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
- 2
- 主要终点
- Proportion of correct diagnoses generated by the LLM compared to the final attending physician diagnosis.
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 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.
结局指标
主要结局
Proportion of correct diagnoses generated by the LLM compared to the final attending physician diagnosis.
时间窗: 3 years
* Description: Measures the percentage of times the LLM's recommended diagnosis matches the final attending physician's diagnosis. * Unit of Measure: Proportion (0.0-1.0) or Percent (0-100%) * How Assessed: Each LLM recommendation is compared to the final documented diagnosis in the medical record. The number of correct diagnoses is divided by the total number of cases.
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.
Accuracy of Next-Step Recommendations
时间窗: Up to 3 years.
Proportion of correct or appropriate next-step management decisions recommended by the LLM. Description: Proportion of correct next-step management decisions recommended by the LLM, compared to either the attending physician's final plan or standard-of-care guidelines. Unit of Measure: Proportion (0.0-1.0) or Percent (0-100%) How Assessed: For each encounter, the LLM's recommended "next step" is recorded and deemed correct if it aligns with the attending plan or guideline.
次要结局
- Cost of Running the LLM(Up to 3 years)
- Staff Compliance With AI(Up to 3 years)
- Transparency/Explainability(3 years)
- Quality of LLM-Generated Clinical Reports(Up to 3 years)
- Staff Acceptance of AI(Up to 3 years)
研究者
Shahar Shelly MD
Chair of Neurology Department
Rambam Health Care Campus