

1.30 

[0.41, 1.49] 

N=19 

0.21 

[0.10, 0.55] 

N=19 

0.10 

[0.10, 0.33] 

N=10 

RCTs only 0.39 

[0.10, 0.73] 

N=28 

1.40 

[0.86, 1.73] 

N=6 

0.56 

[0.37, 1.48] 

N=12 

0.21 

[0.10, 0.50] 

N=16 

0.10 

[0.08, 0.24] 

N=8 

Median 

reduction in 

serum HbA1c 

(%) 

[inter-quartile

 range†

] 

N=Number of 

comparisons Non-RCTs 1.40 

[0.70, 1.78] 

N=10 

1.25 

[0.97, 1.75] 

N=4 

1.40 

[1.25, 1.65] 

N=7 

0.50 

[0.30, 1.25] 

N=3 

0.30 

[0.10, 0.50] 

N=2

All trial 

designs

4.8 

[3.8, 15.0] 

N=17 

4.5 

[2.8, 11.4] 

N=3 

10.6 

[4.6, 17.6] 

N=6 

4.5 

[3.6, 5.8] 

N=11 

4.2 

[3.7, 5.2] 

N=8 

RCTs 4.5 

[3.5, 5.5] 

N=14 

4.5 

[2.8, 11.4] 

N=3 

4.7 

[3.6, 8.2] 

N=4 

4.3 

[3.5, 5.5] 

N=10 

4.3 

[3.7, 5.2] 

N=8 

Median 

Improvement 

in provider 

adherence 

(%) 

[inter-quartile

 range]

‡

N=Number of 

comparisons Non-RCTs 18.0 

[17.2, 21.0] 

N=3 N=0 

17.2 

[16.4, 18.0] 

N=2 

23.9 

- - - - 

N=1 N=0 

* Sample size stratification for HbA1c: 1st quartile=69, median=123, 3rd quartile=226. 

† Sample size stratification for adherence outcomes: 1st quartile=55, median=164, 3rd quartile=229. 

‡

 When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

62
Table 5a. Association between improvement in glycemic control and number of quality improvement 

strategies stratified by study sample size*

 

Median Reduction in HbA1c

[inter-quartile range] 

N=Number of comparisons

Number of QI 

Strategies†

All Comparisons Comparisons 

with sample size 

in lowest 

quartile

Comparisons 

with sample size 

 in lower 2 

quartiles 

Comparisons 

with sample size 

 in upper 2 

quartiles

Comparisons 

with sample size 

in highest 

quartile

Any number of 

strategies 

0.48 

[0.20, 1.38] 

N=38 

1.35 

[0.81,1.73] 

N=10 

1.30 

[0.41, 1.49] 

N=19 

0.21 

[0.10, 0.55] 

N=19 

0.10 

[0.10, 0.33] 

N=10 

1 strategy only 0.00 

[-0.08,‡ 0.16] 

N=6 

0.56 

- - - - 

N=1 

 0.10 

[-0.15,‡ 0.23] 

N=3 

0.00 

[0.00, 0.11] 

N=3 

0.00 

[0.00, 0.00] 

N=2§

≥ 2 strategies 0.60 

 [0.30, 1.40] 

N= 32 

1.40 

[1.10, 1.80] 

N=9 

1.40 

[0.63, 1.58] 

N=16 

0.34 

[0.10, 0.60] 

N=16 

0.15 

[0.10, 0.40] 

N=8 

≥ 3 strategies 0.66 

[0.33, 1. 40] 

N=22 

1.30 

[1.10, 1.40] 

N=5 

1.40 

[0.91, 1.44] 

N=11 

0.47 

[0.20, 0.60] 

N=11 

0.35 

[0.17, 0.53] 

N=4 

≥ 4 strategies 0.48 

[0.30, 0.82] 

N=8 N=0 

1.47 

[0.89, 1.69] 

N=3 

0.47 

[0.30, 0.50] 

N=5 

0.50 

[0.35, 0.55] 

N=3 

5 strategies** 0.53 

[0.40, 0.82] 

N=4 N=0 

1.47 

- - - - 

N=1 

0.47 

[0.33, 0.53] 

N=3 

0.40 

[0.20, 0.60] 

N=2§

*

 Sample size stratification for HbA1c: 1st quartile=69, median=123, 3rd quartile=226 

†

 The median number of strategies was 2, with 16 studies involving 2 of fewer strategies and 22 employing 3 or more. 

‡

 All changes were standardized to reflect reductions. Thus, the negative sign here indicates an increase in serum HbA1c. 

§

 When N=2, the numbers in square brackets reflect the results for each of the two studies rather than the interquartile range. 

** No study involved an intervention with more than 5 QI types. Using the alternate taxonomy shown in Table 1 (Appendix H) in

which substrategies of provider education and organizational change are treated as distinct categories, 2 studies reported 

interventions involving 6 strategies.140,146 

63
Table 5b. Association between improvement in provider adherence and number of quality improvement 

strategies stratified by study sample size* 

Median Improvement in Provider Adherence (%) 

[inter-quartile range]

†

N=Number of comparisons

Number of QI 

Strategies 

All Comparisons Comparisons 

with sample size 

 in lowest 

quartile

Comparisons 

with sample size 

in lower 2 

quartiles

Comparisons Comparisons 

with sample size with sample size 

in upper 2 

quartiles

 in highest 

quartile

Any number (for 

comparison

purposes) 

4.8 

[3.8, 15.0] 

N=17 

4.5 

[2.8, 11.4] 

N=3 

10.6 

[4.6, 17.6] 

N=6 

4.5 

[3.6, 5.8] 

N=11 

4.2 

[3.7, 5.2] 

N=8 

1 strategy only 3.0 

[2.0,3.5] 

N=3 

1.0 

---- 

N=1 

1.0 

---- 

N=1 

3.5 

[3.0, 4.0] 

N=2 

3.5 

[3.0, 4.0] 

N=2

≥ 2 strategies 5.3 

[4.5, 16.1] 

N=14 

11.4 

[4.5, 18.3] 

N=2 

16.4 

[4.9, 18.0] 

N=5 

5.0 

[3.8, 6.0] 

N=9 

4.8 

[4.0, 5.5] 

N=6 

≥ 3 strategies 4.9 

[2.9, 5.4] 

N=3 N=0 

4.9 

---- 

N=1 

3.5 

[1.0, 6.0] 

N=2 N=0 

≥ 4 strategies 1.0 

---- 

N=1 N=0 N=0 

1.0 

---- 

N=1 N=0 

5 strategies‡ 1.0 

---- 

N=1 N=0 N=0 

1.0 

---- 

N=1 N=0 

* Sample size stratification for adherence outcomes: 1st quartile=55, median=164, 3rd quartile=229 

†

 When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

‡

 No study involved an intervention with more than 5 QI types.

64
Table 6. Associations between number of quality improvement strategies and improvements in glycemic control 

and provider adherence stratified by trial design 

Median Reduction in HbA1c

[inter-quartile range]* 

N=Number of comparisons

Median Improvement in provider adherence 

[inter-quartile range]* 

N=Number of comparisons

All 

Comparisons 

RCT Non-RCT All 

Comparisons 

RCT Non-RCT

Any number 

(for 

comparison 

purposes) 

0.48 

[0.20, 1.38] 

N=38 

0.39 

[0.10, 0.73] 

N=28 

1.4 

[0.70, 1.78] 

N=10 

4.9 

[3.8, 15.0] 

N=17 

4.5 

[3.5, 5.4] 

N=14 

18.0 

[17.2, 21.0] 

N=3 

Single 

strategy only

0.00 

[-0.08†, 0.16] 

N=6 

0.00 

[-0.10, 0.00] 

N=5 

0.56 

---- 

N=1 

3.0 

[2.0, 3.5] 

N=3 

3.0 

[2.0, 3.5] 

N=3 N=0 

≥ 2 strategies 0.60 

 [0.30, 1.40] 

N=32 

0.41 

[0.25, 0.94] 

N=23 

1.40 

[1.10, 1.90] 

N=9 

5.3 

[4.5, 16.1] 

N=14 

4.9 

[4.2, 5.8] 

N=11 

18.0 

[17.2, 21.0] 

N=3 

≥ 3 strategies 0.66 

[0.33, 1. 40] 

N=22 

0.44 

[0.22, 0.68] 

N=14 

1.40 

[1.33, 1.92] 

N=8 

4.9 

[2.9, 5.4] 

N=3 

4.9 

[2.9, 5.4] 

N=3 N=0 

≥ 4 strategies 0.48 

[0.30, 0.82] 

N=8 

0.39 

[0.30, 0.57] 

N=6 

1.20 

[0.50, 1.90] 

N=2 

1.0 

---- 

N=1 

1.0 

---- 

N=1 N=0 

5 strategies‡ 0.53 

[0.40, 0.82] 

N=4 

0.53 

[0.40, 0.82] 

N=4 N=0 

1.0 

---- 

N=1 

1.0 

---- 

N=1 N=0 

*

 When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles. 

†

 All changes were standardized to reflect reductions. Thus, the negative sign here indicates an increase in serum HbA1c. 

‡

 No study involved an intervention with more than 5 QI types. 

65
Table 7a. Regression results for impact of general study features on glycemic control and provider

adherence 

HbA1c

(27 studies* ) 

Median adherence

Outcome 

(17 studies) 

Study attribute or 

Methodological feature Regression Coefficient for 

prediction of lowered 

HbA1c

[95%CI; p value] 

Regression coefficient for 

prediction of increased 

adherence

[95%CI; p value] 

Country (U.S. vs. non-U.S.) 0.16 

[-0.38, 0.70; p=0.5] 

-0.05 

[0.24, -0.33; p=0.7] 

Study Period - 0.008 

[-0.08, 0.06; p=0.8] 

-0.04 

[-0.02, -0.07; 0.004] 

Patient selection† 0.16 

[-0.48, 0.81; p=0.6] 

-0.18 

[0.21, -0.63; p=0.3] 

Sample Size‡ -0.46 

[-0.09,-0.72; p=0.02] 

-0.22 

[0.29, -0.63; p=0.4] 

Randomization 

-0.15 

[-0.69, 0.70; p=0.7] 

-0.48 

[-0.23, -0.72; p=0.001] 

Adequate concealment of allocation

-0.15 

[-0.90, 0.60; p=0.7] 

-0.22 

[0.09, -0.53; p=0.2] 

Units of analysis same as unit of 

treatment allocation

-0.02 

[-0.68, 0.64; p=0.95] 

-0.15 

[0.44, -0.74; p=0.6] 

Patient blinding§ 0.17 

[-0.67, 1.01; p=0.68] 

0.18 

[0.61, -0.24; p=0.4] 

* N=27 rather than 38, as in the previous tables showing median effects for studies involving impacts on mean HbA1c, because 11

studies did not report data required to include them in the regression analysis (e.g., standard deviations for the reported means).

† “Patient selection” refers to the explicit selection of patients with more advanced disease (e.g., longer duration or presence of 

major complications), greater prevalence of co-morbid conditions, poor adherence, or decreased access to care. 

‡ The data shown for sample size reflects the Spearman rank correlation coefficient for effective sample size and the Net

reduction in HbA1c, the same outcome used in all of the median effects Tables. This outcome was defined as: 

Net reduction in HbA1c = (Post-intervention HbA1c – Pre-intervention HbA1c)Study group – (Post-intervention HbA1c – Preintervention HbA1c)Control group. 

This outcome was used in place of post-intervention effect size, as the strong inverse correlation would largely reflect the

inclusion of sample size in the calculation of effect size. 

§ Blinding was judged to be present when the subjects were unaware of study group assignment. In studies in which providers 

were randomized and patient level data was abstracted from medical records, patient blinding was coded as present.

66
Table 7b. Regression results for impacts of quality improvement strategies by strategy type and by the 

number of strategies per intervention* 

QI Strategy

Reduction in Serum HbA1c 

 (27 studies) 

Regression 

Coefficient 

[95%CI; p value] 

Improvement in Provider 

Adherence (17 studies) 

Regression 

Coefficient 

[95%CI; p value] 

Provider education 0.34 

[-0.37, 1.05; p=0.33] 

0.25 

[0.00, 0.51; p = 0.05] 

Patient education

0.21 

[-0.31, 0.74; p=0.4] 

-0.01 

[0.37, -0.40; p=0.9] 

Self-management 

- 0.23 

[-0.58, 0.53; p=0.93] 

-0.19 

[0.39, -0.78; p=0.5] 

Patient reminders 0.04 

[-0.58, 0.66; p=0.98] 

-0.13 

[0.30, -0.56; p=0.5] 

Provider reminders -0.42 

[-1.15, 0.31; p=0.25] 

-0.27 

[0.07, -0.60; p = 0.1] 

Facilitated relay 0.20 

[-0.33, 0.73; p=0.44] 

-0.15 

[0.44, -0.74; p = 0.6] 

Audit and feedback - 0.57 

[-1.94, 0.81; p=0.40] 

0.00 

- - - - 

Organizational change

-0.05 

[-0.63, 0.52; p=0.85] 

-0.04 

[0.25, -0.33; p = 0.8] 

Disease management

0.49 

[-0.19, 1.19; p=0.15] 

-0.15 

[0.44, -0.74; p=0.6] 

Change to medical records system 

0.17 

[-0.67, 1.01; p=0.69] 

-0.13 

[0.47, -0.73; p=0.6] 

Personnel or team changes

-0.39 

[-0.95, 0.17; p=17] 

0.23 

[-0.18, 0.64; p=0.2] 

* It is important to note that the regression models aimed to evaluate the relative effectiveness of different intervention components 

and the impact of study features such as trial design and study period. Consequently, a negative coefficient does not imply

“harm” or that the intervention was worse than “usual care.” Rather, a negative result means simply that the average effect

associated with, for instance, the presence of provider reminders (which has a negative coefficient in the analysis of both 

outcomes) was less than the average effects associated with interventions lacking this feature (i.e., studies without provider 

reminders reported larger effect sizes, not that provider reminders were harmful). 

67
Table 7c. Significance tests (Mann-Whitney) for median effects associated with selected methodologic features 

and QI strategies 

Median Reduction in HbA1c

(38 studies) 

Median Increase in Provider adherence 

(17 studies) 

Median effect (%) 

[95% CI] 

P value for 

comparison*

Median effect (%) 

[95% CI] 

P value for 

comparison 

RCT 

(n = 28) 

0.39 

[0.20, 0.60] 

RCT 

(n = 14) 

4.5 

Study [3.3, 5.7] 

design Non-RCT 

(n = 10) 

1.40 

[0.52, 1.96] 

0.008 

Non-RCT 

(n = 3) 

18.0 

[16.4, 23.9] 

0.02 

Upper 2 

quartiles

0.20 

[0.10, 0.57] 

Upper 2 

quartiles

4.4 

Sample [2.4, 18.8] 

size Lower 2 

quartiles

1.20 

[0.42, 1.46] 

0.005 

Lower 2 

quartiles

4.9 

[1.0, 18.3] 

0.7 

Single

(n = 6) 

0.00 

[-0.19, 0.53] 

Single

(n = 3) 

3.0% 

Number of [1.0, 4.0%] QI 

strategies Multiple 

(n = 32) 

0.60 

[0.40, 1.3] 

0.01 

[0.005]†

Multiple 

(n = 14) 

5.3 

[4.4, 16.7] 

0.04 

Yes 

(n=9) 

1.1 

[0.42, 1.87] 

Yes 

(n=11) 

5.6 

Pro [3.0, 18.1] vider 

Education No 

(n = 29) 

0.4 

[0.17, 0.66] 

0.02 

No 

(n = 6) 

4.3 

[3.0, 5.9] 

0.2 

Yes 

(n=8) 

1.09 

[0.67, 2.25] 

Yes 

(n=1) 

4.9 

Disea - - - - se 

Manag. No 

(n=30) 

0.39 

[0.20, 0.55] 

0.009 

No 

(n=16) 

4.8 

[3.6, 15.7] 

1.0 

Yes 

(n=5) 

1.40 

[1.10, 2.80] 

Yes 

(n=1) 

1.0 

 - - - - Change to 

medical 

records 

system 

No 

(n=33) 

0.40 

[0.20, 0.60] 

0.007 

No 

(n=16) 

5.0 

[3.9, 15.7] 

0.1 

Yes 

(n=20) 

0.90 

[0.37, 1.40] 

Yes 

(n=5) 

4.0 

[3.4, 6.0] 

Role for 

clinical 

information 

system‡ No 

(n=18) 

0.35 

[0.20, 0.59] 

0.1 

[0.46] No 

(n=12) 

5.3 

[3.2, 17.9] 

0.3 

* P value represents two-sample Wilcoxon rank-sum (Mann-Whitney) test of hypothesis that the two medians being compared are 

equal.

† Result if the alternate classification scheme shown in Appendix H is used. In this scheme, major substrategies of organizational 

change (e.g., Disease Management) and provider education (e.g., educational outreach and professional meetings) are treated as their 

own categories. 

‡ Focusing on specific roles for clinical information systems resulted in higher p-values (e.g., p=0.26 for interventions with some form 

of computerized decision support compared to all interventions without decision support. 

68
69

Table 8a. Association between improvements in glycemic control and specific substrategies of provider 

education stratified by study sample size*

Median improvement in HbA1c

[inter-quartile range] †

N=Number of comparisons 

Type of provider 

education 

All 

Comparisons 

Comparisons 

with sample 

size in lowest 

quartile 

Comparisons 

with sample 

size in lower 2 

quartiles 

Comparisons 

with sample 

size in upper 2 

quartiles 

Comparisons 

with sample 

size in highest 

quartile 

All QI types 

0.48 

[0.20, 1.38] 

N=38 

1.35 

[0.60, 1.48] 

N=10 

0.80 

[0.41, 1.44] 

N=19 

0.21 

[0.10, 0.60] 

N=19 

0.10 

[0.10, 0.33] 

N=10 

Any form of provider 

education 

1.1 

[0.56, 1.5] 

N=9 

1.10 

[0.71, 1.50] 

N=5 

1.29 

[0.67, 1.60] 

N=8 

0.37 

- - - - 

N=1 

0.37 

- - - - 

N=1 

No form of provider 

education 

0.40 

[0.1, 1.08] 

N=29 

1.40 

[1.30, 1.80] 

N=5 

1.30 

[0.35, 1.40] 

N=11 

0.21 

[0.10, 0.58] 

N=18 

0.10 

[0.10, 0.20] 

N=9 

Meetings/workshops 

1.1 

[0.64, 1.69] 

N=7 

0.91 

[0.67, 1.53] 

N=4 

1.10 

[0.64, 1.69] 

N=7 N=0 N=0 

No meetings or 

workshops 

0.4 

[0.1, 1.19] 

N=31 

1.45 

[1.33, 1.73] 

N=6 

1.35 

[0.47, 1.43] 

N=12 

0.21 

[0.10, 0.55] 

N=19 

0.10 

[0.10, 0.33] 

N=10 

No meetings or 

workshops (has 

provider education) 

0.94 

[0.4, 1.5] 

N=2 

1.5 

---- 

N=1 

1.5 

---- 

N=1 

0.37 

---- 

N=1 

0.37 

---- 

N=1 

Educational 

materials 

0.91 

[0.52, 1.48] 

N=8 

1.10 

[0.71, 1.50] 

N=5 

1.10 

[0.64, 1.49] 

N=7 

0.37 

---- 

N=1 

0.37 

---- 

N=1 

No educational 

materials 

0.4 

[0.1, 1.25] 

N=30 

1.40 

[1.30, 1.80] 

N=5 

1.35 

[0.37, 1.50] 

N=12 

0.21 

[0.10, 0.58] 

N=18 

0.10 

[0.10, 0.20] 

N=9 

No educational 

materials (has 

provider education) 

1.9 

---- 

N=1 N=0 

1.9 

---- 

N=1 N=0 N=0 

Educational 

outreach 

0.71 

---- 

N=1 

0.71 

---- 

N=1 

0.71 

---- 

N=1 N=0 N=0 

No educational 

outreach 

0.47 

[0.20, 1.4] 

N=37 

1.40 

[1.10, 1.80] 

N=9 

1.35 

[0.40, 1.49] 

N=18 

0.21 

[0.10, 0.55] 

N=19 

0.10 

[0.10, 0.33] 

N=10 

No educational 

outreach (has 

provider education) 

1.29 

[0.52, 1.60] 

N=8 

1.30 

[0.97, 1.83] 

N=4 

1.47 

[0.83, 1.70] 

N=7 

0.37 

---- 

N=1 

0.37 

---- 

N=1 

* Sample size stratification for HbA1c: 1st quartile=69, median=123, 3rd quartile=226. 

†

 When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.
Table 8b. Association between improvements in glycemic control and specific substrategies of provider

education stratified by study design 

Median Reduction in HbA1c

[inter-quartile range]* 

N=Number of comparisons

Median Improvement in provider adherence 

[inter-quartile range]* 

N=Number of comparisons

Type of provider

education 

All

Comparisons RCT Non-RCT AllComparisons

RCT Non-RCT

All QI types 

0.48 

[0.20, 1.38] 

N=38 

0.39 

[0.10, 0.73] 

N=28 

1.4 

[0.70, 1.78] 

N=10 

4.85 

[3.8,15.03] 

N=17 

4.5 

[3.5, 5.45] 

N=14 

18.0 

[17.2, 20.95] 

N=3 

Provider education 

1.10 

[0.56, 1.50] 

N=9 

0.71 

[0.41, 1.47] 

N=5 

1.5 

[0.97, 2.13] 

N=4 

5.6 

[4.2, 17.2] 

N=11 

4.8 

[3.1, 8.0] 

N=8 

18.0 

[17.2, 21.0] 

N=3 

No provider 

education 

0.40 

[0.10, 1.08] 

N=29 

0.3 

[0.10, 0.60] 

N=23 

1.40 

[0.73, 1.40] 

N=6 

4.3 

[3.5, 4.8] 

N=6 

4.3 

[3.5, 4.8] 

N=6 

---- 

N=0 

Meetings/workshops

1.10 

[0.64, 1.69] 

N=7 

0.71 

[0.56, 1.09] 

N=3 

1.50 

[0.97, 2.13] 

N=4 

10.7 

[3.6, 18.1] 

N=8 

4.50 

[1.0, 5.0] 

N=5 

18.0 

[17.2, 21.0] 

N=3 

No meetings or 

workshops 

0.40 

[0.10, 1.19] 

N=31 

0.30 

[0.10, 0.6] 

N=25 

1.40 

[0.73, 1.4] 

N=6 

4.50 

[3.8, 5.6] 

N=9 

4.50 

[3.8, 5.6] 

N=9 

---- 

N=0 

No meetings or 

workshops (has 

provider education) 

0.94 

[0.40, 1.5] 

N=2 

0.94 

[0.40, 1.5] 

N=2 

---- 

N=0 

5.6 

[4.7, 10.3] 

N=3 

5.6 

[4.7, 10.3] 

N=3 

---- 

N=0 

Educational 

materials 

0.91 

[0.52, 1.48] 

N=8 

0.71 

[0.41, 1.47] 

N=5 

1.10 

[0.83, 1.95] 

N=3 

5.6 

[4.2, 16.5] 

N=7 

4.5 

[3.8, 5.6] 

N=5 

21.0 

[18.0, 23.9] 

N=2 

No educational 

materials 

0.40 

[0.10, 1.25] 

N=30 

0.30 

[0.10, 0.60] 

N=23 

1.40 

[0.95, 1.65] 

N=7 

4.7 

[3.5, 5.8] 

N=10 

4.5 

[3.4, 5.0] 

N=9 

16.4 

---- 

N=1 

No educational 

materials (has

provider education) 

1.9 

---- 

N=1 

---- 

N=0 

1.9 

---- 

N=1 

10.7 

[4.0, 16.9] 

N=4 

5.0 

[3.0, 11.7] 

N=3 

16.4 

---- 

N=1 

Educational 

outreach

0.71 

---- 

N=1 

0.71 

---- 

N=1 N=0 

5.6 

[4.8, 16.5] 

N=7 

5.3 

[4.6, 12.7] 

N=6 

18.0 

---- 

N=1 

No educational 

outreach

0.47 

[0.20, 1.4] 

N=37 

0.37 

[0.10, 0.70] 

N=27 

1.40 

[0.70, 1.78] 

N=10 

4.3 

[3.5, 5.7] 

N=10 

3.9 

[3.3, 4.6] 

N=8 

20.2 

[16.4, 23.9] 

N=2 

No educational 

outreach (has

provider education) 

1.29 

[0.52, 1.60] 

N=8 

0.94 

[0.40, 1.48] 

N=4 

1.50 

[0.97, 2.13] 

N=4 

10.1 

[3.1, 18.3] 

N=4 

2.4 

[1.0, 3.8] 

N=2 

20.2 

[16.4, 23.9] 

N=2 

* When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

70
Table 9a. Association between improvements in glycemic control and specific substrategies of patient education

stratified by sample size* 

Median improvement in HbA1c 

[inter-quartile range]

†

N=Number of comparisons

Type of patient 

education 

All Comparisons Comparisons

with sample size 

in lowest

quartile

Comparisons

with sample size 

in lower 2 

quartiles 

Comparisons

with sample size 

in upper 2 

quartiles

Comparisons

with sample size 

in highest 

quartile

All QI types 

0.48 

[0.20, 1.38] 

N=38 

1.35 

[0.60, 1.48] 

N=10 

0.80 

[0.41, 1.44] 

N=19 

0.21 

[0.10, 0.60] 

N=19 

0.10 

[0.10, 0.33] 

N=10 

Patient 

education 

0.70 

[0.34, 1.45] 

N=18 

1.50 

[1.40, 1.80] 

N=5 

1.49 

[1.42, 1.73] 

N=6 

0.48 

[0.20, 0.60] 

N=12 

0.35 

[0.17, 0.53] 

N=4 

No patient 

education 

0.39 

[0.1, 0.81] 

N=20 

0.71 

[0.56, 1.10] 

N=5 

0.56 

[0.40, 1.40] 

N=13 

0.10 

[0.05, 0.16] 

N=7 

0.10 

[0.03, 0.10] 

N=6 

Patient 

Education, selfmanagement, or 

patient 

reminders 

0.8 

[0.35, 1.44] 

N=27 

1.45 

[1.25, 1.90] 

N=8 

1.40 

[1.15, 1.73] 

N=14 

0.47 

[0.20, 0.60] 

N=13 

0.20 

[0.10, 0.50] 

N=5 

None of above 

0.10 

[0.0, 0.39] 

N=11 

0.64 

[0.60, 0.70] 

N=2 

0.41 

[0.10, 0.56] 

N=5 

0.10 

[0.03, 0.18] 

N=6 

0.10 

[0.00, 0.10] 

N=5 

Selfmanagement or 

patient 

reminders 

0.48 

[0.28, 1.18] 

N=20 

1.10 

[0.75, 1.95] 

N=3 

1.40 

[0.40, 1.47] 

N=9 

0.30 

[0.15, 0.55] 

N=11 

0.20 

[0.10, 0.50] 

N=5 

No Selfmanagement or 

patient 

reminders 

0.49 

[0.1, 1.38] 

N=18 

1.40 

[1.01, 1.65] 

N=7 

1.01 

[0.45, 1.48] 

N=10 

0.16 

[0.08, 0.48] 

N=8 

0.10 

[0.00, 0.10] 

N=5 

Selfmanagement 

0.4 

[0.2, 0.6] 

N=13 

0.40 

---- 

N=1 

0.90 

[0.38, 1.40] 

N=4 

0.30 

[0.20, 0.50] 

N=9 

0.35 

[0.17, 0.53] 

N=4 

No selfmanagement 

0.71 

[0.1, 1.47] 

N=25 

1.40 

[1.10, 1.80] 

N=9 

1.30 

[0.49, 1.65] 

N=15 

0.16 

[0.10, 0.69] 

N=10 

0.10 

[0.03, 0.10] 

N=6 

Patient 

reminders 

0.60 

[0.35, 1.29] 

N=11 

1.95 

[1.10, 2.80] 

N=2 

1.29 

[0.57, 1.79] 

N=6 

0.47 

[0.20, 0.60] 

N=5 

0.20 

[0.15, 0.40] 

N=3 

No patient 

reminders 

0.41 

[0.1, 1.35] 

N=27 

1.35 

[0.67, 1.58] 

N=8 

1.30 

[0.41, 1.40] 

N=13 

0.21 

[0.10, 0.47] 

N=14 

0.10 

[0.05, 0.24] 

N=7 

* Sample size stratification for HbA1c: 1st quartile=69, median=123, 3rd quartile=226. 

†

 When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

71
Table 9b. Association between improvements in glycemic control and specific substrategies of patient education

stratified by study design 

Median Reduction in HbA1c

[inter-quartile range]* 

N=Number of comparisons

Median Improvement in provider adherence 

[inter-quartile range]* 

N=Number of comparisons

Type of patient 

education 

All

Comparisons RCT Non-RCT AllComparisons

RCT Non-RCT

All QI types 

0.48 

[0.2, 1.38] 

N=38 

0.39 

[0.1, 0.73] 

N=28 

1.4 

[0.7, 1.78] 

N=10 

4.85 

[3.8, 15.03] 

N=17 

4.5 

[3.5, 5.45] 

N=14 

18.0 

[17.2, 20.95] 

N=3 

Patient 

education 

(Broad†) 

0.8 

[0.35, 1.44] 

N=27 

0.47 

[0.25, 1.19] 

N=19 

1.4 

[1.33, 1.92] 

N=8 

4.5 

[4.5, 4.9] 

N=5 

4.5 

[4.5, 4.9] 

N=5 

---- 

N=0 

No Patient 

education 

(Broad) 

0.10 

[0.0, 0.39] 

N=11 

0.1 

[0.0, 0.37] 

N=9 

0.33 

[0.10, 0.60] 

N=2 

5.3 

[3.7, 16.8] 

N=12 

4.0 

[3.4, 5.6] 

N=9 

18.0 

[17.2, 21.0] 

N=3 

Selfmanagement or 

patient 

reminders 

0.48 

[0.28, 1.18] 

N=20 

0.35 

[0.2, 0.57] 

N=14 

1.4 

[1.18, 1.78] 

N=6 

4.5 

[2.8, 5.3] 

N=3 

4.5 

[2.8, 5.3] 

N=3 

---- 

N=0 

No Selfmanagement or 

patient 

reminders 

0.49 

[0.1, 1.38] 

N=18 

0.39 

[0.03, 1.18] 

N=14 

0.98 

[0.45, 1.55] 

N=4 

4.9 

[3.9, 16.1] 

N=14 

4.5 

[3.6, 5.3] 

N=11 

18.0 

[17.2, 21.0] 

N=3 

Patient 

education 

0.7 

[0.34, 1.45] 

N=18 

0.6 

[0.25, 1.39] 

N=15 

1.4 

[0.95, 1.7] 

N=3 

4.9 

[4.7, 5.4] 

N=3 

4.9 

[4.7, 5.4] 

N=3 

---- 

N=0 

No Patient 

education 

0.39 

[0.1,0.81] 

N=20 

0.21 

[0.0,0.4] 

N=13 

1.4 

[0.83,1.65] 

N=7 

4.8 

[3.5,16.1] 

N=14 

4.0 

[3.2,5.3] 

N=11 

18.0 

[17.2,21.0] 

N=3 

Selfmanagement 

0.4 

[0.2, 0.6] 

N=13 

0.3 

[0.2, 0.45] 

N=10 

1.4 

[0.95, 1.4] 

N=3 

6.0 

---- 

N=1 

6.0 

---- 

N=1 

---- 

N=0 

No selfmanagement 

0.71 

[0.1, 1.47] 

N=25 

0.4 

[0.1, 1.25] 

N=18 

1.4 

[0.83, 1.95] 

N=7 

4.7 

[3.7, 15.4] 

N=16 

4.5 

[3.4, 5.0] 

N=13 

18.0 

[17.2, 21.0] 

N=3 

Patient 

reminders 

0.60 

[0.35, 1.29] 

N=11 

0.43 

[0.28, 0.72] 

N=8 

1.90 

[1.5, 2.35] 

N=3 

2.75 

[1.0, 4.5] 

N=2 

2.75 

[1.0, 4.5] 

N=2 

---- 

N=0 

No patient 

reminders 

0.41 

[0.1, 1.35] 

N=27 

0.34 

[0.08, 0.73] 

N=20 

1.4 

[0.53, 1.4] 

N=7 

5.0 

[3.9, 15.7] 

N=15 

4.7 

[3.7, 5.7] 

N=12 

18.0 

[17.2, 21.0] 

N=3 

* When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

72
Table 10a. Association between improvements in glycemic control and specific substrategies of provider

reminder stratified by sample size*

Median improvement in HbA1c 

[inter-quartile range]

†

N=Number of comparisons

Type of provider

reminder 

All Comparisons Comparisons

with sample size 

in lowest

quartile

Comparisons

with sample size 

in lower 2 

quartiles 

Comparisons

with sample size 

in upper 2 

quartiles

Comparisons

with sample size 

in highest 

quartile

All QI types 

0.48 

[0.20, 1.38] 

N=38 

1.35 

[0.60, 1.48] 

N=10 

0.80 

[0.41, 1.44] 

N=19 

0.21 

[0.10, 0.60] 

N=19 

0.10 

[0.10, 0.33] 

N=10 

Provider 

reminder or 

facilitated relay

0.44 

[0.3, 1.4] 

N=20 

1.30 

[0.85, 1.55] 

N=3 

1.35 

[0.40, 1.45] 

N=10 

0.34 

[0.20, 0.49] 

N=10 

0.29 

[0.13, 0.47] 

N=6 

Neither provider

reminder nor 

facilitated relay

0.58 

[0.03, 1.1] 

N=18 

1.40 

[0.91, 1.85] 

N=7 

1.10 

[0.56, 1.50] 

N=9 

0.10 

[0.00, 0.60] 

N=9 

0.05 

[0.00, 0.10] 

N=4 

Provider 

reminder 

0.41 

[0.34, 0.97] 

N=7 N=0 

0.41 

[0.36, 0.94] 

N=3 

0.42 

[0.3 0, 0.85] 

N=4

0.24 

[0.10, 0.40] 

N=2 

No provider 

reminder 

0.56 

[0.15, 1.35] 

N=31 

1.35 

[0.81, 1.73] 

N=10 

1.35 

[0.52, 1.58] 

N=16 

0.20 

[0.10, 0.55] 

N=15 

0.10 

[0.08, 0.27] 

N=8 

Facilitated relay

0.48 

[0.33, 1.38] 

N=14 

1.30 

[0.85, 1.55] 

N=3 

1.40 

[0.85, 1.60] 

N=7 

0.30 

[0.21, 0.48] 

N=7 

0.35 

[0.18, 0.53] 

N=4 

No facilitated 

relay

0.49 

[0.1, 1.18] 

N=24 

1.40 

[0.91, 1.85] 

N=7 

0.91 

[0.38, 1.48] 

N=12 

0.15 

[0.08, 0.65] 

N=12 

0.10 

[0.10, 0.30] 

N=6 

* Sample size stratification for HbA1c: 1st quartile=69, median=123, 3rd quartile=226. 

