Prevalence And Characterisation Of Diagnostic Error Among 7-Day All-Cause Hospital Medicine Readmissions: A Retrospective Cohort Study

BMJ QUALITY & SAFETY(2020)

引用 24|浏览5
暂无评分
摘要
Background The prevalence and aetiology of diagnostic error among hospitalised adults is unknown, though likely contributes to patient morbidity and mortality. We aim to identify and characterise the prevalence and types of diagnostic error among patients readmitted within 7 days of hospital discharge.Methods Retrospective cohort study at a single urban academic hospital examining adult patients discharged from the medical service and readmitted to the same hospital within 7 days between January and December 2018. The primary outcome was diagnostic error presence, identified through two-physician adjudication using validated tools. Secondary outcomes included severity of error impact and characterisation of diagnostic process failures contributing to error.Results There were 391 cases of unplanned 7-day readmission (5.2% of 7507 discharges), of which 376 (96.2%) were reviewed. Twenty-one (5.6%) admissions were found to contain at least one diagnostic error during the index admission. The most common problem areas in the diagnostic process included failure to order needed test(s) (n=11, 52.4%), erroneous clinician interpretation of test(s) (n=10, 47.6%) and failure to consider the correct diagnosis (n=8, 38.1%). Nineteen (90.5%) of the diagnostic errors resulted in moderate clinical impact, primarily due to short-term morbidity or contribution to the readmission.Conclusion The prevalence of diagnostic error among 7-day medical readmissions was 5.6%. The most common drivers of diagnostic error were related to clinician diagnostic reasoning. Efforts to reduce diagnostic error should include strategies to augment diagnostic reasoning and improve clinician decision-making around diagnostic studies.
更多
查看译文
关键词
diagnostic errors, hospital medicine, patient safety, medical error, measurement, epidemiology, adverse events, epidemiology and detection
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要