Evidence-based Synthesis Program Department of Veterans Affairs

Health Services Research & Development Service

July 2012

Prepared for:

Department of Veterans Affairs

Veterans Health Administration

Quality Enhancement Research Initiative

Health Services Research & Development Service

Washington, DC 20420

Prepared by:

Evidence-based Synthesis Program (ESP) Center 

Durham Veterans Affairs Healthcare System

Durham, NC

John W Williams Jr., M.D., M.H.Sc., Director

Shared Medical Appointments for 

Chronic Medical Conditions: 

A Systematic Review

Investigators:

Principal Investigator:

David Edelman, M.D.

Co-Investigators:

Jennifer R. McDuffie, Ph.D.

Eugene Oddone, M.D., M.H.Sc.

Jennifer M. Gierisch, Ph.D., M.P.H.

John W. Williams Jr., M.D., M.H.Sc.

Research Associate:

Avishek Nagi, M.S.

Medical Editor:

Liz Wing, M.A.
i

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

PREFACE

Quality Enhancement Research Initiative’s (QUERI’s) Evidence-based Synthesis Program 

(ESP) was established to provide timely and accurate syntheses of targeted healthcare topics 

of particular importance to Veterans Affairs (VA) managers and policymakers, as they work to 

improve the health and healthcare of Veterans. The ESP disseminates these reports throughout 

VA.

QUERI provides funding for four ESP Centers and each Center has an active VA affiliation. The 

ESP Centers generate evidence syntheses on important clinical practice topics, and these reports 

help:

• develop clinical policies informed by evidence,

• guide the implementation of effective services to improve patient outcomes 

and to support VA clinical practice guidelines and performance measures, 

and 

• set the direction for future research to address gaps in clinical knowledge.

In 2009, the ESP Coordinating Center was created to expand the capacity of QUERI Central 

Office and the four ESP sites by developing and maintaining program processes. In addition, 

the Center established a Steering Committee comprised of QUERI field-based investigators, 

VA Patient Care Services, Office of Quality and Performance, and Veterans Integrated Service 

Networks (VISN) Clinical Management Officers. The Steering Committee provides program 

oversight, guides strategic planning, coordinates dissemination activities, and develops 

collaborations with VA leadership to identify new ESP topics of importance to Veterans and the 

VA healthcare system.

Comments on this evidence report are welcome and can be sent to Nicole Floyd, ESP 

Coordinating Center Program Manager, at nicole.floyd@va.gov.

Recommended citation: Edelman D, McDuffie JR, Oddone E, Gierisch JM, Nagi A, Williams 

JW Jr. Shared Medical Appointments for Chronic Medical Conditions: A Systematic Review. VAESP Project #09-010; 2012.

This report is based on research conducted by the Evidence-based Synthesis 

Program (ESP) Center located at the Durham VA Medical Center, Durham, NC, 

funded by the Department of Veterans Affairs, Veterans Health Administration, Office 

of Research and Development, Quality Enhancement Research Initiative. The findings 

and conclusions in this document are those of the author(s) who are responsible for 

its contents; the findings and conclusions do not necessarily represent the views of 

the Department of Veterans Affairs or the United States government. Therefore, no 

statement in this article should be construed as an official position of the Department 

of Veterans Affairs. No investigators have any affiliations or financial involvement (e.g., 

employment, consultancies, honoraria, stock ownership or options, expert testimony, 

grants or patents received or pending, or royalties) that con¬flict with material 

presented in the report.
ii

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

TABLE OF CONTENTS

EXECUTIVE SUMMARY

Background.................................................................................................................................................... 1

Methods......................................................................................................................................................... 2

Data Synthesis............................................................................................................................................... 2

Rating the Body of Evidence......................................................................................................................... 2

Peer Review................................................................................................................................................... 2

Results ........................................................................................................................................................... 3

Recommendations for Future Research......................................................................................................... 6

Conclusion..................................................................................................................................................... 7

Abbreviations Table....................................................................................................................................... 7

INTRODUCTION

Background.................................................................................................................................................... 8

Objective of this Report................................................................................................................................. 9

METHODS

Topic Development...................................................................................................................................... 10

Analytic Framework.................................................................................................................................... 10

Search Strategy............................................................................................................................................ 11

Study Selection............................................................................................................................................ 12

Data Abstraction .......................................................................................................................................... 13

Quality Assessment ..................................................................................................................................... 13

Data Synthesis............................................................................................................................................. 14

Rating the Body of Evidence....................................................................................................................... 15

Peer Review................................................................................................................................................. 15

RESULTS

Literature Flow ............................................................................................................................................ 16

Study Characteristics................................................................................................................................... 17

Key Question 1: For adults with chronic medical conditions, do shared medical appointments (SMAs) 

compared with usual care improve the following outcomes? ..................................................................... 19

Key Question 2: For adults with chronic medical conditions, do the effects of SMAs vary by patient 

characteristics? ............................................................................................................................................ 26

Key Question 3: Is the intensity of the intervention or the components used by SMAs associated with 

intervention effects? .................................................................................................................................... 26

SUMMARY AND DISCUSSION

Summary of Evidence by Key Question ..................................................................................................... 27

Clinical and Policy Implications ................................................................................................................. 29

Strengths and Limitations............................................................................................................................ 32

Recommendations for Future Research....................................................................................................... 32
iii

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

REFERENCES ................................................................................................................................................... 34

APPENDIX A. Search Strategies .................................................................................................37

APPENDIX B. Excluded Studies...................................................................................................38

APPENDIX C. Data Abstraction Elements ...............................................................................43

APPENDIX D. Criteria Used in Quality Assessment .................................................................... 44

APPENDIX E. Peer Review Comments................................................................................................ 47

APPENDIX F. Ongoing Clinical Trials............................................................................................. 56

APPENDIX G. SMA Study Characteristics...................................................................................... 57

APPENDIX H. SMA Intervention Characteristics ....................................................................... 60

APPENDIX I. Glossary............................................................................................................................. 64

FIGURES

Figure 1. Analytic framework for evaluating shared medical appointments......................................... 11

Figure 2. Literature flow diagram for randomized controlled trials and observational

studies on SMA....................................................................................................................... 16

Figure 3. Effects of shared medical appointments on hemoglobin A1c................................................. 21

Figure 4. Effects of shared medical appointments on total cholesterol ................................................. 22

Figure 5. Effects of shared medical appointments on LDL cholesterol................................................. 22

Figure 6. Effects of shared medical appointments on systolic blood pressure ...................................... 23

Figure 7. Effects of shared medical appointments on health-related quality of life .............................. 23

 TABLES

Table 1. Summary of inclusion and exclusion criteria ......................................................................... 12

Table 2. Overview of studies evaluating SMA..................................................................................... 17

Table 3. Characteristics of shared medical appointment interventions................................................ 18

Table 4. Study details for SMAs enrolling adults with diabetes .......................................................... 20

Table 5. Summary of the intervention effects and SOE for KQ 1........................................................ 28

Table 6. Implementation issues ............................................................................................................ 31

Table 7. Evidence gaps and future research ......................................................................................... 32
8

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

EVIDENCE REPORT

INTRODUCTION

Optimal care of chronic illness requires both high-quality care (i.e., excellent clinical outcomes) 

and ready access to care. However, health care systems struggle with providing access and 

quality simultaneously—and to achieve both these objectives while simultaneously maintaining 

staff job satisfaction is a formidable undertaking. Although Veterans Affairs (VA) has made 

large strides in delivering quality care over the past two decades, quality gaps remain, both 

gaps in technical quality (e.g., still only 70 to 75% of patients have appropriate blood pressure 

control1

) and gaps in timeliness of services. Group medical visits offer the promise of improving 

the effectiveness, timeliness, and efficiency of health care. Additionally, because clinicians may 

prefer to work in collaborative, multidisciplinary settings, group medical visits also have the 

potential to improve staff satisfaction.2-8

Group medical visits are defined as multiple patients seen together while in the same clinical 

setting. A subset of group clinics—referred to as shared medical appointments (SMAs)—is 

defined by groups of patients meeting over time for comprehensive care, usually involving a 

practitioner with prescribing privileges, for a defining chronic condition or health care state. 

SMAs often use educational and/or self-management enhancement strategies, paired with 

medication management, in an effort to achieve improved disease outcomes. 

SMAs have been scientifically studied in an array of primary care settings over the last 10 to 

15 years.3,5,8-23 However, there has been great variability among these studies. In particular, the 

settings of these studies have been heterogeneous; different chronic health care states have been 

assessed; and the impact on clinical, cost, and utilization outcomes has been variable. Most 

important, there has been significant variation in the SMA intervention itself—in particular, 

which types of clinical, educational, and self-efficacy approaches are included in the specific 

SMA under evaluation. This uncertainty regarding the optimal design and impact of SMAs led 

the VA to commission this evidence synthesis report.

BACKGROUND 

The SMA approach developed as a care redesign strategy over the last 15 years. SMAs are 

defined as providing group-based, longitudinal medical care for a number of patients who have 

a common characteristic such as type 2 diabetes.3,5,8,13-20,22,23 This commonality may be a disease 

(e.g., diabetes), a demographic (e.g., patients over 65 years of age), or some other health care–

related element (e.g., high utilization of services). The patient group may stay constant, in an 

attempt to provide group bonding, or patients may be allowed to attend sessions chosen from a 

schedule at their own convenience to promote attendance.