†

 When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

73
Table 10b. Association between improvements in glycemic control and specific substrategies of provider

reminder stratified by trial design

Median Reduction in HbA1c

[inter-quartile range]* 

N=Number of comparisons

Median Improvement in provider adherence 

[inter-quartile range]* 

N=Number of comparisons

Type of 

provider

reminder 

All

Comparisons

RCT Non-RCT All

Comparisons

RCT Non-RCT

All QI types 

0.48 

[0.2, 1.38] 

N=38 

0.39 

[0.1, 0.73] 

N=28 

1.4 

[0.7, 1.78] 

N=10 

4.85 

[3.8, 15.03] 

N=17 

4.5 

[3.5, 5.45] 

N=14 

18.0 

[17.2, 20.95] 

N=3 

Provider 

reminder or 

racilitated relay

0.44 

[0.3, 1.4] 

N=20 

0.4 

[0.3, 0.57] 

N=14 

1.4 

[0.73, 1.78] 

N=6 

3.6 

[2.8, 4.1] 

N=4 

3.6 

[2.8, 4.1] 

N=4 

---- 

N=0 

Neither 

provider

reminder nor 

facilitated relay

0.58 

[0.03, 1.1] 

N=18 

0.15 

[0.0, 0.78] 

N=14 

1.25 

[0.97, 1.75] 

N=4 

5.6 

[4.5, 16.4] 

N=13 

4.8 

[4.1, 5.9] 

N=10 

18.0 

[17.2, 21.0] 

N=3 

Provider 

reminder 

0.41 

[0.34, 0.97] 

N=7 

0.39 

[0.32, 0.45] 

N=6 

1.99 

---- 

N=1 

3.4 

[2.2, 3.6] 

N=3 

3.4 

[2.2, 3.6] 

N=3 

---- 

N=0 

No provider 

reminder 

0.56 

[0.15, 1.35] 

N=31 

0.35 

[0.1, 0.78] 

N=22 

1.4 

[0.56, 1.4] 

N=9 

5.3 

[4.5, 16.1] 

N=14 

4.9 

[4.3, 5.8] 

N=11 

18.0 

[17.2, 21.0] 

N=3 

Facilitated relay

0.48 

[0.33, 1.38] 

N=14 

0.4 

[0.3, 0.6] 

N=9 

1.4 

[0.5, 1.4] 

N=5 

4.9 

---- 

N=1 

4.9 

---- 

N=1 

---- 

N=0 

No facilitated 

relay

0.49 

[0.1, 1.18] 

N=24 

0.3 

[0.05, 0.76] 

N=19 

1.4 

[1.1, 1.99] 

N=5 

4.8 

[3.7, 15.4] 

N=16 

4.5 

[3.4, 5.6] 

N=13 

18.0 

[17.2, 21.0] 

N=3 

* When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

74
Table 11a. Association between improvements in glycemic control and specific substrategies of organizational 

change stratified by study sample size* 

Median improvement in HbA1c 

[inter-quartile range]

†

N=Number of comparisons

Type of 

organizational

change

All comparisons Bottom quartile 

for sample size 

Bottom two 

quartiles for 

sample size 

Top two

quartiles for 

sample size 

Top quartile for 

sample size 

All QI types 

0.48 

[0.20, 1.38] 

N=38 

1.35 

[0.60, 1.48] 

N=10 

0.80 

[0.41, 1.44] 

N=19 

0.21 

[0.10, 0.60] 

N=19 

0.10 

[0.10, 0.33] 

N=10 

All types of

organizational

change

0.60 

[0.10, 1.35] 

N=27 

1.35 

[1.15, 2.0] 

N=6 

1.35 

[0.61, 1.53] 

N=12 

0.20 

[0.10, 0.60] 

N=15 

0.10 

[0.08, 0.27] 

N=8 

No organizational 

change

0.41 

[0.39, 1.02] 

N=11 

1.03 

[0.52, 1.58] 

N=4 

0.56 

[0.41, 1.49] 

N=7 

0.29 

[0.18, 0.40] 

N=4 

0.24 

[0.10, 0.37] 

N=2 

Disease/case 

management 

1.09 

[0.78, 1.55] 

N=8 

1.25 

[1.0, 1.75] 

N=4 

1.25 

[1.0, 1.75] 

N=4 

0.94 

[0.75, 1.31] 

N=4 

0.60 

---- 

N=1 

No disease/case 

management 

0.39 

[0.10, 1.13] 

N=30 

1.40 

[0.75, 1.73] 

N=6 

1.35 

[0.40, 1.49] 

N=15 

0.20 

[0.10, 0.34] 

N=15 

0.10 

[0.10, 0.20] 

N=9 

No disease/case 

management (has

org change) 

0.20 

[0.05, 0.95] 

N=19 

1.75 

[1.30, 2.20] 

N=2 

1.35 

[0.20, 1.53] 

N=8 

0.10 

[0.05, 0.25] 

N=11 

0.10 

[0.05, 0.15] 

N=7 

Team/staffing 

changes 

0.30 

[0.12, 0.68] 

N=14 

1.30 

[1.01, 1.75] 

N=3 

0.71 

[0.30, 1.30] 

N=5 

0.20 

[0.10, 0.50] 

N=9 

0.15 

[0.07, 0.27] 

N=4 

No team/staffing 

changes 

0.58 

[0.33, 1.42] 

N=24 

1.40 

[0.83, 1.65] 

N=7 

1.40 

[0.45, 1.49] 

N=14 

0.29 

[0.10, 0.57] 

N=10 

0.10 

[0.10, 0.30] 

N=6 

No team/staffing 

changes (has org

change) 

1.10 

[0.10, 1.40] 

N=13 

1.40 

[1.25, 2.10] 

N=3 

1.40 

[1.25, 1.65] 

N=7 

0.35 

[0.10, 0.75] 

N=6 

0.10 

[0.08, 0.23] 

N=4 

Medical record

changes 

1.40 

[1.40, 1.9] 

N=5 

1.95 

[1.10, 2.80] 

N=2 

1.40 

[1.40, 1.90] 

N=5 N=0 N=0 

No medical record 

changes 

0.4 

[0.1, 0.8] 

N=33 

1.35 

[0.67, 1.58] 

N=8 

0.64 

[0.40, 1.45] 

N=14 

0.21 

[0.10, 0.55] 

N=19 

0.10 

[0.10, 0.33] 

N=10 

No medical record 

changes (has org

change) 

0.30 

[0.10, 0.78] 

N=22 

1.35 

[1.15, 1.60] 

N=4 

0.71 

[0.10, 1.35] 

N=7 

0.20 

[0.10, 0.60] 

N=15 

0.10 

[0.08, 0.27] 

N=8 

* Sample size stratification for HbA1c: 1st quartile=69, median=123, 3rd quartile=226.

†

 When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

75
Table 11b. Association between improvements in glycemic control and specific substrategies of organizational 

change stratified by study design

Median Reduction in HbA1c

[inter-quartile range]* 

N=Number of comparisons

Median Improvement in provider adherence 

[inter-quartile range]* 

N=Number of comparisons

Type of 

organizational

change

All

Comparisons 

RCT Non-RCT All

Comparisons

RCT Non-RCT 

All QI types 

0.48 

[0.20, 1.38] 

N=38 

0.39 

[0.1, 0.73] 

N=28 

1.4 

[1.1, 1.78] 

N=10 

4.85 

[3.8, 15.03] 

N=17 

4.5 

[3.5, 5.45] 

N=14 

18.0 

[17.2, 20.95] 

N=3 

All types of

organizational

change

0.60 

[0.10, 1.35] 

N=27 

0.25 

[0.03, 0.68] 

N=18 

1.4 

[1.1, 1.9] 

N=9 

4.7 

[4.1, 5.7] 

N=6 

4.5 

[4.0, 4.9] 

N=5 

18.0 

---- 

N=1 

No organizational 

change

0.41 

[0.39, 1.02] 

N=11 

0.41 

[0.38, 1.22] 

N=10 

0.56 

---- 

N=1 

5.0 

[3.6, 15.7] 

N=11 

4.5 

[3.4, 5.6] 

N=9 

20.2 

[16.4, 23.9] 

N=2 

Disease/case 

management 

1.09 

[0.78, 1.55] 

N=8 

0.76 

[0.68, 0.87] 

N=4 

1.7 

[1.33, 2.19] 

N=4 

4.9 

---- 

N=1 

4.9 

---- 

N=1 N=0 

No disease/case 

management 

0.39 

[0.10, 1.13] 

N=30 

0.3 

[0.1, 0.5] 

N=24 

0.98 

[0.52, 1.4] 

N=6 

4.8 

[3.7, 15.4] 

N=16 

4.5 

[3.4, 5.6] 

N=13 

18.0 

[17.2, 21.0] 

N=3 

No disease/case 

management (has

organizational

change) 

0.20 

[0.05, 0.95] 

N=19 

0.15 

[0.0, 0.30] 

N=14 

1.40 

[0.50, 1.40] 

N=5 

4.5 

[4.0, 6.0] 

N=5 

4.3 

[3.3, 4.9] 

N=4 

18.0 

---- 

N=1 

Team/staffing 

changes 

0.30 

[0.12, 0.68] 

N=14 

0.3 

[0.1, 0.71] 

N=13 

0.5 

---- 

N=1 

12.0 

[6.0, 18.0] 

N=2 

6.0 

---- 

N=1 

18.0 

---- 

N=1 

No team/staffing 

changes 

0.58 

[0.33, 1.42] 

N=24 

0.4 

[0.16, 0.7] 

N=15 

1.4 

[1.1, 1.9] 

N=9 

4.5 

[3.6, 10.3] 

N=15 

4.5 

[3.4, 5.0] 

N=13 

20.2 

[16.4, 23.9] 

N=2 

No team/staffing 

changes (has 

organizational

change) 

1.10 

[0.10, 1.40] 

N=13 

0.10 

[0.0, 0.60] 

N=5 

1.40 

[1.33, 1.92] 

N=8 

4.3 

[3.3, 4.6] 

N=4 

4.3 

[3.3, 4.6] 

N=4 N=0 

Medical record

changes 

1.40 

[1.40, 1.9] 

N=5 N=0 

1.40 

[1.40, 1.9] 

N=5 

1.0 

---- 

N=1 

1.0 

---- 

N=1 N=0 

No medical record 

changes 

0.4 

[0.1, 0.8] 

N=33 

0.39 

[0.1, 0.73] 

N=28 

0.56 

[0.5, 1.4] 

N=5 

4.9 

[4.0, 15.4] 

N=16 

4.5 

[3.8, 5.6] 

N=13 

18.0 

[17.2, 21.0] 

N=3 

No medical record 

changes (has 

organizational

change) 

0.30 

[0.10, 0.78] 

N=22 

0.25 

[0.03, 0.68] 

N=18 

0.95 

[0.40, 1.55] 

N=4 

4.9 

[4.5, 6.0] 

N=5 

4.7 

[4.4, 5.1] 

N=4 

18.0 

---- 

N=1 

* When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles. 

76
Table 12a. Association between improvements in glycemic control and various roles for clinical information

systems stratified by quartiles of sample size* 

Median improvement in HbA1c 

[inter-quartile range]

g

N=Number of comparisons

Type of role for 

clinical information 

system‡

All

Comparisons

Comparisons

with sample 

size in lowest 

quartile

Comparisons

with sample 

size in lower 2

quartiles

Comparisons

with sample 

size in upper 2 

quartiles

Comparisons

with sample 

size in highest 

quartile

All Comparisons 

0.48 

[0.20, 1.38] 

N=38 

1.35 

[0.60, 1.48] 

N=10 

0.80 

[0.41, 1.44] 

N=19 

0.21 

[0.10, 0.60] 

N=19 

0.10 

[0.10, 0.33] 

N=10 

Any role

0.9 

[0.3, 1.42] 

N=20 

1.40 

[1.10, 1.50] 

N=5 

1.40 

[0.91, 1.49] 

N=11 

0.10 

[0.10, 0.60] 

N=9 

0.10 

[0.10, 0.24] 

N=7 

No role 

0.35 

[0.2, 0.59] 

N=18 

1.30 

[0.56, 1.80] 

N=5 

0.48 

[0.20, 1.43] 

N=8 

0.26 

[0.20, 0.49] 

N=10 

0.20 

[0.10, 0.35] 

N=3 

Identification of

eligible participants 

0.35 

[0.1, 0.96] 

N=6 

1.40 

---- 

N=1 

1.40 

---- 

N=1 

0.10 

[0.10, 0.60] 

N=5 

0.10 

[0.08, 0.23] 

N=4 

No identification of 

eligible participants 

0.48 

[0.21, 1.4] 

N=32 

1.30 

[0.710, 1.80] 

N=9 

1.20 

[0.40, 1.49] 

N=18 

0.26 

[0.13, 0.49] 

N=14 

0.15 

[0.10, 0.33] 

N=6 

No identification of 

eligible 

participants, but 

has some other 

role for an 

information system 

1.25 

[0.40, 1.49] 

N=14 

1.40 

[1.00, 1.83] 

N=4 

1.40 

[0.81, 1.49] 

N=10 

0.24 

[0.10, 0.78] 

N=4 

0.10 

[0.10, 0.24] 

N=3 

Reminder system 

0.71 

[0.39, 1.69] 

N=11 

1.10 

[0.91, 1.95] 

N=3 

1.10 

[0.56, 1.69] 

N=7 

0.24 

[0.10, 1.78] 

N=4 

0.1 

[0.1, 0.24] 

N=3 

No reminder 

system 

0.47 

[0.15, 1.19] 

N=27 

1.40 

[0.93, 1.65] 

N=7 

1.35 

[0.38, 1.43] 

N=12 

0.21 

[0.10, 0.55] 

N=15 

0.10 

[0.05, 0.35] 

N=7 

No reminder 

system, but has 

some other role for 

an information 

system 

1.08 

[0.10, 1.40] 

N=9 

1.45 

[1.40, 1.50] 

N=2 

1.40 

[1.40, 1.43] 

N=4 

0.10 

[0.10, 0.60] 

N=5 

0.10 

[0.08, 0.23] 

N=4 

Computerized 

decision support 

system (CDSS) 

1.1 

[0.37, 1.99] 

N=5 

1.95 

[1.10, 2.80] 

N=2 

1.95 

[1.10, 2.80] 

N=2 

0.37 

[0.24, 1.18] 

N=3 

0.24 

[0.1, 0.40] 

N=2 

*

 Sample size stratification for HbA1c: 1st quartile=69, median=123, 3rd quartile=226. 

g When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

‡

 In addition to the roles shown in the table, we had included “facilitated communication between providers,” but only 1 study, in 

which the intervention involved an electronic communication designed to enhance shared care between primary care providers and

specialists, exhibited this feature.

129

77
Table 12a (continued). Association between improvements in glycemic control and various roles for clinical 

information systems stratified by quartiles of sample size* 

No CDSS 

0.47 

[0.2, 1.30] 

N=33 

1.35 

[0.67, 1.58] 

N=8 

1.30 

[0.40, 1.47] 

N=17 

0.21 

[0.10, 0.53] 

N=16 

0.10 

[0.08, 0.27] 

N=8 

No CDSS, but has 

some other role for 

an information 

system 

0.71 

[0.25, 1.40] 

N=15 

1.40 

[1.06, 1.45] 

N=3 

1.40 

[0.71, 1.47] 

N=9 

0.10 

[0.10, 0.48] 

N=6 

0.10 

[0.10, 0.10] 

N=5 

Audit system 

1.4 

[0.75, 1.45] 

N=3 

1.45 

[1.40, 1.50] 

N=2 

1.45 

[1.40, 1.50] 

N=2 

1.0 

---- 

N=1 

1.0 

---- 

N=1 

No Audit system 

0.47 

[0.2, 1.2] 

N=35 

1.20 

[0.67, 1.90] 

N=8 

1.10 

[0.40, 1.47] 

N=17 

0.26 

[0.10, 0.58] 

N=18 

0.10 

[0.10, 0.37] 

N=9 

No Audit system, 

but includes other 

role for information 

system 

0.71 

[0.37, 1.40] 

N=17 

1.10 

[0.91, 1.95] 

N=3 

1.40 

[0.71, 1.47] 

0.24 

[0.10, 0.72] 

N=8 

0.10 

[0.10, 0.30] 

N=9 N=6 

* Sample size stratification for HbA1c: 1st quartile=69, median=123, 3rd quartile=226. 

78
Table 12b. Association between improvements in provider adherence and glycemic control for various roles for 

clinical information systems stratified by trial design 

Median Reduction in HbA1c

[inter-quartile range]* 

N=Number of comparisons

Median Improvement in provider adherence

[inter-quartile range]* 

N=Number of comparisons

Type of clinical 

information system 

All

Comparisons

RCT Non-RCT All

Comparisons

RCT Non-RCT

All Comparisons 

0.48 

[0.20, 1.38] 

N=38 

0.39 

[0.1, 0.73] 

N=28 

1.4 

[1.1, 1.78] 

N=10 

4.85 

[3.8, 15.03] 

N=17 

4.5 

[3.5, 5.45] 

N=14 

18.0 

[17.2, 20.95] 

N=3 

Any clinical 

information system 

0.9 

[0.3, 1.42] 

N=20 

0.4 

[0.1, 0.8] 

N=12 

1.4 

[1.33, 1.92] 

N=8 

4.0 

[3.8, 4.5] 

N=5 

4.0 

[3.8, 4.5] 

N=5 

---- 

N=0 

No clinical 

information system 

(CIS) 

0.35 

[0.2, 0.59] 

N=18 

0.3 

[0.15, 0.65] 

N=16 

0.53 

[0.5, 0.56] 

N=2 

5.3 

[4.1, 16.8] 

N=12 

4.9 

[3.0, 5.6] 

N=9 

18.0 

[17.2, 21.0] 

N=3 

Identification of

eligible participants 

0.35 

[0.1, 0.96] 

N=6 

0.1 

[0.1, 0.6] 

N=5 

1.4 

---- 

N=1 

4.5 

[4.3, 5.3] 

N=3 

4.5 

[4.3, 5.3] 

N=3 

---- 

N=0 

 No identification of

eligible participants 

0.48 

[0.21, 1.4] 

N=32 

0.4 

[0.2, 0.76] 

N=23 

1.4 

[0.56, 1.9] 

N=9 

4.9 

[3.5, 16.1] 

N=14 

4.5 

[3.2, 5.3] 

N=11 

18.0 

[17.2, 21.0] 

N=3 

No identification of 

eligible participants 

(but has CIS) 

1.25 

[0.40, 1.49] 

N=14 

0.41 

[0.39, 1.09] 

N=7 

1.40 

[1.25, 1.95] 

N=7 

3.6 

[3.4, 3.8] 

N=2 

3.6 

[3.4, 3.8] 

N=2 

---- 

N=0 

Reminder system 

0.71 

[0.39, 1.69] 

N=11 

0.4 

[0.24, 0.56] 

N=7 

1.95 

[1.7, 2.19] 

N=4 

3.8 

[3.6, 4.2] 

N=3 

3.8 

[3.6, 4.2] 

N=3 

---- 

N=0 

No reminder system

0.47 

[0.15, 1.19] 

N=27 

0.3 

[0.1, 0.8] 

N=21 

0.98 

[0.52, 1.4] 

N=6 

5.3 

[4.1, 16.1] 

N=14 

4.9 

[3.5, 5.8] 

N=11 

18.0 

[17.2, 21.0] 

N=3 

No reminder system

(but has CIS) 

1.08 

[0.10, 1.40] 

N=9 

0.60 

[0.10, 1.08] 

N=5 

1.40 

[1.08, 1.40] 

N=4 

5.0 

[4.0, 6.0] 

N=2 

5.0 

[4.0, 6.0] 

N=2 

---- 

N=0 

Computerized 

decision support 

system (CDSS) 

1.1 

[0.37, 1.99] 

N=5 

0.24 

[0.1, 0.37] 

N=2 

1.99 

[1.55, 2.4] 

N=3 

3.6 

[3.4, 3.8] 

N=2 

3.6 

[3.4, 3.8] 

N=2 

---- 

N=0 

No CDSS 

0.47 

[0.2, 1.30] 

N=33 

0.4 

[0.12, 0.78] 

N=26 

1.4 

[0.53, 1.4] 

N=7 

5.0 

[4.3, 15.7] 

N=15 

4.7 

[3.8, 5.7] 

N=12 

18.0 

[17.2, 21.0] 

N=3 

*

 When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

79
Table 12b (continued). Association between improvements in provider adherence and glycemic control for 

various roles for clinical information systems stratified by trial design*

No CDSS 

(but has CIS) 

0.71 

[0.25, 1.40] 

N=15 

0.51 

[0.17, 0.99] 

N=10 

1.40 

[1.40, 1.40] 

N=5 

4.5 

[4.3, 5.3] 

N=3 

4.5 

[4.3, 5.3] 

N=3 

---- 

N=0 

Audit system 

1.4 

[0.75, 1.45] 

N=3 

0.8 

[0.1, 1.5] 

N=2 

1.4 

---- 

N=1 

3.4 

---- 

N=1 

3.4 

---- 

N=1 

---- 

N=0 

No Audit system 

0.47 

[0.2, 1.2] 

N=35 

0.39 

[0.12, 0.68] 

N=26 

1.4 

[0.56, 1.9] 

N=9 

4.9 

[4.0, 15.4] 

N=16 

4.5 

[3.8, 5.6] 

N=13 

18.0 

[17.2, 21.0] 

N=3 

No Audit system 

(but has CIS) 

0.71 

[0.37, 1.40] 

N=17 

0.40 

[0.17, 0.68] 

N=10 

1.40 

[1.25, 1.95] 

N=7 

4.3 

[4.0, 4.9] 

N=4 

4.3 

[4.0, 4.9] 

N=4 

---- 

N=0 

*

 When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

80
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47. Pouwer F, Snoek FJ, van der Ploeg HM, Ader HJ, Heine RJ. Monitoring of psychological well-being in outpatients 

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48. Reed RL, Revel AO, Carter A, Saadi HF, Dunn EV. A clinical trial of chronic care diabetic clinics in general practice 

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84
Appendix A. Comparison of recommended goals for clinical practice vs. targets for performance measurement 

Goal for clinical practice Target for performance 

measurement 

Comment 

Adequacy of 

Glycemic Control 

“good control”: HbA1c <

7.0% ADA1 Proportion of patients with 

“poor control”: 

 HbA1c ≥ 9% NDQIA2

 HbA1c ≥ 9.5%)3

Many factors effect the appropriateness of 

HbA1c < 7% as a fixed goal for a clinic or 

health care system to achieve (e.g., 

frequency of hypoglycemia, comorbid

conditions, patient preferences), hence 

the focus on percentage of patients with 

“poor control” (≥ 9%) for purposes of 

performance measurement 

Blood pressure

control 

< 130/80 mmHg < 140/90 mmHg Similarly, system-wide reports of BP 

control focus on percentage of patients 

with poor control (> 140/90 mmHg) 

Lipid control LDL-C < 100 mg/dl (2.6 

mmol/l) 

Triglycerides < 150 mg/dl 

(1.7 mmol/l) 

HDL < 40 mg/dl (1.1 

mmol/l) 

LDL-C < 130 mg/dl Elevated LDL-cholesterol is most strongly 

correlated with increased risk of 

cardiovascular disease. Again, optimal 

goal of 100 mg/dl may not be attainable

for numerous reasons. 

Frequency of 

assessing HbA1c 

≥ 2 times/year for patients 

meeting treatment goals;1

≥ 4 times/year patients not 

meeting treatment goals1

> 1 HbA1c test/year 

(NDQIA,2

 HEDIS®3) 

Yearly measurements of HbA1c are 

correlated with actual lower HbA1c levels. 

Frequency of 

assessing lipids 

> 1 complete lipid 

panel/year

1 > 1 LDL-C 

measurement/year 

(NDQIA,2 HEDIS3

) 

Urine protein 

screening 

>1 test for urine 

microalbumin/year1 > 1 test for urine microalbumin/year 

(NDQIA,2 HEDIS3

) 

Receives only an “E” recommendation 

from ADA (“Expert consensus or clinical 

practice”) for type 2 diabetic patients. 

Eye examination >1 dilated retinal 

examination/year1 > 1 dilated retinal examination/year (or every

other year if low risk) 

“Low risk”: non-insulin-requiring, HbA1c < 

8%, no prior evidence of retinopathy

Foot examination > 1 complete foot 

exam/year (including 

sensory exam with

microfilament)1

> 1 foot examination of any 

kind 

Smoking cessation Advise all smokers to quit; 

smoking cessation as part 

of routine diabetic care1

Percentage of patients 

whose smoking 

status was ascertained and 

documented annually

117
Patient education No formal recommendation No formal recommendation 

ADA – American Diabetes Association; NDQIA – National Diabetes Quality Improvement Alliance; HEDIS - Health Plan 

Employer Data and Information Set. The HEDIS measure is actually a composite measures for “comprehensive diabetes care”

consisting of the specific measurements show in the table.

1. American Diabetes Association. Standards of medical care for patients with diabetes mellitus. Diabetes Care. 2003;26 Suppl 

1:S33-50. 

2. National Diabetes Aliance. National Diabetes Quality Improvement Alliance Performance Measurement Set for Adult 

Diabetes. Available at: http://www.nationaldiabetesalliance.org/Final_03_Measures.pdf. Accessed November 16, 2003. 

3. National Committee for Quality Assurance (NCQA). HEDIS Effectiveness of Care Measure-by-Measure Reports: 

Comprehensive Diabetes Care. Available at: http://www.ncqa.org/sohc2002/SOHC_2002_CDIAB.html. Accessed 

November 17, 2003. 

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Appendix B. MEDLINE search for diabetes quality improvement articles 

Search Search String Citations * 

#1 

targets QI strategies that 

tend to be multi-factorial 

using relevant MeSH terms 

and title words

Disease Management [mh] OR Patient Care Planning [mh] OR Patient-Centered Care 

[mh] OR Primary Health Care [mh] OR Progressive Patient Care [mh] OR Critical 

Pathways [mh] OR Delivery of Health Care, Integrated [mh] OR Health Services 

Accessibility [mh] OR Managed Care Programs [mh] OR Product Line Management 

[mh] OR Patient Care Team [mh] OR Patient-Centered Care [mh] OR Behavior Control 

[mh] OR Counseling [mh] OR Health Promotion [mh] OR Patient Compliance [mh] OR 

After-Hours Care [mh] OR ((coordination [ti] OR coordinated [ti] OR Multifactorial [ti] OR 

Multi-factorial [ti] OR Multicomponent [ti] OR Multi-component [ti] OR multidisciplinary 

[ti] OR multi-disciplinary [ti] OR interdisciplinary [ti] OR inter-disciplinary [ti] OR 

integrated [ti] OR community-based [ti] OR organized [ti]) AND (care [ti] OR approach 

[ti] OR intervention [ti] OR strategy [ti] OR strategies [ti] OR management [ti] OR 

managing [ti] OR center* [ti] OR clinic*[ti])) OR Organization and Administration [mh] 

682,850 

#2 

targets TQM and CQI 

Total Quality Management [mh] OR Quality control [mh] OR TQM [ti] OR CQI [ti] OR 

(quality [ti] AND (continuous [ti] OR total [ti]) AND (management [ti] OR improvement 

[ti])) 

28,079 

#3 

targets provider education 

Education, Continuing [mh] OR (Education [ti] AND Continuing [ti] AND (medical [ti] OR 

professional* [ti] OR nursing [ti] OR physician* [ti] OR nurse* [ti])) OR (outreach [ti] AND 

(visit*[ti] OR educational [ti]) OR (academic [ti] AND detailing [ti])) 

35,275 

#4 

targets diffusion of 

innovation

Diffusion of Innovation [mh] OR (Diffusion [ti] AND (Innovation [ti] OR technology [ti])) 4,887 

#5 

targets audit & feedback, 

reminder systems, and 

financial incentives 

Medical audit [mh] OR ((Audit [ti] OR feedback [ti] OR compliance [ti] OR adherence [ti] 

OR training [ti]) AND (improvement* [ti] OR improving [ti] OR improves [ti] OR improve 

[ti] OR guideline* [ti] OR practice* [ti] OR medical [ti] OR provider* [ti] OR physician* [ti] 

OR nurse* [ti] OR clinician* [ti] OR practice guidelines [mh] OR academic [ti] OR visit* 

[ti])) OR Reminder Systems [mh] OR Reminder* [ti] OR ((financial [ti] OR economic [ti] 

OR physician* [ti] OR patient*) AND incentive* [ti]) OR Reimbursement Mechanisms 

[mh]

36,788 

#6 Medical Informatics [mh] OR computer [ti] OR (decision [ti] AND support [ti]) OR 306,619 

119
targets informatics and 

telemedicine 

Telemedicine[mh] OR Telemedicine [ti] OR telecommunication* [ti] OR Internet [mh] 

OR web [ti] OR modem [ti] OR telephone* [ti] OR telephone [mh] 

#7 

combines #1-7 for overall 

set of articles relating to QI 

#1 OR #2 OR #3 OR #4 OR #5 OR #6 988,084 

#8 

combines overall QI search 

key terms for articles 

involving diabetes 

#7 AND (Diabetes Mellitus [mh] OR diabetes [ti] OR diabetic [ti] OR glycemic [ti] OR 

glycaemic [ti] OR sugar* [ti]) 

9,956 

#9 

identifies subset of #9 likely 

to involve original research 

or systematic reviews 

#8 AND (systematic review search string^ OR original research string†) 3,992

#10 #9 Limit to English 3,575 

#11 #10 Limit to publication since 1980 3,460 

#12 #11 BUTNOT (editorial [pt] OR comment [pt] OR letter [pt]) 3,348 

#13 

additional yield of journal 

search 

(#8 AND Journal Search String §) BUTNOT (#9 OR editorial [pt] OR comment [pt] OR 

letter [pt]) [Limited to English, 1980] 

294 

#14 

additional yield of author 

search 

(#8 AND author search**) BUTNOT (#13 OR editorial [pt] OR comment [pt] OR letter 

[pt]) [Limited to English, 1980] 

29 

Total #12 or #13 or #14 3,601

references total 

* Numbers of citations reflect search results from July 8, 2003

^ ((meta-analysis [pt] OR meta-analysis [tw] OR metanalysis [tw]) OR ((review [pt] OR guideline [pt] OR consensus [ti] OR 

guideline* [ti] OR literature [ti] OR overview [ti] OR review [ti] OR Decision Support Techniques [mh]) AND ((Cochrane [tw] OR 

Medline [tw] OR CINAHL [tw] OR (National [tw] AND Library [tw])) OR (handsearch* [tw] OR search* [tw] OR searching [tw]) 

AND (hand [tw] OR manual [tw] OR electronic [tw] OR bibliographi* [tw] OR database* OR (Cochrane [tw] OR Medline [tw] OR 

CINAHL [tw] OR (National [tw] AND Library [tw]))))) OR ((synthesis [ti] OR overview [ti] OR review [ti] OR survey [ti]) AND 

(systematic [ti] OR critical [ti] OR methodologic [ti] OR quantitative [ti] OR qualitative [ti] OR literature [ti] OR evidence [ti] OR 

evidence-based [ti]))) BUTNOT (case report [mh] OR case* [ti] OR report [ti] OR editorial [pt] OR comment [pt] OR letter [pt]) Æ

38,850 MEDLINE records 

120
† Randomised [ti] OR Randomized [ti] OR Controlled [ti] OR intervention [ti] OR evaluation [ti] OR impact [ti] OR effectiveness [ti]

OR Evaluation [ti] OR Studies [ti] OR study [ti] Comparative [ti] OR Feasibility [ti] OR Program [ti] OR Design [ti] OR Clinical 

Trial [pt] OR Randomized Controlled Trial [pt] OR Epidemiologic Studies [mh] OR Evaluation Studies [mh] OR Comparative 

Study [mh] OR Feasibility Studies [mh] OR Intervention Studies [mh] OR Program Evaluation [mh] OR Epidemiologic Research 

Design [mh] —> 2,550,756 MEDLINE records 

§ N Engl J Med [ta] OR JAMA [ta] OR Ann Intern Med [ta] OR Am J Med [ta] OR Arch Intern Med [ta] OR J Gen Intern Med [ta]

OR BMJ [ta] OR Lancet [ta] OR CMAJ [ta] OR Clin Invest Med [ta] OR Arch Fam Med [ta] OR J Fam Pract [ta] OR Fam Pract

[ta] OR Ann Med [ta] OR Br J Gen Pract [ta] OR J Intern Med [ta] OR Med J Aust [ta] OR South Med J [ta] OR West J Med [ta]

OR Aust N Z J Med [ta] OR Med Care [ta] OR Health Serv Res [ta] OR Inquiry [ta] OR Milbank Q [ta] OR Health Aff (Millwood) 

[ta] OR Health Care Financ Rev [ta] OR Med Care Res Rev [ta] OR eff clin pract [ta] OR eval health prof [ta] OR Jt Comm J Qual

Improv [ta] OR Qual Saf Health Care [ta] OR Int J Qual Health Care [mh] OR Qual Health Care [ta] OR Qual Health Res [ta] OR 

Rep Med Guidel Outcomes Res [ta] OR Am J Manag Care [ta] OR Am J Med Qual [ta] OR J Contin Educ Health Prof [ta] OR Prev 

Med [ta] OR Am J Prev Med [ta] OR Patient Educ Couns [ta] OR Ann Behav Med [ta] OR Diabetes Educ [ta] OR Endocrinology

[ta] OR J Clin Endocrinol Metab [ta] OR Diabet Med [ta] OR Diabetes Care [ta] OR Diabetes Res Clin Pract [ta] OR Exp Clin 

Endocrinol Diabetes [ta] OR J Pediatr Endocrinol Metab [ta]

** The author search could not be exhaustive, but, after completion of the initial search, we identified authors who appeared in 

multiple trials or prominent review articles and searched for publications listing any of them as authors (list available on request)

121
Appendix C. Abstraction forms for screening and full-text review

 

Stage 1 

1. Does the article report or evaluate the results of an intervention (whether performed by the investigators 

or not)? 

o Yes 

o No {exclusion}

o Can't Tell {promotion to Stage 2}

 

2. Does the article involve quality improvement or a QI strategy? 

o Yes - involves quality improvement or a QI strategy 

o Yes - systematic review of evaluations of a QI strategy 

o No {exclusion}

o Can't Tell {promotion to Stage 2} 

Stage 2 

1. Should this article proceed to article abstraction stage for this topic? 

o Yes - evaluates a QI strategy involving diabetes 

o No - focused on diabetes in pregnancy, Type I DM or children only, screening for/preventing 

diabetes, hospital care only {exclusion}

o No - off topic (use textbox to indicate if involves other EPC topics) {exclusion} 

o No - not an evaluation or not QI {exclusion} 

o Can't tell - need article {promotion to Stage 3} 

o No - but useful background article {exclusion} 

 

2. What type of study design was used? 

o RCT or quasi-RCT

o CBA* or ITS ** 

o Cohort study; before-after or time series not meeting CBA* or ITS** definitions {exclusion} 

o Observational (e.g., cross-section, case-control) {exclusion} 

o Can't tell (need article) {promotion to Stage 3}

o Systematic review or meta-analysis {exclusion}

o Economic or decision analysis, modeling {exclusion} 

o Non-research (commentary, review, news) {exclusion} 

o Qualitative research (e.g., focus groups) {exclusion} 

o Guideline or consensus statement {exclusion} 

* Controlled Before After (CBA) requires contemporaneous observation periods for control and intervention 

groups AND judgment that control represents a comparable group or setting 

 

** Interrupted time series (ITS) requires statement of well-defined time period for intervention implementation 

AND at least three time points both before and after 

 

Note: At this stage of triage, if there is a reasonable chance article is a clinical trial, CBA or ITS, err on the 

side of inclusion at that level. Stricter criteria can be applied more reliably at next stage of abstraction using 

full text of article. Similarly, if there is a reasonable chance article is a systematic review, designate it as 

such so article can be pulled. 

Stage 3 

1. Does this article merit abstraction at Stage 3? 

o Yes 

o No – not QI or not an evaluation of a QI strategy {exclusion}

o No – study design below Level 2 {exclusion}

o No - excluded topic (focused only on pregnancy, hospital care, Type I DM, or screening) 

{exclusion}

122
o No – no eligible outcomes* {exclusion}

o No - publication prior to 1980 (select only if no other exclusionary answer applies) 

{exclusion}

o No- other {exclusion}

 

 

* Eligible outcomes include measures of disease control, provider adherence, or patient compliance. 

Excluded are: measures of provider or patient understanding, satisfaction, self-efficacy; costs and 

resource use. Also excluded are articles reporting no outcomes specifically related to diabetes (e.g. 

smoking only). Also excluded is provider adherence measured exclusively by provider self-report. 

 

2. Does this article present data overlapping with another article? 

o Exclude this article as a duplicate publication (identify included citation being duplicated) 

{exclusion}

o Include this article, but obtain listed citation to help with abstraction (e.g., separate methods paper; 

identify required citation ) 

o No or N/A 

 

3. What category of study question is addressed by the article? 

o Can screening for or awareness of diabetes be improved? 

o Can provider treatment of diabetes be improved? (e.g., increased adherence to recommended 

care) 

o Can patient glycemic control or diabetic complications be improved 

o Can patient compliance, education or self management be improved? 

o Not sure or Other (describe) 

o N/A 

4. Describe the QI strategy used and its salient features. 

 

 

5. Did the QI strategy involve a provider reminder system* or facilitated relay of clinical data ** back to 

providers? 

o Chart based reminder system* for providers 

o Computer based reminder* or decision support for providers 

o Facilitated relay of clinical data to providers** 

o Not sure 

o No or N/A 

 

* Patient or provider encounter specific information, provided verbally, on paper or on a computer screen, 

which is intended to prompt provider to recall information (e.g., the last time the patient had a HbA1c 

checked and its value, the last time the patient underwent screening colonoscopy and the result) 

 

** Clinical information collected directly from patients and given to the provider using some format other than 

the conventional chart system 

 

6. Did the QI strategy involve provider audit and feedback*? 

o feedback to individual provider (state if confidential) 

o feedback about clinic or practice performance only 

o Public reporting of performance data (state if individual data or data for a group or institution) 

o Benchmarking** 

o Not sure or other 

o No or N/A 

 

* Any summary of clinical performance of health care over a specified period of time. E.g., the percentage of 

a provider's patients who have achieved or have not achieved some clinical target (e.g., BP or HbA1c in

certain range), have or have not been offered some diagnostic test. **Benchmarking refers to the 

provision of performance data from institutions or providers regarded as "leaders in the field." These data 

provide targets for other providers and institutions to emulate. 

