Using a social and behavioral sciences perspective, the authors present insights for aligning behavior with recommendations from experts for managing the COVID-19 pandemic and its impact. Topics include threat perception, leadership, individual and collective interests, science communication, social context, and stress and coping.
Tartari E, Saris K, Kenters N, et al. PLoS One. 2020;15.
Presenteeism among healthcare workers can lead to burnout and healthcare-associated infections, but prior research has found that significant numbers of healthcare workers continue to work despite having influenza-like illness. This study surveyed 249 healthcare workers and 284 non-healthcare workers from 49 countries about their behaviors when experiencing influenza-like illness between October 2018 and January 2019. Overall, 59% of workers would continue to work when experiencing influenza-like illness, and the majority of healthcare workers (89.2-99.2%) and non-healthcare workers (80-96.5%) would continue to work with mild symptoms, such as a mild cough, fatigue or sinus cold. Fewer non-healthcare workers (16.2%) than healthcare workers (26.9%) would continue working with fever alone.
Tschandl P, Codella N, Akay BN, et al. Lancet Oncol. 2019;20:938-947.
Machine learning may have the potential to improve clinical decision-making and diagnosis. In this study, machine-learning algorithms generally performed better than human experts in accurately diagnosing 7 types of pigmented skin lesions and the top 3 algorithms performed better than the 27 physicians.
Bejnordi BE, Veta M, van Diest PJ, et al. JAMA. 2017;318:2199-2210.
Diagnostic error is a growing area of focus within patient safety. Artificial intelligence has the potential to improve the diagnostic process, both in terms of accuracy and efficiency. In this study, investigators compared the use of automated deep learning algorithms for detecting metastatic disease in stained tissue sections of lymph nodes of women with breast cancer to pathologists' diagnoses. The algorithms were developed by researchers as part of a competition and their performance was assessed on a test set of 129 slides, 49 with metastatic disease and 80 without. A panel of 11 pathologists evaluated the same slides with a 2-hour time limit and one pathologist evaluated the slides without any time constraints. The authors conclude that some of the algorithms demonstrated better diagnostic performance than the pathologists did, but they suggest that further testing in a clinical setting is warranted. An accompanying editorial discusses the potential of artificial intelligence in health care.
Plebani M, ed. Clinica Chimica Acta. 2009;404(1):1-86.
This collection of papers presented at an international conference on laboratory medicine focuses on efforts to reduce medical errors in laboratory practice, especially those concerning diagnostic mistakes.
This initiative provides a surgical safety checklist and related educational and training materials building on the Second Global Patient Safety Challenge vision to encourage international adoption of a core set of safety standards. Implementation of this World Health Organization’s checklist has resulted in dramatic reductions in surgical mortality and complications across diverse international hospitals. Surgical checklists have now become one of the clearest success stories in the patient safety movement, although some have described challenges to effective implementation. Dr. Atul Gawande discussed the history of checklists as a quality and safety tool in his book, The Checklist Manifesto: How to Get Things Right.
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