Integrating Wearable Sensor Data With an AI-Based, Protocol-Flexible Triage Platform to Accelerate Decision-Making During the Golden Hour of Combat Casualty Care
Introduction Rapid, accurate triage within the first "golden hour" on the battlefield is critical to survival, yet existing wearable sensors and manual protocols operate in isolation, delaying care and creating inconsistent priority ratings across military and civilian systems. We therefor...
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| Vydáno v: | Curēus (Palo Alto, CA) Ročník 17; číslo 8; s. e91121 |
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01.08.2025
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| ISSN: | 2168-8184, 2168-8184 |
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| Abstract | Introduction Rapid, accurate triage within the first "golden hour" on the battlefield is critical to survival, yet existing wearable sensors and manual protocols operate in isolation, delaying care and creating inconsistent priority ratings across military and civilian systems. We therefore designed a vendor-agnostic, protocol-flexible platform that ingests live biometric streams from military-grade wearables, applies an artificial intelligence (AI)-enhanced hybrid decision engine, and returns colour-coded casualty categories under any standard (e.g., MARCH (Massive hemorrhage, Airway, Respiration, Circulation, and Hypothermia/Head injury), Canadian Triage and Acuity Scale (CTAS), START(Screening Tool to Alert to the Right Treatment), SALT (Sort, Assess, Life-saving Interventions, Treatment/Transport)) in real time. Methods The planned platform will comprise four layers: (i) Data Ingestion, which normalizes ECG, heart-rate, blood-pressure, and oxygen saturation (SpO₂) signals to Open mHealth/Fast Healthcare Interoperability Resources (FHIR) schemas via BLE (Bluetooth Low Energy) or WebSockets; (ii) Secure Transport, which encrypts packets with Transport Layer Security (TLS) 1.3 and uses store-and-forward buffering through message queuing telemetry transport (MQTT)/Kafka brokers; (iii) Triage Engine, which merges continuous machine-learning severity scores with dynamically loaded guideline definitions (JSON (JavaScript Object Notation)/YAML (YAML Ain't Markup Language)) and rule-based overrides; and (iv) Presentation, which delivers one-tap guideline selection and actionable prompts to medics while providing a geo-tagged command dashboard. Validation will combine bench tests on synthetic and de-identified Medical Information Mart for Intensive Care IV (MIMIC-IV) datasets with randomized field-simulation trials that compare manual versus AI-augmented triage, measuring time to triage, protocol concordance, and medic cognitive load (National Aeronautics and Space Administration Task Load Index(NASA-TLX)). Results (projected) We anticipate at least a 30% reduction in triage time, agreement rates of 85% or higher with expert assessments across multiple protocols, and significant decreases in medic workload. Conclusion A dynamic, standards-based artificial intelligence (AI) triage platform could harmonize military and civilian casualty care, accelerate golden-hour decision-making, and improve multinational interoperability. Planned live-exercise evaluations and open-source protocol libraries are expected to facilitate rapid adoption and continuous refinement. |
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| AbstractList | Introduction Rapid, accurate triage within the first "golden hour" on the battlefield is critical to survival, yet existing wearable sensors and manual protocols operate in isolation, delaying care and creating inconsistent priority ratings across military and civilian systems. We therefore designed a vendor-agnostic, protocol-flexible platform that ingests live biometric streams from military-grade wearables, applies an artificial intelligence (AI)-enhanced hybrid decision engine, and returns colour-coded casualty categories under any standard (e.g., MARCH (Massive hemorrhage, Airway, Respiration, Circulation, and Hypothermia/Head injury), Canadian Triage and Acuity Scale (CTAS), START(Screening Tool to Alert to the Right Treatment), SALT (Sort, Assess, Life-saving Interventions, Treatment/Transport)) in real time. Methods The planned platform will comprise four layers: (i) Data Ingestion, which normalizes ECG, heart-rate, blood-pressure, and oxygen saturation (SpO₂) signals to Open mHealth/Fast Healthcare Interoperability Resources (FHIR) schemas via BLE (Bluetooth Low Energy) or WebSockets; (ii) Secure Transport, which encrypts packets with Transport Layer Security (TLS) 1.3 and uses store-and-forward buffering through message queuing telemetry transport (MQTT)/Kafka brokers; (iii) Triage Engine, which merges continuous machine-learning severity scores with dynamically loaded guideline definitions (JSON (JavaScript Object Notation)/YAML (YAML Ain't Markup Language)) and