The Development of a Next-Generation Human Reliability Analysis: Systems Analysis for Formal Pharmaceutical Human Reliability (SAFPH℞ ).

다국어 초록 (Multilingual Abstract)

Medication errors originating in community pharmacies are serious patient safety hazards. However, due to the complexity of the community pharmacy environment, current experimental and observational studies are insufficient to address these problems. This research aims to create a novel, formal, proof-based approach to human reliability analysis (HRA) that will provide pharmacies with the ability to accurately predict error rates, understand why errors are occurring, and better engineer their system to mitigate the errors.Traditional HRA can accurately predict human error rates in a number of environments. However, they are limited in that they are static and thus not able to handle the dynamic environmental elements that can impact human performance. To address this and allow analysts to accurately predict medication error rates, we have developed a next-generation HRA called the Systems Analysis for Formal Pharmaceutical Human Reliability (SAFPHR). This method addresses the limits of previous HRAs by combining concepts from the Cognitive Reliability and Error Analysis Method (CREAM) HRA with probabilistic model checking, a computational tool for automatically proving properties about complex, stochastic systems.By using different estimation methods and cognitive assumptions, we have fully developed the modeling and predictive capabilities of three versions of SAFPHR: basic SAFPHR, CPC-effect extended SAFPHR, and mode-effect extended SAFPHR. These three versions can collectively produce six different methods of computing error rates. To determine which of these estimates were the most accurate and valid, we formally modeled a full, generic, pharmacy dispensing procedure.We then used SAFPHR to make predictions about overall error rates as well as error rates originating from different stages of the dispensing process. These values were computed for all six estimation approaches and then compared with real, comprehensive error rates published in the literature. One method consistently produced accurate predictions both for the overall error rate and the individual stages. The results have important implications for pharmacy because they show that SAFPHR could be used to reduce medication error rates and thus significantly improve patient health and safety. Given its success in pharmacy and the generic nature of its underlying theory, SAFPHR could be used as a general HRA to improve safety and reliability in other critical domains. More avenues of future research are explored in the end.