 

7. Did the QI strategy involve provider education? 

o Educational workshops, meetings (e.g., traditional CME), lectures (live or computer based) 

123
o Educational outreach visits (Use of a trained person who met with providers in their practice 

settings to give information with the intent of changing the provider's practice) 

o Distribution of educational materials (Distribution of published or printed recommendations for 

clinical care, including clinical practice guidelines, audio-visual materials and electronic 

publications) 

o Not sure or other 

o No or N/A 

 

8. Did the QI strategy involve patient education or promote self-management? 

o In-person patient education individually or as a part of a group or community 

o Distribution of printed or audio-visual educational materials 

o Patient reminders (e.g., to keep appointments or comply with other aspect of care) 

o Provision of clinical data back to the patient (e.g., your most recent HbA1c or lipid panel was such 

and such) 

o Distribution of materials or access to a resource that enhances patients' ability to manage their 

condition 

o Not sure or other 

o No or N/A 

 

9. Did the QI strategy involve organizational change? 

o Case management, disease management --coordination of assessment, treatment and 

arrangement for referrals by a person or multidisciplinary team in collaboration with or 

supplementary to the primary care provider 

o Adding new members to team (e.g., adding a diabetes nurse, clinical pharmacist, or nutritionist to 

clinic) or creating multidisciplinary teams (creation of a new team of health professionals of different 

disciplines or additions of new members to the team who work together to care for patients) 

o Communication and case discussion between distant health professionals (e.g., telemedicine) 

o TQM/CQI - cycles of measurement of quality problems, design of interventions, implementation and 

re-measurement 

o Changes in medical records systems -- e.g. changing from paper to computerised records, patient 

tracking systems 

o Revision of professional roles ('professional substitution', 'boundary encroachment') - the shifting of 

roles among health professionals (e.g., nurse midwives providing obstetrical care) 

o Increased staffing without changes in roles (e.g., adding more nurses) 

o Not sure or other 

o No or N/A 

 

10. Did the QI strategy involve financial, regulatory or legislative incentives or actions? 

o Positive or negative financial incentives directed at providers 

o Positive or negative financial incentives directed at patients 

o System-wide changes in reimbursement (e.g., capitation, prospective payment, shift from fee for 

service to salary) 

o Changes to provider licensure requirements 

o Changes to institutional accreditation requirements 

o Not sure or other 

o No or N/A 

 

 

11. Did a clinical information system play a role in design or implementation of intervention (regardless of QI 

strategy type)? 

o Identification and/or group allocation of eligible patients or providers 

o Reminders generated by existing clinical information system 

o Decision support at point of care (e.g., for provider order entry) 

o Facilitated communication between providers (e.g., generated emails between members of care 

team) 

o Audit data gathered from clinical information system to design QI strategy (e.g., audit and 

feedback, TQM, provider education, financial incentives) 

o Not sure or Other 

o No or N/A 

 

12. Who or what was targeted by the intervention? 

o Patients 

124
o Providers (i.e., individual clinicians) 

o Ambulatory clinics or practices 

o Inpatient units or hospitals 

o Public health systems, healthcare delivery systems, policy makers 

o Not sure or Other 

o N/A 

 

13. Among the target group, what was the number of participants? (i.e., study size) 

 

 

14. What type of study design was used? 

o RCT or quasi-RCT 

o CBA* or ITS** 

o Cohort study, retrospective before-after, or time series not meeting ITS definition** 

{exclusion}

o Not sure or other 

o N/A 

 

* Controlled Before After (CBA) requires contemporaneous observation periods for control and intervention 

groups AND judgement that control represents a comparable group or setting 

 

** Interrupted time series (ITS) requires statement of well-defined time period for intervention implementation 

AND at least three time points both before and after 

 

15. What were the outcome types? 

o Measure of disease control (clinical outcomes, HbA1c, glucose control, lipids) 

o Provider adherence (adherence to a guideline or recommended practice) 

o Patient compliance 

o Patient or provider understanding, self-efficacy, empowerment 

o Not sure or other 

o N/A 

 

16. What specific measures of disease control were used? 

o Serum glucose values (mean or percent of patients in certain range) 

o HbA1c (mean or percent of patients in certain range) 

o Cardiovascular risk factor modification (hyperlipidemia, hypertension, smoking cessation) 

o Microvascular complications (retinopathy, neuropathy, microalbuminuria, foot ulcers) 

o Macrovascular complications (MI, stroke, renal failure, amputation) 

o Not sure or other 

o None or N/A 

 

17. For studies reporting measures of clinician adherence, what specific measures were used? 

o Adherence to guideline targets for assessment of glycemic control (e.g., measuring HbA1c at 

certain intervals) 

o Adherence to recommended screening practices for ophthalmologic complications (e.g.,

performance of or referral for dilated retinal exam) 

o Adherence to recommended screening practices for renal complications (e.g., checking urine 

microalbumin) 

o Adherence to recommended screening practices for neuropathy or foot complications (e.g., 

performance of or referral for foot examination) 

o Adherence to treatment choices for achieving glycemic control (e.g., medication choices) 

o Adherence to guideline targets for managing blood pressure or cardiovascular disease 

o Adherence to recommendations for patient education or counseling re: diet, exercise, smoking, or 

other lifestyle factors 

o Not sure or other 

o N/A 

18. For studies reporting measures of patient compliance, how was compliance assessed? 

o Laboratory confirmation (e.g., detection of drug or metabolite in blood or urine; including 

biochemical assays for smoking cessation 

o Pharmacy data (e.g., filled or refilled prescriptions) 

o Specially designed dispensers that record medication use 

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o Home medication counts 

o Office medication counts (e.g., patients bring in bottles with unused pills) 

o Patient self report (via interview or survey) 

o Not sure or other 

o N/A 

 

19. Use textbox to state any important study features or concerns not captured above. 

 

 

20. Does the study intervention primarily consist of patient education or promotion of self-management? 

o Yes: there are no QI strategies other than patient education/promotion of self-management OR 

either of two criteria below is met* {exclusion} 

o No: provider or organizational change represented an intrinsic feature of the intervention 

o Not sure 

 

* Criteria for classifying intervention as primarily patient education: 

 

1) other strategies served only as vehicles for promoting education/self-management (e.g., adding a new

team member whose sole job as to provide patient education, providing financial incentives for patients to 

comply with targets of patient educational program, training providers about how to deliver more effective 

patient education.) 

 

2) non-patient education strategies were delivered to control group and not just intervention group (e.g., 

study of patient education intervention in which all providers received "facilitated relay of clinical

information, " decision support or any other management aide that is common to all providers or all 

organizations caring for patients in the study, not just those in intervention group. 

Stage 4 

1. Does abstraction of this study require information from methods or results reported in other citations (see 

Q2, Stage 3) 

o Yes (specify) 

o No 

 

2. Does the article report data for more than one comparison (i.e., should it be abstracted as more than one 

study)? 

o Yes (specify which comparison is being abstracted here and which others will be abstracted 

elsewhere) 

o No 

A) Study Setting and Participants 

 

3. In what country did the study take place? 

o US only 

o non-US (specify) 

 

 

4. Were the dates of the study period reported? 

o Yes - give dates as exactly as indicated in paper 

o No - indicate duration of study in month or years if reported 

 

 

5. In what setting did the study intervention take place? 

o Primary care clinic 

o Specialist clinic (e.g. diabetes or endocrinology practice) 

o Community 

o Multiple or Other (describe) 

o Not stated or not clear 

 

 

6. Were INCLUDED patients selected on the basis of any of the following? 

126
o Poor compliance with medications or clinic attendance (describe) 

o Poor glycemic control (describe) 

o Presence of specific comorbid conditions or illnesses (specify/describe -- e.g., HTN, hyperlipidemia, 

coronary artery disease, obesity, tobacco use) 

o Presence of specific diabetic complications (specify/describe -- e.g., renal failure, albuminuria, 

neuropathy, retinopathy) 

o Other (explain) 

o None of above 

o Not applicable (no patient involvement in study - e.g., study of provider-based intervention and 

provider outcomes only). 

 

 

7. What type of care was provided to the control population? 

o No intervention or usual care 

o Some form of low intensity intervention (describe) 

o No true control - just two or more different types of intervention (discuss with other reviewers; study

may need to be excluded) 

 

B) Study Design 

 

8. What was the study design? 

o Randomized trial - state method of randomization if described and any descriptive phrases (e.g. 

"randomly assigned") 

o Quasi randomized trial - state basis for treatment allocation (e.g. alternating patients, calendar 

date, eve or odd identification numbers) 

o Controlled before-after study 

 

 

9. Did the study have a cross over design? (Patients randomized to a sequence of interventions such as

treatment A followed by treatment B in one group and treatment B followed by treatment A in the other 

group). 

o Yes (describe) 

o No 

o Not sure - clarify with other reviewers before proceeding 

 

 

10. What was the unit of randomization or treatment allocation? 

o Patient 

o Episode of care 

o Clinic day 

o Provider 

o Practice 

o Firm (describe) 

o Institution 

o Community 

o Other 

 

11. For the unit of treatment allocation (above), state sample size in each group (If sample size differs for 

outcomes, detail differences in "Not stated or not clear" text box): 

o control group 

o intervention group 

o Not stated or not clear (explain) 

 

 

12. If unit of analysis differed from unit of treatment allocation (e.g., providers randomized, but patient 

outcomes analyzed), state sample size in each group: (Use text box for "Not applicable" if sample size for 

any outcomes reported is different-give details) 

o control group 

o intervention group 

o Not stated or not clear 

o Not applicable (unit of analysis same as unit of treatment allocation above) 

 

127
 

13. If unit of analysis differed from unit of treatment allocation, did authors acknowledge this issue and/or

make appropriate adjustments? 

o Yes (describe) 

o No 

o Not applicable (unit of analysis did not differ from unit of treatment allocation) 

 

14. Was there adequate concealment of treatment allocation? 

o Yes -(unit of allocation was institution, team or professional and any random process explicitly

described, e.g. use of random number tables, OR unit of allocation was patient or episode of care 

and some form of centralized randomization scheme or sealed, opaque, serially numbered

envelopes used) 

o Not clear (only partially meets above criteria) or not stated - specify which 

o No - inadequate concealment (enrollment of patients in alternation or through use of even/odd 

identifying numbers OR unit of allocation was patient or episode of care and reported use of any

allocation process that is entirely transparent before assignment (e.g., open list of random 

numbers) OR allocation was altered by investigators, professionals or patients) 

 

 

15. Were patients blind to intervention/treatment allocation? 

o Yes 

o No 

o Not sure (explain) 

o Not applicable (patients not actively involved in study - e.g., provider-focused intervention with 

patient level data obtained retrospectively from charts) 

16. Were providers blind to intervention/treatment allocation? 

o Yes 

o No 

o Not sure (explain) 

o Not applicable - (explain) 

 

 

17. Do any methodologic aspects of the study design not captured above seriously undermine 

appropriateness of inclusion? 

o Yes (explain) 

o No (use text box to document any non-fatal, but still noteworthy methodological features) 

 

 

C) Quality Improvement Attributes of Intervention 

 

18. Did the study intervention involve PATIENT Education? 

o Yes (describe what was taught, where it occurred, duration and frequency of sessions) 

o No 

 

 

19. Did the intervention include access to a resource or provision of a device that promoted Patient SelfManagement? (Patient reminder systems are addressed below in Q40, so do not answer yes here on basis 

of patient reminders.) 

o Yes (describe) 

o No 

 

20. Did the intervention involve a PATIENT REMINDER system? 

o Yes (specify target of reminder - appointments, compliance with meds or recommendations for selfcare) 

o No 

 

21. Did the intervention involve PROVIDER education? 

o Yes (describe nature of education, who administered the education, how often did it occur, etc) 

o No 

 

128
22. Did the intervention involve a PROVIDER REMINDER system? (Facilitated relay of clinical data is 

addressed below , so do not answer yes solely on that basis.) 

o Yes (describe content of reminders and how delivered) 

o No 

 

23. Did the intervention involve Facilitated Relay of clinical information to providers? 

o Yes (describe type of information - e.g., recent glucose or HbA1c, and method of relaying

information) 

o No 

 

24. Did the intervention involve provider AUDIT and FEEDBACK? 

o Yes (describe what was fed back, how often, etc) 

o No 

 

25. Did the intervention involve ORGANIZATIONAL Change (e.g., disease or case management, creation of 

multidisciplinary teams or expansion of professional roles, TQM/CQI, telemedicine, change in medical 

record system)? 

o Yes 

o No 

 

26. If the intervention involved Disease Management or Case Management, which of the following apply? 

o Intervention specifically described as involving "case management" or "disease management" 

o Someone other than physician actively participated in ongoing patient management using 

guidelines or systematic approach to care (protocols/algorithms to guide practitioner and patient 

decisions in specific clinical circumstances (specify type of person playing role of case manager) 

o Person or system actively tracked, scheduled and coordinated patients' appointments 

o Other basis for describing intervention as disease/case management (describe) 

o Not applicable - no component of disease/case management 

 

 

27. Did intervention involve changes to make up of healthcare team or roles of providers? 

o Yes - Creation of multidisciplinary team, addition of new team member, expansion of roles, 

automatic referral for periodic visit with specific provider type (e.g., podiatrist or opthalmologist) 

o Revision/expansion of roles or "shared care" (e.g., nurse or pharmacist operated actively managed 

medications without consulting physician) 

o Other (describe) 

o No changes to team/personnel 

 

28. Did the intervention involve changes to medical records systems? 

o Change from paper to computerised records 

o Implementation of computerized provider order entry (CPOE) 

o New patient tracking system 

o Other (describe) 

o Not applicable - No change to medical record system 

 

29. Did intervention involve any type of organizational change not captured by above questions? 

o Yes (describe) 

o No 

 

30. Did a clinical information system play a role in design or implementation of intervention (see Q11 at 

Stage 3)? 

o Identification and/or group allocation of eligible patients or providers 

o Reminders generated by existing clinical information system 

o Decision support at point of care (e.g., for provider order entry) 

o Facilitated communication between providers (e.g., generated emails between members of care 

team) 

o Audit data gathered from clinical information system to design QI strategy (e.g., audit and 

feedback, TQM, provider education, financial incentives) 

o Other 

o No role for a clinical information system 

 

 

129
D) Results 

 

31. For unit of treatment allocation (e.g., clinics, providers, patients), were results reported for at least 80% 

of participants? 

o Yes (state %) 

o No (state %) 

o Not stated 

 

 

32. If unit of analysis differed from unit of treatment allocation (e.g., providers randomized, but patient level 

outcomes analyzed), were results reported for at least 80% of participants? 

o Yes (state %) 

o No (state %) 

o Not stated or not clear 

o Not applicable (unit of analysis same as unit of treatment allocation) 

 

Measures of Disease Control 

 

33. Did the study report outcomes involving measures of disease control? 

o Yes 

o No 

 

34. Did one measure of disease control involve HbA1c reported as mean and standard deviation in 

intervention and control groups? 

o Yes 

o No 

 

35. For the outcome of disease control involving mean HbA1c, provide the following information for patients 

in CONTROL group; indicate not reported by typing "NR" 

o Mean HbA1c before intervention 

o Standard deviation for HbA1c before intervention 

o Mean HbA1c after intervention 

o Standard deviation for HbA1c after intervention 

o Mean difference between pre- and post-intervention HbA1c values 

o Standard deviation for difference between pre- and post-intervention HbA1c values 

o Not applicable (no measure of HbA1c) 

 

36. For the outcome of disease control involving mean HbA1c, provide the following information for 

INTERVENTION group; indicate not reported by typing "NR" 

o Mean HbA1c value before intervention 

o Standard deviation for HbA1c before intervention 

o Mean HbA1c value after intervention 

o Standard deviation for HbA1c after intervention 

o Mean difference between pre- and post-intervention HbA1c values 

o Standard deviation for difference between pre- and post-intervention HbA1c values 

o Not applicable (no measure of HbA1c) 

 

37. Did study report any measures of disease control involving HbA1c outcomes not captured above (e.g. 

median HbA1c or % of patients with HbA1c in certain range)? 

o Yes (describe) 

o No 

 

38. For articles reporting changes in SYSTOLIC BLOOD PRESSURE using mean and standard deviation, 

provide the following information for patients in CONTROL group (indicate not reported by typing NR) 

o pre-intervention SBP (state mean and standard deviation) 

o post-intervention SBP (state mean and standard deviation) 

o difference between pre- and post-intervention values (state mean and SD) 

o Not applicable - no disease control outcomes involving SBP as mean and SD 

 

39. For articles reporting changes in SYSTOLIC BLOOD PRESSURE using mean and standard deviation, 

provide the following information for patients in INTERVENTION group (indicate not reported by typing NR) 

o pre-intervention SBP (state mean and SD) 

130
o post-intervention SBP (state mean and SD) 

o difference between pre- and post-intervention values (state mean and SD) 

o Not applicable - no disease control outcome involving SBP as mean and SD 

 

40. For articles reporting changes in DIASTOLIC BLOOD PRESSURE using mean and standard deviation, 

provide the following information for patients in the CONTROL group (indicate not reported by typing NR) 

o pre-intervention DBP (state mean and SD) 

o post-intervention DBP (state mean and SD) 

o difference between pre- and post-intervention values (state mean and SD) 

o Not applicable - no disease control outcome involving DBP as mean and SD 

 

41. For articles reporting changes in DIASTOLIC BLOOD PRESSURE using mean and standard deviation, 

provide the following information for patients in INTERVENTION group (indicate not reported by typing NR) 

o pre-intervention DBP (state mean and SD) 

o post-intervention DBP (state mean and SD) 

o difference between pre- and post-intervention values (state mean and SD) 

o Not applicable - no disease control outcome involving DBP and mean and SD 

 

42. Did study report any measures of disease control involving blood pressure outcomes not captured above 

(e.g. median SBP/DBP or % patients with BP in certain range)? 

o Yes (describe) 

o No 

 

43. Indicate results for measures of disease control no captured above: 

o Serum blood glucose 

o Other CV risk factor (e.g. total cholesterol, HDL-C, LDL-C, triglyceride, lipid, smoking, weight) 

o Microalbuminuria or renal failure 

o Other microvascular complications (e.g. foot lesions, retinopathy, neuropathy) 

o Clinical outcomes (e.g. mortality, MI, stroke, amputation) 

o Other (explain) 

o Not applicable - no other outcomes of disease control 

 

Measures of clinician adherence 

 

44. Did the study report outcomes related to clinician adherence? 

o Yes 

o No - none reported or none in usable form (explain) 

 

 

Adherence to Guidelines for Assessing Glycemic Control using HbA1c 

 

45. Did one of the outcomes of clinician adherence involve proportion of patient with HbA1c measured at 

least once during certain time period? 

o Yes (specify definition) 

o No or not reported in usable form (explain) 

 

 

46. For the adherence outcome involving measurement of HbA1c, indicate all that were reported or 

calculable for control group (All results should reflect % patients in designated group with HbA1c checked 

according to stated definition); indicate not reported by typing NR 

o pre-intervention adherence (% patients) 

o post-intervention adherence (% patients) 

o difference between pre- and post-intervention values (% patients) 

o Not applicable - no adherence outcome involving measurement of HbA1c in this format 

 

47. For the adherence outcome involving measurement of HbA1c, indicate all that were reported or 

calculable for intervention group: 

o pre-intervention adherence (% patients) 

o post-intervention adherence (% patients) 

o difference between pre- and post-intervention values (% patients) 

o Not applicable - no adherence outcome involving measurement of HbA1c in this format 

 

131
48. Did study report any outcomes of clinician adherence involving checking HbA1c that are not captured

above? 

o Yes (describe; give results) 

o No 

 

Adherence to Other Guidelines Involving Performance of Laboratory Tests 

 

49. Did the article report outcomes for change in clinician adherence to a guideline for obtaining any lab 

measurements other than HbA1c? 

o Yes - specify definition (if more than one, report below for outcome with median effect attributable 

to intervention; use Excel to calculate and save file) 

o No - none reported or none in usable form 

 

 

50. For the adherence outcome involving measurement of other lab values, indicate all that were reported or 

calculable for control group (All results should reflect % patients in designated group with other lab values

checked according to stated definition); indicate not reported by typing NR 

o pre-intervention adherence (% patients) 

o post-intervention adherence (% patients) 

o difference between pre- and post-intervention values (% patients) 

o Not applicable - no adherence outcome involving measurement of other lab values in this format 

 

51. For the adherence outcome involving measurement of other lab values, indicate all that were reported or 

calculable for intervention group: 

o pre-intervention adherence (% patients) 

o post-intervention adherence (% patients) 

o difference between pre- and post-intervention values (% patients) 

o Not applicable - no adherence outcome involving measurement of other lab values in this format 

 

52. Were there any adherence outcomes for obtaining lab measurements not captured above? (If you had to 

choose outcome with median effect, use textbox for “Yes” answer to list the other adherence outcomes.) 

o Yes (list) 

o No 

 

Adherence to Guidelines for Assessment or Management of Hypertension and/or Coronary Artery Disease 

 

53. Did the article report outcomes for change in clinician adherence to a guideline for assessment or 

management of HTN and/or CAD? 

o Yes - specify definition (if more than one, report below for outcome with median effect attributable 

to intervention; use Excel to calculate and save file) 

o No - none reported or none in usable form 

 

 

54. For the adherence outcome involving assessment or management of HTN and/or CAD, indicate all that 

were reported or calculable for control group (All results should reflect % patients in designated group with 

stated guideline performed); indicate not reported by typing NR 

o pre-intervention adherence (% patients) 

o post-intervention adherence (% patients) 

o difference between pre- and post-intervention values (% patients) 

o Not applicable - no adherence outcome involving assessment or management of HTN and /or CAD 

in this format 

 

55. For the adherence outcome involving assessment or management of HTN and/or CAD, indicate all that 

were reported or calculable for intervention group: 

o pre-intervention adherence (% patients) 

o post-intervention adherence (% patients) 

o difference between pre- and post-intervention values (% patients) 

o Not applicable - no adherence outcome involving assessment or management of HTN and /or CAD 

in this format 

 

132
56. Were there any adherence outcomes for assessment or management of HTN and/OR CAD not captured 

above? (If you had to choose outcome with median effect, use textbox for “Yes” answer to list the other 

adherence outcomes.) 

o Yes (list) 

o No 

 

Adherence to Guidelines for Assessment of Diabetic Complications Involving the Eye or Foot 

 

57. Did the article report outcomes for change in clinician adherence to a guideline for referral for or 

performance of foot exam? 

o Yes - specify definition (if more than one, report below for outcome with median effect attributable 

to intervention; use Excel to calculate and save file) 

o No - none reported or none in usable form 

 

 

58. For the adherence outcome involving referral for or performance of foot exam, indicate all that were

reported or calculable for control group (All results should reflect % patients in designated group with feet 

checked according to stated definition); indicate not reported by typing NR 

o pre-intervention adherence (% patients) 

o post-intervention adherence (% patients) 

o difference between pre- and post-intervention values (% patients) 

o Not applicable - no adherence outcome involving referral for or performance of foot exam in this 

format 

 

59. For the adherence outcome involving referral for or performance of foot exam, indicate all that were

reported or calculable for intervention group: 

o pre-intervention adherence (% patients) 

o post-intervention adherence (% patients) 

o difference between pre- and post-intervention values (% patients) 

o Not applicable - no adherence outcome involving referral for or performance of foot exam in this 

format 

 

60. Were there any adherence outcomes for referral for or performance of foot exam not captured above? (If 

you had to choose outcome with median effect, use textbox for “Yes” answer to list the other adherence 

outcomes.) 

o Yes (describe) 

o No 

 

61. Did the article report outcomes for change in clinician adherence to a guideline for referral for or 

performance of eye exam? 

o Yes - specify definition (if more than one, report below for outcome with median effect attributable 

to intervention; use Excel to calculate and save file) 

o No - none reported or none in usable form 

 

 

62. For the adherence outcome involving referral for or performance of eye exam, indicate all that were

reported or calculable for control group (All results should reflect % patients in designated group with eyes 

checked according to stated definition); indicate not reported by typing NR 

o pre-intervention adherence (% patients) 

o post-intervention adherence (% patients) 

o difference between pre- and post-intervention values (% patients) 

o Not applicable - no adherence outcome involving referral for or performance of eye exam in this 

format 

 

63. For adherence outcome involving referral for or performance of eye exam, indicate all that were reported 

or calculable for intervention group: 

o pre-intervention adherence (% patients) 

o post-intervention adherence (% patients) 

o difference between pre- and post-intervention values (% patients) 

o Not applicable - no adherence outcome involving referral for or performance of eye exam in this 

format 

 

133
64. Were there any adherence outcomes for referral for or performance of eye exam not captured above? (If 

you had to choose outcome with median effect, use textbox for “Yes” answer to list the other adherence 

outcomes.) 

o Yes (describe) 

o No 

 

Adherence to Guidelines for Patient Counseling or Delivery of Patient Education 

 

65. Did the article report outcomes for change in clinician adherence to a guideline for patient counseling or 

delivering of patient education? 

o Yes - specify definition (if more than one, report below for outcome with median effect attributable 

to intervention; use Excel to calculate and save file) 

o No - none reported or none in usable form 

 

 

66. For the adherence outcome involving patient education or counseling, indicate all that were reported or 

calculable for control group (All results should reflect % patients in designated group counseled or educated 

according to stated definition); indicate not reported by typing NR 

o pre-intervention adherence (% patients) 

o post-intervention adherence (% patients) 

o difference between pre- and post-intervention values (% patients) 

o Not applicable - no adherence outcome involving patient education or counseling in this format 

 

67. For the adherence outcome involving patient education or counseling, indicate all that were reported or 

calculable for intervention group: 

o pre-intervention adherence (% patients) 

o post-intervention adherence (% patients) 

o difference between pre- and post-intervention values (% patients) 

o Not applicable - no adherence outcome involving patient education or counseling in this format 

 

68. Were there any adherence outcomes for patient education or counseling not captured above? (If you 

had to choose outcome with median effect, use textbox for “Yes” answer to list the other adherence 

outcomes.) 

o Yes 

o No 

 

69. Did the article report outcomes for change in clinician adherence to any OTHER guideline? 

o Yes (describe and give results) 

o No 

 

Patient compliance outcomes 

 

70. Describe results for any outcomes involving patient compliance 

o Compliance with self-care measures (e.g. self-monitoring of blood glucose), complying with diet or 

exercise, keeping appointments 

o Compliance with medications 

o Other (describe) 

o No patient compliance outcomes 

o Not sure (explain) 

 

 

71. Use textbox to state any important study features or results not captured above. 

 

 

72. Has a senior reviewer checked this Stage 4 abstraction? 

o Yes - completely (indicate which senior reviewer) 

o Partially (indicate where re-review was left off, i.e. question #) 

o No (indicate any important questions/comments for senior reviewer) 

o Not applicable (first reviewer is a senior reviewer) 

134
Appendix D. Articles excluded solely on the basis that intervention focused only on patient 

education or promotion of self-management 

1. Anderson RM, Funnell MM, Butler PM, Arnold MS, Fitzgerald JT, Feste CC. Patient empowerment. 

Results of a randomized controlled trial. Diabetes Care. 1995;18:943-949.

2. Barth R, Campbell LV, Allen S, Jupp JJ, Chisholm DJ. Intensive education improves knowledge, 

compliance, and foot problems in type 2 diabetes. Diabet Med. 1991;8:111-117. 

3. Boehm S, Schlenk EA, Raleigh E, Ronis D. Behavioral analysis and behavioral strategies to 

improve self-management of type II diabetes. Clin Nurs Res. 1993;2:327-344. 

4. Brown SA, Garcia AA, Kouzekanani K, Hanis CL. Culturally competent diabetes self-management 

education for Mexican Americans: the Starr County border health initiative. Diabetes Care. 

2002;25:259-268. 

5. Cooper HC, Booth K, Gill G, IN. Patients' perspectives on diabetes health care education. Health

Education Research. 2003;18:191-206. 

6. Corbett CF, IN. A randomized pilot study of improving foot care in home health patients with

iabetes. Diabetes Educator. 2003;29:273-282. 

7. Domenech MI, Assad D, Mazzei ME, Kronsbein P, Gagliardino JJ. Evaluation of the effectiveness 

of an ambulatory teaching/treatment programme for non-insulin dependent (type 2) diabetic 

patients. Acta Diabetol. 1995;32:143-147. 

8. Estey AL, Tan MH, Mann K. Follow-up intervention: its effect on compliance behavior to a diabetes 

regimen. Diabetes Educ. 1990;16:291-295. 

9. Gilliland SS, Azen SP, Perez GE, Carter JS. Strong in body and spirit: lifestyle intervention for 

Native American adults with diabetes in New Mexico. Diabetes Care. 2002;25:78-83. 

10. Glasgow RE, Toobert DJ, Hampson SE, Strycker LA. Implementation, generalization and long-term 

results of the "choosing well" diabetes self-management intervention. Patient Educ Couns. 

2002;48:115-122. 

11. Goldhaber-Fiebert JD, Goldhaber-Fiebert SN, Tristan ML, Nathan DM. Randomized controlled 

community-based nutrition and exercise intervention improves glycemia and cardiovascular risk 

factors in type 2 diabetic patients in rural Costa Rica. Diabetes Care. 2003;26:24-29. 

12. Greenfield S, Kaplan SH, Ware JE, Jr., Yano EM, Frank HJ. Patients' participation in medical care: 

effects on blood sugar control and quality of life in diabetes. J Gen Intern Med. 1988;3:448-457. 

13. Halbert RJ, Leung KM, Nichol JM, Legorreta AP. Effect of multiple patient reminders in improving 

diabetic retinopathy screening. A randomized trial. Diabetes Care. 1999;22:752-755. 

14. Hendricks LE, Hendricks RT. The effect of diabetes self-management education with frequent 

follow-up on the health outcomes of African American men. Diabetes Educ. 2000;26:995-1002. 

15. Hiss RG, Gillard ML, Armbruster BA, McClure LA. Comprehensive evaluation of community-based 

diabetic patients: effect of feedback to patients and their physicians: a randomized controlled trial. 

Diabetes Care. 2001;24:690-694. 

16. Hopper SV, Miller JP, Birge C, Swift J. A randomized study of the impact of home health aides on 

diabetic control and utilization patterns. Am J Public Health. 1984;74:600-602. 

17. Pieber. Evaluation of a structured teaching and treatment programme for type2 diabetes in general 

ractice in a rural area of Austria. Diabetes Medicine. 1995;112:349-354. 

18. Krier BP, Parker RD, Grayson D, Byrd G. Effect of diabetes education on glucose control. J La 

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138
Appendix E. Summary of results for each included study

Table E1. Main results for glycemic control and provider adherence 

Study details Intervention Provider adherence

outcomes 

Disease control outcomes Patient 

adherence

Ahring, 19921 Intervention group: Patients 

transmitted fingerstick 

readings by telephone system

automatically to provider 

(SMx, FR) 

Control group: no 

intervention/usual care

Disease control: HbA1c 

Control: 11.2 ± 1.8→10.2 ± 

1.2 

Intervention: 10.6 ± 2.8 →

9.2 ± 1.1 

Other outcomes: serum 

blood glucose, weight 

Albisser, 

19962 Comparison 1: 

Intervention group: Patients 

reported HbA1c values, 

symptoms, life changes to 

automated system, received

medication/other advice; 

providers received feedback 

of patient status (SMx, FR, 

OC/medrcrd) 

Control group: Registered 

for, but did not use automated

system 

Disease control 1: HbA1c 

Control: 10.2 ± 1.4→10.3 ± 

1.3 

Intervention: 10.1 ± 1.5→8.8 

± 1.4 

Other outcomes: weight 

Comparison 2:

Intervention group: Patients 

reported HbA1c values, 

symptoms, life changes to 

automated system, received

medication/other advice; 

providers received feedback 

of patient status (SMx, FR, 

OC/medrcrd) 

Control group: Registered 

for, but did not use automated

system

Disease control 1: HbA1c 

Control: 8.2 ± 1.9→8.6 ± 1.7 

Intervention: 8.9 ± 1.7→7.9 ± 

1.2

Other outcomes: weight

Aubert, 19983 Intervention group: Nurse 

case management supervised 

by GP, telephone reminders 

to patient, diabetes education 

(PtE, FR, OC/DxMx)

Control group: Diabetes 

education only 

Other outcomes: % patients 

with dipstick test in past year, 

% patients with

protein/microalbumin test in 

past year 

Other outcomes: median 

HbA1c, median SBP, median 

DBP, fasting blood glucose, 

total cholesterol, HDL-C, LDLC, triglycerides, weight 

Benjamin, 

19994 Intervention group:

Providers developed and 

implemented clinical practice

guidelines based on problemDisease control: HbA1c 

Control: 9.21 ± 2.3→9.15 ± 

2.3 

Intervention: 9.3 ± 2.4 →8.68

139
based learning (PvE) 

Control group: no 

intervention/usual care

± 2.1 

Boucher, 

19875 Intervention group: Diabetes

support service gave lists of 

patients requiring tests to 

provider, provider education in

examination for diabetic fundi 

(PtR, PvE, FR, OC/medrcrd) 

Control group: no 

intervention/usual care

Disease control: HbA1c 

Control: 12.6 ± 3.2→12.5 ± 

2.6 

Intervention: 13.4 ± 

2.9→11.4 ± 2.3 

Branger, 

19986 Intervention group:

Physicians equipped with 

electronic communication 

system to facilitate 

communication between PCP 

and specialist (FR, OC/other) 

Control group: All providers 

had electronic patient records

Disease control: HbA1c 

Control: 6.6 ± NR→ 6.5 ± NR

Intervention: 7.0 ± NR→ 6.8

± NR 

Cagliero, 

19997 Intervention group: Patients 

had HbA1c measured with 

benchtop analyzer with results 

immediately available to 

provider during visit (FR) 

Control group: no 

intervention/usual care

Disease control: HbA1c 

Control: 8.49 ± 1.59→ 8.3 ± 

NR 

Intervention: 8.67 ± 1.79→

8.27 ± NR 

Clancy, 20038 Intervention group: Patients 

had group instead of 

individual physician visits 

consisting of education and 

one-on-one consult (PtE, 

OC/DxMx/team) 

Control group: no 

intervention/usual care

Other outcome: adherence 

to 8 out of 10 diabetes 

guidelines 

Clifford, 

20029 Intervention group:

Pharmacist monitored 

patients and made treatment 

decisions, consulted with MD

(OC/DxMx

Control group: no 

intervention/usual care

Disease control: HbA1c 

Control: 8.5 ± 1.6→8.1 ± 1.6 

Intervention: 8.4 ± 1.4→8.2 ± 

1.5 

Coffey,

199510 Intervention group: Patients 

on managed care plan 

Control group: Traditional 

fee-for-service plan

Patient

compliance: % 

patients compliant 

with home 

monitoring 

Control:

60%→63% 

Intervention:

63%→60% 

Net difference: -

6% 

Dargis, Intervention group: Patients Other outcomes: recurrent 

140
199911 received foot care in specialty 

clinic, special footwear, foot 

care education (PtE, 

OC/team) 

Control group: one time foot 

consult with education/advice

foot ulcers, amputations 

de

Sonnaville, 

199712

Intervention group: Diabetes

support service provides GP 

with consult of podiatrist, 

diabetologist, and diabetes 

nurse educator; lab 

measurements at patient 

home; retinal camera (PtE, 

SMx, FR, OC/team) 

Control group: no 

intervention/usual care

Disease control 1: HbA1c 

Control: 7.6 ± 1.9→7.6 ± 1.5 

Intervention: 7.5 ± 1.7→7.0 ± 

1.3 

Disease control 2: SBP 

Control: 158.2 ± 23.5→

155.3 ± 22.9 

Intervention: 147.2 ± 21.7→

147.7 ± 23.5 

Disease control 2: DBP 

Control: 88.9 ± 11.7→ 85.3 ± 

11.4 

Intervention: 87.7 ± 10.4→

83.0 ± 12.6

Other outcomes: fasting 

blood glucose, BMI, total chol, 

HDL-chol, triglycerides, % 

smokers 

Deeb, 198813 Intervention group:

Providers attended diabetes 

education seminars, had 

quarterly consults with outside

leaders, received printed 

education modules (PvE, 

OC/team) 

Control group: no 

intervention/usual care

Adherence measure: % 

patients with BP measured in 

past year 

Control: 99%→99% 

Intervention: 100%→100% 

Net difference: 0% 

Adherence measure: % 

patients with lower-extremity

exam in past year 

Control: 27%→41% 

Intervention: 66%→94% 

Net difference: -6%

Other outcomes: % patients 

with urinalaysis, lowerextremity history, referral for 

retinopathy exam, retinopathy

history, and retinopathy exam 

in past year 

Feder, 199514 Intervention group: Diabetes Adherence measure: % 

141
care guidelines introduced, 

followed by provider audit and

feedback as follow-up (PvE, 

Aud) 

Control group: Providers 

received guidelines on

asthma, not diabetes 

patients with HbA1c record

Control: 20.6%→30% 

Intervention: 24.8%→48.1%

Net difference: +13.9%

Adherence measure: % 

patients with BP record 

Control: 66.1%→58.3%

Intervention: 69%→79.5%

Net difference: +18.3%

Adherence measure: % 

patients with foot exam record

Control: 28.3%→27.2%

Intervention: 31.4%→51.8%

Net difference: +21.5%

Adherence measure: % 

patients with funduscopy

record 

Control: 19.4%→20% 

Intervention: 20.5%→38.1%

Net difference: +17%

Other outcomes: % patients 

with record of blood glucose 

and weight 

Franz, 199515 Comparison 1

Intervention group: Patients 

received diet therapy with

dietitian who made 

recommendations to PCP on 

medical therapy (PtE, SMx, 

OC/team) 

Control group: Patients had 

one meeting with dietitian to 

discuss diet goals and advice

Disease control : HbA1c 

Control: 8.3 ± 1.7→8.1 ± 1.7 

Intervention: 8.1 ± 1.6→7.7 ± 

1.2

Comparison 2

Intervention group: Patients 

received diet therapy with

dietitian who made 

recommendations to PCP on 

medical therapy (PtE, SMx, 

OC/team) 

Control group: Patients had 

one meeting with dietitian to 

discuss diet goals and advice

Disease control : HbA1c 

Control: 8.2 ± 2.2→6.8 ± 1.3 

Intervention: 8.8 ± 1.9→6.8 ± 

1.0

Frijling,

200216 Intervention group:

Providers received didactic 

education and audit and 

feedback to improve 

compliance with care 

Adherence measure: % 

patients with BP record in last 

year

Control: 89%→92% 

142
guidelines (PvE, Aud) 

Control group: no 

intervention/usual care

Intervention: 91%→94% 

Net difference: -1%

Adherence measure: % 

patients with foot exam record

in last year 

Control: 30%→39% 

Intervention: 24%→43% 

Net difference: +10%

Adherence measure: % 

patients with eye exam 

record in last year 

Control: 69%→67% 

Intervention: 61%→70% 

Net difference: +11%

Other outcomes: % patients

with discussion of body weight

control 

Gaede, 

200317, 18* 

Intervention group: Intensive 

treatment with stepwise

implementation of behaviour 

modification and

pharmacologic therapy, 

dietary and exercise 

counselling (PtE, OC/DxMx) 

Control group: All patients 

received individual diabetic 

dietary advice 

Disease control 1: HbA1c 

(mean and SE)

Control: 8.8 ± 1.7→9.0 ± NR

Intervention: 8.4 ± 1.6→7.8 ± 

NR 

Disease control 2: SBP 

Control: 149 ± 19→ 146 ± 

NR 

Intervention: 146 ± 20→ 132 

± NR 

Disease control 2: DBP 

Control: 86 ± 11→ 78 ± NR

Intervention: 85 ± 10→ 73 ± 

NR

Other outcomes: urinary 

AER, triglycerides, total 

cholesterol, GFR 

Ginsberg,

199619 Intervention group:

Providers trained in Staged 

Diabetes Management 

(benchmarking, flowcharts for 

decision-making, evaluation, 

feedback to community); 

patients received education 

(PtE, PvE) 

Control group: no 

intervention/usual care

Disease control 1: HbA1c 

Control: 10.3 ± 2.1→10.4 ± 

2.1 

Intervention: 10.2 ± 2.8→8.8 

± 0.7

143
Glasgow, 

199620 Intervention group: Brief, 

computer-based assessment 

of dietary compliance. 

