Large Language Model-Generated Messages to Improve Guideline-Directed Medical Therapy in Heart Failure (LLM-GDMT)

April 19, 2026 updated by: Jonathan Cunningham MD MPH, Brigham and Women's Hospital

This study is an investigator-initiated, cluster-randomized implementation trial evaluating a large language model (LLM)-based clinical decision support (CDS) tool designed to improve guideline-directed medical therapy (GDMT) for adult patients with heart failure seen in outpatient cardiology clinics at Mass General Brigham.

For eligible heart failure encounters, the CDS tool reviews existing electronic health record (EHR) data, including diagnoses, medications, vital signs, laboratory results, and recent notes, and generates brief, clinician-facing messages suggesting opportunities to initiate or optimize GDMT and highlighting relevant safety considerations. Messages are delivered to cardiology providers via Epic InBasket and/or institutional email prior to scheduled visits. The tool is advisory only and cannot place orders or change medications automatically; all treatment decisions remain at the discretion of the treating clinician and patient.

Cardiology providers are assigned at the provider/clinic level to early implementation of the CDS tool versus usual care (no messages) during the initial phase. The primary outcome is GDMT optimization within 30 days of an index visit. Secondary outcomes include feasibility of CDS generation and delivery and a 30-day safety composite (e.g., heart failure hospitalization, acute kidney injury, hyperkalemia, hypotension or bradyarrhythmia plausibly related to GDMT).

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Overview and Rationale Guideline-directed medical therapy (GDMT) for heart failure reduces hospitalizations and mortality, yet substantial underuse and suboptimal titration persist in routine practice, even in specialty cardiology clinics. Barriers include limited visit time, complex comorbidities, fragmented information across notes and structured data, and uncertainty about contraindications or prior intolerance. Electronic clinical decision support (CDS) tools that synthesize key patient information and highlight GDMT opportunities at the point of care may help close these gaps.

Large language models (LLMs) can read both structured EHR data (e.g., diagnoses, medications, vital signs, laboratory results) and unstructured narrative notes to generate nuanced, patient-specific recommendations. We developed an LLM-based CDS tool that reviews an adult heart failure patient's EHR and produces a brief, free-text message to the treating cardiology provider summarizing heart failure status, suggesting potential GDMT changes consistent with contemporary guidelines, and flagging relevant safety issues (e.g., low blood pressure, bradycardia, renal dysfunction, hyperkalemia, prior documented intolerance). In retrospective testing, the tool's recommendations were generally concordant with expert clinician judgment.

Study Design

This is an interventional, cluster-randomized, provider-level trial conducted in adult outpatient cardiology clinics at Mass General Brigham. The intervention is a software-only, investigational clinical decision support device ("LLM-GDMT Clinical Decision Support Tool"). Eligible cardiology attendings and advanced practice providers are assigned at the provider/clinic level to one of two parallel arms during the initial phase:

Early Implementation - LLM-GDMT CDS: Providers in this arm receive LLM-generated, clinician-facing messages for eligible heart failure encounters. For scheduled visits that meet predefined inclusion criteria, the tool reviews existing EHR data and generates a brief advisory message that is delivered via Epic InBasket and/or institutional email within the week prior to the visit.

Usual Care (Delayed Implementation): Providers in this arm continue usual care and do not receive LLM-generated messages during the initial evaluation phase. EHR data are used to compute quality metrics for comparison. After the initial evaluation, the CDS tool may be expanded to these providers as part of routine care.

Patients are not contacted for the study. All clinical decisions, including whether to start, stop, or adjust any medication, remain entirely at the discretion of the treating clinician in partnership with the patient. The CDS messages are advisory only and cannot place orders or directly change medications or monitoring plans.

Population and Eligibility The study includes adult patients (age ≥18 years) with a documented heart failure diagnosis who are scheduled for outpatient visits with participating cardiology providers at Mass General Brigham clinics. Additional inclusion criteria require evidence supporting active or prior heart failure (e.g., diagnosis codes, loop diuretic use, echocardiographic findings, or documentation of heart failure signs or symptoms) and at least one prior cardiology visit. Exclusion criteria include hemodynamic instability (e.g., very low blood pressure or heart rate), advanced renal dysfunction below a specified estimated glomerular filtration rate threshold, selected advanced structural heart disease (e.g., cardiac amyloidosis, hypertrophic cardiomyopathy, heart transplant or left ventricular assist device recipients, severe valvular disease), and encounters in adult congenital heart disease clinics.

Intervention and Workflow

For eligible encounters in the early-implementation arm, the CDS tool operates within Mass General Brigham's secure technical environment (Epic, enterprise data warehouse, and Azure OpenAI within the MGB tenant). The tool retrieves relevant structured and unstructured EHR data, uses a large language model to synthesize this information, and generates a brief, human-readable message. The message typically includes:

  • Confirmation of current heart failure status and key comorbidities.
  • A summary of current GDMT and potential opportunities for initiation or titration of evidence-based therapies, taking into account recent vital signs, laboratory values, and documented contraindications or prior intolerance.
  • Safety considerations (e.g., blood pressure, heart rate, renal function, potassium) relevant to GDMT changes.

Messages are delivered to the treating provider via Epic InBasket and/or institutional email in advance of the visit. The tool does not write orders, modify medication lists, or send any direct communication to patients. Providers may choose to use, modify, or ignore the suggestions based on their clinical judgment and patient preferences.

