The Study of a Tele-pharmacy Intervention for Chronic Diseases to Improve Treatment Adherence (STIC2IT)
Phase: NA
Conditions: Hyperlipidemia, Diabetes, Hypertension
Sponsor: Brigham and Women's Hospital

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Study of a Tele-pharmacy Intervention for Chronic diseases to Improve 
Treatment adherence (STIC2IT) 
NCT02512276 
04/27/2017 
 
 
 
 
 
 
 
 
 
 
Table of Contents 
Initial protocol .............................................................................................................................................. 2 
Final protocol ............................................................................................................................................. 16 
Summary of changes to protocol............................................................................................................... 27 
Original analysis plan ............................................................................................................................. 3031 
Final analysis plan ...................................................................................................................................... 32 
Summary of changes to the analysis plan ................................................................................................. 34 
 
 
 
 
 
 
 
 

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Initial protocol 
 
Aims 
Evaluate the impact of a novel tele-pharmacist intervention embedded within a multispecialty group 
practice on the following outcomes over a 12-month period: 
a) Adherence to cholesterol-lowering, anti-hypertensive, and oral hypoglycemic agents 
b) Achievement of ideal control of HbA1c, blood pressure, and cholesterol levels 
 
Background 
Highly effective medications to treat and prevent the complications of chronic disease have 
been developed and evaluated. While rates of prescribing effective therapies have improved 
substantially, long-term adherence remains exceptionally poor. Nearly 50% of patients become non-
adherent within a year of treatment initiation. Interventions that succeed in improving medication 
adherence may confer important clinical benefits across large populations, and may even be cost-saving 
by reducing rates of costly clinical outcomes such as myocardial infarction and stroke. 
The success of interventions to improve adherence has been relatively limited, either because of 
the nature of the intervention or their broad applications to patients who do not require them or will 
not benefit from them. Prior studies have also been limited by recruiting small samples of volunteers, 
following patients for short periods, and not evaluating their impact on clinical outcomes. The efficacy of 
future efforts is likely to be increased by targeting interventions to patients demonstrating both poor 
adherence and poor disease control, tailoring interventions to individual patient needs, and touching 
larger and more representative patient populations and care settings. These goals can be achieved by 

3 
 
the innovative use of information technology including electronic health records (EHR) and other 
advanced communication technologies, including texting. 
We propose a cluster randomized controlled trial (RCT) of a novel tele-pharmacist based 
intervention to improve adherence to medications for hypercholesterolemia, hypertension, and 
diabetes. We will evaluate an innovative intervention that uses of EHR data to identify approximately 
2,000 adult patients cared for by approximately 170 primary care physicians who are both non-adherent 
and not achieving recommended clinical targets for glycemic, blood pressure, and cholesterol control in 
a real-world primary care setting. Following EHR-facilitated referral from the treating primary care 
physicians, a clinical pharmacist will direct an outreach program using multiple communication 
technologies including text messaging and secure email messaging to improve medication adherence. 
 
Study design and randomization 
This study is a randomized, controlled trial of patients treated at Harvard Vanguard Medical 
Associates. The sites involved in this study are all part of HVMA, located in Boston and surrounding cities 
and towns. HVMA is associated with the larger organization, Atrius Health. We will enroll all primary 
care physicians and their eligible patients. Eligible patients include adults 18 years and older with a 
diagnosis of diabetes, hypertension, or hyperlipidemia; evidence of poor disease control (see below 
Table); and evidence of medication non-adherence based on medication refill data. We estimate the 
inclusion of 170 primary care physicians and 2,000 patients. 
 
 
 

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Table. Definition of Poor Disease Control 
Condition 
Cardiac Risk* 
Poor Control Definition 
Diabetes 
High 
Most recent HbAlc>9 
Hypertension 
High 
Moderate 
Low 
Most recent BP>140/80 
Most recent BP>140/90 
Most recent BP>150/100 
Hyperlipidemia 
High 
Moderate 
Low 
Most recent LDL>100 
Most recent LDL>130 
Most recent LDL>160 
* High risk defined as Framingham Risk Score (FRS≥20%) or coronary heart disease equivalent. 
Moderate risk defined as 10%≤FRS<20%. Low risk defined as FRS<10%. 
 
Intervention 
Computerized Alerts to Physicians 
Physicians randomized to the intervention group will receive electronic decision support tools to 
identify patients as non-adherent and facilitate electronic referral to the tele-pharmacist program. 
We will identify eligible study patients based on medication adherence and clinical outcomes on 
a weekly basis and populate a disease registry. We will use this registry to create an automated weekly 
process that places an electronic "flag" in the EHR to identify these patients. These flags will trigger an 
electronic alert that intervention physicians will receive upon accessing the patient chart. For patients 

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that will not be seen by the doctor in an upcoming time period, primary care physicians will receive 
patient lists through Epic staff messaging asking them to approve their patients fro the intervention.  
During the initial 3 months of the project, the study team will work with HVMA clinical leadership and 
primary care physicians to refine the presentation of the alerts. The alert will be directly linked to 
computerized order entry and will allow the physician to place a referral to the tele-pharmacist 
program. We have significant experience in this type of innovation. For example, Dr. Sequist currently 
leads a randomized controlled trial at HVMA, overseen by the Partners IRB, where electronic alerts have 
been used to facilitate primary care referrals of over 1,000 high risk patients with chronic kidney disease 
to nephrology specialists. All electronic referrals will be placed in an “electronic pool” to be monitored 
on a daily basis by the clinical pharmacists. By requiring a direct referral from the primary care physician, 
we will ensure appropriate and safe selection of patients to receive the intervention, and establish 
effective integration of the intervention into the primary care team. 
Tele-Pharmacist Intervention 
Once a referral is generated, the clinical pharmacist will deliver a four part intervention that 
includes 1) motivational interviewing and education, 2) assessing and addressing barriers to medication 
adherence, 3) employing longitudinal reminders and support via text messaging, secure email messages, 
or phone calls, and 4) providing feedback to the referring primary care physician via electronic 
documentation of patient interactions that highlight current care plans. 
The initial telephone intake interview conducted by the tele-pharmacist will be 30 minutes in 
duration. The goal of the initial interview will be to assess a patient's self-reported medication 
adherence and determine key barriers to adherence including forgetfulness, real or perceived side 
effects, perceived effectiveness of medications, or financial barriers. The pharmacist will work with the 
patient during this interview session to provide education regarding their medication regimen and 

