A Randomized Trial of a BE-EHR Module to Guide the Care of Older Adults With Diabetes
Phase: NA
Conditions: Diabetes
Sponsor: NYU Langone Health

 
 
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writing by the study sponsor 
 
 
 
 
 
 
 
 
 A Randomized Trial of a BE-EHR Module to Guide the Care of Older Adults with Diabetes 
 
 
Principal Investigator: 
 
Andrea B. Troxel, ScD 
Department of Population Health 
180 Madison Ave, Room 5-55 
andrea.troxel@nyulangone.org 
646-501-3654 
Additional Investigators: 
 
Devin Mann, MD 
Department of Population Health 
Devin.mann@nyulangone.org 
 
Saul Blecker, MD 
Department of Population Health 
Saul.blecker@nyulangone.org 
 
Sara Chokshi, PhD 
Department of Population Health 
Sara.chokshi@nyulangone.org 
 
Hayley Belli, PhD 
Department of Population Health 
Hayley.belli@nyulangone.org 
NYULH Study Number: 
S19-01464 
Funding Sponsor:  
NIH-NIA 
301-827-6374 
ClinicalTrials.gov Number     NCT04181307 
 
Initial version:   10/11/2019 
Amended: 
 
Amended: 
 
 

Study number: s19-01464 
 
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Statement of Compliance 
 
This study will be conducted in accordance with the Code of Federal Regulations on the Protection of 
Human Subjects (45 CFR Part 46), any other applicable US government research regulations, and 
institutional research policies and procedures. The Principal Investigator will assure that no deviation from, 
or changes to the protocol will take place without prior agreement from the sponsor and documented 
approval from the Institutional Review Board (IRB), except where necessary to eliminate an immediate 
hazard(s) to the study participants. All personnel involved in the conduct of this study have completed 
Human Subjects Protection Training. 
 
 

Study number: s19-01464 
 
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Table of Contents 
 
PROTOCOL SUMMARY ............................................................................................................................... 1 
SCHEMATIC OF STUDY DESIGN ............................................................................................................... 2 
1 
KEY ROLES .......................................................................................................................................... 3 
2 
INTRODUCTION, BACKGROUND INFORMATION AND SCIENTIFIC RATIONALE ....................... 3 
2.1 
BACKGROUND INFORMATION AND RELEVANT LITERATURE ................................................................ 3 
2.2 
RATIONALE ..................................................................................................................................... 6 
2.3 
POTENTIAL RISKS & BENEFITS ........................................................................................................ 6 
2.3.1 
Known Potential Risks ............................................................................................................. 6 
2.3.2 
Known Potential Benefits ......................................................................................................... 6 
3 
OBJECTIVES AND PURPOSE ............................................................................................................ 6 
3.1 
PRIMARY OBJECTIVE ....................................................................................................................... 6 
3.2 
SECONDARY OBJECTIVES (IF APPLICABLE) ....................................................................................... 6 
4 
STUDY DESIGN AND ENDPOINTS .................................................................................................... 6 
4.1 
DESCRIPTION OF STUDY DESIGN ..................................................................................................... 6 
4.2 
INTERVENTION ................................................................................................................................ 6 
4.3 
RANDOMIZATION ............................................................................................................................. 7 
4.4 
STUDY ENDPOINTS ......................................................................................................................... 7 
5 
STUDY ENROLLMENT AND WITHDRAWAL ..................................................................................... 7 
5.1 
INCLUSION CRITERIA ....................................................................................................................... 7 
5.2 
EXCLUSION CRITERIA ...................................................................................................................... 7 
5.3 
VULNERABLE SUBJECTS .................................................................................................................. 7 
5.4 
STRATEGIES FOR RECRUITMENT AND RETENTION ............................................................................ 7 
5.4.1 
Use of DataCore/Epic Information for Recruitment Purposes ................................................. 8 
5.5 
DURATION OF STUDY PARTICIPATION ............................................................................................... 8 
5.6 
TOTAL NUMBER OF PARTICIPANTS AND SITES .................................................................................. 8 
5.7 
PARTICIPANT WITHDRAWAL OR TERMINATION .................................................................................. 8 
5.7.1 
Reasons for Withdrawal or Termination .................................................................................. 8 
5.7.2 
Handling of Participant Withdrawals or Termination ................................................................ 8 
5.7.3 
Premature Termination or Suspension of Study ...................................................................... 8 
6 
STUDY SCHEDULE ............................................................................................................................. 8 
6.1 
SCREENING .................................................................................................................................... 8 
6.2 
ENROLLMENT/BASELINE .................................................................................................................. 8 
6.3 
INTERMEDIATE VISITS...................................................................................................................... 9 
6.4 
FINAL STUDY VISIT.......................................................................................................................... 9 
6.5 
WITHDRAWAL VISIT ......................................................................................................................... 9 
6.6 
UNSCHEDULED VISIT ....................................................................................................................... 9 
7 
STUDY PROCEDURES/EVALUATIONS............................................................................................. 9 
7.1 
PROCEDURES/EVALUATIONS ........................................................................................................... 9 
7.2 
LABORATORY PROCEDURES/EVALUATIONS .................................................................................... 10 
7.3 
STUDY SPECIFIC BIOSPECIMENS ................................................................................................... 10 
7.3.1 
Specimen Collection Procedures ........................................................................................... 10 
7.3.2 
Specimen Preparation, Handling, and Storage ..................................................................... 10 
7.3.3 
Specimen Shipment ............................................................................................................... 10 
7.4 
QUESTIONNAIRE ADMINISTRATION ................................................................................................. 10 
8 
SAFETY AND ADVERSE EVENTS ................................................................................................... 10 
8.1 
DEFINITIONS ................................................................................................................................. 10 

