Leveraging Electronic Health Record (EHR) Tools to Reduce Health Disparities for Patients With Uncontrolled Hypertension
Phase: NA
Conditions: Hypertension, Blood Pressure
Sponsor: Brigham and Women's Hospital

 
Version 2023.08.21 
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Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
 
Principal Investigator: 
Niteesh Choudhry, MD, PhD (contact PI) 
Julie Lauffenburger, PharmD, PhD (multi PI) 
 
Project Title: 
Leveraging Electronic Health Record (EHR) Tools to Help Reduce 
Health Disparities for Patients with Uncontrolled Hypertension 
 
Version Date: 
Version 7: December 4, 2024 
 
 
1. 
 Background and Significance 
 
Hypertension is a major public health problem affecting an estimated of 100 million Americans1 and is 
projected to cost the healthcare system upwards of $220 billion by 2035.2 Racial disparities in 
hypertension control have been recognized for decades.3 The prevalence of hypertension among black 
adults is substantially higher than in non-Hispanic white adults.4 Despite modest improvements in 
treatment initiation, control rates in black patients continue to lag.4,5 These disparities are especially 
problematic because of racial differences in the consequences of disease. For example, a 10-mmHg 
difference in systolic blood pressure is associated with an 8% increase in stroke risk for whites but a 24% 
increase for blacks.6 Rates of hypertension treatment and control are more than 10% lower for 
Hispanic/Latinos than whites.7,8 Hispanics/Latinos also experience worse long-term cardiovascular 
health outcomes, with higher risks of stroke and end-stage renal disease than whites.5 
 
There are many contributors for the persistent racial/ethnic differences in hypertension control 
outcomes. Dr. Khatib, Advocate co-investigator, conducted a widely-cited systematic review of barriers 
to hypertension control and proposed a conceptual framework.9 Of these, a lack of treatment 
intensification, underuse or prescribing of racially-based oral antihypertensives, and suboptimal follow-
up care are thought to be central contributors to racial/ethnic disparities.10-14  
 
In up to half of treatment episodes, healthcare providers do not intensity antihypertensive treatment 
when indicated, often referred to as clinician inertia.15-17 Dr. Khatib’s systematic review identifies several 
contributors including clinicians being inappropriately satisfied with their patient’s current level of 
control, concerns about treatment side effects, and concerns about accuracy of blood pressure readings 
obtained during office visits. Clinical inertia may pose a greater problem for minorities.8,18 The 2014 JNC-
8 guidelines and the 2018 ACC/AHA antihypertensive guidelines now explicitly recommend specific 
treatment choices for hypertension based on race/ethnicity.19,20 Some clinicians remain unaware of the 
nuances in guidelines, and non-guideline concordant antihypertensive treatment, for black patients in 
particular, may still contribute to differences in blood pressure control between groups.21,22 
 
Effectively managing hypertension also relies on the ability to monitor response to therapy. Despite 
guideline recommendations for close follow-up when adjusting drug regimens, minority patients are less 
likely to return to clinical care than white patients.5,23 Patient self-management, such as blood pressure 

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monitoring necessary for titration, are also lower among minority patients, in part because of inability to 
afford in-home blood pressure monitoring cuffs as well as differential recommendations by providers 
about self-monitoring.16 Other social determinants of health, such as poor health literacy and limited 
insurance coverage, are more prevalent among black and Hispanic/Latino patients.24,25 Referral rates to 
social work and nephrologists for renal care are also lower, which further increases gaps in health 
outcomes.26,27 
 
Many EHRs systems contain a range of clinical decision support tools such as alerts, reminders, and 
defaults.25 The widespread adoption of EHRs presents a scalable opportunity for the implementation of 
EHR-based interventions.31 The existing literature supports the potential for health information 
technology (IT) to improve health care quality.32-37 In a recent systematic review, 56% of 236 studies 
found that decision support improved prescribing and preventive care.38 Of 147 studies that evaluated 
clinical outcomes, >75% showed that EHR tools improved these outcomes. Despite this encouraging 
evidence, few studies have evaluated how health IT can be leveraged to reduce disparities,39 particularly 
as other research suggests that the adoption of patient-facing tools, such as EHR portals, is up to 50% 
lower in minority patients.40,41 
 
Despite the promise of EHR-based interventions to improve health care quality, to our knowledge, only 
two trials have specifically sought to prospectively evaluate whether EHR-based interventions can 
reduce racial/ethnic disparities.42 One of these demonstrated that EHR-based “report cards” provided to 
physicians could reduce disparities in diabetes care.43 The other trial found that guideline-based decision 
support reduced disparities in hypertension treatment.42 Other observational studies have also provided 
supportive evidence.44-51 Across these studies, performance reports, decision support, and registries 
appear to be the most successful strategies.43,44  
 
Therefore, we propose a pragmatic randomized trial to test the impact of a multicomponent health IT 
intervention on blood pressure control and racial and ethnic disparities. 
 
2. 
Specific Aims and Objectives  
 
The overall goal of this project is to improve existing decision support for hypertension control and to 
reduce disparities in treatment, consistent with professional guidelines and quality metrics. To achieve 
this, we will conduct and evaluate a cluster randomized trial to determine whether health IT tools 
targeted to providers improve blood pressure control for patients with uncontrolled hypertension.  
 
Our primary hypothesis is that a health IT intervention will improve systolic blood pressure (primary 
outcome), concordance with hypertension guidelines (secondary outcome), diastolic blood pressure 
(secondary outcome), treatment intensification (secondary outcome), and post-visit patient-provider 
follow-up (tertiary outcome) compared with usual care.  
 
The objectives and endpoints are summarize below:  
 
OBJECTIVES 
ENDPOINTS 
JUSTIFICATION FOR ENDPOINTS 
Primary 
To determine whether 
multicomponent EHR-based health 
IT tools designed using behavioral 
Change in systolic blood pressure 
from identification to 12-month 
follow-up, using values in the EHR  
These outcomes are rapidly measurable using 
EHR data and will provide evidence of blood 
pressure control. 

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OBJECTIVES 
ENDPOINTS 
JUSTIFICATION FOR ENDPOINTS 
principles targeted at providers 
improve blood pressure control for 
patients with uncontrolled 
hypertension as compared with 
usual care (no intervention). 
Secondary 
To determine whether 
multicomponent EHR-based health 
IT tools designed using behavioral 
principles targeted at providers 
improve concordance with 
hypertension guidelines, diastolic 
blood pressure control, and 
treatment intensification compared 
with usual care.  
Percentage of patients with well-
controlled blood pressure 
(<140/88mmHg) at 24 months 
follow-up, using values in the EHR. 
 
Percentage of patients with 
intensification of medication in the 
24-month follow-up period, defined 
by addition of therapy or increasing 
dose. 
 
Percentage of patients with 
guideline-concordant medications 
ordered in the follow-up period. 
 
Change in the gap in systolic blood 
pressure between Black and 
Hispanic/Latino patients and non-
Hispanic patients from 
identification to 12-month follow-
up 
 
Change in diastolic blood pressure 
from identification to 12-month 
follow-up 
These outcomes capture the extent to which 
there is an improvement in concordance with 
hypertension guidelines, diastolic blood 
pressure control, and treatment intensification 
during the follow-up period. 
Exploratory 
To better understand patient and 
provider’s perspective on 
hypertension management through 
the AAH healthcare system 
Analysis of de-identified qualitative 
interviews including patients and 
providers from AAH primary care 
sites 
These outcomes will inform the research 
methods and health IT tool design for the 
pragmatic cluster randomized trial.  
To determine whether 
multicomponent EHR-based health 
IT tools designed using behavioral 
principles targeted at providers 
improve post-visit patient-provider 
follow-up compared with usual 
care.  
Clinical encounters, using number 
of office visits, telephone calls and 
patient portal interactions between 
AAH primary care providers and 
patients during the 24-month 
follow-up period 
These outcomes measure the post-visit 
patient-provider follow-up during the follow-
up period. 
To assess the implementation of 
the intervention in practices 
Adoption of the EHR interventions 
and integration of the intervention 
into clinics 
These outcomes will be used to evaluate 
aspects of the implementation of the 
intervention and will provide valuable 
information regardless of the clinical results of 
the trial. 
 

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3. 
General Description of Study Design 
 
3.1 Study site 
The study will be conducted in outpatient primary care practices of Advocate Aurora Health (AAH), a 
large integrated delivery network in Wisconsin and Illinois. AAH has a fully functional EHR, Epic, that 
supports computerized ordering of medication.  
 
3.2 Overall design 
In the first Phase (Aim 1), we will analyze 30 de-identified qualitative interview transcripts that were 
conducted with 15 patients and 15 providers (14 primary care physicians and 1 medical assistant) from 
two AAH primary care clinics to understand perspectives on hypertension management and identify 
opportunities to improve EHR tools for providers. The interviews were previously conducted between 
October 2020 and March 2021 under the oversight and approval of the Advocate Aurora Health IRB. In 
brief, treating physicians provided approval for potential patient participants to be contacted. Eligibility 
criteria for the interviews included a diagnosis of hypertension and seeking care at one of the two pilot 
practices. After approval, patients were conducted for their willingness to be interviewed and consent 
was obtained using a fact sheet. Providers were eligible for the interviews based on their practicing as 
clinicians in the pilot practices. Each interview was conducted using an approved semi-structured 
interview guide and were audio-recorded and subsequently transcribed. Participants were offered 
compensation for their time. All data were previously de-identified. We are seeking approval by the 
MGB IRB for the analysis of the de-identified data collected during these interviews.  
 
In the second Phase, we will conduct a 2-arm pragmatic cluster randomized clinical trial to evaluate 
whether a multicomponent health IT intervention improves blood pressure control and reduces 
racial/ethnic disparities. Participating clinics will be randomly assigned to one of 2 arms: (a) Arm 1: 
multicomponent, intervention and (b) Arm 2: usual care (i.e. no intervention). Providers and in each arm 
will receive the same intervention for up to 24 months after randomization, which will allow us to 
determine the relative effectiveness of each approach as well as their long-term impact on clinical 
outcomes. Primary care providers in the intervention clinics will receive electronic decision support tools 
to guide their care of eligible patients. Providers in usual care clinics will receive usual care at AAH. 
Patients will not receive any intervention or outreach independent of their provider or care team. We 
will use EHR data to implement the EHR tools, identify study subjects, track study progress, and evaluate 
the effect of the interventions.  
 
3.3 Study schema 

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4. 
Subject Selection 
 
4.1 Inclusion Criteria 
The trial will include provider subjects with eligible patients. Specific decision support at the point of 
care will also appear for patients whose latest outpatient/ambulatory blood pressures are elevated (i.e., 
SBP≥140 or DBP≥90). There is no upper limit for SBP or DBP, as this cutpoint is driven by HEDIS criteria 
and would otherwise exclude patients who may be in greatest need of care. Limited inclusion criteria 
will be applied to maximize generalizability in accordance with pragmatic trial principles outlined by 
PRECIS-2 (PRagmatic Explanatory Continuum Indicator Summary). The patient population will only be 
included for the purpose of providers receiving decision support and for the purpose of analyzing the 
effect of the intervention. The study criteria are defined below. 
 
Provider-subjects will receive EHR interventions if they are primary care providers (internal medicine, 
family medicine, geriatrics) employed by AAH at one of the 24 participating clinics cluster randomized to 
the intervention. 
 
EHR patient data will be collected retrospectively for analysis and used for the EHR tools for providers 
based on the following criteria: 
1. Medical Group Medical Home Population: 1 visit with PCP in past 2 years (rolling 24 months) and 
Patient has a medical group PCP as their EPIC General PCP 
2. Current age 18-85 years 

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3. Hypertension diagnosis on EPIC Problem list OR at least 2 visits (office/telehealth/telephonic) with 
an encounter diagnosis of HTN on different dates, with at least one during the last 24 months 
(rolling dates) 
4. Latest outpatient/ambulatory (exclude urgent care) SBP ≥140 or DBP ≥90 (12 months rolling) (lowest 
measure if more than one taken at the same time) (no upper limit) 
 
4.2 Exclusion Criteria 
Providers and patients not meeting the inclusion criteria above will not be included in the analysis of the 
trial. No other exclusion criteria will be used. 
 
4.3 Recruitment and Retention 
 
4.3.1 Inclusivity of study subjects 
The primary study population for receiving any of the EHR-based interventions will be primary care 
providers at one of the 24 clinics. Providers will receive EHR-based decision support tools for the care of 
their eligible patients. Eligible patients are those based on HEDIS quality improvement criteria used at 
AAH and will include those 18-85 years of age, having had 1 visit with their primary care provider in the 
last 2 years and are diagnosed with hypertension. Specific decision support at the point of care will also 
appear for patients whose latest outpatient/ambulatory blood pressures are elevated (i.e., SBP≥140 or 
DBP≥90). The patient population will only be included for the purpose of providers receiving decision 
support and for the purpose of analyzing the effect of the intervention. 
 
Pilot data indicate that participants will cover a broad range of participants by gender and 
race/ethnicity. Of these, 39.8% are White, 37.4% are Black or African American, 7.3% are Hispanic, 5.0% 
are Asian/Pacific Islander, 9.7% are other races or unknown. Further, given the minimal risk nature of 
the study, participants will not receive incentives, remuneration, or be required to provide informed 
consent. 
 
 
5. 
Subject Enrollment 
 
5.1 Waiver of informed consent 
We are seeking a waiver of informed consent and HIPAA authorization for all data collected in this study, 
including the previously conducted deidentified patient interview transcripts. The goal of this project is 
to improve decision support to increase guideline concordant care for patients with poorly controlled 
hypertension. First, the nature of this intervention involves testing EHR decision support directly for 
providers (using information already available to them and a similar infrastructure they use in the course 
of regular clinical care). Second, the ability to understand the true effect of the intervention as it is 
delivered in the real world would be difficult to ascertain if true informed consent was sought. Third, 
obtaining informed consent would predictably reduce the number of patients participating in the study, 
especially those from unrepresented populations, and therefore undermine the generalizability of the 
study results, a foundational aspect of pragmatic clinical trial principles. Fourth, providers will retain all 
oversight of their patients’ care and will be able to make therapeutic choices based using their 
professional judgement. Fourth, this approach has also been approved by Advocate clinical leadership 
and their IRB. We also request a HIPAA waiver of patient authorization to access EHR data 
(retrospectively) necessary for outcome evaluation, as doing so would be impractical and infeasible to 
conduct the study. 

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An organization-wide announcement will be circulated across participating AAH clinics to inform 
providers of the launch of an intervention leveraging clinical decision support tools to support improved 
prescribing for adults. Providers randomized to the intervention group will also receive basic quality 
improvement training on the EHR decision support tools, as is currently done at AAH in usual care.  
 
5.2 Randomization and blinding 
Clinics will be randomized to treatment arms in equal proportions. We will use clinic-based cluster 
randomization to minimize the possibility of contamination in study interventions between practices 
and clinic staff. For the randomization, we will use covariate constrained randomization in R software 
based on clinic characteristics to reduce potential imbalances between the clinics assigned to the 
treatment arms. The clinics/providers will not be blinded to which arm they were assigned to, as 
blinding in the context of an intervention that is intended to motivate action will be infeasible. 
Physicians randomized to usual care will not have any way of accessing the EHR interventions. Patients 
of physicians in the usual care arm will have access to the patient portal but will not be receiving any 
assistance from medical assistants or any of the after-visit activities. Moreover, only primary care 
physicians will be included (specialists will not receive any enhanced clinical decision support or report 
cards), and feasibility data suggest that there is minimal crossover of patients with other primary care 
physicians given the nature hypertension management at Advocate. Therefore, any risk of 
contamination within practices is low, and any contamination that does exist would bias the trial results 
towards the null. 
 
6. 
STUDY PROCEDURES 
 
6.1 Study sites 
The study will be conducted in outpatient primary care practices of Advocate Health, a large integrated 
delivery network in Wisconsin and Illinois. Advocate has a fully functional EHR, Epic, that supports 
computerized ordering of medications.  
 
Based on input from AAH clinical leadership, the following 24 clinics will be randomized to one of two 
study arms (intervention or control).  
 
Clinic 
Advocate Medical Group Beverly 9831 S Western 
Advocate Medical Group Olympia Fields 4001 Vollmer 
Advocate Medical Group Chicago 1357 W 103rd 
Advocate Medical Group South Holland 100 W 162nd 
Advocate Medical Group Sykes 2535 S Martin Luther King 
Advocate Medical Group Des Plaines 77 Rand Rd 
Advocate Medical Group Park Ridge 1775 Ballard 
Advocate Medical Group Chicago 6434 W North 
Advocate Medical Group Chicago 4025 N Western 
Advocate Medical Group Orland Park 9550 167th 
Advocate Medical Group Glenview 1412 Waukegan 
Advocate Medical Group Palos Hills 7620 W 111th 
Advocate Medical Group Morton Grove 6131 Dempster 
Advocate Medical Group Lincolnwood 6540 N Lincoln 
Advocate Medical Group Elgin 1710 Randall 
Advocate Medical Group Chicago 4211 N Cicero 

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 Advocate Medical Group South Elgin 2000 McDonald 
Advocate Medical Group Chicago 3134 Clark 
Advocate Medical Group Frankfort 21160 Lagrange 
Advocate Medical Group Hazel Crest 3330 W 177th 
Advocate Medical Group Libertyville 825 S Milwaukee 
Advocate Medical Group Oak Lawn 4220 W 95th 
Advocate Medical Group Chicago 2301 93rd 
Advocate Medical Group Oak Lawn 9555 S 52nd 
 
6.2 Study interventions 
Clinics randomized to the control arm will not receive any additional EHR-based tools outside of the 
current standard tools offered to Advocate providers and patients. The intervention arm will receive 
EHR-based tools that will be delivered at several time points during a patient’s care episode. 
 
6.2.1 Provider Training  
Prior to study implementation, providers randomized to the intervention arm will receive a brief quality 
improvement training on how to use the new EHR-based tools. Providers will not be required to use 
these tools for the purpose of this study but will be offered these training opportunities to ensure they 
can properly utilize them if they choose to do so, as is standard AAH practice for implementing new EHR 
tools.  
 
6.2.2 Pre-visit interventions 
Providers in the intervention arm will receive an EHR-embedded dashboard which will highlight 
racial/ethnic differences in rates of achieving blood pressure control within their patient panel. Using 
EHR data, we will identify eligible patients: (1) Medical Group Medical Home Population: 1 visit with PCP 
in past 2 years (rolling 24 months) and Patient has a medical group PCP as their EPIC General PCP, (2) 
current age 18-85 years, (3) hypertension diagnosis on EPIC Problem list  OR at least 2 visits 
(office/telehealth/telephonic) with an encounter diagnosis of HTN on different dates, with any during 
the last 24 months (rolling dates) performing provider, and (4) latest outpatient/ambulatory (exclude 
urgent care) SBP ≥140 or DBP ≥90 (12 months rolling) (lowest measure if more than one taken at the 
same time).  
 
Among these eligible patients, patients included in the primary analysis will be those that had an in-
person visit at one of the trial clinics. This is because the primary decision support aimed at providers is 
based on office visits. Patients are first identified as eligible for this analysis based on the first time that 
they had a visit at one of trial clinics. The dashboard will also provide individual-level information for 
patients with poorly-controlled blood pressure including (a) most recent blood pressure values, (b) 
current therapies (based on the medication list), (c) next scheduled appointment, and (d) social 
determinant needs. Patients in the intervention arm will receive outreach via MyChart, a component of 
the patient portal, to answer questions regarding their social determinants of health (SDoH).  
 
6.2.3 During visit interventions 
Providers will receive clinical decision support within relevant patients’ charts that highlight the 
patient’s under-treated blood pressure and its consequences and potential impacts of social needs (i.e., 
social determinants of health collected during clinical care). This clinical decision support will appear 
during chart opening when a patient with blood pressure above goal presents in the clinic. The clinical 
decision support will include enhanced presentation of content, highlighting risks of undertreatment 
and providing guideline-concordant treatment intensification options. To facilitate the intensification of 

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treatment (when appropriate), the clinical decision support will also contain an embedded order set 
(i.e., a SmartSet) to prompt physicians to change or intensify medication based on practice guidelines. 
Templated SmartText will also be embedded into the Notes section of the EHR to improve charting for 
hypertension patients. When providers sign off on patient notes, they may be prompted with 
requirements and recommendations for action items. 
 
Patients who did not complete the SDoH screening pre-visit will be provided with a “SDoH Patient 
Handout” prior to collection of social determinants of health data during clinical care to introduce them 
to the type questions that will be asked and why their providers are collecting this information. This 
handout is currently used as part of clinical care at AAH.  
 
Providers will have the opportunity to provide patient education materials (i.e., information to appear in 
the after-visit summary), which can be automatically ordered within the clinical decision support and are 
already available at Advocate. These will provide pre-templated instructions about treatment, current 
blood pressure values, and self-monitoring recommendations, such as using or buying blood pressure 
cuff, that are easily understood by all patient populations including those with low health literacy.  
 
6.2.4 Post-visit interventions 
We will streamline patient-subjects’ access to the online patient portal by including information about 
the patient portal in the patients’ after-visit summary and enabling providers to order nurse blood 
pressure check visits. Advocate has 24/7 online appointment scheduling capabilities that can be 
leveraged.  
 
6.3 Schedule of visits 
 
There are no study-specific visits in this trial. We have outlined the time points in which data are 
collected below for implementation and evaluation of the interventions. 
Data collection 
Pre-randomization Follow-up 
EHR review for provider eligibility 
X 
 
Provider characteristics (Demographics) 
X 
 
Systolic blood pressure 
X 
X 
Antihypertensive dosage 
X 
X 
Resource use - Clinical encounters, using 
number of office visits, telephone calls and 
patient portal interactions 
X 
X 
 
6.3.1 Implementation evaluation 
 
As part of an implementation evaluation, several assessments will be conducted with the Advocate 
Aurora Health clinic personnel at the clinics which were randomized to the intervention arm.  
 
1. Provider Interviews 
We will conduct recorded phone or virtual interviews with consenting Advocate primary care 
providers who were randomized to the intervention arm to explore: (1) barriers to hypertension 
management, (2) experience with the study-specific EHR tools, and (3) opinions of sustainability 
of the intervention. Providers will be invited to participate via email (see attachment).  

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Prior to the interviews, providers will be asked to provide verbal informed consent for 
participation (see Fact Sheet attachment). Participants will not be forced to participate and may 
withdraw their consent to participate at any time. Participants will be informed that 
participation in voluntary and that the interviews are part of a research study.  
 
Each interview will be conducted by an Advocate investigator and a BWH investigator. A semi-
structured interview guide will be utilized (see attachment). Following provider interviews, the 
recordings will be transcribed. The transcriptions will be used to help evaluate the 
implementation of the intervention. We plan to interview approximately 15-20 providers.  
 
2. Clinic manager survey followed by discussion at clinic manager meeting 
Clinic managers at each of the clinics assigned to the intervention arm will receive an email from 
Advocate leadership asking them to respond via email to a questionnaire (see attachment) 
regarding their experience implementing the study tools into the clinic workflow. They will also 
be invited to attend a regularly-scheduled clinic manager meeting that they currently already 
have scheduled as part of regular clinical care and management to provide additional feedback.  
 
The responses to the clinic manager survey and discussion will be obtained by Advocate 
research staff, who will provide summary of these outcomes to the broader study team. No 
identifying information will be shared with Brigham and Women’s Hospital study staff.  
 
6.4  Data sources and collection 
 
6.4.1 Sources of research material, data that will be recorded, when data will be collected 
Data regarding patients' medical history, disease control, medication use and health care utilization will 
be obtained from AAH EHR data or pharmacy dispensing data for analysis of the trial. De-identified 
transcripts of recordings from patient and provider interviews will also be used for qualitative analysis 
for Aim 1 (interviews previously conducted under the IRB approval of Advocate Aurora Health).  
 
The Advocate data warehouses reside in an Oracle 9i environment and consists of the Clarity and Payer 
databases. The Clarity database is a relational database that contains clinical and financial information 
from the Epic Suite of products; including the electronic medical record system (EpicCare), the 
appointment scheduling system (Cadence), the patient accounting system (Resolute), the patient web 
portal and the master patient index (Identity). The various tables within the Clarity database are 
refreshed on a daily, weekly or monthly basis.  
 
These data are listed in the Attachments.   
 
6.4.2 Linkages to subjects, access to subject identities 
Individually identifiable data are maintained for patient care purposes within the Advocate Network and 
is needed to identify patients and to monitor care recommendations by providers. Without this linkage, 
we could not fulfill the study’s objectives. To protect the confidentiality of these data, only the provider 
group’s data analyst and project manager will have access to personal identifiers while linking the data 
and constructing study variables for the trial.  After linkage is completed and study variables are created, 
all identifiable information will be deleted from the study database. All data transfers between the 
organizations will be accomplished using secure file transfer protocols. Only HIPAA-limited data will be 

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shared with the study team based at Brigham and Women's Hospital, who are all properly trained in 
research management and approved by the IRB. All personally identifiable health information will be 
kept under lock and key. The only HIPAA identifiers that will be received by the study team at Brigham 
and Women’s hospital are dates, which are necessary for the evaluation of study outcomes (e.g., change 
in systolic blood pressure between certain dates.) 
 
6.5 Study endpoints 
The trial will be completed 24 months after trial launch. At the end of the study period, data will be 
extracted retrospectively from electronic health records; the primary analysis will be on patients who 
visited one of the 24 clinics during the study period and who meet the inclusion criteria. 
 
Primary: 
• 
Change is systolic blood pressure from patient identification (i.e., the most recent encounter 
that identified the patient as eligible) through 12-month follow-up, using values recorded in the 
EHR  
 
Secondary: 
• 
Percentage of patients with well-controlled blood pressure (<140/88mmHg) or <130/80 mmHg 
for those with comorbid conditions defined by clinical guidelines at 12 months follow-up, using 
values in the EHR 
• 
Percentage of patients with intensification of medication in the 12-month follow-up period, 
defined by addition of therapy or increasing dose 
• 
Percentage of patients with guideline-concordant medications ordered in the 12-month follow-
up period 
• 
Change in the gap in systolic blood pressure between each of Black and Hispanic/Latino patients 
and non-Hispanic White patients from the last encounter before identification through 12 
months after, using values in the EHR 
• 
Change in diastolic blood pressure from the encounter before identification through 12 months 
after, using values recorded in the EHR 
Exploratory: 
• 
Frequency of viewing the dashboard, SmartSet order set, documentation choices, and 
acknowledgement reasons 
• 
Intervention fidelity: BPAs (alerts) firing as intended 
 
Clinical encounters, using number of office visits, telephone calls and patient portal interactions 
between AAH primary care providers and patients during the 12-month follow-up period. These 
outcomes will also be measured in the remainder of the follow-up period until the end of administrative 
censoring at the end of the 2-year trial.   
 
7. 
Risks and Discomforts 
 
We believe that the risks to participation for both physicians and patients are no more than minimal for 
several reasons. First, the interventions aim to emphasize guideline-recommended treatments for 
patients with persistently elevated blood pressure. Second, all treatment decisions will ultimately be 
made by licensed primary care physicians. Finally, the intervention is physician-focused and delivered 
through interventions that use information already available to physicians that is also guideline 
concordant and intended to reduce disparities in care. We believe there is no more than minimal risk 

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involved to the physician subjects, as the physicians will simply be given tools to alter their behaviors 
towards guideline recommended care. All medical decisions are ultimately made by the physician. This 
trial will not interfere with the ordinary workings of the primary care practices.  
 
