Improving Prediabetes Cardiovascular Care With EHR-Based Decision Support
Phase: NA
Conditions: Pre-diabetes
Sponsor: HealthPartners Institute

Official Title:   
Reducing CV Risk in Prediabetes Patients Using EHR-Based Decision Support 
 
NCT Number:   
NCT02759055 
 
Document Date:    
March 5, 2021

PROTOCOL 
To improve implementation of evidence-based prediabetes care,  a randomized study of the 
implementation and impact of a prediabetes Clinical Decision Support (CDS) intervention on 
cardiovascular (CV) risk in prediabetes patients (18-75 years old, body mass index (BMI) ≥25 kg/m2, 
current smoker, uncontrolled blood pressure [BP] or lipids) is justified based on these considerations: 
(a) over 75% of rural primary care providers (PCP) now use EHR systems22; (b) in a previous 
randomized trial, we showed that CDS in primary care clinics significantly improved BP and 
glucose control in adults with diabetes; (c) this CDS system elicits evidence-informed patient 
treatment preferences using human-computer interfaces; (d) the CDS system is minimally disruptive 
of clinic workflow with 80% use rates in targeted diabetes patients and 95% provider satisfaction; 
and (e) treatment and prioritization algorithms are Web- based and therefore easily updated and 
highly scalable to any medical group that uses EHRs. 
The objective of this project is to systematically and pragmatically improve care and CV risk 
factors in a population of predominantly rural adult prediabetes patients at high risk for developing 
diabetes and CV events by implementing a Web-based CDS system that integrates with local EMR 
systems and presents the patient and PCP with personalized, evidence-based drug and lifestyle 
treatment recommendations. To accomplish this objective, we  addressed the following specific 
aims and hypotheses: 
Specific Aim 1: Cluster-randomize 34 primary care clinics with 450 PCPs and over 11,000 high-
risk adult prediabetes patients to one of two study arms: (a) Usual care (UC) clinics or (b) 
Intervention (CDS) clinics, which use an EHR-linked, Web-based Prediabetes CDS system 
designed to improve prediabetes care. 
Hypothesis 1 (H1): Relative to those treated at UC clinics, eligible 18 to 75-year-old 
prediabetes patients at high CV risk treated at CDS clinics will have significantly more 
favorable trajectories in CV risk estimates  over a median 24-month follow up. 
Hypothesis 2 (H2): Relative to those treated at UC clinics, eligible 18-75 year-old prediabetes 
patients with  uncontrolled CV risk factors at CDS clinics will have significantly more favorable 
trajectories in specific CV risk factors, including BP, lipids, smoking, HbA1c, and weight over a 
median 24-month follow up. 
Hypothesis 3 (H3): Relative to those treated at UC clinics, eligible 18 to 75 year-old 
prediabetes patients at high CV risk treated at CDS clinics will have significantly more 
favorable patterns of metformin and statin use over a median 24-month follow up. 
Specific Aim 2. Assess the cost of the CDS intervention from the health system (payer) perspective 
through utilization analyses. 
Hypothesis 4 (H4): After controlling for demographics and baseline clinical status, eligible 
patients with prediabetes treated in UC clinics versus CDS clinics will have significantly lower 
annual healthcare costs from the  index date over a median follow up of 24 months. 
Specific Aim 3: Describe critical facilitators and barriers for the prediabetes CDS implementation 
process, outcomes, and future dissemination using a mixed-methods approach. 
Note that a previously included cost-effectiveness hypothesis (Hypothesis 5) was not tested due to 
lack of efficacy of the study intervention.  Project results will provide a template for implementation 
of personalized CDS tools in rural and urban health settings, resulting in more efficient, effective 
rural healthcare that can be applied across many clinical domains, incorporates patient treatment 
preferences, and could substantially and sustainably improve the quality of CV care and clinical 
outcomes of millions of Americans with prediabetes in medically underserved areas. 

