Conversational AI in Tactical Casualty Care: Baseline GPT-4o Improves Combat Medic Decision-Making

NCT ID: NCT06796036

Last Updated: 2025-05-13

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 42 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-02-02
• Study Completion Date: 2025-04-30

Brief Summary

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.

Detailed Description

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.

This study offers a proof-of-concept evaluation of LLM applications in combat casualty care, with the potential to improve decision-making and inform the development of specialized AI tools for military use.

Conditions

Artificial Intelligence

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

Combat Medic Decision-Making with and without AI Assistance

All participants will complete 10 Tactical Combat Casualty Care scenarios: 5 with AI assistance using GPT-4 for ventilator management and 5 without AI assistance. The crossover design ensures each participant experiences both conditions.

Group Type: EXPERIMENTAL

Combat Medic Decision-Making with and without artificial intelligence assistance

Intervention Type: OTHER

Participants will complete 10 simulated Tactical Combat Casualty Care (TCCC) scenarios, with 5 scenarios conducted using AI assistance (GPT-4) and 5 without AI. In AI-assisted scenarios, participants will use GPT-4 to query and optimize ventilator settings based on patient data, while non-AI scenarios rely solely on their clinical judgment.

Interventions

Combat Medic Decision-Making with and without artificial intelligence assistance

Participants will complete 10 simulated Tactical Combat Casualty Care (TCCC) scenarios, with 5 scenarios conducted using AI assistance (GPT-4) and 5 without AI. In AI-assisted scenarios, participants will use GPT-4 to query and optimize ventilator settings based on patient data, while non-AI scenarios rely solely on their clinical judgment.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• 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.

Exclusion Criteria

• 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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Czech Technical University in Prague

OTHER

Sponsor Role: collaborator

Charles University, Czech Republic

OTHER

Sponsor Role: lead

Responsible Party

Michal Soták

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Michal Soták, M.D., Ph.D.

Role: PRINCIPAL_INVESTIGATOR

Charles University, Czech Republic

Locations

Military University Hospital Prague

Prague, , Czechia

Countries

Czechia

References

Nemeth C, Amos-Binks A, Rule G, Laufersweiler D, Keeney N, Flint I, Pinevich Y, Herasevich V. TCCC Decision Support With Machine Learning Prediction of Hemorrhage Risk, Shock Probability. Mil Med. 2023 Nov 8;188(Suppl 6):659-665. doi: 10.1093/milmed/usad298.

Reference Type: RESULT

PMID: 37948287 (View on PubMed)

Preiksaitis C, Ashenburg N, Bunney G, Chu A, Kabeer R, Riley F, Ribeira R, Rose C. The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review. JMIR Med Inform. 2024 May 10;12:e53787. doi: 10.2196/53787.

Reference Type: RESULT

PMID: 38728687 (View on PubMed)

Other Identifiers

FieldAI

Identifier Type: -

Identifier Source: org_study_id