Missed diagnosis of stroke in emergency medicine settings is an important patient safety problem. In this study, researchers interviewed emergency medicine physicians about their perspectives on diagnostic neurology and use of clinical decision support (CDS) tools. Themes emerged related to challenges in diagnosis, neurological complaints, and challenges in diagnostic decision-making emergency medicine, more generally. Participating physicians were enthusiastic about the possibility of involving CDS tools to improve diagnosis for non-specific neurological complaints.
Clinical decision support (CDS) systems are designed to improve diagnosis. Researchers surveyed emergency department physicians about their evaluation of human factors-based CDS systems to improve diagnosis of pulmonary embolism. Although perceived usability was high, use of the CDS tool in the real clinical environment was low; the authors identified several barriers to use, including lack of workflow integration.
Stark N, Kerrissey M, Grade M, et al. West J Emerg Med. 2020;21:1095-1101.
This article describes the development and implementation of a digital tool to centralize and standardize COVID-19-related resources for use in the emergency department (ED). Clinician feedback suggests confirms that the tool has affected their management of COVID-19 patients. The tool was found to be easily adaptable to accommodate rapidly evolving guidance and enable organizational capacity for improvisation and resiliency.
Carayon P, Hoonakker P, Hundt AS, et al. BMJ Qual Saf. 2020;29:329-340.
This simulation study assessed whether integrating human factors engineering into a clinical decision support system can improve the diagnosis of pulmonary embolism (PE) in the ED. Authors found that this approach can improve the PE diagnostic process by saving time, reducing perceived workload and improving physician satisfaction with the technology.
Murff HJ, FitzHenry F, Matheny ME, et al. JAMA. 2011;306:848-55.
Many adverse event identification methods cannot detect errors until well after the event has occurred, as they rely on screening administrative data or review of the entire chart after discharge. Electronic medical records (EMRs) offer several potential patient safety advantages, such as decision support for averting medication or diagnostic errors. This study, conducted in the Veterans Affairs system, reports on the successful development of algorithms for screening clinicians' notes within EMRs to detect postoperative complications. The algorithms accurately identified a range of postoperative adverse events, with a lower false negative rate than the Patient Safety Indicators. As the accompanying editorial notes, these results extend the patient safety possibilities of EMRs to potentially allow for real time identification of adverse events.
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