Conversational AI in Tactical Casualty Care: Baseline GPT-4o Improves Combat Medic Decision-Making
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 42
- 试验地点
- 2
- 主要终点
- Accuracy of ventilator settings
研究概览
简要总结
The aim of the project is to investigate whether the integration of artificial intelligence (AI) support, specifically through the GPT-4 model, enhances the decision-making processes of military medical first responders within the framework of Tactical Combat Casualty Care (TCCC). The study focuses on AI's ability to assist in ventilator settings for injured individuals in combat scenarios, emphasizing improved accuracy and decision-making speed. The project tests the hypothesis that the use of AI can positively impact outcomes without compromising the autonomy of first responders. The results have the potential to optimize patient care in challenging conditions and contribute to the advancement of combat medicine.
详细描述
This study investigates the potential of conversational artificial intelligence (AI), specifically GPT-4, to enhance clinical decision-making in Tactical Combat Casualty Care (TCCC) scenarios. The primary objective is to evaluate whether AI support improves the accuracy and efficiency of ventilator management decisions for combat medics in high-pressure environments without compromising their autonomy.
A prospective, randomized, within-subject study design will be employed. Thirty combat medics from the Czech Armed Forces will participate. Each participant will complete 10 simulated TCCC scenarios: five with AI assistance and five without. Scenarios will be matched for complexity and randomized to control for order effects. Participants will use ChatGPT on handheld devices to simulate real-time AI-assisted decision-making.
In scenarios involving AI assistance, medics will query GPT-4 for support in optimizing mechanical ventilator settings based on patient data, including blood gas results, vital signs, and ventilator parameters.
The primary outcome is the accuracy of ventilator settings as categorized into "excellent," "acceptable," or "failing" based on predefined TCCC standards. Secondary outcomes include decision-making speed and participants' perception of AI's utility, measured through post-scenario surveys.
The findings aim to determine the feasibility of integrating large language models (LLMs) into combat medical care to optimize patient outcomes and support medics under combat conditions. The study seeks to advance the understanding of AI's role in military medicine, providing a foundation for future deployment of fine-tuned AI solutions in TCCC and other critical care scenarios.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Other
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Combat medics actively serving in the Czech Armed Forces
- •Completion of standardized Tactical Combat Casualty Care training modules and e-learning on ventilator settings and blood gas interpretation
- •Successful passing of pre-tests to ensure a uniform baseline knowledge level.
- •Willingness to participate and provide informed consent.
- •Availability to complete the full study protocol, including 10 simulated scenarios.
排除标准
- •Failure to pass the pre-tests or complete TCCC and ventilator management training
- •Prior advanced training or professional certification in critical care or mechanical ventilation that could bias results
- •Refusal to provide informed consent or inability to commit to the study schedule
结局指标
主要结局
Accuracy of ventilator settings
时间窗: 1 hour
Accuracy of ventilator settings as categorized into "excellent," "acceptable," or "failing" based on predefined TCCC standards. Excellent means 2 points, acceptable 1 point and failing 0 point.
次要结局
未报告次要终点
研究者
Michal Soták
Principal Investigator
Charles University, Czech Republic