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address all barriers identified, including solutions such as highlighting the effectiveness of the 
medications, facilitating switches to generic medications to lower costs to patients, or recommending 
combination pills to simplify regimens. 
Complex patients (and patients without access to text messaging or email) will receive ongoing 
twice-monthly follow-up telephone calls with the pharmacist to provide in-depth consultation. Patients 
with less complexity who also engage in regular use of either text messaging or the HVMA secure 
messaging system will receive regular automated, yet interactive, outreach. HVMA has significant 
experience working with our text-messaging vendor, Mobile Commons, to deliver interactive and 
tailored text messages with the capacity to store all incoming and outgoing messages in relational 
databases. We will deliver up to weekly text messages to patients to assess self-reported adherence, 
remind them of strategies reviewed to address barriers to adherence and to refill their prescriptions. 
New clinical data such as HbA1c or cholesterol results will be highlighted in the text messages as either 
within goal or outside of goal. For patients enrolled in our secure email messaging system integrated 
into the EHR, we will deliver similar email outreach as to the text messages. Dr. Sequist has significant 
experience using this secure email messaging system to conduct large scale patient outreach. 
For those patients deemed to be not improving on either clinical outcomes or medication 
adherence for 3 consecutive months, the pharmacist will conduct a telephone outreach to readdress 
barriers to adherence. For those patients that demonstrate improvement in both adherence and clinical 
outcomes, the frequency of contact will be decreased to a level appropriate to their support needs. 
The final component of the intervention will be to provide feedback on patient medication 
adherence and clinical outcomes to the referring primary care provider 
 
 

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Physician Orientation 
Within the first 3 months of the project, Drs. Sequist and Choudhry will travel to each of the 17 
health centers to conduct orientation sessions with the primary care physicians, review the goals of the 
project, and solicit feedback on the intervention components. 
Pilot Testing of Interventions 
One health center will have been selected for pilot testing the intervention. The pilot will have 
resolved operational issues and clarified roles. 
Randomization of Primary Care Physicians 
We will employ a cluster randomized study design, with primary care physicians serving as the 
unit of randomization and patients clustered within physicians. We believe the possibility of 
contamination between intervention and control physicians practicing side-by-side is minimal due to the 
current lack of focus on routine assessment and action based on medication adherence. 
Moreover, control physicians will not have any way of accessing the lists of patients eligible for 
medication adherence intervention that we will identify for intervention physicians. 
Randomization of individual physicians will be stratified by practice site. Within each of the 17 
practices, we will array physicians into blocks of 2 based on eligible patient sample size and one 
physician in each block will be randomly assigned to intervention group. Because the intervention will be 
embedded in the EHR and approved by the HVMA leadership, individual providers will not be able to 
opt-out. 

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Outcomes 
We will leverage HVMA's rich EHR data and prescription data. The study sample will consist of 
patients with poorly controlled diabetes, hypertension, and hyperlipidemia as identified by using lab 
values and other coded clinical data such as blood pressure and clinical diagnosis codes contained in the 
EHR. HVMA prescription data, available with a one-month lag, will be used to identify patients with 
suboptimal medication adherence. 
Adherence 
Adherence will be assessed for each medication prescribed for diabetes, hypertension, and 
hyperlipidemia and compared between intervention and control patients. The main measure of patient 
adherence will be proportion of days covered (PDC) for the intervention and control groups, as assessed 
with HVMA pharmacy data. This will be computed using the date the prescription was filled, the ‘days 
supplied’ field in dispensed drug data, and the dates on which the prescription was refilled. 
Clinical outcomes 
Clinical outcomes of interest are the proportion of patients achieving the following clinical 
targets: HbA1c values for patients with diabetes, systolic and diastolic blood pressure for patients with 
hypertension, and LDL values for patients with hypercholesterolemia. All laboratory values are available 
within the EHR. 
 
Analytic plan 
Statistical analyses 
We hypothesize that the intervention will improve medication adherence and disease control. 

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All analyses will use intention-to-treat principles. Medication adherence based on the 
proportion of days covered will be compared using generalized estimating equations, with adjustment 
for the cluster and block randomized design. We will use an identity link function and normally 
distributed errors. The proportion of patients considered adherent by achieving clinical goals will be 
compared using a logit link function and binary distributed errors. We will censor patients if they die, 
lose insurance eligibility, or at the end of the study period. Analyses will be run unadjusted as well as 
after controlling for the potential confounders listed below. 
Potential confounders 
The independent variable of interest is exposure to the pharmacist intervention. We will 
measure several variables to assess the degree of balance between patients and physicians randomized 
to intervention versus control, to adjust for chance imbalances, and to assess whether the efficacy of 
the intervention is modified by particular patient characteristics. Patient demographic characteristics 
and major comorbid conditions, as identified based on EHR data, will be included as independent 
variables 
 
Description of standard of care and how study procedures differ from it 
HVMA currently has an existing clinical pharmacy program. These clinical pharmacists already 
work collaboratively with the primary care physicians in the clinics to provide patient education and 
advice around medication management. Primary care physicians can currently refer patients to the 
clinical pharmacist using an electronic referral in the electronic health record. 
Our intervention differs from standard of care in two ways: 

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1) We will use data from the EHR and medication refill data to prospectively identify patients 
that might benefit most from referral to a clinical pharmacist (e.g. those who are not achieving clinical 
targets and are non-adherent to medications). We will create an alert in the electronic health record 
that advises primary care physicians to refer these patients to the clinical pharmacists. 
2) We will have equipped the clinical pharmacist with a new set of educational tools and 
outreach mechanisms that facilitate better communication with patients and support medication 
adherence. 
 