Overview of Study Implementation Process and Design: The implementation process has four primary 
components (Figure 1): a) initial organizational engagement; b) adapting and piloting the Prediabetes CDS with 
Essentia primary care providers and patients; c) a group-randomized controlled study of the adapted CDS in 34 
primary care clinics blocked on size and Essentia geographic service area; and d) evaluation of the 
implementation process and outcomes. It is critical to note that implementation is an iterative process, both in 
the adaptation 
phase and during 
the randomized 
controlled trial of 
implementation 
and outcomes. 
Continuous 
feedback is 
provided to and 
Figure 1. Study Implementation Process and Design 
from organizational leaders, providers, and patients to better understand the facilitators and barriers to 
implementation and to guide refinements of the CDS. 
Group-Randomized Study Eligibility Criteria 
Study Sites: The group-randomized controlled study will be implemented in 34 Essentia Health primary care 
clinics in Minnesota, North Dakota, and Wisconsin. Essentia  clinics have used the Epic® EHR since 2004 and 
have, on average, 14 PCPs (range: 1-56) and 377 prediabetes patients at high CV risk per clinic. Eighty-four 
percent of the Essentia geographic service area is rural, with 56% of the patient population and 66% of clinics 
located in this rural area. Average household incomes are less than $35,000 for 42% of patients, and 45% live 
in health professional shortage areas. 
Study Participants: PCPs and Patients: To participate, PCPs must practice at one of the 34 Essentia clinics 
and meet these eligibility criteria: (a) be an adult-care general internist, family physician, non-obstetric nurse 
practitioner, or physician assistant, and (b) provide ongoing primary care for 15 or more prediabetes adults 
ages 18-75 with high CV risk. There are currently 441 eligible PCPs which should ensure a more robust 
understanding of implementation barriers and facilitators in a large PCP community. 
To be considered for inclusion in primary or secondary analyses, patients must meet all the following eligibility 
criteria at the time of their index visit: (a) be 18-75 years old, (b) have no evidence of diabetes in the previous 
12 months (based on lab values, diagnosis codes, use of medications, or on problem list), (c) not be pregnant 
(d) have the most recent fasting plasma glucose of 100–125 mg/dL or HbA1c 5.7-6.4% within the prior 24 
months, (e) have the most recent BMI ≥25 kg/m2, (f) have at least one of the following uncontrolled CV risk 
factors: current smoking, a diagnosis
Adapt & Pilot 
Pre-Diabetes CDS
 
PCPs 
Patients 
Organizational 
Stakeholder 
Randomize 
 
Intervention 
 
PCPs 
Patients 
Facilitation & IT Support
24-month Implementation 
and Outcome Assessment
Pre-Diabetes 
CDS 

of hypertension with index visit BP ≥140/90 mm Hg or no diagnosis of hypertension but two consecutive BP 
≥140/90 mm Hg, or ACC/AHA 10-year CV risk ≥7.5% and LDL either untested or ≥70 mg/dL and not on a statin, 
(g) no hospice care or chemotherapy for cancer within previous 12 months, and (h) have at least one 
subsequent primary care visit after the index visit. The patient accrual period (for first index visits) will be 12 
months, followed by a 26-month observation period. Thus, patients will have 26 to 38 months of follow up, with 
an expected median of 32 months follow up after the index visit. Pilot data indicate that non- pregnant eligible 
adults have a median of 6 primary care visits over a 24-month period, ensuring broad inclusion in the analysis. 
We conservatively estimate an average of 25 eligible patients per consented study PCP with few losses due to 
death (<1%) in the study period. Few patients switch primary care clinics in Essentia and we accurately match 
over 98% of patients to a regular PCP. Ultimately, after accounting for all eligibility criteria, we anticipate over 
6,600 eligible patients for Aim 1 and Aim 2 analyses. For Aim 1 and Aim 2 analyses we will retrospectively 
collect CV clinical information and will ask for a waiver of patient consent. The IRB has waived consent in prior 
and existing evidence-based CDS studies. Patients specifically recruited for focus groups and surveys will be 
consented and receive     
modest compensation for those activities. 
Description of Usual Care (UC) and Prediabetes CDS 
Intervention Conditions. 
UC Condition. UC clinics and their consented PCPs  will 
have no access to the CDS intervention. 
Prediabetes CDS System: The Prediabetes CDS 
intervention is rooted in a series of antecedent studies that 
have developed successful forms of CDS for diabetes. The 
process flow of the intervention is shown in Figure 2. Specific 
steps in implementation of the Prediabetes CDS intervention 
are described below. 
Step A. Extract Clinical Data from EHR and Send to Web 
site. In HL102144, we created the programming to securely 
extract pharmacy, laboratory, vital signs, demographic, 
comorbidity, smoking, and drug allergy data from the EHR 
and export it to a Web server within the HealthPartners 
Medical Group (HPMG) firewall. These data pathways are 
debugged and function seamlessly. We will modify the 
programming to identify prediabetes patients 18-75 years at 
high CV risk and adapt the CDS to evidence-based 
prediabetes care recommendations in the ADA Standards of 
Diabetes Care, published every January in Diabetes Care. 
Step B. Treatment Algorithms. Web-based treatment 
algorithms that identify evidence-based treatment options 
appropriate for BP, lipids, tobacco, and BMI are developed  
for 40- to 75-year-old high-CV risk patients. They will be 
adapted for evidence-based prediabetes treatment, 
including use of metformin. Treatment 
 