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8.2 
RECORDING OF ADVERSE EVENTS ................................................................................................. 11 
8.3 
REPORTING OF SERIOUS ADVERSE EVENTS AND UNANTICIPATED PROBLEMS.................................. 12 
8.3.1 
Investigator reporting: notifying the IRB ................................................................................. 12 
9 
STUDY OVERSIGHT .......................................................................................................................... 13 
10 
STATISTICAL CONSIDERATIONS ................................................................................................... 13 
10.1 
STUDY HYPOTHESES .................................................................................................................... 13 
10.2 
SAMPLE SIZE DETERMINATION ...................................................................................................... 13 
10.3 
STATISTICAL METHODS ................................................................................................................. 13 
11 
SOURCE DOCUMENTS AND ACCESS TO SOURCE DATA/DOCUMENTS ................................. 13 
12 
ETHICS/PROTECTION OF HUMAN SUBJECTS ............................................................................. 14 
12.1 
ETHICAL STANDARD ...................................................................................................................... 14 
12.2 
INSTITUTIONAL REVIEW BOARD ..................................................................................................... 14 
12.3 
INFORMED CONSENT PROCESS ..................................................................................................... 14 
12.3.1 
Consent/Assent and Other Informational Documents Provided to Participants ............... 14 
12.3.2 
Consent Procedures and Documentation ......................................................................... 14 
12.3.3 
Research Use of Stored Human Samples, Specimens, or Data....................................... 14 
12.4 
FUTURE USE OF STORED SPECIMENS ............................................................................................ 15 
13 
DATA HANDLING AND RECORD KEEPING ................................................................................... 15 
13.1 
DATA COLLECTION AND MANAGEMENT RESPONSIBILITIES .............................................................. 15 
13.2 
STUDY RECORDS RETENTION........................................................................................................ 15 
13.3 
PROTOCOL DEVIATIONS ................................................................................................................ 15 
13.4 
PUBLICATION AND DATA SHARING POLICY ..................................................................................... 15 
14 
STUDY FINANCES ............................................................................................................................. 15 
14.1 
FUNDING SOURCE ........................................................................................................................ 15 
14.2 
COSTS TO THE PARTICIPANT ......................................................................................................... 15 
14.3 
PARTICIPANT REIMBURSEMENTS OR PAYMENTS ............................................................................. 15 
15 
STUDY ADMINISTRATION ................................................................................................................ 16 
15.1 
STUDY LEADERSHIP ...................................................................................................................... 16 
16 
CONFLICT OF INTEREST POLICY ................................................................................................... 16 
17 
REFERENCES .................................................................................................................................... 17 
18 
ATTACHMENTS ................................................................................................................................. 24 
 
 
 

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List of Abbreviations 
 
AE 
Adverse Event/Adverse Experience 
BE 
Behavioral Economics 
CDS 
Clinical Decision Support 
CFR 
Code of Federal Regulations 
CRF 
Case Report Form 
CSOC 
Clinical Study Oversight Committee 
CW 
Choosing Wisely 
DCC 
Data Coordinating Center 
DHHS 
Department of Health and Human Services 
EHR 
Electronic Health Record 
FFR 
Federal Financial Report 
FWA 
Federalwide Assurance 
HIPAA 
Health Insurance Portability and Accountability Act 
ICF 
Informed Consent Form 
IRB 
Institutional Review Board 
MOP 
Manual of Procedures 
N 
Number (typically refers to participants) 
NIH 
National Institutes of Health 
NYULH 
New York University Langone Health 
OHRP 
Office for Human Research Protections 
OHSR 
Office of Human Subjects Research 
PI 
Principal Investigator 
QA 
Quality Assurance 
QC 
Quality Control 
SAE 
Serious Adverse Event/Serious Adverse Experience 
SOP 
Standard Operating Procedure 
US 
United States 
 
 
 
 
 
 
 

Study number: s19-01464 
 
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authorized in writing by the study sponsor 
Drug/Device Template Version: 13 JUN 2016 
Protocol Summary 
Title 
A randomized trial of a BE-EHR module to guide the care of older adults with 
diabetes 
Short Title 
A randomized trial of a BE-EHR module to guide the care of older adults with 
diabetes 
Brief Summary 
This RCT will test a new electronic health record module to improve 
guideline-compliant care of older adults with diabetes.  The module 
incorporates effective behavioral economics (BE) principles to improve the 
degree to which care of older adults is compliant with Choosing Wisely (CW) 
guidelines; this generally involves less aggressive targets for HbA1c, and 
reductions of medications other than metformin.  The implementation of the 
module is triggered by patient scheduling and medication prescribing in EPIC.  
The BE principles include suggesting alternatives to medications, requiring 
justification, setting of appropriate default order sets, and incorporation of 
anchoring and checklists to guide behavior.  
Objectives 
Primary objective: to test a customized EHR toolkit that applies BE insights to 
promote appropriate diabetes care in older adults based on the American 
Geriatric Society’s Choosing Wisely guideline, and to assess the acceptability 
of the resulting module. 
Secondary objective: to assess the “usability” of the proposed BE-EHR 
module in clinical practice. 
Methodology 
Cluster-randomized controlled trial 
Endpoints 
Primary endpoint: Choosing Wisely guideline compliance  
Secondary endpoint: medication prescribing patterns 
Study Duration 
Eighteen months 
Participant Duration 
Eighteen months 
Population 
Patients age 75 or older with diabetes 
Study Sites 
One single site at NYULH consisting of approximately 70 outpatient practices 
in primary care, geriatrics, or endocrinology 
Number of participants 
Approximately 5000 
Statistical Analysis 
We will begin all analyses with descriptive summary statistics and graphical 
displays of all variables. Primary analyses will utilize Poisson mixed-effects 
models for the provider-level CW compliance rate, with treatment group as 
the primary fixed effect of interest and practice as a random effect to 
accommodate clustering of providers within practices. We will assess whether 
negative binomial regression is warranted because of overdispersion. We will 
also explore non-parametric methods such as quantile regression and other 
rank-based approaches. Although randomization should obviate the need for 
any additional adjustment, we will explore whether adjustment for provider-
level characteristics, such as demographics, panel size, or insurance mix, is 
necessary. 
 
 
 

Study number: s19-01464 
 
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Schematic of Study Design 
 
 
Prior to  
Enrollment 
 
 
 
 
 
Randomization 
 
 
 
 
Evaluation 
 
 
 
 
 
 
Identify ~70 NYULH clinics to participate 
Randomize evenly in a 1:1 ratio to receive the standard EPIC instantiation or the 
standard EPIC instantiation plus the BE-EHR module. 
Evaluate Choosing Wisely compliance rate in patients meeting eligibility seen at 
participating practices 

Study number: s19-01464 
 
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1 
Key Roles 
 
PI:  Andrea B. Troxel, ScD, Professor of Population Health and Director of Biostatistics 
NYU School of Medicine 
180 Madison Avenue 
646-501-3654 
andrea.troxel@nyulangone.org 
 
 
Sub-I:  Devin Mann, MD, Associate Professor of Medicine and Population Health  
NYU School of Medicine 
227 East 30th Street 
212-263-8313 
devin.mann@nyulangone.org 
 
 
Sub-I:  Saul Blecker, MD, Assistant Professor of Medicine and Population Health  
NYU School of Medicine 
227 East 30th Street 
646-501-2513 
saul.blecker@nyulangone.org 
 