Minimizing risks and ensuring safety of subjects 
Our study involves both physician-subjects and patient-subjects. We believe that the risks to 
participation for both sets of subjects are no more than minimal. 
Physician-subjects in the intervention arm will only be asked to refer their eligible patients 
demonstrating non-adherence to chronic medications to the clinical pharmacist via an alert in the 
electronic health record. The information in the alerts is already available to the physicians in the 
electronic health record, and the alerts are only meant to highlight the information for the physicians to 
assist in patient management. Indeed, the alerts will facilitate a clinical practice that is already in place 
within HVMA – referral of patients on chronic medications to the clinical pharmacist for education. All 
physicians will be assigned a scrambled identifier by the implementation team at HVMA, and only these 
scrambled identifiers will be shared with the evaluation team at BWH. 
Patient-subjects may be referred by their physician to a visit with the clinical pharmacist as part 
of an educational intervention to improve chronic medication adherence. The study team will not 
provide direct care to patients, and all treatment decisions will ultimately be made by the patient’s 
medical team at HVMA. The risk to patient-subjects is no more than minimal as we will collect health 

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care data that was generated as a result of routine care, including clinical diagnoses of diabetes, 
hypertension, or hyperlipidemia; as well as relevant laboratory results and medication prescription data. 
The primary risk to patients will be privacy of health information. 
We will minimize the risk to privacy by taking appropriate steps to limit access to data to study 
investigators, creating scrambled patient identifiers, and sharing only deidentified data with Partners 
Healthcare investigators, and ensuring that all team members have received appropriate training in data 
privacy. The link between the identifiers and the medical record number will remain at HVMA in a 
password protected file. 
We will ensure the safety of patient-subjects by leaving ultimate clinical decision making in the 
hands of the evaluating physician and the clinical pharmacist who are in charge of caring for the patient. 
These clinicians already have an established professional relationship in caring for patients at HVMA, 
and we are mainly focused on facilitating referral of appropriate patients between the primary care 
physician and pharmacist. We do not anticipate any safety issues to arise with regards to physician-
subjects that receive the electronic alerts. Given the minimal risks involved in participation in this study, 
we do not anticipate any unacceptable adverse events or need to drop subjects from the study. 
 
Foreseeable risks 
There is no more than minimal risk involved to the physician subjects, as they will only receive 
electronic alerts within the medical record. Patient risks should also be minimized, as our intervention 
aims to promote adherence to medications already prescribed by their primary care physician under the 
guidance of a referral from their physician. Any clinical risks will be minimized in our protocol by 
prompting the evaluating doctor to make a clinical decision as to whether referral to a clinical 
pharmacist is appropriate. The clinical pharmacist intervention is primarily supportive and educational, 

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and all management decisions will be made by the primary care physicians. This model is already in 
place at HVMA, and we are studying the facilitation of this process for patients in most need of 
education and support. 
 
Expected benefits 
This study is designed to improve the management of patients with diabetes, hypertension, and 
hyperlipidemia. The physician-subjects randomly assigned to the intervention may benefit from 
becoming more aware of patient adherence to medications and its importance to achieving improved 
clinical outcomes. Patient-subjects randomly assigned to the intervention may also benefit from the 
receipt of education from clinical pharmacist, gaining a better understanding of their chronic disease 
and the role of their prescription medications. By evaluating three of the most common chronic 
conditions, this study also has the potential to improve care for similar patients in other health care 
organizations across the United States by providing a new model of care. 
 
Equitable selection of subjects 
We will enroll all primary care physicians at Harvard Vanguard Medical Associates (HVMA), as 
well as all of their patients with diabetes, hypertension, or hyperlipidemia; and evidence of poor disease 
control and medication nonadherence. We estimate that 61% of eligible patients are female. The racial 
composition of this population is 72% White, 23% Black, and 5% Hispanic. 
Women and minority patients will be fully eligible for inclusion as study subjects and 
randomized to the intervention or control groups consistent with their proportions in the population of 
patients meeting the eligibility criteria. Physician-subjects will be selected based on current employment 
by HVMA as a primary care physician. 

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All participating clinicians are fluent in English. We will enroll patients regardless of language 
based on referral from the primary care physician. 
 
Recruitment procedures 
Primary care physicians will be enrolled in the study by virtue of being an actively practicing 
clinician in internal medicine at HVMA. Prior to the beginning of the study, we will conduct site visits to 
each of the health centers and provide a 1-hour lunchtime lecture to review the scope of the 
intervention and entertain questions for physicians. Patients will be recruited into the study by a review 
of electronic medical record data to identify patients with diabetes, hypertension, or hyperlipidemia; 
evidence of poor disease control; and evidence of medication nonadherence. Physicians will be 
prompted to refer these patients to the clinical pharmacist via an alert in the electronic health record. 
We will only evaluate clinical outcomes for patients referred by their primary care physicians to the 
clinical pharmacist. The clinical pharmacist will conduct an intake interview with the patient, 
emphasizing the referral from the primary care physician. The pharmacist will describe the educational 
program to the patient and create a treatment plan based on the patient’s needs and desires. The 
patient can decline the referral from the primary care physician (consistent with any clinical referral that 
the primary care physician recommends), and can also decline to participate in any educational offerings 
from the clinical pharmacist. There will be no remunerations to any study subjects. 
 
Consent procedures 
A waiver of informed consent and HIPAA authorization applies to all physician-subjects and 
patient-subjects. This study relies on the PHRC as the IRB of record for HVMA's participation, through an 
existing umbrella IRB agreement between HVMA and Partners. 

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Data and safety monitoring 
The research subjects in this study will be subject to no more than minimal risk and we do not 
anticipate the occurrence of any adverse events. We will be in routine contact with the practice 
managers and internal medicine chiefs at each health center to obtain any feedback from clinicians 
regarding the study. Our plan for data and safety monitoring also includes oversight by the project 
principal investigator (Dr. Sequist) throughout the study period, as well as an independent DSMB that 
will consist of three members of the Harvard Medical School and Brigham and Women’s Hospital faculty 
with experience in health information technology, patient safety, and biostatistics. This committee will 
meet on a semi-annual basis to review data related to the study protocol, and ensure protection of 
patient confidentiality and safety, as well as to monitor the quality of the data collected via the study 
protocol. Our protocol primarily involves delivery of electronic risk alerts to primary care physicians to 
facilitate referral to clinical pharmacists for appropriate education. We will not provide any direct 
treatment, and do not anticipate any adverse events for patients as a result of this study. 
 
