Addressing Diffusion of Responsibility and Prescribing Burden to Improve Use of Diabetes Medications
Phase: NA
Conditions: Diabetes, SGLT-2i and GLP-1RA Therapies, Diffusion of Responsibility, Prescribing Burden
Sponsor: Brigham and Women's Hospital

 
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Institutional Review Board 
Intervention/Interaction Detailed Protocol 
 
 
Principal Investigator: 
Nancy Haff, MD, MPH 
 
Project Title: 
Addressing diffusion of responsibility and prescribing burden to improve 
use of diabetes medications 
 
Version Date: 
July 18, 2024 
 
 
Version Name/Number: 
Version 14 
 
 
 
1. 
 Background and Significance 
Type 2 diabetes control in the US is suboptimal, and poor control is tightly linked to morbidity and 
mortality. For example, one year with a hemoglobin A1c (HbA1c) >7.5% is estimated to equate to a loss 
of approximately 100 life days.2 Thus, disease control is imperative for healthy aging among patients 
with diabetes. Two newer classes of antidiabetic agents, SLGT2is and GLP-1Ras, improve glycemic 
control, induce weight loss, reduce the risk of cardiovascular events and renal disease, and decrease all-
cause mortality.3-5 The American Diabetes Association recommends the use of these medication classes 
for patients with type 2 diabetes and kidney disease, cardiovascular disease, heart failure, or obesity.6 
Despite this, adoption has been slow.7-10 While over 50% of patients with diabetes are eligible, only 1-5% 
of eligible patients receive one of these drugs.1,8,9  
 
While diabetes is commonly managed by primary care providers, SGLT-2i and GLP-1Ras are indicated for 
individuals with type 2 diabetes who also have other comorbidities, and so multiple specialists are often 
involved in this care. This results in significant “diffusion of responsibility,” a behavioral concept that 
refers to the tendency of people (in this context, prescribers) to feel less responsible for individual 
actions in a setting or system when there are multiple actors and responsibility is unclear.11,12,13-15 This 
behavioral barrier is increasingly recognized in the medical context.16-21 In the case of SGLT-2i and GLP-
1RA prescribing, diffusion of responsibility across multiple providers may explain the suboptimal 
prescribing of these medications by primary care providers. 
 
Interventions to address diffusion of responsibility have been evaluated in a few medical and several 
non- medical contexts and suggest promising approaches. Modeling the desired behavior, assigning 
responsibility to individuals or smaller groups, highlighting individuals’ competence to act, and 
highlighting the perceived harm of the situation to be addressed all have been shown to be 
effective.17,22-24 Each of these factors can be translated to an intervention designed to address diffusion 
of responsibility in diabetes prescribing. 
 
Because SGLT-2is and GLP-1Ras are relatively new, prescribing can be complicated. For example, the 
initial medication choice may not be covered by insurance, which results in the patient or pharmacy 
communicating with the prescriber’s office who then communicates with the insurance company to 

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resolve the issue. Sometimes the provider will need to prescribe a different medication; other times a 
prior authorization or “peer to peer” conversation with the insurer is needed. At best, this series of 
denials and changes to the plan delays the start of the medication, but often it prevents initiation 
completely. The complexity and work required for this process is burdensome for providers and can 
deter future prescribing.25 This will be partly addressed through electronic prior authorization, which is 
beginning to be used in routine practice, but further simplification of the process with administrative 
support could also promote prescribing. 
 
As with non-health behaviors,13 the impact of diffusion of responsibility on prescribing, and the success 
of interventions to address it, may vary based on the patient, provider, and clinical scenario. If it were 
possible to predict, using routinely collected data from the EHR, when addressing diffusion of 
responsibility is sufficient or when additional prescribing support is needed, this would allow for more 
precise intervention tailoring and allocation of resources.  
 
2. 
Specific Aims and Objectives  
Sodium-glucose cotransporter-2 inhibitors (SLGT-2is) and glucagon-like peptide-1 receptor agonists 
(GLP-1RAs) are evidence-based medications that reduce morbidity and mortality for individuals with 
diabetes but remain significantly underused.1 Diffusion of responsibility in prescribing across multiple 
providers and a burdensome prescribing process are barriers to their optimal use. To address these 
issues, we propose the following Aims: 
 
Aim 1: Test the impact of an intervention to target diffusion of responsibility with and without 
additional reduction in prescribing burden on SGLT-2i and GLP-1RAs prescribing compared to usual 
care. We will conduct a randomized trial among primary care providers. The primary outcome will be 
SGLT-2i and GLP-1RA prescribing for patients with poorly controlled type 2 diabetes and a clear 
indication for one of these medications. 
 
Aim 2: Identify characteristics of the patient, provider, and their clinical interaction that are associated 
with responsiveness to each intervention using routinely collected EHR data. The effectiveness of 
these interventions may vary based on characteristics of patients, providers, or the clinical scenario. 
Identification of predictors of responsiveness will help target interventions to those most likely to 
benefit. 
 
3. 
General Description of Study Design 
This study will be conducted within MGH primary care. As with prior work, we will use the hospital’s EHR 
database, the Epic Enterprise Data Warehouse, to identify patients. Patients will be eligible if they are: 
adults 18-84 years of age with poorly controlled type 2 diabetes (defined as HbA1c >7.0%) and a 
compelling indication for an SGLT-2i or GLP-1RA (including cardiovascular disease, kidney disease, heart 
failure, or obesity), who are not already prescribed one of these therapies. We will exclude individuals 
with end stage renal disease, dementia, type I diabetes, or an EHR indicator of hospice care. We will also 
exclude patients who are not on the MGH diabetes registry, because patients who are excluded from 
the registry typically have a clinical reason for alternative diabetes goals or care plans. All attending 
MGH PCPs (excluding those involved in the design or delivery of the intervention) will be included in the 
trial. Eligible PCPs will be randomized to one of three arms: (1) intervention to address diffusion of 
responsibility, (2) intervention to address diffusion of responsibility plus additional simplification of the 
prescribing process, and (3) usual care. Providers randomized to arm 1, the intervention to address 
diffusion of responsibility will receive an email from a peer offering encouragement and support in 

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prescribing SGLT-2is and GLP-1RAs that includes specific components designed to reduce diffusion of 
responsibility. The email will also offer for direct support from the peer. Providers randomized to arm 2, 
the intervention addressing diffusion of responsibility and simplifying the prescribing process will 
receive the same contact addressing diffusion of responsibility as in arm 1, but they will additionally 
have access to an experienced administrative team for diabetes medication insurance authorization 
support. The primary outcome will be the rate of prescriptions for SGLT-2is and GLP-1RAs among eligible 
patients.  
 