 
Sub-I:  Sara Chokshi, PhD, Assistant Professor of Population Health  
NYU School of Medicine 
227 East 30th Street 
646-501-2501 
sara.chokshi@nyulangone.org 
 
 
Sub-I:  Hayley Belli, PhD, Postdoctoral Fellow  
NYU Langone Health 
180 Madison Avenue  
541-207-6726 
Hayley.belli@nyulangone.org 
 
 
2 
Introduction, Background Information and Scientific Rationale 
2.1 
Background Information and Relevant Literature 
 
2.1.1  Intensive glycemic control is of unclear benefit and carries increased risk for older adults with 
diabetes. A number of randomized controlled trials, including the Action to Control Cardiovascular Risk in 
Diabetes (ACCORD) trial,1 the Action in Diabetes and Vascular Disease: Preterax and Diamicron 
Modified Release Controlled Evaluation (ADVANCE) trial,2 and the Veterans Affairs Diabetes Trial 
(VADT)3, found that intensive glycemic control was not protective for macrovascular complications of 
diabetes including myocardial infarction or stroke. Although the United Kingdom Prospective Diabetes 
Study (UKPDS) did find a reduction in myocardial infarction in the long term after intensive glycemic 
control, this study enrolled middle-aged patients at time of diabetes diagnosis.4 ACCORD,5 ADVANCE,2 
and UKPDS6 all suggested improvements in microvascular complications with tight glycemic control, but 
these often take years to develop. In total, these trials suggest that tight glycemic control is primarily 
beneficial to patients with newly diagnosed diabetes and a long life expectancy; these characteristics do 
not apply to most older patients, many of whom have been living with diabetes as a chronic disease.7 
Furthermore, these trials demonstrated the potential for harm with tight glycemic control, notably 

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increased risk of hypoglycemia,1,2 as well as a suggestion of increased all-cause mortality.1 Indeed, older 
adults are particularly susceptible to harms related to hypoglycemia in diabetes, including emergent 
hospitalization and neurologic complications.8-12 Intensive glycemic treatment may also lead to increased 
risk of polypharmacy and adverse medicine interactions for older adults with multiple chronic conditions.13  
 
2.1.2  The American Geriatric Society’s Choosing Wisely guideline recommends moderate glycemic 
control. The third Choosing Wisely recommendation from the American Geriatric Society is: “Avoid using 
medications other than metformin to achieve hemoglobin A1c<7.5% in most older adults; moderate 
control is generally better.”14 Furthermore, as shorter life expectancy and greater comorbidity burden may 
decrease the benefits and increase the risks of tight glycemic control, the Choosing Wisely campaign 
suggests using these markers of health status to identify glycemic targets for older patients with diabetes. 
Glycemic target HbA1c ranges are provided for older adults in three categories: 7.0-7.5% for healthy 
adults with a long life expectancy, 7.5-8.0% for patients with moderate comorbidity and a life expectancy 
of less than 10 years, and 8.0-9.0% for patients with multiple comorbid conditions and a shorter life 
expectancy.7,15 These recommendations build on work by Co-investigator Dr. Caroline Blaum on 
categorization of older adults with diabetes into three clinical groups based on health status;16 this has 
been endorsed by numerous expert panels and guidelines.11,17,18 The target ranges for glycemic control 
are similar to HbA1c values that have been associated with the best outcomes for older adults with 
comorbid conditions in observational or modeling studies.19-21 Additionally, the American Diabetes 
Association (ADA) and others have recommended similar health status categories related to HbA1c 
targets in older adults.11,17,18 Although the ADA guidelines do not identify lower targets for HbA1c,11 other 
societies including the American Geriatric Society recommend similar lower thresholds for glycemic 
control.18,22-24  Unfortunately, despite these recommendations, a substantial number of older adults have 
intensive glycemic control that may not be necessary.25-27   Additionally, older patients with intensive 
glycemic control generally do not undergo de-intensification of therapy, suggesting opportunity for 
improving appropriate care.28     
 
2.1.3  Behavioral economic approaches offer promise in influencing hard-to-change behavior. The field of 
behavioral economics (BE) seeks to combine principles of standard economics and psychology to 
recognize the limitations of the classical economic framework that views human decision-makers as 
purely rational actors who make decisions by maximizing the outcomes of available choice sets 
(sometimes termed Homo economicus).29  In reality, humans are predictably irrational,30 making common 
decision errors that are explicable through a set of psychological principles, and are therefore predictable.  
The field of behavioral economics posits that these decision errors, once recognized, can be harnessed 
to encourage desired behaviors rather than inhibit them.31 
 
2.1.4  Decision errors fall into a typology of recognizable classes with known mechanisms. Traits 
contributing to decision errors include loss aversion, anchoring, overweighting of small probabilities, 
present bias, regret aversion, sensitivity to defaults, and the power of social comparisons.32 Once 
recognized, each of these decision errors can be harnessed and overcome, often in the form of gentle 
“nudges” that make a desired behavior more likely.33-36  Loss aversion occurs when a loss is more 
distressing than a gain of equivalent value;37,38 thus, presenting rewards using a loss rather than a gain 
frame can be an effective motivator.  Anchoring occurs when the decision-maker compares potential 
outcomes to some specified or implied reference level;39 responses can be heavily influenced by the first 
information presented, so adjustment of the scale and/or starting point of a choice set can influence the 
resulting decisions. Present bias, also called hyperbolic discounting,40 occurs when outcomes occurring in 
the near future carry much more weight than those occurring further into the future.41  Human beings tend 
to overweigh small probabilities, one feature that contributes to the popularity of lotteries.42,43 Regret 
aversion can be a highly motivating force in determining future actions.44-46  Regret lotteries are a 
behavioral economic intervention simultaneously targeting probabilistic errors, present bias, and regret; in 
this approach, rewards are contingent on a desired behavior and participants are informed that they 
would have won if they had only completed it.47,48   Defaults are a powerful driver of decisions, carrying 
implications of superiority or endorsement and taking advantage of decisional inertia; examples exist in 
such diverse domains as participation in retirement plans49 and organ donation50 that demonstrate 
dramatic differences in participation as a result of the chosen default option. Social comparisons have 

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been shown in a number of settings to be effective in influencing behavior,24,51 as people tend to be 
heavily motivated by their perception of how their performance compares to those around them;52,53 the 
phenomenon of social desirability bias is well documented.54,55 
 