Privacy and confidentiality 
All physicians will be assigned scrambled identifiers by the HVMA project manager, and only 
these identifiers will be made available to investigators at Brigham and Women's Hospital, thereby 
limiting any risks to privacy for physician-subjects. 
All patients will be assigned scrambled identifiers by the HVMA project manager. Following 
medical chart reviews, only these scrambled identifiers will be included in analytic datasets at Brigham 
and Women's Hospital, thereby limiting any risks to privacy for patient-subjects. The link between the 
identifiers and the medical record number will remain at HVMA in a password protected file. 

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Finally, all project staff will have received training in research subject protection. 
 
Sending specimens/data to research collaborators outside Partners 
Once data is received at Partners from HVMA, we will not share these data with any outside 
collaborators. No data will be stored at collaborating sites outside Partners for any use not described in 
this protocol. 
 
 
 

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Final protocol 
 
Background 
Highly effective medications to treat and prevent the complications of chronic disease have been 
developed and evaluated. While rates of prescribing effective therapies have improved substantially, 
long-term adherence remains exceptionally poor. Nearly 50% of patients become non-adherent within a 
year of treatment initiation. Interventions that succeed in improving medication adherence may confer 
important clinical benefits across large populations, and may even be cost-saving by reducing rates of 
costly clinical outcomes such as myocardial infarction and stroke. 
The success of interventions to improve adherence has been relatively limited, either because of 
the nature of the intervention or their broad applications to patients who do not require them or will not 
benefit from them. Prior studies have also been limited by recruiting small samples of volunteers, 
following patients for short periods, and not evaluating their impact on clinical outcomes. The efficacy of 
future efforts is likely to be increased by targeting interventions to patients demonstrating both poor 
adherence and poor disease control, tailoring interventions to individual patient needs, and touching 
larger and more representative patient populations and care settings. These goals can be achieved by the 
innovative use of information technology including electronic health records (EHR) and other advanced 
communication technologies, including texting. 
We propose a cluster randomized controlled trial (RCT) of a novel tele-pharmacist based 
intervention to improve adherence to medications for hypercholesterolemia, hypertension, and diabetes. 
We will evaluate an innovative intervention that uses of EHR data to identify 4076 adult patients cared for 
by approximately 150 primary care physicians who are both non-adherent and not achieving 
recommended clinical targets for glycemic, blood pressure, and cholesterol control in a real-world 
primary care setting. Following EHR-facilitated referral from the treating primary care physicians, a clinical 

17 
 
pharmacist will direct an outreach program using multiple communication technologies including a 
telephone consultation, text messaging and video visits to improve medication adherence. 
 
Study setting 
This trial is being conducted at Harvard Vanguard Medical Associates (Harvard Vanguard), which 
is a practice of Atrius Health, a large multispecialty medical group and a Pioneer Accountable Care 
Organization. Harvard Vanguard employs approximately 150 primary care physicians (PCPs) who provide 
care for approximately 300,000 adult patients at 17 practice sites. Of these, 15 practice sites have 
integrated retail pharmacies, where approximately 50% of patients obtain their prescription medications. 
Within each site, all PCPs and their eligible patients will be randomized to the same study arm. 
 
Study design and randomization 
STIC2IT is a pragmatic, prospective, intention-to-treat, cluster-randomized controlled trial 
designed to test the impact of a technologically-enabled, behaviorally-targeted pharmacist intervention 
designed to improve medication adherence and disease control among the specific group of individuals 
who are most likely to benefit from this intervention – those who are non-adherent to their glucose 
lowering, anti-hypertensive, or statin medications and have evidence of poor disease control based on 
recommended clinical targets.  We randomly selected 1 of the 15 Harvard Vanguard practice sites with 
onsite pharmacies as a pilot site for intervention refinement. The remaining 14 Harvard Vanguard 
practice sites were then cluster-randomized such that all PCPs and their patients in a given practice site 
were assigned to the same study arm. Because the practice sites differ from each other, simple cluster 
randomization may have resulted in imbalances in patient or provider factors that could potentially bias 
outcome assessment. Therefore, we categorized the practice sites based on their size (i.e., small or large, 
based on the number of patients receiving care at each site) and whether clinical pharmacists at the sites 

18 
 
offered disease management counseling directly to patients (i.e., yes or no). Within the resultant 4 blocks, 
practices were then randomized in a 1:1 ratio to intervention or control using a random number 
generator. 
 
Subjects 
We will enroll all primary care physicians and their eligible patients. Eligible patients include 
adults ≥18 years and <85 years with a diagnosis of diabetes, hypertension, or hyperlipidemia; evidence of 
poor or worsening disease control (see Table 1); and evidence of medication non-adherence based on 
medication refill data. Disease control is evaluated using the most recent lab or blood pressure values in 
the electronic health record and is based on clinical guideline targets from the American Diabetes 
Association (ADA), the Eighth Joint National Committee (JNC 8) hypertension guidelines, and the 
American College of Cardiology/American Heart Association (ACC/AHA) cholesterol guidelines. Adherence 
is assessed using prescription claims data. HVMA prescription data are available with a one-month lag. 
For each medication used to treat any one of the targeted conditions, the proportion of days covered 
(PDC) is calculated as the number of days of medication that a patient filled between the first fill date and 
the randomization date divided by the number of days in that same period (up to a maximum of 365 
days). We consider drugs that are chemically related and not intended for use in combination to be 
interchangeable (e.g., two different statins).  We average the PDC for all medications used to treat a 
single condition (e.g., all oral hypoglycemics) and then calculate an overall average adherence for all of 
the conditions that a patient had at the time of their identification.   
 
