Artificial Intelligence for Mass Casualty Clinical Resource Coordination

Abstract

Artificial intelligence (AI) is revolutionizing mass casualty incident (MCI) management by optimizing clinical resource coordination and ensuring efficient triage, allocation, and deployment of critical assets. This review explores the scientific underpinnings, clinical applications, and emerging evidence around AI-driven solutions for mass casualty response, focusing on their impact in improving outcomes, supporting healthcare professionals, and addressing resource limitations. The article synthesizes current research findings, discusses guideline recommendations, and highlights practical implications for integrating AI technologies into disaster medicine workflows.

Introduction

Mass casualty incidents ranging from natural disasters and pandemics to terrorist attacks pose significant challenges to healthcare systems worldwide. Rapid surges in demand can overwhelm available resources, delay care, and adversely affect patient outcomes. Efficient resource coordination is essential to optimize triage, treatment, and logistics in these high-pressure environments. Recent advances in artificial intelligence offer unprecedented opportunities to enhance situational awareness, automate decision support, and streamline clinical workflows during MCIs. This review critically examines the evidence base for AI-driven resource coordination, synthesizing insights from recent PubMed-indexed studies and guidelines to inform best practices for clinicians and health system leaders.

Epidemiology / Disease Burden

Globally, mass casualty events are increasing in frequency and complexity due to urbanization, climate change, and geopolitical instability. Data from the World Health Organization and international disaster databases indicate that thousands of MCIs occur annually, collectively affecting millions of individuals and straining hospital capacities. In the United States alone, the National Trauma Data Bank reports hundreds of incidents each year involving multi-patient trauma, while pandemics such as COVID-19 have demonstrated the devastating impact of protracted large-scale emergencies on healthcare systems. The burden is further amplified in low-resource settings where baseline infrastructure is limited. These epidemiological trends underscore the urgent need for robust, scalable solutions to improve clinical resource allocation and minimize preventable mortality during MCIs.

Pathophysiology

The pathophysiology of MCIs is characterized by an acute mismatch between patient needs and healthcare system capacity. This imbalance leads to delays in triage, under- or over-utilization of available resources, and compromised care quality. The "golden hour" concept emphasizes the critical importance of rapid intervention for trauma and other acute conditions common in MCIs. AI technologies can model these dynamic interactions in real time, predicting resource bottlenecks and guiding optimal patient flow. Mechanistically, AI leverages complex algorithms and real-time data integration from electronic health records (EHRs), wearable devices, and emergency dispatch systems to recognize evolving patterns, prioritize cases, and recommend evidence-based interventions tailored to individual patient risk profiles.

Risk Factors

Several risk factors exacerbate the challenges of clinical resource coordination in MCIs. These include high patient acuity, limited staffing, variable skill mix among responders, infrastructure damage, communication breakdowns, and information overload. Demographic factors such as age, comorbidities, and injury severity further complicate triage and resource allocation. AI systems can account for these variables by analyzing historical and real-time data, identifying at-risk populations, and adjusting resource distribution algorithms accordingly. Additionally, environmental factors such as hazardous material exposure, weather conditions, and logistical hurdles can be dynamically incorporated into AI-driven decision support tools.

Clinical Features

MCIs typically present with a spectrum of clinical features, including polytrauma, burns, blast injuries, acute respiratory distress, and psychological distress. The heterogeneous nature of patient presentations demands rapid assessment and stratification to prioritize care. AI-powered triage tools utilize natural language processing (NLP), image analysis, and predictive analytics to automate initial assessments, flag critical cases, and recommend disposition pathways. For example, machine learning models can analyze prehospital data and vital signs to predict deterioration risk, enabling early intervention and efficient use of critical care resources.

Diagnosis

Accurate and timely diagnosis in MCIs is complicated by high patient volumes, limited diagnostic equipment, and chaotic environments. AI assists in diagnostic decision-making through real-time aggregation of clinical data, risk scoring, and anomaly detection. Algorithms trained on large datasets can support rapid identification of injury patterns, infectious disease outbreaks, or toxic exposures. Integration with EHRs and radiology platforms allows AI to flag abnormal findings, suggest differential diagnoses, and recommend confirmatory tests, reducing diagnostic errors and supporting frontline clinicians in high-stress scenarios.

Treatment & Management

Effective treatment and management of MCI patients rely on coordinated deployment of personnel, equipment, and medications. AI solutions facilitate resource tracking, automate task allocation, and optimize workflows by continuously updating situational awareness dashboards. Decision support systems can recommend evidence-based protocols, monitor adherence to guidelines, and prioritize interventions for high-risk patients. In addition, AI-powered logistics tools can coordinate inter-facility transfers, manage supply chain disruptions, and ensure equitable access to critical resources such as ventilators, blood products, and operating rooms.

Recent Advances / Emerging Therapies

Recent years have seen rapid progress in the development and deployment of AI tools for mass casualty management. Notable advances include deep learning algorithms for image-based triage, real-time patient tracking via mobile applications, and AI-supported telemedicine platforms that extend specialist expertise to remote or overwhelmed facilities. Research published in leading journals highlights the successful implementation of AI-driven protocols in simulated and real-world disaster scenarios, showing improvements in triage accuracy, resource utilization, and patient outcomes. Emerging therapies also include AI-guided mental health support for survivors and responders, leveraging NLP to detect distress and recommend early interventions.

Guideline Recommendations

International and national guidelines are increasingly recognizing the value of AI in disaster response. The World Health Organization, American College of Emergency Physicians, and other bodies advocate for the integration of AI-driven systems into emergency preparedness plans. Key recommendations include investing in interoperable data infrastructure, fostering cross-sector collaboration, and prioritizing transparency and ethical governance in AI deployment. Guidelines emphasize the need for ongoing validation of AI algorithms, clinician training, and continuous quality improvement to ensure safe and effective integration into mass casualty workflows.

Conclusion

Artificial intelligence offers transformative potential for clinical resource coordination during mass casualty incidents. By harnessing advanced analytics, real-time data integration, and automated decision support, AI can enhance triage accuracy, improve resource allocation, and ultimately save lives. Successful implementation depends on robust evidence, interdisciplinary collaboration, and adherence to evolving guidelines. As AI technologies continue to mature, they are poised to become indispensable tools for disaster medicine, supporting healthcare professionals in delivering timely, equitable, and high-quality care under the most challenging circumstances.