2.1.5  Substantial evidence shows the benefits and limitations of clinical decision support on clinical care.  
Electronic health records (EHRs) now dominate the landscape, influencing nearly every clinical decision, 
workflow, and order placed by health care providers.  While EHRs have been successful in standardizing 
documentation, facilitating data sharing, and improving safety, their impact on clinical decision making has 
been mixed.  Clinical decision support (CDS) is the primary EHR tool for influencing clinical decision making 
and promoting adherence to clinical guidelines. CDS entails providing clinicians, patients, and other 
healthcare stakeholders with pertinent knowledge, templates, and/or person-specific information, 
intelligently filtered or presented at appropriate times, to enhance health and healthcare.56 CDS is an 
effective tool for improving provider performance and patient outcomes;57 a meta-analysis of 25 randomized 
controlled trials found strong evidence that CDS improves care (odds ratio 1.42, 95% confidence interval 
1.27 to 1.58).58 Moreover, best practices for optimizing CDS effectiveness have been identified.59,60  
Successful CDS must deliver accurate information in the right clinical context at the point of care, and 
must be integrated into the relevant provider’s workflow.61 CDS can take many forms; to date, alerts and 
reminders have been the dominant CDS tools.62  Large systematic reviews of CDS have demonstrated a 
moderate ability to reduce morbidity, utilization, and costs.63,64  These modest improvements, however, 
are undermined by the well-documented problems of alert fatigue and poor workflow integration, which 
together blunt the potential impact of EHRs and CDS to improve healthcare outcomes.62  
 
2.1.6  New studies indicate that combining behavioral economic and EHR clinical decision support tools 
offers promise for improving guideline adherence. Integrating behavioral economics and electronic health 
records using various CDS tools is a novel approach to improving adherence to guidelines that also 
seeks to minimize negative impacts on clinical workflow and cognitive load.  For example, Meeker et al. 
integrated three BE concepts (suggested alternatives, accountable justification, and peer comparisons) 
into the EHR at 50 primary care practices to significantly (~5-7%) reduce inappropriate antibiotic 
prescribing for upper respiratory infections.51  New approaches like these are needed to complement the 
traditional alerts, reminders, and other CDS tools that disrupt clinical workflow, increase cognitive load, 
and stress the limited capacity of clinicians to rationally process and evaluate the diverse and competing 
demands on their attention. 
 
2.1.7  Innovation. This study will evaluate an innovative tool, implementing behavioral economic principles 
within the electronic health record, to promote clinician adherence to the Choosing Wisely guideline for 
diabetes management in older adults.  Specific areas of innovation include: 
 
• 
Minimal impact on clinician workflow or cognitive workload.  Unlike most new CDS systems, the 
proposed BE-EHR module will have limited negative impact on clinical workflow and cognitive load. 
BE tools inherently bypass the central processing route that requires clinicians to actively think about 
decision making.66 Instead it leverages the peripheral route, which uses contextual cues and other 
influencing tools to nudge clinicians to choose actions consistent with stated guidelines. 
 
• 
Extending the power of BE by combining modalities using the EHR.  Several BE approaches 
have demonstrated efficacy. By combining multiple BE approaches and packaging them in a 
customizable EHR module, we will amplify the potential impact of BE on clinician guideline adherence 
when treating older people with diabetes. 
 
• 
A flexible, scalable, modular intervention.  The creation of the BE-EHR module serves as a highly 
scalable platform for embedding BE-based CDS into any EHR system. The proposed module will 
guide other EHR users through a menu of customizable options that can switch on (or off) various 
BE-derived CDS tools to replicate our intervention for improving clinician adherence to diabetes 
management guidelines in older adults. More importantly, this module can be easily applied to many 
other conditions in older adults and other populations where combining BE with EHR-based clinical 
decision support will be useful for improving guideline adherence. 

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• 
A scalable, interoperable, standards-based solution. The BE-EHR tool will follow standards-
based development approaches, enabling widespread adoption across healthcare systems and 
diverse EHR platforms. 
2.2 
Rationale 
This study aims to evaluate a new BE-EHR module to improve adherence to Choosing Wisely guidelines 
among older adults with diabetes.  
2.3 
Potential Risks & Benefits 
2.3.1 
Known Potential Risks 
The risk is not greater than minimal risk (i.e., risk encountered in daily life or in the course of usual 
clinical practice). Individual case determinations of potential safety gaps will never be released to 
supervisors of any clinicians whose charts are being reviewed. None of the study investigators has any 
oversight capacity over any clinicians. 
2.3.2 
Known Potential Benefits 
There may be benefit to individual participants in this pilot study if their care is improved to comport with 
guidelines. The research will contribute critically to our knowledge of how to integrate behavioral 
economic interventions into EHR-driven clinical workflows. In the past, our team has successfully 
identified the potential benefit of connecting interventions with clinical workflows, and will assess a novel 
BE-EHR module to enable improved, guideline-consistent care of older adults with diabetes. 
 
3 
Objectives and Purpose 
3.1 
Primary Objective 
The primary objective of the study is to evaluate a customized EHR toolkit that applies behavioral 
economic insights to promote appropriate diabetes care in older adults based on the American Geriatric 
Society’s Choosing Wisely (CW) guideline. 
3.2 
Secondary Objectives (if applicable) 
The secondary objective of the study is to continue to assess the usability of the proposed BE-EHR 
module in clinical practice. 
 
4 
Study Design and Endpoints 
4.1 
Description of Study Design 
NYU Langone Health has ambulatory care sites in the five boroughs of New York City (NYC) as well as in 
suburbs of New York and New Jersey. Sites include academic practices, many community-based 
practices and a Federally Qualified Health Center (FQHC); many are multispecialty. Physicians in these 
practices form the NYU Faculty Group Practice (FGP) consisting of 351 total practices, including 81 
primary care and 21 endocrinology practices. The NYU Brooklyn Family Health Center (FHC) is the 
second largest FQHC in the nation and has 9 additional clinics. All have the NYU Epic EHR, connecting 
all ambulatory visits at FGP clinics and at the FQHC.  We will identify approximately 70 practices and 
conduct a parallel-groups cluster-randomized comparison of standard EPIC to standard EPIC + the BE-
EHR module. 
4.2 
Intervention 
The BE-EHR module includes six components: 1) a tailored advisory for patients over 75 with diabetes, 2) 
medication refill protocol with information on Choosing Wisely guidelines, 3) pre-population of the 
medication preference list with metformin, 4) lab result protocol with information on Choosing Wisely 

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guidelines, 5) peer comparisons regarding performance meeting guidelines, and 6) media campaign with 
information about Choosing Wisely guidelines. The set of nudges is referred to collectively as the BE-
EHR module. 
 
To tailor the intervention appropriately, we use a life expectancy algorithm to address different target 
patient populations with different CW guidelines. The life expectancy algorithm was built into the BE-EHR 
module to drive timing and content of module firings that incorporate both patient life expectancy (high, 
medium, low) and target glycemic index per the CW guideline. These categories are defined as follows:   
1) healthy older adults with long life expectancy (>10 years): HbA1c target range of 7-7.5%; 
2) those with moderate comorbidity and a moderate life expectancy (3-10 years): HbA1c target range of 
7.5-8%; 
3) those with multiple comorbidities and shorter life expectancy (3 years): HbA1c target range of 8-9%. 
4.3 
Randomization 
There are currently 81 primary care practices in our FGP. Practice sites will be randomized using variable 
block sizes of 4 and 6 to ensure balance, with an even randomization ratio that will result in half of the 
sites randomized to the BE-EHR module activation (BE-EHR) and half randomized to usual care (UC). 
Randomization will be stratified by practice size (<= 100 patients vs > 100 patients over 75 with diabetes) 
to ensure balance. Dr. Troxel will request that the randomization scheme be generated by one of her 
faculty or staff members in the Division of Biostatistics who is unaffiliated with the project, in order to 
maintain blinding of assignment by the study team. 
4.4 
Study Endpoints 
Outcomes measured at the prescription encounter level include whether the prescription is for a diabetes 
medication other than metformin, and whether the patient’s most recent HbA1c value is within the 
Choosing Wisely HbA1c range guidelines for his/her age and morbidity category; these will be combined 
to classify the encounter as CW-compliant or CW-noncompliant. Outcomes measured at the physician 
level include these two measures, aggregated as a proportion of total prescription encounters within all 
eligible patients. 
5 
Study Enrollment and Withdrawal 
5.1 
Inclusion Criteria 
• 
Patient at NYULH primary care or endocrinology practice 
• 
Practices that have patients aged 75 or older 
• 
Practices that have a diagnosis of diabetes in the EHR chart 
5.2 
Exclusion Criteria 
• 
Under age 75 
5.3 
Vulnerable Subjects 
No vulnerable subjects will be enrolled. 
5.4 
Strategies for Recruitment and Retention 
Eligible patients from the chosen practice sites will be identified electronically within EPIC using an 
algorithm developed by the study team. This algorithm reviews patient records for diabetes codes in the 
Problem List.  Patients 75 and over with one of the included codes are eligible.  The algorithm then 
identifies their life expectancy category using comorbidity codes and assigns them to one of the three 
Choosing Wisely categories described in Section 4.2.  Patients meeting eligibility will be automatically 
included, and all data collection will take place passively via the NYULH EHR system, EPIC. An EPIC 
reporting analyst will extract the relevant parameters from EPIC into a report. No study-specific case 
report forms will be used; rather, information from the patient’s problem list, prescription history, and 

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demographics will be used to compute compliance with CW guidelines. Patients from the selected 
practice sites will undergo their usual interactions with their providers and no study-specific activities will 
take place. 
5.4.1 
Use of DataCore/Epic Information for Recruitment Purposes 
Eligible patients will be identified electronically within EPIC using an algorithm developed by the study 
team. 
5.5 
Duration of Study Participation 
Participants will be followed for 18 months. 
5.6 
Total Number of Participants and Sites  
This is a single site study involving approximately 70 NYULH outpatient clinics serving a total of 
approximately 5000 participants. 
5.7 
Participant Withdrawal or Termination 
5.7.1 
Reasons for Withdrawal or Termination 
Not applicable as the subjects are recruited at the clinic level. 
5.7.2 
Handling of Participant Withdrawals or Termination 
Not applicable. 
5.7.3 
Premature Termination or Suspension of Study 
This study may be temporarily suspended or prematurely terminated if there is sufficient reasonable 
cause. Written notification, documenting the reason for study suspension or termination, will be provided 
by the suspending or terminating party to Drs. Troxel and Mann, the NIH/NIA, and regulatory authorities. 
If the study is prematurely terminated or suspended, the PI will promptly inform the IRB and will provide 
the reason(s) for the termination or suspension. 
 
Circumstances that may warrant termination or suspension include, but are not limited to: 
• 
Determination of unexpected, significant, or unacceptable risk to participants 
• 
Insufficient compliance to protocol requirements 
• 
Data that are not sufficiently complete and/or evaluable 
• 
Determination of futility 
 
Study activities may resume once concerns about safety, protocol compliance, and data quality are 
addressed and satisfy the sponsor and/or IRB. 
 
6 
Study Schedule 
Participants will be included in this study by virtue of meeting eligibility criteria and having a patient 
encounter during the study period (i.e. the trial) of eighteen months from the date of randomization.  
There are no study-specific visits.  Any patient meeting criteria who has one or more patient encounters 
during the 18-month study period will be included.  Therefore, no study-specific visits are relevant for this 
study, so there is no schedule of visits. 
6.1 
Screening 
Not applicable. 
6.2 
Enrollment/Baseline 
Not applicable. 

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6.3 
Intermediate Visits 
Not applicable. 
6.4 
Final Study Visit 
Not applicable. 
6.5 
Withdrawal Visit 
Not applicable. 
6.6 
Unscheduled Visit 
Not applicable. 
 
7 
Study Procedures/Evaluations 
There are no study-specific procedures that patients will undergo.  They will receive care as usual by their 
outpatient providers.  In order to evaluate Choosing Wisely compliance status, the BE-EHR algorithm will 
capture information on demographics, diagnoses in the patient’s problem list, prescription history, and 
relevant lab results (i.e. allergies and blood glucose (HbA1c)).  The study team members will not collect 
this information directly; data will be collected in NYULH’s EHR system, EPIC, and an EPIC Reporting 
analyst will extract the relevant parameters from the EHR into a report. The study team members only 
receive the ultimate determination from the algorithm that the patient is either CW-compliant or CW-
noncompliant. 
7.1 
Procedures/Evaluations 
Practice sites will be randomized using variable block sizes of 4 and 6 to ensure balance, with an even 
randomization ratio that will result in half of the sites randomized to the BE-EHR module activation (BE-
EHR) and half randomized to usual care (UC). Randomization will be stratified by practice size (<= 100 
patients vs > 100 patients over 75 with diabetes) to ensure balance. See Section 4.3 for greater detail. 
 
