Military medicine
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Perioperative death is a harrowing experience that physicians often encounter while needing to continue life sustaining care for others. In this piece, an anesthesiology resident uses poetry to process the recent passing of one of his patients, highlighting the ways that it continues to impact the care that he provides.
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The effectiveness and durability of vaccination to protect against localized anogenital mpox skin lesions is not well defined. Overestimating vaccine effectiveness against mild infection risks degrading vaccine confidence. Acknowledging and communicating vaccine unknowns and limitations is vital to direct mpox research, maintain vaccine confidence, and optimize uptake.
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This column explores the inception, challenges, and prospects of robotic surgery in the military. It highlights the military's role in developing early prototypes, current utilization, training struggles, partnerships with civilian organizations, and potential future applications. The military's influence on the evolving landscape of robotic surgery is emphasized.
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The potential impact of large-scale combat operations and multidomain operations against peer adversaries poses significant challenges to the Military Health System including large volumes of critically ill and injured casualties, prolonged care times in austere care contexts, limited movement, contested logistics, and denied communications. These challenges contribute to the probability of higher casualty mortality and risk that casualty care hinders commanders' forward momentum or opportunities for overmatch on the battlefield. Novel technical solutions and associated concepts of operation that fundamentally change the delivery of casualty care are necessary to achieve desired medical outcomes that include maximizing Warfighter battle-readiness, minimizing return-to-duty time, optimizing medical evacuation that clears casualties from the battlefield while minimizing casualty morbidity and mortality, and minimizing resource consumption across the care continuum. ⋯ Our path to combat casualty care automation starts with mapping and modeling the context of casualty care in realistic environments through passive data collection of large amounts of unstructured data to inform machine learning models. These context-aware models will be matched with patient physiology models to create casualty digital twins that better predict casualty needs and resources required and ultimately inform and accelerate decision-making across the continuum of care. We will draw from the experience of the automotive industry as an exemplar for achieving automation in health care and inculcate automation as a mechanism for optimizing the casualty care survival chain.