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Validation of a Discharge Summary Term Search Method to Detect Adverse Events

Affiliations of the authors: Department of Medicine University of Ottawa (AJF, CVW), Ottawa Health Research Institute (AJF, CVW), Institute for Clinical Evaluative Sciences (CVW), Ottawa, ON, Canada; University of British Columbia, Vancouver, BC, Canada (JA).

Correspondence and reprints: Alan J. Forster, MD, FRCPC, MSc, C406-1053 Carling Avenue, Ottawa, ON, Canada K1Y 4E9; e-mail: <aforster{at}ohri.ca>

Received for publication: 07/16/04; accepted for publication: 10/16/04.

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.

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