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Comparing Manual and AI Patient Screening in Heart Failure

This study is looking at two ways to find patients for clinical trials for heart failure: the traditional method where staff manually review patient charts, and a new method using an AI tool called RECTIFIER. RECTIFIER is a type of artificial intelligence that uses large language models to help assess if a patient meets the study's requirements. The main goal is to see which method is better at determining if someone is eligible for a study. You might be able to join if you are 18 to 90 years old, have a documented diagnosis of heart failure, and have had your heart's pumping ability (left ventricular ejection fraction or LVEF) checked within the last two years. You also need to have seen a Mass General Brigham provider in the last two years. The study is currently unclear on its recruitment status.

Study design
This is an observational study planning to include 4500 participants. Participants will be identified and then assigned to either the manual review arm or the AI-assisted review arm.
What's involved
Not specified in the trial record.
Compensation
Not stated in the trial record.
Follow-up
The primary endpoint, determining study eligibility, will be measured through study completion, an average of 6 months.

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

NCT06588452

Manual Versus AI-Assisted Clinical Trial Screening Using Large-Language Models

Recruiting
Not specifiedAges 18–90Observational
Brigham and Women's Hospital
~4,500 participants
Updated 2024-09-19 on ClinicalTrials.gov
What's tested:RECTIFIER - a generative artificial intelligence screening toolManual clinical trial screening by study staff

At a glance

Recruiting sites
1 of 1 listed site is recruiting right now
RecruitingSuspended, closed, or not yet open
What they're measuring
Determine study eligibility, analyzed using a survival analysis framework, specifically the Fine-Gray subdistribution hazards model, to account for competing risks.
Measured over Through study completion, an average of 6 months

Conditions

Comparing Manual and AI Patient Screening in Heart Failure

Explore related trials

Heart Failure

NCT06588452

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.

  • Brigham and Women's Hospital

    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.

Study leadership

  • Benjamin M Scirica, MD, MSc · STUDY_CHAIR · Brigham and Women's Hospital

Who to contact

Ozan Unlu, MD
[email protected]
617-732-7144
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

Documented diagnosis of heart failure (e.g., ICD-9 codes 428 ICD-10 codes I50 or Problem list in the electronic health record)
Most recent left ventricular ejection fraction (LVEF) assessed within the past 24 months
Seen Mass General Brigham provider within the last 24 months

Exclusion

LVEF \<50% currently prescribed or intolerant to an evidence-based beta-blocker, ARNI, MRA, and SGLT2i at least 50% goal dose
LVEF\>50% currently prescribed or intolerant to SGLT2i
Systolic blood pressure (SBP) \<90 mmHg at last measure

What this trial measures

  • Determine study eligibility, analyzed using a survival analysis framework, specifically the Fine-Gray subdistribution hazards model, to account for competing risks.Through study completion, an average of 6 months

    Assess the likelihood of eligibility determination, comparing the AI-assisted screening group to the manual screening group accounting for the competing risk of ineligibility determination.