There is a small risk associated with altering hypertension medication prescribing, including 
hypotension, bradycardia, allergic reactions, and other adverse effects. However, in prior trials that 
increase intensification of blood pressure regimens, the risks have been similar in intervention and 
control arms. We believe the potential risks of treatment intensification as part of this trial are the 
same, or less than what is encountered during routine, guideline-concordant hypertension care, given 
the focus on patients with persistently elevated blood pressure. Final prescribing decisions will always 
be at the discretion of the patient’s PCP. 
 
Another potential small risk to patients will be privacy of health information. We will minimize the risk 
to privacy by taking appropriate steps to limit access to data to study investigators. Clinical data on the 
care for patients will be retrieved from the electronic medical records. 
 
8. 
Benefits 
 
This study is designed to improve electronic health record prescribing tools for providers caring for adult 
patients with uncontrolled hypertension. Potential benefits for participants in this study include 
improved decision support tools and guideline-concordant prescribing. Thus, the research could have 
both immediate benefits for physicians by increasing hypertension awareness and treatment 
knowledge, as well as for their patients who might benefit from improved blood pressure control. We 
will also evaluate implementation of the interventions to allow for future scalability. 
 
Additionally, the subjects and society may benefit in the future from accumulated knowledge that 
originates from this research. We will also produce several EHR tool deliverables for this work for the 
public, researchers, and policymakers, which will be shared as generalized knowledge. 
 
9. 
Statistical Analysis 
 
9.1. Statistical hypothesis 
We hypothesize that the intervention will lower mean systolic blood pressure to a larger degree than 
usual care. The null hypothesis will be no difference between patients identified as eligible in the 
intervention arm and usual care. All analyses will use intention-to-treat principles. 
 
9.2. Sample size determination 
We powered the study to detect a 5.0 mmHg mean change in SBP between the intervention arm and 
control arm from randomization through 12 months, assuming a standard deviation (SD) of 13.5, an 
alpha threshold of 0.05, and an intra-cluster correlation (ICC) of 0.1. With these assumptions, we 
estimated that at least 24 eligible primary care clinics including 537 providers and 49395 patients that 
were identified as eligible for inclusion in the analysis will provide >80% power to detect differences in 
our primary outcome. The 5.0 mmHg mean change was chosen as this is considered to a clinically 
meaningful change in SBP and was observed in prior systematic reviews of interventions in 
hypertension. This estimate also accounts for regression to the mean and includes SDs that were 
derived from our prior work and Advocate feasibility data.  
 

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9.3. Analyses of endpoints 
Descriptive statistics will be calculated for all variables and presented overall and by group using mean + 
SD for continuous variables, as well as counts or percentages for categorical variables. Comparisons will 
be made between groups for all outcomes using absolute standardized differences. The unit of analysis 
is at the patient-level. 
 
For the primary outcome, we will use generalized estimating equations (GEE) to adjust for the cluster 
design with an identify link function and normally-distributed errors. If >10% of data is missing, we will 
use multiple imputation for our study outcomes, as we have done in prior research. We will test the 
robustness of this approach with sensitivity analyses that use alternative methods of handling missing 
data. For baseline characteristics between arms with a standardized difference >0.1, we will repeat our 
analyses after adjusting for these covariates. We will also conduct analyses by racial/ethnic subgroup. In 
secondary analyses, we will also use longitudinal modeling to account for multiple SBP values over 
follow-up.  
 
For secondary outcomes, we hypothesize that the intervention will lead to more patients with well-
controlled blood pressures, higher rates of treatment intensification and guideline concordance. The 
null hypotheses will be no difference between the intervention arm and usual care. For these outcomes, 
we will use GEE with an identify link and normally-distributed errors for continuous variables and with a 
log-link function and Poisson-distributed errors for categorical variables. For outcomes that are non-
normal, we will use GEE with a log-link function and Poisson-distributed errors, as in our prior work. For 
all primary and secondary outcomes, the study analysts and investigators will be blinded to treatment 
allocation until after the analyses are complete. We will also evaluate other implementation outcomes, 
including clinical outcomes past the initial 12 month follow-up, until 24 months (i.e., the end of the 
trial), to explore durability of treatment effects.  
 
De-identified qualitative interview transcripts (interviews previously conducted under the IRB approval 
of Advocate Aurora Health) will be analyzed using immersion-crystallization methods using Dedoose 
qualitative software (SocioCultural Research Consultants, LLC). In this approach, we immerse ourselves 
in the collected data and then articulate the salient themes during the crystallization process. We will 
continue this approach of cycles of concentrated textual review until all data are examined, and 
meaningful patterns emerge from the data. Collected data will be used to describe and understand both 
patient and providers perspectives on hypertension management. 
 
Because this is a randomized trial, our primary analyses are planned as unadjusted; however, if there are 
strong patient-level predictors of the outcomes not balanced by stratified randomization, we will adjust 
for these in the primary analyses. 
 
10.   Monitoring and Quality Assurance 
 
10.1 Oversight 
General oversight of this project will be performed by the principal investigators (Drs. Choudhry and 
Lauffenburger) and will occur throughout the study period.  Study staff will be trained in appropriate 
study protocols and procedures and will assist in ensuring the study adheres to the IRB-approved 
protocol.  
 
10.2 Monitoring Plan 

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We plan to use a centralized Institutional Review Board (IRB) and a Data Safety Monitoring Board 
(DSMB) for all aspects of this research. Drs. Choudhry and Lauffenburger are the project PIs at MGB. 
Advocate Aurora Health (AAH) will cede review to MGB Institutional Review Board, and the MGB IRB will 
serve as the centralized IRB of record for this trial.  
 
We will establish an independent data and safety monitoring board (DSMB). A DSMB is a requirement 
for all NIH funded Phase III trials. The DSMB will act in an advisory capacity to the NIH to monitor 
participant safety and evaluate the progress of the study, review procedures and management of the 
study. The DSMB reports will be shared with the project and local site PIs within 72 hours of their 
completion. The DSMB will consist of individuals with experience in quality of care, patient safety, and 
statistics. The DSMB will meet regularly, either in person, by teleconference call, or via email at the 
discretion of the DSMB chair, to review data related to the study protocols and ensure protection of 
patient confidentiality and safety, as well as to monitor the quality of the data collected via the study 
protocols. Compliance of regulatory documents and study data accuracy and completeness will be 
maintained through an internal study team quality assurance process.  
 
This trial will be registered with clinicaltrials.gov. 
 
10.3 Adverse events and unanticipated problems 
The PIs and study team will meet on a regular basis throughout the study period and will be in direct 
contact with AAH clinical leadership involved in the project to obtain ongoing feedback.  
 
De-identified study data will be accessible at all times for the MGH PIs and coinvestigators to review, if 
applicable. We will also ensure that all protocol deviations for the pilot study are reported to the NIH 
and the IRB according to the applicable regulatory requirements. Compliance of regulatory documents 
and study data accuracy and completeness will be maintained through an internal study team quality 
assurance process. 
 
10.3.1 Definition: 
Adverse Event (AE): Any untoward or unfavorable medical occurrence in a human study participant, 
including any abnormal sign (e.g. abnormal physical exam or laboratory finding), symptom, or disease, 
temporally associated with the participants’ involvement in the research, whether or not considered 
related to participation in the research. 
 
