Artificial Intelligence Decision Support Systems in Resource-Limited Environments to Save Lives and Reduce Moral Injury

Future military conflicts are likely to involve peer or near-peer adversaries in large-scale combat operations, leading to casualty rates not seen since World War II. Casualty volume, combined with anticipated disruptions in medical evacuation, will create resource-limited environments that challenge medical responders to make complex, repetitive triage decisions. Similarly, pandemics, mass casualty incidents, and natural disasters strain civilian health care providers, increasing their risk for exhaustion, burnout, and moral injury. As opposed to exhaustion and burnout, which can be mitigated with appropriate rest cycles and changes in workload, moral injury is a long-lasting and impairing condition with cognitive, emotional, behavioral, social, and spiritual repercussions. Exhaustion and burnout experienced by providers during COVID-19 correlated with increased disengagement and the desire to leave the health care field. Telemedicine and telementoring expands access to medical expertise, thereby reducing an inexperienced provider's stress levels and uncertainty and improving their confidence in care delivery. Artificial Intelligence Decision Support Systems (AIDeSSAIDeSS) may represent the next phase in clinical decision support systems across the continuum of care. These systems may help address both the anticipated scale of casualties in large-scale combat operations and the critical expertise gaps during future pandemics, mass casualty events, and natural disasters. This study advocates for urgent research at the intersection of high-stress, resource-limited care contexts that may cause moral injury in health care providers and the potential for AIDeSS to reduce that risk. Understanding these dynamics may yield strategies to mitigate psychological distress in medical responders, increase patient survival, and improve the health of our medical systems.