Determinants of Length of Stay for Medical Inpatients Using Survival Analysis

Jaekyeong Kim, Haegak Chang, Seiyoung Ryu, Ilyoung Choi, Angela Eunyoung Kwon and Haeyong Ji ()
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Jaekyeong Kim: School of Management, Kyung Hee University, Seoul 02447, Republic of Korea
Haegak Chang: School of Management, Kyung Hee University, Seoul 02447, Republic of Korea
Seiyoung Ryu: Department of Bigdata Analytics, Kyung Hee University, Seoul 02447, Republic of Korea
Ilyoung Choi: Division of Business Administration, Seo Kyeong Uiversity, Seoul 02713, Republic of Korea
Angela Eunyoung Kwon: Sauder School of Business, University of British Columbia, Vancouver, BC 2053, Canada
Haeyong Ji: Department of Management, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea

IJERPH, 2024, vol. 21, issue 11, 1-14

Abstract: With the increase in insured patients and an aging population, managing the length of stay (LOS) for inpatients has become crucial for controlling medical costs. Analyzing the factors influencing LOS is necessary for effective management. Previous studies often used multiple or logistic regression analyses, which have limitations such as unmet assumptions and the inability to handle time-dependent variables. To address these issues, this study applied survival analysis to examine the factors affecting LOS using the National Health Insurance Service (NHIS) sample cohort data from 2016 to 2019, covering over 4 million records. We used Kaplan–Meier survival estimation to assess LOS probabilities based on sociodemographic, patient, health checkup, and institutional characteristics. Additionally, the Cox proportional hazards model controlled for confounding factors, providing more robust validation. Key findings include the influence of age, gender, type of insurance, and hospital type on LOS. For instance, older patients and medical aid recipients had longer LOS, while general hospitals showed shorter stays. This study is the first in Korea to use survival analysis with a large cohort database to identify LOS determinants. The results provide valuable insights for shaping healthcare policies aimed at optimizing inpatient care and managing hospital resources more efficiently.

Keywords: survival analysis; Kaplan–Meier survival analysis; Cox proportional hazards model; health checkup cohort DB; length of stay; medical data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2024
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