Assessing risk of hospital readmissions for improving medical practice. Health Care Manag Sci 2016 Sep;19(3):291-9
Date
04/17/2015Pubmed ID
25876516DOI
10.1007/s10729-015-9323-5Scopus ID
2-s2.0-84927945803 (requires institutional sign-in at Scopus site) 16 CitationsAbstract
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.
Author List
Kulkarni P, Smith LD, Woeltje KFAuthor
Keith F. Woeltje MD, PhD Associate Dean, Professor in the Medicine department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Age FactorsAged
Decision Trees
Environment
Hospital Bed Capacity
Humans
Insurance, Health
Length of Stay
Logistic Models
Medicine
Middle Aged
Patient Discharge
Patient Readmission
Risk Assessment
Risk Factors
Severity of Illness Index
