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Large Language Model-Generated Messages for Heart Failure Treatment

This study is testing a new software tool called the LLM-GDMT Clinical Decision Support Tool. This tool uses a large language model (like advanced AI) to review your electronic health records if you have heart failure. It then creates short messages for your doctor, suggesting ways to improve your guideline-directed medical therapy (GDMT), which are the recommended treatments for heart failure. The goal is to see if these messages help doctors start or adjust your heart failure medications more effectively within 30 days. We are looking for about 500 adults, aged 18 to 85, who have heart failure and are seeing a cardiologist at Mass General Brigham.

Study design
This is an interventional study involving about 500 participants. It is a cluster-randomized trial, meaning groups of doctors will either use the tool or not.
What's involved
Not specified in the trial record.
Compensation
Not stated in the trial record.
Follow-up
The main outcome is measured within 30 days of a clinic visit.

AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.

NCT07337577

Targeted Artificial Intelligence Language mOdel Recommendations for Heart Failure (TAILOR-HF)

Recruiting
NAAges 18–85InterventionalHealth services
Brigham and Women's Hospital
~500 participants
Updated 2026-08-31 on ClinicalTrials.gov
What's tested:LLM-GDMT Clinical Decision Support Tool

At a glance

Recruiting sites
1 of 1 listed site is recruiting right now
RecruitingSuspended, closed, or not yet open
What they're measuring
Any GDMT optimization within 30 days of index visit
Measured over 30 days

Conditions

Heart Failure

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Heart Failure

NCT07337577

Where you'd take part

This study runs at 1 site. They're the same protocol — you choose where, and that choice sets who your contact draft is addressed to.

  • Mass General Brigham

    Boston, Massachusettsstudy coordinator listed

    Recruiting

Sites open and close at different times, so the status above is per site — it can differ from the study's overall status.

Who to contact

Jonathan W Cunningham, MD, MPH
[email protected]
617-732-8534
Email the study team

Opens a ready-to-send draft in your own email app — review before sending.

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Eligibility criteria

Inclusion

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

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

What this trial measures

  • Any GDMT optimization within 30 days of index visit30 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.