Integration of Blood Glucose Monitoring Into Electronic Health Records
Phase: NA
Conditions: Diabetes Mellitus
Sponsor: Inova Health Care Services

 
 
 
 
 
 
ClinicalTrials.gov ID: NCT03542487 
 
Unique Protocol ID:  17-2642 
GSA OES Project Code: 1729 
Official Title: Integration of Blood Glucose Monitoring Into Electronic Health Records 
Brief Title: Blood Glucose Monitoring in Electronic Health Records 
 
 
 
Document dates: 
Protocol: 2/21/2019 
Informed consent: Waived 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Address 
P 000.000.0000   F 000.000.0000 
inova.org 
 

 
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IRB #17-2642 
 
Integration of Blood Glucose Monitoring into Electronic 
Health Records  
 
Principal Investigator: Season Majors MSN, RN 
Sub Investigator: Christopher Connolly, MD 
 
Research Coordinator Mary Ann Friesen PhD, RN, CPHQ 
 
Key Personnel: Allyson Barnett, MS (GSA, UC Berkeley) 
Key Personnel: Nuole Chen (University of Illinois Urbana-Champaign) 
Academic/Student Advisor: Ben Handel, PhD (UC Berkeley) 
 
Sponsor:  
 
Site of Investigation: Inova Health Systems Primary Care (MyChart) 
 
Date: 2/21/2019 
 
 

 
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1. Introduction 
1.1 Specific Aims 
This study aims to test methods of increasing adoption and integration of blood glucose monitoring into 
electronic medical records, and to measure the impact of widescale adoption on  health status of patients 
with diabetes.  To investigate determinants of adoption, the research will combine and test doctor and 
patient focused approaches to encouraging patient use of blood glucose flow sheets through the online 
patient portal, MyChart.   Adoption will be measured on both the extensive and intensive margin: the 
number of patients who enter data into the flowsheets at all during the study period, and the mean number 
of entries per patient during the study period.  Conditional on statistically significant increases in 
adoption, the study will examine corresponding intent-to-treat effects on patient health, and consider 
possible mechanisms through which health indicators improve or do not improve.   
1.2 Hypotheses 
1.2.1 Interfacing with primary care practices to encourage physicians to implement default online orders 
of blood glucose flowsheets and informational messaging for all patients with diabetes will increase 
patient adoption (as defined above). 
 
1.2.2. Additional reminder messaging to patients that (1) emphasizes the value of tracking blood glucose 
data to the patient OR (2) emphasize the value of tracking blood glucose data to the doctor OR (3) 
informs patient of their selection for a chance to receive an award conditional on tracking will increase 
adoption relative to no reminder messaging. 
 
1.2.3 Promotion of adoption of electronic blood glucose tracking through the means described above will 
result in the following intent-to-treat effects: 
(a)  reduction in patient A1c  
(b)  increases in frequency of doctor-patient interaction 
(c)  changes to treatment plan path 
 
1.2.4 Reminder messaging treatments that induce more intensive use of flowsheets will lead patients to 
experience larger effects as described in 1.2.3.   
 
1.2.5 Entries of blood glucose data will be predictive of A1c on average and will lower over the study 
period. 
1.3 Background and Significance 
The percentage of the US population with diagnosed diabetes increased from 4% to over 7%  from 1999 
to 2014 (CDC 2016), with nearly $1 in $5 of health care dollars spent caring for people with diabetes 
(ADA 2013).  There is substantial evidence that improved average blood sugar control (as measured by 

 
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A1c levels) is associated with significant decreases in the probability of complications from diabetes 
(ADA 2016).  Commercially insured patients with type II diabetes who lower their A1c,  blood pressure 
and lipid levels, experience significant reductions in total medical costs (Fitch et al 2013). Recent 
research also suggests that reduction in blood glucose variability is associated with reduced risk of 
complications and mortality independently of average blood glucose/A1c (Cavalot et al. 2006) (Sorkin et 
al. 2005).   
 
For patients who are insulin-dependent, self monitoring of blood glucose (SMBG) is a critical aspect of 
disease management and regulation of blood glucose levels and variability.  A landmark randomized 
controlled trial comparing intensive insulin therapy guided by frequent blood glucose monitoring to 
conventional insulin treatment.  Intensive therapy delayed the onset and slowed the progression of 
diabetic retinopathy, nephropathy and neuropathy in patients with IDDM (Diabetes Control and 
Complications Trial Research Group 1993).   
 
However, for non-insulin dependent type 2 diabetics, there has been some debate over the value of self 
tracking.  The ASIA randomized controlled trial of 689 patients over a period of 24 weeks found that 
patients assigned to perform 6 SMGB measurements per week had a statistically significant 0.3 reduction 
in A1c (ITT) after 6 months (Guerci et al 2001).  However, the DiGem randomized trial found that 
SMGB without additional training had no effect on A1c after 1 year (Simon et al 2008).  A Cochrane 
meta-analysis of 12 randomized controlled trials evaluating SMBG found a statistically significant mean 
reductions in A1c of 0.3 for studies with 6 month follow-up (effect sizes in individual studies ranged from 
.07 to .69), but no statistically significant change in A1c for studies with a 12 month follow up (Malanda 
et al 2012).  However, the review was criticized for including few studies with 12 month follow-up (two, 
one of which had only 22 subjects).  A more recent meta-study was updated to include the latest RCTs, 
finding a somewhat larger statistically significant reduction in A1c at 6 month follow-up (-.36), and a 
statistically significant reduction in A1c at 12 month follow-up (-.28) (Zhu et al 2016). 
 