Outcomes and Analysis The primary outcome is GDMT optimization within 30 days of the index visit, defined as initiation of at least one new GDMT class not previously prescribed and/or uptitration of at least one existing GDMT medication in eligible patients. Secondary outcomes include: (1) feasibility and fidelity of CDS implementation (e.g., proportion of eligible encounters for which messages are successfully generated and delivered), (2) a 30-day safety composite that includes heart failure hospitalizations, emergency department visits related to decompensated heart failure, acute kidney injury, hyperkalemia above predefined thresholds, and clinically significant hypotension or bradyarrhythmia plausibly related to GDMT, and (3) provider-reported acceptability and perceived usefulness, measured via an optional anonymous survey.

The study anticipates including up to 2,500 unique adult heart failure patients across participating clinics during the implementation period. Analyses will account for clustering at the provider level and will compare GDMT optimization and safety outcomes between early-implementation and usual-care arms during the initial evaluation phase. The overall goal is to determine whether an advisory, LLM-based CDS tool can be implemented safely and feasibly in routine outpatient cardiology practice and whether it improves uptake of guideline-directed heart failure therapies.

Study Type

Interventional

Enrollment (Estimated)

500

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Age ≥18 years
  • Scheduled outpatient visit with a participating cardiology provider in an MGB outpatient cardiology clinic
  • At least one prior cardiology clinic visit in the MGB system within the past 2 years
  • Diagnosis of heart failure by ICD code within the past 2 years
  • Heart failure diagnosis supported by at least one of the following:
  • Current or recent use of a loop diuretic
  • Left ventricular ejection fraction ≤40% on the most recent echocardiogram
  • Explicit documentation of heart failure diagnosis or heart failure signs/symptoms in a prior cardiology note

Exclusion Criteria:

  • Systolic blood pressure <90 mmHg on the most recent recorded measurement
  • Heart rate <50 beats per minute on the most recent recorded measurement
  • eGFR <20 mL/min/1.73 m² on the most recent laboratory assessment
  • Known cardiac amyloidosis or hypertrophic cardiomyopathy
  • History of heart transplant or presence of a left ventricular assist device
  • Severe aortic stenosis, severe aortic insufficiency, or severe mitral stenosis on the most recent echocardiogram
  • Encounter occurs in an adult congenital heart disease clinic

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Health Services Research
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Early Implementation
Providers in this arm receive a large language model-based clinical decision support (LLM-GDMT CDS) intervention. For eligible outpatient heart failure encounters, the CDS tool reviews existing EHR data (diagnoses, medications, vitals, labs, recent notes) and generates a brief, clinician-facing message summarizing HF status, suggesting opportunities to initiate or optimize guideline-directed medical therapy (GDMT), and highlighting safety considerations. Messages are delivered via Epic InBasket and/or institutional email in advance of the visit. The tool is advisory only and cannot place orders or directly change medications; all treatment decisions remain at the discretion of the treating clinician and patient.
Device: LLM-GDMT Clinical Decision Support Tool
Software-only, large language model-based clinical decision support tool that reviews structured and unstructured EHR data for adult heart failure patients and generates brief, clinician-facing messages suggesting opportunities to initiate or optimize guideline-directed medical therapy (GDMT) and highlighting relevant safety considerations. Messages are delivered to cardiology providers via Epic InBasket and/or institutional email prior to eligible outpatient visits. The tool is advisory only and cannot place orders or directly change medications; all treatment decisions remain at the discretion of the treating clinician and patient.
No Intervention: Usual Care (Delayed Implementation)
Providers in this arm continue usual care and do not receive LLM-GDMT CDS messages during the initial evaluation phase. Eligible outpatient heart failure encounters are managed according to routine clinical practice without additional CDS messages. EHR data from these encounters are used to compute GDMT utilization and safety outcomes for comparison with the early-implementation arm. After the initial evaluation phase is complete, the LLM-GDMT CDS tool may be expanded to providers in this arm as part of routine care.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Any GDMT optimization within 30 days of index visit
Time Frame: 30 days
Among eligible HF encounters, the proportion with initiation of at least one new GDMT class not previously prescribed and/or uptitration of at least one existing GDMT medication during or within 30 days after the index visit, comparing early-implementation vs usual care arms.
30 days

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Short-Term Safety Composite (30 days)
Time Frame: 30 days

Proportion of encounters with any of the following within 30 days of the visit:

  • ED visit or hospitalization for hypotension/syncope.
  • ED visit or hospitalization with hyperkalemia above a defined threshold.
  • ED visit or hospitalization with acute kidney injury (e.g., ≥30% increase in creatinine).
  • Symptomatic bradycardia or other arrhythmias plausibly related to GDMT changes.
30 days
Operational Feasibility
Time Frame: 30 days
Proportion of eligible encounters in the early-implementation arm for which messages are successfully generated and delivered (InBasket and/or email).
30 days

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Brigham and Women's Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

June 1, 2026

Primary Completion (Estimated)

July 1, 2027

Study Completion (Estimated)

December 1, 2027

Study Registration Dates

First Submitted

January 12, 2026

First Submitted That Met QC Criteria

January 12, 2026

First Posted (Actual)

January 13, 2026

Study Record Updates

Last Update Posted (Actual)

April 21, 2026

Last Update Submitted That Met QC Criteria

April 19, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2025P003214

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

Yes

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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