In general, SMAs will have more than one health care provider involved; often the care team 

will include a person trained or skilled in delivering patient education or facilitating patient 

interaction (nurse, psychologist) and a prescribing provider empowered to make and initiate a 

comprehensive care plan. Like patients, providers can either be constant with the same patients 

or vary over time. 
9

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

SMA sessions usually last from 60 to 120 minutes. Sessions usually have part of their time 

set aside for social integration, part set aside for interactive education, and part committed to 

changes in the care plan for the common condition. The education piece is designed to improve 

self-management skills; educators will often be formally trained in skills such as motivational 

interviewing to help patients enhance their self-management. Because they involve both selfmanagement improvement along with medication intensification, SMAs have the potential to 

coordinate these strategies to maximize the effects of each. 

SMAs have been touted as a way to improve key elements of health care, particularly access, 

outcomes, and cost. Improved access is thought to occur because in a drop-in group structure, 

patients get their chronic illness care when they want it and/or because a group visit is usually 

shorter than the amount of time it takes to see all patients in the group one-on-one, thereby 

improving the provider’s throughput and patients’ access to that provider. Improved outcomes 

are thought to occur because (1) the group provides enhanced self-management education due to 

more time spent in that education, the use of motivational interviewing by the trained facilitator, 

or the peer support of members of the group, (2) the group provides a focused environment for 

care of the common condition or unifying characteristic (e.g., older age) without the distractions 

of multiple other issues that come up in a brief primary care visit, and/or (3) the group provides 

access to medication changes performed by a provider with special expertise in the common 

condition or by a team of providers with synergistic knowledge, thus leading the group to 

function like a specialty referral. Costs are thought to be lowered because the aforementioned 

efficiency in throughput leads to lower total costs of care or access to a group keeps patients from 

using acute-care settings for management of chronic illness, saving the costs associated with 

unnecessary emergency department visits or hospitalizations.

Early studies of SMAs focused on common demographic characteristics (elderly, high 

utilization) rather than common illnesses.9,12 However, most recent studies have focused on a 

common chronic illness as the unifying theme for the SMA, with diabetes the most commonly 

studied. This may make clinical sense, given that patients with the same chronic illness require 

self-efficacy for the same self-management skills (e.g., patients with diabetes need to feel 

empowered to correctly monitor and record blood sugar readings). The disease focus may also be 

because of the ease of identifying disease-specific clinical outcomes for research studies.

OBJECTIVE OF THIS REPORT

Our objective in this evidence synthesis was to summarize the results of the diverse studies of 

SMAs in an effort to understand their impact on staff satisfaction, patient experience, and clinical 

outcomes along with effects on health care utilization. A second objective was to determine 

whether the impact of SMA visits varies by clinical condition or specific components of the 

intervention.
10

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

METHODS

TOPIC DEVELOPMENT

This review was commissioned by the VA Evidence-based Synthesis Program. The topic was 

nominated after a topic refinement process that included a preliminary review of published peerreviewed literature, consultation with internal partners and investigators, and consultation with 

key stakeholders. We further developed and refined the key questions based on a preliminary 

review of published peer-reviewed literature in consultation with VA experts.

The final key questions were:

Key Question 1. For adults with chronic medical conditions, do shared medical appointments 

(SMAs) compared with usual care improve the following:

•	 Patient and staff experience?

•	 Treatment adherence?

•	 Quality measures such as (a) process of care measures utilized by VA, National Quality 

Forum, or National Committee for Quality Assurance and (b) biophysical markers 

(laboratory or physiological markers of health status such as HbA1c and blood pressure)?

•	 Symptom severity and functional status?

•	 Utilization of medical resources or health care costs?

Key Question 2. For adults with chronic medical conditions, do the effects of SMAs vary by 

patient characteristics such as specific chronic medical conditions and severity of disease?

Key Question 3. Is the intensity of the intervention or the components used by SMAs associated 

with intervention effects?

ANALYTIC FRAMEWORK

We followed a standard protocol for all steps of this review; certain methods map to the PRISMA 

checklist.24 Our approach was guided by the analytic framework shown in Figure 1.
11

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Figure 1. Analytic framework for evaluating shared medical appointments

Figure 1. Analytic framework for evaluating shared medical appointments

Abbreviations: CAD=coronary artery disease; CHF=congestive heart failure; COPD=chronic obstructive pulmonary disease; 

KQ=key question; QOL=quality of life; SMA=shared medical appointment

Abbreviations: CAD=coronary artery disease; CHF=congestive heart failure; COPD=chronic obstructive pulmonary disease; 

KQ=key question; QOL=quality of life; SMA=shared medical appointment

SEARCH STRATEGY

We searched MEDLINE® (via PubMed®), Embase®, CINAHL® , PsycINFO and Web of Science 

for peer-reviewed publications comparing shared medical appointments or group visits with usual 

care from January 1996 through September 2011. Our search strategy used the National Library 

of Medicine’s medical subject headings (MeSH) keyword nomenclature and text words for types 

of visits or clinic appointments, and validated search terms for both randomized controlled trials25

and relevant observational studies adapted from the Cochrane Effective Practice & Organization 

of Care Group search version 1.9. Our final search terms included terms for group visits together 

with terms for trials or relevant observational designs. We limited the search to articles published in 

the English language involving human subjects 18 years of age and older. The full search strategy 

is provided in Appendix A. An updated search for publications in PubMed was conducted in April 

2012. We developed our search strategy in consultation with an experienced search librarian.

We supplemented the electronic searches with a manual search of citations from a set of key 

primary articles, three review articles and one systematic review.3,4,7,10,11,15,18,19,22,26-28 The reference 

list for identified pivotal articles was manually hand-searched and cross-referenced against our 

library in order to retrieve additional manuscripts. All citations were imported into two electronic 

databases: EndNote® Version X5 (Thomson Reuters; Philadelphia, PA) for referencing and 

DistillerSR (Evidence Partners; Manotick, ON, Canada) for data abstraction. As a mechanism 

to assess the risk of publication bias, we searched www.clinicaltrials.gov for completed but 

unpublished studies in March 2012. 
12

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

STUDY SELECTION

Using prespecified inclusion and exclusion criteria, two reviewers assessed titles and abstracts 

for relevance to the Key Questions (KQs). Full-text articles identified by either reviewer as 

potentially relevant were retrieved for further review. Each article retrieved was examined by 

two reviewers against the eligibility criteria (Table 1). Disagreements on inclusion, exclusion, or 

major reason for exclusion were resolved by discussion or by a third reviewer. 

The criteria to screen articles for inclusion or exclusion at both the title-and-abstract and fulltext screening stages are detailed in Table 1. We modified these criteria to include observational 

studies designs recommended by the Cochrane Effective Practice and Organization of Care 

Review Group (i.e., controlled before and after, nonrandomized cluster controlled trials, 

interrupted time-series). Studies excluded at the full-text review stage are listed with the reasons 

for exclusion in Appendix B.

Table 1. Summary of inclusion and exclusion criteria

Study characteristic Inclusion criteria Exclusion criteria

Population Adults (≥18 years) of age with asthma, 

coronary artery disease, congestive heart 

failure, chronic obstructive pulmonary 

disease, diabetes mellitus, hyperlipidemia, 

hypertension, or combinations of these 

chronic medical conditions

Populations selected for individuals with 

substance abuse disorders 

Intervention “Exposure” must meet all the following 

criteria: 

•	 A series of medical visits (at least 

two) where at least one health care 

professional (including a prescribing 

cliniciana

) cares for a groups of patients

•	 The medical provider addresses 

each patient’s unique medical needs 

individually, with the potential to make 

changes in medications, but in the context 

of the group setting

Study excluded if exposure meets any of the 

following criteria:

•	 Not a group visit

•	 No prescribing clinician present at the 

group visit meeting

•	 No plan for adjusting medications when 

indicated

Comparator Usual care or other quality improvement 

strategy

None; study must have a control condition

Outcome •	 Patient and/or staff experience

•	 Treatment adherence (attendance, 

medications, self-management)

•	 Biophysical markers (e.g., HbA1c, blood 

pressure)

•	 Symptom severity

•	 Functional status

•	 Utilization of medical resources

None

Timing Outcomes reported at least 3 months from 

randomization and initiation of intervention

Outcomes reported less than 3 months from 

randomization and initiation of intervention

Setting •	 Outpatient settings; specifically, primary 

care or specialty clinic/practice

•	 Conducted in North America, Western 

Europe, Australia/New Zealand

•	 Conducted in an inpatient or nonmedical 

community setting (i.e., senior centers, 

etc.)

•	 Conducted in countries other than those 

specifically listed as included
13

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Study characteristic Inclusion criteria Exclusion criteria

Study designb •	 Patient or cluster RCTs

•	 Nonrandomized cluster controlled trials

•	 Controlled before-and-after studies

•	 Interrupted time series designs

Cross-sectional studies and other 

observational study designs not specifically 

listed as “included” study designs

Publications •	 English-language only

•	 Published from 1996 to present

•	 Peer-reviewed article

•	 Non-English language publication

•	 Published before 1996c

a

A prescribing clinician may be a medical doctor, doctor of osteopathy, advanced practice registered nurse (APRN), physician 

assistant, or doctor of pharmacy.

b

Study designs recommended by the Cochrane Effective Practice and Organization of Care Group.

c

Shared medical appointments were introduced by Beck et al. with their seminal article in 1997.