Patients received dietary

advice, one-on-one 

counselling, a video and 2 

follow-up calls (PtE, SMx, 

OC/team) 

Control group: no 

intervention/usual care

Disease control: HbA1c 

Control: 7.9 ± NR→7.7 ± NR

Intervention: 7.8 ± NR → 7.6 

± NR 

Other outcomes: serum 

blood glucose 

Groeneveld, 

200121 Intervention group: Patients 

followed-up with structured 

diabetes care service 

including education, testing 

and advice to PCPs on drug

therapy (PtE, FR, OC/DxMx)

Control group: no 

intervention/usual care

Disease control 1: HbA1c 

Control: 7.5 ± 1.8 (post) 

Intervention: 7.1 ± 1.2 (post)

Disease control 2: SBP 

Control: 149 ± 24→ 143 ± 21

Intervention: 137 ± 21→ 135 

± 18 

Disease control 2: DBP 

Control: 86 ± 9.7→ 82 ± 9 

Intervention: 91 ± 9→ 90 ± 8

Other outcomes: serum 

blood glucose, total 

cholesterol, weight, creatinine

Hartmann, 

199522 Intervention group:

Providers participated in a 

series of structured peerreview groups focused on 

diabetes patient records, 

didactic education and roleplay (PvE, Aud) 

Control group: Providers had

same type of group session 

only once 

Adherence measure: % 

patients with at least one 

HbA1c record per quarter 

Control: 24.4%→32.3%

Intervention: 27.6%→26.8%

Net difference: -8.7%

Patient

compliance: % 

patients with

documentation of 

glucose self-mx at 

least once per 

quarter 

Control: 

21.3%→17.1%

Intervention:

7.9%→11.3%

Net difference:

+7.6%

Adherence measure: % 

patients with record of 

fundoscopy and foot exam 

with tuning fork

Control: 1.2%→0.6% 

Intervention: 20.5%→20.5%

Net difference: +0.6%

144
Adherence measure: % 

patients with record of 

fundoscopy at least once per 

year

Control: 5.5%→13.4% 

Intervention: 8.4%→32.2%

Net difference: +15.9%

Other outcomes: % patients 

with pallaesthesia at least 

once per year, % patients with

documentation of 

triglycerides, HDL-C and total 

chol, weight and BP, albumin, 

serum creatinine at least once

per year, blood glucose at 

least once per quarter 

Hayes, 198423 Intervention group: Patients 

received care from a GP 

instead of a diabetes 

specialist clinic, GPs received 

printed care guidelines (PvE, 

OC/team) 

Control group: no 

intervention/usual care

(diabetes specialist clinic) 

Disease control: HbA1c 

Control: 9.5 ± 1.77 (post) 

Intervention: 10.4 ± 1.73

(post) 

Hetlevik, 

200024 Intervention group:

Computerized support system

provided physician with

treatment, test, and referral 

recommendations; providers 

received feedback of missing 

tests at 6 month intervals 

(PvR, Aud) 

Control group: no 

intervention/usual care

Adherence measure: % 

patients with record of HbA1c 

during study period

Control: 77.6%→81.2%

Intervention: 72.5%→79.5%

Net difference: +3.4%

Disease control 1: HbA1c 

Control: 8.2 ± 1.8→ 7.9 ± 1.6

Intervention: 8.2 ± 1.8→ 7.8 

± 1.6 

Adherence measure: % 

patients with record of BP 

during study period

Control: 77.4%→81.5%

Intervention: 78.2%→81.3%

Net difference: -1%

Disease control 2: SBP 

Control: 151.7 ± 21.3→

152.7 ± 19

Intervention: 152.5 ± 21.6→

151.5 ± 22.1 

Other outcomes: % patients 

with record of cholesterol 

during study period

Disease control 3: DBP 

Control: 85.1 ± 10.1→ 85.3 ±

9.9 

Intervention: 84.5 ± 10→

82.8 ± 10.6 

Hirsch, 

200225 Intervention group: Provider 

intervention: reminders for 

tests, diabetes management 

guidelines, didactic teaching, 

feedback of HbA1c levels, 

pharmacist case

Disease control 1: HbA1c 

Control: 7.57 ± NR→ 8.2 ±

NR 

Intervention: 7.64 ± NR→

145
management, access to 

endocrinologist (PvE, Aud, 

OC/DxMx, team) 

Control group: Providers 

received reminders to test 

HbA1c 

7.56 ± NR 

Disease control 2: SBP 

Control: 134 ± NR→ 137.1 ±

NR 

Intervention: 135 ± NR→

133.8 ± NR

Disease control 3: DBP 

Control: 80 ± NR→ 79.19 ±

NR 

Intervention: 79 ± NR→

75.21 ± NR

Hoskins, 

199326 Comparison 1 

Intervention group: GP

received one time letter 

requesting adherence to 

recommended management 

protocol, and measurement of 

HbA1c, BP, weight (OC/other)

Control group: All patients 

received 3-6 mo education 

before intervention. 

Adherence measure: % 

HbA1c results received from 

GP out of number of know GP

attendances 

Control: 98.4% postintervention 

Intervention: 45.6 % postintervention 

Disease control 1: HbA1c 

Control: 8.9 ± 2.5→ 7.3 ± 1.6

Intervention: 8.4 ± 2.6→ 6.9 

± 1.3 

Patient 

compliance: % 

patients attended 

visit 3 (out of 3) to 

their GP during

study period 

Control: 53% 

Intervention: 35% 

Adherence measure: % 

weight measurements 

received from GP out of 

number of know GP 

attendances 

Control: 98.3% postintervention 

Intervention: 70.6 % postintervention 

Disease control 2: SBP 

Control: 150 ± 23→ 133 ± 19

Intervention: 148 ± 23→ 136 

± 14 

Adherence measure: % BP 

measurements received from 

GP out of number of know GP

attendances 

Control: 92.7% postintervention 

Intervention: 89.7 % postintervention 

Disease control 3: DBP 

Control: 90 ± 13→ 81 ± 13

Intervention: 90 ± 15→ 80 ±

11

Other outcomes: weight 

Comparison 2 Adherence measure: % Disease control 1: HbA1c Patient 

146
Intervention group: Shared 

care between patient, GP, 

clinic. Nurse liaised btw

patient and GP, reminded GP 

of measurements. Patient 

received copy of management

protocol (SMx, PtR, PvR, 

OC/team/other) 

Control group: All patients 

received 3-6 mo education 

before intervention. 

HbA1c results received from 

GP out of number of know GP

attendances 

Control: 98.4% postintervention 

Intervention: 66 % postintervention 

Control: 8.9 ± 2.5→ 7.3 ± 1.6

Intervention: 8.5 ± 2.2→ 6.6 

± 1.6 

compliance: % 

patients attended 

visit 3 (out of 3) to 

their GP during

study period 

Control: 53% 

Intervention: 72% 

Adherence measure: % 

weight measurements 

received from GP out of 

number of know GP 

attendances 

Control: 98.3% postintervention 

Intervention: 93.5 % postintervention 

Disease control 2: SBP 

Control: 150 ± 23→ 133 ± 19

Intervention: 145 ± 24→ 130 

± 25 

Adherence measure: % BP 

measurements received from 

GP out of number of know GP

attendances 

Control: 92.7% postintervention 

Intervention: 94.8 % postintervention 

Disease control 3: DBP 

Control: 90 ± 13→ 81 ± 13

Intervention: 88 ± 13→ 81 ±

11

Other outcomes: weight 

Hurwitz, 

199327 Intervention group: Patients 

received computer-generated

reminders to get blood and 

urine tests done. Results 

relayed to providers, 

appointments made as 

necessary (PtR, FR) 

Control group: usual care

with specialist 

Disease control: HbA1c 

Control: 10.3 ± 2.3→ 10.6 ±

2.5 

Intervention: 10.4 ± 2.5→

10.3 ± 2.3 

Other outcomes: serum 

blood glucose, % mortality 

Jaber, 199628 Intervention group:

Pharmacist was responsible

for medication adjustments 

and provided education, 

dietary and exercise 

counselling (PtE, Oc/team) 

Control group: no 

intervention/usual care

Disease control : HbA1c 

Control: 12.2 ± 3.5→ 12.1 ±

3.7 

Intervention: 11.5 ± 2.9→ 9.2 

± 2.1 

Other outcomes: serum 

blood glucose

147
Kiefe, 200129 Intervention group:

Providers had QI education 

and audit and feedback, as 

well as “achievable 

benchmarks” for specific 

processes of care (Aud) 

Control group: Provider 

education and audit and 

feedback 

Adherence measure: % 

patients with at least one 

HbA1c in 18-month period 

Control: 35%→65% 

Intervention: 31%→70% 

Net difference: +9%

Adherence measure: % 

patients with at least one foot 

exam in 18-month period 

Control: 32%→45% 

Intervention: 46%→61% 

Net difference: +2%

Kinmonth,

199830 Intervention group:

Providers received training in

patient-centered care as well 

as materials to give to 

patients (PtE, PvE) 

Control group: Limited 

training on use of guidelines 

and patient materials 

Disease control 1: HbA1c 

Control: 7.17 ± NR (post) 

Intervention: 7.07 ± NR 

(post) 

Disease control 2: SBP 

Control: 141.5 ± NR→ 142.8 

± NR 

Intervention: 144.1 ± NR→

144.3 ± NR

Disease control 3: DBP 

Control: 83.7 ± NR→ 87.2 ±

NR 

Intervention: 85.5 ± NR→ 89

± NR 

Other outcomes: total 

cholesterol, BMI, % smoking

Kogan, 

200331 Intervention group:

Residents received report 

card of individual and group 

disease management 

performance and brief 

discussion with faculty (Aud) 

Control group: no 

intervention/usual care

Other outcomes: % overall 

diabetes management 

(pneumovax, flu shot, 

creatinine, microalbumin, 

appropriate use of ACEinhibitors, dilated eye exam, 

foot exam, lipid screening, 

lipid management, HbA1c, 

diet, exercise) recorded in 

chart 

Legorreta, 

199632 Comparison 1 

Intervention group: Nurse 

case managed patients using 

Disease control: HbA1c 

Control: 8.3 ± 2.6→ 9.1 ± NR

Intervention: 8.9 ± 2.1→ 6.9

148
protocols for management. 

Computer information system 

generated reminders and 

algorithms for medication 

adjustment. Diabetologist 

available for consult (PtR, 

PvE, 

OC/DxMx/medrcrd/other) 

Control group: no 

intervention/usual care

± NR 

Other outcomes: LDLcholesterol 

Comparison 2

Intervention group: Nurse 

case managed patients using 

protocols for management. 

Computer information system 

generated reminders and 

algorithms for medication 

adjustment. Diabetologist 

available for consult (PtR, 

PvE, 

OC/DxMx/medrcrd/other) 

Control group: no 

intervention/usual care

Disease control: HbA1c 

Control: 8.6 ± 2.0→ 8.4 ± NR

Intervention: 10.3 ± 2.5→ 9.0 

± NR 

Other outcomes: LDLcholesterol 

Levetan, 

200233 Intervention group: Patients 

received a wall poster and 

wallet card containing current 

lab values and goals, as well 

as postcards with reminders. 

Providers received lab values 

as well (PtE, SMx, PtR, PvR, 

FR) 

Control group: All patients 

had finished 3 month diabetes

education before enrolment in

study

Disease control 1: HbA1c 

Control: 8.39 ± 2.03→ 7.79 ±

1.91 

Intervention: 8.85 ± 2.48→

7.78 ± 2.22

Other outcomes: % patients 

with HbA1c in range, HDLchol, LDL-chol

Lim, 200234 Intervention group: Case 

management with PCP, case

manager, podiatrist, dietitian. 

Patients received education 

and reminders. Providers 

received reminders for 

testing/referral (PtE, PvR, 

OC/DxMx)

Control group: no 

intervention/usual care

Disease control 1: HbA1c 

Control: 8.28 ± 1.94→ 8.28 ±

1.69 

Intervention: 9.81 ± 2.22→

7.82 ± 1.75

Disease control 2: SBP 

Control: 138.9 ± 19.3→

149
136.8 ± 18.5 

Intervention: 142.1 ± 16.4→

134.2 ± 10.1 

Disease control 3: DBP 

Control: 80.8 ± 8.9→ 79.1 ±

8.41 

Intervention: 82.8 ± 10.3→

80.5 ± 7.8

Other outcomes: % patients 

with HbA1c in range, % 

patients with BP in range 

Litzelman, 

199335 Intervention group: Patients 

received education on foot 

care and reminders by phone 

and mail to continue foot care. 

Provider received reminders 

in chart to educate patient and 

perform foot care (PtE, PtR,

PvE, PvR) 

Control group: no 

intervention/usual care

Other outcomes: % patients 

with serious foot lesions 

Lobach, 

199736 Intervention group:

Computerized system of 

guidelines provided

physicians with reminders for 

tests/referrals (PvR) 

Control group: no 

intervention/usual care

Adherence measure: % 

provider compliance with

chronic glycemia monitoring 

Control: 52.8% (post) 

Intervention: 57.4% (post) 

Adherence measure: % 

provider compliance with

cholesterol level 

determination 

Control: 13.4% (post) 

Intervention: 43.7% (post) 

Adherence measure: % 

provider compliance with foot 

exam 

Control: 30% (post) 

Intervention: 55.6% (post) 

Adherence measure: % 

provider compliance with

opthalmologic exam 

Control: 3.2% (post) 

Intervention: 18.8% (post) 

Other outcomes: % provider 

compliance with urine protein 

150
determination 

Mazzuca, 

198637 Comparison 1 

Intervention group:

Providers had one didactic 

session with three problemoriented follow-up sessions, 

distribution of guidelines, 

access to specialist, 

computer-generated guideline

reminders (PvE, PvR, Aud) 

Control group: no 

intervention/usual care

Disease control 1: HbA1c 

Control: 10.19 ± NR→ 10.74 

± NR 

Intervention: 10.51 ± NR→

10.65 ± NR

Disease control 2: SBP 

Control: 137.2 ± NR→ 144.9 

± NR 

Intervention: 142.5 ± NR→

146.4 ± NR

Disease control 3: DBP 

Control: 81.4 ± NR→ 85.2 ±

NR 

Intervention: 83.1± NR→

83.4 ± NR

Other outcomes: serum 

creatinine, fasting blood 

glucose 

Comparison 2

Intervention group:

Providers had one didactic 

session with three problemoriented follow-up sessions, 

distribution of guidelines, 

access to specialist, 

computer-generated guideline

reminders; patients had 

didactic sessions and dietary 

education (PtE, PtR, PvE, 

PvR, Aud) 

Control group: no 

intervention/usual care

Disease control 1: HbA1c 

Control: 10.19 ± NR→ 10.74 

± NR 

Intervention: 11.34 ± NR→

10.42 ± NR

Disease control 2: SBP 

Control: 137.2 ± NR→ 144.9 

± NR 

Intervention: 140.4 ± NR→

145 ± NR

Disease control 3: DBP 

Control: 81.4 ± NR→ 85.2 ±

NR 

Intervention: 81.8± NR→

151
81.3 ± NR

Other outcomes: serum 

creatinine, fasting blood 

glucose 

McDermott, 

200138 Intervention group: Visit to 

clinic by outreach team 

(diabetologist, nutritionist, 

podiatrist, diabetes healthcare 

worker), reminder and recall 

system (PtR, PvE, PvR, Aud, 

OC/medrcrd) 

Control group: Outreach 

team visits, audit of past 

clinician adherence guidelines

Adherence measure: % 

patients with HbA1c check in 

past 6 mo. 

Control: 60%→62% 

Intervention: 70%→73% 

Net difference: +1%

Other outcomes: % patients 

with HbA1c in range, % 

patients with BP in range 

Adherence measure: % 

patients with BP check in past

6 mo. 

Control: 64%→57% 

Intervention: 76%→65% 

Net difference: -4%

Adherence measure: % 

patients with foot check in 

past year 

Control: 52%→55% 

Intervention: 60%→72% 

Net difference: +9%

Adherence measure: % 

patients with opthalmologist 

check in past year 

Control: 18%→22% 

Intervention: 21%→25% 

Net difference: 0%

Other outcomes: % patients 

with serum creatinine past yr, 

lipids past yr, urinary ACR 

past yr

Meigs, 200339 Intervention group:

Providers given access to 

web-based system of clinical

information and care 

recommendations for 

individual patients to be used 

during visit (PvE, PvR) 

Control group: no 

intervention/usual care

Adherence measure: % 

patients with at least one 

HbA1c in past year 

Control: 88%→87% 

Intervention: 86%→87.6%

Net difference: -2.6%

Disease control 1: HbA1c 

(mean and SE)

Control: 8.1 ± 0.1→ 8.24 ±

NR 

Intervention: 8.4 ± 0.1→ 8.17

± NR 

Adherence measure: % 

patients with at least one BP 

in past year 

Control: 98.6%→97.2%

Intervention: 97.4%→98.4%

Disease control 2: SBP 

Control: 136.9± 1.2→ 134.7±

NR 

Intervention: 138.1 ± 1.2→

138.9 ± NR

152
Net difference: +0.4%

Adherence measure: % 

patients with at least one foot 

exam in past year 

Control: 82.1%→81.4%

Intervention: 65.5%→75.3%

Net difference: +10.5%

Disease control 3: DBP 

Control: 76.4 ± 0.6→ 75.6 ±

NR 

Intervention: 78.3± 0.6→

79.1± NR

Adherence measure: % 

patients with at least one eye 

exam in past year 

Control: 41.2%→42.9%

Intervention: 29.3%→34.8%

Net difference: +3.8%

Other outcomes: % patients 

with HbA1c and BP in range, 

LDL-chol 

Other outcomes: % patients 

with at least on LDL-chol in 

past year 

O’Connor, 

199640 Intervention group: 

Continuous quality

improvement program of: 

audit and feedback, patient 

education by RN, nurse 

responsibility for care of 

poorly controlled diabetics 

(PtE, Aud, OC/DxMx)

Control group: no 

intervention/usual care

Disease control: HbA1c 

(mean ± SE) 

Control: 8.4 ± 0.19→ 8.8 ±

0.17 

Intervention: 8.9 ± 0.22→ 7.9 

± 0.17 

Other outcomes: % patients 

with HbA1c in range 

Oh, 200341 Intervention group: Patients 

received regular telephone 

calls to monitor glucose and 

diet and make medication 

adjustments. Changes and 

data relayed to MD (PtE, FR) 

Control group: no 

intervention, usual care

Disease control: HbA1c 

Control: 8.4 ± 1.0→ 9.0 ± 1.2

Intervention: 8.9 ± 1.2→ 7.7 

± 1.0 

Other outcomes: FBG, 2-hr 

post prandial BG, BMI 

Olivarius, 

200142 Intervention group:

Providers prompted to see 

patients every 3 months and 

follow care guidelines, 

received annual guideline 

seminar and audit and 

feedback of individual patient 

status (PtE, PvE, PvR, Aud) 

Control group: no 

intervention/usual care

Other outcomes: % patients 

with HbA1c and BP in range, 

MI, stroke, amputation, 

retinopathy, neuropathy, 

angina pectoris,

microalbuminuria, smoking; 

mean/median total 

cholesterol, triglycerides, 

serum blood glucose, serum 

creatinine 

Ovhed, 

200043 Intervention group: Patients 

care for primary by RN using 

care guidelines

(OC/team/other) 

Adherence measure: % 

patients with record of HbA1c 

during study year 

Control: 36% postPatient

compliance: % 

patients with

record of urine and 

153
Control group: no 

intervention/usual care

intervention 

Intervention: 97% postintervention 

blood glucose selfmeasurement 

during study year 

Adherence measures

(HTN/CAD): % patients with 

record of body weight, 

cholesterol, TG, BP in study

year

Adherence measure: % 

patients with fundus photo 

during study year 

Control: 47% 

Intervention: 73% 

Other outcome: % patients 

with FBG record during study 

year

Palmer, 

198544 Intervention group: Provider 

quality assurance training and

audit and feedback of 

selected cases (Aud, 

OC/other) 

Control group: no 

intervention/usual care

Other outcomes: % follow-up 

of abnormal glucose levels 

Piette, 200045 Intervention group:

Automated telephone system 

for patient reporting of blood 

glucose, symptoms, self-care 

behaviours. Results relayed 

from nurse to physician, nurse 

follow-up to patient by phone 

(PtE, SMx, FR, OC/team 

Control group: no 

intervention/usual care

Disease control 1: HbA1c 

Control: 8.6 ± 1.8→ 8.3 ± 1.9

Intervention: 8.8 ± 1.8→ 8.2 

± 1.9 

Other outcomes: % patients 

with HbA1c in a range, serum

glucose 

Piette, 200146 Intervention group:

Automated telephone system 

for patient reporting of blood 

glucose, symptoms, self-care 

behaviours. Results relayed 

from nurse to physician, nurse 

follow-up to patient by phone 

(PtE, SMx, PtR, FR, OC/team 

Control group: no 

intervention/usual care

Disease control 1: HbA1c 

Control: 8.1 ± 1.7→ 8.2 ± 0.1 

(SEM) 

Intervention: 8.2 ± 1.7→ 8.1 

± 0.1 (SEM) 

Other outcomes: % patients 

with HbA1c in a range, serum

glucose

Pouwer, 

200147 Intervention group: Patients 

monitored twice by diabetes 

nurse specialist and survey

for psychological wellbeing, 

Disease control: HbA1c 

Control: 7.8 ± NR→ 7.7 ± NR

Intervention: 7.8 ± NR→ 7.7

154
referred to psychologist if 

appropriate (OC/other) 

Control group: Patients had 

two meetings with diabetes 

nurse specialist. 

± NR 

Reed, 200148 Intervention group: 3 

diabetes care clinics created, 

used to introduce care 

guidelines; patients given 

education and clinical data 

cards to bring to 

appointments; provider 

education and access to 

hospital-based specialists 

(PtE, PvE, FR, 

OC/team/medrcrd) 

Control group: no 

intervention/usual care

Disease control 1: SBP 

Control: 133.7 ± 19→ 131.3 

± 16.6 

Intervention: 129.2 ± 18.8→

126.5 ± 16.4 

Disease control 2: DBP 

Control: 82.6 ± 9.5→ 81.1 ±

9.1 

Intervention: 79.4 ± 8.6→

80.9 ± 8.7

Renders, 

200149 Intervention group:

Providers received clinical

guideline education, audit and 

feedback and relay of data 

from central monitoring visit 

(PvE, FR, Aud) 

Control group: annual visit 

with resulting data sent to 

GPs 

Adherence measure: % 

patients with HbA1c at least 

once per year 

Control: 15.6%→29.3%

Intervention: 34.9%→75.5%

Net difference: +26.9%

Other outcomes: % patients 

with HbA1c and BP in range, 

total cholesterol, HDLcholesterol, triglycerides, BMI 

Other outcomes: % patients 

with at least one urine 

albumin, serum creatinine, 

triglycerides, total cholesterol, 

HDL-chol, blood pressure

measurements per year, % 

patients with weight 

measurements and diabetes 

visits 4 per year

Ronnemaa,

199750 Intervention group: Patients 

received foot education and 

primary prevention from 

podiatrist (PtE, OC/team) 

Control group: Patients 

received written information 

on foot care and preventive 

practices 

Other outcomes: % patients 

with: callosities in calcaneal 

region and other regions, 

corns, ingrown toenails, other 

nail disorders, inability to 

spread out toes, inability to 

flex toes 

Patient 

compliance: foot 

self-care score (out 

of 12) 

Control: 5.3 ± 

2.6→ 6.0 ± 2.5

Intervention: 5.4 ± 

2.8→ 7.0 ± 3.2

Sadur, 199951 Intervention group:

Multidisciplinary care team of 

diabetologist, RN educator, 

pharmacist, dietitian, 

Disease control: HbA1c 

Control: 9.55 ± NR→ 9.33 ±

NR 

Patient 

compliance: % 

patients monitoring 

BG at home 

155
behaviourist. Patients 

received education, foot care, 

counselling, feedback (PtE, 

PtR, OC/DxMx/team) 

Control group: no 

intervention/usual care

Intervention: 9.48 ± NR→

8.18 ± NR 

Control:

93.4%→93.6%

Intervention:

90%→97.5%

Smith, 199852 Intervention group:

Physicians entered clinical 

data into electronic system 

during patient visit 

(OC/medrcrd) 

Control group: no 

intervention/usual care

Adherence measure: % 

patients with 4 HbA1c 

measurements per yr

Control: 51.2% post 

Intervention: 76.9% post 

Disease control 1: HbA1c 

Control: 10.2 ± 1.9 post 

Intervention: 9.7 ± 1.7 post 

Adherence measure: % 

patients with dilated eye exam

in past yr 

Control: 65.1% post 

Intervention: 64.1% post

Disease control 2: SBP 

Control: 140.9 ± 19.6 post 

Intervention: 138.3 ± 16.9 

post 

Other outcomes: % patients 

with measurement of urinary 

microalbumin, lipid profile in 

past year; % patients with 

diabetes self-mx and diet 

education; % smoking 

patients with advice to quit 

Disease control 2: DBP 

Control: 93.6 ± 25 post 

Intervention: 80.6 ± 9.6

Stroebel, 

200253 Comparison 1 

Intervention group: 20-

minute physician meeting 

every 2 months to remind to 

use Hot Lists of patients 

needing test (OC/other) 

Control group: Academic 

detailing and “hot list” of 

patients not in compliance 

Adherence measure: % 

patients with HbA1c in 6 

months 

Control: 77%→74% 

Intervention: 74%→76% 

Net difference: 5% 

Disease control: HbA1c 

(mean ± SE) 

Control: 8.1 ± 1.8→ 8.0 ± 1.8

Intervention: 8.3 ± 2→ 8.2 ± 

2 

Other outcomes: % patients 

with LDL-chol in last year 

Other outcomes: % patients 

with HbA1c and BP in a 

range, LDL-Chol 

Comparison 1

Intervention group: 20-

minute physician meeting 

every 2 months to remind to 

use Hot Lists of patients 

needing test and reminders to

patients on Hot Lists to make 

appointments (PtR, OC/other) 

Control group: Academic 

detailing and “hot list” of 

patients not in compliance

Adherence measure: % 

patients with HbA1c in 6 

months 

Control: 77%→74% 

Intervention: 79%→78% 

Net difference: 2% 

Disease control: HbA1c 

(mean ± SE) 

Control: 8.1 ± 1.8→ 8.0 ± 1.8

Intervention: 8.1 ± 2→ 7.9 ± 

1.8 

Other outcomes: % patients 

with LDL-chol in last year 

Other outcomes: % patients 

with HbA1c and BP in a 

range, LDL-Chol 

Thompson, 

199954 Intervention group: Patients 

received 3x weekly calls from 

Disease control: HbA1c 

156
RN who adjusted insulin and 

reviewed with MD (PtE, FR, 

OC/team) 

Control group: no 

intervention/usual care

Control: 9.4 ± 0.8→ 8.9 ± 1.0

Intervention: 9.6 ± 1.0→ 7.8

± 0.8 

Vaughan, 

199655 Intervention group: Patients 

were treated by RN using 

paper algorithms for 

medication adjustment 

(OC/team) 

Control group: no 

intervention/usual care

Adherence measure: % 

patients with HbA1c 

performed 

Control: 79% post 

Intervention: 100% post 

Other outcomes: % patients 

with HbA1c in a range

Wagner, 

200156 Intervention group: Patients 

received care (MD visit, RN 

visit, pharmacist visit, group 

education) at chronic care 

clinic (PtE, SMx, OC/team) 

Control group: no 

intervention/usual care

Adherence measure: % 

patients with foot exam in past

year

Control: 80.8% post 

Intervention: 87.7% post 

Disease control: HbA1c 

Control: 7.4 ± NR→ 7.9 ± NR

Intervention: 7.5 ± NR→ 7.9

± NR 

Adherence measure: % 

patients with retinal eye exam 

in past year 

Control: 62.2%→63.5%

Intervention: 60.6%→67.9%

Net difference: +6%

Other outcomes: total 

cholesterol 

Other outcomes: % patients 

with microalbumin test in past

year

Walker, 

200157 Comparison 1 

Intervention group:

Providers had one time 

problem-based learning 

education program, printed 

guidelines, follow-up (PvE) 

Control group: Clinic was 

sent printed care guidelines 

with no follow-up 

Adherence measure: % 

patients with at least one 

record of HbA1c 

Control: 44%→63% 

Intervention: 53%→73% 

Net difference: +1%

Adherence measure: % 

patients with document of foot 

exam 

Control: 22%→50% 

Intervention: 53%→76% 

Net difference: -5% 

Adherence measure: % 

patients with documented 

DFE or referral for DFE 

Control: 37%→33% 

Intervention: 21%→38% 

157
Net difference: +21% 

Other outcomes: % patients

with at least one serum 

creatinine record 

Comparison 2

Intervention group:

Providers had one time 

problem-based learning 

education program, printed 

guidelines, follow-up; clinic 

received printed patient 

guides to disseminate (PtE, 

PvE) 

Control group: Clinic was 

sent printed care guidelines 

with no follow-up

Adherence measure: % 

patients with at least one 

record of HbA1c 

Control: 44%→63% 

Intervention: 40%→44% 

Net difference: -15% 

Adherence measure: % 

patients with document of foot 

exam 

Control: 22%→50% 

Intervention: 46%→58% 

Net difference: -16% 

Adherence measure: % 

patients with documented 

DFE or referral for DFE 

Control: 37%→33% 

Intervention: 19%→55% 

Net difference: +40% 

Other outcomes: % patients

with at least one serum 

creatinine record 

Ward, 199658 Comparison 1

Intervention group:

Providers received feedback 

of their care records 

compared to the whole group 

and checklists of guidelines; 

providers had interview with a

GP regarding care guidelines 

(PvE, Aud) 

Control group: Providers 

received feedback of their 

care records compared to the 

whole group and checklists of

guidelines

Adherence measure: % 

patients with one HbA1c per 8 

months 

Control: 46.7%→40.7%

Intervention: 36.9%→54.6%

Net difference: +23.7% 

Adherence measure: % 

patients with record of eye 

exam or referral to 

opthalmologist annually

158
Control: 29.6%→31.1%

Intervention: 23.1%→42.3%

Net difference: +17.7% 

Other outcomes: % patients 

with record of urine and blood

glucose (2 per 8 mo); urine 

protein, urine nitrite, 

creatinine, total cholesterol, 

triglycerides (1 per 8 mo); 

blood pressure, weight, foot 

reflex exam, feet pulses, feet 

sensation, nails (annually); 

alcohol inquiry and advice, 

diet, exercise, smoking, 

vaginitis/impotence advice

Comparison 2

Intervention group:

Providers received feedback 

of their care records 

compared to the whole group 

and checklists of guidelines; 

providers had interview with a

nurse regarding care 

guidelines (PvE, Aud) 

Control group: Providers 

received feedback of their 

care records compared to the 

whole group and checklists of

guidelines

Adherence measure: % 

patients with one HbA1c per 8 

months 

Control: 46.7%→40.7%

Intervention: 28.9%→44.6%

Net difference: +21.7% 

Adherence measure: % 

patients with record of eye 

exam or referral to 

opthalmologist annually

Control: 29.6%→31.1%

Intervention: 19.8%→40.5%

Net difference: +19.2% 

Other outcomes: % patients 

with record of urine and blood

glucose (2 per 8 mo); urine 

protein, urine nitrite, 

creatinine, total cholesterol, 

triglycerides (1 per 8 mo); 

blood pressure, weight, foot 

reflex exam, feet pulses, feet 

sensation, nails (annually); 

alcohol inquiry and advice, 

diet, exercise, smoking, 

vaginitis/impotence advice

Weinberger, 

199559 Intervention group: Nurse 

performed patient education, 

medication counselling, 

liaison to MD by telephone 

contact with patients (PtE, 

Disease control: HbA1c 

Control: 10.7 ± 3.4→ 11.1 ± 

2.4 

Intervention: 10.7 ± 3.3→

159
SMx, PtR, FR, OC/DxMx)

Control group: no 

intervention/usual care

10.5 ± 2.7 

Other outcomes: serum 

blood glucose, total 

cholesterol, LDL-C, HDL-C, 

triglycerides 

*Results reported come from both articles. 

