Artificial Intelligence as a Decision Making Tool in Emergency Department

This study will evaluate the performance of a large language model (LLM)-based clinical decision support system in the emergency department at Rambam Health Care Campus. The system analyzes structured patient data from the electronic health record and generates diagnostic and treatment recommendations for physicians. The study will assess the system's ability to support diagnostic reasoning, its impact on diagnostic accuracy when used by physicians, and its perceived clinical usefulness. In addition, a retrospective analysis of de-identified patient records will be conducted to compare LLM-generated recommendations with actual clinical outcomes, including diagnosis, disposition decisions, and length of stay. The study will also examine the performance of the system in a multilingual clinical environment where both Hebrew and English are used in medical documentation and communication.

NCT Number	NCT06902675
Lead Sponsor	Rambam Health Care Campus Collaborators: Technion, Israel Institute of Technology
Conditions	Clinical Decision-making, Medical Reporting, Emergency Department Visit, Information Systems, Electronic Health Records, Artificial Intelligence in Medicine
Enrollment	100,000 participants
Start Date	2000-01-01
Primary Completion	2026-09-01 (estimated)
Study Completion	2026-09-01 (estimated)
Updated on ClinicalTrials.gov	2026-07-31