A Generalized Nash Equilibrium-Seeking Scheme for Trauma Resuscitation

2026-05-21

AI Summary

This study addresses the challenge of quantifying and optimizing collaborative decision-making among medical personnel during trauma resuscitation under heterogeneous constraints of limited resources, varying staff competencies, and workload distributions to improve patient outcomes. The resuscitation process is formulated as a distributed generalized Nash equilibrium problem with coupled inequality constraints, solved over time-varying communication graphs to derive optimal team strategies. Integrating clinical expertise with game-theoretic principles, this work introduces— for the first time—a generalized Nash equilibrium framework tailored specifically to trauma resuscitation, explicitly incorporating practical constraints such as staffing schedules, individual capabilities, and resource availability. The proposed approach enables the automatic generation of efficient coordination strategies in complex clinical environments, offering significant potential to enhance both patient prognosis and overall resuscitation efficiency.

Abstract

Trauma resuscitation is a clinical process for treating life-threatening physiological disorders in safety-critical environments, driven by the experience of healthcare workers (HCWs). Designing and optimizing quantifiable metrics that accurately capture HCW decisions may augment current resuscitation procedures with the potential to improve patient outcomes. This motivates our socio-technical formulation of trauma resuscitation as a distributed generalized Nash equilibrium (GNE)-seeking game with coupled inequality constraints. This method is optimized over a time-varying communication graph. We introduce novel insights from clinical experience to model HCWs behavior. This work facilitates the best possible resuscitation outcome given HCWs workloads, schedules, competencies, and limited resources.

Problem

• trauma resuscitation
• Generalized Nash Equilibrium
• healthcare workers
• resource constraints
• clinical decision-making

Innovation

• Generalized Nash Equilibrium
• Trauma Resuscitation
• Distributed Optimization
• Coupled Constraints
• Time-varying Communication Graph