Abbreviations: HbA1c=glycosylated hemoglobin; KQ=key question; RCT=randomized controlled trial

DATA ABSTRACTION 

Before general use, the abstraction form templates designed specifically for this report were 

pilot-tested on a sample of included articles and revised to ensure that all relevant data elements 

(Appendix C) were captured and that there was consistency and reproducibility between 

abstractors. We designed the data abstraction forms for this project to collect the data required 

to evaluate the specified eligibility criteria for inclusion in this review, as well as population 

characteristics and other data needed for determining outcomes (e.g., biophysical markers, 

resource utilization) and risk of bias. We paid particular attention to describing the details of 

the intervention, including the clinical team (clinical disciplines represented, team size and 

continuity), characteristics of the patient group (group size, group continuity, inclusion of family 

members or peer supports), and group visit processes (individual breakouts, medication changes, 

visit duration, telephone contacts). In addition, we examined the included articles for subgroup 

analyses of relevance to our key questions.

One investigator abstracted the data, and the second reviewed the completed abstraction form 

alongside the original article to check for accuracy and completeness. Disagreements were 

resolved by consensus or by obtaining a third investigator’s opinion if consensus could not be 

reached by the first two. We supplemented abstraction of published data by contacting authors 

for missing information. We contacted 11 of 19 authors; of these, 7 replied with the requested 

information.

QUALITY ASSESSMENT

We also abstracted data necessary for assessing quality and applicability, as described in the 

Agency for Healthcare Research and Quality’s (AHRQ’s) Methods Guide for Effectiveness 

and Comparative Effectiveness Reviews.

29 For RCTs, these key quality criteria consisted of 

(1) adequacy of randomization and allocation concealment, (2) comparability of groups at 

baseline, (3) blinding, (4) completeness of followup and differential loss to followup, (5) whether 

incomplete data were addressed appropriately, (6) validity of outcome measures, and (7) conflicts 

of interest (Appendix D). Using these quality criteria, we assigned a summary quality score 

(good, fair, poor) to individual RCTs studies as defined in the Methods Guide. 
14

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Threats to internal validity of systematic review conclusions based on observational studies 

were identified through assessment of the body of observational literature as a whole, with 

an examination of characteristics of individual studies.29,30 Study-specific issues that were 

considered include (1) potential selection bias (i.e., degree of similarity between intervention 

and control patients), (2) performance bias (i.e., differences in care provided to intervention 

and control patients not related to the study intervention), (3) attribution and detection bias (i.e., 

whether outcomes were differentially detected between intervention and control groups), and (4) 

magnitude of reported intervention effects (see the section on “Selecting Observational Studies 

for Comparing Medical Interventions” in the Methods Guide).29 For each study, one investigator 

assigned a summary quality rating for “hard outcomes” (e.g., laboratory measures) and a separate 

rating for “soft” outcomes (e.g., staff experience), which were then reviewed by a second 

investigator; disagreements were resolved by consensus or by a third investigator if agreement 

could not be reached.

DATA SYNTHESIS

We critically analyzed studies to compare their characteristics, methods, and findings. We 

then determined the feasibility of completing a quantitative synthesis (i.e., meta-analysis) 

by exploring the volume of relevant literature, the completeness of the results reporting and 

the conceptual homogeneity of the studies. Because the elderly and individuals with diabetes 

mellitus are high utilizers of the health care system and are distinct groups of clinical patients 

with distinct primary endpoints, we examined the groups of studies as they pertained to these 

target conditions separately. 

When a meta-analysis was appropriate, we used random-effects models to synthesize the 

available evidence quantitatively. For other outcomes we analyzed the results qualitatively. The 

outcomes amenable to meta-analysis were continuous; we therefore summarized these outcomes 

by a weighted difference of the means when the same scale (e.g., blood pressure) was used and a 

standardized mean difference when the scales (e.g., health-related quality of life) differed across 

studies. We present summary estimates (standardized so that a negative value favors SMA) and 

95 percent confidence intervals (95% CIs). Heterogeneity was examined among the studies 

using graphical displays and test statistics (Cochran’s Q and I2); the I

2 describes the percentage 

of total variation across studies due to heterogeneity rather than to chance.31 Heterogeneity 

was categorized as low, moderate, or high based on I

2

 values of 25 percent, 50 percent, and 75 

percent respectively. We explored heterogeneity in study effects by using subgroup analyses 

for categorical variables (e.g., study quality) and meta-regression analyses for continuous or 

discrete variables (e.g., baseline HbA1c, intervention robustness). We constructed a “robustness 

score” that could range from 0 to 9, based on 7 intervention elements that were chosen a priori: 

theoretical framework guiding the intervention, individual breakouts, continuity between patients 

and clinical team, scheduled visits above the median, and medication changes. The latter two 

characteristics were scored 0 (absent) or 2 (present); all other items were scored as 0 or 1. We 

conducted a sensitivity analyses by using only studies whose populations had type 2 diabetes. 

Our subgroup and meta-regression analyses should be considered hypothesis-generating because 

they consist of indirect comparisons (across studies that may differ in ways other than the target 

condition) and thus are subject to confounding.
15

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

All basic analyses were conducted using Review Manager (RevMan) 5.1.4. (Copenhagen: The 

Nordic Cochrane Centre, The Cochrane Collaboration, 2011). Meta-regression analyses were 

conducted using Comprehensive Meta-Analysis software (Version 2; Biostat, Englewood, NJ).

RATING THE BODY OF EVIDENCE

In addition to rating the quality of individual studies, we evaluated the overall quality of the 

evidence for each KQ as described in the Methods Guide.

29 In brief, this approach requires 

assessment of four domains: risk of bias, consistency, directness, and precision. Additional 

domains considered were strength of association (magnitude of effect) and publication bias. 

For risk of bias, we considered basic (e.g., RCT) and detailed study design (e.g., adequate 

randomization). We used results from meta-analyses when evaluating consistency (forest plots, 

tests for heterogeneity), precision (confidence intervals), strength of association (weighted 

mean difference), and publication bias (clinicaltrials.gov survey). Optimal information size and 

consideration of whether the confidence interval crossed the clinical-decision threshold using a 

treatment model were also used when evaluating precision.32 These domains were considered 

qualitatively, and a summary rating of high, moderate, low, or insufficient strength of evidence 

was assigned after discussion by two investigators. This four-level rating scale consists of the 

following definitions:

•	 High—Further research is very unlikely to change our confidence on the estimate of 

effect.

•	 Moderate—Further research is likely to have an important impact on our confidence in 

the estimate of effect and may change the estimate.

•	 Low—Further research is very likely to have an important impact on our confidence in 

the estimate of effect and is likely to change the estimate.

•	 Insufficient—Evidence on an outcome is absent or too weak, sparse, or inconsistent to 

estimate an effect.

When a rating of high, moderate, or low was not possible or was imprudent to make, a grade 

of insufficient was assigned.33 We also considered the risk of publication bias. Publication bias 

was addressed through a careful search of www.clinicaltrials.gov for identification of any study 

completed but unpublished or ongoing. For the single outcome with at least 10 studies, we used 

graphical (e.g., funnel plots) and test statistics (e.g., Beggs test) to detect publication bias; these 

methods do not perform well with fewer than 10 studies and thus were not performed for the 

other outcomes.34,35

PEER REVIEW

A draft version of the report was reviewed by technical experts and clinical leadership. A 

transcript of their comments can be found in Appendix E, which elucidates how each comment 

was considered in the final report.
16

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

RESULTS

LITERATURE FLOW

The flow of articles through the literature search and screening process is illustrated in Figure 2. 

We identified 1101 unique citations from a combined search of MEDLINE (via PubMed, n=323), 

CINAHL (n=290), Embase (n=145), PsycINFO (n=157) and the Web of Science (n=186). 

Manual searching of included study bibliographies and review articles identified 2 additional 

citations for a total of 1104 citations. After applying inclusion/exclusion criteria at the title-andabstract level, 95 full-text articles were retrieved and screened. Of these, 70 were excluded at the 

full-text screening stage, leaving 25 articles (representing 19 unique studies) for data abstraction. 

All studies compared shared medical appointments with usual care or enhanced usual care; there 

were no direct comparisons between types of quality-improvement strategies. Our search of 

www.clinicaltrials.gov did not suggest publication bias. There were no completed studies that 

were unpublished. We found four ongoing studies (Appendix F), one of which had a methods 

paper. Interestingly, in light of the narrowness of the medical conditions in which SMA has been 

tested, one study is on patients with heart failure.

Figure 2. Literature flow diagram for randomized controlled trials and observational studies on SMA

*See Glossary for definition of companion articles.

Abbreviations: KQ=key question; SMA=shared medical appointment

Search results = 1104 references Excluded = 1009 references

Excluded at title/abstract level 

Retrieved for full-text review = 95 

references

Excluded = 71 references

Not full publication, peer-reviewed, or 

primary data = 34

Not study population of interest = 6

Not eligible study design = 17

Comparator not of interest = 1

Intervention doesn’t meet protocol 

 definition = 7

No outcome of interest = 6

Manual = 1

Post-search

Included 18 unique studies and 6 

companion articles*

KQ 1: 19 unique studies 

+ 1 companion article

KQ 2: 16 unique studies 

+ 4 companion articles

KQ 3: 13 unique studies 

+ 4 companion articles
17

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

STUDY CHARACTERISTICS

Of the 19 studies, 16 (13 trials) evaluated SMA interventions in patients with diabetes mellitus 

and 3 (2 trials) evaluated SMAs in older adults with high utilization of medical resources. Most 

studies were conducted in primary care settings that are part of integrated health systems in the 

United States (Table 2). Of the 19 studies, 15 reported outcomes at 1 year or later. Detailed study 

characteristics are given in Appendix G.

