Learning from error is not just an individual endeavour. Organisations also learn from error. Hospitals provide many learning opportunities, which can be formal or informal. Informal learning from error in hospitals has not been researched in much depth so this narrative review focuses on five learning opportunities: morbidity and mortality conferences, incident reporting systems, patient claims and complaints, chart review and prospective risk analysis. For each of them we describe: (1) what can be learnt, categorised according to the seven CanMEDS competencies; (2) how it is possible to learn from them, analysed against a model of informal and incidental learning; and (3) how this learning can be enhanced. All CanMEDS competencies could be enhanced, but there was a particular focus on the roles of medical expert and manager. Informal learning occurred mostly through reflection and action and was often linked to the learning of others. Most important to enhance informal learning from these learning opportunities was the realisation of a climate of collaboration and trust. Possible new directions for future research on informal learning from error in hospitals might focus on ways to measure informal learning and the balance between formal and informal learning. Finally, 12 recommendations about how hospitals could enhance informal learning within their organisation are given.
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http://dx.doi.org/10.1007/s10459-012-9400-1 | DOI Listing |
Updates Surg
January 2025
Department of Gastrointestinal Surgery, The First People's Hospital of Foshan, No. 81 Lingnan Avenue North, Foshan, China.
The surgical risk is higher for obese patients undergoing laparoscopic left hemicolectomy. To enhance the surgical safety and efficacy for obese patients, we have innovatively integrated the advantages of various surgical approaches to modify a pancreas-guided C-shaped surgical procedure. The safety and quality were assessed through a retrospective analysis.
View Article and Find Full Text PDFBiomech Model Mechanobiol
January 2025
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
When infants are admitted to the hospital with skull fractures, providers must distinguish between cases of accidental and abusive head trauma. Limited information about the incident is available in such cases, and witness statements are not always reliable. In this study, we introduce a novel, data-driven approach to predict fall parameters that lead to skull fractures in infants in order to aid in determinations of abusive head trauma.
View Article and Find Full Text PDFJ Youth Adolesc
January 2025
Research Center of Adolescent Psychology and Behavior, School of Education, Guangzhou University, Guangzhou, China.
Risk-taking is a concerning yet prevalent issue during adolescence and can be life-threatening. Examining its etiological sources and evolving pathways helps inform strategies to mitigate adolescents' risk-taking behavior. Studies have found that unfavorable environmental factors, such as adverse childhood experiences (ACEs), are associated with momentary levels of risk-taking in adolescents, but little is known about whether ACEs shape the developmental trajectory of risk-taking.
View Article and Find Full Text PDFPsychol Res
January 2025
School of Psychology, Shenzhen University, Shenzhen, China.
Extrinsic motivation can foster effortful cognitive control. Moreover, the selective coupling of extrinsic motivation on low- versus high-control demands tasks would exert an additional impact. However, to what extent their influences are further modulated by the level of Need for Cognition (NFC) remains unclear.
View Article and Find Full Text PDFPediatr Cardiol
January 2025
Department of Infectious Disease, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, No. 1678 Dongfang Road, Pudong New Area, Shanghai, 200127, China.
Kawasaki disease (KD) is a febrile vasculitis disorder, with coronary artery lesions (CALs) being the most severe complication. Early detection of CALs is challenging due to limitations in echocardiographic equipment (UCG). This study aimed to develop and validate an artificial intelligence algorithm to distinguish CALs in KD patients and support diagnostic decision-making at admission.
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