Ignite Creation Date:
2024-10-26 @ 3:39 PM
Last Modification Date:
2024-10-26 @ 3:39 PM
Study NCT ID:
NCT06588452
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-09-03
Brief Title:
Manual Versus AI-Assisted Clinical Trial Screening Using Large-Language Models
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Manual Versus AI-Assisted Clinical Trial Screening Using Large-Language Models
Status:
RECRUITING
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
MAPS-LLM
Brief 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
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
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None