 
 

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TABLE 1. Definitions of poor and worsening disease control 
Condition 
Age (years) 
Poor control 
Worsening control 
Diabetes 
 … 
Latest HbA1c > 8 
Latest HbA1C 7.5 ≤ to ≤ 8, and previous 
HbA1C 1% lower 
Hypertension 
≥ 60 
Latest BP > 150/90 
Latest BP 140/80 ≤ to ≤ 150/90, and 
previous BP 20 mmHg lower  
< 60 
Latest BP > 140/90 
Latest BP 130/80 ≤ to ≤ 140/90, and 
previous systolic or diastolic BP 20 mmHg 
lower 
Hyperlipidemia 
 
 … 
Diagnoses of ASCVD  
 … 
40-75 
Type 1 or 2 diabetes and 
use of glucose lowering 
agent  
 … 
 40-79 
ASCVD risk > 7.5% 
 … 
… 
LDL>190 mg/dl 
Latest LDL 175 ≤ to ≤ 190, and previous 
LDL 30 mg/dl lower 
…= N/A; Abbreviations: HbA1c, glycosylated hemoglobin; BP, blood pressure; ASCVD, atherosclerotic 
cardiovascular disease; LDL, low-density lipoprotein 
 
To identify individuals who would benefit most from the intervention, a patient is defined as 
being non-adherent if they (1) have been less than 80% adherent to the specific class of medications used 
to treat the condition(s) for which they were being identified as being poorly controlled and (2) if their 
“average of averages” PDC for all eligible study drugs is less than 80%. For example, patients with poorly 

20 
 
controlled diabetes who are non-adherent to their diabetes medications but adherent to their statins and 
antihypertensives, would only be eligible if their average adherence across all 3 conditions is less than 
80%. Patients are excluded if, prior to randomization, they have less than 6 months of continuous 
enrollment in the health plan (to allow adequate assessment of eligibility), are < 18 or > 85 years of age, 
or have no available telephone contact information, which would preclude contact for enrollment and 
delivery of the intervention.  
 
Study procedures 
Once identified, PCPs of potentially eligible intervention group patients will be sent a secure 
message using the electronic health record to ask permission to include their patient(s) in the study. If a 
physician does not explicitly respond to approve or disapprove of the identified patient(s) to participate in 
the study within 5 days, they are sent a reminder message; one day later, their patients will automatically 
be opted in to the study.  
Patients approved for enrollment in the study are sent a letter informing them about the study 
along with a simple, one compartment per day, pillbox that allows for the storage of one week of 
medication. Patients are then contacted by telephone to schedule a phone consultation with the clinical 
pharmacist.  At this time, they are also administered the Patient Activation Measure (PAM), a 
questionnaire to assess the knowledge, skills, and confidence to manage one’s health and health care. 
The PAM is subsequently used to tailor the intervention for that individual patient. 
 
Intervention  
The central component of the multi-faceted intervention is an individually-tailored telephone 
consultation conducted by a clinical pharmacist who is part of the Harvard Vanguard care team. The 

21 
 
clinical pharmacists work at multiple sites; in order to reduce the chances of contamination, they are 
restricted to providing clinical services only at other intervention sites during the time period of this 
study.  
During this consultation, the clinical pharmacist confirms a patient’s treatment regimen, engages 
the patient in sharing potential barriers to adherence or other factors that may be contributing to poor 
disease control, discusses the patient’s readiness to modify behaviors, and works with the patient to 
agree upon a shared plan of strategies to improve adherence and disease control. The identified 
adherence barriers are grouped into six distinct groups: treatment complexity/forgetfulness, health 
perceptions, lack of knowledge/poor health literacy, side effects, cognitive impairment, and cost-related 
barriers.  Patients may have barriers identified in more than one category.  The solutions and strategies 
offered to patients by the clinical pharmacists are tailored to their PAM level and their identified 
adherence barrier(s). Depending on the barrier, patients with lower levels of activation (e.g., PAM Level 1 
and 2) are offered more intensive solutions, such as daily text messages as reminders or motivational 
support, pillboxes that allow for multiple times per day dosing, follow-up consultations, and video visits 
through the WebEx platform (Santa Clara, CA).  The video visits allow for one-on-one communication, 
delivered remotely. Patients with higher levels of activation (e.g., PAM Level 3 and 4) are offered less 
intensive solutions, such as weekly text messages and pillboxes. The clinical pharmacists then work with 
the primary care physicians and other team members at Atrius Health to implement solutions based on 
the treatment plan. The clinical pharmacists also mail a copy of the shared plan to the patients after the 
initial encounter.  
  
The initial calls last 30 to 45 minutes.  Follow-up calls are scheduled with all patients with low 
levels of activation but only if clinically indicated for highly activated individuals. Depending on the 
barrier(s) identified, patients are offered the opportunity to receive SMS text messages via a secure 
messaging platform (Mobile Commons; Brooklyn, NY) for the 12-month follow-up period or until the 

22 
 
patient opts out.  These 50 unique text messages were developed by the study team to provide 
reminders and motivation to subjects who opted to receive them (see Appendix C).  In addition to the 
motivational text messages, patients are asked questions about their adherence behavior to which they 
can provide answers by directly replying to the text message and receive automated responses using a 
feedback response system. The response system provides different encouraging feedback or advice, 
depending on the patients’ inputs. The content and frequency of the text messages will differ depending 
on patients’ PAM levels and adherence barrier.  The Mobile Commons platform can store all incoming 
and outgoing messages in relational databases. We will deliver text messages to patients to assess self-
reported adherence, remind them of strategies reviewed to address barriers to adherence and to refill 
their prescriptions (see Text_messages). Requested responses are structured (e.g. the number of days 
that they took their medication last week).  If the patient sends anything other than an expected response 
to the question being asked (for example, if they provide their blood pressure reading rather than their 
level of adherence), they will receive an automated message telling them that the text message system is 
not monitored by a live individual and instructing them, in the case of the need for urgent medical 
attention, to call 911, go to their nearest emergency department or calling the 24-hour nurse helpline for 
their HVMA primary care practice.  In addition, the study PIs and the HVMA Site PI (Dr. Thomas Isaac) will 
receive an automated and instant email from the Mobile Commons platform with a copy of any such 
unanticipated texts, and if necessary based on the content will call the 24-hour HVMA nurse helpline to 
have them attempt to address the patient’s concern. 
At the conclusion of the initial pharmacist call, the pharmacists will provide feedback to the 
referring PCP via electronic documentation of patient interactions that highlight current care plans.  In 
situations where the pharmacist is recommending a change in therapy (e.g. a switch from the branded 
version to the generic version of a given drug to reduce cost-related barriers to adherence), they will ask 
the PCP to review their consultation note and based on this to write a new prescription, if appropriate.   

