Records for Alcohol Care Enhancement
Phase: NA
Conditions: Alcohol Use Disorder (AUD)
Sponsor: Boston Medical Center

Records for Alcohol Care Enhancement (RACE) Study 
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Records for Alcohol Care Enhancement (RACE) Study Protocol 
 
Protocol Version Number: 1.6 
Protocol Version Date: March 21, 2023  
ClinicalTrials.gov number: NCT05492942  
Funding Mechanism:  National Institutes on Alcohol Abuse and Alcoholism, Grant Number: 
4R33AA0275970-03 
Principal Investigator: Emily Hurstak  
• 
Phone: 646-509-5189  
• 
E-mail: emily.hurstak@bmc.org 
Statistical Analysis Plan-  pages 30-32 
 
CONFIDENTIAL 
 
This document is confidential and the property of Boston Medical Center. No part of it may be 
transmitted, reproduced, published, or used by other persons without prior written authorization 
from the study sponsor. 
 
Summary of Changes: 
 
Version 
Date 
Description of Change  
Brief Rationale 
1.3 
11/3/2022 
Change to section 14.2. Rather than 
require data from a hard-copy fidelity 
checklist be entered within 7 business to 
the electronic REDCap form, the 
information will be entered into REDCap 
within 30-days business days.  
The change was made to provide 
flexibility for data entry procedures into 
REDCap as the data from fidelity 
checklists will not be used until the end 
of the study.   
1.4 
12/5/22 
• 
Change to section 11 to include 
language regarding compensation for 
clinician participants, and expansion 
of the timeframe for participants to 
consider enrollment in the study.  
• 
Change to section 12 to allow for 
greater time for clinicians to complete 
the follow-up survey at the end of the 
study, and to increase the timeline 
from recruitment to randomization 
for the study team 
 
• 
The change to add compensation 
was made to increase enrollment 
rates in the study. 
• 
The change to the timeframe 
participants have to decide to enter 
the study was made to provide the 
study team with more time to meet 
recruitment goals. 
• 
The change to increase the time 
clinician participants have to 
complete the follow-up survey was 
made to increase follow-up rates of 
the survey.  
• 
The increase in the timeline from 
recruitment to randomization was 
made to provide the study team 
with increased flexibility to 
randomize when recruitment goals 
have been met.  

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1.5 
2/3/2023 
• 
Change to sections 2, 11, and 15.2 is 
to modify the sample size of 
enrollment in RACE study, and clarify 
the total we intend to randomize. 
• 
Change to sections 5 and 12 is to 
allow for randomization to take place 
at more than one time point. A 
majority of the enrolled and 
consented clinicians will be 
randomized at one time point. Any 
remaining clinicians enrolled and 
consented on a rolling basis (until 128 
participants have been randomized). 
With this change of randomization 
occurring at multiple time points, we 
also clarify that rather than having all 
clinicians participate for 18 months, 
participation in the RACE study may 
last up to 18 months  
• 
Change to section 8 is modify the 
method in which de-identified 
quarterly summary reports will be 
sent. Rather than sent via secure 
email, the de-identified quarterly 
reports will be sent via email. The list 
of identifiable high-risk patients will 
continue to be sent to clinicians via 
Epic or secure email.  
• 
Change to section 14.2 is to add a 
potential data source (user metrics 
from an email marketing campaign 
software (e.g., ActiveCampaign)) and 
document how the data collected 
would be stored securely.    
• 
Change to section 15.3 is to state we 
will do an additional analysis to adjust 
for the duration of time each clinician 
spent in the study to account for late 
entry or early withdrawal to see if 
they have an impact on treatment 
estimates. Additionally, upon 
reviewing the variation in clinician 
volume from updated CDW clinician 
volume and panel data, and clinician 
types (i.e., MD, NP, resident/intern) 
enrolled, we have modified the 
randomization stratification.  
• 
Change to section 18.1 it to update 
the schedule of events to note 
recruitment may take place in months 
12-15.  
• 
In our original application and 
protocol, we had used the number 
128 to indicate our total 
enrollment, however, our intention 
was to randomize 128 clinicians. 
We intend to clarify that in order to 
randomize 128 clinician participants 
in the RACE study, we will need to 
consent and enroll more than 128 
individuals to account for those 
who will be disqualified following 
consent because they did not meet 
the enrollment criteria to be 
randomized. 
• 
The change to section 5 and 12 is to 
allow for greater flexibility in the 
recruitment and randomization 
timeline. 95% of the needed sample 
has been enrolled in the RACE 
study, however, to appropriately 
reach our target randomization 
number, and to allow most clinician 
participants to engage in the 
intervention for approximately 18 
months. With the change that 
randomization may occur at 
multiple time points, we clarify that 
the length of time associated with 
participation in the intervention to 
account for clinicians enrolled after 
the first randomization who may 
not participate in the intervention 
for the total 18 months. 
• 
The change to send de-identified 
quarterly summaries via email is to 
allow the study team flexibility to 
send the de-identified quarterly 
reports using email marketing 
campaign software such as 
ActiveCampaign to assist in 
collecting implementation and dose 
received data for the population 
health management (PHM) arm. 
• 
The change to add a potential data 
source, user metrics from email 
marketing campaign software, is to 
increase implementation data 
collected as part of the study that 
may help assess dose of the PHM 
intervention received by clinicians. 
Data collected may include number 

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of clinicians who opened the email 
to view the deidentified quarterly 
report, time viewing the report, etc. 
Data pulled will be stored securely 
in HIPAA compliant storage 
software.  
• 
Change to section 15.3 is to state 
how we will account for and adjust 
for the time of the several clinician 
participants receiving the 
intervention for less than the full 
18-months, as well as make a 
modification to randomization 
stratification to improve efficiency 
and effectiveness of randomization. 
• 
Change to section 18.1 was made 
to in response to the need to 
recruit several clinicians after the 
first initial randomization, and until 
128 in total are randomized.  
1.6 
7/31/2023 
Revise sections 2, 5, 11, 12, 15 to state 
that we intend to randomize at least 128 
clinician participants.   
Our power calculations estimated that 
with 128 clinicians randomized in the 
study, we would have reasonably high 
power to detect a difference between 
study arms for the primary endpoint of 
interest. Since 128 is our randomization 
goal, we are amending the protocol to 
clarify that at minimum we plan to 
randomize 128 clinicians; thus more 
than 128 clinicians may be randomized. 
1.7 
8/15/2024 
Revise section 4.2.2 Secondary Outcome 
Measures to:  
• 
Remove Topiramate from the list 
of AUD medications in the 
outcome pertaining to patients 
prescribed AUD medications 
• 
Change “visits with a BMC 
integrated behavioral health 
social worker” to “visits with a 
BMC mental health clinician”  
We have removed Topiramate from the 
list of AUD medications in our 
secondary outcome because, while 
occasionally prescribed to patients with 
AUD, Topiramate is not FDA approved 
for the treatment of AUD.  Removing 
Topiramate from the AUD medications 
included in our secondary outcome will 
ensure our secondary outcome is 
aligned with AUD medications included 
in national quality of care measures, 
and limited to those FDA approved for 
the treatment of AUD. 
 
We have revised the language 
“integrated behavioral health social 
worker” to “mental health clinician” as 
the original language specific to 
integrated behavioral health social 
worker was unintentionally limiting and 

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narrow and not reflective of the actual 
outcome the study intends to measure 
and capture.  This outcome is meant to 
measure mental health treatment 
utilization (such as visits with a 
psychologist, etc, and not solely mental 
health treatment provided by 
integrated behavioral health social 
workers).  As such, we’ve updated our 
language to more accurately align with 
the secondary outcome the study seeks 
to measure. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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TABLE OF CONTENTS 
1 List of Abbreviations .............................................................................................................. 7 
2 Protocol Summary .................................................................................................................. 8 
3 Background/Rationale & Purpose ....................................................................................... 9 
3.1 Background Information .............................................................................................. 9 
3.2 Rationale and Purpose .............................................................................................. 10 
4 Objectives .............................................................................................................................. 11 
4.1 Study Objectives ......................................................................................................... 11 
4.2 Study Outcome Measures ......................................................................................... 11 
4.2.1 
Primary Outcome Measures .................................................................................. 11 
4.2.2 
Secondary Outcome Measures ............................................................................. 12 
4.2.3 
Exploratory Outcome Measures ............................................................................ 13 
5 Study Design ......................................................................................................................... 13 
6 Potential Risks and Benefits ............................................................................................... 14 
6.1 Risks ............................................................................................................................. 14 
6.2 Potential Benefits ........................................................................................................ 15 
6.3 Analysis of Risks in Relation to Benefits ................................................................. 15 
7 Study Subject Selection ...................................................................................................... 16 
7.1 Subject Inclusion Criteria ........................................................................................... 16 
7.2 Subject Exclusion Criteria ......................................................................................... 16 
8 Study Intervention ................................................................................................................ 16 
9 Recruitment and Retention Procedures ........................................................................... 20 
9.1 Recruitment Procedures ............................................................................................ 20 
9.2 Retention Procedures ................................................................................................ 22 
10 Screening Procedures ......................................................................................................... 22 
11 Consent Procedures ............................................................................................................ 22 
12 Study Procedures ................................................................................................................. 23 
13 Assessment of Safety and Data Safety Monitoring Plan (DSMP) ................................ 25 
13.1 Definitions for Safety Assessment ........................................................................... 26 
13.2 Safety Review ............................................................................................................. 26 
13.2.1 
Multi-Site Safety Monitoring ................................................................................... 27 
13.3 Reporting Plans .......................................................................................................... 27 
13.4 Stopping Rules ............................................................................................................ 27 
14 Data Handling and Record Keeping .................................................................................. 27 
14.1 Confidentiality .............................................................................................................. 27 
14.2 Study Documentation, Source Data, and Case Report Forms (CRFs) ............. 28 
14.3 Study Records Retention .......................................................................................... 30 
15 Statistical Plan ...................................................................................................................... 30 
15.1 Study Hypotheses ...................................................................................................... 30 
15.2 Sample Size Determination ...................................................................................... 30 
15.3 Statistical Methods ..................................................................................................... 31 
16 Ethics/Protection of Human Subjects ................................................................................ 32 
17 Literature References .......................................................................................................... 33 

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18 Appendix ................................................................................................................................ 37 
18.1 Schedule of Events .................................................................................................... 37 

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1 
List of Abbreviations 
 
 
Abbreviation 
Abbreviation definition 
AA 
Alcoholics Anonymous  
ACO 
Accountable Care Organization  
AE 
Adverse Events  
ALT 
Alanine Transaminase 
AST 
Aspartate Transaminase 
AUDIT  
Alcohol Use Disorder Identification Test 
AUD 
Alcohol Use Disorder 
BEDAC 
Biostatics Epidemiology and Data Analysis Center 
BMC 
Boston Medical Center 
BMC ACO 
BMC Medicaid Care Alliance Accountable Care Organization 
BMCHNP 
BMC Healthnet Plan 
BPA 
Best Practice Advisory  
CCM 
Clinical Care Manager 
CDS 
Clinical Decision Support 
CDW 
Clinical Data Warehouse 
COVID-19 
Novel Coronavirus Disease (SARS-CoV-2)  
DSM 
Diagnostic and Statistical Manual of Mental Disorders 
ED 
Emergency Department 
EHR 
Electronic Health Record 
GEE 
Generalized Estimating Equations  
GIM 
General Internal Medicine 
HEDIS 
The Healthcare Effectiveness Data and Information Set 
HCU 
Health Care Utilization 
ICD 
International Classification of Diseases 
MASBIRT 
Massachusetts Screening, Brief Intervention and Referral to 
Treatment 
NIAAAA 
National Institute on Alcohol Abuse and Alcoholism  
NIH 
National Institutes of Health 
NCQA 
National Committee for Quality Assurance  
NP 
Nurse Practitioner 
OBAT 
Office Based Addiction Treatment 
PHM 
Population Health Manager 
PO-NTX 
Oral Naltrexone  
PPV 
Positive Predictive Value 
Principal Investigator 
PI 
Project Coordinator 
PC 
Project Manager 
PM 
RACE 
Records for Alcohol Care Enhancement 
SAE 
Serious Adverse Events 
SAS 
Statistical Analysis System 

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SW 
Social Worker 
VIF 
Variance Inflation Factor 
XR-NTX 
Injectable Naltrexone 
 
 
2 
Protocol Summary 
 
Unhealthy alcohol use, (the spectrum from risky consumption through alcohol use disorder, AUD) is a 
leading cause of preventable death in the US (88,424 deaths annually1), costing $249 billion a year2, and 
associated serious alcohol related health harms (e.g., AUD itself, cirrhosis) are increasing3–6. Though 
individuals with unhealthy alcohol use are frequently in contact with the medical system, most do not 
receive effective interventions to reduce alcohol-related harms7, leaving them vulnerable to negative 
health consequences. Electronic health records (EHRs) are a tool that can be used to improve services 
and interventions for individuals with AUD. Integrated health systems8–12 have the opportunity to 
leverage EHR systems to improve AUD care. However, it is not clear how settings that lack extensive 
infrastructure can utilize EHRs to improve population-based care outcomes beyond screening and 
prevention. The Records for Alcohol Care Enhancement (RACE) study will use data collected from 
routine clinical practices in EHRs along with population health management (PHM) and/or clinical care 
management (CCM) to improve the identification, management, and support for individuals with AUD in 
the Boston Medical Center (BMC) General Internal Medicine (GIM) primary care clinic, where a majority 
of patients are insured through an accountable care organization.  
During the initial (R21) phase of the RACE grant (which is an R21/R33 mechanism), the study team 
updated an Epic-based best practice advisory (BPA) and clinical decision support (CDS) for patients with 
risky alcohol use or alcohol use disorder. The study team also created a usable live registry in Epic of 
BMC primary care patients with risky alcohol use and/or possible or confirmed alcohol use disorder. In 
this next phase of the project (the R33 phase for which this protocol is written), the research team will 
1) maintain the BPA, CDS, and alcohol registry; 2) will re-introduce the features of the BPA/CDS and 
management of risky alcohol use/AUD to the BMC clinicians during study recruitment; and 3) test the 
feasibility and preliminary effectiveness of leveraging the electronic health record (EHR) for AUD care 
with or without population health management and/or clinical care management support. This R33 
portion of the RACE study will implement a four-group randomized control trial whereby clinicians will 
be randomized to the BPA/CDS Epic tools only group (hereafter referred to as “BPA”), or one of three 
other intervention groups following a factorial design (BPA+CCM, BPA+PHM, or BPA+CCM+PHM) that 
provides targeted support to clinicians around identifying and treating their patients with alcohol use 
disorder. The trial will assess whether PHM and/or CCM enhancements improve and facilitate care for 
patients with AUD, and improve outcomes beyond EHR prompting (BPA) alone. We aim to enroll 
approximately 130 primary care clinicians (randomizing at least 128 clinicians), ages 18 years or older, 
and practice primary care at Boston Medical Center (BMC) in the General Internal Medicine (GIM) 
primary care clinic. Enrolled clinician participants will then be randomized to one of the following arms:  
1. Clinician prompting and decision support best practice advisory alone (BPA) 
2. BPA plus population health management (BPA+PHM) 
3. BPA plus clinical care management (BPA+CCM) 
4. BPA plus population health management plus clinical care management (BPA+PHM+ CCM) 

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The study is anticipated to last for a total of 3 years. The first year is used to design and prepare for the 
study, and recruit, enroll and randomize all participants. Following randomization, the active 
intervention with clinicians will last a total of 18 months. Data cleaning and analysis will be an ongoing 
process from the beginning of the intervention through study closure. Clinician participants will be 
involved in the study for up to 18 months regardless of the randomization group to which they are 
assigned.  In the final year, there will be continued data collection from Medicaid claims data (the data 
source for the trial primary outcome), and from the BMC Clinical Data Warehouse (CDW), and data 
analysis, and reporting and dissemination of study results/findings.  
The primary objective is to test the feasibility and obtain preliminary effectiveness estimates of the 4-
arm randomized trial in increasing engagement in treatment for AUD, including AUD medication use, 
counseling, and specialty AUD care, to improve the identification and management of unhealthy alcohol 
use, particularly AUD, in a real-world urban accountable care organization (ACO) primary care setting. In 
achieving these aims, we will demonstrate the feasibility and potential ability of leveraging EHRs in an 
innovative way to improve AUD care. Results will inform improvements in clinical care, and systems of 
care to reduce alcohol-related morbidity and mortality. 
 
3 
Background/Rationale & Purpose 
 
3.1 
Background Information 
Unhealthy alcohol use is very prevalent across the United States, contributing to many preventable 
deaths, and harms related to alcohol use are rising 1,3–6,13,14. Despite high frequency of contacts with the 
medical system, most people with unhealthy alcohol use do not receive evidence-based interventions7. 
This may be due to a variety of factors such as stigma, lack of knowledge, challenges with implementing 
and maintaining tool-based screening, time or prioritization constraints in a busy clinical environment, 
and more15–17.   
EHRs are a useful and practical tool that may be utilized to improve care for individuals with AUD. 
Other integrated health systems have used EHRs (e.g., Veterans Health Administration, Kaiser 
Permanente, etc.) to improve health care in primary care settings8–12, however there are limited 
examples that have not been supported by significant infrastructure. EHRs are also known to improve 
management of chronic disease (e.g., diabetes18) through BPAs, and registries by aggregating 
information about the target population, and by assisting the clinician in reminders, decision support, 
and disease-specific care management19,20. Particularly, EHRs may help clinicians identify, assess, treat 
and monitor care when assisted by targeted staff support such as a clinical care manager and 
population health manager (CCM and PHM)21–25. These support staff may help to track outcomes of 
care and treatments provided, allowing for increased engagement with the population, and facilitation 
of care that otherwise may not be prioritized.  
 
In the R21 phase of the RACE (Records for Alcohol Care Enhancement) grant (2019-2021), the study team 
created a clinically useful live database/registry of patients with unhealthy alcohol use in BMC Epic, and 
updated Epic-based BPAs and clinical decision support (Epic Smart Set) for risky alcohol use and alcohol 
use disorder. Initial findings suggest that the changes made to the EHR were valuable, and that there is a 
need and opportunity to continue to improve screening, identification, diagnosis, and treatment for 
patients with an alcohol use disorder in the primary care setting. The current R33 phase aims to improve 
recognition, management, and overall services provided to patients with AUD by continuing to leverage 
the alcohol registry and changes made to the EHRs, and allocate targeted supports to primary care 

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clinicians – specifically a CCM and PHM, to further prompt screening/identification of AUD and support 
clinicians in engaging their patients with AUD in treatment. A four-group randomized control trial will be 
implemented to determine which of four interventions is most effective at increasing rates of initiation 
and engagement in AUD treatment, as well as other clinical processes and outcomes such as referral to 
AUD specialty care. The trial will compare the use of the 1) BPA alone (only Epic-based clinician 
prompting and clinical decision support), 2) BPA + PHM, 3) BPA + CCM, and 4) BPA + PHM + CCM, on the 
trials’ primary, secondary, and exploratory outcomes. Trial results will be assessed by examining 
outcomes for patients on the clinician’s panel.  
 
The main risks of this research study include inconvenience (to clinicians) and potential loss of 
confidentiality (clinician and/or patient). The main benefits include the ability to improve patient health 
and outcomes, and inform health service organizations about pragmatic and useful innovations for 
improving care for vulnerable populations.  
 
This study will be conducted in compliance with the protocol, applicable regulatory requirements, and 
policies and procedures of the Boston Medical Center and BU Medical Campus Human Research 
Protection Program.  
 
3.2 
Rationale and Purpose  
Current recommended guidelines for alcohol screening and brief intervention have had little 
meaningful impact due to their modest efficacy26, limited attention to treatment for AUD27–29, and 
implementation failures15,30–33. Measurement-based care, the systematic use of patient-reported 
outcomes during clinical care to inform treatment decisions has been shown to improve outcomes in 
mental healthcare (example: depression), but has not been used consistently in SUD care, including 
AUD care34.  Additionally, in primary care settings, the focus has been on intervening on individuals 
with risky alcohol use, rather than on patients with known or potential AUD. While continuing to 
intervene on individuals who have risky alcohol use, the RACE study will facilitate targeted 
interventions on patients with known or potential AUD through enhancement of tools in the EHR and 
through targeted clinician support. AUD is both an under-diagnosed and under-treated medical 
condition. EHRs are an existing mechanism and asset that can be used to improve identification and 
management of AUD. EHRs have previously been used to collect screening information, calculate and 
display results, prompt identification/diagnosis, and provide decision support for brief intervention, 
but they have not been leveraged for their potential to improve quality of AUD care8,35–37. Diverse 
populations in underserviced areas where the prevalence of AUD is high, are often not the 
environments where such technological advancements are applied.  
The research team will work to expand the potential impact of the EHR tools for risky alcohol use and 
alcohol use disorder (the BPA, CDS, registry and registry-based reporting) by incorporating a PHM and 
CCM to augment reach and support to clinicians. This project has the potential to improve AUD care by 
implementing and maintaining an alcohol registry and the BPA/CDS, and by testing feasibility and 
effectiveness of targeted clinic personnel or roles. The combination of systems and service approaches 
employed in this study could be widely disseminated and implemented in many care settings. Though 
there are several barriers (e.g., stigma, lack of knowledge, lack of time, challenges with 
implementation of screening and recommendations for care15–17) to providing care for patients with an 
AUD, the EHR in combination with the PHM and CCM may serve as robust infrastructure for practical 
clinical approaches superior to the EHR alone. The four-group randomized trial will test the feasibility 

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and obtain preliminary effectiveness estimates. The primary hypotheses are that compared to arm 1 
(BPA alone), the BPA combined with PHM (arm 2) and BPA combined with CCM (arm 3) separately, and 
the BPA combined with both CCM and PHM roles (arm 4) will improve engagement (a national quality 
of care measure), receipt of AUD care (e.g., medication, counseling), and reduce acute care utilization 
related to AUD.   
The proposed project has several innovative elements: 1) Use of electronic records to support AUD 
management in an urban safety net ACO hospital-based primary care practice; 2) use of a widely 
implemented EHR to demonstrate feasibility and allow wider dissemination beyond large health systems 
that have substantial infrastructure support; 3) use of both health records and in-person screening to 
identify unhealthy alcohol use in a real world clinical care setting; 4) use of EHR and Medicaid claims 
data to capture clinical outcomes for clinical care and research; 5) and studying the incremental effect of 
PHM and CCM on EHR tools to improve AUD care. 
 
