Artificial Intelligence as a Decision Making Tool in Emergency Department

Condition(s) studied

Study summary

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.

Eligibility

Primary outcome measure(s)

• Length of Stay in Emergency Department — From ED registration until discharge from the emergency department or admission to a hospital ward, assessed up to 24 hours
  Time from ED registration to discharge from emergency department or admission to a hospital ward, focusing in addition on consultation cycle time.

Trial sites (1)

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