NCT06902675

Brief 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.

Trial Health

75
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
20,000

participants targeted

Target at P75+ for all trials

Timeline
4mo left

Started Jan 2000

Longer than P75 for all trials

Geographic Reach
1 country

1 active site

Status
active not recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress99%
Jan 2000Sep 2026

Study Start

First participant enrolled

January 1, 2000

Completed
24.9 years until next milestone

First Submitted

Initial submission to the registry

November 13, 2024

Completed
5 months until next milestone

First Posted

Study publicly available on registry

March 30, 2025

Completed
1.4 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

September 1, 2026

Expected
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

September 1, 2026

Last Updated

April 21, 2026

Status Verified

April 1, 2026

Enrollment Period

26.7 years

First QC Date

November 13, 2024

Last Update Submit

April 16, 2026

Conditions

Clinical Decision-makingMedical ReportingEmergency Department VisitInformation SystemsElectronic Health RecordsArtificial Intelligence in Medicine

Keywords

Clinical decision-makingAIDocumentationReportingInformation SystemEmergency Department

Outcome Measures

Primary Outcomes (1)

  • Length of Stay in Emergency Department

    Time from ED registration to discharge from emergency department or admission to a hospital ward, focusing in addition on consultation cycle time.

    From ED registration until discharge from the emergency department or admission to a hospital ward, assessed up to 24 hours

Other Outcomes (1)

  • The study is organized around four pre-specified aims:

    3 years

Study Arms (2)

Evaluation With AI

A scenario in which the physician receives real-time recommendations only from the model before making the final decision (the final decision will be called on the basis of senior attending, and the treating physician)

Evaluation Without AI

A scenario in which the physician is not exposed to the model's recommendations.

Interventions

Artificial Intelligence
Retrospective Data Extraction of 10,000 Neurology ER Cases

Eligibility Criteria

Age18 Years - 120 Years
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

All adult patients (≥18 years) receiving care in emergency departments wings A and B

You may qualify if:

  • Adults ≥ 18 presented to the ER

You may not qualify if:

  • None

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Rambam healthcare campus

Haifa, 3109601, Israel

Location

Related Publications (3)

  • Gorenshtein A, Perek S, Vaisbuch Y, Shelly S. AI-generated neurology consultation summaries improve efficiency and reduce documentation burden in the emergency department. Sci Rep. 2025 Nov 6;15(1):38868. doi: 10.1038/s41598-025-22769-7.

    PMID: 41198773RESULT

  • Gorenshtein A, Fistel S, Sorka M, Telman G, Winer R, Peretz S, Aran D, Shelly S. AI Based Clinical Decision-Making Tool for Neurologists in the Emergency Department. J Clin Med. 2025 Sep 8;14(17):6333. doi: 10.3390/jcm14176333.

    PMID: 40944092RESULT

  • Gorenshtein A, Weisblat Y, Khateb M, Kenan G, Tsirkin I, Fayn G, Geller S, Shelly S. AI-Based EMG Reporting: A Randomized Controlled Trial. J Neurol. 2025 Aug 22;272(9):586. doi: 10.1007/s00415-025-13261-3.

    PMID: 40844612DERIVED

MeSH Terms

Conditions

Emergencies

Condition Hierarchy (Ancestors)

Disease AttributesPathologic ProcessesPathological Conditions, Signs and Symptoms

Study Officials

  • Shahar Shelly, MD

    Rambam Health Care Campus

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
OTHER
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Chair of Neurology Department

Study Record Dates

First Submitted

November 13, 2024

First Posted

March 30, 2025

Study Start

January 1, 2000

Primary Completion (Estimated)

September 1, 2026

Study Completion (Estimated)

September 1, 2026

Last Updated

April 21, 2026

Record last verified: 2026-04

Data Sharing

IPD Sharing
Will not share

Locations

IL(1)