title: Evaluation of screening criteria for adverse events in medical patients.
abstract: The goals of this study were to evaluate the sensitivity and specificity of 15 screening criteria for adverse events, preventable adverse events, and severe adverse events in medical patients, and to evaluate combinations of these criteria, including those available through hospital billing data, to determine whether a small subset of generic screens might efficiently identify adverse events. The authors studied 3,137 consecutive admissions to a medical service over a 4-month period at an urban tertiary care hospital. Chart reviews were performed after discharge by reviewers blinded to the eventual determination of presence of an adverse event. Judgments regarding presence, severity, and preventability of adverse events were made using guided implicit reviews by physicians. Of all admissions, 341 (11%) were judged to include an adverse event, of which 274 were severe and 145 were preventable. Sensitivity and specificity of individual screens varied widely, with prior hospitalization the most sensitive (68%) but least specific (56%). Death was specific (97%) but not sensitive (9%); readmission was intermediate (sensitivity 28%, specificity 80%). In analyses using severe and preventable adverse events as the outcome, results were generally similar. Combinations of screens also were compared, including some using only screens available through billing data; the most sensitive billing strategy detected just 47% of adverse events, but cost only $3 per admission reviewed and $57 per adverse event, versus $13 per admission and $116 per adverse event for a strategy in which all records were reviewed. It is concluded that no small subset of screens identified a high percentage of adverse events. Using screens available through billing data, although insensitive, would be much less costly.
authors.affiliation: Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
authors.forename: D W
authors.initials: DW
authors.lastname: Bates
authors.forename: A C
authors.initials: AC
authors.lastname: O'Neil
authors.forename: L A
authors.initials: LA
authors.lastname: Petersen
authors.forename: T H
authors.initials: TH
authors.lastname: Lee
authors.forename: T A
authors.initials: TA
authors.lastname: Brennan
mesh_terms: D001900:Boston / Q000453:epidemiology; D016009:Chi-Square Distribution; D006302:Health Services Research / Q000379:methods; D017721:Hospital Costs; D017052:Hospital Mortality; D006785:Hospitals, University / Q000706:statistics & numerical data; D006801:Humans; D007049:Iatrogenic Disease / Q000453:epidemiology*; D016015:Logistic Models; D008485:Medical Audit / Q000191:economics / Q000706:statistics & numerical data; D015999:Multivariate Analysis; D010351:Patient Discharge / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data; D011787:Quality of Health Care / Q000191:economics / Q000706:statistics & numerical data*; D011897:Random Allocation; D012308:Risk Management / Q000191:economics / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity