Missed follow-up of abnormal laboratory result involves complex human–system interaction factors to uncover the related decision-making processes, we chose CDM-based interviews. We applied three human factors methods, including critical decision method (CDM)-based interviews, contextual inquiry (CI)-based analysis methodology, and fault tree analysis (FTA), to understand factors contributing to missed results. By using the human factors methods described in this study, we gathered additional detail about physician's decision-making process, illustrating factors that contribute to missed results. Our objective was to use human factors methods to further illustrate workflow and process issues related to missed results and focus on contributing factors. Prior research on understanding causes of missed results is limited and has used retrospective chart reviews, Human factors methods could advance understanding of physician decision-making processes and uncover factors related to individual decision-making processes. For example, it is imperative to understand individual's intent at the point of care and decision making that contribute to missed results especially within the complex sociotechnical context of electronic health records (EHRs)-enabled health systems. system-level) factors contributing to missed results is evolving. In ambulatory settings, the incidence of missed results can be up to 65% and can lead to delayed diagnosis or treatment. Lack of timely follow-up and missed follow-up of abnormal test results (henceforth, “missed results”) is a recognized patient safety concern. This case report illustrates how human factors–based approaches can enable analysis of contributing factors that lead to missed results, thus informing development of preventive strategies to address them. Key findings including underdeveloped methods to track follow-up, as well as mismatches, in communication channels, timeframes, and expectations between patients and physicians. The flow model highlighted barriers in information flow and decision making, and the hierarchical model identified relationships between contributing factors for delayed action. We then developed a CI-based flow model and conducted a fault tree analysis (FTA) to identify hierarchical relationships between factors that delayed action. We analyzed transcribed responses using a contextual inquiry (CI)-based methodology to identify contextual factors contributing to missed results. We identified 30 cases of missed test results by querying electronic health record data, developed a critical decision method (CDM)-based interview guide to understand decision-making processes, and interviewed physicians who ordered these tests. This study demonstrates application of human factors methods for understanding causes for lack of timely follow-up of abnormal test results (“missed results”) in outpatient settings.
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