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Large Language Model-Generated Messages to Improve Guideline-Directed Medical Therapy in Heart Failure

Status: Recruiting
Location: See location...
Intervention Type: Device
Study Type: Interventional
Study Phase: Not Applicable
SUMMARY

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).

Eligibility
Participation Requirements
Sex: All
Minimum Age: 18
Maximum Age: 85
Healthy Volunteers: f
View:

• 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

Locations
United States
Massachusetts
Mass General Brigham
RECRUITING
Boston
Contact Information
Primary
Jonathan W Cunningham, MD, MPH
jcunningham3@bwh.harvard.edu
617-732-8534
Time Frame
Start Date: 2026-08-27
Estimated Completion Date: 2027-12-01
Participants
Target number of participants: 500
Treatments
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.
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.
Sponsors
Leads: Brigham and Women's Hospital

This content was sourced from clinicaltrials.gov