rule-based overrides; and (iv) Presentation, which delivers one-tap guideline selection and actionable prompts to medics while providing a geo-tagged command dashboard. Validation will combine bench tests on synthetic and de-identified Medical Information Mart for Intensive Care IV (MIMIC-IV) datasets with randomized field-simulation trials that compare manual versus AI-augmented triage, measuring time to triage, protocol concordance, and medic cognitive load (National Aeronautics and Space Administration Task Load Index(NASA-TLX)). Results (projected) We anticipate at least a 30% reduction in triage time, agreement rates of 85% or higher with expert assessments across multiple protocols, and significant decreases in medic workload. Conclusion A dynamic, standards-based artificial intelligence (AI) triage platform could harmonize military and civilian casualty care, accelerate golden-hour decision-making, and improve multinational interoperability. Planned live-exercise evaluations and open-source protocol libraries are expected to facilitate rapid adoption and continuous refinement.Introduction Rapid, accurate triage within the first "golden hour" on the battlefield is critical to survival, yet existing wearable sensors and manual protocols operate in isolation, delaying care and creating inconsistent priority ratings across military and civilian systems. We therefore designed a vendor-agnostic, protocol-flexible platform that ingests live biometric streams from military-grade wearables, applies an artificial intelligence (AI)-enhanced hybrid decision engine, and returns colour-coded casualty categories under any standard (e.g., MARCH (Massive hemorrhage, Airway, Respiration, Circulation, and Hypothermia/Head injury), Canadian Triage and Acuity Scale (CTAS), START(Screening Tool to Alert to the Right Treatment), SALT (Sort, Assess, Life-saving Interventions, Treatment/Transport)) in real time. Methods The planned platform will comprise four layers: (i) Data Ingestion, which normalizes ECG, heart-rate, blood-pressure, and oxygen saturation (SpO₂) signals to Open mHealth/Fast Healthcare Interoperability Resources (FHIR) schemas via BLE (Bluetooth Low Energy) or WebSockets; (ii) Secure Transport, which encrypts packets with Transport Layer Security (TLS) 1.3 and uses store-and-forward buffering through message queuing telemetry transport (MQTT)/Kafka brokers; (iii) Triage Engine, which merges continuous machine-learning severity scores with dynamically loaded guideline definitions (JSON (JavaScript Object Notation)/YAML (YAML Ain't Markup Language)) and rule-based overrides; and (iv) Presentation, which delivers one-tap guideline selection and actionable prompts to medics while providing a geo-tagged command dashboard. Validation will combine bench tests on synthetic and de-identified Medical Information Mart for Intensive Care IV (MIMIC-IV) datasets with randomized field-simulation trials that compare manual versus AI-augmented triage, measuring time to triage, protocol concordance, and medic cognitive load (National Aeronautics and Space Administration Task Load Index(NASA-TLX)). Results (projected) We anticipate at least a 30% reduction in triage time, agreement rates of 85% or higher with expert assessments across multiple protocols, and significant decreases in medic workload. Conclusion A dynamic, standards-based artificial intelligence (AI) triage platform could harmonize military and civilian casualty care, accelerate golden-hour decision-making, and improve multinational interoperability. Planned live-exercise evaluations and open-source protocol libraries are expected to facilitate rapid adoption and continuous refinement. Introduction Rapid, accurate triage within the first "golden hour" on the battlefield is critical to survival, yet existing wearable sensors and manual protocols operate in isolation, delaying care and creating inconsistent priority ratings across military and civilian systems. We therefore designed a vendor-agnostic, protocol-flexible platform that ingests live biometric streams from military-grade wearables, applies an artificial intelligence (AI)-enhanced hybrid decision engine, and returns colour-coded casualty categories under any standard (e.g., MARCH (Massive hemorrhage, Airway, Respiration, Circulation, and Hypothermia/Head injury), Canadian Triage and Acuity Scale (CTAS), START(Screening Tool to Alert to the Right Treatment), SALT (Sort, Assess, Life-saving Interventions, Treatment/Transport)) in real time. Methods The planned platform will comprise four layers: (i) Data Ingestion, which normalizes ECG, heart-rate, blood-pressure, and oxygen saturation (SpO₂) signals to Open mHealth/Fast Healthcare Interoperability Resources (FHIR) schemas via BLE (Bluetooth Low Energy) or WebSockets; (ii) Secure Transport, which encrypts packets with Transport Layer Security (TLS) 1.3 and uses store-and-forward buffering through message