Physician Response Evaluation With Contextual Insights vs. Standard Engines - Artificial Intelligence RAG vs LLM Clinical Decision Support

Not Applicable

Recruiting

• Conditions: Large Language Models

• Registration Number: NCT07037940
• Lead Sponsor: Montefiore Medical Center

Brief Summary

Clinical decision support tools powered by artificial intelligence are being rapidly integrated into medical practice. Two leading systems currently available to clinicians are OpenEvidence, which uses retrieval-augmented generation to access medical literature, and GPT-4, a large language model. While both tools show promise, their relative effectiveness in supporting clinical decision-making has not been directly compared. This study aims to evaluate how these tools influence diagnostic reasoning and management decisions among internal medicine physicians.

Detailed Description

Internal medicine attendings and residents are invited to participate in a study investigating how physicians using a RAG-based LLM (OpenEvidence) perform compared to those using a standard general-purpose LLM (ChatGPT) on both diagnostic reasoning and complex management decisions. As AI tools increasingly enter clinical practice, evidence is needed about which approaches best support physician decision-making. This study will help determine if specialized medical knowledge retrieval systems (OpenEvidence) provide advantages over general AI assistants (ChatGPT) when solving real clinical cases.

Participants will complete one 90-minute Zoom session where clinical cases derived from real, de-identified patient encounters will be solved. Participants will be randomly assigned to use either OpenEvidence or ChatGPT and all responses evaluated by blinded scorers using a validated rubric.

Study Timeline

• Status Verified: 2025-06
• Study First Submitted: 2025-06-17
• Study First Submitted QC: 2025-06-17
• Last Update Submitted: 2025-06-25
• Study First Posted: 2025-06-26
• Last Update Posted: 2025-06-29
• Study Start: 2025-07-01
• Primary Completion: 2025-09
• Study Completion: 2025-09-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 56

Inclusion Criteria

• Internal medicine residents
• Internal medicine attending physicians

Exclusion Criteria

• Not meeting Inclusion Criteria

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Clinical Reasoning Performance as determined by Rater Scores	15-minutes upon completion of cases, up to approximately 90 minutes total	Clinical reasoning performance will be evaluated based upon the accuracy of the rater scores to responses to the surveys administered. Six blinded, trained independent raters will independently score each participant's response using a validated scoring rubric. Possible response scores can range from 0-100% with higher scores indicating increased clinical reasoning performance. Results for each assessment will be summarized by study arm using basic descriptive statistics and analyzed using mixed-effects models to account for within-subject correlation and between-subject factors.

Secondary Outcome Measures

Name	Time	Method
Time efficiency	Up to approximately 75 minutes	Time efficiency will be assessed based on the amount of time it takes for participants to complete the surveys. Each survey will automatically be time stamped to record the amount of time needed for each participant to answer each case. Results for the virtual session will be summarized by study arm using basic descriptive statistics and analyzed.
Decision confidence	15-minutes upon completion of cases, up to approximately 90 minutes total	Decision confidence will be determined by asking participants to assess the level of confidence in survey answers using a scale ranging from 1-5 (1 being least confident, 5 being most confident) such that higher scores are associated with increased confidence in responses. Scores will be summarized by study arm using basic descriptive statistics.

Trial Locations

Locations (1)

MontefioreMC; 🇺🇸 Bronx, New York, United States