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

Last updated: September 10, 2024
Sponsor: Brigham and Women's Hospital
Overall Status: Active - Recruiting

Phase

N/A

Condition

Heart Failure

Chest Pain

Congestive Heart Failure

Treatment

Manual clinical trial screening by study staff

RECTIFIER - a generative artificial intelligence screening tool

Clinical Study ID

NCT06588452
2022P002809-LLM
  • Ages 18-90
  • All Genders
  • Accepts Healthy Volunteers

Study Summary

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.

Eligibility Criteria

Inclusion

Inclusion Criteria:

  • Documented diagnosis of heart failure (e.g., ICD-9 codes 428 ICD-10 codes I50 orProblem list in the electronic health record)

  • Most recent left ventricular ejection fraction (LVEF) assessed within the past 24months

  • Seen Mass General Brigham provider within the last 24 months

Exclusion

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

Total Participants: 4500
Treatment Group(s): 2
Primary Treatment: Manual clinical trial screening by study staff
Phase:
Study Start date:
March 04, 2024
Estimated Completion Date:
September 13, 2024

Study 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.

Connect with a study center

  • Brigham and Women's Hospital

    Boston, Massachusetts 02115
    United States

    Active - Recruiting

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