Potential sources of variation in effects of SMBG on A1c for insulin-naive patients across these studies 
include the characteristics of the patient population, differences in involvement of physicians and 
training/education provided to patients, as well as heterogeneity in adherence by patients. Much larger 
effects were found for newly diagnosed patients in comparison to those with a diagnosis greater than 1 
year  (-0.54 vs -.28 change in A1c).  Some of the studies finding no effect of SMBG included mostly 
patient populations with already well-controlled A1c.  Both meta-studies pooled interventions with 
different levels of guidance and structure to glucose testing. 
 
Two key factors in enhancing the effectiveness of SMBG seem to be patient adherence and physician 
involvement (Clark 2007). In one study, structured SMBG was compared to enhanced standard care for 
483 poorly controlled insulin-naive type 2 diabetics (Polonsky 2011).  Analysis revealed much larger 
effects for patients who adhered to the intervention (-0.5 A1c change). Additionally, patients in both the 
treatment and control group of this intervention were assigned to quarterly office visits, with structured 
SMBG patients instructed to bring their readings to consult with their physician.  Availability of this data 
encouraged primary care physicians to treat glycemia earlier, more frequently, and more effectively.  
Significantly more patients assigned to structured SMBG group received recommendations for a 

 
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treatment change as compared with control subjects.  These findings highlight the key role that physician 
engagement with SMBG data plays in its effectiveness. 
 
Nearly all randomized controlled trials of SMBG have had patients monitor their glucose using either pen 
and paper or store the information on the monitoring device itself to be brought to an office visit for 
physician viewing.  As emphasized above, physicians play an important role in interpreting blood glucose 
trends, but likely do not have access to this patient generated data between office visits.  Though 
technology to electronically transmit blood glucose readings is available, it is not widely used as a 
standard practice of care.  The TELEDIAB-1 study piloted the Diabeo system (a smartphone coupled to a 
website) which incorporated automated advice on the insulin doses required; and remote monitoring by 
teleconsultation.  Use of the system improved A1c by 0.9% vs controls in patients with chronic, poorly 
controlled type 1 diabetes (Charpentier et al 2011).  However, there are few examples of integration of 
such technologies into Electronic Medical Records systems in a manner that would allow for wide-scale 
use.  One study demonstrated the feasibility of automatically sending data from continuous glucose 
monitors to EMR patient portals for physician viewing, but did not test the impact of this on patient 
outcomes (Kumar et al 2016).  To our knowledge, there is no randomized trial or prior research testing the 
causal effects of integrating of data from patient self blood glucose monitoring into EMRs on a wide-
scale. 
 
Inova patients can track their blood glucose electronically through  MyChart, allowing physicians to view 
their data in real time and be notified if results are out of range.  More recently, functionality has been 
developed to connect Apple’s HealthKit to MyChart, such that patients with compatible glucometers can 
link them to their smartphones, which can in turn be linked to MyChart to automatically transfer 
glucometer readings to the EHR.  This update streamlines the tracking process for patients with 
compatible devices.  Despite these capabilities however, few doctors and patients at Inova use MyChart’s 
blood glucose flowsheets.  In order for patients to use the flowsheets, their physician must place an order 
through the EMR, and this initial step is rarely taken.   
 
Recent research suggests that  that informational frictions are a key barrier to updating convention across 
medical practices (Chan 2016). Anecdotal evidence supports that many physicians at Inova are not aware 
of blood glucose tracking features in MyChart or how to set up tracking.  This study will seek to test an 
intervention to inform physicians of the tracking capabilities, and give guidance for placing bulk glucose 
flowsheet orders for all patients with diabetes.  This aspect of the intervention is intended to remove 
barriers to physician action, setting a default such that patients have access to the tracking feature. 
 
However, SMBG is most effective when patients track regularly.  Many of the studies discussed above 
show a correlation between adherence and reduction in A1c.  This study will also test the effect of 
reminder messaging on patient use of the flowsheets. One version of messaging will emphasize physician 
engagement and monitoring of flowsheet entries. Previous research has shown doctor patient 
communication is predictive of adherence (Friedman et al 2008).  Patients may feel more accountability 
and value to tracking if they anticipate their physicians will be looking at their results.  Additionally, as 
part of this design, some patients will be given a chance to receive a gift card if they fill out the 
flowsheets, intended to provide compensation for time spent setting up and learning how to use the 

 
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tracking features. Past research in other contexts has shown higher adherence to patient driven behaviors 
when such compensation is provided (Roski et al 2003).   
 
This research will address two key questions related to integration of blood glucose tracking data into 
EHRs in a large health system.  First, effective use of this new technology requires doctors and patients to 
update the production process, which may be stalled by inertia or lack of knowledge about the new 
feature.  This study will examine methods for encouraging adoption of the new technology.  Additionally, 
it is not known how systematic, widespread use of blood glucose data by providers through an EHR will 
impact patient health. These questions will be addressed using a clustered randomized design to test 
patient and provider focused interventions which encourage uptake of blood glucose tracking through the 
EHR.  This encouragement design will also allow for testing effects on patient health, as measured by 
A1c test results. 
 
