Observational studyBMJ open2025
Promising solution for standardised length of hospital stay based on time-to-event models and contemporary Australian administrative data.
Observational study in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
10 authors.
Funding
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Abstract
objectiveHospital length of stay (LOS) is a key indicator of hospital efficiency and quality of care, but a reliable metric for benchmarking LOS remains problematic. This report describes a time-to-event methodology to generate a hospital standardised LOS ratio (HSLR).
designRetrospective observational analysis of LOS from a jurisdictional administrative dataset using a time-to-event (hazard of discharge) analytic approach to generate risk-adjusted LOS (predicted LOS-pLOS), and the HSLR (= (sum observed LOS)/(sum total pLOS)).
setting219 (public and private) acute-care hospitals in the State of Victoria, Australia, adult population 5.28 million.
participants2.73 million adult multiday separations and 15.53 million bed-days from July 2019 to June 2024.
interventionsNil. OUTCOME MEASURES: Descriptive statistics for annual mean LOS (aLOS), pLOS and HSLR at the hospital level with model fit assessed for calibration (Cox-Snell residuals), classification (aLOS and HSLR results for hospital-years compared to benchmark), variance (intraclass correlation coefficient (ICC) at provider level) and model dispersion (value (φ) and random effect SD (τ)) characteristics.
resultsObserved LOS was markedly right skewed and autocorrelated (p<0.001); population aLOS 5.68±4.98 days, median 3.94 (IQR: 2.48-6.76) days. LOS prediction model included six demographic covariates (age, sex, aged-care residency, emergent, admission source, unplanned transfer) and 12 145 separate principal diagnoses aggregated into nine ranked LOS risk-categories. 572 (61% of 940 hospital-year) aLOS values were outliers (>3 SD of benchmark); whereas 936 (99.5%) HSLR values were inliers (<±3 SD); 98% within ±2 SD. Some overdispersion (φ=14.5 ±1.7, τ=0.09 ±0.001) remained, but ICC at provider level (0.025) was low.
conclusionsaLOS is a simple descriptor but poor comparator. Time-to-event survival analytic models furnish risk-adjusted pLOS and HSLR metrics which indicate that the majority of LOS variation is due to patient-related, not hospital, factors.
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