Purpose

A prospective randomized controlled trial comparing manual review and AI screening for patient eligibility determination and enrollments. A structured query will identify potentially eligible patients from the Mass General Brigham Electronic Data Warehouse (EDW), who will then be randomized into either the manual review arm or the AI-assisted review arm.

Condition

Eligibility

Eligible Ages
Between 18 Years and 90 Years
Eligible Genders
All
Accepts Healthy Volunteers
Yes

Inclusion Criteria

  • 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 Criteria

  • 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

Study Design

Phase
Study Type
Observational
Observational Model
Cohort
Time Perspective
Prospective

Arm Groups

ArmDescriptionAssigned Intervention
Manual Review Arm Study staff manually reviews patient eligibility.
  • Other: Manual clinical trial screening by study staff
    The current gold standard - study staff manually review potentially eligible patients through chart review.
Artificial Intelligence (AI) Review Arm AI screens for eligibility, followed by an abbreviated final review by study staff.
  • Other: RECTIFIER - a generative artificial intelligence screening tool
    RECTIFIER is a large-language model based, generative artificial intelligence-enabled inclusion and exclusion criteria assessment tool.

Recruiting Locations

Brigham and Women's Hospital
Boston, Massachusetts 02115
Contact:
Alexander J Blood, MD, MSc
617-732-7144
ablood@bwh.harvard.edu

More Details

Status
Recruiting
Sponsor
Brigham and Women's Hospital

Study Contact

Ozan Unlu, MD
617-732-7144
ounlu@bwh.harvard.edu

Detailed Description

Screening participants for clinical trials is a critical yet challenging process that requires significant time and resources. Traditionally, patient screening has been manual, relying on the diligence and judgment of study staff. However, manual screening is prone to human error and inefficiencies, contributing to high costs and prolonged trial durations. Recent advancements in natural language processing (NLP) and large language models (LLMs), such as GPT-4, offer potential solutions to improve the accuracy, efficiency, and reliability of the screening process. Retrieval-Augmented Generation (RAG)-enabled systems, like RECTIFIER, have shown promise in enhancing clinical trial screening by automating the extraction and analysis of relevant data from electronic health records (EHRs). In the investigators' previous study, RECTIFIER demonstrated high accuracy in screening patients for clinical trials, aligning closely with expert clinician reviews and outperforming manual study staff in several criteria. It underscored the potential for LLMs to transform clinical trial screening, making it more efficient and cost-effective while maintaining high standards of accuracy and reliability. However, before RECTIFIER is scaled to be used across many domains of clinical trials, it should be validated prospectively in the real-world setting to enroll patients. In the Co-Operative Program for Implementation of Optimal Therapy in Heart Failure (COPILOT-HF) trial (NCT05734690), the investigators will identify potential participants through EHR queries followed by manual review, which provides an opportunity for RECTIFIER to improve the screening process. By leveraging RECTIFIER, this study aims to evaluate the effectiveness of automated AI screening compared to traditional manual methods for enrollments of patients into an ongoing clinical trial.

Notice

Study information shown on this site is derived from ClinicalTrials.gov (a public registry operated by the National Institutes of Health). The listing of studies provided is not certain to be all studies for which you might be eligible. Furthermore, study eligibility requirements can be difficult to understand and may change over time, so it is wise to speak with your medical care provider and individual research study teams when making decisions related to participation.