title: Measuring potentially avoidable hospital readmissions.
abstract: The objectives of this study were to develop a computerized method to screen for potentially avoidable hospital readmissions using routinely collected data and a prediction model to adjust rates for case mix. We studied hospital information system data of a random sample of 3,474 inpatients discharged alive in 1997 from a university hospital and medical records of those (1,115) readmitted within 1 year. The gold standard was set on the basis of the hospital data and medical records: all readmissions were classified as foreseen readmissions, unforeseen readmissions for a new affection, or unforeseen readmissions for a previously known affection. The latter category was submitted to a systematic medical record review to identify the main cause of readmission. Potentially avoidable readmissions were defined as a subgroup of unforeseen readmissions for a previously known affection occurring within an appropriate interval, set to maximize the chance of detecting avoidable readmissions. The computerized screening algorithm was strictly based on routine statistics: diagnosis and procedures coding and admission mode. The prediction was based on a Poisson regression model. There were 454 (13.1%) unforeseen readmissions for a previously known affection within 1 year. Fifty-nine readmissions (1.7%) were judged avoidable, most of them occurring within 1 month, which was the interval used to define potentially avoidable readmissions (n = 174, 5.0%). The intra-sample sensitivity and specificity of the screening algorithm both reached approximately 96%. Higher risk for potentially avoidable readmission was associated with previous hospitalizations, high comorbidity index, and long length of stay; lower risk was associated with surgery and delivery. The model offers satisfactory predictive performance and a good medical plausibility. The proposed measure could be used as an indicator of inpatient care outcome. However, the instrument should be validated using other sets of data from various hospitals.
authors.affiliation: Institut Universitaire de Médecine Sociale et Préventive, University of, Lausanne, Switzerland. Patricia.Halfon@hospvd.ch
authors.forename: Patricia
authors.initials: P
authors.lastname: Halfon
authors.forename: Yves
authors.initials: Y
authors.lastname: Eggli
authors.forename: Guy
authors.initials: G
authors.lastname: van Melle
authors.forename: Julia
authors.initials: J
authors.lastname: Chevalier
authors.forename: Jean Blaise
authors.initials: JB
authors.lastname: Wasserfallen
authors.forename: Bernard
authors.initials: B
authors.lastname: Burnand
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000704:Analysis of Variance; D002648:Child; D002675:Child, Preschool; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data; D006300:Health Services Misuse / Q000706:statistics & numerical data*; D006785:Hospitals, University / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D016347:Medical Records Systems, Computerized / Q000592:standards; D008875:Middle Aged; D010043:Outcome and Process Assessment, Health Care / Q000379:methods*; D010359:Patient Readmission / Q000592:standards* / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012307:Risk Factors; D012680:Sensitivity and Specificity; D013557:Switzerland