160
 Table E2. Main results for blood pressure

Setting Study period

(duration) 

Study Design*

(Number of 

Patients) 

QI strategies 

employed ^

Systolic blood

pressure results

28 general practices 

(Netherlands)12

1989-1995 

(6 years) 

CBA 

(505, 28 providers) 

Patient Ed, SelfMx, Facil relay,

Org change 

Control: 

158.2±23.5 →

155.3±22.9 

Intervention: 

147.2±21.7 →

147.7±23.5 

Diabetes clinic 

(Denmark)17, 18† 

1993-2001 

(9 years) 

RCT

(160) 

Patient Ed, Org 

change 

Control: 149±19 →

146±NR 

Intervention: 

146±20 → 132±NR 

Specialized diabetic 

service 

(Netherlands)21

--- 

(1 year) 

RCT

(246, 15 practices) 

Patient Ed, Facil 

relay, Org change 

Control: 149±24 →

143±21 

Intervention: 

137±21 → 135±18

29 general practices 

(Norway)

24

--- 

(21 months) 

RCT

(1034, 17 

practices) 

Prvdr Remind, 

Audit & Fdbck 

Control: 

151.7±21.3 →

152.7±19

Intervention: 

152.5±21.6 →

151.5±22.1 

U. of Washington 

Family Medical 

Center 

(Seattle, WA)

25

1998-1999 

(14 months) 

RCT

(109, 2 firms) 

Prvdr Ed, Audit & 

Fdbck, Org change 

Control: 134±NR

→ 137.1±NR 

Intervention: 

135±NR →

133.8±NR 

Royal Prince Alfred 

Hospital Diabetes 

Clinic 

(Australia)

26

--- 

(1 year) 

RCT

(137) 

Org change Control: 150±23 →

133±19 

Intervention: 

148±23 → 136±14

Royal Prince Alfred 

Hospital Diabetes 

Clinic 

(Australia)

26

--- 

(1 year) 

RCT

(134) 

Self-Mx, Pt 

Remind, Prvdr 

Remind, Org 

change 

Control: 150±23 →

133±19 

Intervention: 

145±24 → 130±25

Cedars Sinai 

Medical Center

(Los Angeles, CA)30

1994-1995 

(1 year) 

RCT

(360, 43 practice 

teams) 

Patient Ed, Prvdr 

Ed 

Control: 141.5±NR 

→ 142.8±NR 

Intervention: 

144.1±NR →

144.3±NR 

Diabetes education 

program 

1998-1999 RCT Patient Ed, SelfMx, Pt Remind, Control: 143±NR

→ NR±NR 

161
(unspecified US 

city)

33 (6 months) (150) Prvdr Remind, 

Facil relay 

Intervention: 

142±NR →

NR±NR 

Choa Chu Kang

polyclinic 

(Singapore)34

2000-2001 

(7 months) 

CBA 

(211) 

Patient Ed, Prvdr 

Remind, Org 

change 

Control: 

138.9±19.3 →

136.8±18.5 

Intervention: 

142.1±16.4 →

134.2±10.1 

General medicine 

clinic, Indiana 

University Medical 

Center37

1978-1982 

(3 years) 

RCT

(260, 13 firms)

Prvdr Ed, Prvdr 

Remind, Audit & 

Fdbck 

Control: 137.2±NR 

→ 144.9±NR 

Intervention: 

142.5±NR →

146.4±NR 

General medicine 

clinic, Indiana 

University Medical 

Center37

1978-1982 

(3 years) 

RCT

(273, 14 firms)

Patient Ed, Pt 

Remind, Prvdr Ed, 

Prvdr Remind, 

Audit & Fdbck 

Control: 137.2±NR 

→ 144.9±NR 

Intervention: 

140.4±NR →

145±NR 

Hospital-based Adult 

Medicine Clinic

(unspecified US 

city)

39

1998-1999 

(1 year) 

RCT

(598, 2 firms) 

Prvdr Ed, Prvdr 

Remind

Control: 136.9±NR 

→ 134.7±NR 

Intervention: 

138.1±NR →

138.9±NR 

9 primary health

centers 

(United Arab 

Emirates)48

--- 

(18 months) 

CBA 

(219, 9 practices) 

Patient Ed, Prvdr 

Ed, Facil relay, Org 

change 

Control: 133.7±19 

→ 131.3±16.6 

Intervention: 

129.2±18.8 →

126.5±16.4 

Sub-specialty

diabetes clinic 

(unspecified US 

city)

52

1996 

(3 months) 

CBA 

(82) 

Org change Control: NR±NR →

140.9±19.6 

Intervention: 

NR±NR →

138.3±16.9 

* RCT – randomized controlled trial; CBA – controlled before after comparison.

^ Pt Remind – patient reminder, Prvdr Remind – provider reminder, Org change – organizational change, 

Self-Mx — self-management, Facil Relay — facilitated relay of clinical data to providers, Patient Ed — patient 

education, Prvdr Ed — provider education, Audit & Fdbck — audit and feedback to provider Financial — financial 

or regulatory intervention 

162
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163
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24. Hetlevik I, Holmen J, Kruger O, Kristensen P, Iversen H, Furuseth K. Implementing clinical guidelines in the 

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implementation of a computer-based decision support system. Int J Technol Assess Health Care. Winter 

2000;16(1):210-227:PrvRmndr, AuditFdbck:clin information\Rmndr, CDSS, Audit:Dz/HbA1c/BP,

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PrvdrEd/workshops/materials:Dz/HbA1c/BP/other

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35. Litzelman DK, Slemenda CW, Langefeld CD, et al. Reduction of lower extremity clinical abnormalities in 

patients with non-insulin-dependent diabetes mellitus. A randomized, controlled trial. Ann Intern Med. Jul 1 

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166
Appendix F. Calculation of effective sample sizes for trials with clustering 

A substantial number of studies exhibited “clustering,” in that the units of analysis were patient level 

outcomes but the unit of allocation had been clusters of patients (e.g., randomization involved providers or 

clinics). The significance of clustering is that patients within a cluster are not independent – i.e., patients at 

one clinic have greater resemblance to each other than to patients at other sites or cared for by other 

providers in the trial. Unit of analysis errors do not affect point estimates for effect sizes, but they may

spuriously narrow the associated confidence interval, potentially leading to a false-positive result in a trial.1-8

To avoid the same inflation of precision in our analysis, we calculated an effective sample size*

 for each 

study. Importantly, from the point of view of our analysis, the degree to which investigators acknowledged or 

accounted for cluster effects did not affect our analysis, except in so far as investigators who did consider 

cluster effects in the design or analysis of the trial were more likely to report data such as the number of 

providers randomized, rather than just the total numbers of patients in each group, as well as provide more 

technical details, such as values for the intra-cluster coefficient (ICC). 9-11 

The table below compares the effective and originally reported sample sizes for effective sample

sizes. Only values at baseline in the control and intervention groups are shown, but the same calculations 

were carried out for reported sample sizes in the post-intervention period for all study groups. Because so 

few studies reported ICC values,9-11 we imputed values based on published estimates. Specifically, we used 

ICC=0.03 for the HbA1c outcome and ICC=0.10 for measures of provider adherence, based no the 

midpoints of published values for process and outcome measures in primary care settings. 3

 As a sensitivity

analysis, we re-ran the effective sample size calculations and regression analyses with the upper bounds of 

these ranges (ICC=0.10 and ICC=0.33, for outcome and process measures, respectively), with no

substantial impacts no the results. 

Table F1. Reported vs. effective sample sizes for clustered studies evaluating reduction in HbA1c 

No. of clusters

Reported N 

(patients) 

Effective N 

(patients) Percent reduction (%) 

Benjamin 199912 2 106 46 57

Boucher 199713 6 183 105 43

Olivarius 200114 484 874 857 2

de Sonnaville 199715 28 563 376 33

Frijling 20029 123 1430 1128 21

Groeneveld 200116 15 224 165 26

Hetlevik 200017 30 733 452 38

Hirsch 200218 2 109 46 58

Kiefe 200119 84 1352 974 28

Kinmonth 199810 42 240 197 18

Kogan 200320 44 283 249 12

Litzelman 199321 4 353 111 69

Mazzuca 198622 13 127 104 18

McDermott 200123 21 678 380 44

167
Meigs 200311 66 598 377 37

O’Connor 199624 2 241 60 75

Ovhed 200025 2 394 66 83

Reed 200126 9 189 123 35

Renders 200127 27 389 291 25

Wagner 200128 34 609 425 30

Walker 200129 2 345 65 81

Deeb 198830 6 636 173 73

Feder 199531 24 21 21 0

Legorreta 199632 2 205 58 72

Hartmann 199533 17 376 246 35

Walker 200129 2 345 65 81

Legorreta 199632 2 185 55 70

Branger 199934 32 275 227 17

Mazzuca 198622 14 120 101 16

* Effective N equals sample size adjusted for presence of clustering. It was calculated as NEffective = (k*m) / (1 + (m1)*r), where ‘k ‘is the number of clusters, ‘m’ is the number of patients per cluster, and ‘r’ is the intracluster 

coefficient (ICC). When r = 0, then NEffective = k*m (i.e., the reported sample size). When r = 1, then NEffective = k

(i.e., the number of clusters) 1-8

Appendix F References

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10. Kinmonth AL, Woodcock A, Griffin S, Spiegal N, Campbell MJ. Randomised controlled trial of patient 

centred care of diabetes in general practice: impact on current wellbeing and future disease risk. The Diabetes

Care From Diagnosis Research Team. BMJ. 1998;317:1202-1208. 

168
11. Meigs JB, Cagliero E, Dubey A, et al. A controlled trial of web-based diabetes disease management: the 

MGH diabetes primary care improvement project. Diabetes Care. 2003;26:750-757. 

12. Benjamin EM, Schneider MS, Hinchey KT. Implementing practice guidelines for diabetes care using 

problem-based learning. A prospective controlled trial using firm systems. Diabetes Care. 1999;22:1672-

1678. 

13. Boucher BJ, Claff HR, Edmonson M, et al. A pilot Diabetic Support Service based on family practice 

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15. de Sonnaville JJ, Bouma M, Colly LP, Deville W, Wijkel D, Heine RJ. Sustained good glycaemic control in 

NIDDM patients by implementation of structured care in general practice: 2-year follow-up study.

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16. Groeneveld Y, Petri H, Hermans J, Springer M, IN. An assessment of structured care assistance in the 

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17. Hetlevik I, Holmen J, Kruger O, Kristensen P, Iversen H, Furuseth K. Implementing clinical guidelines in the 

treatment of diabetes mellitus in general practice. Evaluation of effort, process, and patient outcome related to 

implementation of a computer-based decision support system. Int J Technol Assess Health Care. 

2000;16:210-227. 

18. Hirsch IB, Goldberg HI, Ellsworth A, et al. A multifaceted intervention in support of diabetes treatment 

guidelines: a controlled trial. Diabetes Res Clin Pract. 2002;58:27-36. 

19. Kiefe CI, Allison JJ, Williams OD, Person SD, Weaver MT, Weissman NW. Improving quality improvement 

using achievable benchmarks for physician feedback: a randomized controlled trial. JAMA. 2001;285:2871-

2879. 

20. Kogan JR, Reynolds EE, Shea JA. Effectiveness of report cards based on chart audits of residents' adherence 

to practice guidelines on practice performance: a randomized controlled trial. Teach Learn Med. 2003;15:25-

30. 

21. Litzelman DK, Slemenda CW, Langefeld CD, et al. Reduction of lower extremity clinical abnormalities in 

patients with non-insulin-dependent diabetes mellitus. A randomized, controlled trial. Ann Intern Med. 

1993;119:36-41. 

22. Mazzuca SA, Moorman NH, Wheeler ML, et al. The diabetes education study: a controlled trial of the effects

of diabetes patient education. Diabetes Care. 1986;9:1-10. 

23. McDermott RA, Schmidt BA, Sinha A, Mills P. Improving diabetes care in the primary healthcare setting: a

randomised cluster trial in remote Indigenous communities. Med J Aust. 2001;174:497-502. 

24. O'Connor PJ, Rush WA, Peterson J, et al. Continuous quality improvement can improve glycemic control for 

HMO patients with diabetes. Arch Fam Med. 1996;5:502-506. 

25. Ovhed I, Johansson E, Odeberg H, Rastam L. A comparison of two different team models for treatment of

diabetes mellitus in primary care. Scand J Caring Sci. 2000;14:253-258. 

26. Reed RL, Revel AO, Carter A, Saadi HF, Dunn EV. A clinical trial of chronic care diabetic clinics in general 

practice in the United Arab Emirates: a preliminary analysis. Arch Physiol Biochem. 2001;109:272-280. 

27. Renders CM, Valk GD, Franse LV, et al. Long-term effectiveness of a quality improvement program for 

patients with type 2 diabetes in general practice. Diabetes Care. 2001;24:1365-1370. 

28. Wagner EH, Grothaus LC, Sandhu N, et al. Chronic care clinics for diabetes in primary care: a system-wide 

randomized trial. Diabetes Care. 2001;24:695-700. 

29. Walker EA, Engel SS, Zybert PA, IN. Dissemination of diabetes care guidelines: lessons learned from 

community health centers. Diabetes Educator. 2001;27:101-110. 

30. Deeb LC, Pettijohn FP, Shirah JK, Freeman G. Interventions among primary-care practitioners to improve 

care for preventable complications of diabetes. Diabetes Care. 1988;11:275-280. 

31. Feder G, Griffiths C, Highton C, Eldridge S, Spence M, Southgate L. Do clinical guidelines introduced with

practice based education improve care of asthmatic and diabetic patients? A randomised controlled trial in 

general practices in east London. BMJ. 1995;311:1473-1478. 

32. Legorreta AP, Peters AL, Ossorio RC, Lopez RJ, Jatulis D, Davidson MB. Effect of a comprehensive nursemanaged diabetes program: an HMO prospective study. Am J Manag Care. 1996;2:1024-1030. 

33. Hartmann, et al. Effects of peer-review groups on physicians' practice. Eur J Gen Pract. 1995;1:107. 

34. Branger PJ, van't Hooft A, van der Wouden JC, Moorman PW, van Bemmel JH. Shared care for diabetes: 

supporting communication between primary and secondary care. Int J Med Inf. 1999;53:133-142. 

169
Appendix G. Articles excluded at the level of full-text review

Citation Reason for Exclusion 

1. Selected methods for the management of diabetes mellitus. Ann 

Intern Med 1983; 99:272-4. 

Not an evaluation of a QI intervention 

2. Impact of policy and procedure changes on hospital days among 

diabetic nursing-home residents--Colorado. MMWR Morb Mortal Wkly 

Rep 1984; 33:621-4, 629. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

3. Private practitioners, public health join forces in unique diabetes 

program. Urban Health 1985; 14:23-7, 46-8. 

Not an evaluation of a QI intervention 

4. Leads from the MMWR. Improving eye care for persons with diabetes 

mellitus--Michigan. JAMA 1985; 254:3293-4.

Not an evaluation of a QI intervention 

5. Community-based exercise intervention--the Zuni Diabetes Project. 

MMWR Morb Mortal Wkly Rep 1987; 36:661-4. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

6. Leads from the MMWR. Demonstration to improve care practices for 

diabetic patients in primary care centers--Florida. JAMA 1987; 

257:1580-1. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

7. Program reimburses pharmacists who counsel patients on glyburide 

product. Am J Hosp Pharm 1992; 49:2645-6.

Not an evaluation of a QI intervention 

8. Integrated care for diabetes: clinical, psychosocial, and economic 

evaluation. Diabetes Integrated Care Evaluation Team. BMJ 1994; 

308:1208-12. 

Other*

9. Longitudinal electronic records for diabetic patients. Part II: Clinical 

aspects. J Clin Comput 1995; 22:1-36. 

Study design did not meet criteria for 

RCT, CBA, or ITS/outcomes 

10. Longitudinal electronic records for diabetic patients. Part I: Clinical 

aspects. J Clin Comput 1995; 22:1-88. 

Study design did not meet criteria for 

RCT, CBA, or ITS/outcomes 

11. National Standards for Diabetes Self-Management Education 

Programs. Task Force to Revise the National Standards. Diabetes 

Care 1995; 18:141-3. 

Not an evaluation of a QI intervention 

12. From the Centers for Disease Control and Prevention. Availability of 

diabetes information on the Internet. JAMA 1997; 278:1565. 

Not an evaluation of a QI intervention 

170
13. From the Centers for Disease Control and Prevention. National 

Diabetes Awareness Month--November 1997. JAMA 1997; 278:1564. 

Not an evaluation of a QI intervention 

14. Putting primary care guidelines into practice. Med Manag Netw 1998; 

6:1-5. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

15. Disease management program improves diabetes outcomes, curbs 

hospital costs, utilization. Health Care Cost Reengineering Rep 1998; 

3:42-5. 

Not an evaluation of a QI intervention 

16. Innovative touchscreen-driven solution for diabetics. Health Manag 

Technol 1999; 20:20-1. 

Not an evaluation of a QI intervention 

17. Pilot study proves CM's effectiveness. Hosp Case Manag 2000; 8:17-

9. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

18. Compliance and combinations. Diabetes Obes Metab 2001; 3:383-4. Not an evaluation of a QI intervention 

19. 'Behavior change' is centerpiece of new DM approach for diabetics. 

Dis Manag Advis 2001; 7:118-21, 113. 

Not an evaluation of a QI intervention 

20. Diabetes education program in Bulgaria. Patient Educ Couns 2001; 

43:111-4. 

No eligible outcomes 

21. In-house diabetes program delivers on a 'shoestring' budget. Dis 

Manag Advis 2002; 8:181-5, 177. 

Not an evaluation of a QI intervention 

22. The Translating Research Into Action for Diabetes (TRIAD) study: a 

multicenter study of diabetes in managed care. Diabetes Care 2002; 

25:386-9. 

No eligible outcomes 

23. From the Centers for Disease Control and Prevention. Preventive-care 

practices among persons with diabetes--United States, 1995 and 

2001. JAMA 2002; 288:2814-5. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

24. Incentive program focuses physicians on improving outcomes. 

Perform Improv Advis 2003; 7:49-54. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

25. Abourizk NN, O'Connor PJ, Crabtree BF, Schnatz JD. An outpatient 

model of integrated diabetes treatment and education: functional, 

metabolic, and knowledge outcomes. Diabetes Educ 1994; 20:416-21. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

26. Achtmeyer CE, Payne TH, Anawalt BD. Computer order entry system Excluded topic (hospital care only) 

171
decreased use of sliding scale insulin regimens. Methods Inf Med

2002; 41:277-81. 

27. Ackerman SJ. Benefits of preventive programs in eye care are visible 

on the bottom line. A new nationwide effort to improve eye care for 

people with diabetes gets backing from a study on the costeffectiveness of screening for retinopathy. Diabetes Care 1992; 

15:580-1. 

Not an evaluation of a QI intervention 

28. Adams CE, Biggerstaff N. Reduced resource utilization through 

standardized outcome-focused care plans. J Nurs Adm 1995; 25:43-

50. 

No eligible outcomes 

29. Adamson TE, Gullion DS. Assessment of 8 continuing medical 

education. Diabetes Care 1986; 9:11-16. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

30. Agurs C, et al. A randomized controlled trial of weight reduction and 

exercise for diabetes management in African-American subjects. 

Diabetes Care 1997; 20:1503.

Patient education or self-management 

only 

31. Albisser AM, Schiffrin A, Schulz M, Tiran J, Leibel BS. Insulin dosage 

adjustment using manual methods and computer algorithms: a 

comparative study. Med Biol Eng Comput 1986; 24:577-84. 

No eligible outcomes 

32. Albisser AM, Harris RI, Sperlich M, Albisser JB. Getting referrals for 

diabetes education and self-management training. Diabetes Educ

1999; 25:959-60, 963-4, 966 passim. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

33. Albisser AM. Clinical studies with home glucose clamping. Ann 

Endocrinol (Paris) 2001; 62:11-8. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

34. Allanach EJ, Allanach BC. Diabetes teaching follow-up compliance: a 

quality assurance evaluation. Mil Med 1984; 149:73-5. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

35. Ambrosiadou BV, Goulis DG, Pappas C. Clinical evaluation of the 

DIABETES expert system for decision support by multiple regimen 

insulin dose adjustment. Comput Methods Programs Biomed 1996; 

49:105-15. 

No eligible outcomes 

36. Anderson RM, Zimbelman L, Green J, Saunders JT, Pohl S. The 

development of a competency-based interdisciplinary clinical training 

program in diabetes. Diabetes Educ 1986:200-3. 

Not an evaluation of a QI intervention 

172
37. Anderson RM, Funnell MM, Barr PA, Dedrick RF, Davis WK. Learning 

to empower patients. Results of professional education program for 

diabetes educators. Diabetes Care 1991; 14:584-90. 

No eligible outcomes 

38. Anderson RM, Funnell MM, Butler PM, Arnold MS, Fitzgerald JT, 

Feste CC. Patient empowerment. Results of a randomized controlled 

trial. Diabetes Care 1995; 18:943-9. 

Patient education or self-management 

only 

39. Ardron M, MacFarlane IA, Robinson C, van Heyningen C, Calverley 

PM. Anti-smoking advice for young diabetic smokers: is it a waste of 

breath? Diabet Med 1988; 5:667-70. 

Excluded topic (smoking cessation)/No 

eligible outcomes 

40. Arlen D, Harkless L, Frykberg R, Garcia I. Skilled nursing facility units. 

Preliminary screening of geriatric infections. J Am Podiatr Med Assoc

1990; 80:385-92. 

Not an evaluation of a QI intervention 

41. Assal. Patient education as the basis for diabetes care in clinical 

practice and research. Diabetologia 1985; 283:602-603. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

42. Bacon GE, Ladu C, Shein HE, Rucknagel DL. Evaluation of 

glycosylated hemoglobin in the management of young patients with

insulin-dependent diabetes mellitus. J Adolesc Health Care 1986; 

7:187-90. 

Excluded topic (Type 1 diabetes only) 

43. Baker SB, Vallbona C, Pavlik V, et al. A diabetes control program in a 

public health care setting. Public Health Rep 1993; 108:595-605. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

44. Baksi AK, Brand J, Nicholas M, Tavabie A, Cartwright BJ, Waterfield 

MR. Non-consultant peripheral clinics: a new approach to diabetic 

care. Health Trends 1984; 16:38-40. 

Not an evaluation of a QI intervention 

45. Balas EA, Boren SA, Griffing G. Computerized management of 

diabetes: a synthesis of controlled trials. Proc AMIA Symp 1998:295-

9. 

Not an evaluation of a QI intervention 

46. Balik B, Moynihan PM, Haig B. Community-based diabetes education: 

an outreach program. Diabetes Educ 1985; 11:19-23. 

Not an evaluation of a QI intervention 

47. Barrera M, Jr., Glasgow RE, McKay HG, Boles SM, Feil EG. Do 

Internet-based support interventions change perceptions of social 

support?: An experimental trial of approaches for supporting diabetes 

No eligible outcomes 

173
self-management. Am J Community Psychol 2002; 30:637-54. 

48. Bartel J. A comprehensive diabetes education program. Nurs Manage

1992; 23:74-6. 

Not an evaluation of a QI intervention 

49. Barth R, Campbell LV, Allen S, Jupp JJ, Chisholm DJ. Intensive 

education improves knowledge, compliance, and foot problems in type

2 diabetes. Diabet Med 1991; 8:111-7. 

Patient education or self-management 

only 

50. Bashook PG. Clinical assessment: a state-of-the-art review. Diabetes 

Educ 1985; 11 Suppl:30-6. 

Not an evaluation of a QI intervention 

51. Beaven DW, Scott RS, Brown LJ. Diabetes care--a continuing 

challenge. Aust N Z J Med 1988; 18:297-301.

Not an evaluation of a QI intervention 

52. Beck MJ, Evans BJ, Quarry-Horn JL, Kerrigan JR. Type 2 diabetes 

mellitus: issues for the medical care of pediatric and adult patients. 

South Med J 2002; 95:992-1000. 

Not an evaluation of a QI intervention 

53. Bell R, Norman M, Lamb B, Holliday J, Leggett-Frazier N. Assessing 

the effectiveness of a clinical fellowship on diabetes in enhancing 

diabetes care in North Carolina. Diabetes Educ 2000; 26:41-2, 53-4, 

57. 

No eligible outcomes 

54. Bellazzi R, Riva A, Montani S, et al. Application report: preliminary 

evaluation of the T-IDDM project in Pavia. Stud Health Technol Inform

1999; 68:99-101. 

Not an evaluation of a QI intervention 

55. Bender AP, Sprafka JM, Jagger H, Wannamaker J, Muckala KH. 

Evaluation of the effect of record source on the profiles of patients with 

diabetes mellitus in Wadena, Minnesota. Minn Med 1983; 66:383-7. 

Not an evaluation of a QI intervention 

56. Ben-Noun L. Poorly controlled diabetes in the elderly. Practitioner

1989; 233:14, 16. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

57. Berger A. Action on clinical audit: progress report 2. BMJ 1998; 

317:880-1. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

58. Berkowitz KJ, Anderson LA, Panayioto RM, Ziemer DC, Gallina DL. 

Mini-residency on diabetes care for healthcare providers: enhanced 

knowledge and attitudes with unexpected challenges to assessing

behavior change. Diabetes Educ 1998; 24:143-4, 149-50. 

No eligible outcomes 

174
59. Bessman AN. Comparison of medical care in nurse clinician and 

physician clinics in medical school affiliated hospitals. Journal of 

Chronic Diseases 1974; 27:115-125. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

60. Birke JA, Horswell R, Patout CA, Jr., Chen SL. The impact of a staged 

management approach to diabetes foot care in the Louisiana public 

hospital system. J La State Med Soc 2003; 155:37-42. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

61. Blonde L, Guthrie R, Testa M, et al. Diabetes management by a team 

of diabetes nurse educators, endocrinologists and primary care 

physicians in a managed care setting [abstract]. Book/Association for 

Health Services Research; 16:318-319. 

Other ^

62. Bloomgarden, et al. Randomized, controlled trial of diabetic patient 

education: improved knowledge without improved metabolic status. 

Diabetes Care 1987; 10:263. 

Patient education or self-management 

only 

63. Bloomgarden ZT. International Diabetes Federation meeting, 1997. 

Neuropathy, information technology, cost of diabetes care, and 

epidemiology. Diabetes Care 1998; 21:1198-202. 

Not an evaluation of a QI intervention 

64. Blum A. Sugar 'n' spice-- 'n' public relations. Med J Aust 1982; 2:16-9. Not an evaluation of a QI intervention 

65. Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for 

patients with chronic illness: the chronic care model, Part 2. JAMA

2002; 288:1909-14. 

Not an evaluation of a QI intervention 

66. Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for 

patients with chronic illness. JAMA 2002; 288:1775-9. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

67. Boehm S, Schlenk EA, Raleigh E, Ronis D. Behavioral analysis and 

behavioral strategies to improve self-management of type II diabetes. 

Clin Nurs Res 1993; 2:327-44. 

Patient education or self-management 

only 

68. Bohannon NJ, Zilbergeld B, Bullard DG, Stoklosa JM. Treatable 

impotence in diabetic patients. West J Med 1982; 136:6-10. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

69. Boswell EJ, Pichert JW, Penha ML. Negotiating independent practice 

in diabetes education. Diabetes Educ 1992; 18:288, 290. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

70. Bowyer NK. A primary care team approach to the prevention of ocular 

complications of diabetes: a program review. J Am Optom Assoc

1997; 68:233-42. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

175
71. Boyd AF, Hartzema AG. Computerized monitoring protocols as a 

pharmaceutical care practice enhancement: a conceptual illustration 

using diabetes mellitus. Ann Pharmacother 1993; 27:963-6. 

Not an evaluation of a QI intervention 

72. Branger PJ, van 't Hooft A, van der Wouden JC, Duisterhout JS, van 

Bemmel JH. dup Shared care for diabetes: supporting communication 

between primary and secondary care. Medinfo 1998; 9 Pt 1:412-6. 

(Duplication/overlap of included article †) 

Overlaps with or duplicates another 

article that was included 

73. Brooks RJ, Legoretta AP, Silver AL, Fabius RJ, Krakovitz J. 

Implementing guidelines for eye care of diabetic patients: results from 

an HMO intervention study. American Journal of Managed Care 1996; 

2:365-369. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

74. Brouard N, Mounier F, Schaub C. Ocular lesions of diabetic

retinopathy: a computer documentation by correspondence factor 

analysis. Med Inform (Lond) 1981; 6:235-7. 

Not an evaluation of a QI intervention 

75. Brown SA, Hedges LV. Predicting metabolic control in diabetes: a pilot 

study using meta-analysis to estimate a linear model. Nurs Res 1994; 

43:362-8. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

76. Brown SA, Hanis CL. A community-based, culturally sensitive 

education and group-support intervention for Mexican Americans with 

NIDDM: a pilot study of efficacy. Diabetes Educ 1995; 21:203-10. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

77. Brown SA, Upchurch S, Anding R, Winter M, Ramirez G. Promoting 

weight loss in type II diabetes. Diabetes Care 1996; 19:613-24. 

Not an evaluation of a QI intervention 

78. Brown SA, Hanis CL. Culturally competent diabetes education for 

Mexican Americans: the Starr County Study. Diabetes Educ 1999; 

25:226-36. (Duplicate/overlap of article #80 from this table) 

Overlaps with or duplicates another 

article that was included 

79. Brown JB, Nichols GA, Glauber HS. Case-control study of 10 years of 

comprehensive diabetes care. West J Med 2000; 172:85-90. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

80. Brown SA, Garcia AA, Kouzekanani K, Hanis CL. Culturally competent 

diabetes self-management education for Mexican Americans: the 

Starr County border health initiative. Diabetes Care 2002; 25:259-68. 

Patient education or self-management 

only 

81. Bruckner M, Mangan M, Godin S, Pogach L. Project LEAP of New

Jersey: lower extremity amputation prevention in persons with type 2 

Study design did not meet criteria for 

RCT, CBA, or ITS 

176
diabetes. Am J Manag Care 1999; 5:609-16. 

82. Burden ML, Woghiren O, Burden AC. Diabetes in African Caribbean, 

and Indo-Asian ethnic minority people. J R Coll Physicians Lond 2000; 

34:343-6. 

Not an evaluation of a QI intervention 

83. Burnett S, Hurwitz B, Davey C, et al. The implementation of prompted 

retinal screening for diabetic eye disease by accredited optometrists in 

an inner-city district of North London: a quality of care study. Diabet 

Med 1998; 15 Suppl 3:S38-43. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

84. Burton WN, Connerty CM, IN. Worksite-based diabetes disease 

management program. Disease Management, 5(1):1-8, 2002 

Spring.(20 ref) JC CP 2002:1-8. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

85. Calvo CB, Rubinstein A. Influence of new evidence on prescription

patterns. J Am Board Fam Pract 2002; 15:457-62. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

86. Campbell LV, Chisholm DJ, Barth R. Evaluation of the benefits of a 

diabetes education programme. Diabetes Educ 1984; 10 SPEC 

NO:46-7. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

87. Campbell RK. Pharmaceutical services for patients with diabetes. 

Module 1. Professional and economic impact of diabetes on pharmacy

practice. Am Pharm 1986; NS26:suppl 1-8. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

88. Campbell, et al. The relative effectiveness of educational and

behavioral instruction programs for patients with NIDDM: a 

randomized trial. Diabetes Educ 1996; 22:379. 

Patient education or self-management 

only 

89. Campbell TM, Stamm PL, Johnson JR. Improving drug use in a 

capitated program for the poor. Am J Health Syst Pharm 1997; 

54:2449-50. 

Not an evaluation of a QI intervention 

90. Campbell R, Pound P, Pope C, et al. Evaluating meta-ethnography: a 

synthesis of qualitative research on lay experiences of diabetes and 

diabetes care. Social Science & Medicine 2003; 56:671-684.

Not an evaluation of a QI intervention 

91. Canga N, De Irala J, Vara E, Duaso MJ, Ferrer A, Martinez-Gonzalez 

MA. Intervention study for smoking cessation in diabetic patients: a 

randomized controlled trial in both clinical and primary care settings. 

Diabetes Care 2000; 23:1455-60. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

177
92. Carlson A, Rosenqvist U. Locally developed plans for quality diabetes 

care: Worker and consumer participation in the public healthcare 

system. Health Educ Res 1990; 5:41. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

93. Carlson A, Rosenqvist U. Diabetes care organization, process, and 

patient outcomes: effects of a diabetes control program. Diabetes 

Educ 1991; 17:42-8. 

No eligible outcomes 

94. Carney T, Helliwell C. Effect of structured postgraduate medical 

education on the care of patients with diabetes. Br J Gen Pract 1995; 

45:149-51. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

95. Carter IR, Nash C, Ridgway A. On any Saturday--a practical model for 

diabetes education. J Natl Med Assoc 2002; 94:67-72. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

96. Cherry JC, Moffatt TP, Rodriguez C, Dryden K. Diabetes disease 

management program for an indigent population empowered by

telemedicine technology. Diabetes Technol Ther 2002; 4:783-91. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

97. Chicoye L, Roethel CR, Hatch MH, Wesolowski W. Diabetes care 

management: a managed care approach. Wmj 1998; 97:32-4. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

98. Chiou ST, Lin HD, Yu NC, et al. An initial assessment of the feasibility 

and effectiveness of implementing diabetes shared care system in 

Taiwan--some experiences from I-Lan County. Diabetes Res Clin 

Pract 2001; 54 Suppl 1:S67-73. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

99. Clancy DE, Cope DW, Magruder KM, et al. Evaluating group visits in 

an uninsured or inadequately insured patient population with

uncontrolled type 2 diabetes. Diabetes Educator. 2003; 29:292-302. 

No eligible outcomes 

100. Clark CM, Jr., Chin MH, Davis SN, et al. Incorporating the results of 

diabetes research into clinical practice: celebrating 25 years of 

diabetes research and training center translation research. Diabetes 

Care 2001; 24:2134-42. 

Not an evaluation of a QI intervention 

101. Clark CM, Jr., Snyder JW, Meek RL, Stutz LM, Parkin CG. A 

systematic approach to risk stratification and intervention within a 

managed care environment improves diabetes outcomes and patient 

satisfaction. Diabetes Care 2001; 24:1079-86. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

102. Clarke J, Crawford A, Nash DB. Evaluation of a Comprehensive 

Diabetes Disease Management Program: Progress in the Stuggle for 

Study design did not meet criteria for 

178
Sustained Behavior Change. Disease Management 2002; 5:77. RCT, CBA, or ITS/No eligible outcomes 

103. Coid DR, Duncan C, Carmichael C, McLeod J, Campbell IW. 

Evaluation of a Fife 'Novopen' clinic held within a diabetes patient 

education centre. Health Bull (Edinb) 1990; 48:243-8. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

104. Constance A, Crawford K, Hare J, et al. MDON: a network of 

community partnerships. Fam Community Health 2002; 25:52-60. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

105. Cook S, Cohen RM. Evaluating a workshop model for improving 

diabetes patient education programs: is it really successful? Diabetes 

Educ 1986; 12:48-50. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

106. Cook GB. A computer program for teaching and auditing patients'

knowledge of diabetes. Diabetes Educ 1987; 13:306-8. 

Not an evaluation of a QI intervention 

107. Cook CB, Ziemer DC, El-Kebbi IM, et al. Diabetes in urban AfricanAmericans. XVI. Overcoming clinical inertia improves glycemic control 

in patients with type 2 diabetes. Diabetes Care 1999; 22:1494-500. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

108. Cooper HC, Booth K, Gill G, IN. Patients' perspectives on diabetes 

health care education. Health Education Research 2003; 18:191-206. 

Patient education or self-management 

only 

109. Corbett CF, IN. A randomized pilot study of improving foot care in 

home health patients with diabetes. Diabetes Educator. 2003; 29:273-

282. 

Patient education or self-management 

only 

110. Corkery, et al. Effect of a bicultural community health worker on 

completion of diabetes education in a Hispanic population. Diabetes 

Care 1997; 20:254. 

Patient education or self-management 

only 

111. Courtney L, Gordon M, Romer L. A clinical path for adult diabetes. 

Diabetes Educ 1997; 23:664-71. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

112. Crane M, Werber B. Critical pathway approach to diabetic pedal 

infections in a multidisciplinary setting. J Foot Ankle Surg 1999; 38:30-

3; discussion 82-3. 

Excluded topic (in-patient care only) 

113. Cranor CW, Bunting BA, Christensen DB. The Asheville Project: longterm clinical and economic outcomes of a community pharmacy

diabetes care program. J Am Pharm Assoc (Wash) 2003; 43:173-84. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

114. Cranor CW, Christensen DB. The Asheville Project: factors associated

with outcomes of a community pharmacy diabetes care program. J 

Study design did not meet criteria for 

179
Am Pharm Assoc (Wash) 2003; 43:160-72. RCT, CBA, or ITS 

115. Cranor CW, Christensen DB. The Asheville Project: short-term 

outcomes of a community pharmacy diabetes care program. J Am

Pharm Assoc (Wash) 2003; 43:149-59. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

116. Crawford MJ, Rutter D, Manley C, et al. Systematic review of involving 

patients in the planning and development of health care. BMJ 2002; 

325:1263. 

design 

117. Cretin S, Farley DO, Dolter KJ, Nicholas W. Evaluating an integrated

approach to clinical quality improvement: clinical guidelines, quality

measurement, and supportive system design. Med Care 2001; 

39:II70-84. 

Excluded topic (unrelated to 

diabetes)/No eligible outcomes

118. Cummings DM, Morrissey S, Barondes MJ, Rogers L, Gustke S. 

Screening for diabetic retinopathy in rural areas: the potential of 

telemedicine. J Rural Health 2001; 17:25-31. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

119. Czech MS. A mastery learning program for self blood glucose 

monitoring. Diabetes Educ 1984; 10:27-30. 

Not an evaluation of a QI intervention 

120. Dally DL, Dahar W, Scott A, Roblin D, Khoury AT. The impact of a 

health education program targeting patients with high visit rates in a 

managed care organization. Am J Health Promot 2002; 17:101-11. 

No eligible outcomes 

121. Daniel M, Green LW, Marion SA, et al. Effectiveness of communitydirected diabetes prevention and control in a rural Aboriginal 

population in British Columbia, Canada. Soc Sci Med 1999; 48:815-

32. 

Patient education or self-management 

only 

122. Dans PE, King TM. An office of medical practice evaluation: what is it 

and why have one? QRB Qual Rev Bull 1986; 12:320-5. 

