MedTrace Logo

Artificial Intelligence as a Decision Making Tool in Emergency Department

NCT06902675 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 100000

Last updated 2026-07-31

No results posted yet for this 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.

Conditions

  • Clinical Decision-making
  • Medical Reporting
  • Emergency Department Visit
  • Information Systems
  • Electronic Health Records
  • Artificial Intelligence in Medicine

Sponsors & Collaborators

  • Technion, Israel Institute of Technology

    collaborator OTHER

  • Rambam Health Care Campus

    lead OTHER

Principal Investigators

  • Shahar Shelly, MD · Rambam Health Care Campus

Eligibility

Min Age
0 Years
Max Age
120 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2000-01-01
Primary Completion
2026-09-01
Completion
2026-09-01

Countries

  • Israel

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06902675 on ClinicalTrials.gov