3.1 Study Schema 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4. 
Subject Selection 
This study will be conducted within MGH primary care, which consists of over 190 attending PCPs caring 
for more than 13,300 patients with diabetes, approximately 25% of whom have an HbA1c above goal. 
There will be two participant populations included in the study. First, we expect to enroll 191 providers 
to receive the intervention. Second, although they will not be receiving the intervention, we expect to 
accrue 1468 patients whose information will be used to determine study outcomes. Details for waiver of 
informed consent and waiver of HIPAA authorization are detailed below.  
 
4.1 Inclusion/Exclusion Criteria 
Eligible Primary Care Providers 
N = 191 
Randomize (1:1:1 ratio) 
Arm 1  
Intervention to 
address DOR 
Arm 2 
Address DOR + 
Simplification 
Arm 3 
Usual Care 
Assess rates SGLT-2i and GLP-1RA prescribing among eligible patients 
Determine association of demographic and clinical characteristics with 
intervention responsiveness 

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We will include attending primary care physicians meeting the following criteria: 
• 
Practicing in primary care at Massachusetts General Hospital 
 
Providers will be excluded if they are involved in the design or conduct of this study (e.g. Drs. Haff and 
Horn, and the peer PCPs delivering the study intervention). 
 
We will accrue patients as part of study intervention delivery and outcome collection. Patient inclusion 
criteria are:  
• 
18-84 years of age 
• 
HbA1c >7.0% and a compelling indication for an SGLT-2i or GLP-1RA (including cardiovascular 
disease, kidney disease, heart failure, or obesity) 
• 
Not already prescribed an SGLT-2i or GLP-1RA at the time of the intervention 
 
We will exclude individuals with end stage renal disease, dementia, type I diabetes, or an EHR indicator 
of hospice care. We will also exclude patients who are not on the MGH diabetes registry, because 
patients who are excluded from the registry typically have a clinical reason for alternative diabetes goals 
or care plans. 
 
4.2 Patient Identification 
Patients will not directly receive intervention, but their clinical parameters will be used to identify 
upcoming visits with potential opportunities for prescription of an SGLT-2i or GLP-1RA medication.  As 
with prior work, we will use the hospital’s EHR database, the Epic Enterprise Data Warehouse, to 
identify eligible patients.  
 
4.3 Recruitment Procedures 
We will use the hospital’s EHR database, the Epic Enterprise Data Warehouse, to identify eligible 
patients.  
 
As with similar minimal-risk quality-improvement studies that we have performed that involve the use 
of routine clinical tools such as patient portals or physician reminders, informed consent will not be 
sought in this study, and we request a waiver of informed consent. The nature of this intervention 
involves the use of messages delivered via email and in-basket (using information already available to 
providers and delivered via a similar infrastructure they use during regular clinical care). Obtaining 
written informed consent would also not produce generalizable knowledge about the effectiveness of 
the intervention, a foundational aspect of pragmatic clinical trial principles. With an approved waiver of 
informed consent for provider participation, all attending MGH primary care providers will be directly 
included in the study. 
 
While providers and patients will not be consented into the study, an organization-wide announcement 
will be circulated across MGH Primary Care Practices to inform providers of the launch of this support 
for improved prescribing for adults with sub-optimally controlled diabetes. 
 
Providers will be able to request unenrollment from the study at any time by replying to the 
intervention email. However, because the intervention includes only emails and in-basket messages that 
support prescribing, we anticipate that retention rates in the study will be very high. Study staff and 
data analysts will track any physician turnover in the study as they have done in prior work, and though 
minimal physician turnover is expected, we expect any turnover to bias the trial results towards the null. 

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5. 
Subject Enrollment 
We will use the hospital’s EHR database, the Epic Enterprise Data Warehouse, to identify eligible 
patients. 
 
5.1 Informed Consent 
As with other minimal-risk studies we have performed where informed consent is impracticable, we are 
requesting a waiver of informed consent and HIPAA authorization for patients and providers. There are 
several reasons for this. First, we plan to use routinely collected EHR data to identify eligible providers, 
administer interventions, and assess study outcomes.  Because this is of minimal risk to patients, the 
study team will have no interaction with patients, all medical decisions will be made by the patients’ PCP 
as in usual care, and it would be impracticable to obtain patient consent, we feel that a waiver of 
consent and HIPAA authorization for patients is needed for this study.  Similarly, for providers, the 
interventions are minimal risk, designed to provide decision support for diabetes care, are consistent 
with professional guidelines and MGH-specific quality metrics, and are similar to supports that are put in 
place in routine quality improvement interventions. The interventions will be delivered using email and 
Epic in-basket which are routinely used in primary care. Providers will be given the opportunity to 
unenroll from the study at any time by replying to a study email or by contacting the study team; if they 
request to be unenrolled they will receive no further study-related contact.  
 
5.2 Treatment assignment and randomization 
All eligible providers will be randomized in a 1:1:1 ratio to one of three arms: (1) intervention to address 
diffusion of responsibility, (2) intervention to address diffusion of responsibility plus additional 
simplification of the prescribing process, and (3) usual care.  Randomization will be stratified by primary 
care clinic.  
 
6. 
STUDY PROCEDURES 
 
6.1 Study Site 
Study participants will be selected from Mass General Brigham (MGB), a large integrated delivery 
network in Boston, MA, specifically from Mass General Hospital primary care clinics, where Dr. Haff (PI) 
practices. 
 
6.2 Overall Design 
This study will be conducted within MGH primary care, which consists of over 250 PCPs caring for more 
than 13,300 patients with diabetes, approximately 25% of whom have an HbA1c above goal. As with 
prior work, we will use the hospital’s EHR database, the Epic Enterprise Data Warehouse, to identify 
eligible patients and providers. All eligible PCPs will be randomized to one of three arms: (1) intervention 
to address diffusion of responsibility, (2) intervention to address diffusion of responsibility plus 
additional simplification of the prescribing process, and (3) usual care. 
 