Not an evaluation of a QI intervention 

123. Davidson JK, Delcher HK, Englund A. Spin-off cost/benefits of 

expanded nutritional care. Journal of the American Dietetic 

Association 1979; 75:250-257.

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

124. Davidson MB, Morgan R, Bales B, Pearce MA, Crane M, Graham B. 

The establishment of a reimbursable diabetes education program. 

Diabetes Educ 1982; 8:31-3, 38. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

125. Davidson. The impact of managed care on the care of diabetes Not an evaluation of a QI intervention 

180
patients. Diabetes Spectrum 1996; 9:169-90. 

126. Davis ED, Beckman JS, Harris NL, Howe JD, Steele RM. 

Implementing a nursing care quality program to improve diabetes 

patient education. J Nurs Care Qual 1992; 6:67-77. 

No eligible outcomes 

127. Day JL, Humphreys H, Alban-Davies H. Problems of comprehensive 

shared diabetes care. Br Med J (Clin Res Ed) 1987; 294:1590-2. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

128. Day JL, Johnson P, Rayman G, Walker R. The feasibility of a 

potentially 'ideal' system of integrated diabetes care and education 

based on a day centre. Diabet Med 1988; 5:70-5. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

129. Day JL, Metcalfe J, Johnson P. Benefits provided by an integrated

education and clinical diabetes centre: a follow-up study. Diabet Med

1992; 9:855-9. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

130. de Grauw WJ, van Gerwen WH, van de Lisdonk EH, van den Hoogen 

HJ, van den Bosch WJ, van Weel C. Outcomes of audit-enhanced

monitoring of patients with type 2 diabetes. J Fam Pract 2002; 51:459-

64. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

131. de Weerdt I, Visser AP, Kok GJ, de Weerdt O, van der Veen EA. 

Randomized controlled multicentre evaluation of an education 

programme for insulin-treated diabetic patients: effects on metabolic 

control, quality of life, and costs of therapy. Diabet Med 1991; 8:338-

45. 

Patient education or self-management 

only 

132. Deichmann RE, Castello E, Horswell R, Friday KE. Improvements in 

diabetic care as measured by HbA1c after a physician education 

project. Diabetes Care 1999; 22:1612-6. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

133. D'Eramo-Melkus GA, Wylie-Rosett J, Hagan JA. Metabolic impact of 

education in NIDDM. Diabetes Care 1992; 15:864-9. 

Patient education or self-management 

only 

134. Diehl AK, Sugarek NJ, Bauer RL. Medication compliance in noninsulin-dependent diabetes: a randomized comparison of 

chlorpropamide and insulin. Diabetes Care 1985; 8:219-23. 

Not an evaluation of a QI intervention 

135. Dimmick SL, Burgiss SG, Robbins S, Black D, Jarnagin B, Anders M. 

Outcomes of an integrated telehealth network demonstration project. 

Telemed J E Health 2003; 9:13-23. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

181
136. Domenech MI, Assad D, Mazzei ME, Kronsbein P, Gagliardino JJ. 

Evaluation of the effectiveness of an ambulatory teaching/treatment 

programme for non-insulin dependent (type 2) diabetic patients. Acta 

Diabetol 1995; 32:143-7. 

Patient education or self-management 

only 

137. Domurat ES. Diabetes managed care and clinical outcomes: the 

Harbor City, California Kaiser Permanente diabetes care system. Am

J Manag Care 1999; 5:1299-307. 

Not an evaluation of a QI intervention 

138. Donohoe ME, Fletton JA, Hook A, et al. Improving foot care for people 

with diabetes mellitus--a randomized controlled trial of an integrated 

care approach. Diabet Med 2000; 17:581-7. 

No eligible outcomes 

139. Dornan C, Fowler G, Mann JI, Markus A, Thorogood M. A community

study of diabetes in Oxfordshire. J R Coll Gen Pract 1983; 33:151-5. 

Not an evaluation of a QI intervention 

140. Drapin L. I/S component crucial to diabetes management program. 

Health Manag Technol 1995; 16:30, 32. 

Not an evaluation of a QI intervention 

141. Dunn SM, Beeney LJ, Hoskins PL, Turtle JR. Knowledge and attitude

change as predictors of metabolic improvement in diabetes education. 

Soc Sci Med 1990; 31:1135-41. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

142. Eakin EG, Bull SS, Glasgow RE, Mason M. Reaching those most in 

need: a review of diabetes self-management interventions in

disadvantaged populations. Diabetes Metab Res Rev 2002; 18:26-35. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

143. Edmonds. Improved survival of the diabetic foot: the role of the 

specialised foot clinic. QJ Med 1986; 60:763.

Study design did not meet criteria for 

RCT, CBA, or ITS 

144. Edmonds M, Bauer M, Osborn S, et al. Using the Vista 350 telephone 

to communicate the results of home monitoring of diabetes mellitus to 

a central database and to provide feedback. Int J Med Inf 1998; 

51:117-25. 

Excluded topic (Type 1 diabetes 

only)/No eligible outcomes 

145. Engelman KK, Ellerbeck EF, Totten B. Improving systems for 

preventive care via academic detailing by students. Acad Med 2001; 

76:565-6. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

146. Erdman DM, Cook CB, Greenlund KJ, et al. The impact of outpatient 

diabetes management on serum lipids in urban African-Americans 

with type 2 diabetes. Diabetes Care 2002; 25:9-15. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

182
147. Estey AL, Tan MH, Mann K. Follow-up intervention: its effect on 

compliance behavior to a diabetes regimen. Diabetes Educ 1990; 

16:291-5. 

Patient education or self-management 

only 

148. Falkenberg, et al. Problem oriented participatory education in the 

guidance of adults with non-insulin-treated type-II diabetes mellitus. 

Scand J Prim Health Care 1986; 4:157. 

Patient education or self-management 

only 

149. Felig P, Bergman M. Intensive ambulatory treatment of insulindependent diabetes. Ann Intern Med 1982; 97:225-30. Not an evaluation of a QI intervention 

150. Fernando DJ, Perera SD. The work of a diabetes clinic: an audit. 

Ceylon Med J 1994; 39:138-9.

Study design did not meet criteria for 

RCT, CBA, or ITS 

151. Filippi A, Sabatini A, Badioli L, et al. Effects of an Automated

Electronic Reminder in Changing the Antiplatelet Drug-Prescribing

Behavior Among Italian General Practitioners in Diabetic Patients: An 

intervention trial. Diabetes Care 2003; 26:1497-500. 

No eligible outcomes 

152. Fox CH, Mahoney MC. Improving diabetes preventive care in a family

practice residency program: a case study in continuous quality

improvement. Fam Med 1998; 30:441-5. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

153. Frame. Computer based vs manual health maintenence tracking: a 

controlled trial. Arch Fam Med 1994; 3:581. 

Excluded topic (unrelated to 

diabetes)/No eligible outcomes

154. Franz MJ, Splett PL, Monk A, et al. Cost-effectiveness of medical 

nutrition therapy provided by dietitians for persons with non-insulindependent diabetes mellitus [see comments]. Journal of the American 

Dietetic Association 1995; 95:1018-1024. (Duplicate/overlap of 

included article §) 

Overlaps with or duplicates another 

article that was included 

155. Friedman N. Diabetes and managed care: the Lovelace Health 

System's Episode of Care Program. Manag Care Q 1996; 4:43-9. 

Not an evaluation of a QI intervention 

156. Friedman NM, Gleeson JM, Kent MJ, Foris M, Rodriguez DJ, Cypress 

M. Management of diabetes mellitus in the Lovelace Health Systems' 

EPISODES OF CARE program. Eff Clin Pract 1998; 1:5-11.

Study design did not meet criteria for 

RCT, CBA, or ITS 

157. Friedman RH. Automated telephone conversations to assess health 

behavior and deliver behavioral interventions. J Med Syst 1998; 

22:95-102. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

183
158. Friedrich MJ. Enhancing diabetes care in a low-income, high-risk 

population. JAMA 2000; 283:467-8. 

Not an evaluation of a QI intervention 

159. Funnell MM, Arnold MS, Fogler J, Merritt JH, Anderson LA. 

Participation in a diabetes education and care program: experience 

from the diabetes care for older adults project. Diabetes Educ 1998; 

24:163-7. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

160. Fuqua L. Marketing and diabetes education: "a harmonious chorus". 

Diabetes Educ 1989; 15:210-3.

Not an evaluation of a QI intervention 

161. Gaede P, Vedel P, Parving HH, Pedersen O. Intensified multifactorial

intervention in patients with type 2 diabetes mellitus and 

microalbuminuria: the Steno type 2 randomised study. Lancet 1999; 

353:617-22. (Duplicate/overlap of included article**) 

Overlaps with or duplicates another 

article that was included 

162. Gagliardino JJ, Etchegoyen G. A model educational program for 

people with type 2 diabetes: a cooperative Latin American 

implementation study (PEDNID-LA). Diabetes Care 2001; 24:1001-7. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

163. Gary TL, Genkinger JM, Guallar E, Peyrot M, Brancati FL. Metaanalysis of randomized educational behavioral interventions in type 2 

diabetes. Diabetes Educ 2003; 29:488-501. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

164. Gegick CG, Altheimer MD, Kissling GE. Benefits of computerized

outcome analysis in diabetes management. Endocr Pract 2000; 6:253-

9. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

165. Genev NM, McGill M, Hoskins PL, et al. Continuing diabetes

education by telephone. Diabet Med 1990; 7:920-1. 

No eligible outcomes 

166. Gerstein HC, Reddy SS, Dawson KG, Yale JF, Shannon S, Norman 

G. A controlled evaluation of a national continuing medical education 

programme designed to improve family physicians' implementation of 

diabetes-specific clinical practice guidelines. Diabet Med 1999; 

16:964-9. 

No eligible outcomes 

167. Ghosh S, Aronow WS. Utilization of lipid-lowering drugs in elderly 

persons with increased serum low-density lipoprotein cholesterol 

associated with coronary artery disease, symptomatic peripheral 

arterial disease, prior stroke, or diabetes mellitus before and after an 

educational program on dyslipidemia treatment. J Gerontol A Biol Sci 

Study design did not meet criteria for 

RCT, CBA, or ITS 

184
Med Sci 2003; 58:M432-5. 

168. Gibbins RL, Saunders J. Develop diabetic care in general practice. 

BMJ 1988; 297:187-9. 

Not an evaluation of a QI intervention 

169. Gilden, et al. Diabetes support groups improve health care of older 

diabetic patients. J Am Geriatr Soc 1992; 40:147. 

Patient education or self-management 

only 

170. Gilliland SS, Azen SP, Perez GE, Carter JS. Strong in body and spirit: 

lifestyle intervention for Native American adults with diabetes in New

Mexico. Diabetes Care 2002; 25:78-83. 

Patient education or self-management 

only 

171. Ginsberg BH, Tan MH, Mazze R, Bergelson A. Staged diabetes 

management: computerizing a disease state management program. J 

Med Syst 1998; 22:77-87. 

Not an evaluation of a QI intervention 

172. Glasgow RE, Toobert DJ, Hampson SE, Brown JE, Lewinsohn PM, 

Donnelly J. Improving self-care among older patients with type II 

diabetes: the "Sixty Something..." Study. Patient Educ Couns 1992; 

19:61-74. 

Patient education or self-management 

only 

173. Glasgow RE, Toobert DJ. Brief, computer-assisted diabetes dietary 

self-management counseling: effects on behavior, physiologic 

outcomes, and quality of life. Med Care 2000; 38:1062-73. 

(Duplicate/overlap of article #174 from this table) 

Overlaps with or duplicates another 

article that was included 

174. Glasgow RE, Toobert DJ, Hampson SE, Strycker LA. Implementation, 

generalization and long-term results of the "choosing well" diabetes 

self-management intervention. Patient Educ Couns 2002; 48:115-22. 

Patient education or self-management 

only 

175. Glasgow RE, Funnell MM, Bonomi AE, Davis C, Beckham V, Wagner 

EH. Self-management aspects of the improving chronic illness care 

breakthrough series: implementation with diabetes and heart failure 

teams. Ann Behav Med 2002; 24:80-7. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

176. Gleeson. Diabetes mellitus: disease management in a multispecialty

group practice. Dis Manag Health Outcomes 1999; 5:63. 

Not an evaluation of a QI intervention 

177. Goldberg HI, Neighbor WE, Hirsch IB, Cheadle AD, Ramsey SD, Gore 

E. Evidence-based management: using serial firm trials to improve 

diabetes care quality. Jt Comm J Qual Improv 2002; 28:155-66. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

178. Goldhaber-Fiebert JD, Goldhaber-Fiebert SN, Tristan ML, Nathan DM. Patient education or self-management 

185
Randomized controlled community-based nutrition and exercise 

intervention improves glycemia and cardiovascular risk factors in type 

2 diabetic patients in rural Costa Rica. Diabetes Care 2003; 26:24-9. 

only 

179. Gomez EJ, Hernando ME, Garcia A, et al. Telemedicine as a tool for 

intensive management of diabetes: the DIABTel experience. Comput 

Methods Programs Biomed 2002; 69:163-77. 

Excluded topic (Type 1 diabetes only) 

180. Goodman RM, Liburd LC, Green-Phillips A. The formation of a 

complex community program for diabetes control: lessons learned 

from a case study of Project DIRECT. J Public Health Manag Pract

2001; 7:19-29. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

181. Goyder EC, McNally PG, Drucquer M, Spiers N, Botha JL. Shifting of 

care for diabetes from secondary to primary care, 1990-5: review of 

general practices. BMJ 1998; 316:1505-6. 

Not an evaluation of a QI intervention 

182. Graber AL, Elasy TA, Quinn D, Wolff K, Brown A. Improving glycemic 

control in adults with diabetes mellitus: shared responsibility in primary 

care practices. South Med J 2002; 95:684-90. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

183. Grabert M, Schweiggert F, Holl RW. A framework for diabetes 

documentation and quality management in Germany: 10 years of 

experience with DPV. Comput Methods Programs Biomed 2002; 

69:115-21. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

184. Greenfield S, Kaplan SH, Ware JE, Jr., Yano EM, Frank HJ. Patients'

participation in medical care: effects on blood sugar control and quality 

of life in diabetes. J Gen Intern Med 1988; 3:448-57. 

Patient education or self-management 

only 

185. Greenhalgh PM. Shared care for diabetes. A systematic review. 

Occas Pap R Coll Gen Pract 1994:i-viii, 1-35. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

186. Grey N, Maljanian R, Staff I, Cruzmarino de Aponte M. Improving care

of diabetic patients through a collaborative care model. Conn Med

2002; 66:7-11. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

187. Grieve R, Beech R, Vincent J, Mazurkiewicz J. Near patient testing in

diabetes clinics: appraising the costs and outcomes. Health Technol 

Assess 1999; 3:1-74. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

188. Griffin S. Diabetes care in general practice: meta-analysis of 

randomised control trials. BMJ 1998; 317:390-6. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

186
189. Griffin S, Kinmonth AL. Diabetes care: the effectiveness of systems for 

routine surveillance for people with diabetes. Cochrane Database Syst 

Rev 2000:CD000541. 

Not an evaluation of a QI intervention 

190. Gruesser M, Hartmann P, Schlottmann N, Joergens V. Structured 

treatment and teaching programme for type 2 diabetic patients on 

conventional insulin treatment: evaluation of reimbursement policy. 

Patient Educ Couns 1996; 29:123-30. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

191. Grundel BL, White GL, Jr., Eichold BH, 2nd. Diabetes in the managed 

care setting: a prospective plan. South Med J 1999; 92:459-64. 

Not an evaluation of a QI intervention 

192. Halbert RJ, Leung KM, Nichol JM, Legorreta AP. Effect of multiple

patient reminders in improving diabetic retinopathy screening. A 

randomized trial. Diabetes Care 1999; 22:752-5. 

Patient education or self-management 

only 

193. Hanefeld M, Fischer S, Schmechel H, et al. Diabetes Intervention 

Study. Multi-intervention trial in newly diagnosed NIDDM. Diabetes 

Care 1991; 14:308-17. 

Patient education or self-management 

only 

194. Haring OM. Improving Patient Care by Automated Record Summaries. 

Final rept. 15 Jun 72-31 May 76,. 1976.May 31 1976; 60 p. NTIS 

Order Number:B-267. 

Publication prior to 1980 

195. Harrower. Improvement in psychological well-being in new NIDDMs. 

DSNVs medical initial assessment. Pract Diab Int 1995; 12:120. 

No eligible outcomes 

196. Hart JT. Community general practitioners. Br Med J (Clin Res Ed)

1984; 288:1670-3. 

Not an evaluation of a QI intervention 

197. Harwell TS, McDowall JM, Gohdes D, Helgerson SD. Measuring and 

improving preventive care for patients with diabetes in primary health 

centers. Am J Med Qual 2002; 17:179-84. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

198. Hawkins, et al. Evaluation of a clinical pharmacist in caring for 

hypertensive and diabetic patients. American Journal of Hospital 

Pharmacy 1979; 36:1321. 

Not an evaluation of a QI intervention 

199. Hawkins DW. Clinical pharmacy functions in ambulatory patient care. 

J Clin Pharmacol 1981; 21:245-50. 

Not an evaluation of a QI intervention 

200. Hawthorne K, Tomlinson S. One-to-one teaching with pictures - 

flashcard health education for British Asians with diabetes. Br J Gen 

Patient education or self-management 

only 

187
Pract 1997; 47:301. 

201. Heath GW, Leonard BE, Wilson RH, Kendrick JS, Powell KE. 

Community-based exercise intervention: Zuni Diabetes Project. 

Diabetes Care 1987; 10:579-83. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

202. Heatlie JM. Reducing insulin medication errors: evaluation of a quality

improvement initiative. J Nurses Staff Dev 2003; 19:92-8. 

Study design did not meet criteria for 

RCT, CBA, or ITS/Excluded topic 

(hospital care only) 

203. Heller, et al. Group education for obese patients with type 2 diabetes: 

greater success at less cost. Diabet Med 1988; 5:552. 

Patient education or self-management 

only 

204. Heller S, MacKinnon M. Education health professionals. Diabetes Rev 

Int 1998; 7:16-18. 

Not an evaluation of a QI intervention 

205. Hendricks LE, Hendricks RT. The effect of diabetes self-management 

education with frequent follow-up on the health outcomes of African 

American men. Diabetes Educ 2000; 26:995-1002. 

Patient education or self-management 

only 

206. Hidaka H, Terada M, Maegawa H, et al. Evaluation of a new care 

system provided to diabetic patients in the outpatient clinic. Intern Med

2000; 39:783-7.

Study design did not meet criteria for 

RCT, CBA, or ITS 

207. Hilton RA. Does diabetic control really make a difference? Can Nurse

1982; 78:49-52. 

Not an evaluation of a QI intervention 

208. Hiss RG, Gillard ML, Armbruster BA, McClure LA. Comprehensive 

evaluation of community-based diabetic patients: effect of feedback to 

patients and their physicians: a randomized controlled trial. Diabetes 

Care 2001; 24:690-4. 

Patient education or self-management 

only 

209. Hood FJ. Diabetes coding. South Med J 2002; 95:64-6. Not an evaluation of a QI intervention 

210. Hoppener P, Knottnerus JA, Grol R, Metsemakers JF. 

Computerization of general practices and quality control. Blood 

glucose regulation in type 2 diabetics investigated in the Registration

Network family practices. Fam Pract 1992; 9:353-6. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

211. Hopper SV, Miller JP, Birge C, Swift J. A randomized study of the 

impact of home health aides on diabetic control and utilization 

patterns. Am J Public Health 1984; 74:600-2. 

Patient education or self-management 

only 

188
212. Hoskings PH, Alford J, Fowler P, et al. Outpatient stabilisation 

program. An innovative approach in the management of diabetes in a 

large teaching hospital. Aust Clin Rev 1984:8-11. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

213. Hoskins PL, Alford JB, Handelsman DJ, Yue DK, Turtle JR. 

Comparison of different models of diabetes care on compliance with 

self-monitoring of blood glucose by memory glucometer. Diabetes 

Care 1988; 11:719-24. 

No eligible outcomes 

214. Hosler AS, Godley K, Rowland DH. An initiative to improve diabetes 

care standards in healthcare organizations serving minorities. 

Diabetes Educ 2002; 28:581-9.

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

215. Howorka K, Pumprla J, Wagner-Nosiska D, Grillmayr H, Schlusche C, 

Schabmann A. Empowering diabetes out-patients with structured 

education: short-term and long-term effects of functional insulin 

treatment on perceived control over diabetes. J Psychosom Res 2000; 

48:37-44. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

216. Hull M. How to set up a diabetes education program. Rn 1989; 52:61-

4. 

Not an evaluation of a QI intervention 

217. Humphry J, Jameson LM, Beckham S. Overcoming social and cultural 

barriers to care for patients with diabetes. West J Med 1997; 167:138-

44. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

218. Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computer-based

clinical decision support systems on physician performance and 

patient outcomes: a systematic review. JAMA 1998; 280:1339-46. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

219. Ibrahim IA, Beich J, Sidorov J, Gabbay R, Yu L. Measuring outcomes 

of type 2 diabetes disease management program in an HMO setting. 

South Med J 2002; 95:78-87. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

220. Ivey JM, Ehle J. Establishing unit-based glucose monitoring. Diabetes 

Educ 1988; 14:487-91. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

221. Izquierdo PEKSMJKRP-S, Weinstock RS. A comparison of diabetes 

education administered through telemedicine versus in person. 

Diabetes Care 2003; 26:1002-7. 

Patient education or self-management 

only 

222. Jacobs M, Feit J. Healthy lifestyles: a pilot program to influence Study design did not meet criteria for 

189
behavior change. Diabetes Educ 2000; 26:230-2, 234, 240 passim. RCT, CBA, or ITS 

223. Jacobson JM, O'Rourke PJ, Wolf AE, IN. Impact of a diabetes 

teaching program on health care trends in an Air Force Medical 

Center. Military Medicine 1983; 148:46-47. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

224. Jarrett J, Stewart T, Rogers L. Diabetes mellitus. III: Complications 

and organising long-term management. Br Med J (Clin Res Ed) 1981; 

283:763-5. 

Not an evaluation of a QI intervention 

225. Joergens V, Gruesser M. Three years' experience after national 

introduction of teaching programs for type II diabetic patients in 

Germany: how to train general practitioners. Patient Educ Couns

1995; 26:195-202. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

226. Johnson NE, Nash DB. Key factors in the implementation of a clinical 

quality improvement project: successes and challenges. Am J Med 

Qual 1993; 8:118-22. 

Not an evaluation of a QI intervention 

227. Johnson EQ, Valera S. Medical nutrition therapy in non-insulindependent diabetes mellitus improves clinical outcome. J Am Diet 

Assoc 1995; 95:700-1. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

228. Jones PM. Use of a course on self-control behavior techniques to 

increase adherence to prescribed frequency for self-monitoring blood 

glucose. Diabetes Educ 1990; 16:296-303. 

No eligible outcomes 

229. Jones PC, Silverman BG, Athanasoulis M, et al. Nationwide telecare 

for diabetics: a pilot implementation of the HOLON architecture. Proc 

AMIA Symp 1998:346-50. 

Not an evaluation of a QI intervention 

230. Jones PM. Quality improvement initiative to integrate teaching 

diabetes standards into home care visits. Diabetes Educ 2002; 

28:1009-20. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

231. Jovanovic L, Peterson CM. Toward normoglycemia: studies in 

computer-assisted insulin delivery. Diabetes Educ 1987; 13:302-5. 

Study design did not meet criteria for 

RCT, CBA, or ITS/Excluded topic (Type 

1 diabetes only) 

232. Joyner L, McNeeley S, Kahn R. ADA's (American Diabetes 

Association) provider recognition program. HMO Pract 1997; 11:168-

70. 

Not an evaluation of a QI intervention 

190
233. Kalten MR, Ardito DA, Cimino C, Wylie-Rosett J. A Web-accessible 

core weight management program. Diabetes Educ 2000; 26:929-36. 

No eligible outcomes 

234. Kaplan RM, Wilson DK, Hartwell SL, Merino KL, Wallace JP. 

Prospective evaluation of HDL cholesterol changes after diet and 

physical conditioning programs for patients with type II diabetes 

mellitus. Diabetes Care 1985; 8:343-8. 

No eligible outcomes 

235. Kaplan SH, Greenfield S, Ware JE, Jr. Assessing the effects of 

physician-patient interactions on the outcomes of chronic disease 

[published erratum appears in Med Care 1989 Jul;27(7):679]. Medical 

Care 1989; 27:S110-S127. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

236. Karlander SG, Kindstedt K. Effects of a formalized diabetes education. 

Acta Med Scand 1983; 213:41-3. 

Patient education or self-management 

only 

237. Kay S. A standard architecture for sustainability and integration of 

care. Diabetes Nutr Metab 2001; 14:89-92. 

Not an evaluation of a QI intervention 

238. Kelling DG, Wentworth JA, Wright JB. Diabetes mellitus. Using a 

database to implement a systematic management program. N C Med 

J 1997; 58:368-71. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

239. Kendall PA, Jansen CM, Sjogren DD, Jansen GR. A comparison of 

nutrient-based and exchange-group methods of diet instruction for 

patients with noninsulin-dependent diabetes. Am J Clin Nutr 1987; 

45:625-37. 

Not an evaluation of a QI intervention 

240. Kendall D, Lunt H, Moore MP, McSweeney WP. Diabetes 

complication screening in general practice: a two pass audit with 

benchmarking. N Z Med J 1999; 112:141-4. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

241. Kerr D, Haigh R. Intensive diabetes treatment: a new deal for old 

people? Age Ageing 1994; 23:89-90. 

Not an evaluation of a QI intervention 

242. Khunti K, Baker R, Ganguli S. Clinical governance for diabetes in 

primary care: use of practice guidelines and participation in multipractice audit. Br J Gen Pract 2000; 50:877-81. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

243. Kiechle FL. The impact of continuous glucose monitoring on hospital 

point-of-care testing programs. Diabetes Technol Ther 2001; 3:647-

50. 

Not an evaluation of a QI intervention 

191
244. Kilo C, Miller JP, Williamson JR. The crux of the UGDP. Spurious 

results and biologically inappropriate data analysis. Diabetologia 1980; 

18:179-85. 

Not an evaluation of a QI intervention 

245. Kim J, Philips TL. The effectiveness of two forms of corrective 

feedback in diabetes education. J computer-based educ 1991; 19:14. 

No eligible outcomes 

246. Kirk AF, Higgins LA, Hughes AR, et al. A randomized, controlled trial 

to study the effect of exercise consultation on the promotion of 

physical activity in people with Type 2 diabetes: a pilot study. Diabet 

Med 2001; 18:877-82. 

No eligible outcomes 

247. Kirkman MS, Weinberger M, Landsman PB, et al. A telephonedelivered intervention for patients with NIDDM. Effect on coronary risk 

factors. Diabetes Care 1994; 17:840-6. (Duplicate/overlap of included 

article ^^) 

Overlaps with or duplicates another 

article that was included 

248. Kirkman MS, Williams SR, Caffrey HH, Marrero DG. Impact of a 

program to improve adherence to diabetes guidelines by primary care

physicians. Diabetes Care 2002; 25:1946-51. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

249. Kleschen MZ, Holbrook J, Rothbaum AK, Stringer RA, McInerney MJ, 

Helgerson SD. Improving the pneumococcal immunization rate for 

patients with diabetes in a managed care population: a simple 

intervention with a rapid effect. Jt Comm J Qual Improv 2000; 26:538-

46. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

250. Knopf RF, Kittel PR, Funnell MM, Wolf FM. Development and 

evaluation of diabetes continuing education courses for health 

professionals: a synthesis of eight years of experience. Diabetes Educ

1988; 14:136-41. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

251. Knowles EA, Gem J, Boulton AJ. The diabetic foot and the role of a 

multidisciplinary clinic. J Wound Care 1996; 5:452-4. 

Not an evaluation of a QI intervention 

252. Korhonen T, Huttunen JK, Aro A, et al. A controlled trial on the effects 

of patient education in the treatment of insulin-dependent diabetes. 

Diabetes Care 1983; 6:256-61. 

Excluded topic (Type 1 diabetes only) 

253. Krier BP, Parker RD, Grayson D, Byrd G. Effect of diabetes education 

on glucose control. J La State Med Soc 1999; 151:86-92. 

Patient education or self-management 

only 

192
254. Krishna S, Balas EA, Spencer DC, Griffin JZ, Boren SA. Clinical trials 

of interactive computerized patient education: implications for family

practice. J Fam Pract 1997; 45:25-33. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

255. Kronsbein, et al. Evaluation of a structured treatment and teaching 

programme on non-insulin dependent diabetes. Lancet 1988; 2:1407. 

Patient education or self-management 

only 

256. Lafata JE, Baker AM, Divine GW, McCarthy BD, Xi H. The use of 

computerized birthday greeting reminders in the management of 

diabetes. J Gen Intern Med 2002; 17:521-30. 

Patient education or self-management 

only 

257. Larkin M. Diabetes on the rise worldwide and webwide. Lancet 2001; 

357:815. 

Not an evaluation of a QI intervention 

258. Laron Z, Faiman G, Flexer Z, Rapaport M. Use of a computer program 

in the treatment and education of young diabetics. Acta Paediatr Jpn

1987; 29:378-84. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

259. Larsen ML, Horder M, Mogensen EF. Effect of long-term monitoring of 

glycosylated hemoglobin levels in insulin-dependent diabetes mellitus. 

N Engl J Med 1990; 323:1021-5. 

Excluded topic (Type I diabetes only

 † †) 

260. Lee SJ, Sicari C, Harper CA, et al. Examination compliance and 

screening for diabetic retinopathy: a 2-year follow-up study. Clin 

Experiment Ophthalmol 2000; 28:149-52. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

261. Lee SJ, McCarty CA, Taylor HR, Keeffe JE. Costs of mobile screening 

for diabetic retinopathy: a practical framework for rural populations. 

Aust J Rural Health 2001; 9:186-92. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

262. Leese GP, Ahmed S, Newton RW, et al. Use of mobile screening unit 

for diabetic retinopathy in rural and urban areas. BMJ 1993; 306:187-

9. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

263. Leichter SB, Hernandez C, Harvill C, Rice G. The Kentucky Diabetes 

Control Program and the feasibility of the pyramidal model for public 

health intervention in diabetes mellitus. Diabetes Educ 1988; 14:218-

22. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

264. Lenz ER, Mundinger MO, Hopkins SC, Lin SX, Smolowitz JL. 

Diabetes care processes and outcomes in patients treated by nurse 

practitioners or physicians. Diabetes Educ 2002; 28:590-8. 

Not an evaluation of a QI intervention 

193
265. Leonard B, Leonard C, Wilson R. Zuni Diabetes Project. Public Health 

Rep 1986; 101:282-8. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

266. Levenson D. Benchmarking project spurs better guideline adherence. 

Rep Med Guidel Outcomes Res 2001; 12:5-7. 

Not an evaluation of a QI intervention 

/Excluded topic (unrelated to diabetes) 

267. Lewis D, Nath C. Feasibility of a kiosk-based patient education system 

in a busy outpatient clinic setting. Diabetes Educ 1997; 23:577-81, 

585-6. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

268. Lo R, Lo B, Wells E, Chard M, Hathaway J. The development and 

evaluation of a computer-aided diabetes education program. Aust J 

Adv Nurs 1996; 13:19-27. 

Patient education or self-management 

only 

269. Lobach DF, Hammond WE. Development and evaluation of a 

Computer-Assisted Management Protocol (CAMP): improved

compliance with care guidelines for diabetes mellitus. Proc Annu 

Symp Comput Appl Med Care 1994:787-91. (Duplicate/overlap of 

included article §§) 

Overlaps with or duplicates another 

article that was included 

270. Lorenz RA, Pichert JW, Enns SJ, Hanson SL. Impact of organizational 

interventions on the delivery of patient education in a diabetes clinic. 

Patient Educ Couns 1986; 8:115-23. 

No eligible outcomes 

271. Lorenz RA. Training health professionals to improve the effectiveness 

of patient education programs. Diabetes Educ 1986:204-9. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

272. Lurie N, Ward NB, Shapiro MF, Brook RH. Termination from Medi-Cal-

-does it affect health? N Engl J Med 1984; 311:480-4. 

Not an evaluation of a QI intervention 

273. Lustman PJ, Griffith LS, Freedland KE, Kissel SS, Clouse RE. 

Cognitive behavior therapy for depression in type 2 diabetes mellitus. 

A randomized, controlled trial. Ann Intern Med 1998; 129:613-21. 

Patient education or self-management 

only 

274. Luz C. National campaign yields positive results for patients with 

diabetes. Healthplan 2002; 43:50-1. 

Not an evaluation of a QI intervention 

275. Maislos M, Weisman D, Sherf M. Western Negev Mobile Diabetes 

Care Program: a model for interdisciplinary diabetes care in a semirural setting. Acta Diabetol 2002; 39:49-53. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

276. Major S, Salti I, Masri A, Van Lerberghe W, Boelaert M, Khogali M. 

Managing diabetes mellitus in a Lebanese primary care centre. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

194
Working towards change. J Med Liban 1998; 46:182-8. 

277. Malone JM, Snyder M, Anderson G, Bernhard VM, Holloway GA, Jr., 

Bunt TJ. Prevention of amputation by diabetic education. American 

Journal of Surgery 1989; 158:520-523. 

Patient education or self-management 

only 

278. Mancino JM, Rhia LC, McHattie K. Comparison of type 2 diabetes 

medical nutrition therapy to practice guidelines in a community health 

system. J Am Diet Assoc 2002; 102:1129-31. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

279. Marrero. Increasing the use of intensified managment of diabetes: an 

education program for physicians. The Journal of Continuing

Education in the Health Professions 1991; 11:283. 

Study design did not meet criteria for 

RCT, CBA, or ITS/Excluded topic (Type 

1 diabetes only) 

280. Marrero DG, Vandagriff JL, Kronz K, et al. Using telecommunication 

technology to manage children with diabetes: the Computer-Linked 

Outpatient Clinic (CLOC) Study. Diabetes Educ 1995; 21:313-9. 

Excluded topic (Children/adolescents 

only) 

281. Marshall CL, Bluestein M, Briere E, et al. Improving outpatient 

diabetes management through a collaboration of six competing, 

capitated Medicare managed care plans. American Journal of Medical 

Quality 2000; 15:65-71. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

282. Martin EE. Organising a practice. Problem identification, disease 

management, and audit. Br Med J (Clin Res Ed) 1982; 285:265-6. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

283. Martin DA, McNeal B, Kronenfeld JJ, Wheeler FC. The impact of 

professional education on nursing behavior in the practice setting. J 

Contin Educ Nurs 1986; 17:40-2. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible No eligible 

outcomes 

284. Matsuyama JR, Mason BJ, Jue SG. Pharmacists' interventions using 

an electronic medication-event monitoring device's adherence data 

versus pill counts. Ann Pharmacother 1993; 27:851-5. 

No eligible outcomes 

285. Mazze RS, I. Staged diabetes management. Toward an integrated 

model of diabetes care. Diabetes Care 1994; 17:66. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

286. Mazzuca SA, Moorman NH, Wheeler ML, et al. The diabetes 

education study: a controlled trial of the effects of diabetes patient 

education. Diabetes Care 1986; 9:1-10.***

Patient education or self-management 

only 

287. Mazzuca SA, Vinicor F, Cohen SJ, et al. The Diabetes Education 

Study: a controlled trial of the effects of intensive instruction of internal 

Other ^^^

195
medicine residents on the management of diabetes mellitus. J Gen 

Intern Med 1988; 3:1-8. 

288. McCabe CJ, Stevenson RC, Dolan AM. Evaluation of a diabetic foot 

screening and protection programme. Diabet Med 1998; 15:80-4. 

Patient education or self-management 

only 

289. McCulloch DK, Mitchell RD, Ambler J, Tattersall RB. Influence of 

imaginative teaching of diet on compliance and metabolic control in 

insulin dependent diabetes. Br Med J (Clin Res Ed) 1983; 287:1858-

61. 

Excluded topic (Type 1 diabetes only) 

290. McDonald CJ. Use of a computer to detect and respond to clinical 

events: its effect on clinician behavior. Annals of Internal Medicine

1976; 84:162-167. 

Publication prior to 1980 

291. McDonald CJ, Hui SL, Smith DM, et al. Reminders to physicians from 

an introspective computer medical record. A two-year randomized 

trial. Ann Intern Med 1984; 100:130-8. 

Excluded topic (unrelated to 

diabetes)/No eligible No eligible

outcomes 

292. McKay HG, King D, Eakin EG, Seeley JR, Glasgow RE. The diabetes 

network internet-based physical activity intervention: a randomized 

pilot study. Diabetes Care 2001; 24:1328-34. 

No eligible outcomes 

293. McKenna M, Dolan M, Loughlin T, et al. Evaluation of impact on 

diabetic control of increased surveillance, and of human and U-100 

insulins. Ir J Med Sci 1987; 156:347-52. 