4 
Objectives 
 
4.1 
Study Objectives 
 
The primary objective is to test the feasibility and obtain preliminary effectiveness estimates of a 4-arm 
randomized trial that provides targeted support for alcohol use disorder as a chronic disease by 
enhancing the utility of EHR tools to improve initiation and engagement in treatment for AUD (a 
national quality of care measure), as well as other outcomes such as rates of alcohol-related acute care 
utilization (emergency department visits and inpatient encounters). In achieving these aims, the 
research will demonstrate the feasibility and potential ability of leveraging EHRs to improve the 
identification and management of unhealthy alcohol use, particularly AUD, in a real-world urban ACO 
primary care setting. Preliminary testing will inform around effectiveness for improving care that could 
lead to better clinical outcomes including a reduction in heavy drinking, fewer alcohol-related 
emergency department visits, and less alcohol-related morbidity and mortality in the US.  
 
4.2 
Study Outcome Measures 
 
Outcomes for the RACE trial include two outcomes that map onto the Healthcare Effectiveness Data and 
Information Set (HEDIS) initiation and engagement of Alcohol Abuse or Dependence Treatment, a 
national quality of care measure: initiation and engagement in AUD treatment (defined below)38,39.  We 
will also measure alcohol attributable care such as acute (emergency department and inpatient) and 
outpatient healthcare utilization, and other AUD related health outcomes including AUD medication 
prescribing, AUD specialty care, and referrals to counseling. Outcome data will come from Medicaid 
claims data and BMC Epic data from the BMC CDW to assess the effect of the study intervention on 
outcomes. These data will be collected at multiple time points throughout the intervention period and 
after the intervention has concluded (due to lag times in processing Medicaid claims data and such data 
becoming available for researcher access and use). 
  
4.2.1 
Primary Outcome Measures 
 
The primary outcome in the RACE study is engagement in AUD treatment. Engagement in AUD 
treatment was chosen as it is a national quality of care measure, and is feasible and generalizable to 
other settings39,40. Treatment engagement is associated with a reduction in mortality for individuals with 

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a substance use disorder, lower addiction severity especially for outpatients with AUD, improved 
employment and wages for individuals involved in the justice system, and fewer arrests for crimes38,40,41. 
Engagement is defined as having two or more healthcare services (inclusive of AUD medication) with a 
diagnosis of alcohol use disorder within 34 days of initiating care42,43; initiation is defined as having a 
healthcare service (inclusive of medication) with a diagnosis of AUD within 14 days of a new AUD 
diagnosis43.  A new AUD diagnosis is defined as a healthcare service in which a patient receives a new 
AUD diagnosis when there has not been an AUD diagnosis for a healthcare service during the prior 194 
days, excluding diagnoses assigned in the emergency department/detox43. BMC Healthnet Plan 
(BMCHMP), a longstanding managed care organization under MassHealth, serves under the Boston 
Accountable Care Organization (ACO) and collects this information based on Medicaid claims. The study 
will gather data for the primary outcome from BMC ACO Medicaid claims data at multiple time points 
throughout the study due to the 3–9-month lag time for claims completion. Initial collection of data will 
be completed as the intervention ends, and conclude with a final request for data approximately 6-
months post intervention. Overall, using engagement as the primary outcome balances what is 
achievable by the intervention, and has the potential to demonstrate improvement on a measure with 
significant clinical meaning.  
 
4.2.2 
Secondary Outcome Measures 
 
There will be several secondary outcome measures. Initiation of AUD treatment (as defined in section 
4.2.1 (having a healthcare service (inclusive of medication) with a diagnosis of AUD within 14 days of a 
new diagnosis)) is a secondary outcome. Initiation alone is a secondary outcome instead of the primary 
outcome because there are few studies that find improved outcomes associated with initiation 
compared to engagement. Initiation will be measured using BMC ACO Medicaid data. Additional 
secondary outcomes for this study will be assessed using primarily BMC Epic data from the BMC CDW 
(although some measures will use Medicaid claims data) and include the following outcomes among 
BMC primary care patients with a new AUD diagnosis:  
• 
Patients who have been prescribed AUD medication such as Naltrexone, Intramuscular (IM) 
Naltrexone, Acamprosate, or Disulfiram44 within 90 days of a new AUD diagnosis, among 
patients with a new AUD diagnosis on a clinician's panel. Where possible, we will also look at fills 
of these prescriptions in Medicaid ACO data. 
• 
Any and number of Boston Medical Center (BMC) outpatient encounters with an AUD billing 
diagnosis within 90 days of a new AUD diagnosis, among patients with a new AUD diagnosis on a 
clinician's panel. 
• 
Any and number of visits with a BMC mental health clinician with an AUD diagnosis (encounter 
with AUD as a billing diagnosis) within 90 days of a new AUD diagnosis, among patients with a 
new AUD diagnosis on a clinician's panel. 
• 
Any and number of referrals for counseling or specialty AUD care such as the BMC Office Based 
Addiction Treatment (OBAT), Center for Addiction Treatment for AdoLescent/Young adults who 
use SubsTances (CATALYST), etc., within 90 days of a new AUD diagnosis, among patients with a 
new AUD diagnosis on a clinician's panel. 
 
Additionally, we will examine any and number of completed encounters for AUD specialty care as 
recorded in the EHR within 90 days of a new AUD diagnosis, among patients with a new AUD diagnosis 
on a clinician's panel. Lastly, we will assess any and number of acute care utilization (emergency 
department visits and hospitalizations), as well as any and number of acute care utilization (emergency 
department visits and hospitalizations) with an alcohol-related diagnosis within 90 days of a new AUD 

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diagnosis, among patients with a new AUD diagnosis on a clinician's panel from Medicaid ACO data to 
capture care utilization outside of BMC. Initial collection of Medicaid ACO and clinical data warehouse 
data will be collected as the intervention ends and conclude with a final request approximately within 9-
months post intervention.  These additional outcomes will help support the primary outcome, and 
assess additional effects of the intervention.   
 
4.2.3 
Exploratory Outcome Measures 
 
We will examine the number of heavy drinking days in the past year reported on the single item screen 
in the patient’s Epic record. Currently, when a patient is seen int the BMC primary care clinic, they 
receive a single item screening question that asks how many times in the past year have they had 
greater than 5(males)/4(females) drinks in one day, which is entered into the patient’s chart as a 
numerical result of the number of heavy drinking day episodes the patient has had in the past year. This 
result is used operationally in the clinic to help identify if a patient is likely to have unhealthy alcohol use 
and requires additional screening with the AUDIT. Since screening for alcohol use is not consistently 
completed in the primary care clinic, we will examine number of heavy drinking days as an exploratory 
outcome.  
 
5 
Study Design 
The R33 implementation phase of the study will assess feasibility and obtain preliminary effectiveness 
estimates in a 4-arm factorial randomized trial comparing 1) clinician prompting and decision support 
(BPA alone), 2) BPA + PHM support, 3) BPA + CCM support, and 4) BPA + PHM + CCM support, to 
increase engagement in AUD care. Participants in the study are medical clinicians (physicians, nurse 
practitioners, and residents, and fellows) that provide clinical care to patients who receive care in the 
BMC GIM primary care clinic. A majority of the enrolled and consented clinicians will be randomized at 
one time point using a permuted block strategy. Any remaining clinicians enrolled and consented on a 
rolling basis (until at least 128 participants have been randomized), will subsequently be randomized 
using a permuted block strategy. Randomization will be stratified by clinician type and patient panel 
volume to ensure patients are evenly distributed across randomized groups. Clinicians are the enrolled 
and randomized research participants in the study and will complete informed consent procedures. 
Patient participants will not complete research study activities, rather, patients may receive 
improvements in quality of care as a result of the targeted support provided to their clinician (PCP) 
participants. Patients will not receive investigational or experimental treatments or services as the 
intervention is designed to increase patient access to standard of care. The RACE study intervention(s) 
are designed to improve GIM clinician delivery of gold standard evidence-based care for AUD. 
Identifiable data from patient participants will be collected from the medical record and Medicaid 
claims. 
 
The conceptual framework/logic of using the EHR to improve AUD care involves collection of data that is 
otherwise not collected systematically, presentation of those data at the time of a clinician visit when it 
is relevant in the form of a prompt, provision of clinical decision support to facilitate assessment, brief 
advice and counseling, medication prescribing, and referral for counseling and AUD specialty care, and a 
mechanism for measurement-(monitoring-) based care in follow-up (repeat prompts). Although EHR 
enhancements can improve care, impact is modest. This may be even more likely for AUD, a condition 
that is associated with stigma, and about which primary care clinicians may have less knowledge and skill 
than other chronic conditions. Thus, the study aims to expand the potential impact of the EHR with 

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enhancements that provide additional clinical prompting and support (the PHM and CCM). The EHR for 
all clinicians will include prompting and decision support through the BPA. For clinicians who receive 
support from the PHM, the PHM will NOT directly help clinicians manage and implement patient care 
plans, rather the PHM will provide clinician panel-level feedback from the registry and decision support 
tools through quarterly reports and weekly summaries of high-risk patients. For clinicians who receive 
support from the CCM, the CCM is expected to not only suggest potential care opportunities to clinicians 
for patients with AUD, but also to help facilitate AUD treatment, referral, monitoring, and follow-up 
through direct interactions with patients. The CCM will be an existing BMC nurse who will facilitate AUD 
care through existing nursing practices and protocols. The study will not evaluate new systems of care 
delivered by nursing, but rather it will evaluate having a nurse with dedicated time to support AUD as a 
chronic disease model of care. 
 
Primary outcome and some secondary outcome data will come from BMC ACO Medicaid claims, and 
data for secondary outcomes will be obtained from the EHR (BMC Epic) via the BMC CDW as well as 
BMC ACO Medicaid claims data. The CCM and PHM will also regularly (approximately weekly) complete 
a Fidelity to the Intervention Checklist, that will supplement EHR assessment of fidelity to the 
intervention.  Additionally, after a clinician consents to participate in the trial, they will be asked to 
complete a brief socio-demographic survey via a REDCap form. At the close of their time in the study, 
participating clinicians will be asked to complete a brief follow-up survey, similar to the baseline socio-
demographics survey. These surveys will be used to understand baseline characteristics of enrolled 
clinicians, clinician confidence as it relates to providing alcohol related care, and include an open-ended 
question on their experience as a participant in the trial. Other relevant clinician characteristics (title, 
highest degree completed, etc.) will be collected via public data. 
 
6 
Potential Risks and Benefits 
 
6.1 
Risks 
 
Potential risks for clinician participants include inconvenience related to the time required to participate 
in the intervention and loss of confidentiality. Potential risks for patient participants include loss of 
confidentiality. These are minimal risks, consequences are not likely, and will be further minimized by 
researchers as described below. 
 
Inconveniences: For clinician participants, inconvenience is a potential risk that may occur due to time 
spent engaging in the intervention activities with the CCM and PHM.  It may be inconvenient for 
enrolled clinicians to take time to review reports and messages from the population health manager 
and/or clinical care manager and to facilitate and provide additional AUD patient care that they may not 
otherwise have provided. However, the extent to which enrolled clinicians actively engage with the PHM 
or CCM, and the reports are up to the clinicians and are entirely voluntary. Additionally, it is routine 
practice for clinicians to receive information about their performance on healthcare quality metrics, and 
while the HEDIS initiation and engagement measure (the target of this study) is not currently a routine 
performance metric shared with BMC GIM clinicians, performance on other similar quality measures 
such as depression care management, are routinely shared. Thus, we would not consider this risk of 
inconvenience to be more than minimal, as it would be similar to inconveniences they already 
encounter outside of the research setting.  
 