Eligible patients from the chosen practice sites will be identified electronically within EPIC using an 
algorithm developed by the study team. This algorithm reviews patient records for diabetes codes in the 
Problem List.  Patients 75 and over with one of the included codes are eligible.  The algorithm is outlined 
below as well as a description of each component of the interventional module: 
1. Evaluation of Choosing Wisely compliance using a built-in algorithm. The life expectancy 
algorithm was built into the BE-EHR module to drive timing and content of module firings that 
incorporate both patient life expectancy (high, medium, low) and target glycemic index per the 
CW guideline. These categories are defined as follows: 
1) healthy older adults with long life expectancy (>10 years): HbA1c target range of 7-7.5% 
2) those with moderate comorbidity and a moderate life expectancy (3-10 years): HbA1c target 
range of 7.5-8% 
3) those with multiple comorbidities and shorter life expectancy (3 years): HbA1c target range 
of 8-9% 
2. Firings of various module components using internal EPIC reporting. Each of the six components 
is triggered when a participant who has lower than recommended glycemic control is encountered 
in the system. 
1) Tailored Advisory: The Tailored Advisory nudge activates in Epic for any patient who is not 
CW compliant. For each patient seen, clinicians can respond by clicking the “Agree with 

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recommendation. Action taken” button, or by selecting the “Clinically inappropriate. Please 
explain” option, with space for free text comments. A response is not required. 
2) Refill Protocol: The Refill Protocol nudge activates in Epic any time a medication refill for 
either Metformin or a non-Metformin diabetes medication is generated for a patient over 75 
3) Preference List: The Preference List nudge is a system level nudge at the pilot sites. 
Metformin is listed at the top of the page as the choice for “First-line Type 2 Diabetes,” 
without restricting orders for non-Metformin medications. 
4) Lab Result: The Lab Result nudge activates in Epic whenever there is a new A1c lab result 
for a non-CW compliant patient; the alert remains active in Epic for seven days following the 
result. 
5) Peer Comparison: The Peer Comparison nudge is sent via a secured Microsoft Outlook 
account once per month. The subject line of the email is “Message from the desk of Dr. 
[Insert Practice Director Name]” and the email content includes three graphics: a CW 
compliance rate for the individual provider, a CW compliance rate for the clinician’s practice 
site, and a CW compliance rate across all NYU Langone practices. Depending on whether 
the clinician’s CW compliance rate was above or below the rate of their respective practice, 
the provider receives either a “negative” or “positive” version of the email 
6) Campaign: The campaign toolkit for dissemination includes three gameshow-themed 
animations (Price is Right, Jeopardy, and Who Wants to Be a Millionaire), as well as a 
flashcard deck that quizzes physicians on CW best practices. The Price is Right campaign 
includes three unique variations and the Jeopardy campaign includes four unique variations. 
7.2 
Laboratory Procedures/Evaluations 
Not applicable. 
7.3 
Study Specific Biospecimens 
7.3.1 
Specimen Collection Procedures 
Not applicable. 
7.3.2 
Specimen Preparation, Handling, and Storage 
Not applicable. 
7.3.3 
Specimen Shipment 
Not applicable. 
7.4 
Questionnaire Administration 
Not applicable. 
 
8 
Safety and Adverse Events 
8.1 
Definitions 
Unanticipated Problems Involving Risk to Subjects or Others 
Any incident, experience, or outcome that meets all of the following criteria:  
• Unexpected in nature, severity, or frequency (i.e., not described in study-related documents such 
as the IRB-approved protocol or consent form, the investigators brochure, etc) 
• Related or possibly related to participation in the research (i.e., possibly related means there is a 
reasonable possibility that the incident experience, or outcome may have been caused by the 
procedures involved in the research) 

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• Suggests that the research places subjects or others at greater risk of harm (including physical, 
psychological, economic, or social harm). 
 
Adverse Event 
An adverse event (AE) is any symptom, sign, illness or experience that develops or worsens in severity 
during the course of the study.  Intercurrent illnesses or injuries should be regarded as adverse events.  
Abnormal results of diagnostic procedures are considered to be adverse events if the abnormality: 
• results in study withdrawal 
• is associated with a serious adverse event 
• is associated with clinical signs or symptoms 
• leads to additional treatment or to further diagnostic tests 
• is considered by the investigator to be of clinical significance 
 
Serious Adverse Event 
Adverse events are classified as serious or non-serious.  A serious adverse event is any AE that is:  
• fatal 
• life-threatening 
• requires or prolongs hospital stay 
• results in persistent or significant disability or incapacity 
• a congenital anomaly or birth defect 
• an important medical event 
 
Important medical events are those that may not be immediately life threatening, but are clearly of major 
clinical significance.   They may jeopardize the subject, and may require intervention to prevent one of the 
other serious outcomes noted above.  For example, drug overdose or abuse, a seizure that did not result 
in in-patient hospitalization, or intensive treatment of bronchospasm in an emergency department would 
typically be considered serious.  
 
All adverse events that do not meet any of the criteria for serious should be regarded as non-serious 
adverse events.  
 
Post-study Adverse Event 
All unresolved adverse events should be followed by the investigator until the events are resolved, the 
subject is lost to follow-up, or the adverse event is otherwise explained.  At the last scheduled visit, the 
investigator should instruct each subject to report any subsequent event(s) that the subject, or the 
subject’s personal physician, believes might reasonably be related to participation in this study.  The 
investigator should notify the study sponsor of any death or adverse event occurring at any time after a 
subject has discontinued or terminated study participation that may reasonably be related to this study.  
8.2 
Recording of Adverse Events 
At each contact with the subject, the investigator must seek information on adverse events by specific 
questioning and, as appropriate, by examination.  Information on all adverse events should be recorded 
immediately in the source document, and also in the appropriate adverse event module of the case report 
form (CRF).  All clearly related signs, symptoms, and abnormal diagnostic procedures results should 
recorded in the source document, though should be grouped under one diagnosis. 
 
All adverse events occurring during the study period must be recorded.  The clinical course of each event 
should be followed until resolution, stabilization, or until it has been determined that the study treatment or 
participation is not the cause.  Serious adverse events that are still ongoing at the end of the study period 
must be followed up to determine the final outcome.  Any serious adverse event that occurs after the 
study period and is considered to be possibly related to study participation should be recorded and 
reported immediately. 

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8.3 
Reporting of Serious Adverse Events and Unanticipated Problems 
Serious adverse events and unanticipated problems must be reported if they are: 
• related to study participation, 
• unexpected, and  
• serious or involve risks to subjects or others. 
 