1.4 Preliminary Studies 
Season Majors is the MyChart (Patient Portal) Manager at Inova Health System. In her role she 
is responsible for the management of the portal as well as for increasing patient engagement and 
use. Season has worked with the portal since 2013 and has coordinated the implementation of 
multiple features within the portal to allow patients to better manage their care along with their 
care team. Her nursing experience has helped her to better understand the needs of the patients as 
well as the clinical aspects of care that can be enhanced through portal usage. 
 
Dr, Christopher Connolly is Board Certified in Internal Medicine and is currently the EPIC Certified 
Physician Builder for the Inova Health System- Inova Medical Group (IMG). He has served as the 
Clinical Informatics Chief Medical Information Officer from 2008-2016 for Medical Group and Urgent 
Care. He completed a Research Fellowship at New York Medical College and has been in practice for 11 
years. 
 
Dr. Mary Ann Friesen  is the Nursing Research and Evidence Based Practice Coordinator for Inova and 
chair of the Inova Health System Nursing Research and Evidence Based Council she also serves on the 
Institutional Review Board. In her role she coordinates research efforts across the system and has served 
as Principal Investigator for various  studies focused on nursing,  patient- centered care, and 
communication.  Dr. Friesen has presented at numerous professional conferences and published in various 
journals. Dr. Friesen is a Certified Professional in Healthcare Quality (CPHQ).  In 2016 she received the 
Distinguished Alumni Award for George Mason University College of Health and Human Services. 
 
Allyson Barnett is a PhD student at UC Berkeley studying health economics and current Associate Fellow 
with the GSA Office of Evaluation Sciences.  She has extensive experience conducting data analysis on 
large administrative datasets as a research analyst, and designing randomized field trials. She is currently 
authoring another paper on the effects of health IT on preventative care, leveraging a large insurer claims 
database and detailed IT login data.  She is also working with the Center for Medicare and Medicaid 

 
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Services to design a randomized experiment testing updated versions of a notice sent out to low income 
Part D beneficiaries. 
 
Nuole Chen is a PhD student studying International Relations in the Department of Political Science at 
the University of Illinois at Urbana-Champaign and current Associate Fellow with the GSA Office of 
Evaluation Sciences. 
2. Study Design and Subject Selection 
2.1 Study Type 
Cluster randomized encouragement design. 
2.2 Setting/Location 
Research will be conducted through the MyChart electronic medical records system with patients of Inova 
Health Systems primary care offices (27 locations).  Half of the 27 primary care offices will be randomly 
selected to receive treatment, but outcome data (existing electronic medical records data) will be analyzed 
for the entire study population. 
2.3 Duration of Study 
Primary care practices identified in the treatment group will be encouraged to place bulk orders for 
electronic glucose flowsheets over a period of two weeks.  Patients will be actively encouraged to use 
flowsheets for 12 weeks following the administration period.  Outcome data will be collected for a total of 
38 weeks: 
(a) 12 weeks retrospective at baseline 
(b) 2 week administration period 
(c) 12 week implementation period 
(d) 12 week follow up period 
2.4 Number of Subjects 
Estimated intake over study duration: 9,000 (only half selected for treatment group).  ; Maximum number 
of subjects: 15,000. 
2.5 Study Population 
Non-pregnant adult patients of Inova physicians at primary care sites other than Ashburn II Primary Care, 
Lake Ridge Primary Care and Springfield Primary Care with a current diabetes mellitus diagnosis and 
active MyChart account at time of treatment administration will be included in the study. There will be no 
gender, age, racial or ethnic exclusions of adult patients, and study population is expected to match the 

 
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distribution of diabetic patient characteristics in Inova health system. Patients will not be formally 
recruited for participation in the study.  The intervention involves practice-level promotion of an existing 
feature of Inova’s MyChart: electronic blood glucose flowsheets.  Promotion of this feature will not be 
formally mandated by the study design. All communications and interactions included in the study will 
take place electronically through MyChart.  
 
Physicians will  exclude from initial bulk flow sheet orders any individual patients whom they identify as 
having contraindications for tracking of blood glucose.  
2.6 Recruitment 
Patients will not be formally recruited for participation in the study.  The intervention involves practice-
level promotion of an existing feach of Inova’s MyChart: electronic blood glucose flowsheets.  Promotion 
of this feature does not represent a change in standard of care, and will not be formally mandated by the 
study design.  
3. Study Methods and Procedures 
3.1 Study Treatment/Intervention 
The focus of the study treatment will be to encourage adoption of the blood glucose monitoring feature of 
MyChart by patients with diabetes and their providers.  Currently, providers must place an order to allow 
patients to enter their blood glucose into the online system.  If patients enter data, providers are then able 
to view these readings through the patient portal.  This feature is currently available, but awareness and 
utilization are low. The sections describe intervention design, which will promote adoption. 
 