The primary outcome will be the rate of prescriptions for SGLT-2is and GLP- 1RAs among eligible 
patients. Secondary outcomes will include the rate of SGLT-2i and GLP-1RA prescribing among all 
patients with diabetes, change in A1c among eligible patients between baseline and last HbA1c within 6 
months after intervention start, and implementation outcomes including frequency and nature of 
interactions with the emails, links, administrative support team, and prescribing model PCP peer. We 
will extract routinely collected data on prescribing and HbA1c from the EDW, as we have done with prior 

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projects. Implementation outcomes will be tracked by research assistants utilizing a secure RedCap 
database. 
 
6.2.1 Aim 1 Design 
In Aim 1 of the study, we will conduct a 3-arm randomized controlled trial to compare the effectiveness 
of two interventions compared to usual care on SGLT-2i and GLP-1RA prescribing. We will include 
Massachusetts General Hospital Attending Primary Care Providers. Interventions will be linked to 
upcoming visits with patient aged 18-84 years with poorly controlled type 2 diabetes (HbA1c >7.0%) and 
a compelling indication for an SGLT-2i or GLP-1RA (including cardiovascular disease, kidney disease, 
heart failure, or obesity), who are not already prescribed one of these therapies. We will exclude 
patients with a documented allergy to both medication classes, end stage renal disease, dementia, type I 
diabetes, an EHR indicator of hospice care and those who are excluded from the MGH diabetes registry.  
 
Providers will be randomized equally to one of three arms: (1) intervention to address diffusion of 
responsibility, (2) intervention to address diffusion of responsibility plus additional simplification of the 
prescribing process, and (3) usual care. The intervention period will continue for 6 months.  
 
Providers randomized to arm 1, an intervention to address diffusion of responsibility, will receive an 
email from a peer offering encouragement and support in prescribing SGLT-2is and GLP-1RAs that 
includes specific components designed to reduce diffusion of responsibility. Specifically, these elements 
are adapted from interventions that mitigate diffusion of responsibility in other contexts, including (1) 
assigning a responsibility to individuals or smaller groups, (2) increased perceived harm of the situation 
to be addressed (3) highlighting competence to act, and (4) modeling the desired behavior. The email 
will also contain an offer for direct support from the peer. Four MGH primary care physicians will 
administer the intervention. The email will be sent by the study team on their behalf and will contain a 
personalized subject line and/or text to increase the likelihood of opening and reading (see Study 
Interventions Document]).  
 
The email will be accompanied by an additional personal outreach like walking through the practice and 
initiating brief, informal conversation or a short, hand-written note left on the PCP’s desk 3-4 and 6 
months after the start of the intervention. After the initial email, PCPs will receive up to 3 additional 
follow-up Epic in-basket messages, per provider, per week, notifying them of patients with upcoming 
visits who are eligible for one of these therapies and offering support in prescribing. The PCPs delivering 
the intervention will be trained in the clinical and administrative nuances of prescribing SGLT-2is and 
GLP-1RAs and remunerated for time spent assisting their peers. They will also have access to a 
diabetologist to consult for challenging clinical questions. Any additional interactions between study 
PCPs and the peer delivering the intervention will be offered and tracked but not required. If providers 
assigned to this arm reach out with questions about prescribing, the peer will respond and may provide 
clinically existing resources that are currently available in routine care. 
 
Providers randomized to arm 2, an intervention to address diffusion of responsibility plus additional 
simplification of the prescribing process, will receive the same contact addressing diffusion of 
responsibility as in arm 1, but they will additionally have access to an experienced administrative team 
for diabetes medication insurance authorization support. In this arm, the list of each PCP’s patients who 
are potentially eligible for an SLGT-2i or GLP-1RA will be reviewed by MGH’s Central Authorization Unit 
(CAU). The CAU is an administrative team that currently supports prescribing for MGH diabetes 
specialists and other divisions by completing insurance authorization requests and helping providers 

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navigate variable insurance coverage for medications. For eligible study patients, the CAU will review 
the patient’s chart and insurance coverage and provide information on which medication options are 
likely to be most affordable for each patient. This information will then be sent to PCPs via Epic in-basket 
message 3 business days before the patient’s visit. In-basket messages will come from the shared in-
basket pool, be signed by both the PCP peer and the CAU team, and will contain information about 
which SGLT-2is and GLP-1RAs are covered for that patient. In-basket messages will be capped at no 
more than 3 per provider per week, providers will be messaged about a single patient no more than 3 
times over the course of the study. PCPs will also be informed in the initial outreach how to access the 
administrative team through Epic in-basket or email with any administrative questions or for any follow-
up.  
 
As in Arm 1, the initial email will be accompanied the additional personal outreach 3-4 and 6 months 
after the start of the intervention, and up to 3 follow-up Epic in-basket messages per provider, per 
week, but in Arm 2 the follow-up messages will also include information on the covered medication 
choice(s) for each patient how to access additional CAU support.  Providers will be invited to reply 
directly that in-basket message to request assistance with prior authorization if needed. This team will 
then follow up with the pharmacy to determine coverage, communicate with clinic medical assistants, 
complete prior authorizations, determine alternative covered options, and track the process, as needed 
in each case. From the primary care doctor’s perspective, this will be a markedly smoother process than 
current prescribing. If providers assigned to this arm reach out with questions regarding insurance 
coverage or prior authorization for an eligible patient that does not have an upcoming visit, the CAU 
staff will deliver the intervention with covered medication choice(s) to the provider at that time. If the 
patient later has an upcoming visit and has still not been prescribed the medication, the CAU staff will 
deliver the intervention again in the typical time frame (3 business days prior to the visit). Interventions 
regarding a single patient will be capped at no more than 3, including any interventions not associated 
with visits but delivered after PCP request. If providers assigned to this arm reach out with questions 
regarding ineligible patients, PCP peers will provide them with the same generic resources given in arm 
1 that are currently available in routine care. 
 
Prior to enrollment of providers into this trial, the CAU will pilot test Epic in-basket versus email 
workflow to determine the most efficient workflow. This pilot testing will be done with panels of those 
PCPs who are not included in the study due involvement in the study (i.e., PI and peer providers).  
 