Table 2. Overview of studies evaluating SMA

Study Characteristic Adults With Diabetes Older Adults

N studies (participants) 16 (3221)a 3 (1851)

Mean age of sample: median (range) 60.8 (27 to 69.8) 74.1 (73.5 to 78.2)

Setting: N studies (participants)

 Primary care

 Medical Subspecialty

13 (2232)

3 (989)

3 (1851)

0

Health care system: N studies (participants)

Government (VA, FQC)

Private integrated system (HMO)

University-affiliated clinic

7 (771)

2 (892)

7 (1558)

0

3 (1851)

0

Country: N studies (participants)

 United States

Europe

13 (2232)

3 (989)

3 (1851)

0

Study design: N studies (participants)

 Randomized controlled trial

 Observational

13 (2921)

3 (300)

2 (615)

1 (1236)

Sites: N studies (participants)

Single

Multisite

14 (2106)

2 (1115)

1 (321)

2 (1530)

Study durationb

: N studies (participants)

 6 to 12 months

 >12 months

4 (410)

12 (2811)

0

3 (1851)

a

Participant number is based on the number randomized. 

b

Study duration is measured from time of randomization to most distal followup.

Abbreviations: FQC=Federally qualified center; VA=Veterans Administration; HMO=health maintenance organization

Characteristics of Shared Medical Appointments

In the studies we assessed, SMAs were led by teams of 1 to 3 clinicians that included a physician 

(n=15), clinical pharmacists (n=9; the prescribing clinician in 3 studies) and a registered nurse 

(Table 3). The clinical team was multidisciplinary in most studies; pharmacists and licensed mental 

health professionals participated in almost half the studies. Sessions were designed for closed 

panels of patients in all but three studies; these later studies used drop-in models. Group size was 

6 to 10 for most studies, with group size ranging between 10 and 20 in 4 studies and group size as 

large as 25 members in one study. The planned visit frequency ranged from approximately every 3 

weeks to every 3 months. SMA visits were a median of 2 hours (range 1 to 3.5 hours). 

At least 16 of 19 studies offered individual breakouts with a physician or clinical pharmacist as 

part of the SMA design specified that medication changes could be made at group visits. Three 

studies did not report this information. About half the studies invited participation by family 

members or friends. Three studies described the educational approach as “patient-centered adult 

learning,”20-22 and two studies used the stages-of-change model to design the intervention;8,26

no other study described a theoretical model. In about half the studies, patients participated in 
18

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

selecting or prioritizing educational topics, and printed materials were tailored to the individual 

patient. Few studies used telephone contact as a part of the SMA intervention. Details of the 

SMA interventions are given in Appendix H.

Table 3. Characteristics of shared medical appointment interventions

Characteristic Diabetes (16 Studies) Older Adults (3 Studies)

Intervention team disciplines: N studies (participants)

Medical doctor

Nurse practitioner

Pharmacist

Registered nurse

Dietician

Physical therapist/exercise specialist

Psychologist/Behavioral specialist

Health educator

Social worker

Othera

12 (2731)

3 (298)

8 (1609)

10 (2791)

4 (1208)

3 (269)

3 (326)

3 (1116)

2 (164)

6 (1238)

3 (1851)

1 (1236)

1 (294)

2 (615)

0

1 (294)

1 (321)

0

0

2 (1530)

Intervention team size

Median number of members (range) 2.5 (2 to 7) 2.5 (2 to 3)

Average visit duration 

Median minutes (range) 120 (60 to 210) 120 (90 to 120)

Number of planned visits

Median (range) 7.5 (4 to 36) 12 (12 to 24)

Medication changes made during sessions

Yes

Not reported/unclear

13 (2217)

3 (995)

3 (1851)

0

Individual breakouts: N studies (participants)

Yes

No

Not reported/unclear

12 (2850)

1 (88)

3 (273)

3 (1851)

0

0

Behavioral components

Licensed mental health professional led group 

education session

Yes

No

Not reported/unclear

Family/friend participation

Yes

No

Not reported/unclear

6 (1356)

9 (1149)

1 (707)

7 (1346)

4 (527)

5 (1169)

1 (321)

2 (1530)

0

2 (615)

0

1 (1236)

Patient-clinician group continuity: N studies 

(participants)

Group member continuityb

Closed

Open/drop-in

Team continuity

Consistent care team

Care team changes/rotates

Not reported/unclear

13 (2942)

3 (270)

12(2120)

3 (989)

1 (103)

2 (1557)

1 (294)

2 (1530)

1 (321)

0

Some intervention components delivered by telephone

Yes

No

Not reported/unclear

2 (424)

10 (2385)

4 (403)

1 (312)

1 (294)

1 (1236)

a

Disciplines that were present on only one team: occupational therapist, medical assistant, research assistant or undefined; there 

were no physician assistants used, although this would be a valid clinical discipline for teams.

b

Group membership was classified as closed when the same group of patients were scheduled for each SMA visit.
19

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Comparison Condition

In all studies, the comparison condition was some form of usual care. This care was 

inconsistently described. Three studies by one group20-22 and one other study18 used a structured 

or enhanced form of usual care. In one study,22 this care consisted of individual visits with 

a forced interval of 3 months; in another study,18 this was VA usual care supplemented with 

a single diabetes education session; and in the other two studies,20,21 usual primary care was 

enhanced by one-on-one education sessions with the group facilitator. Three studies conducted in 

the VA8,26,36 described usual care at some length, including average visit frequencies of 4 months, 

online clinical tools, electronic medical records with clinical reminders related to diabetes care, 

and a full range of referral services including diabetes education. Three other VA studies5,14,15

very briefly described usual care. The other nine studies did not describe usual care at all. 

KEY QUESTION 1: For adults with chronic medical conditions, do 

shared medical appointments (SMAs) compared with usual care 

improve the following:

•	Patient and staff experience?

•	Treatment adherence?

•	Quality measures such as (a) process of care measures utilized

by VA, National Quality Forum, or National Committee for

Quality Assurance and (b) biophysical markers (laboratory or

physiological markers of health status such as HbA1c and blood

pressure)?

•	Symptom severity and functional status?

•	Utilization of medical resources or health care costs?

Effects of Shared Medical Appointments on Clinical, Process, and Economic 

Outcomes

The outcomes reported varied widely across studies and between studies for adults with diabetes 

and older adults. We describe the results separately for these two populations.

Effect of SMAs on Outcomes for Adults With Diabetes 

Patient selection for SMA studies among patients with diabetes

Patient characteristics are reported in Table 4. Briefly, 10 of 15 studies required patients to be 

“out of control” with regard to their A1c; however, this inclusion floor varied from a low of 

6.5% to a high of 9.0%. Four studies required elevated blood pressure, and two required elevated 

lipids. Other criteria were used by no more than two studies (e.g., efforts to assure that diabetes 

was type 2, insulin-requiring, high utilization in past year).

We identified 13 randomized trials that evaluated the effects of SMAs on outcomes for 

patients with diabetes.3,8,14,15,17-22,26,36,37 Of these, ten enrolled only patients with type 2 

diabetes,3,8,14,15,17,18,20,22,26,36 two enrolled mixed samples,19,37 and one enrolled only patients 
20

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

with type 1 diabetes.21 Three observational studies evaluated SMAs.5,13,16 All but one of these 

16 studies compared SMAs with usual care. One study18 compared SMAs with a traditional, 

two-session, diabetes education intervention. Study quality was rated as good for 6 trials, fair 

for 6 trials and 2 observational studies, and poor for the two remaining studies. For trials, 

methodological problems included (1) failure to describe allocation concealment (n=9), (2) 

outcomes assessed without blinding to intervention (n=6), and (3) an inadequate approach to 

addressing incomplete data (n=6). Except for the study in patients with type 1 diabetes, patients 

were older adults with representative gender and racial mixes (Table 4).

Table 4. Study details for SMAs enrolling adults with diabetes

Characteristic Randomized Trials Observational studies

N studies (participants) 13 (2921)a 3 (300)

Median age of sample (range)b 60.8 (29 to 69.8) 59.4 (56.8 to 61.0)

Sex: N (%)

 Male

 Female

 Not reported (3 studies)

1585 (54.3%)

1137 (38.9%)

190 (6.8%)

93 (31.0%)

128 (42.7%)

79 (26.3%)

Race: N (%)c

 African American

 White

 Other

 Not reported

425 (16.4%)

952 (36.7%)

127 (4.9%)

1088 (42.0%)

–

–

–

300 (100%)

Study quality: N (%)

Good

Fair

Poor

6 (46%)

6 (46%)

1 (8%)

0

2 (67%)

1 (33%)

a

Participant number is based on the number included in description of population characteristics, which is a smaller sample than 

those randomized.

b

Mean age was not reported in one study.

c

Of studies reporting race, 329 participants were not accounted for; therefore, percentage is of n=2592.

Treatment Experience and Adherence Outcomes 

Only two trials21,37 described the effects on patient experience, and none reported effects on staff 

experience. Neither of those trials showed greater satisfaction among those in SMAs compared with 

usual care. One study reported no effects on medication adherence,3 another reported no effects 

on blood glucose self-monitoring,20 and two studies reported mixed effects on self-management 

behaviors.19,36 In both studies, patients in the SMA group increased the frequency of home glucose 

monitoring more than in the usual care group. Foot self-exams increased significantly in one study,36

and exercise time increased by a statistically nonsignificant degree compared with usual care. 

