Application of a Systems Theory-Based Accident Analysis Technique to Perioperative Safety Reports From the COVID-19 Pandemic.

Abstract

Objectives: Nonlinear retrospective analytic techniques can allow for in-depth understanding of accidents and their causes, yet they are infrequently used in health care. The purpose of this study was to provide an example, using Causal Analysis based on Systems Theory (CAST) together with an inductive thematic analysis to understand the contextual factors contributing to one hospital's perioperative safety events.

Methods: We created a hierarchical control structure of the hospital's perioperative system with input from a multidisciplinary group. We then analyzed safety events that were self-reported during a COVID surge (April 2020) using CAST to understand their contributing factors. Next, we analyzed the contributing factors using inductive qualitative thematic coding to identify system-level safety risks. We mapped each system-level safety risk to a recommendation for future mitigation.

Results: We screened 122 safety reports and found 19 safety events that met inclusion criteria. The analysis revealed 245 contributing factors represented by 22 subthemes corresponding to 3 major themes: (1) vulnerable processes, being problems with workflows or communication channels; (2) personnel challenges including challenges with staff redeployment as well as cognitive and behavioural challenges; and (3) poorly designed or unavailable equipment. Each subtheme corresponded to a prevention strategy, such as creation of a central protocol hub.

Conclusions: Using a nonlinear accident analysis technique together with thematic analysis, we were able to identify system-wide contributing factors to safety events. These contributing factors led to recommendations for future pandemics or crises characterized by scarce resources, limited data, and a rapidly changing environment.