All providers will receive the second personal outreach 6 months after the start of the intervention. The 
outreach will consist of an email notifying them that the study is ending and inviting them to participate 
in a brief survey. The fact sheet for survey participation will be attached to the email. The survey will be 
accessed through a unique link to a secure REDCap survey embedded into the email. REDCap is a secure, 
HIPAA-compliant web-based platform hosted on Mass General Brigham servers. Providers who click on 
the link to the survey will be met with a landing page that displays the fact sheet describing the study. 
They will then be able to click a button to indicate that they agree to participate. Providers who select 
that they agree will then proceed to the rest of the survey. All providers will be allows to respond. 
Participants will be offered remuneration in the form of a $25.00 Amazon gift card for completing the 
survey. An email will be sent to the physician upon completion of the survey containing the claim code 
they will use to access their gift card. In addition, an electronic copy of the prescribing chart given to 
intervention providers in the first personalized outreach will be attached for all providers. In both 
intervention arms, providers will also receive a list of the patients who remain eligible for one of these 
medications but who have not had a visit during the study intervention period. Providers will then have 

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the option to ask their administrative staff to schedule visits with these patients, consider prescribing 
asynchronously, or otherwise generate a plan of care. The survey to intervention providers will capture 
opinions on the interventions, mechanisms behind diffusion of responsibility in prescribing these 
medications, and thoughts about potential helpful interventions moving forward. Providers in the 
control arm will only be asked the questions about mechanisms behind diffusion of responsibility in 
prescribing these medications and thoughts about potential helpful interventions moving forward. All 
providers who have not completed the survey within 2-3 weeks of receiving the email invitation will 
receive a follow-up email asking them again to participate in the survey.  
 
Providers randomized to arm 3, usual care, will not receive any outreach outside of the end of study 
notification and survey. 
 
After study completion, we will conduct an exploratory chart review of the visit notes from a random 
sample of patients in all three arms. Within each study arm, we will randomly sample visit notes from 
charts of 20 patients who were prescribed an SGLT-2i or a GLP1RA during the target visit (primary 
outcome occurred) and 20 patients who did not receive a prescription (primary outcome did not occur) 
to qualitatively explore the nature of the conversations that were documented at the visit. This will also 
include reviewing other encounter types in patients charts like telephone notes, patient gateway 
messaging, and other visits. This data collection will come from Epic chart review only. We will review 
the chart and summarize the nature of that conversation that was had around initiating SGLT-2is or GLP-
1s, if one was documented. We will review the notes from the visit that was targeted by the 
intervention as well as subsequent notes in the chart as needed (such as telephone encounters, patient 
gateway encounters, and specialists visits notes) in order to understand if a medication was prescribed, 
and if not, then what the barriers were to starting. We will collect information on whether the patient 
filled the new prescription at the pharmacy, if available. This will include a total of 120 sampled charts, 
and the qualitative analyses will help us identify potential characteristics to extract from routine EHR 
data for Aim 2 and help understand remaining barriers to prescribing as well as potential mechanisms 
behind intervention effectiveness. Finally, we will also review the charts of all patients who received a 
prescription for a new medication to see what proportion of these prescriptions were actually filled by 
patients.  
 
We will use the MGB standardized deployments of AI/ML services in MGB’s secure cloud infrastructure, 
specifically the Azure OpenAI models as a tool to analyze qualitative data from the study and compare 
results from the LLM to those obtained by investigator analyses. Use of the LLM will permit faster 
analysis of large amounts of qualitative free-text data.  
 
6.2.2 Aim 2 Design 
In Aim 2, we will identify characteristics of providers, patients, and their interactions, that are 
measurable in routinely collected EHR data, that are associated with intervention responsiveness. We 
will extract provider demographic and practice data, patient demographic and clinical information, and 
data on the patient-provider interaction. We will also pre-specify which of these characteristics may be 
related to diffusion of responsibility. 
 
6.3 Participant Remuneration 
Subjects will be remunerated for participation in the end-of-study survey. Providers who complete the 
survey will receive a $25 Amazon gift card; an access code for their payment will be delivered by email. 
 

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7. 
Risks and Discomforts 
We believe there is no more than minimal risk involved to the physician subjects, as the physicians will 
receive interventions designed to help with the provision of guideline-concordant diabetes care. The 
study recommendations are consistent with professional guidelines and MGH-specific quality metrics 
and are similar to supports that are put in place in routine quality improvement interventions. The 
interventions will be delivered using email and epic in-basket which are routinely used in primary care. 
The main potential discomfort for providers is burden from receiving additional messages.  To mitigate 
this, messages will be kept to the minimum necessary to deliver the intervention, and providers will be 
given the opportunity to unenroll from the study at any time by replying to a study email or in-basket 
message. If they request to be unenrolled, they will receive no further study-related contact.  
 
For patient-subjects, we also believe that the risks, to participation is no more than minimal for several 
reasons. First, the interventions aim to emphasize guideline-recommended treatments for patients with 
diabetes and a compelling indication for these treatments. Second, all treatment decisions will 
ultimately be made by licensed primary care physicians. Finally, the intervention is specifically physician-
focused and uses information already available to physicians.  There is a small risk associated with 
altering diabetes medication prescribing, including hypoglycemia, allergic reactions, and others. 
However, the diabetes control and cardiovascular benefits of these medications are typically considered 
to outweigh the infrequent risks, and PCPs can weigh these risks and benefits with each individual 
patient and decide whether or not to prescribe, as they always do in usual care. For this reason, we 
believe the potential risks of treatment initiation as part of this trial are the same as what is 
encountered during routine, guideline-concordant diabetes care. The study team will not be providing 
any direct care to patients and all medical decisions are ultimately made by the physician. This trial will 
otherwise not interfere with the usual workings of primary care practices. Thus, the main potential risk 
to subjects in this study is related to privacy of data, and we will take several measures to ensure that 
this risk is minimal, and that patient information is safeguarded.  
 
Patient data for study outcome evaluation will be drawn from EHR information in the Epic Enterprise 
Data Warehouse (EDW). The investigators are aware of the sensitive nature of the data and are 
committed to protecting patient privacy. Only the minimum necessary identifiable health care data 
needed to achieve the intended purpose will be used. All the data in the registry is contained within the 
Mass General Brigham firewall, and its usage is logged and audited. It is only accessible to IRB-approved 
researchers. In addition, supplementary qualitative data for implementation evaluation will be drawn 
through traditional chart review by the research assistants. Research assistants will review 
conversations documented through multiple encounter types, like telephone notes, patient gateway 
messaging, and other visits and summarize what happened. Information will only be summarized from 
encounters that are viewable from the patient’s chart; we do not need access to provider-to-provider 
inbasket messaging that is not saved into the chart. Summarized qualitative information will be stored 
using a secure REDCap database and all efforts will be taken to ensure that this information is 
safeguarded and only accessible to study investigators who are actively involved in the research.  
 