 

 
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3.1.1 PRACTICE LEVEL PROMOTION 
Providers in the randomly selected group of treatment primary care practices will be encouraged to batch 
order blood glucose flowsheets for all patients with diabetes with active MyChart accounts.  The research 
team will contact physicians and practice managers with an explanation of the initiative and instructions 
for completing batch orders and viewing entries through the system.  The practice will be advised to 
complete filling orders over a two week administration period, after which the implementation period will 
begin.  A batch order will trigger two forms of alerts sent to providers, according to the default settings 
for electronic glucose flowsheets: 
 
1. A bi-weekly report received of values that the patient has entered, or a notification that the patient 
has not entered data during the interval.  
2. A notification when patient entered values are out of range (the patient also gets a notification 
that their doctor will see this value) according to the following current settings: 
 
Notification Setting 
Current Default 
Pre-Meal Range (entry outside range=alert) 
60-180 mg/dl 
Post-Meal Range 
100-240 mg/dl 
Nighttime Range 
60-300 mg/dl 
 
Patients will automatically be notified when the flowsheet has been ordered for them.  Additionally, 
providers will be given text for a secure smart-text message to send to all patients receiving the 
flowsheets, instructing them to enter data for the study period.  The secure message will also provide 
them with information on how to enter data, and on the benefits of tracking blood glucose.  Draft text of 
this message is included below: 
 
Dear [Patient], 
You may have seen that there is a new electronic form available for you to enter your blood glucose 
results in the MyChart portal.  Keeping track of your blood glucose can help you manage your diabetes 
and reduce your chances for developing complications like heart disease, kidney damage and eye 
damage. As part of a new Inova program to help patients with diabetes get healthy, I would like you to try 
entering the results of your home blood glucose tests on MyChart for the next 3 months, through [Date]. 
Please let me know if you have any questions.  
Sincerely,  
[Practitioner] 
 
WHAT TO DO NEXT 
 
1. Get help: If you need help learning how to measure your blood glucose or getting testing supplies, you 
can contact [Practice Contact]. 

 
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2. Learn how: See [insert link to instructions/screenshots] to learn how to use the electronic blood 
glucose flowsheets on MyChart 
3.  Link your devices:  You don’t need to have special equipment to use the flowsheets, but if you have an 
Apple/iOS device and compatible glucometer, you can set up automatic data transfer.  This will pull the 
results of your glucose tests into MyChart automatically, making it even easier to track.  See  [insert link 
to instructions] for help setting this up, and for a list of compatible devices. 
4. Track your blood glucose: For the next 3 months, try to enter at least 6 measurements total per week, 
spread out over a few different days.  However, don’t be discouraged if you miss a day-- even tracking 
occasionally could be beneficial to your health.  
 
Note: If you’ve never tested your blood glucose before, get in touch with [Practice Contact] to make sure 
you have all the materials and instructions you need, and to confirm that testing is right for you. Patients 
who think they might be pregnant should also be sure to talk to a physician before starting any new 
tracking regime. 
 
3.1.2 INDIVIDUAL LEVEL REMINDERS 
After the initial two-week roll-out/administration period, follow-up emails will be sent out by the research 
team to patients at treatment practices whose doctors have placed blood glucose flowsheet orders.  
Patients will be assigned at the individual level to one of four reminder treatment conditions, which will 
be administered by the research team.  The number of reminder conditions may be reduced (to 2 or 3 
instead of 4) depending on the rate of flowsheet orders by physicians to preserve statistical power.  
Assignment to any of these treatments will not preclude or provide guidelines for other communication 
between providers and patients (e.g. providers are free to send out their own reminders or messaging 
regardless of patient assignment to treatment group). The four reminder treatment conditions are 
described below: 
 
(1) No additional reminders from research group 
 
(2) Standard secure message reminder, addressed from Inova Medical Group, sent every two weeks 
during 12 week implementation period.  Draft text below: 
 
Dear [Patient], 
Don’t forget to track your blood glucose through MyChart!  Keeping track of your blood glucose can 
help you manage your diabetes and reduce your chances for developing complications like heart disease, 
kidney damage and eye damage. 
Sincerely,  
Inova Medical Group 
 
(3) Secure message reminder with chance to receive gift card, addressed from Inova, sent every two 
weeks during 12 week implementation period.  Draft text below: 
Dear [Patient], 
You have been chosen for a special program to help you get started tracking your blood glucose through 
MyChart.  For each day that you track your blood glucose on MyChart through [DATE], you will be 

 
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entered to receive one of fifty $50 gift cards to Amazon.  You’ll be sent a secure message through 
MyChart after [DATE] if you’ve been selected to receive a gift card.  Keeping track of your blood glucose 
can help you manage your diabetes and reduce your chances for developing complications like heart 
disease, kidney damage and eye damage. 
Sincerely,  
Inova Medical Group 
 
(4) Secure message reminder, addressed from primary care doctor, sent every two weeks during 12 
week implementation period.  Draft text below: 
Dear [Patient], 
Don’t forget to track your blood glucose through MyChart! Viewing your results helps me to respond if 
they are out of range, and improve your diabetes treatment to help you stay healthy.  We will talk about 
your results at your next office visit. Keeping track of your blood glucose can help you manage your 
diabetes and reduce your chances for developing complications like heart disease, kidney damage and 
eye damage. 
Sincerely,  
[Practitioner] 
3.2 Control Group 
The control group will be randomly selected half of the 27 primary care practices, and will receive no 
encouragement of adoption.  However, this group will not be explicitly discouraged from making 
flowsheet orders. 
3.3 Randomization 
Provider-side treatments will be cluster randomized at the practice level.  Randomization will stratify 
across practices by number of diabetic patients (cluster size), and will be conducted using a random 
number generator at the outset of the study.  Reminder messaging treatments will be assigned 
alphabetically by first two letters of patient last name, as it is logistically infeasible to do individual level 
patient messaging without sorting on an existing field in the patient’s EHR .  There are some concerns 
that ethnicity could correlate with assignment based on last name spelling, so this form of assignment is 
“pseudo-random”.  However, the patient’s race/ethnicity recorded in the medical record will be controlled 
for in the analysis.  Causal interpretation of the results of the reminder messaging portion of the 
experiment will thus require the assumption that grouped last name spelling is not independently related 
to likelihood of flowsheet adoption. 
3.4 Endpoints/Outcomes Measurements 
Hypothesis: Interfacing with primary care practices to encourage physicians to implement default online 
orders of blood glucose flowsheets and informational messaging for all patients with diabetes will 
increase patient adoption. 