• 
Adverse Events will be classified using the following rating scales: 
o Severity: Mild, Moderate or Severe 
• 
Mild: Awareness of signs or symptoms but are easily tolerated 
• 
Moderate: Events introduce a low level of inconvenience or concern but may 
interfere with daily activities but are usually improved by simple therapeutic 
measures. 
▪ 
Severe: Events interrupt the participants’ normal daily activities and generally 
require systemic drug therapy 
o Expectedness: Unexpected or Expected 
• 
Unexpected: nature or severity of the event is not consistent with the condition 
under study 
• 
Expected: event is known to be associated with the intervention or condition 
under study. 

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Serious Adverse Event (SAE): Any adverse event that: 
• 
results in death 
• 
is life threatening, or places the participant at immediate risk of death from the event as it 
occurred 
• 
requires or prolongs hospitalization 
• 
causes persistent or significant disability or incapacity 
• 
results in congenital anomalies or birth defects 
• 
is another condition which investigators judge to represent significant hazards. 
10.3.2 Determination 
Given the minimal risk nature of the study which aims to increase guideline concordant care for patients 
with persistently elevated blood pressures, we do not anticipate any SAEs or AEs. Patients’ own primary 
care physicians will have ultimate decision-making authority for prescribing choices, as they would in 
routine clinical practice, and we expect any adverse events related to hypertension medication 
intensification, such as hypotension or bradycardia, to be the same or less as those experienced in 
routine clinical care, given the focus on patients with uncontrolled hypertension. For example, in the 
SPRINT14 trial, which tested intensive BP control among patients age 75 or older, similar rates of serious 
adverse events occurred in the intensive-treatment group and in the standard-treatment group. In this 
study, we are using standard blood pressure targets and include a younger patient sample who have 
persistently elevated blood pressure.  
 
10.3.3 Reporting 
As previously described, any adverse events as part of this study are expected to be similar, or lower, 
than those anticipated in routine hypertension care. The study team will not be providing any direct care 
to patients and all treatment decisions will ultimately be made by the patients’ medical teams at 
Advocate Aurora Health. Any adverse event will be handled in the course of regular clinical care. 
Because we are not directly intervening upon patients and will have no patient contact during this study, 
prospectively tracking adverse events is infeasible. However, physicians will have contact information 
for our research team to report any concerns, and we will be in regular contact with practice and AAH 
leadership to monitor for any adverse events that come to attention through any of those channels. 
Therefore, while we will not be actively monitoring the occurrence of adverse events, which if done 
would require patient contact and detailed patient-level chart-reviews, we anticipate that the study 
team will be informed of any AEs or SAEs that do occur. Marlon Everett, MD, cardiologist practicing at 
AAH, with support by Rasha Khatib, PhD, research manager at AAH, are engaged in support of this project 
and will help with monitoring of physician feedback that gets routed through traditional departmental 
channels.  
 
If we become aware of any AEs or SAEs throughout the course of the study, we will collect this 
information. Any reports of deaths will be submitted to the NIH Program Officer within 24 hours. Any 
unexpected SAEs will be reported to the NIH PO and the IRB within 48 hours of the study’s knowledge of 
the SAE. All other reported SAEs and AEs received by the study team will be reported to the NIH 
Program Officer quarterly, unless otherwise requested by the DSMB. 
 
11.   Privacy and Confidentiality 
 
☒ Study procedures will be conducted in a private setting 
☒ Only data and/or specimens necessary for the conduct of the study will be collected 

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☒ Data collected (paper and/or electronic) will be maintained in a secure location with appropriate 
protections such as password protection, encryption, physical security measures (locked 
files/areas) 
☐ Specimens collected will be maintained in a secure location with appropriate protections (e.g. 
locked storage spaces, laboratory areas) – N/A 
☒ Data and specimens will only be shared with individuals who are members of the IRB-approved 
research team or approved for sharing as described in this IRB protocol 
☒  Data and/or specimens requiring transportation from one location or electronic space to 
another will be transported only in a secure manner (e.g. encrypted files, password protection, 
using chain-of-custody procedures, etc.) 
☒   All electronic communication with participants will comply with Mass General Brigham secure 
communication policies 
☒ Identifiers will be coded or removed as soon as feasible and access to files linking identifiers 
with coded data or specimens will be limited to the minimal necessary members of the research 
team required to conduct the research 
☒ All staff are trained on and will follow the Mass General Brigham policies and procedures for 
maintaining appropriate confidentiality of research data and specimens 
☒ The PI will ensure that all staff implement and follow any Research Information Service Office 
(RISO) requirements for this research 
☒ Additional privacy and/or confidentiality protections 
 
 
Advocate investigators will extract data that contains patient health information (PHI) including MRN, 
first name, last name, date of birth, encounter dates, and Zip Codes. To protect against the risk of 
inappropriate disclosure of personal health information, only limited data will be shared with study 
collaborators at Mass General Brigham (MGB)  who will oversee and conduct the analysis with insights 
from the Advocate team. The only PHI that will be shared with the BWH study investigators outside of 
Advocate are dates (e.g., date of birth, admission/discharge dates, and dates of medication fills), which 
are necessary to evaluate exposures and outcomes retrospectively. No other study investigators outside 
of BWH will receive any PHI. Advocate will then disclose HIPAA-limited datasets encrypted by a study 
key only known to the Advocate investigators to conduct the analyses. These datasets will consist of 
structured information from the EHR. Sharing this information will be necessary to assess the impact of 
the interventions.  
 
The electronic data stored at Advocate will be safeguarded by state-of-the-art security protocols. All 
patient-specific information will be collected by querying Advocate’s inpatient and outpatient 
databases; inpatient data will be necessary to evaluate outcomes such as hospitalizations. Identified 
patient information will be recorded on a password-protected electronic spreadsheet, with each patient 
assigned a specific study number. All identifiers (excluding dates) linked to study numbers will be locked 
and stored separately from the data analysis spreadsheet. The coded spreadsheet will also be stored on 
a password-protected computer in a locked office with access limited to study investigators only. Only 
the limited dataset will be utilized for analyses. All identifiers will be destroyed after completion of data 
analysis. Only study team members will have access to study related documents. All investigators have 
completed CITI training. 
 
Mass General Brigham (MGB) has computer networks in place that employ up to date virus protection 
software and enable password-protected access only to study investigators. All data transfers between 
the organizations will be accomplished using secure file transfer protocols. To ensure the confidentiality 

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and security of all data, the research team operates a secure, state-of-the-art computing facility housed 
at MGB Healthcare’s data center. The MGB data center is a secure facility that houses both computing 
environments as well as clinical systems and electronic medical records for several large hospitals in 
Eastern Massachusetts. Entry into the computer room requires staffed computer room security. The 
Division’s computers are connected to the MGB networking backbone with 10 gigabit-per-second fiber 
links. Network security is overseen by electronic medical records systems to the research team’s data. 
All data are transmitted to programmers’ workstations in an encrypted state. Backups are created using 
the current Department of Defense standard for data security and are stored in a locked facility. The 
redundancy, extensive data power, and security of our computer facility confirm our capacity to collect 
and manage data and ensure confidentiality for all project participants.  
 