Figure. 2 Schematic representation of Prediabetes CDS 
showing how treatment options are identified, prioritized, and 
presented to patients and primary care providers at the point 
of care. 
recommendations provided through the CDS intervention are based on: (a) all current medication 
prescriptions for glucose (including metformin), BP, lipids, and smoking cessation; (b) current renal function, liver 
function, creatinine kinase level, and comorbid conditions such as coronary heart disease, congestive heart 
failure, chronic kidney disease, dementia, and depression, which affect the appropriate use of these classes of 
medications; (c) personalized evidence-based  glucose, BP, lipid, and weight management goals; and (d) 
medication allergies listed in the EHR. Medication recommendations are specific (what drug, what dose) based 
on clinical care guidelines for prediabetes and CV risk factors. The CDS identifies risky prescribing events and 
suggests alternative clinical actions. A diabetes glucose-management algorithm also exists and will deploy upon 
conversion from prediabetes to diabetes. These treatment algorithms have been effective with adult diabetes 
patients (R01DK068314) and will be updated every 6-12 months or when major changes occur to reflect new 
care guidelines and U.S. Food and Drug Administration medication actions.  
In this study, prediabetes clinical goals will emphasize appropriate management of BP, lipids, smoking 
 
of CDS (Figure 3) with options 
 
 
 
 
version of clinical decision 
 
 
 
 
 
 