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To minimize the risk of inconvenience, we will describe the potential burden of time associated with the 
study and study contacts in the informed consent. It will be stressed that participation is completely 
voluntary and that they can withdraw from the study at any time. Additionally, when the PHM shares 
reports clinicians enrolled in the PHM arm(s), they will only get information in 1-on-1 meetings with the 
PHM, or through emails directly between the PHM and the clinician. Identifiable individual performance, 
information will never be shared with anyone other than the enrolled clinician. We may share 
blinded (for practice variability) and/or as aggregate data by suite for providers within a suite who have 
been randomized to the PHM arm(s). Blinded data would allow people to see where they stand in 
comparison to their peers, but not actually see who those other individuals are.  
 
Loss of confidentiality: Loss of confidentiality is potentially the most serious risk to clinician 
participants in the proposed study. Loss of confidentiality for clinician participants could occur 
is someone sees responses to interview assessments or others quality metric reports, or when research 
staff make attempts to contact them throughout the study period, though it is very unlikely because 
specific procedures will be implemented to prevent such disclosure. Additionally, loss of confidentiality 
is the principal risk for patient participants, however we will not have direct contact with patient 
participants for data collection. Data will be solely collected through Medicaid data claims and Epic data 
via the clinical data warehouse (CDW). The risk is minimal because all of this information is already 
collected in the electronic health record (EHR). Though the information collected is already available in 
the source databases, and we will take measures to secure data appropriately, it is sensitive information 
relating to health status and alcohol use.  
 
To assure confidentiality, data will be kept in a secure electronic environment (e.g., REDCap, Box) only 
accessible to research staff such as the principal investigator, the project manager, and research 
assistants. Any clinician participant direct identifiers (e.g., names, addresses, contact information, etc.,) 
and patient direct identifiers will be will be password protected, and accessible only to staff needing the 
information for appropriate study procedures. Study data collected on paper forms will be stored in 
locked filing cabinets. 
 
To minimize risk for all clinician participants, they will be informed of the potential for breach of 
confidentiality during the informed consent process. The CCM and PHM will follow routine GIM clinic 
and BMC practices, and policies and procedures for ensuring privacy and confidentiality when working 
with patients and/or accessing PHI. Therefore, any risk of loss of confidentiality for patient-participants 
will be no greater than minimal risk. To minimize risk of loss of confidentiality to clinician and patient 
participants, we have a Certificate of Confidentiality from the National Institutes of Health to further 
protect against loss of confidentiality. 
 
6.2 
Potential Benefits 
 
There may be no benefits to any participants from participation in the study. Clinician participants may 
benefit from increased support and information around the patients on their panel with AUD. Patient 
participants may benefit from the increased assessment and access to evidence-based, standard of care 
treatment for AUD. Society, medical science, and the health care system may benefit from the 
information obtained about advancements to EHR and care innovations provided by the BMC primary 
care clinic. Particularly, the study has the potential to improve quality of care for patients with AUD.  
 
6.3 
Analysis of Risks in Relation to Benefits 

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The principal risk of participating in the RACE study is loss of confidentiality to clinician and patient 
participants. The occurrence of this risk is not likely. The anticipated benefits for patient participants 
include potential for improved quality of care and receipt of evidence-based care through improved 
identification of unhealthy alcohol use, intervention tools, and targeted support. The anticipated 
benefits for clinician participants include improved decision support through the EHR, as well as 
increased targeted support by the CCM and PHM to improve well-being of patients on their panel. The 
knowledge resulting from this study has potential to help patients and health service organizations as 
information about the changes in care outcomes will inform future care and future innovations. This 
information could be useful in understanding the successes and challenges of new interventions, and 
widely disseminated and implemented in care settings. The opinion of the research team is that the 
potential benefits outweigh the risks of this study. 
 
7 
Study Subject Selection 
 
7.1 
Subject Inclusion Criteria 
 
In order to be eligible to participate in this study, an individual must meet all of the following criteria: 
 
Primary Care Clinician Subjects: 
• 
Adult (18 years or older) 
• 
Physician or Nurse Practitioner  
• 
Practices primary care at Boston Medical Center in the General Internal Medicine Primary Care 
Clinic 
• 
Current position in the practice expected to be unchanged for a minimum of 18 months (not a 
graduating trainee) 
 
Patient Subjects: 
Records (EHR, Medicaid ACO claims) from all patients empaneled (patient is assigned to PCP’s primary 
care panel) by study enrolled clinicians who are: 
• 
Adult (18 years or older) 
• 
Have had at least 1 completed visit in general internal medicine at BMC during the last 18 
months.  
• 
Eligible for alcohol related care based on high-risk screening results or an alcohol related 
diagnosis 
 
7.2 
Subject Exclusion Criteria 
 
An individual who meets any of the following criteria will be excluded from participation in this study: 
• 
Clinicians who, at the time of study recruitment, are expected to remain in their BMC GIM 
position for less than 18 months (e.g. resident or fellow trainees expected to graduate within 
the study time period). 
• 
Limited and non-readers 
 
8 
Study Intervention 
 

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The RACE study has four intervention groups (BPA only, BPA+CCM, BPA+PHM, or BPA+CCM+PHM) that 
provide various targeted support to assist clinicians in identifying patients with unhealthy alcohol use 
and engaging patients in treatment for AUD.   
 
Four-arm randomized control trial: 
All groups in the RACE randomized control trial (and all clinicians in the BMC primary care clinic, 
regardless of RACE trial enrollment) will receive Epic-based prompting, a “best practice advisory” (BPA), 
that will appear when a clinician opens an Epic record for a visit with a patient who has screened positive 
on the single item screening question, AUDIT screening survey, or by having past-year utilization with an 
alcohol-attributable ICD-10 diagnosis code. Most often, alcohol use screening is be done by a medical 
assistant in the GIM clinical setting as is current standard of care. Medical assistants administer the single 
item screening tool; if this result is positive the patient will be given the 10 item Alcohol Use Disorders 
Identification Test (AUDIT). If the single item screening is positive (> 0 <4) and/or the AUDIT is 8-13 
(female) or 15(male), the BPA will fire with “Risky Alcohol” decision support. If the single item screening is 
>= 4 and or the AUDIT > 13/15 or has a sum >=4 on the AUDIT dependence questions (questions 4-6), the 
BPA will fire with AUD decision support45. The EHR prompt that the primary care clinician receives 
includes the numerical result from patients’ responses to self-report alcohol screening questions. From 
the BPA, the clinician has the option to open a Smart Set which contains clinical decision support for 
diagnosis and management of risky alcohol use and AUD. Decision support will depend on the screening 
results but may include the following: 1) suggestion that the clinician assess the patient using the AUDIT 
or interview the patient about AUD DSM criteria (both of which will be presented on the screen along 
with EHR data fields for recording findings) and 2) approaches to managing risky alcohol use and/or 
alcohol use disorder. For those with risky use (positive screen but no current AUD) guidance will be 
provided regarding brief intervention (feedback, advice to cut down or abstain [e.g. pregnancy or trying 
to conceive, contraindicated medications or medical conditions, past disorder], goal setting, and follow-
up [to measure/monitor by reassessing heavy drinking episode frequency, and AUD symptom 
appearance]). For those with AUD, clinicians will be presented with a range of options to discuss with 
patients focusing on 1) medications and how to prescribe them (naltrexone, acamprosate, disulfiram, 
and topiramate), 2) click-button referrals in the EHR to Office Based Addiction Treatment (OBAT) and 
Integrated Behavioral Health (IBH), and 3) information and printed patient referral materials for seeking 
AUD specialty care outside BMC (including links to the NIAAA Treatment Navigator 
https://alcoholtreatment.niaaa.nih.gov/ posted fall of 2017). Although not treatment, information will 
be provided on how to connect patients with mutual help groups (Example: alcoholics anonymous). 
 
PHM Description of Activities:  
The PHM will use weekly and monthly generated registry and work bench reports to examine the 
clinician panel-level data on patients with an active AUD (i.e. not in remission) on their problem list, 
those with suspected AUD on each clinician panel, and to identify high-risk AUD patients who should be 
targeted for outreach. The lists of identified high-risk patients will be sent to clinicians securely via Epic 
and/or secure email. The quarterly reports on summary metric data will be sent via  email, however, the 
clinicians will choose how little or much they would like to engage with the PHM and reports. A fidelity 
checklist will be used to document other information such as the types of work reports pulled, if graphs 
and charts were sent to the clinician, the frequency of reports pulled, etc.   
 
Additionally, the PHM may use a fidelity checklist to track frequency, types of work reports pulled in 
Epic, etc., to understand fidelity to the intervention. Particularly, the fidelity checklist may be important 

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for providing context for reproducibility, and determining aspects of the intervention that were 
successful to understand reach and acceptability of the intervention. 
For clinicians assigned to the BPA and PHM group, they will have access to the following resources: 
• 
Continued access to the existing Epic BPA and Clinical Decision Support (Smart Set) for Risky 
Alcohol Use and AUD.  
o The BPA will continue to operate routinely, wherein the clinician participant will 
receive prompting when they open an Epic record for an encounter with a patient who 
has risky alcohol use, or a possible or confirmed alcohol use disorder.  
o From the BPA, the clinician participant will have the option to open a Smart Set which 
contains clinical decision support for diagnosis and management of risky alcohol use 
and AUD.  
• 
The clinician participant will be supported by a population health manager who will access an 
existing registry of patients with possible or confirmed alcohol use disorder and utilize work 
bench reports to examine outcomes and quality metrics for patients with alcohol use on their 
patient panel.  
o The PHM will send the clinician participant quarterly reports with summaries of (de-
identified) data, charts, and graphs that show the numbers and percentages of patients 
on their panel who need additional prompting and screening for alcohol use care, as 
well as the number and percentage of patients on their panel with AUD who have 
initiated and engaged in treatment for their AUD. These will be circulated via secure 
email to clinicians.  
▪ 
The PHM may offer clinicians the opportunity to review the reports in one-on-
one meetings to further assess barriers and facilitators to AUD care, and to help 
explain and interpret results.  
o When applicable, the PHM will send the clinician participant a weekly Epic message 
about higher risk patient(s) (patients who were recently (past 7 days) seen in the acute 
care setting or by BMC Faster Paths for an alcohol-related health problem for whom 
outreach may be beneficial.  
▪ 
In addition to sending this information to the clinician participant via secure Epic 
staff message, the PHM will send the General Internal Medicine (GIM) front 
desk staff an Epic message regarding a high-risk patient to facilitate scheduling a 
GIM visit for the patient within two weeks of their new AUD encounter, and 
copy the clinician participant on the staff message.  
▪ 
The Epic message to the GIM staff will include instructions on what can be 
communicated to the patient, and the GIM staff will be instructed to inform the 
clinician and the PHM (and CCM if in the combined PHM and CCM arm), the 
outcome of the outreach call. 
o The PHM may share aggregated and deidentified data that may be reviewed in group 
meetings to identify systems barriers or other issues related to clinical care for AUD.  
o Data reviewed may include number and percentage of patients screened with results 
suggestive of unhealthy alcohol use or AUD, patients with a new AUD diagnosis meeting 
criteria for initiation and engagement (as defined by the HEDIS measure), and patients 
with AUD referred to AUD specialty care or behavioral health support.  
 
