Decision making in trauma centers from the standpoint of complex adaptive systems

Purpose: This paper aims to examine complex clinical decision‐making processes in trauma center units of hospitals in terms of the immediate impact of complexity on the medical team involved in the trauma event. Design/methodology/approach: It is proposed to develop a model of decision‐making processes in trauma events that uses a Bayesian classifier model with convolution and deconvolution operators to study real‐time observed trauma data for the decision‐making process under tremendous stress. The objective is to explore and explain physicians' decision‐making processes under stress and time constraints during actual trauma events from the perspective of complexity. Findings: Because physicians have blurred information and cues that are tainted by random environmental noise during injury‐related events, they must de‐blur (de‐convolute) the collected data to find a best approximation of the real data for decision‐making processes. Research limitations/implications: The data collection and analysis is innovative and the permission to access raw audio and video data from an active trauma center will differentiate this study from similar studies that rely on simulations, self report and case study approaches. Practical implications: Clinical decision makers in trauma centers are placed in situations that are increasingly complex, making decision‐making and problem‐solving processes multifaceted. Originality/value: The science of complex adaptive systems, together with human judgment theories, provide important concepts and tools for responding to the challenges of healthcare this century and beyond.