12.   References 
 
1. 
Whelton PK, Carey RM, Aronow WS, et al. 2017 
ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, 
Detection, Evaluation, and Management of High Blood Pressure in Adults. Journal of the 
American College of Cardiology. 2018;71(19):e127. 
 
2. 
Khavjou OP, Diana; Leib, Alyssa. Projections of Cardiovascular Disease Prevalence and 
Costs: 2015-2035. AHA Centers for Health Metrics and Evaluation;2016. 
 
3. 
Lackland DT. Racial differences in hypertension: implications for high blood pressure 
management. Am J Med Sci. 2014;348(2):135-138. 
 
4. 
Nwankwo T, Yoon SS, Burt V, Gu Q. Hypertension among adults in the United States: 
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5. 
Gu A, Yue Y, Desai RP, Argulian E. Racial and Ethnic Differences in Antihypertensive 
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Howard G, Lackland DT, Kleindorfer DO, et al. Racial differences in the impact of 
elevated systolic blood pressure on stroke risk. JAMA Intern Med. 2013;173(1):46-51. 
 
7. 
Guzman NJ. Epidemiology and management of hypertension in the Hispanic population: 
a review of the available literature. American journal of cardiovascular drugs : drugs, devices, 
and other interventions. 2012;12(3):165-178. 
 
8. 
Sorlie PD, Allison MA, Aviles-Santa ML, et al. Prevalence of hypertension, awareness, 
treatment, and control in the Hispanic Community Health Study/Study of Latinos. Am J 
Hypertens. 2014;27(6):793-800. 
 

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9. 
Khatib R, Schwalm JD, Yusuf S, et al. Patient and healthcare provider barriers to 
hypertension awareness, treatment and follow up: a systematic review and meta-analysis of 
qualitative and quantitative studies. PLoS One. 2014;9(1):e84238. 
 
10. 
Lafata JE, Karter AJ, O'Connor PJ, et al. Medication Adherence Does Not Explain Black-
White Differences in Cardiometabolic Risk Factor Control among Insured Patients with 
Diabetes. Journal of general internal medicine. 2016;31(2):188-195. 
 
11. 
Steiner JF, Ho PM, Beaty BL, et al. Sociodemographic and clinical characteristics are not 
clinically useful predictors of refill adherence in patients with hypertension. Circ Cardiovasc 
Qual Outcomes. 2009;2(5):451-457. 
 
12. 
Lauffenburger JC, Robinson JG, Oramasionwu C, Fang G. Racial/Ethnic and gender gaps 
in the use of and adherence to evidence-based preventive therapies among elderly Medicare 
Part D beneficiaries after acute myocardial infarction. Circulation. 2014;129(7):754-763. 
 
13. 
Lewey J, Shrank WH, Bowry AD, Kilabuk E, Brennan TA, Choudhry NK. Gender and racial 
disparities in adherence to statin therapy: a meta-analysis. American heart journal. 
2013;165(5):665-678, 678 e661. 
 
14. 
Lewey J, Choudhry NK. The current state of ethnic and racial disparities in cardiovascular 
care: lessons from the past and opportunities for the future. Current cardiology reports. 
2014;16(10):530. 
 
15. 
Ziemer DC, Doyle JP, Barnes CS, et al. An intervention to overcome clinical inertia and 
improve diabetes mellitus control in a primary care setting: Improving primary care of african 
americans with diabetes (ipcaad) 8. Archives of Internal Medicine. 2006;166(5):507-513. 
 
16. 
Huebschmann AG, Mizrahi T, Soenksen A, Beaty BL, Denberg TD. Reducing clinical 
inertia in hypertension treatment: a pragmatic randomized controlled trial. J Clin Hypertens 
(Greenwich). 2012;14(5):322-329. 
 
17. 
Milman T, Joundi RA, Alotaibi NM, Saposnik G. Clinical inertia in the pharmacological 
management of hypertension: A systematic review and meta-analysis. Medicine (Baltimore). 
2018;97(25):e11121. 
 
18. 
Rodriguez F, Ferdinand KC. Hypertension in minority populations: new guidelines and 
emerging concepts. Adv Chronic Kidney Dis. 2015;22(2):145-153. 
 
19. 
James PA, Oparil S, Carter BL, et al. 2014 evidence-based guideline for the management 
of high blood pressure in adults: report from the panel members appointed to the Eighth Joint 
National Committee (JNC 8). Jama. 2014;311(5):507-520. 
 

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20. 
Whelton PK, Carey RM, Aronow WS, et al. 2017 
ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, 
Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the 
American College of Cardiology/American Heart Association Task Force on Clinical Practice 
Guidelines. Journal of the American College of Cardiology. 2018;71(19):e127-e248. 
 
21. 
Bennett A, Parto P, Krim SR. Hypertension and ethnicity. Curr Opin Cardiol. 
2016;31(4):381-386. 
 
22. 
Ferdinand KC, Yadav K, Nasser SA, et al. Disparities in hypertension and cardiovascular 
disease in blacks: The critical role of medication adherence. J Clin Hypertens (Greenwich). 
2017;19(10):1015-1024. 
 
23. 
Manze M, Rose AJ, Orner MB, Berlowitz DR, Kressin NR. Understanding racial disparities 
in treatment intensification for hypertension management. J Gen Intern Med. 2010;25(8):819-
825. 
 
24. 
Gellad WF, Grenard JL, Marcum ZA. A systematic review of barriers to medication 
adherence in the elderly: looking beyond cost and regimen complexity. The American journal of 
geriatric pharmacotherapy. 2011;9(1):11-23. 
 
25. 
Haviland AM, Elliott MN, Weech-Maldonado R, Hambarsoomian K, Orr N, Hays RD. 
Racial/ethnic disparities in Medicare Part D experiences. Medical care. 2012;50 Suppl:S40-47. 
 
26. 
Gulla J, Neri PM, Bates DW, Samal L. User Requirements for a Chronic Kidney Disease 
Clinical Decision Support Tool to Promote Timely Referral. Int J Med Inform. 2017;101:50-57. 
 
27. 
Singh K, Waikar SS, Samal L. Evaluating the feasibility of the KDIGO CKD referral 
recommendations. BMC Nephrol. 2017;18(1):223. 
 
28. 
Lammers EJ, McLaughlin CG, Barna M. Physician EHR Adoption and Potentially 
Preventable Hospital Admissions among Medicare Beneficiaries: Panel Data Evidence, 2010-
2013. Health services research. 2016;51(6):2056-2075. 
 
29. 
Xierali IM, Hsiao CJ, Puffer JC, et al. The rise of electronic health record adoption among 
family physicians. Annals of family medicine. 2013;11(1):14-19. 
 
30. 
Payne TH, Hines LE, Chan RC, et al. Recommendations to improve the usability of drug-
drug interaction clinical decision support alerts. Journal of the American Medical Informatics 
Association : JAMIA. 2015;22(6):1243-1250. 
 