Effects on medication treatment were reported in 8 of 13 studies, but outcomes were reported 

inconsistently. One of four studies26 reported more medication starts or dose titrations for oral 

hypoglycemic medications, and one of two studies8

 reported more insulin starts and increased 

insulin doses for the SMA group. One of three studies26 found more antihypertensive medication 

starts or dose titrations overall in the SMA intervention group, and two studies8,15 found greater 

use of dose titrations for selected antihypertensive medications. Only one of five studies8

 found a 

statistically significant increase in lipid-lowering medications and this was only for niacin. Most 

of the positive intervention effects were in studies led by clinical pharmacists. Patient or staff 

experience was not reported in any of the observational studies.
21

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Biophysical Outcomes

Hemoglobin A1c. Figure 3 shows the forest plots for the random-effects meta-analyses of the 

effect of SMAs on glucose. All studies reported effects on average glucose (A1c) at the end of 

the intervention, assessed at 6 months to 4 years. SMAs were associated with lower A1c than 

usual care (mean difference=-0.55; 95% CI, -0.99 to -0.11). However, effects varied significantly 

across studies (Q=179.9, df=12, p < 0.001; I2

 =93%)—variability that was not explained by 

study quality. Because of the variability in effects between studies, we conducted analyses to 

evaluate this variability. First, we conducted a sensitivity analysis, excluding the study in patients 

with type 1 diabetes,21 but variability remained high (I2

=94%). Next, we used meta-regression 

analyses to evaluate the association between baseline A1c and intervention robustness with 

treatment effects. Neither baseline A1c nor intervention robustness (B=0.02 decrease in A1c per 

1 point increase in robustness; CI, -0.23 to 0.26) was associated with treatment effects (p=0.90). 

Thus, SMAs were associated with a mean decrease in A1c, but effects varied markedly and were 

not explained by factors we hypothesized a priori to be associated with variation in treatment 

effect. 

Effects of SMAs on glucose from the observational studies were generally consistent with the 

trial data. Two of the three observational studies5,13 found statistically significant reductions in 

A1c from baseline to followup among patients participating in SMAs. Only one study5

 compared 

this change with a control group, finding a statistically significant benefit from SMA participation 

(p=0.002).

Figure 3. Effects of shared medical appointments on hemoglobin A1c
22

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Cholesterol. Figures 4 and 5 show the forest plots for the random-effects analyses of the effect of 

SMAs on total cholesterol (5 studies) and LDL cholesterol (5 studies). For both outcomes, SMAs 

were associated with a statistically nonsignificant decrease in cholesterol. For each outcome, 

treatment effects varied significantly across studies. Because of the small number of studies, 

we did not complete meta-regression analyses to examine variability in treatment effects. One 

additional study17 reported a statistically nonsignificant increase in the proportion of patients 

achieving an LDL of less than 100—findings that are consistent with the analysis of mean 

change in LDL. Only two of the observational studies reported effects on cholesterol. Both found 

reductions in LDL cholesterol, but only one5

 compared the SMA with the control group, and the 

differences were not statistically significant.

Figure 4. Effects of shared medical appointments on total cholesterol

Figure 5. Effects of shared medical appointments on LDL cholesterol

Blood pressure. Figure 6 shows the forest plots for the random-effects analyses of the effect of 

SMAs on systolic blood pressure. Five studies reported effects on systolic blood pressure;3,8,22,26,36

four of these were conducted in VA. SMAs were associated with improved blood pressure control (MD, -5.22; 95% CI, -7.40 to -3.05). Results were consistent across studies (Q=1.82, df=4, 

p=0.77, I2=0%). Of the three observational studies, only one5 found a statistically significant pre–

post change in systolic blood pressure for the SMA participants. In this study, the blood pressure 

effects were also greater for the SMA group (-14.93 mmHg) than for the control group (-2.54 

mmHg, p=0.04). 
23

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Figure 6. Effects of shared medical appointments on systolic blood pressure

Health-Related Quality-of-Life Outcomes

Figure 7 shows the random-effects meta-analysis of the effect of SMAs on health-related quality 

of life (HRQOL). Six studies17,20-22,36,37 reported measuring HRQOL, but only five of these 

reported outcomes.20-22,36,37 The studies by Trento et al. measured HRQOL with the Diabetes 

Quality-of-Life Measure, Cohen et al. reported the mental and physical components of the SF36, and Wagner et al. reported the general health subscale of the SF-36. Because these measures 

differ, we analyzed the data using standardized mean difference. SMAs were associated with 

a large improvement in HRQOL (SMD -0.84; 95% CI, -1.64 to -0.03), but effects varied 

substantially across studies (Q=191.99, df=4, p<0.001; I2

=98%). There were too few studies to 

evaluate the variability in treatment effects quantitatively. However, the studies with the smallest 

effects36,37 used general rather than disease-specific measures.

Figure 7. Effects of shared medical appointments on health-related quality of life
24

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Economic Outcomes

Rates for hospital admissions and emergency department visits. The effect of SMAs on hospital 

admissions was reported in five studies.3,14,19,26,37 Four studies reported admission rates involving 

603 patients followed for 6 to 18 months. In three of these, admission rates were lower with 

SMAs, but the result was statistically significant in only one study.

19 The fifth study37 followed 

707 patients for 2 years and reported a statistically nonsignificant lower proportion of patients 

with a hospital admission who were randomized to SMAs (16.9% versus 21.0%, p=0.10).

Effects on emergency department visits were reported in the same five studies. Two studies 

reported significantly lower visit rates3

 or the proportion with an emergency department visit.37

Rates were not significantly different in the other three studies. Observational studies did not 

report comparative effects on admission rates or emergency department visits.

Costs. Four studies reported effects on total costs, one in a large HMO,37 two in a universityaffiliated general medical clinic serving low-income patients,14,15 and another in an Italian 

diabetes clinic.20 Findings were mixed. In the largest trial testing a low-intensity intervention,37

the total health care costs (excluding the clinical study personnel) did not differ significantly. The 

studies by Clancy et al.14,15 tested more robust interventions. The earlier study found significantly 

higher total costs (inpatient, outpatient, and emergency department costs) for SMAs compared 

with usual care ($2,886 versus $1,490 per patient over six months; p=0.0003). Total costs were 

heavily influence by higher inpatient costs for the SMA group. In the later study, 1-year charges 

were significantly lower for the SMA group ($5,869 versus $8,412 per patient, p<0.05). Lower 

modeled charges were driven primarily by lower outpatient charges, in particular for specialty 

visits. The study by Trento et al.,20 conducted in Italy, reports costs that may not be applicable to 

the U.S. health system. An evaluation that included staff costs, medications, and transportation 

costs for diabetes care showed a small increase for SMA patients ($597 versus $570 over 4 years, 

p=NR). Observational studies did not report comparative costs.

Effect of SMAs on Outcomes for Older Adults

Patient selection for SMA studies among older adults

Only three studies evaluated SMA interventions in older adults. Two of the four studies required 

a minimum age of 60; the other two used 65. All studies required some elevated use of health 

care in the past year; two operationalized that directly, while the third required a hospitalization 

in the past year. 

We identified two randomized trials9,11 that evaluated the effects of SMAs in 615 older adults 

with a recent hospitalization or other criteria for increased utilization. One observational study 

evaluated a similar population of 2251 older adults.10 All studies were conducted in primary 

care, in group-model HMO settings in the United States, and compared SMAs with usual care. 

The mean age of participants ranged from 73.5 to 78.2 years of age. The most common chronic 

conditions were arthritis, hypertension, difficulty hearing, heart disease, liver disease, and 

bladder/kidney disease. All studies reported effects on utilization or costs at 1 year or greater. 

One trial was rated fair quality11 and one poor quality;9 the observational study was rated fair 

quality.10 In the trial by Scott et al.,11 only participants expressing a strong interest in group 

care (37% of those eligible) were randomized. Methodological problems included failure to 
25

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

describe allocation concealment, outcomes assessed without blinding to intervention, and poor 

specification of outcome measures. Additional study details are in Appendix G. 

The design of SMA visits was similar to the diabetes studies, except that fewer disciplines 

participated in the clinical teams. Detailed intervention descriptions are in Appendix H.

Treatment Experience and Adherence Outcomes

All studies reported a measure of patient experience. The two trials reported patient perceptions 

of quality of care, and both reported higher quality ratings with SMAs compared with usual 

care. In the study by Beck et al.,9

 more patients rated the overall quality of care as excellent 

(37% versus 27%, p=0.019), and Scott et al.11 found that patients assigned to SMAs rated the 

quality of care 0.3 points higher on a 1-to-4 scale than usual care patients did (p=0.048). In the 

observational study, only SMA participants rated satisfaction, and 90 percent of participants 

reported satisfaction with four aspects of group visits, including the visit overall. In aggregate, 

these results support high levels of satisfaction with group visits among older adults. No study 

evaluated staff satisfaction using a validated measure, and no study reported comparative data 

on medication adherence. In the study by Levine et al.,10 90 percent of SMA providers agreed 

or strongly agreed that they felt a lot of satisfaction from group visits, and 50 percent endorsed 

that group visits enhance their practice. Beck et al.9 reported that participants attended 55 percent 

of scheduled SMA visits. Among participants with a high interest in group visits, Scott et al.11

reported 2 or fewer visits over 24 months by approximately 25 percent of patients.

Biophysical Outcomes

Biophysical outcomes were not reported, likely because patients were selected on the basis of 

age and health care utilization rather than a particular illness. 

Health-Related Quality-of-Life Outcomes

Both trials reported effects on overall health status (via the Likert scale) and functional status 

using activities of daily living or instrumental activities of daily living; there were no differences 

in outcomes for any of these measures. Scott et al.11 reported effects on HRQOL using a 10-point 

scale with 10 indicating the highest quality of life possible. Participants randomized to SMAs 

rated HRQOL higher at 24-month followup (mean score, SMA 7.2 [1.8] versus usual care 6.3 

[2.0]; p=0.002). The single observational study did not reported effects on HRQOL or functional 

status.