 
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Outcome Measures: Comparison of individuals between treatment and control practices-- (a) fraction of 
patients entering blood glucose measurements into MyChart flowsheets during implementation period & 
follow-up period (dichotomous), (b) mean number of flowsheet entries during implementation period and 
follow-up period (c) fraction of patients for whom order is placed by doctor 
 
Hypothesis: Additional reminder messaging to patients that (1) emphasizes the value of tracking blood 
glucose data to the patient OR (2) emphasize the value of tracking blood glucose data to the doctor OR 
(3) informs patient of their selection for a chance to receive an award conditional on tracking will increase 
adoption relative to no reminder messaging. 
Outcome Measure: Comparison of individuals across reminder messaging assignment groups (within 
treatment practices)-- (a) fraction of patients entering blood glucose measurements into MyChart 
flowsheets during implementation period & follow-up period (dichotomous), (b) mean number of 
flowsheet entries during implementation period and follow-up period  
 
Hypothesis: Promotion of adoption of electronic blood glucose tracking through the means described 
above will result in the following intent-to-treat effects: 
(a)  reduction in patient A1c  
Outcome Measures: Intent to treat comparison of individuals between treatment and control 
practices of the following measures at the end of the intervention period and follow-up period:  
(1) Mean A1c (2) Quartiles of A1c using quantile regression analysis (3) fraction of patients 
experiencing improvement [reduction] in A1c from baseline (dichotomous) (4) fraction of 
patients below benchmark 7.0% A1c (dichotomous) 
(b)  increase in frequency of doctor-patient interaction 
Outcome Measures: Intent to treat comparison of individuals between treatment and control 
practices of the following measures during the intervention period and follow-up period: (1) 
number of securing messaging (2) number of phone interactions, and (3) number of in-office 
appointments 
(c)  changes to treatment plan path 
Outcome Measure:  Intent to treat comparison of individuals between treatment and control 
practices of number of alterations to patient prescriptions/medication list  during the intervention 
period and follow-up period 
 
Hypothesis: Reminder messaging treatments that induce more intensive use of flowsheets will lead 
patients to experience larger effects as described above. 
Outcome Measure: Intent to treat and treatment on the treated comparison of individuals across 
reminder messaging assignment groups (within treatment practices)-- reduction in A1c, increase 
in frequency of doctor-patient interactions, changes to treatment plan path (outcomes as described 
above in a-c) 
 
Hypothesis: Entries of blood glucose data will be predictive of A1c and will lower over the study period.  
Outcome Measure: Descriptive analysis of flowsheet entry values in the treatment group during 
the implementation and follow-up period. 
 

 
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3.5 Consent 
A waiver of informed consent and waiver of HIPAA authorization is requested.  The study is an 
encouragement design aiming to increase uptake of an existing service (electronic flowsheets) provided 
through Inova’s MyChart electronic medical record system, and will not change standards of care.  
Patients and providers in both the treatment and control groups will have access to electronic flowsheets 
throughout the study unchanged from baseline.  The research presents no more than minimal risk of harm 
to participants and involves no procedures for which written consent is normally required outside of the 
research context. Research could not practicably be conducted without a waiver of consent and HIPAA 
authorization due to the number of subjects, online nature of the experiment, and design of the study 
which will seek to examine outcomes for the identified population regardless of actual use of electronic 
flowsheets. Outcomes of the research could not practicably be studied without access to and use of 
protected health information. See sections on Data Storage, Risks and Confidentiality for more details. 
3.6 Monitoring Subjects and Criteria for Withdrawal 
Not applicable-- nature of encouragement design means that the intervention is discrete administration. 
4. Statistical Considerations/Data Analysis 
4.1 Sample Size 
Non-pregnant adult patients of Inova primary care physicians with a current diabetes mellitus diagnosis 
and active MyChart account at time of treatment administration will be included in the study. The 
estimated number of patients is around 7200, from 71 primary care physicians across 24 selected Inova 
primary care practices.   
 
Power Calculations 
Power calculations were performed using the “clustersampsi” command in Stata.  Knowledge of available 
sample and estimates/assumptions of control outcome mean, variance, and intracluster correlation were 
used to calculate a minimum detectable effect size for the key outcomes of flowsheet adoption (extensive 
margin, dichotomous rate of adoption) and changes in mean A1c.  
 
