Inferring causal networks of health care resilience and safety performance indicators: A two-stage fuzzy cognitive map approach

Abstract

In the literature, there are three major concepts used to model resilience and safety performance: (i) integrated resilience engineering (IRE), (ii) work motivational factors, (iii) health, safety, environment, and ergonomics (HSEE). Over the last few decades, health care has developed a compartmentalized vision of performance, safety, and resilience. The issues caused by this are not yet clearly understood since the developments in each dimension are in their infancy. This paper presents a two-stage fuzzy cognitive map (FCM) using a non-linear Hebbian learning (NHL) algorithm and three evolutionary algorithms (EAs) to model the causal relations among these concepts and their impact on health care performance. This paper aims to infer the causal network of these concepts. We use an FCM trained in two stages to show the synergistic relationships among the health and safety paradigms and their favorable effects on the organization's safety performance. The methodology developed in this study targets achieving the minimum learning error by running an NHL algorithm followed by three EAs comprising genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA). The method is applied to a case study of a large general hospital. While a mere defuzzification of the experts' judgments yields virtually no distinguishability between the individual factors and their impact on performance, our approach extracts a much-improved degree of differentiation. One striking example is the obtained high impact of self-organization and overall workload on resilience and safety performance.