Brigham and Women's Hospital
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.
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 Generatio…
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
RECTIFIER is a large-language model based, generative artificial intelligence-enabled inclusion and exclusion criteria assessment tool.
The current gold standard - study staff manually review potentially eligible patients through chart review.
Arctuva estimate
Verify or correct the compensation shown for this listing.