Flowsheet Adoption: Practice Level Treatment-Control Comparison 
Sample Size: 7200 
Number of Treatment Arms: 2 
Number of Clusters: 24 
Assumed Control Adoption Rate: 2% 
Assumed Intra-Cluster Correlation (within practices): 0.1 
Minimum Detectable Effect: 9.5 percentage point increase in flowsheet orders 
 
Mean HbA1c: Practice Level Treatment Control Comparison 

 
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Sample Size: 7200 
Number of Treatment Arms: 2 
Number of Clusters: 24 
Assumed Control HbA1c Mean: 6.74 
Assumed Control HbA1c Standard Deviation: 1.39 
Assumed Intra-Cluster Correlation (within practices): 0.07 
Assumed Baseline Correlation: 0.80 
ITT Minimum Detectable Effect: 0.27 change in A1c 
 
Flowsheet Adoption: Individual Level Comparison between Messaging Assignment Groups in Treatment 
Practices 
Sample Size: 3600 
Number of Arms (including no reminder): 4 
Assumed No Reminder Adoption Rate: 20% 
Minimum Detectable Effect: 5.5 percentage point increase in use of flowsheets when compared to no 
reminder 
 
Justification 
The sample size represents the entire population of Inova primary care patients with diabetes who have 
active MyChart accounts and are therefore able to access the blood glucose tracking feature.  The 
minimum detectable effects resulting from power calculations above are in-line with similar studies cited 
in the protocol background.  Metastudy reviews of the effect of self monitoring of blood glucose found a 
0.33 point change in A1c.  
 
4.2 Method of Data Analysis 
All data analyzed in this study will be administrative health and use data that is collected as part of 
routine medical care and record keeping.  EPIC has offered to build the SQL queries required to produce 
the outcome datasets described in this protocol.  Outcomes listed in Section 3.4. will be analyzed using 
regression analysis, including (a) a version with no covariates, (b) a version controlling for patient 
age/gender, race/ethnicity, A1c at baseline, days since most recent office visit at baseline, and doctor 
fixed effects.  A flag identifying patients who are eventual participants of "Impact of Innovative 
Interventions on Diabetic Adherence with Digital Sensors" (a study recently submitted to the IRB that 
may have limited overlap with this population) will also be included in the controls.  For outcomes 
indicating analysis of practice level treatment and control groups, standard errors will be clustered at the 
practice level.  
4.3 Data Storage 
All data will be stored on Inova systems, and authorized collaborating researchers and personnel will  
access the data remotely through Citrix.  A data use agreement (draft attached) will be entered into by 
Inova and the General Services Administration, and specified personnel from GSA will be authorized to 

 
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access the limited dataset and perform data analysis.  The limited dataset accessed through Citrix will be 
have facial identifiers removed in accordance with the HIPAA definition of limited dataset and personnel 
authorized to access will agree to (i) not use or disclose the information other than as permitted by the 
DUA or as otherwise required by law; (ii) use appropriate safeguards to prevent the use or disclosure of 
the information other than as provided for in the DUA; (iii) report to Inova any use or disclosure of the 
information not provided for by the DUA of which the recipient becomes aware; and (iv) not to identify 
the information or contact the individual. Data will be fully anonymized and linkages to identifying 
information will be permanently destroyed three (3) years after the conclusion of the study. 
5. Human Subjects Protections 
5.1 Risks 
The risks to participation in this study are minimal.  The randomized encouragement design means that 
doctors will make the final determination of whether use of electronic blood glucose flow sheets is 
suitable for the patient.  The study does not advise against or preclude the provider or patient from 
switching to alternative methods for self monitoring of blood glucose, or using other forms of treatment 
concurrently. Previous research has shown positive health effects of self monitoring of blood glucose, and 
it is a recommended treatment for diabetes.  However, one low risk area is potential loss of privacy.  The 
risk will be minimized by implementing an adequate plan to protect the identifiers from improper use and 
disclosure: a limited dataset will be created and made available to authorized researchers via remote 
access on Citrix.  Data will be fully anonymized and linkages to identifying information will be 
permanently destroyed three (3) years after the conclusion of the study. Protected health information will 
not be reused or disclosed to any other person or entity, except as required by law, or for authorized 
oversight of the research project. 
 
5.2 Benefits 
The intervention seeks to test methods for increasing utilization of an existing feature of the MyChart 
EHR system.  Lessons from this aspect of the study may be applied to increase awareness and utilization 
of other new EHR features, potentially benefiting patients and doctors indirectly. 
 
Patients who decide to track their blood sugar through MyChart as a results of the intervention may 
experience increased ability to control and manage their diabetes.  Because providers will be able to view 
and interpret blood glucose data more easily and frequently, they may be more likely to adjust and tailor 
treatment plans to this information.  Both of these factors may lead to improved patient health indicators 
(specifically A1c). 
5.3 Alternatives 
Not applicable. 