cessation, weight, and glucose using metformin or other pharmacologic agents, and referral to nutrition, 
physical activity, or weight-loss therapy. For lifestyle strategies, providers can refer willing  patients directly to 
appropriate evidence-based clinical therapy or community resources. 
Step C. Prioritization Algorithms. The CDS will provide diabetes prevention recommendations to all patients. 
CV risk reduction will be prioritized. Web-based prioritization algorithms use a patient’s data to estimate the 
potential achievable reduction in CV risk with management of each out-of-control CV risk factor (BP, lipids, BMI, 
and tobacco). In previous work, we developed a 4-step approach to estimate the CV risk reduction that can be 
achieved for each CV risk factor: (a) Run the CV risk equation with real values of systolic BP, HDL, total 
cholesterol, BMI, and smoking status, and save this first score. (b) Re-run the same CV risk equation but 
replace one observed elevated risk factor value (eg, observed systolic BP ≥140 mm Hg) with the evidence-
based optimal value (systolic BP=140 mm Hg) and save this second score; (c) subtract the two scores to 
estimate CV risk reduction that could theoretically be achieved by control of the selected out-of- control CV risk 
factor (SBP in this example); (d) Repeat this process for each out-of-range CV risk factor, one at a time, thus 
estimating CV risk reduction that may be achieved for each individual risk factor that is out of control. The 
estimates are then ranked based on the potential CV benefit of treating each individual risk factor. We will use 
this approach with prediabetes patients’ at high CV risk. We acknowledge that this approach is imperfect. It 
assumes additivity and extrapolates risk equation estimates to individuals; however, prioritization is based on 
relative risk reduction within a patient using the same CV risk equation. Furthermore the ACC/AHA 10-year 
pooled ASCVD risk engine must be used for 40-75 years olds while the Framingham 30-year CV risk score is 
used for 18-39 year olds. In previous projects, these estimates were found clinically credible with PCPs, who 
view them as superior to intuitive judgments of CV risk reduction. Drug effects on BP and lipids are based on 
published reports and are adjusted downward for patients already on multidrug regimens. A  similar evidence-
based approach will be used to incorporate the effect of metformin use on CV risk. 
Step D: Presentation of Prioritized CV Treatment 
Options to PCPs and Patients. The CDS clinic 
workflow is illustrated in Figure 2. At a clinic visit, the 
following protocol is automatically implemented: 
(1) When the rooming nurse enters the patient BP in 
the EHR, biometric data is automatically transferred 
to a secure Web server. If the patient has 
prediabetes, is overweight or obese, and has at least 
one other elevated CV risk factor, a “Best Practice 
Alert” (BPA) automatically displays; (2) based on the 
BPA, the nurse deploys the CDS with a single click 
on the visit navigator and, with an additional click, 
prints the patient and PCP interfaces; (3) if a 
patient’s mental and physical status appears stable, 
the nurse hands the patient interface sheet(Figure 3) 
to the patient, saying, “The yellow marks show how 
you can reduce your danger of diabetes, stroke, or 
heart attack later in life. If you are ready to work on 
any of these things, please talk with your doctor 
during your visit today.” 
(4) If the PCP prefers, the printed PCP interface is placed in the basket outside the exam room for visit 
planning before entering the exam room. Otherwise, the PCP interface is displayed when the PCP opens the 
EHR during the visit. The printed PCP interface is a powerful visit-planning tool that most PCPs prefer to view 
before entering the exam room; (5) the PCP discusses patient questions or treatment preferences related to 
the prioritized CV risk reduction or diabetes prevention CDS recommendations; and (6) based on patient 
preferences, the PCP completes the Prediabetes Smartset (see below) or books a follow-up visit. 
The prediabetes CDS PCP interface is patient-specific. If medications are indicated, the CDS will specify either 
initiation or titration of drugs based on current medications, distance from goal, and other clinical and 
comorbidity considerations. Options for local clinic- or community-based lifestyle interventions are provided to 
facilitate lifestyle management referrals. On the Patient Interface, the estimated CV risk reduction of each 
treatment is a simple visual display (Figure 3). This approach is preferred because many patients have low 
numeracy, and time-dependent risk information is difficult to interpret. This visual display has been well-
received in earlier studies and shown to be a strong motivational strategy. However, for 

