Using the Generic Analysis Method to Analyze Sentinel Event Reports Across Hospitals: A Retrospective Cross-Sectional Study.

OBJECTIVES: Improving patient safety by investigating sentinel events (SEs) is hampered by the focus on isolated events within hospitals and a narrow scope of traditional root cause analysis methods. We aimed to examine if performing cross-hospital aggregate analysis of SEs applying a novel generic analysis method (GAM) bearing a human factor perspective can enhance learning from SEs. METHODS: A retrospective cross-sectional review of SE reports from 28 Dutch general hospitals using the GAM to reanalyze events was performed. A qualitative approach was used to identify contributing factors and system issues. Findings were discussed with a patient safety expert panel. Descriptive statistics and measures of associations between domains were calculated. RESULTS: Sixty-nine SE reports were reviewed. Applying the GAM provided a more holistic SE analysis than a traditional method. Of the 405 identified contributing factors in all SEs, the majority was related to the persons involved (patients and professionals, n = 146 [36.2%]) and the organization (n = 121 [30%]). The most frequently recurring pattern was the combination of factors related to the persons involved, the technology used, the tasks of professionals, and organizational factors influencing the event. Cross-hospital aggregate GAM analysis of SEs helped to identify system issues and propose more system-oriented overarching recommendations. CONCLUSIONS: This study found that applying the GAM to analyze SEs across hospitals can help to improve learning from SEs and may result in proposing stronger recommendations. The method can support hospitals, working together in a network of hospitals, to jointly learn from SEs.

What Can We Learn From In-Depth Analysis of Human Errors Resulting in Diagnostic Errors in the Emergency Department: An Analysis of Serious Adverse Event Reports.

INTRODUCTION: Human error plays a vital role in diagnostic errors in the emergency department. A thorough analysis of these human errors, using information-rich reports of serious adverse events (SAEs), could help to better study and understand the causes of these errors and formulate more specific recommendations. METHODS: We studied 23 SAE reports of diagnostic events in emergency departments of Dutch general hospitals and identified human errors. Two researchers independently applied the Safer Dx Instrument, Diagnostic Error Evaluation and Research Taxonomy, and the Model of Unsafe acts to analyze reports. RESULTS: Twenty-one reports contained a diagnostic error, in which we identified 73 human errors, which were mainly based on intended actions (n = 69) and could be classified as mistakes (n = 56) or violations (n = 13). Most human errors occurred during the assessment and testing phase of the diagnostic process. DISCUSSION: The combination of different instruments and information-rich SAE reports allowed for a deeper understanding of the mechanisms underlying diagnostic error. Results indicated that errors occurred most often during the assessment and the testing phase of the diagnostic process. Most often, the errors could be classified as mistakes and violations, both intended actions. These types of errors are in need of different recommendations for improvement, as mistakes are often knowledge based, whereas violations often happen because of work and time pressure. These analyses provided valuable insights for more overarching recommendations to improve diagnostic safety and would be recommended to use in future research and analysis of (serious) adverse events.