Artificial Intelligence as a Decision Making Tool in Emergency Medicine
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 100,000
- 试验地点
- 1
- 主要终点
- Length of Stay in Emergency Department
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 0 Years 至 120 Years（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients aged 0 to 120 years presented to the emergency department
排除标准
- 未提供
研究组 & 干预措施
Evaluation Without AI
A scenario in which the physician is not exposed to the model's recommendations.
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)
结局指标
主要结局
Length of Stay in Emergency Department
时间窗: 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.
次要结局
未报告次要终点
研究者
Shahar Shelly MD
Chair of Neurology Department
Rambam Health Care Campus