Application of Human Factors Analysis and Classification System to Healthcare Accidents

Abstract:

Root cause analysis proves an effective method of investigating serious events. With an objective of identifying and mitigating the causal sequence of events, the study modifies theoretical framework of Human Factors Analysis and Classification System (HFACS) based on Reason’s “Swiss Cheese” model of human error. Latent failures are categorized into four levels in the framework, including unsafe acts, preconditions, supervision, and organizational influences. Fallible decisions in upper management levels can directly affect supervisory practices, thereby creating adverse preconditions for unsafe acts and hence indirectly influencing the performance of operators, ultimately leading to accidents. Human factors principles are applied to support each factor, making HFACS more standardized and reliable in comprehensive investigation. Analysis of actual reports on healthcare incidents/accidents aims to figure out “why” the error occurred by tracing back the causal chain. Moreover, after recording each HFACS factor for accidents, statistical path of relationship can describe the strength of associations both between categories at adjacent levels and factors from the same category. HFACS shows great potential to detect latent failures, provide specific interventions and improve effectiveness of safety plans and strategies in healthcare.