The effect of PHM is anticipated to be on motivation to respond to the screening prompt and fully 
engage with the BPA. The PHM helps clinicians know how they are doing managing AUD. However, PHM 

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does not directly help clinicians with implementation of care with individual patients. PHM has no 
patient contact. 
CCM Description of Activities:  
The CCM will use weekly study generated Epic work bench reports to assess and identify patients on the 
clinician panel who may need additional alcohol related care follow-up. The work bench reports will 
streamline identification of relevant patients based on certain characteristics as they relate to the study 
outcomes. For example, Epic work bench reports will include patients who had a new AUD diagnosis and 
are eligible for initiation and engagement for AUD care. The CCM and the clinician participant will 
collaborate to assist patients in receiving care for AUD; however, the clinician participant can choose 
how little or how much to engage with the CCM in providing alcohol use related care to patients.  
 
Additionally, the CCM may use a fidelity checklist to track frequency, types of work reports pulled in 
Epic, etc., to understand fidelity to the intervention. Particularly, the fidelity checklist may be important 
for providing context for reproducibility, and determining aspects of the intervention that may have 
been successful to assist in understanding the reach and acceptability of the intervention. 
For clinicians assigned to receive BPA and CCM, they will receive the following resources: 
• 
Continued access to the existing Epic BPA and Clinical Decision Support (Smart Set) for Risky 
Alcohol Use and AUD. 
o The BPA will continue to operate routinely, wherein the clinician participant will receive 
prompting when they open an Epic record for an encounter with a patient who has risky 
alcohol use, or a possible or confirmed alcohol use disorder.   
o From the BPA, the clinician participant will have the option to open a Smart Set which 
contains clinical decision support for diagnosis and management of risky alcohol use and 
AUD.  
• 
The clinician participant will be supported by a clinical care manager who can assist in 
identifying patients who need further assessment, those who may be appropriate for AUD 
medication, counseling, referral, and will assist in conducting outreach to those patients 
regarding alcohol use care.  
o The CCM will review weekly reports to identify patients who may have had a recent or 
new alcohol related diagnosis, but have not received follow-up care related to their 
alcohol use, OR patients with a positive AUDIT score (greater than 13 for women, and 
greater for 15 for men, or >= 4 on the dependence questions) in the last 7 days but no 
alcohol attributable visit/encounter diagnosis associated with that encounter. 
Individuals on these reports may be eligible and appropriate for treatment initiation and 
engagement 
▪ 
Adapting from the HEDIS definition for outreach, we will define a new AUD 
diagnosis as a new AUD diagnosis in the BMC outpatient setting or acute care 
settings within the last 7 days when there has been no AUD encounter 
diagnoses (inpatient or outpatient) in the prior 194 days, excluding diagnoses 
assigned in the ED/detox. 
o To further identify patients on the work bench reports appropriate for outreach, the 
CCM may do brief chart reviews of patient charts to determine who to approach and 
who not to approach (e.g., exclude people in remission, already engaged, in OBAT etc.).  

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▪ 
The CCM may reach out to the clinician if they have identified a patient to 
approach in person or over the phone to give the clinician an opportunity to 
provide further insight and information on the patient. 
o The CCM themselves may conduct outreach to patients to schedule visits, and follow-up 
about a medication, upcoming appointments, schedule visit, specialty referrals, etc. The 
CCM will not complete a telephone screening, rather they can facilitate scheduling for 
further evaluation by clinicians. The CCM may engage in motivational interviewing to 
help support patients in the initial connection to existing clinical pathways. Using their 
clinical expertise, the CCM may suggest appropriate care to the patients and will 
interact with patients to help manage their care. 
o If the CCM has any clinical interaction (in-person or remotely) with the patient, all 
information related to the interaction will be documented in the EHR using standard 
general internal medicine documentation procedures using non-billable encounters (no 
charge clinical documentation encounters). 
▪ 
The CCM may create and update care coordination notes for patients who are 
engaged in AUD care for 1 or more encounter, and may list themselves as a 
contact for the AUD care. 
▪ 
Specifically, telephone calls, whether the patient was reached or not, and in-
person encounters will be documented in Epic. This will be used to assist the 
primary outcome measure in the claims data.  
▪ 
The CCM will communicate any relevant information to the clinician, and share 
clinical encounter notes with the clinician and other team members (e.g., 
complex care management team, OBAT team, suite nurses, behavioral health 
team etc.). 
o The CCM will communicate with clinician participants electronically (e.g., Epic 
messaging, secure email) and in-person to discuss potential care plans, and then assist 
the clinician in implementing those plans. As needed by the clinician or for efficiency, 
communication may also occur over secure email or by telephone. 
o 
Further assistance by the CCM may include facilitating prescription sign-off, assuring 
refills, finding, selecting, and coordinating specialty AUD care, and contacting patients to 
facilitate appointments related to care.  
The effect of CCM is expected to be to facilitate individual patient care—to suggest it to the clinician and 
to help make it happen. The CCM may help clinicians get individual care done, and improve reception of 
quality evidence-based care. 
Clinicians assigned to receive the BPA, PHM and CCM combined support will receive prompting from 
the BPA as described above, the aforementioned data and reports issued by the PHM, and direct 
patient assistance from the CCM. The PHM reports will be delivered to individual clinicians, and the 
data reviewed will include elements described above. The CCM will support the clinician and will 
conduct patient outreach as described above.  
 
9 
Recruitment and Retention Procedures 
 
9.1 
Recruitment Procedures 
 
To identify potentially eligible clinician participants, research staff may use the following methods: 

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• 
Research staff, in collaboration with the BMC primary care leadership team in the general 
internal medicine unit, will compile a list of potentially eligible clinician participants to invite 
them to participate in the research study.  
• 
Potential clinician participants may be identified through the professional network of the study 
team and through clinician referral of colleagues to the study team.  
• 
Research staff will attend a variety of GIM meetings (e.g., suite, clinician, business, etc.)to 
present information on the study to clinicians. This may occur in person and over Zoom.  
o When meetings occur over Zoom, research staff will ask clinicians to private chat them 
through the Zoom chat box to sign-up and/or request more information. 
o When meetings occur in person, research staff will bring devices (e.g., tablets, 
computers) to allow clinicians to review and sign the consent in-person.  
▪ 
If approached in a public space (e.g., a charting room, precepting room, etc.), 
clinicians will be provided with the opportunity to speak with research staff in a 
private location about the study about any questions or concerns.  
▪ 
Though recruitment may take place in person, clinicians will not be required to 
consent in-person. They will have the option to consent on their own time, and 
not in a group setting where they may feel pressure to consent and enroll in the 
study.  
o Research staff will send additional follow-up emails (up to 8 emails over the course of 
the recruitment period) to GIM clinicians as a reminder of what was discussed, how they 
can participate, and the benefit of participating in the study.  
• 
Research staff may approach clinicians in-person in charting rooms, precepting rooms, or other 
work spaces where clinicians congregate or complete administrative tasks to discuss the study, 
answer questions, and facilitate enrollment/consent into the study. Clinicians will be able to use 
a QR code or link to access the REDCap eConsent on study devices (e.g., tablets, computers) 
when recruitment is attempted in-person.  
o If approached in a public space (e.g., a charting room, precepting room, etc.), clinicians 
will be provided with the opportunity to speak with research staff in a private location 
about the study about any questions or concerns.  
o Though recruitment may take place in person, clinicians will not be required to consent 
in-person. They will have the option to consent on their own time, and not in a group 
setting where they may feel pressure to consent and enroll in the study.  
 
 
If clinicians are interested in participating, study staff will provide them a link and/or QR code to the 
REDCap Adult eConsent Form in the Zoom chat box (if interest in participation is expressed during a 
zoom GIM Meeting) or via email. Potential clinician participants will have the opportunity to read 
through the eConsent form on their own time, and confirm if they would like to participate by signing 
the electronic form. If clinicians have questions about the study after reading the eConsent form, they 
will be provided with contact information of research staff to ask questions via email or request to set-
up a meeting or phone call with research staff to ask their questions. Clinician participants will be able to 
download the consent form from the REDCap page after confirming their participation. To support 
recruitment efforts, clinicians who decide to enroll into the study will be offered $25 to compensate 
them for their time and effort. 
 
Patient participants will not be recruited for enrollment in the study.  
 

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9.2 
Retention Procedures 
 
We anticipate a small number of participating primary care clinicians will leave the BMC primary care 
clinic during the study period; however, we expect this number to be minimal. We will not enroll 
clinicians who are expected to leave BMC GIM during the study period due to graduation from clinical 
training or other expected departures. Additionally, for the patient population we expect attrition to be 
small because primary care patients largely remain patients at BMC since they are seen regardless of 
ability to pay, and patients infrequently move out of the region due to socioeconomic status. To assure 
there is a sufficient number of patients and time for engagement, we chose a study duration of 18-
months.  
 
10 Screening Procedures 
 
Clinician participants will not be directly screened (no self-report eligibility screening) for inclusion in the 
study because the research team we will only approach clinicians deemed eligible by the clinical 
operations leadership team (based on knowledge of graduation(s) or planned departures). Clinic 
leadership/expert judgement will be used to help determine if a clinician’s current role in the clinic 
anticipates that they will be working in the clinic for at least 18 months.  
 
The study team or clinican leadership cannot determine if clinicians intend to leave the BMC primary 
care clinic within 18-month of the study because clinicians may not want to disclose a planned leave, job 
departure, and plans can change in either direction at any point. Instead, language in the eConsent form 
will indicate the characteristics (e.g. include clinicians who plan to be in the practice for the next 18-
months) of our target population so they can be followed through the duration of the intervention. 
Providing this language will allow clinicians who anticipate a departure to opt out of participation. 
 
11 Consent Procedures  
 
The study will utilize an eConsent REDCap process using an adult informed consent form because it is 
the most logistically feasible way to obtain consent from the clinician participants. Obtaining verbal 
consent would be challenging given time constraints, clinical activities, remote work, and clinicians are 
more likely to review the eConsent form on their own time.  
 
Clinician participants will confirm agreement to participate in the study by signing the electronic consent 
form in REDCap. This will be sought by trained research staff (e.g., Research Assistant, Research 
Coordinator, Project Manager) under the supervision of study investigators. Potential clinician 
participants will be sent the REDCap link to the eConsent form via Zoom chat box (when the opportunity 
to participate in the study is presented during a GIM meeting) and/or email, and clinicians will have the 
opportunity to read and review the information on their own time. Clinician participants will be 
provided with research staff contact information in case they have any questions or there are elements 
of the consent they would like to review with study staff. They will have until the recruitment goal (at 
least 128 clinicians randomized) has been achieved to decide if they would like to participate in the 
research study. Clinician participants who choose to participate in this RACE trial be able to download a 
copy of the informed consent document to keep, and those who consent to participate in the study will 
be offered a $25 ClinCard for their time and effort. Agreement to participate in the RACE trial will be 
obtained directly before participants complete the brief demographics survey in REDCap.  
 

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The REDCap eConsent form will include all required elements: description of study procedures, the 
potential risks, discomforts, and benefits, the alternative to participating in the study including to not 
participate in the research, the purpose and duration of the study, confidentiality, the availability of 
compensation, and that participation is voluntary. Clinician participants who choose to participate will 
be able to download a copy of the informed consent form for their own records.  
 
