Workflow Automation for a Virtual Hypertension Management Program

Randomized Evaluation of a Remote Management Program to Improve Guideline-Directed Medical Therapy: The DRIVE Trial

BACKGROUND

Several SGLT2i (sodium-glucose transport protein 2 inhibitors) and GLP1-RA (glucagon-like peptide-1 receptor agonists) reduce cardiovascular events and improve kidney outcomes in patients with type 2 diabetes; however, utilization remains low despite guideline recommendations.

METHODS

A randomized, remote implementation trial in the Mass General Brigham network enrolled patients with type 2 diabetes with increased cardiovascular or kidney risk. Patients eligible for, but not prescribed, SGLT2i or GLP1-RA were randomly assigned to simultaneous virtual patient education with concurrent prescription of SGLT2i or GLP1-RA (ie, Simultaneous) or 2 months of virtual education followed by medication prescription (ie, Education-First) delivered by a multidisciplinary team driven by nonlicensed navigators and clinical pharmacists who prescribed SGLT2i or GLP1-RA using a standardized treatment algorithm. The primary outcome was the proportion of patients with prescriptions for either SGLT2i or GLP1-RA by 6 months.

RESULTS

Between March 2021 and December 2022, 200 patients were randomized. The mean age was 66.5 years; 36.5% were female, and 22.0% were non-White. Overall, 30.0% had cardiovascular disease, 5.0% had cerebrovascular disease, and 1.5% had both. Mean estimated glomerular filtration rate was 77.9 mL/(min‧1.73 m2), and mean urine/albumin creatinine ratio was 88.6 mg/g. After 2 months, 69 of 200 (34.5%) patients received a new prescription for either SGLT2i or GLP1-RA: 53.4% of patients in the Simultaneous arm and 8.3% of patients in the Education-First arm (P<0.001). After 6 months, 128 of 200 (64.0%) received a new prescription: 69.8% of patients in the Simultaneous arm and 56.0% of patients in Education-First (P<0.001). Patient self-report of taking SGLT2i or GLP1-RA within 6 months of trial entry was similarly greater in the Simultaneous versus Education-First arm (69 of 116 [59.5%] versus 37 of 84 [44.0%]; P<0.001) Median time to first prescription was 24 (interquartile range [IQR], 13-50) versus 85 days (IQR, 65-106), respectively (P<0.001).

CONCLUSIONS

In this randomized trial, a remote, team-based program identifies patients with type 2 diabetes and high cardiovascular or kidney risk, provides virtual education, prescribes SGLT2i or GLP1-RA, and improves guideline-directed medical therapy. These findings support greater utilization of virtual team-based approaches to optimize chronic disease management.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT06046560.

Methods, rationale, and design for a remote pharmacist and navigator-driven disease management program to improve guideline-directed medical therapy in patients with type 2 diabetes at elevated cardiovascular and/or kidney risk

AIM

Describe the rationale for and design of Diabetes Remote Intervention to improVe use of Evidence-based medications (DRIVE), a remote medication management program designed to initiate and titrate guideline-directed medical therapy (GDMT) in patients with type 2 diabetes (T2D) at elevated cardiovascular (CV) and/or kidney risk by leveraging non-physician providers.

METHODS

An electronic health record based algorithm is used to identify patients with T2D and either established atherosclerotic CV disease (ASCVD), high risk for ASCVD, chronic kidney disease, and/or heart failure within our health system. Patients are invited to participate and randomly assigned to either simultaneous education and medication management, or a period of education prior to medication management. Patient navigators (trained, non-licensed staff) are the primary points of contact while a pharmacist or nurse practitioner reviews and authorizes each medication initiation and titration under an institution-approved collaborative drug therapy management protocol with supervision from a cardiologist and/or endocrinologist. Patient engagement is managed through software to support communication, automation, workflow, and standardization.

CONCLUSION

We are testing a remote, navigator-driven, pharmacist-led, and physician-overseen management strategy to optimize GDMT for T2D as a population-level strategy to close the gap between guidelines and clinical practice for patients with T2D at elevated CV and/or kidney risk.

Retrieval Augmented Generation Enabled Generative Pre-Trained Transformer 4 (GPT-4) Performance for Clinical Trial Screening

Background

Subject screening is a key aspect of all clinical trials; however, traditionally, it is a labor-intensive and error-prone task, demanding significant time and resources. With the advent of large language models (LLMs) and related technologies, a paradigm shift in natural language processing capabilities offers a promising avenue for increasing both quality and efficiency of screening efforts. This study aimed to test the Retrieval-Augmented Generation (RAG) process enabled Generative Pretrained Transformer Version 4 (GPT-4) to accurately identify and report on inclusion and exclusion criteria for a clinical trial.

