Root Cause Analysis Of Adverse Events Using A Human Reliability Analysis Approach

Abstract

Large scale analysis of adverse event data is challenging due to the unstructured nature of event reporting and narrative textual data in adverse event repositories. This issue is further complicated for human error adverse events, which are routinely treated as a root cause instead of as initiating events in a causal chain. Human error events are commonly misunderstood and underreported, which hinders the analysis of trends and the identification of risk mitigation strategies across industries. Currently, the prevailing means of human error investigation is the analysis of accident and incident data which are not designed around a framework of human cognition or psychomotor function. Existing approaches lack a theoretical foundation with sufficient cognitive granularity to identify root causes of human error. This research provides a cognitive task decomposition to standardize the investigation, reporting, and analysis of human error adverse event data in narrative textual form. The proposed method includes a qualitative structure to answer six questions (when, who, what, where, how, why) that are critical to comprehensively understand the events surrounding human error. This process is accomplished in five main stages: (1) Develop guidelines for a cognitively-driven adverse event investigation; (2) Perform a baseline cognitive task analysis (when) to document relevant stakeholders (who), products or processes (what), and environments (where) based on a taxonomy of cognitive and psychomotor function; (3) Identify deviations for the baseline task analysis in the form of unsafe acts (how) using a human error classification; (4) and Develop a root cause mapping to identify the performance shaping factors (PSFs) (why) for each unsafe act. The outcome of the proposed method will advance the fields of risk analysis and regulatory science by providing a standardized and repeatable process to input and analyze human error in adverse event databases. The method provides a foundation for more effective human error trending and accident analysis at a greater level of cognitive granularity. Application of this method to adverse event risk mitigations can inform prospective strategies such as resource allocation and system design, with the ultimate long-term goal of reducing the human contribution to risk.