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.

This pre-post study found that a human factors approach improved blood collection procedures in the emergency department, which is important for preventing adverse events such as transfusion errors. This demonstrates the benefits of applying human factors engineering in patient safety efforts across health care settings.

Diagnosis is a critical area of patient safety. Prior research demonstrates that physicians perceive time pressure as an impediment to diagnosis, but this has not been objectively documented. This educational simulation study examined the ability of internal medicine residents to correctly diagnose written cases with and without time pressure. Residents under time pressure had reduced diagnostic accuracy, and this decrement was more marked for difficult cases. These results demonstrate the benefit of allowing physicians more time for accurate diagnosis, consistent with recent Institute of Medicine recommendations to examine novel models of care and reimbursement to foster diagnostic safety. A recent PSNet interview discussed diagnostic errors and how to reduce them.

Root cause analysis was used to explore contributing factors to both active errors and latent errors in the diagnostic evaluation of trauma patients.