 
15 
5.4 Confidentiality 
To minimize the risk of loss of confidentiality, the research team will implement a plan to protect the 
identifiers from improper use and disclosure: a limited dataset will be created and made available only to 
to authorized researchers via secure remote access to Inova systems on Citrix.  Data will be fully 
anonymized and linkages to identifying information will be permanently destroyed three (3) years after 
the conclusion of the study. Protected health information will not be reused or disclosed to any other 
person or entity, except as required by law, or for authorized oversight of the research project. 
6. Subject Compensation 
6.1 Costs 
There are no direct costs to participants.  Indirect costs to patients may include inconvenience of receiving 
additional secure messages.  Due to encouragement of flowsheet orders by physicians in the treatment 
group, patients choosing not to follow their doctor’s recommendation of electronic glucose tracking may 
feel guilt, and patients choosing to complete the online flowsheets will need to spend additional time 
entering their values.  Amount of time spent will depend on whether they set up an automatic download 
of electronic readings through their smartphone, or manually enter the values via an internet-connected 
device.  If patients choose to start tracking their blood glucose and have not already been doing so, they 
may face additional supply costs. 
6.2 Payment 
There will be no payment for participation.  A subset of participants in the treatment group--one quarter 
of patients with diabetes in 10 randomly selected practices--will have the chance to receive one of 50 $50 
gift card to Amazon.com if they choose to enter data into the flowsheet for any day during the study 
period (chance will be increasing in the number of days flowsheet is used).  This gift card is intended as 
compensation to offset hassle costs of entering data into the flowsheet, and not as an incentive to 
participate in the study.  Study personnel will follow guidelines from section 11.10 of IRC on the 
distribution of gift cards. 
 
The code for online redemption of the gift card will be sent to selected recipients who remain MyChart 
activated at the time of distribution in a secure message through MyChart.  The subject line will read Gift 
Card Recipient: MyChart Glucose Tracking Program.   Patient replies will be disabled for this 
communication.  We will track read-receipts after one month, but will not re-send messages or take any 
action based on this information.  The message will read as follows: 
 
We're pleased to let you know that you were selected to receive a $50 gift card to Amazon.com!     
You were chosen for a special program through Inova to help you get started tracking your blood glucose 
through MyChart.  For each day that you tracked your blood glucose on MyChart between May and 
August 2018, you were entered to receive one of fifty gift cards. You tracked, and one of your entries was 
randomly selected!  To redeem your gift card, visit https://www.amazon.com/gc/redeem and enter [code]. 

 
16 
 
7. Adverse Event Reporting 
Risks from participation in this study are minimal, but one possible adverse event is breach of 
confidentiality. Adverse events will be reported in accordance with IRC 11.16. 
8. Funding 
Key Personnel Allyson Barnett has received a small J-PAL North America HCDI Project Development 
grant (less that $5000) to cover gift cards, and personal travel costs. The grant will be administered 
directly to Allyson and will not pass through the Inova grant office. 
9. Conflicts of Interest 
None. 
10. Facilities and Equipment 
Data analysis will be conducted through remote access to Inova Citrix systems.  The intervention will be 
administered primarily through through MyChart, and will not require any additional equipment or 
facilities.  
11. Outside Collaborators 
General Services Administration, Office of Evaluation Sciences: Key Personnel will conduct data 
analysis for the study, see attached DUA 
Department of Health and Human Services, Office of the National Coordinator for Health Information 
Technology: Will not have access to data or serve as investigators on the study, but is coordinating with 
OES to incorporate results into guidelines and informational materials 
Ben Handel, UC Berkeley: Will not have access to data.  Academic advising of key personnel. 
12. References 
American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2012. Diabetes Care Mar 
2013, DC_122625; DOI: 10.2337/dc12-2625 
 
American Diabetes Association. Glycemic targets. Sec. 5. In Standards of Medical Care in Diabetes--
2016. Diabetes Care 2016;39(Suppl. 1):S39–S46 
 

 
17 
Barnett AH, Krentz AJ, Strojek K, et al. The efficacy of self-monitoring of blood glucose in the 
management of patients with type 2 diabetes treated with a gliclazide modified release-based regimen.  
Diabetes Obes Metab. 2008;10(12):1239-47. 
 
Cavalot F, Petrelli A, Traversa M, et al. Postprandial Blood Glucose Is a Stronger Predictor of 
Cardiovascular Events Than Fasting Blood Glucose in Type 2 Diabetes Mellitus, Particularly in Women: 
Lessons from the San Luigi Gonzaga Diabetes Study. J Clin Endocrinol Metab 2006; 91 (3): 813-819. 
 
Center for Disease Control. Long Term Trends in Diabetes. https://www.cdc.gov/diabetes/data/. 
Published April 2016. Accessed January 4, 2017. 
 
Chan, David. Informational Frictions and Practice Variation: Evidence from Physicians in Training. 
NBER Working paper. 2016; e21855. 
 
Charles M. Clark, Jr. Why the DiGEM study does not help us decide the value of SMBG in people with 
type 2 diabetes not on insulin. BJM 2007, response. 
 
Charpentier G, Benhamou P-Y, Dardari D, et al. The Diabeo Software Enabling Individualized Insulin 
Dose Adjustments Combined With Telemedicine Support Improves A1c in Poorly Controlled Type 1 
Diabetic Patients: A 6-month, randomized, open-label, parallel-group, multicenter trial (TeleDiab 1 
Study). Diabetes Care. 2011;34(3):533-539.  
 
Cho J, Chang SA, Kwon HS, et al. Long-Term Effect of the Internet-Based Glucose Monitoring System 
on HbA1c Reduction and Glucose Stability. Diabetes Care. 2006; 29 (12): 2625-2631. 
 
Cordts S. Self-monitoring of blood glucose in patients with type 2 diabetics not using insulin. Am Fam 
Physician. 2012 May 1;85(9):866-7. 
 