Methods

The Co-Operative Program for Implementation of Optimal Therapy in Heart Failure (COPILOT-HF) trial aims to recruit patients with symptomatic heart failure. As part of the screening process, a list of potentially eligible patients is created through an electronic health record (EHR) query. Currently, structured data in the EHR can only be used to determine 5 out of 6 inclusion and 5 out of 17 exclusion criteria. Trained, but non-licensed, study staff complete manual chart review to determine patient eligibility and record their assessment of the inclusion and exclusion criteria. We obtained the structured assessments completed by the study staff and clinical notes for the past two years and developed a workflow of clinical note-based question answering system powered by RAG architecture and GPT-4 that we named RECTIFIER (RAG-Enabled Clinical Trial Infrastructure for Inclusion Exclusion Review). We used notes from 100 patients as a development dataset, 282 patients as a validation dataset, and 1894 patients as a test set. An expert clinician completed a blinded review of patients' charts to answer the eligibility questions and determine the "gold standard" answers. We calculated the sensitivity, specificity, accuracy, and Matthews correlation coefficient (MCC) for each question and screening method. We also performed bootstrapping to calculate the confidence intervals for each statistic.

Results

Both RECTIFIER and study staff answers closely aligned with the expert clinician answers across criteria with accuracy ranging between 97.9% and 100% (MCC 0.837 and 1) for RECTIFIER and 91.7% and 100% (MCC 0.644 and 1) for study staff. RECTIFIER performed better than study staff to determine the inclusion criteria of "symptomatic heart failure" with an accuracy of 97.9% vs 91.7% and an MCC of 0.924 vs 0.721, respectively. Overall, the sensitivity and specificity of determining eligibility for the RECTIFIER was 92.3% (CI) and 93.9% (CI), and study staff was 90.1% (CI) and 83.6% (CI), respectively.

Conclusion

GPT-4 based solutions have the potential to improve efficiency and reduce costs in clinical trial screening. When incorporating new tools such as RECTIFIER, it is important to consider the potential hazards of automating the screening process and set up appropriate mitigation strategies such as final clinician review before patient engagement.

Implementation of a Scalable Online Weight Management Programme in Clinical Settings: Protocol for the PROPS 2.0 Programme (Partnerships for Reducing Overweight and Obesity with Patient-Centered Strategies 2.0)

INTRODUCTION

There is an urgent need for scalable strategies for treating overweight and obesity in clinical settings. PROPS 2.0 (Partnerships for Reducing Overweight and Obesity with Patient-Centered Strategies 2.0) aims to adapt and implement the combined intervention from the PROPS Study at scale, in a diverse cross-section of patients and providers.

METHODS AND ANALYSIS

We are implementing PROPS 2.0 across a variety of clinics at Brigham and Women's Hospital, targeting enrolment of 5000 patients. Providers can refer patients or patients can self-refer. Eligible patients must be ≥20 years old and have a body mass index (BMI) of ≥30 kg/m2 or a BMI of 25-29.9 kg/m2 plus another cardiovascular risk factor or obesity-related condition. After enrolment, patients register for the RestoreHealth online programme/app (HealthFleet Inc.) and participate for 12 months. Patients can engage with the programme and receive personalized feedback from a coach. Patient navigators help to enrol patients, enter updates in the electronic health record, and refer patients to additional resources. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework is guiding the evaluation.

ETHICS AND DISSEMINATION

The Mass General Brigham Human Research Committee approved this protocol. An implementation guide will be created and disseminated, to help other sites adopt the intervention in the future.

TRIAL REGISTRATION NUMBER

NCT0555925.

The ACTIVATE Digital Health Pilot Program for Diabetes and Hypertension in an Underserved and Rural Community

BACKGROUND

Community health centers and patients in rural and agricultural communities struggle to address diabetes and hypertension in the face of health disparities and technology barriers. The stark reality of these digital health disparities were highlighted during the coronavirus disease 2019 pandemic.

OBJECTIVES

The objective of the ACTIVATE (Accountability, Coordination, and Telehealth in the Valley to Achieve Transformation and Equity) project was to codesign a platform for remote patient monitoring and program for chronic illness management that would address these disparities and offer a solution that fit the needs and context of the community.

METHODS

ACTIVATE was a digital health intervention implemented in three phases: community codesign, feasibility assessment, and a pilot phase. Pre- and postintervention outcomes included regularly collected hemoglobin A1c (A1c) for participants with diabetes and blood pressure for those with hypertension.

RESULTS

Participants were adult patients with uncontrolled diabetes and/or hypertension (n = 50). Most were White and Hispanic or Latino (84%) with Spanish as a primary language (69%), and the mean age was 55. There was substantial adoption and use of the technology: over 10,000 glucose and blood pressure measures were transmitted using connected remote monitoring devices over a 6-month period. Participants with diabetes achieved a mean reduction in A1c of 3.28 percentage points (standard deviation [SD]: 2.81) at 3 months and 4.19 percentage points (SD: 2.69) at 6 months. The vast majority of patients achieved an A1c in the target range for control (7.0-8.0%). Participants with hypertension achieved reductions in systolic blood pressure of 14.81 mm Hg (SD: 21.40) at 3 months and 13.55 mm Hg (SD: 23.31) at 6 months, with smaller reductions in diastolic blood pressure. The majority of participants also reached target blood pressure (less than 130/80).

