Clinical trial Recruiting at 1 site Interventional Ages 18 to 85

Tailored AI Guidelines for Heart Failure Care

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

Run by Brigham and Women's Hospital · NCT07337577 · Data updated 2026-11-12

A large language model–based decision-support tool tests whether it helps cardiology clinicians improve guideline-directed heart failure therapy for adults in outpatient clinics.

Quick facts

Study typeInterventional
Ages18 Years to 85 Years
SexAll
Enrollment500 (estimated)
SponsorBrigham and Women's Hospital
Academic / other
Locations1 site (Boston, Massachusetts)
StatusRecruiting
StartedAugust 27, 2026
Main results expectedSeptember 1, 2027
Study endsDecember 1, 2027
Registry record updatedAugust 31, 2026
Trial IDNCT07337577 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Adults (18+) with heart failure who have a scheduled outpatient visit with a participating cardiology provider in MGB and a recent history of heart failure management.

Probably not a fit

Patients with very unstable hemodynamics or severe comorbidities may not experience GDMT changes or benefit.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This cluster-randomized interventional trial tests an AI-driven clinical decision support tool that reviews a patient’s EHR data and provides brief, clinician-facing messages about GDMT opportunities and safety considerations before visits. The tool is advisory and does not place orders, leaving all treatment decisions to the clinician and patient. The study is conducted in Mass General Brigham outpatient cardiology clinics with providers randomized to use the CDS and aims to see if GDMT optimization improves in routine practice. Outcomes focus on uptake of GDMT adjustments and safety flagging. The approach builds on prior retrospective concordance between AI recommendations and expert judgment and seeks to reduce practice gaps in GDMT use.

Why it matters

Potential benefit: If successful, clinicians may identify and optimize GDMT opportunities more consistently, potentially reducing hospitalizations and mortality.

How similar studies have gone: Previous work with AI-assisted CDS in heart failure has shown concordance with clinician judgment, suggesting potential for meaningful aid in practice.

Who can join: the full rules

Show full inclusion / exclusion criteria
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

Where it's running

  • Mass General Brigham
    Boston, Massachusetts, United States
    Recruiting

Study contacts

Similar studies

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How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.