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Search results for "Diagnostic Errors"
Journal Article > Study
Automated identification of postoperative complications within an electronic medical record using natural language processing.
Murff HJ, FitzHenry F, Matheny ME, et al. JAMA. 2011;306:848-855.
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.
Journal Article > Commentary
Graber ML, Berg D, Jerde W, Kibort P, Olson APJ, Parkash V. Diagnosis (Berl). 2018;5:257-266.
This commentary provides a clinical review of a missed diagnosis of Epstein-Barr virus infection that was identified via autopsy and summarizes contributing factors to the incident with an emphasis on the role of cognitive bias. The piece includes the perspectives of the patient's family and from the organization regarding what happened and what could have been done to prevent this outcome. This discussion is the first in a series of diagnostic error case presentations to be published in this journal.
Journal Article > Study
Readmission after delayed diagnosis of surgical site infection: a focus on prevention using the American College of Surgeons National Surgical Quality Improvement Program.
Gibson A, Tevis S, Kennedy G. Am J Surg. 2014;207:832-839.
The National Surgical Quality Improvement Program (NSQIP) was developed to monitor and enhance the quality of surgical care. This retrospective study used the NSQIP indicators to identify cases of surgical site infections. Researchers found that nearly 50% of patients were diagnosed following hospital discharge, and many of these infections led to readmissions. Patients who presented with a surgical site infection after discharge were less likely to smoke or have chronic cardiopulmonary illness. The authors suggest that closer postdischarge follow-up might have prevented some readmissions they identified. However, prior studies did not show a benefit to early follow-up. A past AHRQ WebM&M commentary discussed environmental safety in the operating room and its relationship to surgical site infections.