patients with strong numeracy and health literacy, the PCP interface may be given. The presentation of CV risk 
factor domains with treatment options facilitates patient and provider decision-making based on patient readiness 
to change. Patient readiness is a key predictor of subsequent adherence and success of treatment, as we and 
others have shown. 
Step E: Prediabetes CDS Smartset: The Prediabetes CDS will also include a personalized ‘virtual’ checklist, or 
Smartset, to streamline prediabetes patient management by grouping diagnoses, medications, tests and 
procedures, and patient follow-up. The Smartset serves multiple purposes, it: (a) invites PCPs and patients to 
select evidence-based prediabetes and CV care-management options that best fit their current needs and 
readiness, including metformin use and lifestyle change referrals, (b) incentivizes the PCP and patient to take an 
action, (c) quantifies the percentage of visits at which each intervention PCP is using the CDS, and (d) enables 
us to give feedback to PCPs and clinic leaders on comparative use of the CDS. 
Step F: Iterative Use of the Prediabetes CDS over a Series of Visits. A key design feature of this 
intervention is its repeated use at all office visits of eligible patients during the intervention period. The CDS is 
automatically activated at any visit during the study period for eligible patients and provides updated treatment 
priorities and recommendations at each visit. Pilot data indicate that eligible patients average 4-6 primary care 
visits during a 24 month period, assuring that nearly all intervention patients have at least 3 exposures to the 
CDS. The CDS can also be manually activated by the PCP. In our previous study, almost 70% of eligible PCPs 
maintained use of the CDS system. With strong support from Essentia leaders and PCP leaders, we expect 
similar use rates. 
Prediabetes patients converting to diabetes will continue to receive appropriate CV risk reduction prioritization 
and evidence-based glucose and aspirin treatment recommendations, thus facilitating timely treatment 
transition for newly diagnosed diabetes and reducing the lag time often reported between conversion to 
diabetes and being clinically diagnosed and treated. 
Implementation of the Prediabetes CDS Intervention: Key implementation stages include: organizational 
engagement; adapting, piloting, and refining the Prediabetes CDS; and implementing the Prediabetes CDS 
group-randomized study. Key informant interviews, focus groups, surveys, usability testing, EMR data, and 
continuous quantitative and qualitative feedback between researchers and stakeholders will be used to monitor 
the implementation process and outcomes. 
Organizational Engagement: As part of the initial engagement process, Essentia leaders and PCP leaders 
have already provided letters of support for the prediabetes CDS. Further engagement with Essentia 
leadership, informatics personnel, and key providers will occur through in-person meetings, conference calls, 
informational meetings, and identification of critical personnel to participate in the development, adaptation, 
and piloting processes of this study. 
Adapt and Pilot Test the Prediabetes CDS: All CDS algorithms and interfaces will be extensively tested to 
confirm clinical validity and acceptability to providers and patients. Special attention will be given to the 
prioritization and treatment algorithms related to metformin use, to usability testing of the PCP and patient 
interface screens and the Prediabetes Smartset, and to the lifestyle intervention referral resource tool. The 
resource tool, updated semi-annually, will include a map and list of evidence-based programs and descriptions 
of location, delivery mode, program type, program intensity, potential benefits, costs, and contact information. 
Essentia leaders, providers, Patient Council, patients, informatics personnel, and local community 
organizations will be engaged in the development and adaptation process through meetings, in-person usability 
testing, and key informant interviews. 
After initial programming, the research team will pilot the treatment and prioritization algorithms, the patient and 
PCP interfaces, and the Prediabetes Smartset on a series of eligible prediabetes patients. After several rounds 
of iterative testing, feedback, and modifications, we will recruit five PCP-nurse teams from Essentia clinics not 
included in the randomized implementation study and pilot the Prediabetes CDS and Care Coordination Team 
(CCT) protocols in eligible patients for 4 weeks. PCPs and nurses will provide written consent and offered 
compensation to complete online surveys and provide within-CDS and CCT protocol feedback on their 
experience with the CDS system, including the clinical utility of prompts and their impact on clinic workflow. 
Implementation of the Randomized Control Study: We will train intervention clinics to use the CDS using 
group and individual meetings with all intervention clinic PCPs, rooming nurses, and clinic staff, plus 

