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Electronically screening discharge summaries for adverse medical events.

机译:电子检查出院摘要以了解不良医疗事件。

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OBJECTIVE: Detecting adverse events is pivotal for measuring and improving medical safety, yet current techniques discourage routine screening. The authors hypothesized that discharge summaries would include information on adverse events, and they developed and evaluated an electronic method for screening medical discharge summaries for adverse events. DESIGN: A cohort study including 424 randomly selected admissions to the medical services of an academic medical center was conducted between January and July 2000. The authors developed a computerized screening tool that searched free-text discharge summaries for trigger words representing possible adverse events. MEASUREMENTS: All discharge summaries with a trigger word present underwent chart review by two independent physician reviewers. The presence of adverse events was assessed using structured implicit judgment. A random sample of discharge summaries without trigger words also was reviewed. RESULTS: Fifty-nine percent (251 of 424) of the discharge summaries contained trigger words. Based on discharge summary review, 44.8% (327 of 730) of the alerted trigger words indicated a possible adverse event. After medical record review, the tool detected 131 adverse events. The sensitivity and specificity of the screening tool were 69% and 48%, respectively. The positive predictive value of the tool was 52%. CONCLUSION: Medical discharge summaries contain information regarding adverse events. Electronic screening of discharge summaries for adverse events using keyword searches is feasible but thus far has poor specificity. Nonetheless, computerized clinical narrative screening methods could potentially offer researchers and quality managers a means to routinely detect adverse events.
机译:目的:检测不良事件对于衡量和改善医疗安全至关重要,但目前的技术不利于常规筛查。作者假设出院总结将包括不良事件的信息,他们开发并评估了一种电子方法,用于筛选出院摘要中是否存在不良事件。设计:2000年1月至2000年7月进行了一项队列研究,包括424个随机选择的学术医疗中心的医疗服务。作者开发了一种计算机化的筛选工具,该工具可搜索自由文本出处摘要以表示可能的不良事件的触发词。测量:由两名独立的医师审阅者对所有带有触发词的出院摘要进行图表审阅。使用结构化的隐含判断评估不良事件的存在。还审查了无触发词的放电摘要的随机样本。结果:59%(424的251)中的放电词包含触发词。根据放电总结,警报触发词中有44.8%(730个中的327个)表示可能发生不良事件。经过病历审查后,该工具检测到131例不良事件。筛选工具的敏感性和特异性分别为69%和48%。该工具的阳性预测值为52%。结论:出院摘要包含有关不良事件的信息。使用关键词搜索对不良事件的放电摘要进行电子筛选是可行的,但到目前为止其特异性较差。但是,计算机化的临床叙事筛选方法可能会为研究人员和质量管理人员提供常规检测不良事件的手段。

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