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Validation of a discharge summary term search method to detect adverse events.

机译:验证放电不良术语搜索方法以检测不良事件。

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OBJECTIVE: Adverse events are poor health outcomes caused by medical care. Measuring them is necessary for quality improvements, but current detection methods are inadequate. We performed this study to validate a previously derived method of adverse event detection using term searching in physician-dictated discharge summaries. DESIGN: This was a retrospective, chart review study of a random sample of 245 adult medicine and surgery patients admitted to a multicampus academic medical center in 2002. MEASUREMENTS: The authors used a commercially available search engine to scan discharge summaries for the presence of 104 terms that potentially indicate an adverse event. Summaries with any of these terms were reviewed by a physician to determine the term's context. Screen-positive summaries had a term that was contextually indicative of an adverse event. We used a two-stage chart review as the gold standard to determine the true presence or absence of an adverse event. RESULTS: The average patient age was 62 years (standard deviation 18.6) and 55% were admitted to a medical service. By gold standard criteria, 48 of 245 patients had an adverse event. Term searching classified 27 cases with an adverse event, with 11 true positives; 218 cases were classified as not having an adverse event, with 181 true negatives. The sensitivity, specificity, and positive and negative predictive values were 0.23 (95% confidence interval [CI]=0.11-0.35), 0.92 (95% CI=0.88-0.96), 0.41 (95% CI=0.25-0.59), and 0.83 (95% CI=95% 0.77-0.97), respectively. CONCLUSION: Although the sensitivity of the method is low, its high specificity means that the method could be used to replace expensive manual chart reviews by nurses.
机译:目的:不良事件是由医疗引起的不良健康结果。测量它们对于提高质量是必要的,但是当前的检测方法是不够的。我们进行了这项研究,以验证先前导出的不良事件检测方法,该方法使用了医师指定的出院摘要中的术语搜索。设计:这是一项回顾性图表回顾性研究,对2002年进入多校学术医学中心的245名成年医学和外科手术患者进行了随机抽样。测量:作者使用商业搜索引擎扫描了出院摘要中是否存在104项内容。可能表示不良事件的术语。医生会审查这些术语中的任何一个的摘要,以确定术语的上下文。筛查阳性摘要的用语在上下文中指示不良事件。我们使用两阶段图表审查作为黄金标准,以确定不良事件的真实存在与否。结果:平均患者年龄为62岁(标准差18.6),有55%的患者接受了医疗服务。按照金标准,245名患者中有48名发生了不良事件。术语搜索对27例不良事件进行了分类,其中11例为阳性。 218例未发生不良事件,其中181例为阴性。敏感性,特异性以及阳性和阴性预测值分别为0.23(95%置信区间[CI] = 0.11-0.35),0.92(95%CI = 0.88-0.96),0.41(95%CI = 0.25-0.59)和分别为0.83(95%CI = 95%0.77-0.97)。结论:尽管该方法的敏感性较低,但其高特异性意味着该方法可用于代替护士昂贵的人工检查表。

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