23 
 
All intervention patients who do not opt out of trial participation are mailed progress reports at 6 
months and 9 months after randomization on behalf of their PCP. These progress reports provide 
personalized and updated information about disease control generated using data from the electronic 
health record.  For patients whose administrative claims data are believed by the clinical pharmacist to be 
an accurate representation of actual filling behavior (e.g., for patients who only use their prescription 
insurance plan to pay for their prescriptions), the progress reports will also provide patient-specific 
medication adherence information.  For those patients deemed to be not improving on either clinical 
outcomes or medication adherence for 3 consecutive months, the pharmacist will conduct a telephone 
outreach to readdress barriers to adherence. For those patients that demonstrate improvement in both 
adherence and clinical outcomes, the frequency of contact will be decreased to a level appropriate to 
their support needs 
The specific components of the intervention that are administered to each patient, including the 
number, frequency and length of the phone consultations that they receive, will be explicitly tracked to 
facilitate future reproducibility and scalability. 
At the conclusion of the 12-month follow-up period, a brief survey will be administered to 
intervention patients who did not decline to speak to a clinical pharmacist. These surveys will assess each 
patient’s self-reported medication adherence, activation, and perceptions of the clinical pharmacist 
program. The surveys will also be used to validate medication adherence collected by retail pharmacy 
filling data and administrative pharmacy claims. These surveys will be delivered via a paper mailing on 
behalf of the PCP. Within these paper surveys, patients will be given the option to complete the survey 
online through Qualtrics. Non-respondents will receive a second copy of the survey with a $5 gift card as 
an incentive (see “12-month survey” in attachments section).  
 

24 
 
Outcomes 
The trial’s primary outcome is medication adherence assessed at 12 months after randomization.  
For this outcome, medication adherence will be assessed using prescription claims data and measured as 
the mean PDC over the 12 months after randomization using the “average of averages” approach used 
for study eligibility.  Adherence will be measured only for medications that qualified a patient for inclusion 
in the study and follow-up will begin at the point of randomization.  Medications that were filled before 
randomization but had a supply that extended into the follow-up period will have their carry-over supply 
included in the adherence calculation. 
In sensitivity analyses, we will measure medication adherence by calculating PDC beginning from the 
first fill of a medication after randomization until the end of the 12-month follow-up period.   We will also 
repeat our analyses censoring patients with diabetes when they initiate insulin.  
The secondary outcomes for the study include disease control and rates of healthcare utilization.  
Disease control will be measured as the following two different outcomes: (1) the proportion of patients 
achieving good disease control for all of their eligible conditions and (2) the proportion of patients 
achieving good disease control for at least one of their eligible conditions.  Because disease control will be 
evaluated using biometrics that are collected during routine care rather than at study-prescribed 
intervals, we will use those values that are closest to each patient’s 12-month end of follow-up period. If 
more than 10% of subjects have missing data we will repeat our analyses using multiple imputation 
Rates of healthcare utilization will also be measured using administrative claims data and will include 
all-cause emergency room visits, physician office visits, and all-cause hospitalizations during the 12-month 
follow-up period. 
 

25 
 
Analytic plan 
We will report the means and frequencies of pre-randomization variables separately for 
intervention and control subjects.  Comparisons of these values will be performed using t-tests and chi 
square tests and their non-parametric analogs, as appropriate. The outcomes will be evaluated using 
intention-to-treat principles among all randomized patients.  
In the primary analysis, the outcomes will be compared using generalized estimating equations 
with an identity link function and normally distributed errors to account for the clustering of subjects 
within practice sites.  Our primary models will also adjust for the block-randomized design.  If there are 
differences in baseline characteristics between study groups that are believed to be confounders of the 
intervention–outcome association, we will repeat our analyses after adjusting for these covariates. 
A similar approach will be taken for the analysis of the secondary outcomes, except using logit 
link functions with binary errors and log link functions with Poisson errors, as appropriate for disease 
control and rates of healthcare utilization, respectively.  
Several additional analyses will also be conducted. First, we will assess the correlation between 
calculated adherence based on insurer claims, self-reported adherence, and pharmacy transaction 
records (for patients filling prescriptions at Harvard Vanguard pharmacies). Second, we will use Markov 
modeling to assess the long-term impact of the intervention on cost, quality-adjusted survival, and cost-
effectiveness of the intervention. 
Sample size considerations 
Our study should be sufficiently powered to detect small, clinically meaningful changes in the 
primary outcome.  We powered the study to detect a 2.5% mean change in adherence between the 
intervention and control groups, assuming a standard deviation of 0.25 (a conservative assumption), 
clustering at the practice level with a design effect of 1.10, and a 15% non-differential loss to follow-up 

26 
 
rate.  We assumed that 95% of potentially eligible intervention patients would be approved for study 
inclusion by their PCPs and that 50% of these patients would agree to a pharmacist consultation. With 
these assumptions, we estimated that we would need a total sample size of 4000 eligible patients to 
provide more than 80% power to detect differences in our primary study outcome of medication 
adherence.  In other words, if those patients receiving the intervention demonstrate a 4.6% improvement 
in adherence, we will still have more than 80% power to detect an improvement of 2.5% in the overall 
sample. We also have substantial power to detect improvements in disease control, assuming a rate of 
controlled disease of 30% based on our pilot data. 
 