For all study data, we will safeguard any identifiable information in accordance with IRB practices, limit 
access to the information to study investigators actively involved in the research who have all 
undergone human subjects research training and store any data in accordance with IRB practices. 
Finally, as is our routine practice, great care will be taken to ensure the confidentiality of all data and to 
protect the privacy of participants through translation of all potentially traceable identifiers into 
untraceable coded subject numbers whenever possible. 

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To ensure the confidentiality and security of all data, the research team operates a secure, state-of-the-
art computing facility housed at MGB’s data center. The MGB data center is a secure facility that houses 
both computing environments as well as clinical systems and electronic medical records for several large 
hospitals in Eastern Massachusetts. Entry into the computer room requires staffed computer room 
security. The Division’s computers are connected to the MGB networking backbone with 10 gigabit-per-
second fiber links. Network security is overseen by electronic medical records systems to the research 
team’s data. All data are transmitted to programmers’ workstations in an encrypted state. Backups are 
created using 256-bit AES encryption, the current Department of Defense standard for data security, 
and are stored in a locked facility. The redundancy, extensive data power, and security of our computer 
facility confirm our capacity to collect and manage data and ensure confidentiality for all project 
participants.  
 
For the use of the Azure Open AI models, we will utilize the secure MGB environment on the Azure 
cloud to access the Open AI tools. The cloud setup has been previously established by MGB for research 
team use and complies with the legal, cybersecurity, and data security requirements at MGB. Our team 
has completed a Cloud Architecture Consult with the IT team and we will abide by MGB’s published 
AI/LLM Guidance. Within the Azure environment, only team members included on the IRB who are 
participating in the data analysis will be given access to the LLM and data, following the same principles 
for our data management in other systems.  
 
8. 
Benefits 
Interventions to address diffusion of responsibility and simplify the prescribing process could help 
increase utilization of SGLT-2is and GLP-1RAs in diabetes care. Thus, the research could have both 
immediate benefits for physicians by increasing diabetes treatment knowledge, as well as for their 
patients who might benefit from improved glycemic control.  
 
9. 
Statistical Analysis 
9.1 Data Analysis Plan 
9.1.1 Aim 1 Analysis Plan 
For analyses we will use generalized estimating equations to compare outcomes between each 
intervention versus usual care and each other. 
 
Qualitative analyses will be conducted on the random sampling of charts from patients who did and did 
not have the primary outcome in each study arm. Data will be coded and themes extracted and used to 
inform interpretation of the primary and secondary outcome results.  
 
We will use the Azure Open AI LLM to assist with data analysis of free text qualitative data including that 
from chart review and free-text responses from physicians to the survey. Specifically, we will engineer 
prompts to guide the LLM in coding of data, extraction of themes, and summary of content. This will be 
used to compliment direct qualitative analysis by study investigators and will permit faster analysis of 
qualitative data. Results from this will be reported as part of trial results and will also help inform which 
predictors may be impactful for Aim 2.  
 
9.1.2 Aim 2 Analysis Plan 
For analyses, we will fit separate logistic and boosted regression models using sets of these predictors to 
evaluate the ability to predict responsiveness to each intervention, and we will incorporate classification 

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and regression trees where appropriate. Boosted regression is a machine learning method robust to 
multi-collinearity and overfitting. For each regression, we will use 10-fold cross-validation to compare C-
statistics for the ability to predict intervention response and calculate a continuous net reclassification 
index to assess changes in predicted response with additional predictors.26,27 
 
9.2 Power Analysis 
We estimate that 191 providers caring for 1468 eligible patients will provide 80% power to observe an 
11 percentage-point difference in prescribing rates between each intervention arm and control, 
assuming a control arm prescribing rate of 30%, an ICC of 0.07, an average of 7 patients per provider, 
and a type I error rate of 5%.  
 
10.   Monitoring and Quality Assurance 
10.1 Adverse Events and Reporting 
Oversight of the pilot will be the responsibility of the pilot PI: Nancy Haff, MD, MPH. Dr. Haff and study 
investigators will meet on a regular basis throughout the study period and will be in direct contact with 
the peer PCPs to obtain ongoing feedback. In addition, the protocol will undergo Institutional Review 
Board (IRB) evaluation. 
 
De-identified study data will be accessible at all times for the BWH pilot PI and coinvestigators to review, 
if applicable. The study team will also ensure that all protocol deviations for the pilot study are reported 
to the NIH and the IRB according to the applicable regulatory requirements. Compliance of regulatory 
documents and study data accuracy and completeness will be maintained through an internal study 
team quality assurance process. 
 
Definition:  
Adverse Event (AE): Any untoward or unfavorable medical occurrence in a human study participant, 
including any abnormal sign (e.g., abnormal physical exam or laboratory finding), symptom, or disease, 
temporally associated with the participants’ involvement in the research, whether or not considered 
related to participation in the research.  
Adverse Events will be classified using the following rating scales: 
• 
Severity: Mild, Moderate or Severe 
o Mild: Awareness of signs or symptoms but are easily tolerated 
o Moderate: Events introduce a low level of inconvenience or concern but may interfere 
with daily activities but are usually improved by simple therapeutic measures.  
o Severe: Events interrupt the participants’ normal daily activities and generally require 
systemic drug therapy  
• 
Expectedness: Unexpected or Expected 
o Unexpected: nature or severity of the event is not consistent with the condition under 
study. 
o Expected: event is known to be associated with the intervention or condition under 
study 
 
Serious Adverse Event (SAE): Any adverse event that: 
• 
Results in death  
• 
Is life threatening, or places the participant at immediate risk of death from the event as it 
occurred  
• 
Requires or prolongs hospitalization  

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• 
Causes persistent or significant disability or incapacity  
• 
Results in congenital anomalies or birth defects  
• 
Is another condition which investigators judge to represent significant hazards  
 
Given the minimal risk nature of the study, in which the intervention involves enhanced education and 
guideline concordant care for patients with at MGB, the PI does not expect any SAEs or AEs related to 
the information delivered in this trial.  Patients’ own primary care physicians will have ultimate decision-
making authority for prescribing choices, as they would in routine clinical practice, and we expect any 
adverse events related to diabetes medication prescribing, such as hypoglycemia, to be the same as 
those experienced in routine clinical care.  
 
