Determinants of Length of Stay for Medical Inpatients Using Survival Analysis
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".