webinars and email reminders with links to a short instructional video demonstrating rooming nurse and PCP 
roles in prediabetes CDS use. Training will be completed and the CDS fully implemented at all intervention 
clinics within 30 working days of clinic randomization. To ensure high intervention implementation fidelity, all 
intervention clinic staff will receive weekly email reports showing CDS rates of use among all eligible patients. 
The project manager will communicate (in-person, by e-mail, or by telephone) with each Intervention clinic’s 
nurse managers each month throughout the full implementation period to assess continued use of the CDS 
and to gather feedback. Furthermore, the CDS provides an open-ended feedback mechanism for providers to 
give implementation researchers and staff timely feedback on issues, concerns, and proposed refinements  
about the CDS so it can be more effectively and efficiently implemented and used at the intervention clinic 
sites.  
Ongoing communication and feedback with stakeholders throughout the engagement, adaptation, pilot testing, 
and implementation phases will allow us to assess various components of the RE-AIM and CFIR frameworks 
to best determine mediators of CDS implementation, use, clinical outcomes, and future implementation and 
dissemination throughout Essentia primary care clinics and other health systems. 
Definition and Measurement of Variables: We will use a mixed-methods approach to examine the facilitators and 
barriers for (a) successfully achieving and maintaining CV risk factor control among prediabetes patients at high 
CV risk (Specific Aim 1) and for (b) sustainable implementation in Essentia primary care clinics (Specific Aims 
2 & 3). Appendix A contains the protocol for measuring the study variables. 
Aim #1- (a) Cardiovascular Risk Trajectories (H1): There is no validated CV risk engine developed with a 
prediabetes population. Therefore, we propose to use the ACC/AHA 10-year pooled CV risk engine to 
estimate annual rate of change in CV risk as our primary outcome. We will use the lipid equation for patients 
with lipid data available to calculate CV risk at the index and all post-index visits and, to minimize missing CV 
risk values, the BMI equation at the index and all post-index visits for the remaining patients. The BMI 
equation estimates higher CV risk relative to the lipid equation. Because the calculation method will be 
consistent across all visits within each patient this difference will not bias the estimated rate of change in CV 
risk. CV risk scores for each patient will be calculated at each primary care visit from the index through the 
entire follow-up period using available EHR data elements. As depicted in Figure 2 Step A, at each visit we 
will search for the most recent data elements, looking back over a period appropriate for each risk component 
(i.e., 12 months for BP values or medications, DM diagnosis, smoking, BMI; 48 months for lipids). H1 will be 
supported if the annual rate of change  in CV risk scores from the index visit through the end of the follow-up 
period is lower (ie, less increase) among patients in the CDS relative to UC clinics; (b) Modifiable diabetes 
and CV risk factors (H2): Risk factors needed to calculate the CV risk scores (weight, systolic blood 
pressure, HDL-c, LDL-c, total cholesterol, smoking), are extracted from EHR files as in Figure 2 Step A. H2 
will be supported if annual rate of change in CV risk component  trajectories are lower among patients in the 
CDS relative to UC clinics; (c) Metformin and statin use (H3): Data to assess drug treatment are from EHR 
files such as prescribed medications. The presence or absence of appropriate drug use will be assessed at 
each visit. H3 will be supported if appropriate drug use improves over the follow-up period among patients in 
the CDS clinics relative to UC patients.  
Aim #2 – Health Care Costs (H4): Costs for this analysis include intervention costs and incremental medical 
care costs, defined from the health system perspective. Intervention costs include implementation and 
maintenance and training but exclude research and development costs. Standard accounting methods will be 
used to measure the cost of the CDS intervention. Medical care costs will include the cost of all prescribed 
medications and clinic services—including laboratory, lifestyle counseling, and physician services—incurred 
prior to and up to a 24-month post-index date period by participants in each study group, as indicated by 
Essentia billing and clinical encounter data. Reliance on Essentia billing records for measuring medical care 
utilization may miss costs incurred in other health systems; however, this opportunity is expected to be equal 
across randomized study arms, and cross-system medical utilization at the clinic level is expected to be 
limited in this study population. For this reason, though, emergency visits and hospitalizations will be 
excluded from the  analysis—and generally, these events are too infrequent in a sample of this size to 
accurately predict impact of the study intervention on hospital costs. 
 
While market prices generally are a good estimate of the costs for medical services, the paid amount in this 
claims system is specific to Essentia at a particular time and may provide a biased view of costs between 
pre- and post-intervention periods. To address this, we will use Total Care Relative Resource ValuesTM 
(TCRRVs), which are a nationally standardized set of measures that have been endorsed by the National 
Quality Forum and are derived from Centers for Medicare and Medicaid Services (CMS) relative value units 