31. 
Barker PW, Heisey-Grove DM. EHR adoption among ambulatory care teams. The 
American journal of managed care. 2015;21(12):894-899. 

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32. 
Sequist TD, Morong SM, Marston A, et al. Electronic risk alerts to improve primary care 
management of chest pain: a randomized, controlled trial. Journal of general internal medicine. 
2012;27(4):438-444. 
 
33. 
Field TS, Rochon P, Lee M, Gavendo L, Baril JL, Gurwitz JH. Computerized clinical 
decision support during medication ordering for long-term care residents with renal 
insufficiency. Journal of the American Medical Informatics Association : JAMIA. 2009;16(4):480-
485. 
 
34. 
Steele AW, Eisert S, Witter J, et al. The effect of automated alerts on provider ordering 
behavior in an outpatient setting. PLoS medicine. 2005;2(9):e255. 
 
35. 
Tamblyn R, Huang A, Perreault R, et al. The medical office of the 21st century (MOXXI): 
effectiveness of computerized decision-making support in reducing inappropriate prescribing in 
primary care. CMAJ : Canadian Medical Association journal = journal de l'Association medicale 
canadienne. 2003;169(6):549-556. 
 
36. 
Embi PJ, Leonard AC. Evaluating alert fatigue over time to EHR-based clinical trial alerts: 
findings from a randomized controlled study. Journal of the American Medical Informatics 
Association : JAMIA. 2012;19(e1):e145-148. 
 
37. 
Pell JM, Cheung D, Jones MA, Cumbler E. Don't fuel the fire: decreasing intravenous 
haloperidol use in high risk patients via a customized electronic alert. Journal of the American 
Medical Informatics Association : JAMIA. 2014;21(6):1109-1112. 
 
38. 
Jaspers MW, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support 
systems on practitioner performance and patient outcomes: a synthesis of high-quality 
systematic review findings. Journal of the American Medical Informatics Association : JAMIA. 
2011;18(3):327-334. 
 
39. 
Kruse CS, Stein A, Thomas H, Kaur H. The use of Electronic Health Records to Support 
Population Health: A Systematic Review of the Literature. J Med Syst. 2018;42(11):214. 
 
40. 
Smith SG, O'Conor R, Aitken W, Curtis LM, Wolf MS, Goel MS. Disparities in registration 
and use of an online patient portal among older adults: findings from the LitCog cohort. J Am 
Med Inform Assoc. 2015;22(4):888-895. 
 
41. 
Jhamb M, Cavanaugh KL, Bian A, et al. Disparities in Electronic Health Record Patient 
Portal Use in Nephrology Clinics. Clin J Am Soc Nephrol. 2015;10(11):2013-2022. 
 

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42. 
Hicks LS, Sequist TD, Ayanian JZ, et al. Impact of computerized decision support on 
blood pressure management and control: a randomized controlled trial. Journal of general 
internal medicine. 2008;23(4):429-441. 
 
43. 
Sequist TD, Fitzmaurice GM, Marshall R, et al. Cultural competency training and 
performance reports to improve diabetes care for black patients: a cluster randomized, 
controlled trial. Ann Intern Med. 2010;152(1):40-46. 
 
44. 
Bickell NA, Shastri K, Fei K, et al. A tracking and feedback registry to reduce racial 
disparities in breast cancer care. J Natl Cancer Inst. 2008;100(23):1717-1723. 
 
45. 
Samal L, Lipsitz SR, Hicks LS. Impact of electronic health records on racial and ethnic 
disparities in blood pressure control at us primary care visits. Archives of Internal Medicine. 
2012;172(1):75-76. 
 
46. 
Ziemer DC, Doyle JP, Barnes CS, et al. An intervention to overcome clinical inertia and 
improve diabetes mellitus control in a primary care setting: Improving Primary Care of African 
Americans with Diabetes (IPCAAD) 8. Arch Intern Med. 2006;166(5):507-513. 
 
47. 
Hsu L, Bowlus CL, Stewart SL, et al. Electronic messages increase hepatitis B screening in 
at-risk Asian American patients: a randomized, controlled trial. Dig Dis Sci. 2013;58(3):807-814. 
 
48. 
Balicer RD, Hoshen M, Cohen-Stavi C, et al. Sustained Reduction in Health Disparities 
Achieved through Targeted Quality Improvement: One-Year Follow-up on a Three-Year 
Intervention. Health Serv Res. 2015;50(6):1891-1909. 
 
49. 
Des Jardins T, Drone SA, Hashisaka S, et al. Leveraging Health IT to Reduce Disparities in 
Three Underserved Beacon Communities. J Health Care Poor Underserved. 2016;27(2):377-385. 
 
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Jean-Jacques M, Persell SD, Thompson JA, Hasnain-Wynia R, Baker DW. Changes in 
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APPENDIX A 
 
Data Monitoring Committee / Data and Safety Monitoring Board 
Appendix 
 
 
A Data Monitoring Committee (DMC) or Data and Safety Monitoring Board (DSMB) will be convened for 
safety monitoring of this research study.  The following characteristics describe the DMC/DSMB 
convened for this study (Check all that apply): 
 
☒ The DMC/DSMB is independent from the study team and study sponsor. 
 
☒ A process has been implemented to ensure absence of conflicts of interest by DMC/DSMB 
members. 
 
☒ The DMC/DSMB has the authority to intervene on study progress in the event of safety 
concerns, e.g., to suspend or terminate a study if new safety concerns have been identified or 
need to be investigated.   
 
☒ Describe number and types of (i.e., qualifications of) members: 
Sagar B. Dugani, MD, PhD 
Dr. Dugani is an Assistant Professor of Medicine and practicing hospitalist physician at the Mayo Clinic 
with expertise in the design and evaluation of interventions to promote better quality of care in 
cardiometabolic disease. His research focuses on disparities in the care of diabetes and heart disease.   
 
Macarius M. Donneyong, PhD, MPH 
Dr. Donneyong is an Assistant Professor and epidemiologist at The Ohio State University; he has 
expertise in hypertension, particularly in the evaluation of disparities in medication use and 
hypertension outcomes among Black patients and other socioeconomically disadvantaged groups.  
 
Anne Mobley Butler, PhD 
Dr. Butler is an Assistant Professor of Medicine at Washington University in St. Louis and epidemiologist 
with expertise in the study of disease-related burden, prevention, treatment and outcomes in clinical 
care. Her research focuses on epidemiologic and biostatistical methods; she also has experience in large 
scale evaluations of disparities. 
 
☒  Describe planned frequency of meetings: 
The DSMB will meet twice annually, either in person, by teleconference call, or via email at the 
discretion of the DSMB chair, to review data related to the study protocols and ensure 
protection of patient confidentiality and safety, as well as to monitor the quality of the data 
collected via the study protocols. 
 
☒ DMC/DSMB reports with no findings (i.e., “continue without modifications”) will be submitted 
to the IRB at the time of Continuing Review. 
 

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☒ DMC/DSMB reports with findings/modifications required will be submitted promptly (within 5 
business days/7 calendar days of becoming aware) to the IRB as an Other Event.