BackgroundHospitals in various countries such as the Netherlands investigate and analyse serious adverse events (SAEs) to learn from previous events and attempt to prevent recurrence. However, current methods for SAE analysis do not address the complexity of healthcare and investigations typically focus on single events on the hospital level. This hampers hospitals in their ambition to learn from SAEs. Integrating human factors thinking and using a holistic and more consistent method could improve learning from SAEs.AimThis study aims to develop a novel generic analysis method (GAM) to: (1) facilitate a holistic event analysis using a human factors perspective and (2) ease aggregate analysis of events across hospitals.MethodsMultiple steps of carefully evaluating, testing and continuously refining prototypes of the method were performed. Various Dutch stakeholders in the field of patient safety were involved in each step. Theoretical experts were consulted, and the prototype was pretested using information-rich SAE reports from Dutch hospitals. Expert panels, engaging quality and safety experts and medical specialists from various hospitals were consulted for face and content validity evaluation. User test sessions concluded the development of the method.ResultsThe final version of the GAM consists of a framework and affiliated questionnaire. GAM combines elements of three methods for SAE analysis currently practised by Dutch hospitals. It is structured according to the Systems Engineering Initiative for Patient Safety model, which incorporates a human factors perspective into the analysis. These eases aggregated analysis of SAEs across hospitals and helps to consider the complexity of healthcare work systems.ConclusionThe GAM is a valuable new tool for hospitals to learn from SAEs. The method can facilitate a holistic aggregate analysis of SAEs across hospitals using a human factors perspective, and is now ready for further extensive testing.

ObjectivesUnintended events (UEs) are prevalent in healthcare facilities, and learning from them is key to improve patient safety. The Prevention and Recovery Information System for Monitoring and Analysis (PRISMA)-method is a root cause analysis method used in healthcare facilities. The aims of this systematic review are to map the use of the PRISMA-method in healthcare facilities worldwide, to assess the insights that the PRISMA-method offers, and to propose recommendations to increase its usability in healthcare facilities.MethodsPubMed, EMBASE.com, CINAHL, and The Cochrane Library were systematically searched from inception to February 26, 2020. Studies were included if the PRISMA-method for analyzing UEs was applied in healthcare facilities. A quality appraisal was performed, and relevant data based on an appraisal checklist were extracted.ResultsThe search provided 2773 references, of which 25 articles reporting 10,816 UEs met our inclusion criteria. The most frequently identified root causes were human-related, followed by organizational factors. Most studies took place in the Netherlands (n = 20), and the sample size ranged from 1 to 2028 UEs. The study setting and collected data used for PRISMA varied widely. The PRISMA-method performed by multiple persons resulted in more root causes per event.ConclusionsTo better understand UEs in healthcare facilities and formulate optimal countermeasures, our recommendations to further improve the PRISMA-method mainly focus on combining information from patient files and reports with interviews, including multiple PRISMA-trained researchers in an analysis, and modify the Eindhoven Classification Model if needed.

IntroductionHuman 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.MethodsWe 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.ResultsTwenty-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.DiscussionThe 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.

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.