Assessing risk of hospital readmissions for improving medical practice

Parimal Kulkarni, L. Douglas Smith, Keith F. Woeltje

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish
Pages (from-to)291-299
Number of pages9
JournalHealth Care Management Science
Issue number3
StatePublished - Sep 1 2016


  • Decision trees
  • Healthcare analytics
  • Neural networks
  • Readmissions
  • Regression


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