For Narrative Reports of Safety Events 
If the report is supplied as a narrative, the minimum necessary information to be provided at the time of 
the initial report includes: 
• 
Study identifier 
• 
Study Center 
• 
Subject number 
• 
A description of the event 
• 
Date of onset 
• 
Current status 
• 
Whether study treatment was discontinued 
• 
The reason why the event is classified as 
serious 
• 
Investigator assessment of the association 
between the event and study treatment 
8.3.1 
Investigator reporting: notifying the IRB 
Federal regulations require timely reporting by investigators to their local IRB of unanticipated problems 
posing risks to subjects or others.  The following describes the NYULMC IRB reporting requirements, 
though Investigators at participating sites are responsible for meeting the specific requirements of their 
IRB of record.  
 
Report Promptly, but no later than 5 working days: 
Researchers are required to submit reports of the following problems promptly but no later than 5 working 
days from the time the investigator becomes aware of the event: 
• 
Unanticipated problems including adverse events that are unexpected and related 
– Unexpected: An event is “unexpected” when its specificity and severity are not accurately 
reflected in the protocol-related documents, such as the IRB-approved research protocol, any 
applicable investigator brochure, and the current IRB-approved informed consent document 
and other relevant sources of information, such as product labeling and package inserts.  
– Related to the research procedures: An event is related to the research procedures if in the 
opinion of the principal investigator or sponsor, the event was more likely than not to be 
caused by the research procedures.  
– Harmful: either caused harm to subjects or others, or placed them at increased risk 
 
Other Reportable events: 
The following events also require prompt reporting to the IRB, though no later than 5 working days: 
• Complaint of a research subject when the complaint indicates unexpected risks or the complaint 
cannot be resolved by the research team. 
• Protocol deviations or violations (includes intentional and accidental/unintentional deviations 
from the IRB approved protocol) for any of the following situations:  
– one or more participants were placed at increased risk of harm  
– the event has the potential to occur again 
– the deviation was necessary to protect a subject from immediate harm 
• Breach of confidentiality 
• Incarceration of a participant when the research was not previously approved under Subpart C 
and the investigator believes it is in the best interest of the subject to remain on the study. 
• New Information indicating a change to the risks or potential benefits of the research, in 
terms of severity or frequency. (e.g. analysis indicates lower-than-expected response rate or a 

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more severe or frequent side effect; Other research finds arm of study has no therapeutic value; 
FDA labeling change or withdrawal from market) 
 
Reporting Process 
The reportable events noted above will be reported to the IRB using a Reportable New Information 
submission and will include a description of the event with information regarding its fulfillment of the 
above criteria, follow-up/resolution, and need for revision to consent form and/or other study 
documentation. Copies of each report and documentation of IRB notification and receipt will be kept in the 
Clinical Investigator’s study file. 
9 
Study Oversight 
It is the responsibility of the Principal Investigator to oversee the safety of the study at her site.  This 
safety monitoring will include careful assessment and appropriate reporting of adverse events as noted 
above, as well as the construction and implementation of a site data and safety-monitoring plan (see 
Section 11: Study Auditing, Monitoring and Inspecting).  Medical monitoring will include a regular 
assessment of the number and type of serious adverse events. 
10 Statistical Considerations 
10.1 Study Hypotheses 
Patient in clinics receiving the BE-EHR module will have higher rates of CW compliance. 
10.2 Sample Size Determination 
Our preliminary data indicate that approximately 30-40% of eligible patients have care that is CW-
compliant. We wish to be able to detect an increase in the rate of CW-compliant care of ten percentage 
points or more. We conservatively estimate an ICC (intra-class correlation coefficient, a measure of the 
degree of additional correlation among providers within the same practice) of 0.05; with an average of 
four providers per practice site at 70 sites, this leads to a design effect of 1.15. The sample of 280 
providers becomes an effective sample size of 244, providing approximately 87% power to detect a 
difference of ten percentage points between the study arms. 
10.3 Statistical Methods 
We will begin all analyses with descriptive summary statistics and graphical displays of all variables. 
Primary analyses will utilize Poisson mixed-effects models for the provider-level CW compliance rate, with 
treatment group as the primary fixed effect of interest and practice as a random effect to accommodate 
clustering of providers within practices. We will assess whether negative binomial regression is warranted 
because of overdispersion. We will also explore non-parametric methods such as quantile regression and 
other rank-based approaches. Although randomization should obviate the need for any additional 
adjustment, we will explore whether adjustment for provider-level characteristics, such as demographics, 
panel size, or insurance mix, is necessary. 
11 Source Documents and Access to Source Data/Documents 
Source data is all information, original records of clinical findings, observations, or other activities in a 
study necessary for the reconstruction and evaluation of the study.  Source data are contained in source 
documents.  Examples of these original documents, and data records include: hospital records, clinical 
and office charts, laboratory notes, memoranda, subjects’ diaries or evaluation checklists, pharmacy 
dispensing records, recorded data from automated instruments, copies or transcriptions certified after 
verification as being accurate and complete, microfiches, photographic negatives, microfilm or magnetic 
media, x-rays, subject files, and records kept at the pharmacy, at the laboratories, and at medico-
technical departments involved in the study.  
 
The EPIC electronic health record is the primary data collection instrument for the study.  Study-specific 
case report forms will not be used. 

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Access to study records will be limited to IRB-approved members of the study team. The investigator will 
permit study-related monitoring, audits, and inspections by the IRB/EC, the sponsor, government 
regulatory bodies, and University compliance and quality assurance groups of all study related 
documents (e.g., source documents, regulatory documents, data collection instruments, study data etc.).  
The investigator will ensure the capability for inspections of applicable study-related facilities (e.g., 
pharmacy, diagnostic laboratory, etc.). 
12 Ethics/Protection of Human Subjects 
12.1 Ethical Standard 
The investigator will ensure that this study is conducted in full conformity with Regulations for the 
Protection of Human Subjects of Research codified in 45 CFR Part 46. 
12.2 Institutional Review Board 
The protocol for study i19-01464,  request for waiver of informed consent, and request for waiver of 
HIPAA authorization will be submitted to the IRB for review and approval. Approval of the protocol, the 
waiver of consent, and the waiver of HIPAA authorization must be obtained before any participant is 
enrolled. Any amendment to the protocol will require review and approval by the IRB before the changes 
are implemented to the study.  
12.3 Informed Consent Process 
We will obtain a waiver of informed consent and a waiver of HIPAA authorization for patients seen in the 
participating clinics to acknowledge their participation at a clinic level. The risks to subjects are exactly 
equivalent to the risks experienced in the standard setting of patient care.  The new module to be 
activated in EPIC relates only to guidance for physician decision-making regarding diabetes care, and 
has no impact on disclosure or treatment of PHI. Subjects will receive care from their providers in the 
usual way.  The only difference is the interface with the electronic health record that is used by the 
physician during the course of the patient encounter.  The autonomy of the provider to make decisions 
about patient care will not be affected. It is impracticable to obtain consent from patients for receipt of 
standard medical care by their usual providers.  It is unclear what they would be providing consent for, 
since they will receive care as usual from their providers. Patients will not be provided with additional 
information.  They will be treated as usual by their providers and the providers’ experience with the 
module is irrelevant to their patient experience. 
12.3.1 Consent/Assent and Other Informational Documents Provided to Participants 
Not applicable. 
12.3.2 Consent Procedures and Documentation 
Not applicable. 
12.3.3 Research Use of Stored Human Samples, Specimens, or Data 
No human samples or specimens will be collected. 
 
