A Generalized Nash Equilibrium-Seeking Scheme for Trauma Resuscitation

TL;DR

This paper models trauma resuscitation as a distributed game to optimize healthcare workers' decisions, aiming to improve patient outcomes in safety-critical environments.

Contribution

It introduces a novel GNE-seeking game framework incorporating clinical insights and dynamic communication for trauma resuscitation.

Findings

The proposed method effectively models HCW behavior and decision-making.

It optimizes resuscitation outcomes considering workloads, schedules, and resources.

The framework adapts to time-varying communication networks.

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.