Excluded topic (Type 1 diabetes only) 

294. McMahon LM, Longabaugh R, Desrosiers M, Kriebel GW, Jr. 

Management of physical problems in a psychiatric hospital: a study of 

problem-oriented patient care. QRB Qual Rev Bull 1981; 7:13-9. 

No eligible outcomes 

295. McNabb WL, Cook S, Fischer B, Quinn MT, Haas L. Dissemination of 

a continuing education program in diabetes to health care 

professionals. Diabetes Educ 1994; 20:35-40. 

Not an evaluation of a QI intervention 

/No eligible No eligible outcomes 

296. McNally PG, MacIver DH, Jowett NI, Hearnshaw JR. Hypoglycaemia 

in insulin dependent diabetics: is advice heeded? Br Med J (Clin Res 

Ed) 1987; 294:1655-6. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

297. Michael P. Impact and components of the Medicare MNT benefit. J 

Am Diet Assoc 2001; 101:1140-1. 

Not an evaluation of a QI intervention 

298. Mitchell. Does primary medical practitioner involvement with a Study design did not meet criteria for 

196
specialist team improve patient outcomes? A systematic review. Br J 

Gen Pract 2002; 52:934. 

RCT, CBA, or ITS 

No eligible outcomes 

299. Montani S, Bellazzi R, Quaglini S, d'Annunzio G. Meta-analysis of the 

effect of the use of computer-based systems on the metabolic control 

of patients with diabetes mellitus. Diabetes Technol Ther 2001; 3:347-

56. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

300. Moore K, Mengel M. Expanding the team: the use of volunteers in a 

diabetes education program. Diabetes Educ 2002; 28:554-8, 560, 562. 

Not an evaluation of a QI intervention 

301. Morrison N, Dooley J. The Sioux Lookout Diabetes Program: diabetes 

prevention and management in northwestern Ontario. Int J 

Circumpolar Health 1998; 57 Suppl 1:364-9.

Study design did not meet criteria for 

RCT, CBA, or ITS 

302. Most RS, Gross AM, Davidson PC, Richardson P. The accuracy of 

glucose monitoring by diabetic individuals in their home setting. 

Diabetes Educ 1986; 12:24-7. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

303. Muhlhauser I, Berger M. Diabetes education and insulin therapy: when

will they ever learn? J Intern Med 1993; 233:321-6. 

Not an evaluation of a QI intervention 

304. Mulcahy K, Peeples M, Tomky D, Weaver T. National Diabetes 

Education Outcomes System: application to practice. Diabetes Educ

2000; 26:957-64. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

305. Mulrow C, Bailey S, Sonksen PH, Slavin B. Evaluation of an 

Audiovisual Diabetes Education Program: negative results of a 

randomized trial of patients with non-insulin-dependent diabetes 

mellitus. J Gen Intern Med 1987; 2:215-9. 

Patient education or self-management 

only 

306. Munro N, Felton A, McIntosh C. Is multidisciplinary learning effective 

among those caring for people with diabetes? Diabet Med 2002; 

19:799-803. 

Not an evaluation of a QI intervention 

307. Narayan KM, Hoskin M, Kozak D, et al. Randomized clinical trial of 

lifestyle interventions in Pima Indians: a pilot study. Diabet Med 1998; 

15:66-72. 

Excluded topic (normoglycemic

patients) 

308. New JP, Hollis S, Campbell F, et al. Measuring clinical performance 

and outcomes from diabetes information systems: an observational 

study. Diabetologia 2000; 43:836-43. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

197
309. Nilasena DS, Lincoln MJ. A computer-generated reminder system 

improves physician compliance with diabetes preventive care

guidelines. Proc Annu Symp Comput Appl Med Care 1995:640-5. 

Other †††

310. Nodhturft VL, MacMullen JA. Standardized nursing care plans... the 

effect of care plans on documentation of diabetic nursing care. 

Nursing Management (Chicago.) 1982; 13:33-6, 40-2. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

311. Noel PH, Larme AC, Meyer J, Marsh G, Correa A, Pugh JA. Patient 

choice in diabetes education curriculum. Nutritional versus standard 

content for type 2 diabetes. Diabetes Care 1998; 21:896-901. 

Patient education or self-management 

only 

312. Norris SL, Engelgau MM, Narayan KM. Effectiveness of selfmanagement training in type 2 diabetes: a systematic review of 

randomized controlled trials. Diabetes Care 2001; 24:561-87. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

313. Norris SL, Lau J, Smith SJ, Schmid CH, Engelgau MM. Selfmanagement education for adults with type 2 diabetes: a metaanalysis of the effect on glycemic control. Diabetes Care 2002; 

25:1159-71. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

314. Norris SL, Nichols PJ, Caspersen CJ, et al. The effectiveness of 

disease and case management for people with diabetes. A systematic 

review. Am J Prev Med 2002; 22:15-38. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

315. Norris SL, Nichols PJ, Caspersen CJ. Increasing diabetes selfmanagement education in comunity settings. A systematic review. Am

J Prev Med 2002; 22:39-66. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

316. O'Connor PJ, Pronk NP, IN. Integrating population health concepts, 

clinical guidelines, and ambulatory medical systems to improve 

diabetes care. Journal of Ambulatory.Care Management 1998; 21:67-

73. 

Not an evaluation of a QI intervention 

317. Ornstein SM, Garr DR, Jenkins RG, Rust PF, Arnon A. Computergenerated physician and patient reminders. Tools to improve 

population adherence to selected preventive services. J Fam Pract

1991; 32:82-90. 

Excluded topic (unrelated to 

diabetes)/No eligible outcomes

318. Orton P. Shared care. Lancet 1994; 344:1413-5. Study design did not meet criteria for 

RCT, CBA, or ITS 

198
319. Overland J, Mira M, Yue DK. Differential shared care for diabetes: 

does it provide the optimal partition between primary and specialist 

care? Diabet Med 2001; 18:554-7. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

320. Oxman AD, Thomson MA, Davis DA, Haynes RB. No magic bullets: a 

systematic review of 102 trials of interventions to improve professional 

practice. CMAJ 1995; 153:1423-31. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

321. Park JY, Daly JM. Evaluation of diabetes management software. 

Diabetes Educ 2003; 29:255-62, 267. 

Not an evaluation of a QI intervention 

No eligible outcomes 

322. Parker MT, Leggett-Frazier N, Vincent PA, Swanson MS. The impact 

of an educational program on improving diabetes knowledge and 

changing behaviors of nurses in long-term care facilities. Diabetes 

Educ 1995; 21:541-5. 

No eligible outcomes 

323. Perry TL, Mann JI, Lewis-Barned NJ, Duncan AW, Waldron MA, 

Thompson C. Lifestyle intervention in people with insulin-dependent 

diabetes mellitus (IDDM). Eur J Clin Nutr 1997; 51:757-63. 

Patient education or self-management 

only 

324. Peters A, Rubsamen M, Jacob U, Look D, Scriba PC. Clinical 

evaluation of decision support system for insulin-dose adjustment in 

IDDM. Diabetes Care 1991; 14:875-80. 

Excluded topic (Type 1 diabetes only) 

325. Peters AL. The effect of a diabetes management program on diabetes 

health care outcomes in health maintenance organizations. Diabetes 

43 suppl 1:84 A 1994.JC CP 1994; 43:84. (Duplicate/overlap of article 

#327 from this table) 

Overlaps with or duplicates another 

article that was included 

326. Peters AL, Davidson MB, Ossorio RC. Management of patients with 

diabetes by nurses with support of subspecialists. HMO Pract 1995; 

9:8-13. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

327. Peters AL, Davidson MB. Application of a diabetes managed care 

program. The feasibility of using nurses and a computer system to 

provide effective care. Diabetes Care 1998; 21:1037-43. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

328. Petitti DB, Contreras R, Ziel FH, Dudl J, Domurat ES, Hyatt JA. 

Evaluation of the effect of performance monitoring and feedback on 

care process, utilization, and outcome. Diabetes Care 2000; 23:192-6. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

329. Petranyi G, Petranyi M, Scobie IN, et al. Quality control of home Not an evaluation of a QI intervention 

199
monitoring of blood glucose concentrations. Br Med J (Clin Res Ed)

1984; 288:757.

330. Philis-Tsimikas A, Walker C. Improved care for diabetes in

underserved populations. J Ambul Care Manage 2001; 24:39-43. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

331. Pichert JW. Outcomes of a diabetes professional education seminar. 

Diabetes Educ 1984; 9:37-9. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

No eligible outcomes 

332. Pichert JW, Penha ML. Institutionalization of diabetes care and 

education programs: a tale of two cities. Diabetes Educ 1993; 19:273, 

276-7. 

Not an evaluation of a QI intervention 

333. Pieber. Evaluation of a structured teaching and treatment programme

for type2 diabetes in general practice in a rural area of Austria. 

Diabetes Medicine 1995; 112:349-54. 

Patient education or self-management 

only 

334. Piette JD, Mah CA. The feasibility of automated voice messaging as 

an adjunct to diabetes outpatient care. Diabetes Care 1997; 20:15-21. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

No eligible outcomes 

335. Piette JD. Perceived access problems among patients with diabetes in

two public systems of care. J Gen Intern Med 2000; 15:797-804. 

No eligible outcomes 

336. Piette JD, Weinberger M, McPhee SJ. The effect of automated calls 

with telephone nurse follow-up on patient-centered outcomes of 

diabetes care: a randomized, controlled trial. Med Care 2000; 38:218-

30. 

No eligible outcomes 

337. Pill R, Stott NC, Rollnick SR, Rees M. A randomized controlled trial of 

an intervention designed to improve the care given in general practice

to Type II diabetic patients: patient outcomes and professional ability

to change behaviour. Fam Pract 1998; 15:229-35. 

Patient education or self-management 

only 

338. Piwernetz K, Renner R, Mohrlein A, et al. Analysis and processing of 

data in a hospital-based diabetes management system. Horm Metab 

Res Suppl 1990; 24:109-15. 

Not an evaluation of a QI intervention 

339. Porter AM. Organisation of diabetic care [letter]. British Medical 

Journal Clinical Research Ed 1982; 285:1121. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

200
340. Prasad S, Kamath GG, Jones K, Clearkin LG, Phillips RP. 

Effectiveness of optometrist screening for diabetic retinopathy using 

slit-lamp biomicroscopy. Eye 2001; 15:595-601. 

No eligible outcomes 

341. Pratt, et al. Peer support and nutrition education for older adults with 

diabetes. J Nutr Elder 1987; 6:31. 

Patient education or self-management 

only 

342. Probert CS, Maddison W, Roland JM. Diet, diabetes, and male 

chauvinism. BMJ 1990; 301:1430-1. 

No eligible outcomes 

343. Pugh KB, Jenkins AJ, Zheng D, Chinniss S, Hermayer K, Jenkins C. 

Foot problems and foot care practices in diabetes. A survey of public 

and private diabetes clinics affiliated with a university hospital. J S C 

Med Assoc 2002; 98:305-10. 

Not an evaluation of a QI intervention 

344. Quevedo SF, Blenkiron P, Lynch K. Diabetes case management: 

experience in the staff and IPA model HMO. HMO Pract 1998; 12:44-

6. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

345. Quinn DC, Graber AL, Elasy TA, Thomas J, Wolff K, Brown A. 

Overcoming turf battles: developing a pragmatic, collaborative model 

to improve glycemic control in patients with diabetes. Jt Comm J Qual 

Improv 2001; 27:255-64. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

346. Rabkin SW, Boyko E, Wilson A, Streja DA. A randomized clinical trial 

comparing behavior modification and individual counseling in the 

nutritional therapy of non-insulin-dependent diabetes mellitus: 

comparison of the effect on blood sugar, body weight, and serum 

lipids. Diabetes Care 1983; 6:50-6. 

Patient education or self-management 

only 

347. Raji A, Gomes H, Beard JO, MacDonald P, Conlin PR. A randomized

trial comparing intensive and passive education in patients with

diabetes mellitus. Arch Intern Med 2002; 162:1301-4. 

Patient education or self-management 

only 

348. Ratner RE. CLIA 1988: impact on diabetes care. Diabetes Care 1992; 

15:1814-7. 

Not an evaluation of a QI intervention 

349. Raz I, Soskolne V, Stein P. Influence of small-group education 

sessions on glucose homeostasis in NIDDM. Diabetes Care 1988; 

11:67-71. 

Patient education or self-management 

only 

350. Redhead J, Hussain A, Gedling P, McCulloch AJ. The effectiveness of 

a primary-care-based diabetes education service. Diabet Med 1993; 

Study design did not meet criteria for 

201
10:672-5. RCT, CBA, or ITS 

351. Reiber GE, Smith DG, Boone DA, et al. Design and pilot testing of the 

DVA/Seattle Footwear System for diabetic patients with foot 

insensitivity. J Rehabil Res Dev 1997; 34:1-8. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

352. Renders CM, Valk GD, Griffin SJ, Wagner EH, Eijk Van JT, Assendelft 

WJ. Interventions to improve the management of diabetes in primary 

care, outpatient, and community settings: a systematic review.

Diabetes Care 2001; 24:1821-33. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

353. Renders CM, Valk GD, Griffin S, Wagner EH, Eijk JT, Assendelft WJ. 

Interventions to improve the management of diabetes mellitus in 

primary care, outpatient and community settings. Cochrane Database 

Syst Rev 2001:CD001481. 

Not an evaluation of a QI intervention 

354. Riddle MC. A strategy for chronic disease. Lancet 1980; 2:734-6. Not an evaluation of a QI intervention 

355. Ridgeway NA, Harvill DR, Harvill LM, Falin TM, Forester GM, Gose 

OD. Improved control of type 2 diabetes mellitus: a practical

education/behavior modification program in a primary care clinic. 

South Med J 1999; 92:667-72. 

Patient education or self-management 

only 

356. Rimmer JH, Silverman K, Braunschweig C, Quinn L, Liu Y. Feasibility

of a health promotion intervention for a group of predominantly African 

American women with type 2 diabetes. Diabetes Educ 2002; 28:571-

80. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

357. Rith-Najarian S, Branchaud C, Beaulieu O, Gohdes D, Simonson G, 

Mazze R. Reducing lower-extremity amputations due to diabetes. 

Application of the staged diabetes management approach in a primary 

care setting. J Fam Pract 1998; 47:127-32. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

358. Roberts S. Effectiveness of a hospital diabetes specialist nursing 

service. Diabet Med 2002; 19 Suppl 1:9-11. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

359. Robinson TN. Community health behavior change through computer 

network health promotion: preliminary findings from Stanford HealthNet. Computer Methods Programs Biomed 1989; 30:137. 

Not an evaluation of a QI intervention 

/No eligible outcomes 

360. Rodgers J, Walker R. Glycaemic control in type 2 diabetes. Nurs 

Times 2002; 98:56-7. 

Not an evaluation of a QI intervention 

202
361. Rothman R, Malone R, Bryant B, Horlen C, Pignone M, IN. 

Pharmacist-led, primary care-based disease management improves 

hemoglobin A1c in high-risk patients with diabetes. American Journal 

of Medical Quality 2003; 18:51-58. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

362. Rotman. A randomized controlled trial of a computer-based physician 

workstation in an outpatient setting: implementation barriers to 

outcome evaluation. J Am Med Inform Assoc 1996; 3:340. 

Excluded topic (unrelated to 

diabetes)/No eligible outcomes

363. Rotvold GH, Knarvik U, Johansen MA, Fossen K. Telemedicine 

screening for diabetic retinopathy: staff and patient satisfaction. J 

Telemed Telecare 2003; 9:109-13. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

364. Rubin RJ, Dietrich KA, Hawk AD. Clinical and economic impact of 

implementing a comprehensive diabetes management program in 

managed care. J Clin Endocrinol Metab 1998; 83:2635-42. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

365. Rundall TG, Shortell SM, Wang MC, et al. As good as it gets? Chronic 

care management in nine leading US physician organisations. BMJ

2002; 325:958-61. 

Not an evaluation of a QI intervention 

366. Ruoff G, Gray LS. Using a flow sheet to improve performance in 

treatment of elderly patients with type 2 diabetes. Fam Med 1999; 

31:331-6. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

367. Rutten G, van Eijk J, de Nobel E, Beek M, van der Velden H.

Feasibility and effects of a diabetes type II protocol with blood glucose 

self-monitoring in general practice. Fam Pract 1990; 7:273-8. 

Patient education or self-management 

only 

368. Ryff-de Leche A, Engler H, Nutzi E, Berger M, Berger W. Clinical 

application of two computerized diabetes management systems: 

comparison with the log-book method. Diabetes Res 1992; 19:97-105. 

Excluded topic (Type 1 diabetes only) 

369. Sacerdote AS. Impact of budget cuts on diabetic control in urban adult 

diabetes clinic. Diabetes Care 1988; 11:302-3. 

Not an evaluation of a QI intervention 

370. Samaras K, Ashwell S, Mackintosh AM, Fleury AC, Campbell LV, 

Chisholm DJ. Will older sedentary people with non-insulin-dependent 

diabetes mellitus start exercising? A health promotion model. Diabetes 

Res Clin Pract 1997; 37:121-8.

Patient education or self-management 

only 

371. Sanders KM, Satyvavolu A. Improving blood pressure control in 

diabetes: limitations of a clinical reminder in influencing physician 

No eligible outcomes 

203
behavior. J Contin Educ Health Prof 2002; 22:23-32. 

372. Sarkisian. A systematic review of diabetes self-care interventions for 

older, African American, or Latino adults. Diabetes Educ. 2003; 

29:467. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

373. Schachat AP, Lee PP, Wu WC. A quality assurance program for an 

inpatient department of ophthalmology. 'Indicators and criteria'. Arch 

Ophthalmol 1989; 107:1293-6.

Not an evaluation of a QI intervention 

374. Schneider SH, Khachadurian AK, Amorosa LF, Clemow L, Ruderman 

NB. Ten-year experience with an exercise-based outpatient life-style 

modification program in the treatment of diabetes mellitus. Diabetes 

Care 1992; 15:1800-10. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

375. Scholz V, Jorgens V, Berger M, et al. Evaluation of a postgraduate 

course for diabetes educators. Diabetes Educ 1984; 10 SPEC NO:80-

4. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

376. Schwartz R, Zaremba M, Ra K. Third-party coverage for diabetes 

education program. QRB Qual Rev Bull 1985; 11:213-7. 

Not an evaluation of a QI intervention 

377. Schwedes U, Siebolds M, Mertes G. Meal-related structured selfmonitoring of blood glucose: effect on diabetes control in non-insulintreated type 2 diabetic patients. Diabetes Care 2002; 25:1928-32. Patient education or self-management 

only 

378. Scott, et al. The effectiveness of diabetes education for non-insulindependent diabetic persons. Diabetes Educ 1984; 10:36. Patient education or self-management 

only 

379. Scott M. The NSW Aboriginal Vascular Health Program. N S W Public 

Health Bull 2002; 13:152-4. 

Not an evaluation of a QI intervention 

380. Sczupak CA, Conrad WF. Relationship between patient-oriented 

pharmaceutical services and therapeutic outcomes of ambulatory

patients with diabetes mellitus. American Journal of Hospital

Pharmacy 1977; 34:1238-1242. 

Publication prior to 1980 

381. Shandro MT, Pick ME, Gruninger A, Ryan EA, IN. Diabetes care: 

interventions in the community. Diabetes Care 2002; 25:941-942. 

Patient education or self-management 

only 

382. Sharp LK, Lipsky MS. The short-term impact of a continuing medical 

education program on providers' attitudes toward treating diabetes. 

Diabetes Care 1999; 22:1929-32. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

204
383. Sharp LK, Lipsky MS. Continuing medical education and attitudes of 

health care providers toward treating diabetes. J Contin Educ Health 

Prof 2002; 22:103-12. 

No eligible outcomes 

384. Shultz EK, Bauman A, Hayward M, Rodbard D, Holzman R. Improved 

diabetic prognosis following telecommunication and graphical 

processing of diabetic data. Proc Annu Symp Comput Appl Med Care

1991:53-7. Duplicate/overlap of article #385 from this table)

Overlaps with or duplicates another 

article that was included 

385. Shultz EK, Bauman A, Hayward M, Holzman R. Improved care of 

patients with diabetes through telecommunications. Ann N Y Acad Sci

1992; 670:141-5. 

Other §§§

386. Sibbald RG, Kensholme A, Carter L, Knowles A, Tyrrell W. Special 

foot clinics for patients with diabetes. J Wound Care 1996; 5:238-43. 

Not an evaluation of a QI intervention 

387. Siddall R. Diabetes. Sugar refiner. Health Serv J 1999; 109:suppl 3-5. Not an evaluation of a QI intervention 

388. Sideman K, Padilla B, Huff TA, Stachura ME. Capillary blood glucose 

monitoring: a comparison of two hospital quality control programs. 

Diabetes Educ 1988; 14:223-6.

Not an evaluation of a QI intervention 

/Excluded topic (capillary blood glucose 

testing) 

389. Sidorov J, Gabbay R, Harris R, et al. Disease management for 

diabetes mellitus: impact on hemoglobin A1c. Am J Manag Care 2000; 

6:1217-26. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

390. Sidorov J, Shull R, Tomcavage J, Girolami S, Lawton N, Harris R. 

Does diabetes disease management save money and improve 

outcomes? A report of simultaneous short-term savings and quality

improvement associated with a health maintenance organizationsponsored disease management program among patients fulfilling 

health employer data and information set criteria. Diabetes Care 2002; 

25:684-9. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

391. Sifuentes F, Chang L, Nieman LZ, Foxhall LE. Evaluating a diabetes 

foot care program in a preceptorship for medical students. Diabetes 

Educ 2002; 28:930-2, 935-7. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

392. Sikka R, Waters J, Moore W, Sutton DR, Herman WH, Aubert RE. 

Renal assessment practices and the effect of nurse case 

management of health maintenance organization patients with

diabetes. Diabetes Care 1999; 22:1-6. (Duplicate/overlap of included 

Overlaps with or duplicates another 

article that was included 

205
article****) 

393. Simmons D, Voyle JA. Reaching hard-to-reach, high-risk populations: 

piloting a health promotion and diabetes disease prevention

programme on an urban marae in New Zealand. Health Promot

Internation 2003; 18:41-50. 

Study design did not meet criteria for 

RCT, CBA, or ITS/Excluded topic 

(promotion of diabetes prevention 

program) 

394. Singh BM, Holland MR, Thorn PA. Metabolic control of diabetes in 

general practice clinics: comparison with a hospital clinic. Br Med J 

(Clin Res Ed) 1984; 289:726-8.

Not an evaluation of a QI intervention 

395. Sinnock P. The use of hospitalization data to evaluate patient 

education programs. Diabetes Educ 1984; 10 SPEC NO:43-5. 

Not an evaluation of a QI intervention 

396. Skaer TL, Sclar DA, Markowski DJ, Won JK. Effect of value-added 

utilities on prescription refill compliance and Medicaid health care 

expenditures--a study of patients with non-insulin-dependent diabetes 

mellitus. J Clin Pharm Ther 1993; 18:295-9. 

Patient education or self-management 

only 

397. Smith DM, Norton JA, Weinberger M, McDonald CJ, Katz BP. 

Increasing prescribed office visits. A controlled trial in patients with

diabetes mellitus. Med Care 1986; 24:189-99. (Duplicate/overlap of 

article #398 from this table) 

Overlaps with or duplicates another 

article that was included 

398. Smith DM, Weinberger M, Katz BP. A controlled trial to increase office 

visits and reduce hospitalizations of diabetic patients. J Gen Intern 

Med 1987; 2:232-8. 

Patient education or self-management 

only 

399. Smith SC, Folk JC, Losch ME. Effects of collaborative education on 

patient satisfaction and knowledge. Insight 1992; 17:20-4. 

No eligible outcomes 

400. Smith DE, Heckemeyer CM, Kratt PP, Mason DA. Motivational 

interviewing to improve adherence to a behavioral weight-control 

program for older obese women with NIDDM. A pilot study. Diabetes 

Care 1997; 20:52-4. 

Patient education or self-management 

only 

401. Smith L, Weinert C. Telecommunication support for rural women with

diabetes. Diabetes Educ 2000; 26:645-55. 

No eligible outcomes 

402. Snoek FJ, Skinner TC. Psychological counselling in problematic 

diabetes: does it help? Diabet Med 2002; 19:265-73. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

403. Solberg LI, Reger LA, Pearson TL, et al. Using continuous quality Not an evaluation of a QI intervention 

206
improvement to improve diabetes care in populations: the IDEAL 

model. Improving care for Diabetics through Empowerment Active 

collaboration and Leadership. Jt Comm J Qual Improv 1997; 23:581-

92. 

404. Sperl-Hillen J, O'Connor PJ, Carlson RR, et al. Improving diabetes 

care in a large health care system: an enhanced primary care 

approach. Jt Comm J Qual Improv 2000; 26:615-22. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

405. Sperl-Hillen J, Isham GJ. HealthPartners' diabetes care improvement 

program. Healthplan 2002; 43:46-8, 50-1, 53. 

Not an evaluation of a QI intervention 

406. Sprafka JM, Crozier M, Whipple D, Bishop D, Kurth D. Response of 

diabetic patients to a community-based education program. Diabetes 

Educ 1988; 14:148-51. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

407. Stafford J, Helm AM, Beaven DW. The value of short courses: an 

attempt ot evaluate a four-day teaching programme in Christchurch for 

diabetes educators. New Zealand Nursing Journal 1979; 72:24. 

Not an evaluation of a QI intervention 

408. Stange KC, Miller WL, Crabtree BF, O'Connor PJ, Zyzanski SJ. 

Multimethod research: approaches for integrating qualitative and 

quantitative methods. J Gen Intern Med 1994; 9:278-82. 

Not an evaluation of a QI intervention 

409. Starren J, Hripcsak G, Sengupta S, et al. Columbia University's

Informatics for Diabetes Education and Telemedicine (IDEATel) 

project: technical implementation. J Am Med Inform Assoc 2002; 9:25-

36. 

Not an evaluation of a QI intervention 

410. Steel JM, Cramb R, Duncan LJ. How useful are patient-operated 

blood glucose meters? Practitioner 1980; 224:651-3. 

Not an evaluation of a QI intervention 

411. Steffens B. Cost-effective management of type 2 diabetes: providing 

quality care in a cost-constrained environment. Am J Manag Care

2000; 6:S697-703; discussion S704-9. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

412. Stegmayer P, Lovrien FC, Smith M, Keller T, Gohdes DM. Designing a 

diabetes nutrition education program for a Native American

community. Diabetes Educ 1988; 14:64-6. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

413. Stein GH. The use of a nurse practitioner in the management of 

patients with diabetes mellitus. Medical Care 1974; 12:885-890. 

Not an evaluation of a QI intervention 

207
414. Stoner KL, Lasar NJ, Butcher MK, et al. Improving glycemic control: 

can techniques used in a managed care setting be successfully

adapted to a rural fee-for-service practice? Am J Med Qual 2001; 

16:93-8. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

415. Stott NC, Rollnick S, Rees MR, Pill RM. Innovation in clinical method: 

diabetes care and negotiating skills. Fam Pract 1995; 12:413-8. 

Not an evaluation of a QI intervention 

416. Strock ES, Sandell JL. The ambulatory insulin program: initiating

insulin therapy in an outpatient setting. Diabetes Educ 1988; 14:338-

45. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

417. Sturmberg JP, Overend D. General practice based diabetes clinics. 

An integration model. Aust Fam Physician 1999; 28:240-5. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

418. Sullivan FM. Whose problem is the diabetic who does not attend a 

hospital clinic? Scott Med J 1988; 33:259-60. 

Not an evaluation of a QI intervention 

419. Sullivan FM, Menzies A. The costs and benefits of introducing a 

nurse-run diabetic review service into general practice. Practical 

Diabetes 1991;8(2):47-50 

Study design did not meet criteria for 

RCT, CBA, or ITS 

420. Surwit RS, van Tilburg MA, Zucker N, et al. Stress management 

improves long-term glycemic control in type 2 diabetes. Diabetes Care

2002; 25:30-4. 

Patient education or self-management 

only 

421. Svac J. Analysis of discharge data in a large hospital in Slovakia. Its 

application to diabetic patients. Stud Health Technol Inform 1994; 

14:110-23. 

Not an evaluation of a QI intervention 

422. Tai SS, Nazareth I, Donegan C, Haines A. Evaluation of general

practice computer templates. Lessons from a pilot randomised 

controlled trial. Methods Inf Med 1999; 38:177-81. 

Study design did not meet criteria for 

RCT, CBA, or ITS/No eligible outcomes 

423. Tape TG, Campbell JR. Computerized medical records and preventive 

health care: success depends on many factors. Am J Med 1993; 

94:619-25. 

No eligible outcomes 

424. Taplin S, Galvin MS, Payne T, Coole D, Wagner E. Putting populationbased care into practice: real option or rhetoric? J Am Board Fam

Pract 1998; 11:116-26. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

425. Tariq SH, Karcic E, Thomas DR, et al. The use of a no-concentrated- Not an evaluation of a QI intervention 

208
sweets diet in the management of type 2 diabetes in nursing homes. J 

Am Diet Assoc 2001; 101:1463-6. 

426. Tasker PR. Diabetes care: whose responsibility? Br Med J (Clin Res 

Ed) 1985; 290:1632. 

Not an evaluation of a QI intervention 

427. Tattersall R, Gale E. Patient self-monitoring of blood glucose and 

refinements of conventional insulin treatment. Am J Med 1981; 

70:177-82. 

Not an evaluation of a QI intervention 

428. Taylor R. Practical community screening for diabetic retinopathy using 

the mobile retinal camera: report of a 12 centre study. British Diabetic 

Association Mobile Retinal Screening Group. Diabet Med 1996; 

13:946-52. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

429. Testa MA, Simonson DC. Health economic benefits and quality of life 

during improved glycemic control in patients with type 2 diabetes 

mellitus: a randomized, controlled, double-blind trial. JAMA 1998; 

280:1490-6. 

Not an evaluation of a QI intervention 

430. Teza SL, DeVito AV, 2nd, Hiss RG. Resource manual to help your 

program meet the national standards. Diabetes Educ 1987; 13 

Suppl:210-28. 

Not an evaluation of a QI intervention 

431. Thackeray R, Neiger BL. Using social marketing to develop diabetes 

self-management education interventions. Diabetes Educ 2002; 

28:536-40, 542-4. 

Not an evaluation of a QI intervention 

432. Thompson. A team approach to diabetic foot care: the Manchester 

experience. Foot 1991; 1:75-82. 

Other ^^^^

433. Thorn PA, Watkins PJ. Organisation of diabetic care. Br Med J (Clin 

Res Ed) 1982; 285:787-9. 

Not an evaluation of a QI intervention 

434. Tildesley HD, Mair K, Sharpe J, Piaseczny M. Diabetes teaching--

outcome analysis. Patient Educ Couns 1996; 29:59-65. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

435. Tobe SW, McFarlane PA, Naimark DM. Microalbuminuria in diabetes 

mellitus. CMAJ 2002; 167:499-503. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

436. Trento M, al. e. Therapeutic group education in the follow-up of 

patients with non-insulin treated, non-insulin dependent diabetes 

mellitus. Diabetes Metab Clin Exp 1998; 11:212-216. 

Patient education or self-management 

only 

209
437. Trento M, Passera P, Tomalino M, et al. Group visits improve 

metabolic control in type 2 diabetes: a 2-year follow-up. Diabetes Care

2001; 24:995-1000. 

Patient education or self-management 

only 

438. Trento M, Passera P, Bajardi M, et al. Lifestyle intervention by group 

care prevents deterioration of Type II diabetes: a 4-year randomized 

controlled clinical trial. Diabetologia 2002; 45:1231-9. 

Patient education or self-management 

only 

439. Tsang MW, Mok M, Kam G, et al. Improvement in diabetes control 

with a monitoring system based on a hand-held, touch-screen 

electronic diary. J Telemed Telecare 2001; 7:47-50. 

Patient education or self-management 

only 

440. Tu KS, McDaniel G, Gay JT. Diabetes self-care knowledge, behaviors, 

and metabolic control of older adults--the effect of a posteducational 

follow-up program. Diabetes Educator. 1993; 19:25-30. 

Patient education or self-management 

only 

441. Turner RC, Peden JG, Jr., O'Brien K. Patient-carried card prompts vs 

computer-generated prompts to remind private practice physicians to 

perform health maintenance measures. Arch Intern Med 1994; 

154:1957-60. 

No eligible outcomes 

442. Turnin MC, Beddok RH, Clottes JP, et al. Telematic expert system 

Diabeto. New tool for diet self-monitoring for diabetic patients. 

Diabetes Care 1992; 15:204-12. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

443. Tyrrell W. Orthotic intervention in patients with diabetic foot ulceration. 

J Wound Care 1999; 8:530-2. 

Not an evaluation of a QI intervention 

444. Uusitupa, et al. The maintenance of improved metabolic control after 

intensified diet therapy in recent type 2 diabetes. Diabetes Res Clinic 

Pract 1993; 19:227. 

Patient education or self-management 

only 

445. Uusitupa MI. Early lifestyle intervention in patients with non-insulindependent diabetes mellitus and impaired glucose tolerance. Ann Med

1996; 28:445-9. (Duplicate/overlap of article #444 from this table) 

Overlaps with or duplicates another 

article that was included 

446. Valk GD, Kriegsman DM, Assendelft WJ. Patient education for 

preventing diabetic foot ulceration. A systematic review. Endocrinol 

Metab Clin North Am 2002; 31:633-58. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

447. Varroud-Vial M, Charpentier G, Vaur L, et al. Effects of clinical audit 

on the quality of care in patients with type 2 diabetes: results of the 

Not an evaluation of a QI intervention 

210
DIABEST pilot study. Diabetes Metab 2001; 27:666-74. 

448. Vijan S, Stevens DL, Herman WH, Funnell MM, Standiford CJ. 

Screening, prevention, counseling, and treatment for the 

complications of type II diabetes mellitus. Putting evidence into 

practice. J Gen Intern Med 1997; 12:567-80. 

Not an evaluation of a QI intervention 

449. Vinicor F, Cohen SJ, Mazzuca SA, et al. DIABEDS: a randomized trial 

of the effects of physician and/or patient education on diabetes patient 

outcomes. Journal of Chronic Diseases 1987; 40:345-356. 

(Duplicate/overlap of included article^^)

Overlaps with or duplicates another 

article that was included 

450. Vrijhoef HJ, Diederiks JP, Spreeuwenberg C, Wolffenbuttel BH. 

Substitution model with central role for nurse specialist is justified in

the care for stable type 2 diabetic outpatients. J Adv Nurs 2001; 

36:546-55. 

No eligible outcomes 

451. Vrijhoef HJ, Spreeuwenberg C, Eijkelberg IM, Wolffenbuttel BH, van 

Merode GG. Adoption of disease management model for diabetes in 

region of Maastricht. BMJ 2001; 323:983-5. 

Not an evaluation of a QI intervention 

452. Vrijhoef HJ, Diederiks JP, Spreeuwenberg C, Wolffenbuttel BH, van 

Wilderen LJ, IN. The nurse specialist as main care-provider for 

patients with type 2 diabetes in a primary care setting: effects on 

patient outcomes. International Journal of Nursing Studies 2002; 

39:441-451. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

No eligible outcomes 

453. Watkins GB, Sutcliffe T, Pyke DA, Watkins PJ. Computerisation of 

diabetic clinic records. Br Med J 1980; 281:1402-3. 

Not an evaluation of a QI intervention 

454. Watts GF, Macleod AF, Benn JJ, et al. Comparison of the real-time 

use of glycosylated haemoglobin and plasma fructosamine in the 

diabetic clinic. Diabet Med 1991; 8:573-9. 

Not an evaluation of a QI intervention 

455. Weinberger M, Ault KA, Vinicor F. Prospective reimbursement and 

diabetes mellitus. Impact upon glycemic control and utilization of 

health services. Med Care 1988; 26:77-83. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

No eligible outcomes 

456. Weinberger M, Oddone EZ, Henderson WG. Does increased access 

to primary care reduce hospital readmissions? Veterans Affairs 

Cooperative Study Group on Primary Care and Hospital Readmission. 

N Engl J Med 1996; 334:1441-7. 

No eligible outcomes 

211
457. Werdier D, Jesdinsky HJ, Helmich P. A randomized, controlled study 

on the effect of diabetes counseling in the offices of 12 general 

practitioners. Rev Epidemiol Sante Publique 1984; 32:225-9.

Patient education or self-management 

only 

458. White N, Carnahan J, Nugent CA, Iwaoka T, Dodson MA. 

Management of obese patients with diabetes mellitus: comparison of 

advice education with group management. Diabetes Care 1986; 

9:490-6. 