Reporting:  
As previously described, no additional SAEs or AEs are expected as part of this study. Any adverse event 
from medication prescribing will be handled in the course of regular clinical care, as is done currently, 
and through established safety reporting and review systems within MGH. In his role as the Director or 
Population Health and Quality in the Division of General Internal Medicine at MGH, Dr. Horn routinely 
reviews the safety reporting within MGH primary care and can be alerted to any reports that may relate 
to the study intervention.  
 
Any reports of deaths will be submitted to the NIA Program Officer and to the NIA-appointed Standing 
Roybal DSMB Chair within 24 hours of the study team’s knowledge. Any unexpected SAEs will be 
reported to the NIA PO and DSMB Chair within 48 hours of the study’s knowledge of the SAE. All other 
reported SAEs received by the study team will be reported to the NIA Program Officer and to the DSMB 
quarterly, unless otherwise requested by the DSMB. AEs will be reported per IRB policies. They will also 
be reported to the NIA and DSMB at a frequency requested by NIA and/or by the DSMB (annually, at a 
minimum).  
 
10.2 Planned Safety Monitoring 
General oversight of this project by the Brigham and Women’s Hospital (BWH) pilot PI (Dr. Haff) will 
occur throughout the study period, including regular contact with MGB clinical leadership involved in 
the project to obtain ongoing feedback. In addition, this protocol will undergo Institutional Review 
Board (IRB) evaluation.  
 
We will have oversight from both the NIA Director-approved Roybal Centers Program Data Safety 
Monitoring Board (DSMB) for all aspects of this research (see Appendix A). The DSMB will act in an 
advisory capacity to the NIA to monitor participant safety, evaluate the progress of the study, and 
review procedures and management of the study. Our plan for data and safety monitoring also includes 
oversight by the project principal investigators (Dr. Haff) throughout the study period.  
 
Meetings of the DSMB will be held regularly (e.g., every six to nine months) at the call of NIA or the 
DSMB Chair and review data related to study protocols and ensure protection of patient confidentiality 
and safety. At each meeting, the DSMB will make recommendations as to whether the studies should 
continue or if changes to the protocol are necessary for continuation. This trial will also be registered 
with clinicaltrials.gov.  
 
10.3 
Data Management 

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To protect against the risk of inappropriate disclosure of personal health information, the investigators 
at MGH will only access study data with encrypted identifiers. As described, all members of the research 
team have completed or will complete appropriate human subjects research training and patient privacy 
training related to the Health Insurance Portability and Accountability Act (HIPAA). We have a history of 
collaborative evaluations with delivery organizations that involves transfer of the minimum data 
necessary to complete rigorous evaluations, involving the use of encrypted identifiers to ensure patient 
confidentiality.  
 
To ensure the confidentiality and security of all data, the research team operates a secure, state-of-the-
art computing facility housed at MGB’s data center. The MGB data center is a secure facility that houses 
both computing environments as well as clinical systems and electronic medical records for several large 
hospitals in Eastern Massachusetts. Entry into the computer room requires staffed computer room 
security. The Division’s computers are connected to the MGB networking backbone with 10 gigabit-per-
second fiber links. Network security is overseen by electronic medical records systems to the research 
team’s data. All data are transmitted to programmers’ workstations in an encrypted state. Backups are 
created using 256-bit AES encryption, the current Department of Defense standard for data security, 
and are stored in a locked facility. The redundancy, extensive data power, and security of our computer 
facility confirm our capacity to collect and manage data and ensure confidentiality for all project 
participants.  
 
We will also safeguard any identifiable information from the physicians in accordance with IRB practices, 
limit access to any information in accordance with IRB practices, and limit access to the information to 
study investigators actively involved in the research who have all undergone human subjects research 
training. 
 
For the use of the Azure Open AI models, we will utilize the secure MGB environment on the Azure 
cloud to access the Open AI tools. The cloud setup has been previously established by MGB for research 
team use and complies with the legal, cybersecurity, and data security requirements at MGB. Our team 
has completed a Cloud Architecture Consult with the MGB IT team and we will abide by MGB’s 
published AI/LLM guidelines. Within the Azure environment, only team members included on the IRB 
who are participating in the data analysis will be given access to the LLM and data, following the same 
principles for our data management in other systems.  
 
10.4 
Outcomes monitoring 
Given the minimal risks involved in participation in the study and its short duration, we will not perform 
any interim analysis for study outcomes.  
 
10.5 
Internal monitoring of source data, protocol adherence, and recordkeeping 
The PI and study team will meet at least monthly to review study activity including recruitment, status of 
enrolled subjects, safety issues, internal quality assurance and peer review information. Meeting 
minutes will be maintained documenting the date of the meeting, names of those in attendance, and 
summary of information discussed and reviewed.  
 
The Program Manager will complete an internal quality review of regulatory files prior to study 
enrollment begins and at least annually. The quality assurance review of regulatory files will be 
completed using the MGB Human Research Affairs Compliance and Education Office Regulatory Binder 

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Checklist. Any missing documents will be noted on the Regulatory Binder Checklist and then will be 
retrieved and filed as appropriate.  
 
The Research Assistant(s) and Research Scientist will perform quality assurance reviews of the data 
entered into electronic case report forms for accuracy and completeness. Source data verification will 
be completed. Discrepancies will be documented for the study team to resolve. Once data entered on 
the eCRF have been verified and determined to be accurate and complete, the form will be locked the 
indicate that review is complete.  
 
