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 type | Interventional |
|---|---|
| Ages | 18 Years to 85 Years |
| Sex | All |
| Enrollment | 500 (estimated) |
| Sponsor | Brigham and Women's Hospital Academic / other |
| Locations | 1 site (Boston, Massachusetts) |
| Status | Recruiting |
| Started | August 27, 2026 |
| Main results expected | September 1, 2027 |
| Study ends | December 1, 2027 |
| Registry record updated | August 31, 2026 |
| Trial ID | NCT07337577 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
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Mass General Brigham
Boston, Massachusetts, United States Recruiting
Study contacts
- Study coordinator: Jonathan W Cunningham, MD, MPH
- Email: jcunningham3@bwh.harvard.edu
- Phone: 617-732-8534
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.