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Big Data-Driven Determinants of Length of Stay for Patients with Hip Fracture

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  • Jihye Lim

    (Department of Healthcare Management, Youngsan University, Yangsan 50510, Korea)

Abstract

It is important that length of stay (LOS) management for alleviating health care financial burdens and improving patient outcomes. The aim of this study was to report the differences of LOS and the factors affecting LOS of hip fracture patients using big data. A total of 463,194 data were collected from 2016 to 2017 KNHDS. Of those, 2238 patients with the hip fracture primary diagnosis were included in the study population. As independent variables were used gender, age, type of insurance, admission route, result of treatment, number of hospital beds, the presence of surgery, and comorbidities. Statistical analysis performed using the IBM SPSS Statistics for Windows, version 25.0. A statistically significant difference was observed in the length of stay of hip fracture patients according to the healthcare insurance type. The difference in LOS associated with comorbidities was statistically significant for hypertension, peptic ulcer disease, coagulopathy, and alcohol abuse ( p < 0.05). Independent variables that affected LOS of hip fracture patients with national health insurance were the treatment result, operation presence, comorbidity count, and hospital beds ( p < 0.001). The factors associated with the length of stay for hip fracture patients were the difference according to the healthcare insurance type. The results of this study can be used as a basic data for the national health policy for the proper distribution and utilization of medical resources.

Suggested Citation

  • Jihye Lim, 2020. "Big Data-Driven Determinants of Length of Stay for Patients with Hip Fracture," IJERPH, MDPI, vol. 17(14), pages 1-9, July.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:14:p:4949-:d:382232
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    Cited by:

    1. Ying-Jen Chang & Kuo-Chuan Hung & Li-Kai Wang & Chia-Hung Yu & Chao-Kun Chen & Hung-Tze Tay & Jhi-Joung Wang & Chung-Feng Liu, 2021. "A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery," IJERPH, MDPI, vol. 18(5), pages 1-14, March.

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