Clinical Decision-makingNCT06902675
Artificial Intelligence as a Decision Making Tool in Emergency Department
An observational study of Clinical Decision-making and Medical Reporting, run by Rambam Health Care Campus at 1 site in Israel.
Trial 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. Read more about this study
- Who is it for?
- Ages 0 to 120 years
- Conditions the study is for
- Clinical Decision-making, Medical Reporting, Emergency Department Visit
- How many people
- 100,000 participants
- Where
- 1 siteIsrael
- Study dates
- 1 Jan 2000to 1 Sep 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 0 to 120 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: All patients (0-120 years) receiving care in emergency departments (main and pediatric)
Who may be able to join
- Patients aged 0 to 120 years presented to the emergency department
Who cannot join
- None
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Artificial Intelligence as a Decision Making Tool in Emergency Medicine
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.
Read the detailed description
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.
- Conditions
- Clinical Decision-making, Medical Reporting, Emergency Department Visit, Information Systems, Electronic Health Records, Artificial Intelligence in Medicine
- Keywords
- Clinical decision-making, AI, Documentation, Reporting, Information System, Emergency Department
- Medical subject headings
- Emergencies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: From ED registration until discharge from the emergency department or admission to a hospital ward, assessed up to 24 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Mar 2025
- Study start1 Jan 2000
- Primary completion1 Sep 2026 (estimated)
- Study completion1 Sep 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Clinical Decision-makingStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 100,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Rambam Health Care Campus
- Sponsor type
- University, hospital or other
- Collaborators
- Technion, Israel Institute of Technology
- Responsible party
- Principal investigator, Shahar Shelly MD, Rambam Health Care Campus
- Investigators
- Shahar Shelly, MD, Principal investigator, Rambam Health Care Campus
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06902675
- Sponsor's protocol ID
- 0026-24RMB
- Status last verified
- Jul 2026
- First submitted
- 13 Nov 2024
- Last updated
- 31 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 41198773Gorenshtein 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.
PubMed 40944092Leibovitch L, Ahituv A, Gorenshtein A, Aran D, Sorka M, Miron K, Shelly S. Prospective evaluation of a large language model clinical decision support system in the emergency department. Nat Med. 2026 Aug 19. doi: 10.1038/s41591-026-04601-5. Online ahead of print.
PubMed 42618632Gorenshtein 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.
PubMed 40844612
Study at a glance
From the registry record, last updated by the sponsor on 31 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence as a Decision Making Tool in Emergency Medicine
- ClinicalTrials.gov ID
- NCT06902675
- Acronym / sponsor ID
- 0026-24RMB
- Conditions
- Clinical Decision-making, Medical Reporting, Emergency Department Visit, Information Systems, Electronic Health Records, Artificial Intelligence in Medicine
- Keywords
- Clinical decision-making, AI, Documentation, Reporting, Information System, Emergency Department
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 100,000 participants (estimated)
- Lead sponsor
- Rambam Health Care CampusUniversity, hospital or other
- Collaborators
- Technion, Israel Institute of Technology
- Study start
- 1 Jan 2000 (actual)
- Primary completion
- 1 Sep 2026 (estimated)
- Study completion
- 1 Sep 2026 (estimated)
- First posted
- 30 Mar 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 0 to 120 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- All patients (0-120 years) receiving care in emergency departments (main and pediatric)
Key inclusion criteria (1 listed)
- Patients aged 0 to 120 years presented to the emergency department
Key exclusion criteria (1 listed)
- None
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Length of Stay in Emergency Department
Time frame: 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Jul 2026.
- Results publication: PubMed 41198773Gorenshtein 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.
- Results publication: PubMed 40944092Gorenshtein 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.
- Derived from this study: PubMed 42618632Leibovitch L, Ahituv A, Gorenshtein A, Aran D, Sorka M, Miron K, Shelly S. Prospective evaluation of a large language model clinical decision support system in the emergency department. Nat Med. 2026 Aug 19. doi: 10.1038/s41591-026-04601-5. Online ahead of print.
- Derived from this study: PubMed 40844612Gorenshtein 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Shahar Shelly, MD principal investigator
Rambam Health Care Campus
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Clinical Decision-making and Medical Reporting, run by Rambam Health Care Campus at 1 site in Israel.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 31 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Israel. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Length of Stay in Emergency Department (time frame: From ED registration until discharge from the emergency department or admission to a hospital ward, assessed up to 24 hours). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06902675, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Jul 2026.