Economic Outcomes

Rates for hospital admissions and emergency department visits. All studies showed fewer 

admissions in the SMA group, but the difference was statistically significant in only one 

study (mean admissions/patient, 0.44 [0.89] versus 0.82 [1.7]; p=0.013).11 SMA visits were 

also associated with a statistically significant decrease in emergency department visits in both 

trials (mean difference in visit rates/year, 0.22 to 0.26); the observational study did not report 

emergency department visits. Other outpatient utilization was not significantly lower in the 

SMA groups. Primary care visits were not lower in any of the three studies, and only one of two 

studies9 found significantly lower specialty visits.
26

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Costs. The specific approach to cost analyses varied, but all studies included estimated costs of 

SMA visits. Total costs were lower for the SMA group in each study (range in mean difference in 

annual costs, -$178 to -$1599) but varied substantially across studies and did not reach statistical 

significance for any study. The two trials reported lower hospital costs, ranging from 

-$178/person per year (p=NR) to -$1145/person per year (p=0.07); the observational study did 

not report hospital costs. Other cost data were not reported consistently across studies.

KEY QUESTION 2: For adults with chronic medical conditions, do the 

effects of SMAs vary by patient characteristics (e.g., specific chronic

medical conditions and severity of disease)?

We planned to address this question using two approaches, beginning with comparing the 

effects of SMAs across conditions. However, studies did not examine subgroups within their 

populations, and there was too little variability in diagnosis across studies for analysis—all 

condition-specific studies enrolled patients with diabetes. The single study enrolling adults with 

type 1 diabetes found similar treatment effects compared with those enrolling adults with type 2 

diabetes. Second, we planned and conducted an evaluation of the association between treatment 

effects and baseline severity of disease. This analysis was possible only for the studies enrolling 

patients with diabetes. We used meta-regression analysis to examine the baseline association 

between A1c and treatment effects on glucose control. Baseline A1c was not associated with 

treatment effects (B=0.14 increase in A1c per 1 point increase in baseline A1c; 95% CI, -0.47 

to 0.75; p=0.66). However, this analysis is limited by the relatively small number of studies, 

indirect comparisons, and potential for ecological fallacy since only the average baseline A1c for 

the study sample was available. A more robust approach would be a meta-analysis at the patient 

level, where baseline A1c is evaluated for each patient; however, these data were not available. 

KEY QUESTION 3: Is the intensity of the intervention or the 

components used by SMAs associated with intervention effects?

Characteristics of the SMA interventions are summarized in Table 3 (KQ 1). Detailed 

descriptions for each study are given in Appendix H. As described in the Methods section, we 

developed a measure of intervention robustness based on seven intervention components. Two 

of the components (involving a behavioral health specialist or a medication change during SMA 

visits) were weighted double, and thus scores could range from zero to nine. For these analyses, 

we limited the sample to the trials in patients with diabetes and used A1c as the outcome, 

yielding a set of studies with similar characteristics except for the independent variable of 

interest (intervention robustness). We used meta-regression analyses to examine the relationship 

between robustness and intervention effects on A1c. For the 12 trials, robustness scores ranged 

from 3 to 8 (median=5). There was no association between intervention robustness score and 

treatment effects (B=0.02 decrease in A1c per 1-point increase in robustness score; 95% CI, 

-0.30 to 0.25; p=0.88).
27

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

SUMMARY AND DISCUSSION

SMAs have the potential to offer chronic disease care that is more efficient while improving staff 

satisfaction and patient outcomes. We identified 15 RCTs and 4 observational studies of varying 

quality comparing SMAs with usual care or enhanced usual care. Studies were conducted 

exclusively in patients with diabetes or in older adults with higher than average medical 

utilization. No eligible studies enrolled patients with the other chronic conditions of interest: 

coronary artery disease, chronic heart failure, asthma, chronic obstructive pulmonary disease, 

hyperlipidemia, or hypertension. This limited diversity in patient populations compromised 

our ability to determine if effects varied by condition. However, the included studies reported 

outcomes ranging from patient experience to biophysical and economic outcomes. These findings 

and the overall strength of evidence are summarized and discussed by key question.

SUMMARY OF EVIDENCE BY KEY QUESTION

Key Question 1

Few studies (0 to 3) reported effects on staff experience, patient experience, or treatment 

adherence. The strength of evidence for each of these outcomes was judged to be insufficient to 

estimate an effect of the SMA intervention in both patients with diabetes and older adults.

The most robust finding of this evidence synthesis is that SMAs for patients with diabetes 

appear to have a significant impact on biophysical outcomes. Hemoglobin A1c improved by 

approximately 0.6 percentage points, and systolic blood pressure by about 5 mmHg; both these 

findings were statistically significant. LDL-C improved by approximately 7 mg/dl, but this was 

not statistically significant. While each individual finding is only moderately robust given the 

limitations in study quality and unexplained variability in intervention effects, the constellation 

of findings taken together indicates that SMAs help intermediate clinical outcomes for type 2 

diabetes. Similar outcomes were not reported in older adults.

For patients with diabetes, there was significant improvement on HRQOL, measured in 3 of 

4 studies with a relatively sensitive, disease-specific, quality-of-life scale. Positive effects on 

HRQOL were found in one trial conducted in older adults, but functional status was not affected 

in these studies. Studies in older adults show a pattern of lower health care utilization, but the 

number of studies and participants are relatively few and these results should be considered 

preliminary. In patients with diabetes, lower hospitalization was the most consistent effect, but 

effects on other economic outcomes were too preliminary to estimate an effect. Our judgments 

about the strength of evidence (SOE) prioritized data from RCTs. 
28

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Table 5. Summary of the intervention effects and SOE for KQ 1

Population

Number of

Studiesa 

(Subjects)

Domains Pertaining to SOE SOE

Risk of

Bias: Study 

Design/ 

Quality

Consistency Directness Precision Effect Estimate 

(95% CI)

Staff 

experience Insufficient

Diabetes 0 NA NA NA NA Not estimable

Older adults 1 (1236) Obs/Fair NA Direct Imprecise Not estimable

Patient 

experience Insufficient

Diabetes 2 (769) RCT/Fair Consistent Direct Imprecise No effect

Older adults 2 (444) RCT/Fair Inconsistent Direct Imprecise Small to large positive 

effect

Treatment 

adherence Insufficient

Diabetes 3 (536) RCT/Fair Some 

inconsistency Direct Imprecise Not estimable

Older adults 0 NA NA NA NA Not estimable

Biophysical

Diabetes:

A1c 13 (2921) RCT/Good Inconsistent Direct Some imprecision

MD = -0.55 

(-0.99 to -0.11)

Moderate SOE

Diabetes: 

Total 

Cholesterol

LDL 

Cholesterol

5 (1556)

5 (997)

RCT/Fair

RCT/Fair

Inconsistent

Inconsistent

Direct

Direct

Imprecise

Imprecise

MD = -4.9 (-17.8 to 

7.9)

Low SOE

MD -6.6 (-16.1 to 2.8)

Low SOE

Diabetes: 

Blood 

pressure

5 (1125) RCT/Good Consistent Direct Some 

imprecision

MD = -5.2 

(-7.4 to -3.1)

Moderate SOE

Older adults 0 NA NA NA NA Not estimable

Healthrelated 

quality 

of life or 

functional 

status

Diabetes 5 (1561) RCT/Fair Inconsistent Direct Imprecise

SMD = -0.84 

(-1.6 to -0.03)

Low SOE

Older adults 2 (615) RCT/Fair Inconsistent Direct Imprecise Not estimable

Economic

Diabetes

5 (1339)

5 (1339)

4 (1125)

RCT/Good

RCT/Good

RCT/Fair

Inconsistent

Consistent

Inconsistent

Direct

Direct

Direct

Imprecise

Some 

imprecision

Imprecise

ED visits lower rates 

in 2 of 5 studies 

Insufficient SOE

Hospitalizations lower 

in 4 of 5 studies

Low SOE

Total costs range 

from lower to higher 

Insufficient SOE
29

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Population

Number of

Studiesa 

(Subjects)

Domains Pertaining to SOE SOE

Risk of

Bias: Study 

Design/ 

Quality

Consistency Directness Precision Effect Estimate 

(95% CI)

Older adults

2 (615)

2 (615)

2 (615)

RCT/Fair

RCT/Fair

RCT/Fair

Consistent

Some 

inconsistency

Inconsistent

Direct

Direct

Direct

Imprecise

Imprecise

Imprecise

ED visits lower rates 

in 2 of 2 studies

Low SOE

Hospitalizations lower 

in 1 of 2 studies

Insufficient SOE

Total costs lower 

but not statistically 

significant

Insufficient SOE

a

Studies(subjects) given are for randomized trials; observational studies were also considered in 

SOE ratings but are not listed separately in the table.

Abbreviations: CI=confidence interval; ED=emergency department; MD=mean difference; NA=not applicable; RCT=randomized 

controlled trial; RD=risk difference; RR=risk ratio; SMD=standardized mean difference; SOE=strength of evidence

Key Questions 2 and 3

No studies explored KQ 2 (identifying the subgroups of patients that would benefit most from 

an SMA intervention) or KQ 3 (identifying the specific components of an SMA intervention that 

were most potent). We devised a robustness score to attempt to address KQ 3, but it was not able 

to discriminate degrees of effectiveness among intervention components. More than 70 percent 

of all studies were similar on six of the seven variables used in the robustness score: (1) whether 

the team was continuous, (2) whether the group was closed, (3) whether individual breakout 

sessions were conducted, (4) whether medication changes were made, (5) how long each session 

was, and (6) whether there was contact outside the session. It is possible that there are other 

more important variables that are not being measured with current approaches. The strength of 

evidence for both questions was judged to be insufficient.

