Measuring potentially avoidable hospital readmissions

• Published in: Journal of clinical epidemiology Vol. 55; no. 6; pp. 573 - 587
• Main Authors: Halfon, Patricia, Eggli, Yves, van Melle, Guy, Chevalier, Julia, Wasserfallen, Jean-Blaise, Burnand, Bernard
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.06.2002; Elsevier
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Analysis of Variance; Avoidable; Biological and medical sciences; Child; Child, Preschool; Diagnosis-Related Groups - statistics & numerical data; Epidemiology; General aspects; Health Services Misuse - statistics & numerical data; Hospitalization, Hospital quality, Risk factors; Hospitals, University - statistics & numerical data; Humans; Infant; Medical Records Systems, Computerized - standards; Medical sciences; Methodology; Middle Aged; Outcome and Process Assessment (Health Care) - methods; Patient Readmission - standards; Patient Readmission - statistics & numerical data; Public health. Hygiene; Public health. Hygiene-occupational medicine; Readmission; Reproducibility of Results; Risk Factors; Sensitivity and Specificity; Switzerland; Hospitalization, Hospital quality, Risk factors; Avoidable; Readmission; Human; Evaluation; Hospital admission; Risk factor; Prediction; Hospitalization; Models; Epidemiology; Public health
• ISSN: 0895-4356; 1878-5921
• DOI: 10.1016/S0895-4356(01)00521-2

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.