TY - JOUR
T1 - Assessing risk of hospital readmissions for improving medical practice
AU - Kulkarni, Parimal
AU - Smith, L. Douglas
AU - Woeltje, Keith F.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - 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.
AB - 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.
KW - Decision trees
KW - Healthcare analytics
KW - Neural networks
KW - Readmissions
KW - Regression
UR - http://www.scopus.com/inward/record.url?scp=84927945803&partnerID=8YFLogxK
U2 - 10.1007/s10729-015-9323-5
DO - 10.1007/s10729-015-9323-5
M3 - Article
C2 - 25876516
AN - SCOPUS:84927945803
SN - 1386-9620
VL - 19
SP - 291
EP - 299
JO - Health Care Management Science
JF - Health Care Management Science
IS - 3
ER -