CONCLUSION

The ACTIVATE pilot demonstrated that a codesigned solution for remote patient monitoring and chronic illness management delivered by community health centers can overcome digital divide barriers and show positive health outcomes for rural and agricultural residents.

Innovative Remote Management Solutions for the Control of Hypertension

We stand at a critical juncture in the delivery of health care for hypertension. Blood pressure control rates have stagnated, and traditional health care is failing. Fortunately, hypertension is exceptionally well-suited to remote management, and innovative digital solutions are proliferating. Early strategies arose with the spread of digital medicine, long before the COVID-19 pandemic forced lasting changes to the way medicine is practiced. Highlighting one contemporary example, this review explores salient features of remote management hypertensive programs, including: an automated algorithm to guide clinical decisions, home (as opposed to office) blood pressure measurements, an interdisciplinary care team, and robust information technology and analytics. Dozens of emerging hypertension management solutions are contributing to a highly fragmented and competitive landscape. Beyond viability, profit and scalability are critical. We explore the challenges impeding large-scale acceptance of these programs and conclude with a hopeful look to the future when remote hypertension care will have dramatic impact on global cardiovascular health.

Results of a Remotely Delivered Hypertension and Lipid Program in More Than 10 000 Patients Across a Diverse Health Care Network

Importance

Blood pressure (BP) and cholesterol control remain challenging. Remote care can deliver more effective care outside of traditional clinician-patient settings but scaling and ensuring access to care among diverse populations remains elusive.

Objective

To implement and evaluate a remote hypertension and cholesterol management program across a diverse health care network.

Design, Setting, and Participants

Between January 2018 and July 2021, 20 454 patients in a large integrated health network were screened; 18 444 were approached, and 10 803 were enrolled in a comprehensive remote hypertension and cholesterol program (3658 patients with hypertension, 8103 patients with cholesterol, and 958 patients with both). A total of 1266 patients requested education only without medication titration. Enrolled patients received education, home BP device integration, and medication titration. Nonlicensed navigators and pharmacists, supported by cardiovascular clinicians, coordinated care using standardized algorithms, task management and automation software, and omnichannel communication. BP and laboratory test results were actively monitored.

Main Outcomes and Measures

Changes in BP and low-density lipoprotein cholesterol (LDL-C).

Results

The mean (SD) age among 10 803 patients was 65 (11.4) years; 6009 participants (56%) were female; 1321 (12%) identified as Black, 1190 (11%) as Hispanic, 7758 (72%) as White, and 1727 (16%) as another or multiple races (including American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, unknown, other, and declined to respond; consolidated owing to small numbers); and 142 (11%) reported a preferred language other than English. A total of 424 482 BP readings and 139 263 laboratory reports were collected. In the hypertension program, the mean (SD) office BP prior to enrollment was 150/83 (18/10) mm Hg, and the mean (SD) home BP was 145/83 (20/12) mm Hg. For those engaged in remote medication management, the mean (SD) clinic BP 6 and 12 months after enrollment decreased by 8.7/3.8 (21.4/12.4) and 9.7/5.2 (22.2/12.6) mm Hg, respectively. In the education-only cohort, BP changed by a mean (SD) -1.5/-0.7 (23.0/11.1) and by +0.2/-1.9 (30.3/11.2) mm Hg, respectively (P < .001 for between cohort difference). In the lipids program, patients in remote medication management experienced a reduction in LDL-C by a mean (SD) 35.4 (43.1) and 37.5 (43.9) mg/dL at 6 and 12 months, respectively, while the education-only cohort experienced a mean (SD) reduction in LDL-C of 9.3 (34.3) and 10.2 (35.5) mg/dL at 6 and 12 months, respectively (P < .001). Similar rates of enrollment and reductions in BP and lipids were observed across different racial, ethnic, and primary language groups.

Conclusions and Relevance

The results of this study indicate that a standardized remote BP and cholesterol management program may help optimize guideline-directed therapy at scale, reduce cardiovascular risk, and minimize the need for in-person visits among diverse populations.

A remote hypertension management program clinical algorithm

INTRODUCTION

Hypertension is the leading risk factor for death, affecting over one billion people worldwide, yet control rates are poor and stagnant. We developed a remote hypertension management program that leverages digitally transmitted home blood pressure (BP) measurements, algorithmic care pathways, and patient-navigator communications to aid patients in achieving guideline-directed BP goals.