We are seeking a waiver of Informed Consent for the patient sample in this study. This study meets the 
criteria for a waiver of consent with regards to patient participants, i.e. 1) it is minimal risk (e.g., the 
benign intervention is designed to support and facilitate a patient’s clinician in providing standard of 
care treatment for AUD that already exists in GIM such as providing resources, facilitating referrals to 
existing treatment programs, and cueing FDA approved prescriptions), 2) the rights and welfare of the 
subjects would not be adversely affected by the waiver (or alteration) of consent- all information 
collected from patient participants for use in this study is already routinely collected, recorded, and 
available in the electronic health record, and the intervention (BPA, PHM, CCM) itself is designed to 
increase patient access to standard of care, evidence based treatment for unhealthy alcohol use 3) it 
would not be practicable to carry out the research without the waiver of consent- it would be confusing 
and an undue burden to participants to be contacted regarding accessing records to seek consent. The 
primary outcome is engagement in care for AUD (measured using Medicaid claims). If the research team 
were required to obtain consent from patient participants for record review it would bias our sample for 
over inclusion of patients who are engaged in care, and would not be representative of the subject 
population of patients with AUD (undiagnosed and diagnosed) in GIM,  4) the research cannot 
practicably be carried out without using information in an identifiable format because the study team 
will perform quality assurance checks on data provided by the Boston Medical Center (BMC) clinical data 
warehouse (CDW) for research to ensure data integrity prior to conducting analyses, which is not 
practicable to complete without access to identifiers, and 5) when feasible, subjects are told about the 
research after the fact- we will not be informing participants of participation in the research after the 
fact. 
 
12 Study Procedures 
 
See the Appendix for the schedule of events.  
 
Eligibility of clinician participants will be determined and assessed by the primary care leadership team 
who will provide a list of eligible clinicians to the study team. There will be no screening data collected 
as part of the study. We will include language in the eConsent form that will indicate the characteristics 
of our intended population (e.g. include clinicians who plan to be in the practice for the next 18-months) 
so as to not exclude individuals who do not know or do not want to disclose a planned leave and job 
departure, and to consider plans may change in either direction at any point.  
 
There are no specific study visits or tests performed as part of the study. After a clinician participant 
consents, they will be asked to answer a brief socio-demographic survey. The clinicians will also provide 
contact information to the study team via a separate form in REDCap, which will be stored on password 
protected and secure servers. This contact information will be collected so clinicians can receive follow-
up information about the study, as well as their randomization assignment. At the end of the 
intervention (up to 18 months from randomization), the participants will be sent a single brief follow-up 
survey to measure confidence around AUD care and provide feedback.  

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The CCM and PHM will complete fidelity checklists as part of the study. The CCM fidelity checklists will 
be used to demonstrate how often the CCM has run and reviewed their reports, performed chart review 
for patients who may be eligible for outreach, and provide basic numerical totals on how many patients 
were attempted to be outreached to, how many were not eligible, not reached, etc. The PHM will 
complete a weekly checklist to demonstrate how often they ran the high-risk patient report, document 
the number of patients each week eligible on this report, how often they distributed quarterly reports to 
the clinician participants, if any one-one meetings were held between a clinician participant and a 
provider, etc. 
 
Study data for the patient sample will be collected through Medicaid claims data, and EHR data accessed 
via the BMC Clinical Data Warehouse. For the primary outcome, and initiation and acute care utilization 
we will utilize BMC ACO Medicaid claims data. For all other outcomes we will primarily obtain data from 
the EHR via the Clinical Data Warehouse. We will use existing clinical source data and source documents 
from patients on the clinician’s panel. This information is generated for purposes outside of research, 
however, will be used for research purposes. Data from the EHR and from Medicaid Claims will be 
collected at several timepoints at the end of the study period to account for lag times in data.  
 
Early termination in the study will only be necessary if a clinician participant no longer is employed by 
BMC primary care during the course of the study or takes a leave of absence and is not providing clinical 
care in the practice. If this happens, the study team will ask the participant to complete the follow-up 
survey as they leave the clinic rather than waiting until the end of the study period. The study team will 
consider how this may impact primary outcome data. The study team plans to complete an intention to 
treat analysis including clinicians who had early departures in determination of the final outcome. We 
will request contact information for departing clinicians to allow the study team to send follow-up study 
survey at study completion. 
 
Overview of Study Contacts: 
 
The RACE trial will not have any formal study visits with clinician or patient participants.  
 
Upon reviewing and signing the eConsent form in REDCap, clinician participants will be asked to 
complete a brief baseline sociodemographic survey in REDCap. They survey will not be administered for 
study staff, rather the clinicians will independently complete the survey on their own time. Eligible and 
consented clinician participants enrolled in the study will be randomized to an intervention group. 
Randomization may take place at more than one time point as clinician participants consent and enroll 
(until at least 128 clinicians are randomized). The clinicians will be notified of their randomization 
assignment by secure email within 2 weeks of randomization.  
 
At the end of the intervention, up to 18 months after randomization, clinician participants will be asked 
to complete a brief follow-up survey. Clinician participants will be sent a secure email with a link to the 
follow-up survey in REDCap, and be given approximately eight weeks to complete the survey.  
 
There are no clinical or laboratory evaluations for study-related purposes for clinician or patient 
participants in the RACE study. Patients included in the patient sample for the RACE study may receive 
clinical or laboratory evaluations performed as part of their routine clinical care, but no labs or clinical 
evaluations will be ordered for exclusively research purposes Clinician participants will only place clinical 

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orders consistent with usual evidence-based care for AUD. Any and all patient care will be documented 
in the EHR per typical clinical documentation procedures. The CCM role will not be producing billable 
encounters. 
 
Study Duration: 
The study will last a total of three years. In the first year, the research will team hire and train the PHM 
and CCM staff, develop the protocol and obtain IRB approval, complete clinical trial registration, work 
with the BMC ACO and the CDW to make plans for future data collection, and work with primary care 
leadership. Towards the end of the first year of the grant, the research team will recruit and consent all 
clinician participants from the primary care clinic. Clinicians may be consented and enrolled on a rolling 
basis until at least 128 are randomized. Clinician participants begin the intervention upon randomization 
into a study group. The anticipated time between enrollment and end of study activities for each 
individual clinician participant is up to 18-months. At the end of the intervention, the clinician 
participants will be sent a brief follow-up survey to answer. Medicaid claims and EHR data collection will 
last for about 6 months afterwards. Analysis and reporting are planned for the last 6 months of the 
study. 
 
Randomization: 
Randomization may occur at more than one time point. Only participants who remain eligible at the 
time of randomization will be randomized.  
 
A majority of the enrolled and consented clinicians will be randomized at one time point. Any remaining 
clinicians enrolled and consented on a rolling basis (until at least 128 clinicians are randomized), will 
subsequently be randomized. Randomization will be stratified by clinician type (e.g., nurse practitioner, 
physician, resident or fellow trainee) and by clinician patient panel volume (the mean number of 
patients eligible for the initiation and engagement outcome per clinician, at the time of randomization 
or as close to it as feasibly possible). Stratifying by patient volume will help ensure that clinicians with 
many/few patients with AUD are evenly distributed across randomized groups. To ensure balance with 
respect to the number of clinicians in each arm, the permuted blocks strategy will be used. Once 
randomization occurs, clinicians will be informed of their randomization by email, and provided with 
information on next steps.  
 
Participant Withdrawal: 
The research team does not anticipate needing to withdraw clinician-subjects without their consent, 
however, participation may be terminated for inappropriate behavior towards staff, or if the study is 
stopped early for any reason. Early termination or withdrawal of subjects may occur if a clinician 
unexpectedly leaves the clinic during the intervention period. If this occurs, the research team will 
consider and evaluate for any contamination issues related to the patient panel being divided among 
clinicians who are assigned to a different group of the study. Study clinicians that leave the practice prior 
to the close of the study will be asked to complete the follow-up survey as soon as possible.  
 
Blinding: 
Clinicians will not be blinded in that they will receive notice when they are randomized with details 
about which randomization group they have been assigned to.  
 
 
13 Assessment of Safety and Data Safety Monitoring Plan (DSMP) 

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13.1 Definitions for Safety Assessment 
 
The following definitions will be used in the assessment of safety: 
 
In a medical study, an adverse event (AE) is any untoward or unfavorable medical occurrence in a human 
subject, including any abnormal sign (for example, abnormal physical exam or laboratory finding), 
symptom, or disease, temporally associated with the subject’s participation in the research, whether or 
not considered related to the subject’s participation in the research. 
 
In the context of the RACE study, a non-medical study, an adverse event for a clinician participant may 
include a breach of confidentiality, inconvenience, invasion of privacy, or emotional distress related to 
or concerns about perception of job performance related to the study. An adverse event as it relates to 
a patient participant would be a breach of confidentiality.  
 
Unanticipated Problem is defined as an event, experience or outcome that meets all three of the 
following criteria:  
• 
is unexpected; AND 
• 
is related or possibly related to participation in the research; AND 
• 
suggests that the research places subjects or others at a greater risk of harm (including physical, 
psychological, economic, or social harm) than was previously known or recognized.  
 
Possibly related means there is a reasonable possibility that the incident, experience, or outcome may 
have been caused by the procedures involved in the research 
 
Unexpected means the nature, severity, or frequency of the event is not consistent with either: 
• 
the known or foreseeable risk of adverse events associated with the procedures involved in the 
research that are described in (a) the protocol–related documents, such as the IRB-approved 
research protocol, any applicable investigator brochure, and the current IRB-approved informed 
consent document, and (b) other relevant sources of information, such as product labeling and 
package inserts; or 
• 
the expected natural progression of any underlying disease, disorder, or condition of the 
subject(s) experiencing the adverse event and the subject’s predisposing risk factor profile for the 
adverse event. 
 
13.2 Safety Review 
 
Both the risks listed in Section 6.1 and unknown risks will be monitored as follows:  
 
The primary risks in the RACE study are inconvenience due to potential time associated with study team 
contact for clinician participants, and loss of confidentiality for clinician and patient participants. Though 
the risk of these occurring is low, clinicians will be provided with contact information of the study team 
to report any inconvenience on a on a case-by-case basis to the study team. Inconveniences will be 
reviewed and monitored as they occur by the CCM and PHM as they will be primarily in contact with the 
study participants. The project coordinator, and director of research operations may assist in monitoring 
of risks as needed. Loss of confidentiality will be monitored for and reported by all research study team 

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members as they occur. The PI will be responsible for assuring that study procedures are adhered to 
regarding data security, and will regularly meet with the study staff.   
 
Adverse events in this study will not be medical. Adverse events will be evaluated for severity, 
seriousness, relatedness, and expectedness on a form that allows the person who has identified the AE 
to provide a description of the event, and the Principal Investigator (PI) will review the AE to determine 
if the event is related to the protocol, expected from the population, and severity.  All adverse events 
will be reviewed by the research team on a monthly basis to determine if a pattern of adverse events 
exists and suggests a change to the protocol may be necessary. Physicians will not be blinded, and 
patients will be aware that a CCM is involved in their medical care if they are contacted by them, or if a 
physician discusses the CCM with the patient. There will be no unblinding required as the study team 
will know which arm clinicians are associated with.  
 
13.2.1 Multi-Site Safety Monitoring 
 
This is not a multi-site study.  
 
13.3 Reporting Plans 
    
The Principal Investigator at BMC/BU Medical Campus will report unanticipated problems, safety 
monitors’ reports, and adverse events to the BMC/BU Medical Center IRB in accordance with IRB 
policies: 
• 
Unanticipated Problems occurring at BMC/BU Medical Campus will be reported to the BMC/BU 
Medical Campus IRB within 7 days of the investigator learning of the event. 
• 
Adverse Events (including Serious Adverse Events) will be reported in summary at the time of 
continuing review, along with a statement that the pattern of adverse events, in total, does not 
suggest that the research places subjects or others at a greater risk of harm than was previously 
known. 
 