CLINICAL AND POLICY IMPLICATIONS

A key finding is that SMAs have been evaluated primarily in patients with diabetes, and to a lesser 

extent and with a narrow range of outcomes for older adults with high utilization. Even where the 

data are more robust in those with diabetes, it is challenging to place into context the improvements 

seen in biophysical parameters with SMAs. However, we can discuss the clinical importance of 

these findings in at least two ways. First, we can compare the results to clinical trial data relative to 

starting any agent for these conditions. The improvement seen in one year on systolic blood pressure 

across all arms of the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial 

(ALLHAT), trial, after adding the chosen first medication, was approximately 6.6 mmHg; patients in 

SMAs achieved approximately 75 percent of that level of improvement.38 Similarly, adding a firstline oral hypoglycemic agent at a maximally tolerated dose usually lowers A1c by 1 to 1.5 percentage 

points;38 patients in SMAs achieve 33 to 50 percent of that goal. The change in LDL-C of 7 mg/dl is 

much smaller compared with drug effect, approximately 15 percent of what would be expected with 

clinical trial doses of an HMG-CoA reductase inhibitor (“statin”). However, each drug comparison 
30

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

is made relative to placebo controls. For SMA interventions, the comparator is usual care, which 

typically includes medication treatment, and thus one would expect the effects to be smaller.

Another way to evaluate the improvements observed with SMA is against the known standard 

deviations for the outcomes in the population of patients with disease, and then calculate effect 

sizes. While many different values for standard deviations for the relevant parameters are 

reported in the literature, effect sizes of SMA interventions for systolic BP, A1c, and LDL-C are 

approximately 0.5, 0.33, and 0.25, respectively. These are considered moderate to small effect 

sizes, but all would be considered important.39

The improvements in A1c and blood pressure, and the more modest improvement in LDL-C are 

possibly synergistic, or at least additive, in prevention of the macrovascular and microvascular 

complications of diabetes.40 Thus, as a whole, SMAs may impact the risk of complications 

among patients with diabetes. Even if half the effect were lost in translation due to lower 

treatment fidelity when implemented outside of clinical trials, there would still likely be an 

important improvement in complication risk for patients enrolled in a diabetes SMA intervention. 

However, it is important to remember that the degree of synergy in the context of improvements 

in multiple outcomes is guesswork at best; SMAs—and indeed multicomponent health services 

in general—have not been studied with enough patients to determine their actual effects on major 

cardiovascular or microvascular complications. 

Finally, many authors propose that SMAs are more satisfying than standard outpatient visits for 

both patients and providers, but few have measured patient and staff satisfaction. Because SMAs 

are a major shift in clinic organization, more data are needed on these variables as well as costto-benefit ratios before a general policy recommendation can be made.

Generalizability of Findings

The results of the diabetes studies have limitations to their external validity. Using the PICOTS 

framework (population, intervention, comparator, outcome, timing, setting), the applicability 

of the findings appears strong with respect to (1) population because a reasonable balance of 

race and sex was achieved among patients, (2) outcomes because there is general consensus that 

A1c, blood pressure, and LDL-C are the important outcomes in diabetes, and (3) timeframes 

because there is general consensus that improvement of 6 months or longer is clinically relevant. 

However, none of the studies examined maintenance of effect after the intervention ended. 

Although similar in many aspects, there were enough differences in intervention process that a 

conclusion as to what makes an SMA intervention particularly successful could not be drawn. 

In addition, what constituted usual care was inconsistently defined. Therefore, intervention 

heterogeneity and the types of usual care comparators, may also be important limitations to the 

generalizability of our results.

The heterogeneity of the studies is concerning. Complex health services interventions are often 

a black box; that is, they contain many components that are hard to capture and tease out even in 

a well-conducted analysis. If there was a particular aspect of these interventions that was critical 

to predicting improved clinical outcomes, we were unable to capture that with the available 

data. This raises the question of a possible uncaptured element of SMAs that is important for 

potency, effectiveness, or generalizability. Without further, more mechanistic studies that attempt 
31

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

to elucidate the key components of an SMA intervention, implementation of a diabetes SMA or 

design of an SMA for another condition will be at least partially based on reasoned judgment 

rather than strict evidence-based decision making.

An additional concern is that none of these studies was conducted in “real world” settings. All of 

the diabetes studies were conducted in academic, government, or vertically integrated systems. 

There are two potential reasons for this. First, all complex chronic care redesign interventions 

are easier to implement in systems that are either highly controlled or in which there is interest 

in research. Second, SMAs are difficult to implement in fee-for-service, independent clinics 

because they are unlikely to derive any financial benefits from improved quality of care but 

would have to absorb the cost in time and money of implementing the SMA. It is possible that 

this barrier could be relieved by Accountable Care Organizations, but this theory is still untested. 

Lastly, academic, government, or vertically integrated systems may also have very high quality 

usual care. While factors related to setting may not negatively impact the generalizability of 

these results for implementation of diabetes or other SMAs within vertically integrated systems 

such as the VA, they do suggest caution when considering the use of SMAs outside such systems. 

Should SMAs Be Implemented?

The clearest finding of this evidence synthesis is that the existing knowledge base does not 

provide enough evidence to make a strictly evidence-driven decision about implementation of 

SMAs in any context except diabetes. Regarding diabetes SMA implementation, this evidence 

synthesis raises several key issues summarized in Table 6.

Table 6. Implementation issues

Issue for Implementation of Diabetes SMAs Potential Solution

Enrollment

There was no clear indication of which patients will 

receive the most benefit from this alternative structure.

Allow selection criteria for SMAs to fit specific local 

needs. 

Elements of intervention

There was no clear indication of which elements were 

most effective.

Use the most prevalent common elements:

Prescribing clinician

A consistent clinical lead for the SMA group

At least 3 team members

Closed group participants

Individual time with clinician (brief)

Medications evaluated

Group duration of 90 to 120 minutes

Variable elements that could be tailored to clinic or 

patient population:

Group size

Participation of family and friends

Contact with participants outside of group

Potential mechanisms of intervention

Very few studies reported any intermediate or 

mechanistic outcomes such as self-management, 

medication change, or access to care.

Measure these at implementation; use Plan-DoStudy-Act approach to allow these factors to change 

intervention over time.
32

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Issue for Implementation of Diabetes SMAs Potential Solution

Infrastructure changes

There was no clear indication whether the change in 

clinic structure was more effective or efficient.

In already vertically integrated settings, such as VA, 

these changes are not as difficult.

The broad-based improvement seen was clinically 

meaningful balanced against satisfaction and cost, 

especially for older adults.

SMAs also have costs. These costs are not just the labor cost of redirecting providers away from 

their existing clinical responsibilities to conduct an SMA; there is also the time and labor cost to 

establish a new structure for care. This lack of information about both direct costs and changes 

in utilization in all but older adults who are high utilizers of the health care system is a key gap 

in the existing literature. For those patients, hospital admissions, ER visits and total costs were 

consistently lower with SMAs. Also, implementation of SMAs will not succeed if either patients 

or providers are unsatisfied with the new structure, and effects on patient and staff experience 

remain largely unknown, again with the exception of older adults who expressed increased 

satisfaction with SMAs. 

STRENGTHS AND LIMITATIONS

Our study has a number of strengths, including a protocol-driven review, a comprehensive 

search, careful quality assessment, and rigorous quantitative synthesis methods. Our report, 

and the literature, also has limitations. An important limitation is the lack of breadth to the 

types of patients and illnesses that have been studied in the context of an SMA. The evidence 

synthesis found no explicit data regarding system-level, as opposed to patient-level, benefits of 

SMAs; the fact that as many studies viewed the SMA as an add-on to, rather than a replacement 

for, usual primary care suggests that improvements in access may not be as great as desired. 

In addition, the components of the interventions were often not described adequately for 

replication, especially the content of the group education time. Finally, outcomes reported varied 

substantially across studies and our attempts to explain the observed variability in intervention 

effects were unsuccessful. With unexplained variability, summary measures of treatment effect 

may not adequately describe the expected effects of the intervention. 

RECOMMENDATIONS FOR FUTURE RESEARCH

We used the framework recommended Robinson et al.41 to identify gaps in evidence and classify 

why these gaps exist (Table 7). The next generation of research in SMAs for patients with 

diabetes and other conditions should close the gaps outlined in the previous section. 

Table 7. Evidence gaps and future research

Evidence Gap Reason Type of Studies to Consider

Patients

Absence of data for patients with 

conditions other than diabetes mellitus 

and high utilization

Insufficient information Single and multisite RCTs

Quasi-experimental studies

Interventions
33

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

Evidence Gap Reason Type of Studies to Consider

Uncertain which elements of an SMA 

intervention are most effective and 

efficient

Insufficient information RCTs of head-to-head comparisons 

of different types of SMAs; 

Disaggregation trials

Outcomes

Uncertain effects on patient and staff 

satisfaction

Insufficient information Nonrandomized or cluster 

randomized, multisite implementation 

studies, qualitative studies

Uncertain effects on physiological 

variables other than HbA1c

Insufficient information Large scale RCTs

Nonrandomized, cluster controlled 

trials, controlled before-and-after 

studies, interrupted time series

Uncertain effects on health system costs 

with the exception of the elderly high 

utilizers of the health system

Insufficient information Costs analyses 

Uncertain whether there would be 

unintended consequences to other 

aspects of the health care system if 

SMAs were implemented

Insufficient information Multisite observational studies

Abbreviation: RCT=randomized controlled trial

Our review shows that SMAs, typically using closed panels with individual breakouts and 

the opportunity for medication management, help intermediate clinical outcomes for type 2 

diabetes. A smaller literature shows positive effects on patient experience in older adults and the 

possibility of lower health care utilization. SMAs may be most effective for illnesses such as 

diabetes that have a phase in which the risk of complication is relatively high while the disease 

is simultaneously asymptomatic, and in which medication titration and self-management are 

important. Until further studies are done that allow for comparisons across conditions, the 

targeting of SMA for chronic conditions other than diabetes will remain speculative. Finally, 

repeating the existing diabetes SMA efficacy trials in fee-for-service settings would be important 

to understand the extent to which SMAs work when the profit motive is essential to the practice 

model.
34

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

REFERENCES

1. Fletcher RD, Amdur RL, Kolodner R, et al. Blood Pressure Control Among US 

Veterans: A Large Multi-Year Analysis of Blood Pressure Data from the VA Health Data 

Repository. Circulation. 2012.