METHODS

Patients with uncontrolled hypertension are identified through provider referrals and electronic health record screening aided by population health managers within the Mass General Brigham (MGB) health system. Non-licensed patient navigators supervised by pharmacists, nurse practitioners, and physicians engage and educate patients. Patients receive cellular or Bluetooth-enabled BP devices with which they monitor and transmit scheduled home BP readings. Evidence-based medication changes are made according to a custom hypertension algorithm approved within a collaborative drug therapy management (CDTM) agreement with MGB and implemented by pharmacists. Using patient-specific characteristics, we developed different pathways to optimize medication regimens. The renin-angiotensin-aldosterone system-blocker pathway prescribed ARBs/ACE inhibitors first for patients with diabetes, impaired renal function, and microalbuminuria; the standard pathway started patients on calcium channel blockers. Regimens were escalated frequently, adding thiazide-type diuretics, and including beta blockers and mineralocorticoid receptor antagonists if needed.

DISCUSSION

We have developed an algorithmic approach for the remote management of hypertension with demonstrated success. A focus on algorithmic decision-making streamlines tasks and responsibilities, easing the potential for scalability of this model. As the backbone of our remote management program, this clinical algorithm can improve BP control and innovate the management of hypertension in large populations.

I2b2-etl: Python application for importing electronic health data into the informatics for integrating biology and the bedside platform

MOTIVATION

The i2b2 platform is used at major academic health institutions and research consortia for querying for electronic health data. However, a major obstacle for wider utilization of the platform is the complexity of data loading that entails a steep curve of learning the platform's complex data schemas. To address this problem, we have developed the i2b2-etl package that simplifies the data loading process, which will facilitate wider deployment and utilization of the platform.

RESULTS

We have implemented i2b2-etl as a Python application that imports ontology and patient data using simplified input file schemas and provides inbuilt record number de-identification and data validation. We describe a real-world deployment of i2b2-etl for a population-management initiative at MassGeneral Brigham.

AVAILABILITY AND IMPLEMENTATION

i2b2-etl is a free, open-source application implemented in Python available under the Mozilla 2 license. The application can be downloaded as compiled docker images. A live demo is available at https://i2b2clinical.org/demo-i2b2etl/ (username: demo, password: Etl@2021).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Provider Perspectives on Patient- and Provider-Facing High Blood Pressure Clinical Decision Support

BACKGROUND

Hypertension, persistent high blood pressures (HBP) leading to chronic physiologic changes, is a common condition that is a major predictor of heart attacks, strokes, and other conditions. Despite strong evidence, care teams and patients are inconsistently adherent to HBP guideline recommendations. Patient-facing clinical decision support (CDS) could help improve recommendation adherence but must also be acceptable to clinicians and patients.

OBJECTIVE

This study aimed to partly address the challenge of developing a patient-facing CDS application, we sought to understand provider variations and rationales related to HBP guideline recommendations and perceptions regarding patient role and use of digital tools.

METHODS

We engaged hypertension experts and primary care respondents to iteratively develop and implement a pilot survey and a final survey which presented five clinical cases that queried clinicians' attitudes related to actions; variations; prioritization; patient input; importance; and barriers for HBP diagnosis, monitoring, and treatment. Analysis of Likert's scale scores was descriptive with content analysis for free-text answers.

RESULTS

Fifteen hypertension experts and 14 providers took the pilot and final version of the surveys, respectively. The majority (>80%) of providers felt the recommendations were important, yet found them difficult to follow-up to 90% of the time. Perceptions of relative amounts of patient input and patient work for effective HBP management ranged from 22 to 100%. Stated reasons for variation included adverse effects of treatment, patient comorbidities, shared decision-making, and health care cost and access issues. Providers were generally positive toward patient use of electronic CDS applications but worried about access to health care, nuance of recommendations, and patient understanding of the tools.

CONCLUSION

At baseline, provider management of HBP is heterogeneous. Providers were accepting of patient-facing CDS but reported preferences for that CDS to capture the complexity and nuance of guideline recommendations.

Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis

Analysis of health data typically requires development of queries using structured query language (SQL) by a data-analyst. As the SQL queries are manually created, they are prone to errors. In addition, accurate implementation of the queries depends on effective communication with clinical experts, that further makes the analysis error prone. As a potential resolution, we explore an alternative approach wherein a graphical interface that automatically generates the SQL queries is used to perform the analysis. The latter allows clinical experts to directly perform complex queries on the data, despite their unfamiliarity with SQL syntax. The interface provides an intuitive understanding of the query logic which makes the analysis transparent and comprehensible to the clinical study-staff, thereby enhancing the transparency and validity of the analysis. This study demonstrates the feasibility of using a user-friendly interface that automatically generate SQL for analysis of health data. It outlines challenges that will be useful for designing user-friendly tools to improve transparency and reproducibility of data analysis.