(RVUs).  TCRRVs extend CMS RVU measures to include utilization categories, such as laboratory services 
and medications, which do not have CMS RVU weights.  Specifically, TCCRVs defined during the midpoint of 
patient accrual and follow-up (e.g., 2019) will be used to convert claims to represent U.S. dollars. 
Aim #3 Facilitators and Barriers of Implementation: For this analysis, we use the personalized medicine 
RE-AIM framework described by Gaglio and Glasgow and selected components of the CFIR model. Key RE-
AIM metrics are: a) the percent and type of patients reached, b) for whom is the intervention effective in 
improving outcomes, c) what percent of clinics and providers have adopted the intervention, d) how 
consistently is the intervention implemented, and e) how well are the intervention components and their 
effects maintained and spread. Key components of the CFIR conceptual model that will be examined include 
but are not limited to stakeholder perceptions on: intervention source; strength and quality of the evidence-
base; iterative adaptability and testing of the CDS, including usability testing; complexity and packaging of the 
CDS; whether the CDS meets provider and patient needs; external policies and incentives affecting 
implementation; informal and formal communication processes, including CDS feedback loops; implementation 
climate and readiness; engagement of stakeholders; fidelity of implementation; and provider knowledge, 
attitudes, beliefs, and self- efficacy about the CDS. 
We will examine facilitators, barriers, and solutions to the implementation process using the following mixed 
methods approach: (a) meeting minutes; (b) semi-structured interviews conducted with 8-10 key 
stakeholders among Essentia leaders conducted during Year 1 to understand organizational support and 
guide CDS adaptation and implementation and Year 4 to better understand future dissemination the CDS 
system; (c) CDS patient focus groups conducted in Year 4, with discussion focused on perceived barriers and 
facilitators to achieving CV risk factor control, use of the CDS, and interactions with providers; (d) CDS 
provider focus groups conducted in Year 4 among four rural and two urban CDS intervention clinic teams and 
conducted during routine morning clinic meetings with discussions on patient-provider decision making, how to 
improve CDS use and effectiveness, and the potential for future CDS adoption by other primary care clinics; 
(e) cross- sectional provider surveys conducted in Year 2, (prior to implementation) and Year 4 (post-
implementation) among 50 UC and 50 CDS providers to assess perceptions of primary practice systems using 
the 20-item Physician Practice Connection Research Survey,  the 10-item System (i.e., CDS) User Scale, and 
other measures; and, (f) patient surveys conducted during the implementation study on a cohort of 250 CDS 
patients and 250 UC patients. Patient survey eligibility criteria include at least two PCP visits within 12 months 
before their index date, and the index visit will automatically trigger delivery of the first of two patient surveys. 
Surveys will be available in Spanish. Our analysis will be on participants with an index visit and at least one 
more visit during the study. Patient experience will be assessed using the Clinic and Group Consumer 
Assessment of Healthcare Providers and Systems (CG-CAHPS). Patient perceptions of prediabetes care 
management will be assessed using the Patient Assessment of Care for Chronic Conditions (PACIC-plus).  
We will also assess patient-reported depression (PHQ-9), physical activity (BRFSS), select dietary behaviors 
(5-A-Day Fruit and Vegetable screener and BRFSS), and participation in clinic- and/or community-based 
lifestyle education, counseling, and programs. 
Measures of Covariates for Primary and Secondary Analyses: One predictor of CV risk, risk factor, and 
drug use outcomes is the binary indicator for study condition (CDS or UC). The second key predictor is time 
elapsed in years between the patient’s index visit to each subsequent measurement. Several other measures 
merit consideration as dependent outcomes, moderators or mediators in primary analyses. These include (a) 
number of PCP patient encounters, (b) patient characteristics including demographics and pre-intervention clinical 
values and comorbidities (EHR ICD-10 diagnosis codes and problem lists), and (c) provider characteristics such as age, 
gender, type of provider, full-time or part-time work status,, specialty board certification status, and proportion of eligible 
study patients. Patient or provider characteristics may also be used to adjust for covariate imbalance introduced 
by clinic randomization. 
Analysis Plan. 
Aim 1 Analytic Approach. Aim 1 pertains to the effectiveness of the Prediabetes CDS intervention, 
predicting that it will improve the annual rate of change in CV risk (CVR) and individual risk factors relative to 
UC. Patients age 40-75 at index are included in the Aim 1 analyses because the CDS was programmed to 
deliver the intervention via treatment recommendations (Figure 2, Step B) to this age group. Rate of change 
outcomes (e.g., H1, H2) will be tested using random coefficients models in which all available index and post-
index outcomes (H1 CV risk, H2 CV risk factors) will be predicted from fixed effects for clinic-randomized 
treatment group, years elapsed since index CVR, the treatment by years elapsed interaction and the index 
outcome value. Outcomes measured only once per person will follow the same approach, omitting predictors 
that incorporate years elapsed since index and where warranted including an offset for follow-up time. 