queuing telemetry transport (MQTT)/Kafka brokers; (iii) Triage Engine, which merges continuous machine-learning severity scores with dynamically loaded guideline definitions (JSON (JavaScript Object Notation)/YAML (YAML Ain't Markup Language)) and rule-based overrides; and (iv) Presentation, which delivers one-tap guideline selection and actionable prompts to medics while providing a geo-tagged command dashboard. Validation will combine bench tests on synthetic and de-identified Medical Information Mart for Intensive Care IV (MIMIC-IV) datasets with randomized field-simulation trials that compare manual versus AI-augmented triage, measuring time to triage, protocol concordance, and medic cognitive load (National Aeronautics and Space Administration Task Load Index(NASA-TLX)). Results (projected) We anticipate at least a 30% reduction in triage time, agreement rates of 85% or higher with expert assessments across multiple protocols, and significant decreases in medic workload. Conclusion A dynamic, standards-based artificial intelligence (AI) triage platform could harmonize military and civilian casualty care, accelerate golden-hour decision-making, and improve multinational interoperability. Planned live-exercise evaluations and open-source protocol libraries are expected to facilitate rapid adoption and continuous refinement. |
| Author | Alnuaimi, Maitha K |
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| Cites_doi | 10.1097/DMP.0b013e318182194e 10.3768/rtipress.2024.OP.0090.2402 10.1371/journal.pone.0318290 10.3390/bioengineering12050519 10.1097/TA.0b013e3182755dcc 10.1001/jamasurg.2015.3104 10.1016/S0166-4115(08)62386-9 10.1017/s1049023x0004276x 10.1016/j.iccn.2025.104058 10.7205/milmed.172.supplement_1.1 10.3390/s24248204 10.13026/a3wn-hq05 10.1109/IEMBS.2007.4352597 |
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| Keywords | interoperability wearable sensors triage protocols golden hour combat casualty care artificial intelligence |
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| References_xml | – volume: 2 Suppl 1 year: 2008 ident: ref8 article-title: Mass casualty triage: an evaluation of the data and development of a proposed national guideline publication-title: Disaster Med Public Health Prep doi: 10.1097/DMP.0b013e318182194e – year: 2024 ident: ref3 article-title: Wearable Sensors for Service Members and First Responders: Considerations for Using Commercially Available Sensors in Continuous Monitoring doi: 10.3768/rtipress.2024.OP.0090.2402 – volume: 20 year: 2025 ident: ref5 article-title: A new trauma severity scoring system adapted to wearable monitoring: a pilot study publication-title: PLoS One doi: 10.1371/journal.pone.0318290 – volume: 12 year: 2025 ident: ref14 article-title: Opportunities for artificial intelligence in operational medicine: lessons from the United States military publication-title: Bioengineering (Basel) doi: 10.3390/bioengineering12050519 – year: 1998 ident: ref9 article-title: Canadian Emergency Department Triage and Acuity Scale: implementation guidelines – volume: 73 year: 2012 ident: ref1 article-title: Death on the battlefield (2001-2011): implications for the future of combat casualty care publication-title: J Trauma Acute Care Surg doi: 10.1097/TA.0b013e3182755dcc – volume: 151 year: 2016 ident: ref2 article-title: The effect of a golden hour policy on the morbidity and mortality of combat casualties publication-title: JAMA Surg doi: 10.1001/jamasurg.2015.3104 – volume: 52 year: 1988 ident: ref12 article-title: Development of NASA-TLX (task load index): results of empirical and theoretical research publication-title: Adv Psychol doi: 10.1016/S0166-4115(08)62386-9 – volume: 11 year: 1996 ident: ref7 article-title: Disaster triage: START, then SAVE--a new method of dynamic triage for victims of a catastrophic earthquake publication-title: Prehosp Disaster Med doi: 10.1017/s1049023x0004276x – volume: 89 year: 2025 ident: ref13 article-title: The role of AI in emergency department triage: an integrative systematic review publication-title: Intensive Crit Care Nurs doi: 10.1016/j.iccn.2025.104058 – volume: 172 year: 2007 ident: ref10 article-title: Tactical combat casualty care 2007: evolving concepts and battlefield experience publication-title: Mil Med doi: 10.7205/milmed.172.supplement_1.1 – volume: 24 year: 2024 ident: ref6 article-title: Overview of wearable healthcare devices for clinical decision support in the prehospital setting publication-title: Sensors (Basel) doi: 10.3390/s24248204 – year: 2020 ident: ref11 article-title: MIMIC-IV (version 0.4) publication-title: PhysioNet doi: 10.13026/a3wn-hq05 – volume: 2007 year: 2007 ident: ref4 article-title: Motion tolerance in wearable sensors--the challenge of motion artifact publication-title: Annu Int Conf IEEE Eng Med Biol Soc doi: 10.1109/IEMBS.2007.4352597 – ident: ref16 – ident: ref15 |
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