13.4 Stopping Rules 
 
The study has no pre-defined stopping rules. Since the study is no more than minimal risk study, and we 
do not anticipate there being harm associated with the study, we have not built-in stopping rules. The 
major risk due to the study is inconvenience, however, clinicians can voluntarily end their participation 
in the study at any point. Though we may gather data during the study, due to the time required for 
data management, cleaning and quality control procedures, we will not be able to meaningfully analyze 
the data prior to the end of the study. Therefore, no interim analyses will be performed to determine 
effectiveness of the different arms in the study.  
 
14 Data Handling and Record Keeping 
 
14.1 Confidentiality 
All web and database servers will be housed in a secure, professionally managed server room under the 
control of the Boston University Medical Camps (BUMC) Office of Information Technology (OIT). Staff 
from the Biostatistics and Epidemiology Data Analytic Center (BEDAC) and BUMC OIT work to continually 

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enhance their systems, procedures and processes to ensure compliance with industry and government 
standards for data security. All central systems are secured and physically behind locked doors with 
access restricted to key personnel in the BUMC OIT. All data are stored on servers that are password-
protected. To protect against security breaches, data will be electronically encrypted so that only the 
intended recipient can decode. Any PHI will be stored in a HIPAA compliant environment that meets 
BUMC standards for storage of PHI. 
Clinician participants in the RACE Study directly enter data into a REDCap survey that will be attached to 
clinician participant identifiers (e.g., name, phone number, email). This data will be stored using the 
security features that allows the system to collect, process, and store data classified as restricted use. 
The BU REDCAP configuration includes a virtual Restricted Use environment (vRU), a firewall, SSL/TLS 
encryption, two-factor authentication, and user rights management policies to enhance security and 
compliance for study data. Additionally, data kept in REDCap will only be accessible to relevant research 
staff. Paper records will be stored in locked filing cabinets and locked offices. 
Additionally, research data will not be collected directly from patient participants. Identifiable patient 
participant data will be collected solely through electronic health record (EHR) review and through 
review of Boston Accountable Care Organization (BACO) claims data. Though the information collected 
is already available in the source databases, and we will take measures to secure data appropriately, it is 
sensitive information relating to health status and alcohol use. These data will be kept in a secure 
electronic environment (e.g., Box.com), accessible to relevant research staff need the information for 
appropriate study procedures. Specifically, Box.com is the HIPAA compliant electronic environment that 
meets BMC standards for storage of PHI, and is the primary platform the clinical data warehouse (CDW) 
for research utilizes to data they provide to research teams. 
 
As a National Institutes of Health (NIH) funded study we automatically have a Certificate of 
Confidentiality to protect participants in this research. 
 
Data will be kept for a minimum of seven years. Identifiable data and/or a master-code provided by the 
CDW for research that links data to identifiers for clinician or patient participants will be kept for the 
duration of the study. Once all analyses have been completed and the study PI determines that 
identifiable data and/or links to identifiers are no longer needed, these identifiers and/or links to 
identifiers will be destroyed by permanently deleting identifiers from all datasets.  If an identifier is 
deemed important for analysis but is no longer needed for identification purposes (e.g. dates), we will 
scramble the dates such that they remaining meaningful for analysis purposes (i.e. that an interval can 
still be determined) but do not remain identifiable.  
 
There are no current plans for external data sharing. The project has been registered on 
ClinicalTrials.gov., and will be updated during each milestone (e.g., recruitment, randomization, data 
collection, study closeout) of the study. 
 
14.2 Study Documentation, Source Data, and Case Report Forms (CRFs) 
The CCM will utilize non-billable Epic encounter documentation notes to record data that may be used 
as research data. The CCM will document standard of care clinical processes in the EHR following usual 
general internal medicine standard documentation processes and policies. Any Epic encounter or 
progress note documentation will not be used to record research data, but rather for clinical care 

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coordination and communication in the EMR. If paper copies of work bench reports are printed, the 
paper copies will be shredded after the work is completed. The PHM will not document in the clinical 
record. 
Data sources in the RACE study include the REDCap baseline and follow-up survey completed by clinician 
participants, fidelity checklists, and source data: EHR data from Epic and BMC ACO Medicaid Claims 
data. We may also include clinician use metrics tracked and pulled from email marketing campaign 
software (e.g., ActiveCampaign) to assess number of clinicians who open the email to view the report, 
the amount of time a clinician participant views the report, how many clinicians click on the hyperlinks, 
etc. For the primary outcome, and initiation and acute care utilization we utilize BMC ACO Medicaid 
claims data for the patient sample. For all other outcomes we will obtain them from the EHR via the 
Clinical Data Warehouse. We will use existing clinical source data and source documents from patients 
on the clinician’s panel. This information is generated for purposes outside of research, however, will be 
used for research purposes. The source data will include diagnoses and ICD-10 codes, health care 
utilization, medications and mentions of medications, counseling and referrals to counseling or specialty 
care, any EHR fields related to the BPA prompts, and patient demographics (e.g., age, gender, 
race/ethnicity). These data will be securely sent to the study team per typical CDW and Medicaid claims 
data procedures with the minimal amount of identifiable information included necessary to complete 
outcome analyses.  
Initial and follow-up socio-demographic information will be collected and directly entered by clinician- 
participants into a REDCap survey. These surveys are used to understand some baseline characteristics 
of each clinician participant, confidence as it relates to providing alcohol related care, and impact of the 
intervention that cannot be collected from the EHR. These will be programmed into a secure electronic 
platform (REDCap), and managed by BEDAC. Other relevant clinician characteristics (e.g., title, other 
degrees completed) will be collected via public data sources.  
User metrics from the email marketing campaign software (e.g., ActiveCampaign) will be stored on the 
software platform. Data pulled from the software for research purposes will be stored in Box.com, a 
HIPAA compliant BMC approved storage software. 
Additionally, the CCM and PHM will complete regular fidelity checklists to confirm fidelity to the 
intervention for replication purposes. This checklist may be completed on a hard copy paper document 
or electronically. If a paper form is used, the data from this source paper form will be manually entered 
into the programmed electronic REDCap form within 30 business days. Hard copy documents will be 
stored in a locked filing cabinet, and electronic forms will be saved on a password protected platform.   
Corrections on data collection forms (i.e., fidelity checklist): If any entry error has been made to 
hardcopy data fidelity checklists, to correct such an error, a single straight line will be drawn through the 
incorrect entry and the correct data will be entered above it. All such changes will be initialed and dated.  
There will be no erasures or white-out on hardcopy fidelity checklists. For clarification of illegible or 
uncertain entries, the clarification will be printed above the item, then initialed and dated. If fidelity 
checklists are directly entered in the REDCap programmed form, any data entry errors will be captured 
through the audit trail in REDCap.  
 
See Section 18 Appendix for the following CRFs: 
• 
Baseline Socio-Demographic Clinician Survey 

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• 
Follow-up Socio-Demographic Clinician Survey 
• 
CCM and PHM Fidelity Checklist 
 
14.3 Study Records Retention 
 
All study records (e.g., informed consent, REDCap survey records, source data, fidelity checklists) from 
the RACE study will be retained for at least seven years after completion of the study per Boston 
Medical Center and Boston University requirements. Data will be managed and retained by the 
Biostatistics and Epidemiology Data Analytics Center (BEDAC). After the seven years of required data 
retention, the study PI will determine when the information can be destroyed.  
 
 
15 Statistical Plan 
Note: there will not be a separate attached stastical analysis plan 
 
15.1 Study Hypotheses 
 
Our primary hypothesis is that PHM and CCM separately and combined will improve engagement with 
AUD care (3 primary comparisons with no PHM or CCM). Secondary hypotheses are that PHM and CCM 
separately and combined will improve initiation of AUD care, prescription of AUD medication, referral to 
and receipt of AUD behavioral counseling in primary care, and referral to and receipt of specialty AUD 
care. 
 
15.2 Sample Size Determination 
 
Sample Size/Power: To define the limits of the study, we present power calculations to assess the 
differences we will be able to detect with reasonably high power for the primary endpoint of interest, 
engagement. Calculations assume a 2-sided test, with an overall significance level of 0.05. For the 
purposes of power calculations, we consider a simple, conservative setting based on a Bonferroni 
adjustment for multiple comparisons, although in our analyses we will use the Hochberg sequential 
correction method, an approach that will result in higher power than the Bonferroni method. To 
maintain an overall type I error rate of 5%, we assume each of the 3 primary pairwise comparisons will 
be conducted at an alpha level of 0.0167 for the following power calculations. It is expected that at least 
128 total clinicians will be randomized in the study (approximately 32 per randomized group), with an 
average of approximately 8 eligible patients per clinician. We conservatively estimate a total of 1000 
patients (approximately 250 patients per randomized group). We expect the interclass correlation 
coefficient (ICC) will be <0.10 for the outcomes of interest, and conservatively assume a value of 0.10 in 
the following calculations. Calculations for engagement are based on a chi-square test with continuity 
correction, with estimates adjusted for clustering based on the inflation factor (also referred to as the 
design effect). Because the primary outcome, engagement, will be assessed using medical records, there 
will be no loss to follow-up. Based on rates from BMCHNP and the primary care clinic itself (sources and 
specifics above under “primary outcome”), we expect 15% of patients with AUD in the BPA only group 
will be engaged in care. Based on the above assumptions, the proposed study has 80% power to detect 
an absolute difference of 17% (i.e., 15% in the BPA only group vs. 32% in any of the 3 combined 
intervention arms) in the proportions with engagement. A difference of this magnitude represents a 
clinically meaningful effect of the intervention.  
 

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15.3 Statistical Methods 
 
For the primary outcome, and initiation and acute care utilization we will access BMC ACO Medicaid 
data. For all other outcomes we will obtain them from the EHR via the Clinical Data Warehouse. 
Demographic information from participating clinicians will be collected using REDCap at the time of 
consent. Sociodemographic patient data will primarily be obtained from Epic, however if there is missing 
sociodemographic data in Epic, we may use Medicaid claims data to fill in the missing data. Descriptive 
statistics will be calculated for patient-specific and clinician-specific variables at baseline. All baseline 
variables will be assessed to see whether there appear to be any differences across randomized arms. 
This study will use an intent-to-treat analysis including all subjects according to their randomized 
assignment. The primary goal of this aim is to estimate the effectiveness of the 3 combination 
interventions (BPA+PHM+CCM; BPA+CCM; BPA+PHM) vs. BPA alone on the primary outcome 
engagement in care. Randomization and the intervention occur at the clinician level while the unit of 
observation is at the patient level. Thus, analyses of patients must account for clustering by the clinician. 
The main analysis evaluating the effect of the interventions on the binary study outcomes (primary: 
engagement; secondary: initiation, receipt of AUD medication, AUD counseling and specialty AUD care 
by referral) will use generalized estimating equations (GEE) logistic regression models with empirical 
(robust) standard errors to account for clustering by clinicians. We will include indicator variables to 
represent the study arms and will also adjust for the randomization stratification factors clinician type 
(attending/nurse practitioner vs. resident) and clinician volume (above the median, or below the median 
by clinician type) to improve efficiency, and explore suite effects not already accounted for. In addition, 
the models will control for important baseline characteristics (individual-specific, clinician-specific, or 
both) that differ between groups to avoid confounding. An additional analysis will be further adjusted 
for the duration of time each clinician spent in the study, accounting for late study entry and early 
withdrawal to see if this has any impact on the treatment estimates. Secondary, confirmatory analyses 
will be conducted using mixed effects logistic regression models, sometimes referred to as random 
effects models or hierarchical regression models accounting for clustering by including a random effect 
for clinician. Spearman correlation coefficients will be obtained to identify pairs of variables that may be 
collinear (r>0.4) and would therefore not be included together in regression analyses. In addition, the 
variance inflation factor (VIF) will be assessed to detect possible collinearity. The 3 pairwise comparisons 
of primary interest are between each combination intervention vs. BPA alone and we will adjust for the 
multiple comparisons using the Hochberg sequential test procedures. 
 
