title: Assessing risk of hospital readmissions for improving medical practice.
abstract: We compare statistical approaches for predicting the likelihood that individual patients will require readmission to hospital within 30 days of their discharge and for setting quality-control standards in that regard. Logistic regression, neural networks and decision trees are found to have comparable discriminating power when applied to cases that were not used to calibrate the respective models. Significant factors for predicting likelihood of readmission are the patient's medical condition upon admission and discharge, length (days) of the hospital visit, care rendered during the hospital stay, size and role of the medical facility, the type of medical insurance, and the environment into which the patient is discharged. Separately constructed models for major medical specialties (Surgery/Gynecology, Cardiorespiratory, Cardiovascular, Neurology, and Medicine) can improve the ability to identify high-risk patients for possible intervention, while consolidated models (with indicator variables for the specialties) can serve well for assessing overall quality of care.
authors: BJC HealthCare & University of Missouri-St. Louis, BJC Learning Institute, Ste 400A, St Louis, MO, 63144, USA. parimal.kulkarni@bjc.org.
authors: Parimal
authors: P
authors: Kulkarni
authors: Center for Business and Industrial Studies, College of Business Administration, University of Missouri-St. Louis, St Louis, MO, 63121, USA.
authors: L Douglas
authors: LD
authors: Smith
authors: BJC HealthCare & Washington University School of Medicine, Center for Clinical Excellence, BJC Learning Institute, Ste 400A, St Louis, MO, 63144, USA.
authors: Keith F
authors: KF
authors: Woeltje
mesh_terms: D000367:Age Factors; D000368:Aged; D003663:Decision Trees; D004777:Environment; D006742:Hospital Bed Capacity / Q000706:statistics & numerical data; D006801:Humans; D007348:Insurance, Health / Q000706:statistics & numerical data; D007902:Length of Stay / Q000706:statistics & numerical data; D016015:Logistic Models; D008511:Medicine / Q000706:statistics & numerical data; D008875:Middle Aged; D016571:Neural Networks, Computer; D010351:Patient Discharge / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data*; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index
keywords: Decision trees; Healthcare analytics; Neural networks; Readmissions; Regression