11   Privacy and Confidentiality 
☒ Study procedures will be conducted in a private setting 
☒ Only data and/or specimens necessary for the conduct of the study will be collected 
☒ Data collected (paper and/or electronic) will be maintained in a secure location with appropriate 
protections such as password protection, encryption, physical security measures (locked 
files/areas) 
☐ Specimens collected will be maintained in a secure location with appropriate protections (e.g. 
locked storage spaces, laboratory areas) 
☒ Data and specimens will only be shared with individuals who are members of the IRB-approved 
research team or approved for sharing as described in this IRB protocol 
☒  Data and/or specimens requiring transportation from one location or electronic space to 
another will be transported only in a secure manner (e.g. encrypted files, password protection, 
using chain-of-custody procedures, etc.) 
☒   All electronic communication with participants will comply with Mass General Brigham secure 
communication policies 
☒ Identifiers will be coded or removed as soon as feasible and access to files linking identifiers 
with coded data or specimens will be limited to the minimal necessary members of the research 
team required to conduct the research 
☒ All staff are trained on and will follow the Mass General Brigham policies and procedures for 
maintaining appropriate confidentiality of research data and specimens 
☒ The PI will ensure that all staff implement and follow any Research Information Service Office 
(RISO) requirements for this research 
☒ Additional privacy and/or confidentiality protections 
 
Data for the study will be safeguarded by state-of-the-art security protocols. The facilities have 24-hour 
security and are protected by locked entrances. MGB has computer networks in place that employ up to 
date virus protection software and enable password protected access only to study investigators. The 
setup for analysis of these data will be the same as all the other IRB applications that the MGB research 
division submits for secondary use of data. All the datasets, including limited protected health 
information (PHI), will be stored only on secure servers at MGB’s data center and will only be accessed 
by a limited number of individuals in the study team from this division who are all trained in data 
security and patient privacy.  
 
To ensure the confidentiality and security of all data, the research team operates a secure, state-of-the-
art computing facility housed at MGB’s data center. The MGB data center is a secure facility that houses 
both computing environments as well as clinical systems and electronic medical records for several large 
hospitals in Eastern Massachusetts. Entry into the computer room requires staffed computer room 

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security. The Division of Pharmacoepidemiology’s computers are connected to the MGB networking 
backbone with 10 gigabit-per-second fiber links. Network security is overseen by electronic medical 
records systems to the research team’s data. All data are transmitted to programmers’ workstations in 
an encrypted state. Backups are created using 256-bit AES encryption, the current Department of 
Defense standard for data security, and are stored in a locked facility. The redundancy, extensive data 
power, and security of the computer facility confirm the capacity to collect and manage data and ensure 
confidentiality for all project participants. 
 
The study team will also safeguard any identifiable information from the physicians in accordance with 
IRB practices, limit access to any information in accordance with IRB practices, limit access to the 
information to study investigators actively involved in the research who have all undergone human 
subjects research training.  
 
For the use of the Azure Open AI models, we will utilize the secure MGB environment on the Azure 
cloud to access the Open AI tools. The cloud setup has been previously established by MGB for research 
team use and complies with the legal, cybersecurity, and data security requirements at MGB. Our team 
has completed a Cloud Architecture Consult with the MGB IT team and we will abide by MGB’s 
published AI/LLM guidelines. Within the Azure environment, only team members included on the IRB 
who are participating in the data analysis will be given access to the LLM and data, following the same 
principles for our data management in other systems.  
 
All members of the research team have completed or will complete appropriate human subjects 
research training and patient privacy training related to the Health Insurance Portability and 
Accountability Act (HIPAA). 
 
 
 
 

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12.   References 
1. Nelson AJ, O’Brien EC, Kaltenbach LA, et al. Use of Lipid-, Blood Pressure–, and Glucose-Lowering 
Pharmacotherapy in Patients With Type 2 Diabetes and Atherosclerotic Cardiovascular Disease. 
JAMA Netw Open. 2022;5(2):e2148030. doi:10.1001/jamanetworkopen.2021.48030  
2. Heald AH, Stedman M, Davies M, et al. Estimating life years lost to diabetes: outcomes from analysis 
of National Diabetes Audit and Office of National Statistics data. Cardiovasc Endocrinol Metab. 
2020;9(4):183- 185. doi:10.1097/XCE.0000000000000210  
3. Kosiborod M, Lam CSP, Kohsaka S, et al. Lower Cardiovascular Risk Associated with SGLT-2i in 
&gt;400,000 Patients: The CVD-REAL 2 Study. J Am Coll Cardiol. Published online March 11, 
2018:24748. doi:10.1016/j.jacc.2018.03.009  
4. Kosiborod M, Cavender MA, Fu AZ, et al. Lower Risk of Heart Failure and Death in Patients Initiated 
on Sodium-Glucose Cotransporter-2 Inhibitors Versus Other Glucose-Lowering Drugs: The CVD-REAL 
Study (Comparative Effectiveness of Cardiovascular Outcomes in New Users of Sodium-Glucose 
Cotransporter-2 Inhibitors). Circulation. 2017;136(3):249-259. 
doi:10.1161/CIRCULATIONAHA.117.029190  
5. Kristensen SL, Rørth R, Jhund PS, et al. Cardiovascular, mortality, and kidney outcomes with GLP-1 
receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of 
cardiovascular outcome trials. Lancet Diabetes Endocrinol. 2019;7(10):776-785. doi:10.1016/S2213-
8587(19)30249-9  
6. American Diabetes Association. 9. Pharmacologic Approaches to Glycemic Treatment: Standards of 
Medical Care in Diabetes—2021. Diabetes Care. 2020;44(Supplement_1):S111-S124. 
doi:10.2337/dc21-S009  
7. Schernthaner G, Shehadeh N, Ametov AS, et al. Worldwide inertia to the use of cardiorenal 
protective glucose-lowering drugs (SGLT2i and GLP-1 RA) in high-risk patients with type 2 diabetes. 
Cardiovasc Diabetol. 2020;19(1):185. doi:10.1186/s12933-020-01154-w  
8. Hamid A, Vaduganathan M, Oshunbade AA, et al. Antihyperglycemic Therapies With Expansions of 
US Food and Drug Administration Indications to Reduce Cardiovascular Events: Prescribing Patterns 
Within an Academic Medical Center. J Cardiovasc Pharmacol. 2020;76(3):313-320. 
doi:10.1097/FJC.0000000000000864  
9. Nargesi AA, Jeyashanmugaraja GP, Desai N, Lipska K, Krumholz H, Khera R. Contemporary National 
Patterns of Eligibility and Use of Novel Cardioprotective Antihyperglycemic Agents in Type 2 
Diabetes Mellitus. J Am Heart Assoc. 2021;10(13):e021084. doi:10.1161/JAHA.121.021084  
10. Dave CV, Schneeweiss S, Wexler DJ, Brill G, Patorno E. Trends in Clinical Characteristics and 
Prescribing Preferences for SGLT2 Inhibitors and GLP-1 Receptor Agonists, 2013-2018. Diabetes 
Care. 2020;43(4):921- 924. doi:10.2337/dc19-1943  
11. Wallach MA, Kogan N, Bem DJ. Diffusion of responsibility and level of risk taking in groups. J Abnorm 
Soc Psychol. 1964;68(3):263-274. doi:10.1037/h0042190  
12. Guerin B. Diffusion of Responsibility. In: The Encyclopedia of Peace Psychology. John Wiley & Sons, 
Ltd; 2011. doi:10.1002/9780470672532.wbepp084  
13. P F, Ji K, T G, et al. The bystander-effect: a meta-analytic review on bystander intervention in 
dangerous and non-dangerous emergencies. Psychol Bull. 2011;137(4). doi:10.1037/a0023304  
14. Darley JM, Latane B. Bystander intervention in emergencies: Diffusion of responsibility. J Pers Soc 
Psychol. 1968;8(4, Pt.1):377-383. doi:10.1037/h0025589  
15. 15. LATANÉ B, DARLEY JM. BYSTANDER “APATHY.” Am Sci. 1969;57(2):244-268.  
16. Marcotte LM, Krimmel-Morrison J, Liao JM. How to Keep Diffusion of Responsibility From 
Undermining Value- Based Care. AMA J Ethics. 2020;22(9):802-807. 
doi:10.1001/amajethics.2020.802  