For the trial, our plans to randomize and analyze groups by intention to treat, have outcome assessment 
done by those unaware of group assignment, the likelihood of minimal loss to follow-up, and an 
adequate power and sample size will support the validity of the results. It is possible that the 
interventions tested could be too weak to have an effect. However, PHM and CCM are effective 
approaches for other conditions and are feasible. More intensive models are more difficult and costly to 
implement. BPA, PHM and CCM balance intervention strength and feasibility well. We will attribute 
patients to their assigned primary care clinician when they meet eligibility for initiation of AUD 
treatment (e.g. have a new AUD encounter diagnosis, as per the HEDIS definition of new AUD diagnosis). 
There is a small possibility that individual patients will be reassigned to a different clinician (potentially 
in a different treatment arm) during the follow-up period for the outcomes, which could introduce 
contamination. We feel that this will be rare occurrence and that it will not interfere with the pragmatic 
intent-to-treat analysis using the clinician at patient inclusion in the study. But, we will perform 
additional secondary analyses allowing time-varying clinician assignments to verify that changes in 
clinician assignment do not compromise the treatment effect estimates. Otherwise, contamination is 

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unlikely because clinicians, while they may be aware that other clinicians are receiving PHM or CCM, will 
not be receiving it. The primary outcome of the trial, engagement in AUD treatment, will have to have 
technical specification modified (as has been done for prior research to define the index visit). It is also 
different from data fed back by PHMs to clinicians from the EHR in real time (because engagement is 
collected at the end of the trial). Results are likely to apply widely to similar settings. 
 
16 Ethics/Protection of Human Subjects 
 
This study is to be conducted according to applicable US federal regulations and institutional policies 
(which are based in federal regulations, guidance, and ICH Good Clinical Practice guidelines). 
 
This protocol and any amendments will be submitted to the Boston Medical Center and Boston 
University Medical Campus IRB for formal approval of the study conduct. The decision of the IRB 
concerning the conduct of the study will be made in writing to the investigator. 
 
All clinician subjects for this study will be provided a consent form describing this study and providing 
sufficient information for subjects to make an informed decision about their participation in this study. 
The consent form will be submitted with the protocol for review and approval by the IRB. The consent of 
a subject, using the IRB-approved consent form, must be obtained before that subject is submitted to 
any study procedure. Consent will be documented as required by the IRB.   
 
 

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17 Literature References 
 
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https://www.cdc.gov/alcohol/data-stats.htm 
3. Grant BF, Chou SP, Saha TD, et al. Prevalence of 12-Month Alcohol Use, High-Risk Drinking, and 
DSM-IV Alcohol Use Disorder in the United States, 2001-2002 to 2012-2013: Results From the 
National Epidemiologic Survey on Alcohol and Related Conditions. JAMA Psychiatry. 2017;74(9):911. 
doi:10.1001/jamapsychiatry.2017.2161 
4. Case A, Deaton A. Rising morbidity and mortality in midlife among white non-Hispanic Americans in 
the 21st century. Proc Natl Acad Sci USA. 2015;112(49):15078-15083. 
doi:10.1073/pnas.1518393112 
5. Yoon YH, Chen CM. LIVER CIRRHOSIS MORTALITY IN THE UNITED STATES: NATIONAL, STATE, AND 
REGIONAL TRENDS, 2000–2013. :71. 
6. Mullins PM, Mazer-Amirshahi M, Pines JM. Alcohol-Related Visits to US Emergency Departments, 
2001–2011. Alcohol and Alcoholism. 2017;52(1):119-125. doi:10.1093/alcalc/agw074 
7. Lipari R, Park-Lee E, Van Horn S. AMERICA’S NEED FOR AND RECEIPT OF SUBSTANCE USE 
TREATMENT IN 2015. https://www.samhsa.gov/data/sites/default/files/report_2716/ShortReport-
2716.html 
8. Williams EC, Achtmeyer CE, Kivlahan DR, et al. Evaluation of an electronic clinical reminder to 
facilitate brief alcohol-counseling interventions in primary care. J Stud Alcohol Drugs. 
2010;71(5):720-725. doi:10.15288/jsad.2010.71.720 
9. Bradley K, Williams E, Achtmeyer C, Volpp B, Collins B, Kivlahan DR. Implementation of Evidence-
based Alcohol Screening in the Veterans Health Administration. AJMC. 2006;12(10). Accessed May 
9, 2022. https://www.ajmc.com/view/oct06-2375p597-606 
10. Mertens JR, Chi FW, Weisner CM, et al. Physician versus non-physician delivery of alcohol screening, 
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11. Wallace A. Alcohol as a Vital Sign: We Ask Everyone – NCAL Research Spotlight. Kaiser Permanente. 
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14. Roberts A, Rogers J, Mason R, et al. Alcohol and other substance use during the COVID-19 
pandemic: A systematic review. Drug and Alcohol Dependence. 2021;229:109150. 
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15. Rehm J, Anderson P, Manthey J, et al. Alcohol Use Disorders in Primary Health Care: What Do We 
Know and Where Do We Go? Alcohol and Alcoholism. 2016;51(4):422-427. 
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16. Williams EC, Achtmeyer CE, Young JP, et al. Local Implementation of Alcohol Screening and Brief 
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and Administrative Staff. Journal of Substance Abuse Treatment. 2016;60:27-35. 
doi:10.1016/j.jsat.2015.07.011 
17. Williams EC, Johnson ML, Lapham GT, et al. Strategies to implement alcohol screening and brief 
intervention in primary care settings: A structured literature review. Psychology of Addictive 
Behaviors. 2011;25(2):206-214. doi:10.1037/a0022102 
18. O’Connor PJ, Sperl-Hillen JM, Rush WA, et al. Impact of Electronic Health Record Clinical Decision 
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19. Bodenheimer T. Improving Primary Care for Patients With Chronic Illness. JAMA. 
2002;288(14):1775. doi:10.1001/jama.288.14.1775 
20. Population Health Management:  A Roadmap for Provider-Based Automation in a New Era of 
Healthcare. 
https://www.exerciseismedicine.org/assets/page_documents/PHM%20Roadmap%20HL.pdf 
21. Hong CS, Siegel AL, Ferris TG. Caring for high-need, high-cost patients: what makes for a successful 
care management program? Issue Brief (Commonw Fund). 2014;19:1-19. 
22. Felt-Lisk S, Higgins T. Exploring the Promise of Population Health Management Programs to Improve 
Health. Published online August 2011. https://www.mathematica.org/publications/exploring-the-
promise-of-population-health-management-programs-to-improve-health 
23. Oslin DW, Lynch KG, Maisto SA, et al. A Randomized Clinical Trial of Alcohol Care Management 
Delivered in Department of Veterans Affairs Primary Care Clinics Versus Specialty Addiction 
Treatment. J GEN INTERN MED. 2014;29(1):162-168. doi:10.1007/s11606-013-2625-8 
24. Bradley KA, Bobb JF, Ludman EJ, et al. Alcohol-Related Nurse Care Management in Primary Care: A 
Randomized Clinical Trial. JAMA Intern Med. 2018;178(5):613. 
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25. Watkins KE, Ober AJ, Lamp K, et al. Collaborative Care for Opioid and Alcohol Use Disorders in 
Primary Care: The SUMMIT Randomized Clinical Trial. JAMA Intern Med. 2017;177(10):1480. 
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26. Jonas DE, Garbutt JC, Amick HR, et al. Behavioral Counseling After Screening for Alcohol Misuse in 
Primary Care: A Systematic Review and Meta-analysis for the U.S. Preventive Services Task Force. 
Ann Intern Med. 2012;157(9):645. doi:10.7326/0003-4819-157-9-201211060-00544 
27. Borsky A, Zhan C, Miller T, Ngo-Metzger Q, Bierman AS, Meyers D. Few Americans Receive All High-
Priority, Appropriate Clinical Preventive Services. Health Affairs. 2018;37(6):925-928. 
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28. Saitz R. Alcohol screening and brief intervention in primary care: Absence of evidence for efficacy in 
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services following brief alcohol intervention: a meta-analysis of randomized controlled trials: SBI 
and referral to treatment. Addiction. 2015;110(9):1404-1415. doi:10.1111/add.12950 
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interventions. Drug and Alcohol Dependence. 2008;98(3):235-240. 
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31. Kaner E, Bland M, Cassidy P, et al. Effectiveness of screening and brief alcohol intervention in 
primary care (SIPS trial): pragmatic cluster randomised controlled trial. BMJ. 2013;346(jan09 
2):e8501-e8501. doi:10.1136/bmj.e8501 
32. Hilbink M, Voerman G, van Beurden I, Penninx B, Laurant M. A Randomized Controlled Trial of a 
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INTERN MED. 2015;30(8):1125-1132. doi:10.1007/s11606-015-3248-z 
36. Williams EC, Rubinsky AD, Chavez LJ, et al. An early evaluation of implementation of brief 
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38. Harris AH, Humphreys K, Bowe T, Tiet Q, Finney JW. Does Meeting the HEDIS Substance Abuse 
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39. doi:10.1007/s11414-008-9142-2 
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40. Paddock SM, Hepner KA, Hudson T, et al. Association Between Process Based Quality Indicators and 
Mortality for Patients With Substance Use Disorders. J Stud Alcohol Drugs. 2017;78(4):588-596. 
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18 Appendix 
 
18.1 Schedule of Events 
 
 
 
Activities 
Months 
0-
3 
3-
6 
6-
9 
9-
12 
12-
15 
15-
18 
18-
21 
21-
24 
24-
27 
27-
30 
30-
33 
33-
36 
36-
39 
39-
42 
42-
45 
45-
48 
Recruitment 
 
 
 
X 
X 
 
 
 
 
 
 
 
 
 
 
 
Consent 
 
 
 
X 
 
 
 
 
 
 
 
 
 
 
 
 
Baseline Survey 
 
 
 
X 
 
 
 
 
 
 
 
 
 
 
 
 
CDW Data Pull 
 
 
 
X 
X 
 
 
 
 
 
 
 
 
 
 
 
Randomization 
 
 
 
 
X 
 
 
 
 
 
 
 
 
 
 
 
PHM Monthly Data Pull and 
Review  
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
PHM Quarterly Reports 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
PHM 1-on-1 meetings (as 
requested) 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
PHM Weekly Reports 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
CCM Weekly Reports 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
CCM Chart Review 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
CCM+ Clinician 
Communication/Collaboration 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
CCM + Patient 
Communication 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
CCM Epic Clinical Interaction 
Documentation  
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
Fidelity Checklist 
Documentation (PHM + CCM) 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
Adverse Events 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
 
 
Complete Follow-up Survey 
 
 
 
 
 
 
 
 
 
 
X 
 
 
 
 
 
Gather BMC ACO Medicaid 
Claims Data 
 
 
 
 
 
 
 
 
 
 
X 
X 
 
 
 
 
CDW Request 
 
 
 
 
 
 
 
 
 
 
X 
X 
 
 
 
 
Data Analyses 
 
 
 
 
 
 
 
 
 
 
 
X 
X 
X 
X