 

27 
 
Summary of changes to protocol 
 
Date of 
submission 
Description of modification 
Rationale for modification 
Approval 
date 
11/20/2013 1- Adding new funding source: NIH - 1 R01 
HL117918-01 
Our NIH application will be 
funded 
12/31/2013 
07/01/2014 1- Adding that this will utilize HVMA sites, 
which is part of a larger organization 
(Atrius). 
2- Including information clinical QI efforts 
have been removed and language 
simplified.  
3- Discussing how roles will be determined 
from the pilot study 
4- Updating the number of sites/ health 
centers 
5- Discussing waiver of informed consent as 
well as stating that HVMA is relying on 
PHRC for IRB through an already existing 
umbrella IRB agreement between Partners 
and HVMA. 
These changes have been 
updated as part of a 
continuing review as noted 
during the review process. 
07/03/2014 
03/13/2015 1- Updating definitions of "poor disease 
control" to reflect current practice 
guidelines.  
2- Changing randomization process from 
the level of the physician to the level of the 
practice site.  
3- Outlining the process by which physicians 
will approve patients for study participation 
in greater detail 
4- Describing the actual tele-pharmacist 
intervention in more detail.  
5- Increasing the number of patients 
included in the study has increased from 
2000 to 3000, as this is roughly the number 
of eligible patients at our practice sites.  
6- Uploading new documents including the 
letter notifying patients of the intervention, 
the research assistant telephone script, a 
list of FAQs that may be relevant when the 
research assistant (RA) calls a patient, a list 
of text messages that will be sent to 
patients, and the letter that will be sent to 
patients summarizing their new relevant 
clinical data. 
Processes have been 
updated to reflect realistic 
work flows per the request 
of our partners at HVMA 
and the number of patients 
in the study based on 
eligible patients at the 
practice sites. We also 
included new documents 
that the research assistants 
will use to schedule 
patients for a telephone 
visit with their clinical 
pharmacist.  
04/14/2015 
05/19/2015 1- Slighting revising several documents 
including: the patient recruitment letter, 
The first three documents 
propose minor updates, 
06/22/2015 

28 
 
research assistant FAQs, research assistant 
call script, and reminder call message to 
patients for their clinical pharmacist call.  
such as "clinical 
pharmacist" as opposed to 
"pharmacist” and wording 
in an automatic reminder 
call that will be sent to 
patients prior to their 
appointments 
06/26/2015 1- Submitting revised text messages to be 
sent to patients who consent to receiving 
this service. 
2- Including the text for a follow-up 
recruitment letter that will be sent to 
patients who could not be reached with the 
first 
recruitment letter mailing. 
1- The new text messages 
better address specific 
barriers that may hinder 
optimal medication 
adherence.  
2- The follow-up 
recruitment letter will allow 
us to re-contact patients 
who could not be reached 
with the first mailing. 
07/02/2015 
07/22/2015 1. Updating with Spanish-translated 
versions of the initial and follow-up patient 
recruitment letters 
2. Updating the initial patient recruitment 
letter to include verbiage about the 
enclosed pillbox 
3. Providing voicemail messages that RAs 
will leave to patients who could not be 
reached via phone 
4. Adding additional questions to the RA 
FAQs  
5. Revising the RA script 
1. Spanish translations of 
the patient recruitment 
letters will allow us to 
appeal to patients whose 
primary language is 
Spanish. 
2. We changed our 
workflow to mail a pillbox 
with the initial recruitment 
letter, so we want to 
reference this tool in the 
letter. 
3. Voicemail messages will 
provide patients with the 
opportunity to return the 
RAs' calls. 
4. Additional information in 
the RA FAQs will help RAs 
answer any questions 
about clinical pharmacist 
responsibilities. 
5. Edits to the RA script 
reflect improved verbiage 
for recruitment. 
08/04/2015 
09/03/2015 1- Revising RA permissions to include access 
to contact patients via MyHealth, a secure, 
web-based program that allows patients to 
contact their physician's office about non-
urgent medical matters.  
2- Revising the re-contact letter that is 
already IRB approved to include reference 
1- RA access to MyHealth 
would provide an 
alternative method to 
contact patients who could 
not be reached via 
telephone. 
09/24/2015 

29 
 
to a small nominal collapsible drinking cup 
with pill container that will be included in 
the envelopes. 
2- A small collapsible 
drinking cup with pill 
container could serve as a 
useful tool to increase 
adherence.  
01/12/2016 1- Updating the report card (progress 
report), which contains information on 
medication adherence and disease control 
according to each patient's claims data and 
most recent lab values. 
The updated version of the 
report card reflects 
changes in both content 
and language. We included 
patients' most recently 
filled medications, last filled 
dates (according to claims 
data), and most recent lab 
values and incorporated 
more verbiage about 
medication adherence and 
next steps for better 
disease control. 
01/25/2016 
02/18/2016 1- Changing the enrollment number target 
from 3,000 to 4,080 patients to reflect 
findings from the first few months of 
enrollment. 
After randomization, the 
referral rates by the PCPs 
were somewhat lower than 
originally estimated. 
Second, fewer patients 
randomized to the 
intervention were able to 
be reached by the RAs. We  
previously expected that 
60% of patients would be 
reachable; that estimate is 
closer to 40%. Lastly, 
patients are censored when 
they lose insurance 
eligibility during follow-up, 
and more patients than 
originally estimated lose 
insurance. These affect the 
sample size estimates. 
03/03/2016 
06/29/2016 1- Adding the specific patient surveys and 
processes that will be conducted at the 
conclusion of the 12-month follow-up 
period for intervention patients who did not 
decline to speak to a clinical pharmacist. 
The surveys will be delivered via paper 
mailing with an online survey. Non-
respondents will be mailed a second copy of 
the survey with an enclosed $5 gift card as 
an incentive. 
These surveys will be used 
to evaluate the 
effectiveness of the 
telepharmacist 
intervention on patient 
adherence and activation. 
The surveys will also be 
used to validate the 
adherence measures 
collected by retail 
pharmacy data and 
07/22/2016 

30 
 
pharmacy claims. We will 
compare adherence  
and assess the validity of 
these alternative measures. 
11/02/2016 1- Slightly modifying the protocol to clarify 
which data are being used by Partners 
investigators for the purposes of analysis. 
The present amendment includes this 
clarification in the protocol and in the 
related data forms, as applicable. 
The study team will use 
HIPAA-limited PHI data 
from HVMA for the 
purpose of 
analyzing the study.  
The only PHI that will be 
shared with Partners 
investigators are dates 
(e.g., date of birth, 
admission/discharge dates, 
and dates of procedures).  
12/16/2016 
03/08/2017 1- Slightly modifying the protocol to clarify 
how the data are being used by Partners 
investigators for the purposes of analyses to 
inform the DUA between the BWH and 
HVMA.  
Like other insurer 
administrative claims, these 
data may contain some 
diagnosis codes and 
procedures for potentially 
sensitive information (e.g., 
HIV diagnosis, reproductive 
history) as these are 
adjudicated claims from 
insurers. In this study, 
except for mental health 
conditions, we will not be 
specifically looking at these 
sensitive conditions as 
comorbidities, effect 
modifiers or outcomes. We 
are only planning to include 
these in aggregated counts 
of resource utilization.  
04/27/2017 
 
