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Prediction of Hospitalization Length. Quantile Regression Predicts Hospitalization Length and its Related Factors
M Kazemi1, S Nazari1, N Motamed2
1Department of Biostatistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.
Annali Di Igiene : Medicina Preventiva E Di Comunita
|February 11, 2021
Summary
Hospital length of stay varies significantly by unit and cause. Quantile regression offers a more precise prediction of hospitalization duration due to data skewness.
Area of Science:
- Healthcare Management
- Medical Statistics
- Health Services Research
Background:
- Hospital length of stay (LOS) is a key metric for evaluating hospital efficiency and resource utilization.
- Identifying factors influencing LOS is crucial for optimizing healthcare delivery.
- This study focuses on Zanjan teaching hospitals in 2018.
Purpose of the Study:
- To investigate the factors affecting the length of hospitalization.
- To apply the Quantile regression model for a more precise analysis of LOS.
- To understand variations in LOS across different hospital units and patient demographics.
Main Methods:
- A cross-sectional study involving 1,031 patients from various units (orthopaedic, pediatric, internal, surgical, intensive care).
- Data collected using a pre-designed checklist via multistage random sampling.
- Analysis performed using both Quantile regression and ordinary regression models.
Main Results:
- The average LOS was 7.58±5.83 days, with 90% of patients hospitalized for less than 14 days.
- Significant differences in LOS were observed between surgical/orthopedic units and intensive care units.
- Patients hospitalized for injuries and poisonings showed statistically significant differences in LOS compared to other causes (p<0.05).
Conclusions:
- Quantile regression provides a more accurate prediction of LOS compared to ordinary regression, especially given the heterogeneity (skewness) of stay durations.
- Understanding these factors can aid in better resource allocation and hospital management.
- The findings highlight the need for unit-specific and cause-specific approaches to managing hospital stays.
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