| Autor: |
Kulkarni P; BJC HealthCare & University of Missouri-St. Louis, BJC Learning Institute, Ste 400A, St Louis, MO, 63144, USA. parimal.kulkarni@bjc.org., Smith LD; Center for Business and Industrial Studies, College of Business Administration, University of Missouri-St. Louis, St Louis, MO, 63121, USA., Woeltje KF; BJC HealthCare & Washington University School of Medicine, Center for Clinical Excellence, BJC Learning Institute, Ste 400A, St Louis, MO, 63144, USA. |
| Jazyk: |
angličtina |
| Zdroj: |
Health care management science [Health Care Manag Sci] 2016 Sep; Vol. 19 (3), pp. 291-9. Date of Electronic Publication: 2015 Apr 16. |
| DOI: |
10.1007/s10729-015-9323-5 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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