 
 
 

31 
 
Original analysis plan 
 
Outcome Measures 
Adherence 
Adherence will be assessed for each medication prescribed for diabetes, hypertension, and 
hyperlipidemia and compared between intervention and control patients. The main measure of patient 
adherence will be proportion of days covered (PDC) for the intervention and control groups, as assessed 
with HVMA pharmacy data. This will be computed using the date the prescription was filled, the ‘days 
supplied’ field in dispensed drug data, and the dates on which the prescription was refilled. 
Clinical outcomes 
Clinical outcomes of interest are the proportion of patients achieving the following clinical targets: HbA1c 
values for patients with diabetes, systolic and diastolic blood pressure for patients with hypertension, and 
LDL values for patients with hypercholesterolemia. All laboratory values are available within the EHR. 
Statistical analyses 
We hypothesize that the intervention will improve medication adherence and disease control. 
All analyses will use intention-to-treat principles. Medication adherence based on the proportion of days 
covered will be compared using generalized estimating equations, with adjustment for the cluster and 
block randomized design. We will use an identity link function and normally distributed errors. The 
proportion of patients considered adherent by achieving clinical goals will be compared using a logit link 
function and binary distributed errors. We will censor patients if they die, lose insurance eligibility, or at 
the end of the study period. Analyses will be run unadjusted as well as after controlling for the potential 
confounders listed below. 
Potential confounders 
The independent variable of interest is exposure to the pharmacist intervention. We will measure several 
variables to assess the degree of balance between patients and physicians randomized to intervention 
versus control, to adjust for chance imbalances, and to assess whether the efficacy of the intervention is 
modified by particular patient characteristics. Patient demographic characteristics and major comorbid 
conditions, as identified based on EHR data, will be included as independent variables 
HVMA currently has an existing clinical pharmacy program. These clinical pharmacists already work 
collaboratively with the primary care physicians in the clinics to provide patient education and advice 
around medication management. Primary care physicians can currently refer patients to the clinical 
pharmacist using an electronic referral in the electronic health record. 
 
 
 

32 
 
Final analysis plan 
 
Sample size considerations 
Our study should be sufficiently powered to detect small, clinically meaningful changes in the primary 
outcome.  We powered the study to detect a 2.5% mean change in adherence between the intervention 
and control groups, assuming a standard deviation of 0.25 (a conservative assumption), clustering at the 
practice level with a design effect of 1.10, and a 15% non-differential loss to follow-up rate.  We assumed 
that 95% of potentially eligible intervention patients would be approved for study inclusion by their PCPs 
and that 50% of these patients would agree to a pharmacist consultation. With these assumptions, we 
estimated that we would need a total sample size of 4000 eligible patients to provide more than 80% 
power to detect differences in our primary study outcome of medication adherence.  In other words, if 
those patients receiving the intervention demonstrate a 4.6% improvement in adherence, we will still 
have more than 80% power to detect an improvement of 2.5% in the overall sample. We also have 
substantial power to detect improvements in disease control, assuming a rate of controlled disease of 
30% based on our pilot data. 
Outcome Measures 
Primary outcome - Adherence 
The trial’s primary outcome is medication adherence assessed at 12 months after randomization.  
Medication adherence will be assessed using prescription claims data and measured as the mean PDC 
over the 12 months after randomization using the “average of averages” approach used for study 
eligibility.  Adherence will be measured only for medications that qualified a patient for inclusion in the 
study and follow-up will begin at the point of randomization 
Secondary outcomes 
The secondary outcomes for the study include disease control and rates of healthcare utilization. 
Disease control will be measured as the following two different outcomes: (1) the proportion of patients 
achieving good disease control for all of their eligible conditions and (2) the proportion of patients 
achieving good disease control for at least one of their eligible conditions.  Because disease control will be 
evaluated using biometrics that are collected during routine care rather than at study-prescribed 
intervals, we will use those values that are closest to each patient’s 12-month end of follow-up period up 
to a maximum of 15 months after randomization 
Rates of healthcare utilization will also be measured using administrative claims data and will include all-
cause emergency room visits, physician office visits, and hospitalizations during follow-up 
Statistical analyses 
We will report the means and frequencies of pre-randomization variables separately for intervention and 
control subjects.  Comparisons of these values will be performed using t-tests and chi square tests and 
their non-parametric analogs, as appropriate. The outcomes will be evaluated using intention-to-treat 
principles among all randomized patients. In the primary analysis, the outcomes will be compared using 
generalized estimating equations with an identity link function and normally distributed errors to account 

33 
 
for the clustering of subjects within practice sites.  Our primary models will also adjust for the block-
randomized design.  If there are differences in baseline characteristics between study groups that are 
believed to be confounders of the intervention–outcome association, we will repeat our analyses after 
adjusting for these covariates. 
A similar approach will be taken for the analysis of the secondary outcomes, except using logit link 
functions with binary errors and log link functions with Poisson errors, as appropriate. If more than 10% 
of subjects have missing outcome data, we will repeat our analyses using the latest post-randomization 
lab values available and using multiple imputation.  
Several additional analyses will also be conducted.  First, we will assess the correlation between 
calculated adherence based on insurer claims, self-reported adherence, and pharmacy transaction 
records (for patients filling prescriptions at Harvard Vanguard pharmacies).  Second, we will use Markov 
modeling to assess the long-term impact of the intervention on cost, quality-adjusted survival, and cost-
effectiveness of the intervention 
Potential confounders 
We will measure several variables to assess the degree of balance between patients and physicians 
randomized to intervention versus control, to adjust for chance imbalances, and to assess whether the 
efficacy of the intervention is modified by particular patient characteristics such as baseline patient 
demographic characteristics, major comorbid conditions, medication use, and healthcare utilization as 
identified based on claims and EHR data. 
 

34 
 
Summary of changes to the analysis plan 
Detailed sample size calculations are now provided which was not included in the original plan. The 
primary and secondary outcomes are now clarified along with the specific analytic strategy for these 
outcomes.