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17. Christensen SS. Escape from the diffusion of responsibility: A review and guide for nurses. J Nurs 
Manag. 2019;27(2):264-270. doi:10.1111/jonm.12677  
18. Mcintosh E. The implications of diffusion of responsibility on patient safety during anaesthesia, ‘So 
that others may learn and even more may live’ – Martin Bromiley. J Perioper Pract. 2019;29(10):341-
345. doi:10.1177/1750458918816572  
19. Diffusion of Responsibility Leads to Danger. Accessed February 19, 2022. 
https://psnet.ahrq.gov/web- mm/diffusion-responsibility-leads-danger  
20. McNulty S, Williams P. A call to reduce diffusion of responsibilities. BMJ. 2014;348:g1627. 
doi:10.1136/bmj.g1627  
21. Mannion R, Thompson C. Systematic biases in group decision-making: implications for patient 
safety. Int J Qual Health Care J Int Soc Qual Health Care. 2014;26(6):606-612. 
doi:10.1093/intqhc/mzu083  
22. Vernon JL. Overcoming the bystander effect. Am Sci. 2016;104(4):194-195.  
23. Cramer RE, Mcmaster MR, Bartell PA, Dragna M. Subject Competence and Minimization of the 
Bystander Effect. J Appl Soc Psychol. 1988;18(13):1133-1148. doi:10.1111/j.1559-
1816.1988.tb01198.x  
24. Ganti N, Baek S. Why People Stand By: A Comprehensive Study About the Bystander Effect. J Stud 
Res. 2021;10(1). doi:10.47611/jsrhs.v10i1.1390  
25. Gao Y, Peterson E, Pagidipati N. Barriers to prescribing glucose-lowering therapies with 
cardiometabolic benefits. Am Heart J. 2020;224:47-53. doi:10.1016/j.ahj.2020.03.017 
26. Steyerberg EW, Harrell FE, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal 
validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin 
Epidemiol. 2001;54(8):774-781. doi:10.1016/s0895-4356(01)00341-9  
27. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a 
framework for traditional and novel measures. Epidemiol Camb Mass. 2010;21(1):128-138. 
doi:10.1097/EDE.0b013e3181c30fb2  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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APPENDIX A 
 
Data Monitoring Committee / Data and Safety Monitoring Board 
Appendix 
 
 
 
• 
To be completed for studies monitored by Data Monitoring Committee (DMC) or Data and Safety 
Monitoring Board (DSMB) if a full DMC/DSMB charter is not available at the time of initial IRB 
review. 
• 
DMC/DSMB Charter and/or Roster can be submitted to the IRB later via Amendment, though these 
are not required.  
 
A Data and Safety Monitoring Board (DSMB) will be convened for safety monitoring of this research 
study.  The following characteristics describe the DSMB convened for this study (Check all that apply): 
 
☒ The DSMB is independent from the study team and study sponsor. 
 
☒ A process has been implemented to ensure absence of conflicts of interest by DSMB members. 
 
☒ The DSMB has the authority to intervene on study progress in the event of safety concerns, e.g., 
to suspend or terminate a study if new safety concerns have been identified or need to be 
investigated.   
 
☒ Describe number and types of (i.e., qualifications of) members: 
This study will be monitored by the NIA-appointed standing Roybal DSMB, which acts in an 
advisory capacity to the National Institute of Aging (NIA) Director to monitor participant safety, 
data quality and progress of the Roybal Centers. DSMB members, which have been approved by 
the NIA Director include: 
• 
Andrea B. Troxel, ScD (Chair), Professor, NYU School of Medicine 
• 
Abby King, PhD, Professor, Stanford University School of Medicine 
• 
Jerry Gurwitz, MD, Professor, University of Massachusetts Medical School and University 
of Massachusetts Graduate School of Biomedical Sciences 
• 
Hae-Ra Han, PhD, RN, FAAN, Professor, Johns Hopkins University School of Nursing 
• 
Hang Lee, PhD, Associate Professor, Harvard Medical School 
• 
Ezra Golberstein, PhD, Associate Professor, University of Minnesota School of Public 
health 
• 
David Kim, MD, PhD, Chief Resident, Stanford University 
• 
Christopher Celano, MD, Assistant Professor, Massachusetts General Hospital 
 
☒  Describe planned frequency of meetings: 
Per the NIA Notice of Award, recruitment is restricted until the DSMB has reviewed and 
recommended approval to NIA, with NIA’s concurrence, the DSMP, IRB-approved study 
protocol, consent documents, and Manual of Operating Procedures. 
 

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Per the Data Safety Monitoring Plan, the DSMB will meet twice annually, either in-person or by 
teleconference call to review study progress, data quality, and participants safety. 
 
☒ DSMB reports with no findings (i.e., “continue without modifications”) will be submitted to the 
IRB at the time of Continuing Review. 
 
☒ DSMB reports with findings/modifications required will be submitted promptly (within 5 
business days/7 calendar days of becoming aware) to the IRB as an Other Event.