Evaluating readmission rates and discharge planning by analyzing the length-of-stay of patients

Wanlu Gu, Neng Fan () and Haitao Liao
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Wanlu Gu: University of Arizona
Neng Fan: University of Arizona
Haitao Liao: University of Arkansas

Annals of Operations Research, 2019, vol. 276, issue 1, No 4, 89-108

Abstract: Abstract The length-of-stay (LOS) is an important quality metric in health care, and the use of phase-type (PH) distribution provides a flexible method for modeling LOS. In this paper, we model the patient flow information collected in a hospital for patients of distinct diseases, including headache, liveborn infant, alcohol abuse, acute upper respiratory infection, and secondary cataract. Based on the results obtained from fitting Coxian PH distributions to the LOS data, the patients can be divided into different groups. By analyzing each group to find out their common characteristics, the corresponding readmission rate and other useful information can be evaluated. Furthermore, a comparison of patterns for each disease is analyzed. We conclude that it is important to offering better service and avoiding waste of sources, by the analysis of the relations between groups and readmission. In addition, comparing the patterns within distinct diseases, a better decision for assigning resources and improving the insurance policy can be made.

Keywords: Phase-type distribution; Healthcare quality; Length-of-stay; Markov chains; Readmission rate (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10479-018-2957-1

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