Patient education or self-management 

only 

459. White B. 13 months of quality improvement: did it work? Fam Pract 

Manag 2001; 8:55-7. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

460. Williams D, Munroe C, Hospedales CJ, Greenwood RH. A Three-Year 

Evaluation of the Quality of Diabetes Care in the Norwich Community

Care Scheme. Diab Medicine 1990; 7:74. 

Study design did not meet criteria for 

RCT, CBA, or ITS 

461. Wilson W, Pratt C. The impact of diabetes education and peer support 

upon weight and glycemic control of elderly persons with noninsulin

dependent diabetes mellitus (NIDDM). Am J Public Health 1987; 

77:634-5. 

Patient education or self-management 

only 

462. Wing RR, Epstein LH, Nowalk MP, Scott N, Koeske R, Hagg S. Does 

self-monitoring of blood glucose levels improve dietary compliance for 

obese patients with type II diabetes? Am J Med 1986; 81:830-6. 

Patient education or self-management 

only 

463. Wing. Self-regulation in the treatment of type II diabetes. Behav Ther

1988; 19:11. (Duplicate/overlap of article #462 from this table) 

Overlaps with or duplicates another 

article that was included 

464. Wing RR, Anglin K. Effectiveness of a behavioral weight control 

program for blacks and whites with NIDDM. Diabetes Care 1996; 

19:409-13. 

No eligible outcomes 

465. Wise PH, Dowlatshahi DC, Farrant S, Fromson S, Meadows KA. 

Effect of computer-based learning on diabetes knowledge and control. 

Diabetes Care 1986; 9:504-8. 

Patient education or self-management 

only 

466. Woodcock AJ, Kinmonth AL, Campbell MJ, Griffin SJ, Spiegal NM. 

Diabetes care from diagnosis: effects of training in patient-centred 

care on beliefs, attitudes and behaviour of primary care professionals. 

Patient Educ Couns 1999; 37:65-79. 

No eligible outcomes 

467. Wooldridge J, Moreno L. Evaluation of the costs to Medicare of 

covering therapeutic shoes for diabetic patients. Diabetes Care 1994; 

No eligible outcomes 

212
17:541-547. 

468. Worth. Shared care for diabetes in Chester: preliminary experience 

with a 'clinic-wide' scheme. Practical Diabetes 1990; 7:266.

Study design did not meet criteria for 

RCT, CBA, or ITS 

469. Wylie-Rosett J, Engel S, D'Eramo G, et al. Delivery of diabetes care to 

low--income patients: assessment of a federally funded program. 

Diabetes Educ 1989; 15:366-9.

Not an evaluation of a QI intervention 

470. Wylie-Rosett J, Villeneuve M, IN. Overcoming resistance to change in

a long-term care facility: analysis of the team approach and 

consensus process. Diabetes Educator. 1989; 15:122-123. 

Not an evaluation of a QI intervention 

471. Young-Hyman D. Provider impact in diabetes education. What we

know, what we would like to know, paradigms for asking. Diabetes 

Educ 1999; 25:34-42. 

Not an evaluation of a QI intervention 

* Combination of reasons: 1) follow-up < 80%; patients were allowed to change treatment assignment, but there was no ITT; main

outcome was HbA1c, but all 4 values (control pre/post, Intervention pre/post) were equal AND the common value of 5.3 is too low, 

possibly b/c study was conducted before assay standardization. Lastly, borderline QI vs. equivalency study

^ Insufficient information; contacted first author - still not available as journal publication or completed manuscript 

† Branger, P.J., van't Hooft, A., van der Wouden, J.C., Moorman, P.W., van Bemmel, J.H. Shared care for diabetes: supporting 

communication between primary and secondary care. Int J Med Inf. 1999;53(2-3):133-42. 

§ Franz, M.J., Monk, A., Barry, B., et al. Effectiveness of medical nutrition therapy provided by dietitians in the management of noninsulin-dependent diabetes mellitus: a randomized, controlled clinical trial. Journal of the American Dietetic Association. 

1995;95(9):1009-1017. 

** Gaede, P., Vedel, P., Larsen, N., Jensen, G.V., Parving, H.H., Pedersen, O. Multifactorial intervention and cardiovascular disease 

in patients with type 2 diabetes. N Engl J Med. 2003;348(5):383-93. 

^^ Weinberger, M., Kirkman, M.S., Samsa, G.P., et al. A nurse-coordinated intervention for primary care patients with non-insulindependent diabetes mellitus: impact on glycemic control and health-related quality of life. J Gen Intern Med. 1995;10(2):59-66. 

†† Specified patients only as “insulin dependent diabetics,” but average age and duration of disease suggested that patients were all Type

I diabetics. 

§§ Lobach, D.F., Hammond, W.E. Computerized decision support based on a clinical practice guideline improves 

compliance with care standards. Am J Med. 1997;102(1):89-98. 

*** Article compares usual care versus provider education only, usual care versus patient and provider education, and 

usual care versus patient education only. Exclusion is of usual care versus patient education only comparison. 

^^^ Design makes results unusable, study too old to contact authors for more information. 

††† Insufficient patient population size information. 

§§§ Cross-over trial with insufficient wash out period and insufficient information to split into two. 

**** Aubert, R.E., Herman, W.H., Waters, J., et al. Nurse case management to improve glycemic control in diabetic patients in a

health maintenance organization. A randomized, controlled trial. Ann Intern Med. 1998;129(8):605-12. 

^^^^ Unable to obtain article. 

213
Appendix H. Additional tables for diabetes results and analysis 

Tables H1-H3. Re-analysis of adherence in terms of maximum rather than median effect reported by

each study 

In the main analysis, we pooled the various adherence outcomes reported by the included 

studies into a general provider adherence outcome. For each study, this measure was 

based on the adherence outcome exhibiting the median effect. 

In Appendix Tables H1-H3, we show the results obtained if, instead of taking the 

outcome with median effect, one selects the outcome with maximum effect (i.e., the 

outcome for which the intervention group showed the greatest increase in adherence 

above any increases in the control group). 

The purpose of the comparison is not the different magnitudes size between the 

maximum and median adherence (as these are expected to differ in this regard) but rather, 

the degree to which the patterns across QI types, strata of sample size and trial design are 

the same. In other words, it is unlikely that the relationships discussed in the main 

analysis simply reflect the definition of the generalized adherence outcome in terms of 

the median effect associated with each study. The same general relationships emerge on 

analyzing the studies in terms of the maximum impact on adherence achieved by each 

study. 

 

Tables H4a-c. Alternate classification scheme for numbers of QI strategies per intervention 

The main analysis showed a benefit for intervention with at least 2 QI strategies

compared to single-faceted ones. Since this finding relates to one of our a priori

hypotheses (that multi-faceted interventions achieve greater impact than single faceted 

ones), we further explored this question by reclassifying each intervention using a 

scheme similar to that used by other authors, in which major subtypes are regarded as 

their own category. Specifically, we replaced the broad category of provider education 

with separate categories for workshops or meeting, educational outreach or academic 

detailing, and distribution of educational materials. Similarly, we promoted disease 

management, team or personnel changes, and changes to the existing medical record 

system to their own categories, rather than pooling them within the broad category of 

organizational change. 

214
Table H1a. Association between maximum improvements in provider adherence† and type of quality

improvement strategy stratified by study sample size (median adherence included for comparison)

Median

Improvement 

in clinician 

adherence 

[inter-quartile

range] 

N=Number of 

trials 

Maximum Improvement in provider adherence 

[inter-quartile range] 

N=Number of trials 

All Trials All Trials Trials with

sample size in

lowest quartile 

Trials with

sample size in

lower 2 

quartiles 

Trials with

sample size in

upper 2 

quartiles

Trials with

sample size in

highest

quartile

All QI types 4.8 

[3.8, 15.0] 

N=17 

11.6 

[7.0, 23.7] 

N=17 

21.8 

[21.4,30.9] 

N=3 

25.9 

[21.2,37.5] 

N=6 

11.0 

[6.5,13.5] 

N=11 

10.8 

[6.5,12.6] 

N=8 

Provider 

Education 

5.6 

[4.15, 17.2] 

N=11 

21.8 

[11.3,28.5] 

N=11 

21.8 

[21.4,30.9] 

N=3 

30.0 

[21.8,40.0] 

N=5 

11.3 

[11.0,2.07] 

N=6 

11.3 

[10.9,14.6] 

N=4 

Provider 

Reminders 

3.4 

[2.2, 3.6] 

N=3 

11.0 

[10.8,13.2] 

N=3 

- - - - 

N=0 

- - - - 

N=0 

11.0 

[10.8,13.2] 

N=3 

13.0 

[10.5,15.4] 

N=2

Facilitated 

relay

4.85 

- - - - 

N=1

4.85 

- - - - 

N=1

- - - - 

N=0

4.85 

- - - - 

N=1

- - - - 

N=0 

- - - - 

N=0 

Patient 

Education 

4.9 

[4.7, 5.4] 

N=3 

6.0 

[5.4,23.0] 

N=3 

40.0 

- - - - 

N=1 

22.4 

[4.9,40.0] 

N=2 

6.0 

- - - - 

N=1 

- - - - 

N=0 

Selfmanagement 

6.0 

- - - - 

N=1 

6.0 

- - - - 

N=1 

- - - - 

N=0 

- - - - 

N=0 

6.0 

- - - - 

N=1 

- - - - 

N=0 

Patient 

reminders 

2.8 

[1.0,4.5] 

N=2 

9.0 

[7.0,11.0] 

N=2 N=0 N=0 

9.0 

[7.0,11.0] 

N=2 

7.0 

- - - - 

N=1 

215
Table H1a. Association between maximum improvements in provider adherence† and type of quality

improvement strategy stratified by study sample size (median adherence included for comparison) (continued) 

Audit &

feedback 

5.6 

[3.4, 16.4] 

N=9 

15.4 

[11.0,23.7] 

N=9 

21.8 

- - - - 

N=1 

31.8 

[21.8,41.7] 

N=2 

11.6 

[11.0,19.6] 

N=7 

11.6 

[11.0,15.4] 

N=5 

Organizational 

Change

4.7 

[4.1, 5.7] 

N=6 

6.5 

[5.3,10.0] 

N=6 

4.9 

- - - - 

N=1 

17.4 

[4.9,30.0] 

N=2 

6.5 

[5.8,8.0] 

N=4 

6.0 

[5.0,7.0] 

N=2 

† For each study, the general provider adherence outcome captured the adherence outcome with the median effect size reported by

that study. In this set of appendix tables, we show the results obtained if, instead of taking the outcome with median effect, one 

selects the outcome with maximum effect (i.e., the outcome for which the intervention group showed the greatest increase in

adherence above any increases in the control group). 

* When N=2, square brackets show the actual results of each study rather than interpolated 25th and 75th percentiles.

216
Table H1b. Associations between improvements in glycemic control and provider adherence stratified by trial 

design (median adherence included for comparison) 

Median Improvement in provider adherence 

[inter-quartile range*

] 

N=Number of trials 

Maximum Improvement in provider adherence 

[inter-quartile range*] 

N=Number of trials 

All Trials RCT Non-RCT† All Trials RCT Non-RCT† 

All QI types 4.9 

[3.8, 15.0] 

N=17 

4.5 

[3.5, 5.4] 

N=14 

18.0 

[17.2, 21.0] 

N=3 

11.6 

[7.0,23.7] 

N=17 

11.0 

[6.3,19.6] 

N=14 

30.0 

[28.5,35.9] 

N=3 

Provider 

Education 

5.6 

[4.2, 17.2] 

N=11 

4.8 

[3.1,8.0] 

N=8 

18.0 

[17.2, 21.0] 

N=3 

21.8 

[11.3,28.5] 

N=11 

16.3 

[11.0,22.3] 

N=8 

30.0 

[28.5,35.9] 

N=3 

Provider 

Reminders 

3.4 

[2.2, 3.6] 

N=3 

3.4 

[2.2, 3.6] 

N=3 

- - - - 

 

N=0 

11.0 

[10.8,13.2] 

N=3 

11.0 

[10.8,13.2] 

N=3 

- - - - 

 

N=0 

Facilitated 

relay

4.9 

- - - - 

N=1 

4.9 

- - - - 

N=1 

- - - - 

 

N=0 

4.85 

- - - - 

N=1

4.85 

- - - - 

N=1 

- - - - 

 

N=0 

Patient 

Education 

4.9 

[4.7, 5.4] 

N=3 

4.9 

[4.7, 5.4] 

N=3 

- - - - 

 

N=0 

6.0 

[5.4,23.0] 

N=3 

6.0 

[5.4,23.0] 

N=3 

- - - - 

 

N=0 

Selfmanagement 

6.0 

- - - - 

N=1 

6.0 

- - - - 

N=1 

- - - - 

 

N=0 

6.0 

- - - - 

N=1 

6.0 

- - - - 

N=1 

- - - - 

 

N=0 

Patient 

reminders 

2.8 

[1.0,4.5] 

N=2 

2.8 

[1.0, 4.5] 

N=2 

- - - - 

 

N=0 

9.0 

[7.0,11.0] 

N=2 

9.0 

[7.0,11.0] 

N=2 

- - - - 

 

N=0 

Audit &

feedback 

5.6 

[3.4, 16.4] 

N=9 

5.0 

[3.2, 10.3] 

N=7 

20.2 

[16.4,23.9] 

N=2* 

15.4 

[11.0,23.7] 

N=9 

11.6 

[11.0,18.6] 

N=7 

34.3 

[26.9,41.7] 

N=2* 

Organizational 

Change

4.7 

[4.1, 5.7] 

N=6 

4.5 

[4.0, 4.9] 

N=5 

18.0 

- - - - 

N=1 

6.5 

[5.3,10.0] 

N=6 

6.0 

[5.0,7.0] 

N=5 

30.0 

- - - - 

N=1 

* When N=2, square brackets show the actual results of each study rather than interpolated inter-quartile range. 

† Non-RCT included 16 controlled before-after studies and 1 quasi-randomized trial. 

217
Table H1c. Impacts on glycemic control and provider adherence stratified by trial design and sample size 

(median adherence included for comparison) 

All sizes Trials with

sample size 

in lowest

quartile

Trials with

sample size 

in lower 2 

quartiles 

Trials with

sample size 

in upper 2 

quartiles

Trials with

sample size 

in highest 

quartile

All trial 

designs

4.8 

[3.8, 15.0] 

N=17 

4.5 

[2.8, 11.4] 

N=3 

10.6 

[4.6, 17.6] 

N=6 

4.5 

[3.6, 5.8] 

N=11 

4.2 

[3.7, 5.2] 

N=8 

RCTs 

4.5 

[3.5,5.5] 

N=14 

4.5 

[2.8,11.4] 

N=3 

4.7 

[3.6,8.2] 

N=4 

4.3 

[3.5,5.5] 

N=10 

4.3 

[3.7,5.2] 

N=8 

Median 

Improvement 

in provider 

adherence

(%) 

[inter-quartile

range†

] 

N=Number of 

trials 

Non-RCTs 

18.0 

[17.2,21.0] 

N=3 N=0 

17.2 

[16.4,18.0] 

N=2 

23.9 

- - - - 

N=1 

N=0 

All trial 

designs

11.6 

[7.0, 23.7] 

N=17 

21.8 

[21.4,30.9] 

N=3 

25.9 

[21.2,37.5] 

N=6 

11.0 

[6.5,13.5] 

N=11 

10.8 

[6.5,12.6] 

N=8 

RCTs 

11.0 

[6.3,19.6] 

N=14 

21.8 

[21.4,30.9] 

N=3 

21.4 

[17.0,26.4] 

N=4 

10.8 

[6.3,11.5] 

N=10 

10.8 

[6.5,12.6] 

N=8 

Maximum 

Improvement 

in provider 

adherence

(%) 

[inter-quartile

range†

] 

N=Number of 

trials 

Non-RCTs 

30.0 

[28.5,35.9] 

N=3 N=0 

35.9 

[30.0,41.7] 

N=2 

26.9 

- - - - 

N=1 

N=0 

218
Table H2a. Association between improvement in provider adherence and number of QI strategies stratified by

study sample size (median adherence included for comparison) 

Median 

Improvement 

in Provider 

Adherence (%) 

[inter-quartile

range] 

N=Number of 

trials 

Maximum Improvement in Provider Adherence (%) 

[inter-quartile range] 

N=Number of trials 

Number of QI 

Strategies 

All Trials All Trials Trials with

sample size in

lowest quartile 

Trials with

sample size in

lower 2 

quartiles 

Trials with

sample size in

upper 2 

quartiles

Trials with

sample size in

highest

quartile

Any number 

(for 

comparison

purposes) 

4.8 

[3.8, 15.0] 

N=17 

11.6 

[7.0, 23.7] 

N=17 

21.8 

[21.4,30.9] 

N=3 

25.9 

[21.2,37.5] 

N=6 

11.0 

[6.5,13.5] 

N=11 

10.8 

[6.5,12.6] 

N=8 

1 strategy

only

3.0 

[2.0,3.5] 

N=3 

5.0 

[4.5,13.0] 

N=3 

21.0 

---- 

N=1 

21.0 

---- 

N=1 

4.5 

[4.0,5.0] 

N=2 

4.5 

[4.0,5.0] 

N=2

≥ 2 strategies 5.3 

[4.5,16.1] 

N=14 

13.5 

[10.6,26.1] 

N=14 

30.9 

[21.8,40.0] 

N=2 

30.0 

[21.8,40.0] 

N=5 

11.0 

[10.5,15.4] 

N=9 

11.3 

[10.6,14.5] 

N=6 

≥ 3 strategies 4.9 

[2.9,5.4] 

N=3 

6.0 

[5.4,8.5] 

N=3 N=0 

4.9 

---- 

N=1 

8.5 

[6.0,11.0] 

N=2 N=0 

≥ 4 strategies 1.0 

---- 

N=1 

11.0 

---- 

N=1 N=0 N=0 

11.0 

---- 

N=1 N=0 

5 strategies * 1.0 

---- 

N=1 

11.0 

---- 

N=1 N=0 N=0 

11.0 

---- 

N=1 N=0 

* No study involved an intervention with more than 5 QI types.

219
Table H2b. Associations between number of quality improvement strategies and improvements in glycemic 

control and provider adherence stratified by trial design 

Median Reduction in HbA1c

[inter-quartile range*

] 

N=Number of trials 

Maximum Improvement in provider adherence 

[inter-quartile range*] 

N=Number of trials 

All Trials RCT Non-RCT† All Trials RCT Non-RCT† 

Any number 

(for 

comparison

purposes) 

4.9 

[3.8, 15.0] 

N=17 

4.5 

[3.5, 5.4] 

N=14 

18.0 

[17.2, 21.0] 

N=3 

11.6 

[7.0,23.7] 

N=17 

11.0 

[6.3,19.6] 

N=14 

30.0 

[28.5,35.9] 

N=3 

Single

strategy only

3.0 

[2.0,3.5] 

N=3 

3.0 

[2.0,3.5] 

N=3 N=0 

5.0 

[4.5,13.0] 

N=3 

5.0 

[4.5,13.0] 

N=3 N=0 

≥ 2 strategies 5.3 

[4.5,16.1] 

N=14 

4.9 

[4.2,5.8] 

N=11 

18.0 

[17.2,21.0] 

N=3 

13.5 

[10.6,26.1] 

N=14 

11.0 

[8.8,18.6] 

N=11 

30.0 

[28.5,35.9] 

N=3 

≥ 3 strategies 4.9 

[2.9, 5.4] 

N=3 

4.9 

[2.9, 5.4] 

N=3 N=0 

6.0 

[5.4,8.5] 

N=3 

6.0 

[5.4,8.5] 

N=3 N=0 

≥ 4 strategies 1.0 

---- 

N=1 

1.0 

---- 

N=1 N=0 

11.0 

---- 

N=1 

11.0 

---- 

N=1 N=0 

5 strategies* 1.0 

---- 

N=1 

1.0 

---- 

N=1 N=0 

11.0 

---- 

N=1 

11.0 

---- 

N=1 N=0 

* No study involved an intervention with more than 5 QI types. 

220
Table H3a. Median and maximum improvements in provider adherence associated with specific 

substrategies of organizational change stratified by study design 

Median Reduction in HbA1c

[inter-quartile range *] 

N=Number of trials 

Maximum Improvement in provider adherence 

[inter-quartile range] 

N=Number of trials 

Type of 

organizational

change

All Trials RCT Non-RCT All Trials RCT Non-RCT 

All QI types 

4.85 

[3.8,15.03] 

N=17 

4.5 

[3.5,5.45] 

N=14 

18.0 

[17.2,20.95] 

N=3 

11.6 

[7.0,23.7] 

N=17 

11.0 

[6.3,19.6] 

N=14 

30.0 

[28.5,35.9] 

N=3 

Any type of 

organizational

change

4.7 

[4.1,5.7] 

N=6 

4.5 

[4.0,4.9] 

N=5 

18.0 

---- 

N=1 

6.5 

[5.3,10.0] 

N=6 

6.0 

[5.0,7.0] 

N=5 

30.0 

---- 

N=1 

No organizational 

change

5.0 

[3.6,15.7] 

N=11 

4.5 

[3.4,5.6] 

N=9 

20.2 

[16.4,23.9] 

N=2 

21.0 

[11.3,25.3] 

N=11 

15.4 

[11.0,21.8] 

N=9 

34.3 

[26.9,41.7] 

N=2 

Disease/case 

management 

4.9 

---- 

N=1 

4.9 

---- 

N=1 N=0 

4.9 

---- 

N=1 

4.9 

---- 

N=1 N=0 

No disease/case 

management 

4.8 

[3.7,15.4] 

N=16 

4.5 

[3.4,5.6] 

N=13 

18.0 

[17.2,21.0] 

N=3 

13.5 

[9.6,24.5] 

N=16 

11.0 

[7.0,21.0] 

N=13 

30.0 

[28.5,35.9] 

N=3 

No disease/case 

management 

(some other 

organizational

change present) 

4.5 

[4.0,6.0] 

N=5 

4.3 

[3.3,4.9] 

N=4 

18.0 

---- 

N=1 

7.0 

[6.0,11.0] 

N=5 

6.5 

[5.8,8.0] 

N=4 

30.0 

---- 

N=1 

*

No study involved an intervention with more than 5 QI types.

221
Table H3a. Median and maximum improvements in provider adherence associated with specific 

substrategies of organizational change stratified by study design (continued) 

Team/staffing 

changes 

12.0 

[6.0,18.0] 

N=2 

6.0 

---- 

N=1 

18.0 

---- 

N=1 

18.0 

[6.0,30.0] 

N=2 

6.0 

---- 

N=1 

30.0 

---- 

N=1 

No team/staffing 

changes 

4.5 

[3.6,10.3] 

N=15 

4.5 

[3.4,5.0] 

N=13 

20.2 

[16.4,23.9] 

N=2 

11.6 

[8.8,22.8] 

N=15 

11.0 

[7.0,21.0] 

N=13 

34.3 

[26.9,41.7] 

N=2 

No team/staffing 

changes 

 (some other 

organizational

change present) 

4.3 

[3.3,4.6] 

N=4 

4.3 

[3.3,4.6] 

N=4 N=0 

6.0 

[5.0,8.0] 

N=4 

6.0 

[5.0,8.0] 

N=4 N=0 

Medical record

changes 

1.0 

---- 

N=1 

1.0 

---- 

N=1 N=0 

11.0 

---- 

N=1 

11.0 

---- 

N=1 N=0 

No medical record 

changes 

4.9 

[4.0,15.4] 

N=16 

4.5 

[3.8,5.6] 

N=13 

18.0 

[17.2,21.0] 

N=3 

13.5 

[16.8,24.5] 

N=16 

11.0 

[6.0,21.0] 

N=13 

30.0 

[28.5,35.9] 

N=3 

No medical record 

changes 

(some other 

organizational

change present) 

4.9 

[4.5,6.0] 

N=5 

4.7 

[4.4,5.1] 

N=4 

18.0 

---- 

N=1 

6.0 

[5.0,7.0] 

N=5 

5.5 

[5.0,6.3] 

N=4 

30.0 

---- 

N=1 

222
Table H3b. Median and maximum improvements in provider adherence associated with various roles for 

clinical information systems 

Median Reduction in HbA1c

[inter-quartile range *] 

N=Number of trials 

Maximum Improvement in provider 

adherence 

[inter-quartile range] 

N=Number of trials 

Type of clinical 

information system 

(CIS) 

All Trials RCT Non-RCT All Trials RCT Non-RCT 

All Trials 

0.48 

[0.20, 1.38] 

N=38 

0.39 

[0.1,0.73] 

N=28 

1.4 

[1.1,1.78] 

N=10 

11.6 

[7.0,23.7] 

N=17 

11.0 

[6.3,19.6] 

N=14 

30.0 

[28.5,35.9] 

N=3 

Any CIS 0.9 

[0.3,1.42] 

N=20 

0.4 

[0.1,0.8] 

N=12 

1.4 

[1.33,1.92] 

N=8 

7.0 

[6.0,10.5] 

N=5 

7.0 

[6.0,10.5] 

N=5 

---- 

N=0 

No CIS 

0.35 

[0.2,0.59] 

N=18 

0.3 

[0.15,0.65] 

N=16 

0.53 

[0.5,0.56] 

N=2 

21.4 

[11.0,27.7] 

N=12 

11.6 

[11.0,21.8] 

N=9 

30.0 

[28.5,35.9] 

N=3 

Identification of

eligible participants 

0.35 

[0.1,0.96] 

N=6 

0.1 

[0.1,0.6] 

N=5 

1.4 

---- 

N=1 

6.0 

[5.5,6.5] 

N=3 

6.0 

[5.5,6.5] 

N=3 

---- 

N=0 

 No identification of

eligible participants 

0.48 

[0.21,1.4] 

N=32 

0.4 

[0.2,0.76] 

N=23 

1.4 

[0.56,1.9] 

N=9 

18.2 

[11.0,26.1] 

N=14 

11.6 

[10.8,21.4] 

N=11 

30.0 

[28.5,35.9] 

N=3 

No identification of 

eligible participants 

(some other role for 

CIS present ) 

1.25 

[0.401.49] 

N=14 

0.41 

[0.39,1.09] 

N=7 

1.40 

[1.25,1.95] 

N=7 

13.0 

[10.5,15.4] 

N=2 

13.0 

[10.5,15.4] 

N=2 

---- 

N=0 

* When N=2, square brackets show the actual results of each study rather than interpolated inter-quartile range. 

223
Table H3b. Median and maximum improvements in provider adherence associated with various roles for 

clinical information systems (continued)

Reminder system 0.71 

[0.39,1.69] 

N=11 

0.4 

[0.24,0.56] 

N=7 

1.95 

[1.7,2.19] 

N=4 

10.5 

[8.8,13.0] 

N=3 

10.5 

[8.8,13.0] 

N=3 

---- 

N=0 

No reminder system 0.47 

[0.15,1.19] 

N=27 

0.3 

[0.1,0.8] 

N=21 

0.98 

[0.52,1.4] 

N=6 

16.3 

[7.3,26.1] 

N=14 

11.0 

[5.5,21.4] 

N=11 

30.0 

[28.5,35.9] 

N=3 

No reminder system

(some other role for 

CIS present ) 

1.08 

[0.10,1.40] 

N=9 

0.60 

[0.10,1.08] 

N=5 

1.40 

[1.08,1.40] 

N=4 

5.5 

[5.0,6.0] 

N=2 

5.5 

[5.0,6.0] 

N=2 

---- 

N=0 

Computerized

decision support 

system (CDSS) 

1.1 

[0.37,1.99] 

N=5 

0.24 

[0.1,0.37] 

N=2 

1.99 

[1.55,2.4] 

N=3 

13.0 

[10.5,15.4] 

N=2 

13.0 

[10.5,15.4] 

N=2 

---- 

N=0 

No CDSS 0.47 

[0.2,1.30] 

N=33 

0.4 

[0.12,0.78] 

N=26 

1.4 

[0.53,1.4] 

N=7 

11.6 

[6.5,25.3] 

N=15 

11.0 

[5.8,21.2] 

N=12 

30.0 

[28.5,35.9] 

N=3 

No CDSS 

(some other role for 

CIS present ) 

0.71 

[0.25,1.40] 

N=15 

0.51 

[0.17,0.99] 

N=10 

1.40 

[1.40,1.40] 

N=5 

6.0 

[5.5,6.5] 

N=3 

6.0 

[5.5,6.5] 

N=3 

---- 

N=0 

Audit system 1.4 

[0.75,1.45] 

N=3 

0.8 

[0.1,1.5] 

N=2 

1.4 

---- 

N=1 

15.4 

---- 

N=1 

15.4 

---- 

N=1 

---- 

N=0 

No Audit system 0.47 

[0.2,1.2] 

N=35 

0.39 

[0.12,0.68] 

N=26 

1.4 

[0.56,1.9] 

N=9 

11.3 

[6.8,24.5] 

N=16 

11.0 

[6.0,21.0] 

N=13 

30.0 

[28.5,35.9] 

N=3 

224
Table H3b. Median and maximum improvements in provider adherence associated with various roles for 

clinical information systems (continued)

No Audit system 

(some other role for 

CIS present ) 

0.71 

[0.37,1.40] 

N=17 

0.40 

[0.17,0.68] 

N=10 

1.40 

[1.25,1.95] 

N=7 

6.5 

[5.8,7.9] 

N=4 

6.5 

[5.8,7.9] 

N=4 

---- 

N=0 

225
Table H4a. Association between improvement in glycemic control and number of quality improvement 

strategies stratified by study sample size, but with number of QI strategies including important 

substrategies

Median Reduction in HbA1c

[inter-quartile range] 

N=Number of trials 

Number of QI 

Strategies * 

All Trials Trials with

sample size in

lowest quartile 

Trials with

sample size in

lower 2 

quartiles 

Trials with

sample size in

upper 2 

quartiles

Trials with

sample size in

highest quartile 

Any number of 

strategies 

0.48 

[0.20, 1.38] 

N=38 

1.35 

[0.60,1.48] 

N=10 

0.80 

[0.41, 1.44] 

N=19 

0.21 

[0.10, 0.60] 

N=19 

0.10 

[0.10, 0.33] 

N=10 

1 strategy only 0.00 

[0.0,0.10] 

N=5 

-0.20 ^

- - - - 

N=1 

0.15 

[0.10,0.20] 

N=2 †

0.00 

[0.00,0.11] 

N=3 

0.00 

[0.00,0.00] 

N=2 

≥ 2 strategies 0.60 

 [0.30, 1.40] 

N= 33 

1.40 

[0.71,1.50] 

N=9 

1.08 

[0.47, 1.47] 

N=17 

0.34 

[0.10, 0.73] 

N=16 

0.15 

[0.10, 0.40] 

N=8 

≥ 3 strategies 0.66 

[0.33, 1. 40] 

N=22 

1.35 

[1.15, 1.40] 

N=4 

1.19 

[0.53, 1.40] 

N=10 

0.55 

[0.20, 1.18] 

N=12 

0.35 

[0.17, 0.53] 

N=4 

≥ 4 strategies 0.6 

[0.41,1.1] 

N=13 

0.71 

- - - - 

N=1 

0.59 

[0.43,0.99] 

N=6 

0.6 

[0.4, 1.5] 

N=7 

0.50 

[0.35,0.55] 

N=3 

≥ 5 strategies 0.71 

[0.53,1.29] 

N=7 

0.71 

- - - - 

N=1 

0.71 

[0.59,1.09] 

N=3 

0.85 

[0.50,1.53] 

N=4 

0.40 

[0.20,0.60] 

N=2 

6 strategies* 1.09 

[0.71,1.47] 

N=2 

0.71 

- - -- 

N=1 

1.09 

[0.71,1.47] 

N=2 N=0 N=0 

* Under this alternate classification of the QI strategies, Five studies were still single-faceted, 1-5 but the median number of 

strategies increased from two to three and the maximum number of strategies increased from five to six (seen in two 

comparisons 6, 7). 

^ All changes were standardized to reflect reductions. Thus, the negative sign here indicates an increase in serum HbA1c. 

†

 When N=2, the numbers in square brackets reflect the results for each of the two studies rather than the inter-quartile 

range. 

226
Table H4b. Association between improvement in provider adherence and number of quality improvement 

strategies stratified by study sample size, but with number of QI strategies including important subtypes*

Median Improvement in Provider Adherence (%) 

[inter-quartile range] 

N=Number of trials 

Number of QI 

Strategies 

All Trials Trials with

sample size in

lowest quartile 

Trials with

sample size in

lower 2 quartiles 

Trials with

sample size in

upper 2 

quartiles

Trials with

sample size in

highest quartile 

Any number (for 

comparison

purposes) 

4.9 

[3.8, 15.0] 

N=17 

5.2 

[4.4, 8.8] 

N=4 

5.2 

[4.1, 15.8] 

N=8 

4.5 

[3.8, 6.0] 

N=9 

4.5 

[4.0, 5.0] 

N=5 

1 strategy only 3.0 

[3.0,4.0] 

N=2 ^

3.0 

---- 

N=1 

3.0 

---- 

N=1 

4.0 

---- 

N=1 

4.0 

---- 

N=1

≥ 2 strategies 5.0 

[4.2,15.7] 

N=15 

5.6 

[5.2,12.0] 

N=3 

5.6 

[4.7,16.7] 

N=7 

4.8 

[3.7,8.6] 

N=8 

4.8 

[4.2,8.3] 

N=4 

≥ 3 strategies 5.6 

[4.7,16.5] 

N=11 

5.6 

[5.2,12.0] 

N=3 

5.6 

[4.7,16.7] 

N=7 

5.5 

[4.0,9.0] 

N=4 

11.5 

[5.0,18.0] 

N=2 

≥ 4 strategies 4.5 

[2.8,11.3] 

N=3 

---- 

N=0 

4.5 

---- 

N=1 

9.5 

[1.0,18.0] 

N=2 

18.0 

---- 

N=1 

≥ 5 strategies 1.0 

---- 

N=1 

---- 

N=0 

---- 

N=0 

1.0 

---- 

N=1 

---- 

N=0 

6 strategies ---- 

N=0 

---- 

N=0 

---- 

N=0 

---- 

N=0 

---- 

N=0 

* Analogous to Table 5b, but with major substrategies within provider education and organizational change counted as 

separate strategies. Specifically, the broad category of provider education has been replaced by three categories

(workshops or meetings, distribution of educational materials, and educational outreach) and organizational change has 

been replaced by three strategies (disease or case management, changes to team structure or personnel, changes to

medical records system, and “other organizational change”). 

^ When N=2, the numbers in square brackets reflect the results for each of the two studies rather than the inter-quartile 

range.

227
Table H4c. Associations between number of QI strategies and improvements in glycemic control and 

provider adherence stratified by trial design, but with number of QI strategies including important subtypes

Median Reduction in HbA1c

[inter-quartile range*

] 

N=Number of trials 

Median Improvement in provider adherence 

[inter-quartile range*] 

N=Number of trials 

All Trials RCT Non-RCT† All Trials RCT Non-RCT† 

Any number 

(for 

comparison

purposes) 

0.48 

[0.20, 1.38] 

N=38 

0.39 

[0.10, 0.73] 

N=28 

1.4 

[0.70, 1.78] 

N=10 

4.9 

[3.8, 15.0] 

N=17 

4.5 

[3.5, 5.4] 

N=14 

18.0 

[17.2, 21.0] 

N=3 

1 strategy

only

0.00 

[0.0,-0.1] 

N=5 

0.00 

[0.0,-0.1] 

N=5 

---- 

N=0 

3.0 

[3.0,4.0] 

N=2 

3.0 

[3.0,4.0] 

N=2 

---- 

N=0 

≥ 2 strategies 0.60 

 [0.30, 1.40] 

N= 33 

0.41 

[0.25,0.94] 

N=23 

1.4 

[0.7,1.78] 

N=10 

5.0 

[4.2,15.7] 

N=15 

4.7 

[3.7,5.7] 

N=12 

18.0 

[17.2,21.0] 

N=3 

≥ 3 strategies 0.66 

[0.33, 1. 40] 

N=22 

0.44 

[0.22,0.68] 

N=14 

1.4 

[1.33,1.92] 

N=8 

5.6 

[4.7,16.5] 

N=11 

5.0 

[4.5,6.0] 

N=9 

21.0 

[18.0,23.9] 

N=2 

≥ 4 strategies 0.6 

[0.41,1.1] 

N=13 

0.47 

[0.3,0.71] 

N=9 

1.5 

[0.95,2.13] 

N=4 

4.5 

[2.8,11.3] 

N=3 

2.8 

[1.0,4.5] 

N=2 

18.0 

---- 

N=1 

≥ 5 strategies 0.71 

[0.53,1.29] 

N=7 

0.6 

[0.47,0.71] 

N=5 

1.95 

[1.1,2.8] 

N=2 

1.0 

---- 

N=1 

1.0 

---- 

N=1 

---- 

N=0 

6 strategies 1.09 

[0.71,1.47] 

N=2 

1.09 

[0.71,1.47] 

N=2 

---- 

N=0 

---- 

N=0 

---- 

N=0 

---- 

N=0 

228
Appendix H References

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229