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Artificial Intelligence as a Decision Making Tool in Emergency Department

StatusActive, Not Recruiting
PhaseNot specified
Started
View on ClinicalTrials.gov ↗
Record status
This record was last updated July 31, 2026 (before its estimated September 1, 2026 completion). Its status may not reflect the trial's current state.

Record History

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  1. Eligibility criteria expanded, about 4 wordsv6
  2. Enrollment increased: 20000 → 100000 participantsv5
  3. Re-verified, no change to tracked fieldsv4
  4. Primary completion pushed from Sep 9, 2025 to Sep 1, 2026v3 Show 12 more changesShow less
    • Primary endpoint(s) modified
    • Enrollment increased: 1000 → 20000 participants
    • Study completion moved earlier from Sep 9, 2028 to Sep 1, 2026
    • Trial arms changed: 3 → 2
    • Start date: Jan 21, 2025 to Jan 1, 2000
    • Interventions: Artificial Intelligence (Other), Retrospective Data Extraction of 10,000 Neurology ER Cases (Other) to not stated
    • Secondary endpoints shortened, about 19 words
    • Study description shortened, about 44 words
    • Publications: 2 to 4
    • Eligibility criteria expanded, about 1 word
    • Collaborators expanded, about 5 words
    • Study title expanded, about 1 word
  5. Primary completion date changed from estimate of Aug 2025 to Sep 9, 2025
    • Study completion date: Sep 12, 2025 to Sep 9, 2028
    • Study title revised
    v2
  6. Study First Submitted
  • Stopped: an ongoing trial was terminated, suspended or withdrawn.
  • Notable: the trial’s plan changed such as a big move in its timeline or size, a new primary endpoint, sponsor or phase, or recruiting opened or resumed.
  • Minor: routine progress and housekeeping, such as sites added or enrollment closed.
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.
Trial Details
NCT Number NCT06902675
Lead Sponsor Rambam Health Care Campus
Collaborators: Technion, Israel Institute of Technology
Conditions
(from sponsor)		 Clinical Decision-making, Medical Reporting, Emergency Department Visit, Information Systems, Electronic Health Records, Artificial Intelligence in Medicine
Related terms Emergencies
Enrollment 100,000 participants
Start Date
Primary Completion (estimated)
Study Completion (estimated)
Updated on ClinicalTrials.gov