Random clinic and patient intercepts will account for clustered observations as appropriate.  The link function 
and error distribution for each model will be determined by the distribution of each outcome. The 
relationships between years elapsed and CV risk and risk factor outcomes are expected to be linear. The 
tenability of this assumption will be checked for each outcome and more complex relationships (e.g., non-
linear, spline) modeled if warranted.  
The years elapsed parameter will estimate the annual rate of change in CV risk and risk factors among 
patients in UC clinics in the absence of intervention, while the treatment by years elapsed parameter estimates 
the difference in the annual rate of change among patients in the CDS relative to UC clinics. It is expected that 
CV risk will decrease, or increase at a slower rate, among patients in CDS relative to UC clinics. Similar 
patterns of improvement, or slower worsening, are expected for CV risk factors. Finally, the treatment group 
parameter is expected to estimate more appropriate pharmacologic use among patients in CDS compared to 
UC clinics.  
2.2.6.1 H1 Sample size justification. We conducted a power analysis to estimate the minimum detectable 
slope of the treatment by time parameter (power=.80, α2=.05) given the anticipated sample size (15 CDS 
clinics, 7-11 providers per clinic * 25 patients per provider = 2625-4125 CDS patients, 3 CVR per patient).. The 
patient sample size was divided by the design effect, Neff = N / [1 + (nclus – 1)], to approximate the effective 
independent patient sample size (Neff) implied by clinic intraclass correlations (ICC) of  = .001-.02. The CVR 
sample sizes (Neff patient * 3 CVR/pt) were divided by the design effect implied by a patient ICC=0.80. The 
resulting effective CVR sample sizes were used to estimate the minimum detectable standardized linear 
regression coefficient (βCDS) relative to βUC=0. When ICCclin=0.001, the minimum detectable βCDS =0.044-0.055; 
when ICCclin =0.005 βCDS =0.061-0.070; and when ICCclin =0.02 βCDS =0.103-0.108.  
 
Aim 2 Analytic Approach   : Incremental medical costs will be estimated using standard health econometric 
methods. A generalized estimating equation (typically assuming a gamma distribution and log link function) will 
be used to estimate annualized costs by study arm while allowing clustering by clinic and controlling for 
demographics, time, and baseline clinical risk factors. The marginal effect of being assigned to an intervention 
clinic will provide an estimate of the incremental medical cost associated with the CDS intervention. The 
estimates assume that the CDS is implemented in a large health system with an EHR capable of exporting 
data to Web-based clinical algorithms. Aim 3 Analytic Approach: For measures not addressed in Specific 
Aim #1 and #2, measures of centrality will be computed for each of the RE-AIM measures. As an example, 24-
month CDS use rates will be  determined to assess the completeness of implementation. Mixed models will 
assess changes in patient- and provider-reported outcomes using the Aim 1 analytic approach, with 
distributional and model modifications made as appropriate to each outcome. For patient and provider 
experience survey metrics we will describe the proportion of respondents indicating highest satisfaction for 
specific items at each time point and change over time for these items and for composite scales. Spearman 
correlations will be used to examine relationships between items, scales, and other study outcomes. 
Information first will be extracted and orchestrated into categories, themes, and patterns that emerge from and 
are grounded in data. Coding schemes will be developed and tested using the system recommended by 
Strauss and Corbin. Classification schemes and typologies will be used to identify and develop themes and 
concepts emerging from the data. Once coding has been completed and thematic and conceptual material 
developed, we will also use descriptive data analysis to examine the importance and intensity of categories by 
their repetition within and across interviews, using counts and frequencies.  
Missing data. Because all data elements required for calculating the Aim 1 primary outcome measures (eg, 
HbA1c, weight, systolic BP, LDL-c, smoking) are drawn from EHR, they will be high quality and available for 
virtually all tests performed for patients in the analytic dataset. The absence of a measured CV risk factor will 
result from lack of a factor having been assessed rather than its failure to be documented. As such, missing 
values will be rare and can be assumed to be missing at random. 
Secondary analyses. The primary analytic model is sufficiently flexible to accommodate non-Gaussian data by 
specifying alternate error distributions and link functions. As such, secondary efficacy outcomes (eg, treatment 
mediators and modifiers) and treatment-effect patterns (heterogeneity across demographic and clinical 
subgroups, persistence of effects) will be analyzed using the Aim 1 approach, with distributional 
accommodations and model modifications made as needed.