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.

This is a mixed-methods study combining a prospective controlled component and a retrospective chart review. Prospective Component * Setting: Emergency Department, Rambam Health Care Campus * The LLM will receive structured patient input (chief complaint, vitals, relevant history, laboratory and imaging results) via a secure interface. * LLM-generated recommendations will be logged and made available to the treating physician; final clinical decisions remain entirely with the physician. * The system operates in decision-support mode only it does not autonomously initiate any clinical action. Retrospective Component • De-identified historical ED records will be used to evaluate LLM performance against documented clinical outcomes. Primary metrics: diagnostic concordance, appropriateness of suggested workup, and disposition accuracy.

Inclusion Criteria: Adults ≥ 18 presented to the ER Exclusion Criteria: None