• Intended Use: Data collected under this protocol may be used to study the effectiveness of the 
module.  
• Tracking: Data will be tracked using Epic reports generated by study team members.  
o 
Disposition at the completion of the study.  Data will be retained in de-identified electronic 
form on secure, password-protected digital storage media at the NYU central site, under 
the supervision of Drs. Troxel and Mann, for use by other researchers including those 
outside of the study. 

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12.4 Future Use of Stored Human Samples, Specimens, or Data 
No specimens will be collected. 
 
Data collected for this study will be analyzed and stored at NYU Langone Health. After the study is 
completed, the de-identified, archived data will be transmitted to and stored at the NYU central site, under 
the supervision of Drs. Troxel and Mann, for use by other researchers including those outside of the 
study.  
 
When the study is completed, access to study data will be provided through the study database. 
13 Data Handling and Record Keeping 
13.1 Data Collection and Management Responsibilities 
 
Data collection is the responsibility of the study staff at NYULH under the supervision of Dr. Troxel. The 
investigator is responsible for ensuring the accuracy, completeness, legibility, and timeliness of the data 
reported. 
13.2 Study Records Retention 
Study documents and data will be retained for the longer of 3 years after close out or 5 years after final 
reporting/publication. These documents should be retained for a longer period, however, if required by 
local regulations. No records will be destroyed without the written consent of the sponsor, if applicable. It 
is the responsibility of the sponsor to inform the investigator when these documents no longer need to be 
retained. 
13.3 Protocol Deviations 
A protocol deviation is any noncompliance with the study protocol or MOP requirements. The 
noncompliance may be either on the part of the participant, the investigator, or the study site staff. As a 
result of deviations, corrective actions are to be developed by the site and implemented promptly. 
 
It is the responsibility of the site to use continuous vigilance to identify and report deviations within five 
working days of identification of the protocol deviation. All deviations must be addressed in study source 
documents and reported to the designated Safety Officer. Protocol deviations must be reported to the 
local IRB per their guidelines. The site PI/study staff is responsible for knowing and adhering to their IRB 
requirements. Further details about the handling of protocol deviations will be included in the MOP. 
13.4 Publication and Data Sharing Policy 
This study will comply with the NIH Public Access Policy, which ensures that the public has access to the 
published results of NIH funded research. It requires scientists to submit final peer-reviewed journal 
manuscripts that arise from NIH funds to the digital archive PubMed Central upon acceptance for 
publication. 
14 Study Finances 
14.1 Funding Source 
This study is financed through a grant from the National Institutes of Health – National Institute on Aging. 
14.2 Costs to the Participant 
There are no costs to participants in this study. 
14.3 Participant Reimbursements or Payments 
There are no participant reimbursements or payments. 

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15 Study Administration 
15.1 Study Leadership 
Dr. Troxel takes full responsibility for the study. The Safety Officer will govern the conduct of the study. 
The study PI will meet with the Safety Officer via teleconference at least annually. 
16 Conflict of Interest Policy 
 
The independence of this study from any actual or perceived influence, such as by the pharmaceutical 
industry, is critical. Therefore, any actual conflict of interest of persons who have a role in the design, 
conduct, analysis, publication, or any aspect of this study will be disclosed and managed. Furthermore, 
persons who have a perceived conflict of interest will be required to have such conflicts managed in a 
way that is appropriate to their participation in the study. The study leadership in conjunction with the NIH 
NIA has established policies and procedures for all study group members to disclose all conflicts of 
interest and will establish a mechanism for the management of all reported dualities of interest.  
 
Any investigator who has a conflict of interest with this study (patent ownership, royalties, or financial gain 
greater than the minimum allowable by their institution, etc.) must have the conflict reviewed by the NYU 
Langone Conflict of Interest Committee with a Committee-sanctioned conflict management plan that has 
been reviewed and approved by the study sponsor prior to participation in this study.  All NYULH 
investigators will follow the applicable conflict of interest policies. 
 
 

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17 References 
1. 
Gerstein HC, Miller ME, Byington RP, et al. Effects of intensive glucose lowering in type 2 
diabetes. N Engl J Med. 2008;358(24):2545-2559. 
2. 
Patel A, MacMahon S, Chalmers J, et al. Intensive blood glucose control and vascular outcomes 
in patients with type 2 diabetes. N Engl J Med. 2008;358(24):2560-2572. 
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Duckworth W, Abraira C, Moritz T, et al. Glucose control and vascular complications in veterans 
with type 2 diabetes. N Engl J Med. 2009;360(2):129-139. 
4. 
Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose 
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Ismail-Beigi F, Craven T, Banerji MA, et al. Effect of intensive treatment of hyperglycaemia on 
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Intensive blood-glucose control with sulphonylureas or insulin compared with conventional 
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Huang ES, Laiteerapong N, Liu JY, John PM, Moffet HH, Karter AJ. Rates of complications and 
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Kirkman MS, Briscoe VJ, Clark N, et al. Diabetes in older adults: a consensus report. Journal of 
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American Geriatrics Society Expert Panel on Care of Older Adults with Diabetes M, Moreno G, 
Mangione CM, Kimbro L, Vaisberg E. Guidelines abstracted from the American Geriatrics Society 
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Huang ES, Zhang Q, Gandra N, Chin MH, Meltzer DO. The effect of comorbid illness and 
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Blecker S, Park H, Katz SD. Association of HbA1c with hospitalization and mortality among 
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Aguilar D, Bozkurt B, Ramasubbu K, Deswal A. Relationship of hemoglobin A1C and mortality in 
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22. 
Huang ES. Potential Overtreatment of Older, Complex Adults With Diabetes. JAMA. 
2015;314(12):1280-1281. 
23. 
International Diabetes Federation Guideline Development G. Global guideline for type 2 diabetes. 
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