2. King H, Aubert RE, Herman WH. Global burden of diabetes, 1995-2025: prevalence, 

numerical estimates, and projections. Diabetes Care. 1998;21(9):1414-31.

3. Edelman D, Fredrickson SK, Melnyk SD, et al. Medical clinics versus usual care for 

patients with both diabetes and hypertension: a randomized trial. Ann Intern Med. 

2010;152(11):689-96.

4. Jaber R, Braksmajer A, Trilling JS. Group visits: A qualitative review of current research. 

Journal of the American Board of Family Medicine. 2006;19(3):276-290.

5. Kirsh S, Watts S, Pascuzzi K, et al. Shared medical appointments based on the chronic 

care model: a quality improvement project to address the challenges of patients with 

diabetes with high cardiovascular risk. Qual Saf Health Care. 2007;16(5):349-53.

6. Noffsinger E. Increasing quality care and access while reducing cost through drop-in 

group medical appointments (DIGMAs). Group Pract J. 1999;48(1):12-18.

7. Riley SB, Marshall ES. Group visits in diabetes care: a systematic review. Diabetes Educ. 

2010;36(6):936-44.

8. Taveira TH, Friedmann PD, Cohen LB, et al. Pharmacist-led group medical appointment 

model in type 2 diabetes. Diabetes Educ. 2010;36(1):109-17.

9. Beck A, Scott J, Williams P, et al. A randomized trial of group outpatient visits for 

chronically ill older HMO members: the Cooperative Health Care Clinic. J Am Geriatr 

Soc. 1997;45(5):543-9.

10. Levine MD, Ross TR, Balderson BH, et al. Implementing group medical visits for older 

adults at group health cooperative. J Am Geriatr Soc. 2010;58(1):168-72.

11. Scott JC, Conner DA, Venohr I, et al. Effectiveness of a group outpatient visit model for 

chronically ill older health maintenance organization members: a 2-year randomized trial 

of the cooperative health care clinic. J Am Geriatr Soc. 2004;52(9):1463-70.

12. Coleman EA, Eilertsen TB, Kramer AM, et al. Reducing emergency visits in older 

adults with chronic illness. A randomized, controlled trial of group visits. Eff Clin Pract. 

2001;4(2):49-57.

13. Bray P, Thompson D, Wynn JD, et al. Confronting disparities in diabetes care: the clinical 

effectiveness of redesigning care management for minority patients in rural primary care 

practices. J Rural Health. 2005;21(4):317-21.

14. Clancy DE, Cope DW, Magruder KM, et al. Evaluating concordance to American 

Diabetes Association standards of care for type 2 diabetes through group visits in an 

uninsured or inadequately insured patient population. Diabetes Care. 2003;26(7):2032-6.
35

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

15. Clancy DE, Huang P, Okonofua E, et al. Group visits: promoting adherence to diabetes 

guidelines. J Gen Intern Med. 2007;22(5):620-4.

16. Culhane-Pera K, Peterson KA, Crain AL, et al. Group visits for Hmong adults with type 2 

diabetes mellitus: a pre-post analysis. J Health Care Poor Underserved. 2005;16(2):315-27.

17. Gutierrez N, Gimple NE, Dallo FJ, et al. Shared medical appointments in a residency 

clinic: an exploratory study among Hispanics with diabetes. Am J Manag Care. 

2011;17(6 Spec No.):e212-4.

18. Naik AD, Palmer N, Petersen NJ, et al. Comparative effectiveness of goal setting in diabetes 

mellitus group clinics: randomized clinical trial. Arch Intern Med. 2011;171(5):453-9.

19. Sadur CN, Moline N, Costa M, et al. Diabetes management in a health maintenance 

organization. Efficacy of care management using cluster visits. Diabetes Care. 

1999;22(12):2011-7.

20. 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(6):995-1000.

21. Trento M, Passera P, Borgo E, et al. A 3-year prospective randomized controlled clinical 

trial of group care in type 1 diabetes. Nutr Metab Cardiovasc Dis. 2005;15(4):293-301.

22. Trento M, Gamba S, Gentile L, et al. Rethink Organization to iMprove Education and 

Outcomes (ROMEO): a multicenter randomized trial of lifestyle intervention by group 

care to manage type 2 diabetes. Diabetes Care. 2010;33(4):745-7.

23. Krzywkowski-Mohn SM. Diabetic control and patient perception of the scheduled in 

group medical appointment at the Cincinnati Veterans Administration Medical Center. 

ProQuest Information & Learning; 2009.

24. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews 

and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.

25. Wilczynski NL, McKibbon KA, Haynes RB. Response to Glanville et al.: How to 

identify randomized controlled trials in MEDLINE: ten years on. J Med Libr Assoc. 

2007;95(2):117-8; author reply 119-20.

26. Taveira TH, Dooley AG, Cohen LB, et al. Pharmacist-led group medical appointments 

for the management of type 2 diabetes with comorbid depression in older adults. Ann 

Pharmacother. 2011;45(11):1346-55.

27. Loney-Hutchinson LM, Provilus AD, Jean-Louis G, et al. Group visits in the 

management of diabetes and hypertension: effect on glycemic and blood pressure control. 

Current Diabetes Reports. 2009;9(3):238-242.

28. Kirsh SR, Aron DC. Integrating the chronic-care model and the ACGME competencies: 

using shared medical appointments to focus on systems-based practice... Accreditation 

Council for Graduate Medical Education. Quality & Safety in Health Care. 

2008;17(1):15-19.
36

Shared Medical Appointments for Chronic Medical Conditions Evidence-based Synthesis Program

29. Agency for Healthcare Research and Quality. Methods Guide for Effectiveness and 

Comparative Effectiveness Reviews. Rockville, MD: Agency for Healthcare Research and 

Quality. Available at: http://www.effectivehealthcare.ahrq.gov/index.cfm/search-for-guidesreviews-and-reports/?pageaction=displayproduct&productid=318. Accessed May 10, 2012.

30. Viswanathan M, Berkman ND. Development of the RTI Item Bank on Risk of Bias and 

Precision of Observational Studies. Methods Research Report. (Prepared by the RTI 

International-University of North Carolina Evidence-based Practice Center under Contract 

No. 290-2007-0056-I.) AHRQ Publication No. 11-EHC028-EF. Rockville, MD: Agency for 

Healthcare Research and Quality. Available at: http://www.effectivehealthcare.ahrq.gov/ehc/

products/350/784/RTI-Risk-of-Bias_Final-Report_20110916.pdf. Accessed May 15, 2012.

31. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 

2002;21(11):1539-58.

32. Guyatt GH, Oxman AD, Kunz R, et al. GRADE guidelines 6. Rating the quality of 

evidence--imprecision. J Clin Epidemiol. 2011;64(12):1283-93.

33. Schunemann HJ, Oxman AD, Brozek J, et al. Grading quality of evidence and strength of 

recommendations for diagnostic tests and strategies. BMJ. 2008;336(7653):1106-10.

34. Egger M, Davey Smith G, Schneider M, et al. Bias in meta-analysis detected by a simple, 

graphical test. BMJ. 1997;315(7109):629-34.

35. Sterne JA, Egger M, Smith GD. Systematic reviews in health care: Investigating and 

dealing with publication and other biases in meta-analysis. BMJ. 2001;323(7304):101-5.

36. Cohen LB, Taveira TH, Khatana SA, et al. Pharmacist-led shared medical appointments 

for multiple cardiovascular risk reduction in patients with type 2 diabetes. Diabetes Educ. 

2011;37(6):801-12.

37. 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(4):695-700.

38. Fowler MJ. Diabetes Treatment, Part 2:Oral Agents for Glycemic Management. Clinical 

Diabetes. October 2007 vol. 25 no. 4 131-134.

39. Cohen J. Statistical Power Analysis for the Behavioral Sciences (second ed.). Hillsdale, 

NJ: Lawrence Erlbaum Associates; 1988.

40. Sehestedt T, Hansen TW, Li Y, et al. Are blood pressure and diabetes additive or 

synergistic risk factors? Outcome in 8494 subjects randomly recruited from 10 

populations. Hypertens Res. 2011;34(6):714-21.

41. Robinson KA, Saldanha IJ, Mckoy NA. Frameworks for Determining Research Gaps 

During Systematic Reviews. Methods Future Research Needs Report No. 2. (Prepared by 

the Johns Hopkins University Evidence-based Practice Center under Contract No. HHSA 

290-2007-10061-I.) AHRQ Publication No. 11-EHC043-EF. Rockville, MD: Agency for 

Healthcare Research and Quality. June 2011. Available at: www.effectivehealthcare.ahrq.

gov/reports/final.cfm. Accessed May 22, 2012.