Fitch K, Pyenson B, Iwasaki K. Medical Claim Cost Impact of Improved Diabetes Control for Medicare 
and Commercially Insured Patients with Type 2 Diabetes. J Manag Care Pharm. 2013;19(8):609-20 
 
Franciosi M, Lucisano G, Pellegrini F, et al. ROSES: role of self-monitoring of blood glucose and 
intensive education in patients with Type 2 diabetes not receiving insulin. Diabet Med.  2011 
Jul;28(7):789-96.  
 
Friedman, David et al. Doctor–Patient Communication, Health-Related Beliefs, and Adherence in 
Glaucoma. Ophthalmology. 115(8): 1320-1327. 
 
Guerci B1, Drouin P, Grangé V, et al. Self-monitoring of blood glucose significantly improves metabolic 
control in patients with type 2 diabetes mellitus: the Auto-Surveillance Intervention Active (ASIA) study. 
Diabetes Metab. 2003 Dec;29(6):587-94. 
 

 
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Hirsch I.  Blood Glucose Monitoring Technology: Translating Data into Practice. Endocrine Practice. 
2004, 10(1): 67-76. 
 
Kumar RB, Goren ND, Stark DE, et al. Automated integration of continuous glucose monitor data in the 
electronic health record using consumer technology. J Am Med Inform Assoc 2016; 23 (3): 532-537.  
Journal of the American Medical Informatics Association. 2016; ocv206. 
 
Landolina M, Perego G, Lunati M, et al. Remote Monitoring Reduces Healthcare Use and Improves 
Quality of Care in Heart Failure Patients With Implantable Defibrillators. Circulation. 2012; 125:2985-
2992. 
 
Malanda UL, Welschen LMC, Riphagen II, et al. Self-monitoring of blood glucose in patients with type 2 
diabetes mellitus who are not using insulin. Cochrane Database of Systematic Reviews. 2012; 
1(CD005060). 
 
Moreland EC1, Volkening LK, Lawlor MT, et al. Use of a blood glucose monitoring manual to enhance 
monitoring adherence in adults with diabetes: a randomized controlled trial. Arch Intern Med. 2006 Mar 
27;166(6):689-95. 
 
Murata GH1, Shah JH, Hoffman RM, et al. Intensified blood glucose monitoring improves glycemic 
control in stable, insulin-treated veterans with type 2 diabetes: the Diabetes Outcomes in Veterans Study 
(DOVES). Diabetes Care. 2003 Jun;26(6):1759-63. 
 
O’Connor PJ, Sperl-Hillen JM, Rush WA, et al. Impact of Electronic Health Record Clinical Decision 
Support on Diabetes Care: A Randomized Trial. Annals of Family Medicine. 2011;9(1):12-21.  
 
O’Kane M, Bunting B, Copeland M, et al. Efficacy of self monitoring of blood glucose in patients with 
newly diagnosed type 2 diabetes (ESMON study): randomised controlled trial BMJ 2008; 336 :1174. 
 
Parkin CG, Davidson JA. Value of Self-Monitoring Blood Glucose Pattern Analysis in Improving 
Diabetes Outcomes. Journal of diabetes science and technology (Online). 2009;3(3):500-508. 
 
Polonsky WH, Fisher L, Schikman CH, et al. Structured Self-Monitoring of Blood Glucose Significantly 
Reduces A1C Levels in Poorly Controlled, Noninsulin-Treated Type 2 Diabetes: Results from the 
Structured Testing Program study. Diabetes Care. 2011;34(2):262-267. 
 
Rodbard HW, Schnell O, Unger J, et al. Use of an Automated Decision Support Tool Optimizes 
Clinicians’ Ability to Interpret and Appropriately Respond to Structured Self-Monitoring of Blood 
Glucose Data. Diabetes Care. 2012;35(4):693-698.  
 
Roski J, Jeddeloh R, An L, et al. The impact of financial incentives and a patient registry on preventive 
care quality: increasing provider adherence to evidence-based smoking cessation practice guidelines. 
Preventative Medicine, 2003. 36(3). 291-299. 

 
19 
 
Simon J, Gray A, Clarke P, et al. Cost effectiveness of self monitoring of blood glucose in patients with 
non-insulin treated type 2 diabetes: economic evaluation of data from the DiGEM trial BMJ 2008; 336 
:1177. 
 
Sorkin J, Muller D, Fleg J, et al. The Relation of Fasting and 2-h Postchallenge Plasma Glucose 
Concentrations to Mortality. Diabetes Care. 2005, 28 (11) 2626-2632. 
 
The Diabetes Control and Complications Trial Research Group. The Effect of Intensive Treatment of 
Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent 
Diabetes Mellitus. New England Journal of Medicine. 1993; 329:977-986 
 
Tracy S. Tylee, Dace L. Trence. Glycemic Variability: Looking Beyond the A1C. Diabetes Spectrum Aug 
2012, 25 (3) 149-153. 
 
Zhu H, Zhu Y, Leung S. Is self-monitoring of blood glucose effective in improving glycaemic control in 
type 2 diabetes without insulin treatment: a meta-analysis of randomised controlled trial. BMJ Open 
2016;6:e010524. 
13. Appendix 
Documents to be included:  
● Pre-review checklist 
● Collaborator agreements 
● Data Use Agreement 
● Financial Disclosure From 
● IIA with Berkeley IRB 
● Department Impact Form 
● Department Facility Signature Form