Clinical trial NCT06902675: Artificial Intelligence as a Decision Making Tool in Neurology
Official title: Artificial Intelligence as a Decision Making Tool in Neurology
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Clinical Decision-making
Medical Reporting
Neurological Diseases or Conditions
Emergency Department Visit
Lead sponsor: Rambam Health Care Campus
Sponsor class: OTHER
First posted: 2025-03-30
Enrollment: 1000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 120
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All adult patients (≥18 years) receiving care in Neurology Department A, including both patients hospitalized in the neurological ward (Department A) and outpatients from the neurology department's clinics.

Brief summary: Background: The establishment of neuroinformatics as a distinct field has enabled the integration of computational biology and informatics to improve neurological research. This interdisciplinary approach enhances the capacity to integrate diverse datasets, unravel complex neural networks, and develop computational models that can improve clinical management. The investigators aim to evaluate whether an artificial-intelligence-based tool is effective in non-English-speaking regions.

Hypothesis: Integrating a language model-based clinical assistance system within the neurology ward will significantly enhance the efficiency and accuracy of patient care by leveraging neuroinformatics principles. The investigators hypothesize that combining natural language processing and data analytics will improve diagnostic and treatment processes.

Detailed description: Research Design:

A pre-post-intervention design will be used, measuring outcomes before and after the implementation of a neuroinformatics-driven clinical assistance system. Changes in diagnostic accuracy, treatment decisions, and workflow efficiency will be quantified.

Workflow:

1. A neurologist evaluates each patient, and the patient signs informed consent.
2. A medical student manually uploads de-identified clinical information to a secure interface.
3. Data are analyzed by a large language model (LLM) system through a hospital-approved application.
4. A senior physician must approve any decision based on the LLM's recommendation.
5. For half of the prospectively enrolled participants, the LLM's recommendation is presented to the resident.

Retrospective Data:

1. The investigators will extract data from 10,000 patients who were evaluated by a neurologist in the Emergency Department.
2. Clinical information (without patient identifiers) will be uploaded to a secure, hospital-approved LLM with a security key.
3. The model's output will be compared with actual clinical decisions and patient outcomes (e.g., mortality, discharge status).

Eligibility criteria: Inclusion Criteria:

* Informed consent

Exclusion Criteria:

* Unable to give informed consent

Primary outcomes:
measure: Proportion of correct diagnoses generated by the LLM compared to the final attending physician diagnosis.
timeFrame: 3 years
measure: Accuracy of Next-Step Recommendations
timeFrame: Up to 3 years.
Secondary outcomes:
measure: Cost of Running the LLM
timeFrame: Up to 3 years
measure: Staff Compliance With AI
timeFrame: Up to 3 years
measure: Transparency/Explainability
timeFrame: 3 years
measure: Quality of LLM-Generated Clinical Reports
timeFrame: Up to 3 years
measure: Staff Acceptance of AI
timeFrame: Up to 3 years
Other outcomes:
Locations:
city: Haifa
country: Israel
facility: Rambam healthcare campus
Study officials:
References: