ArticleFrontiers in digital health2025
Applying a logistic regression-clustering joint model to analyze the causes of prolonged pre-analytic turnaround time for urine culture testing in hospital wards.
Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Closed loop full process management and application of body fluid specimens in hospitalised patients.BMJ open quality · 2026Article
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4 authors.
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Abstract
Introduction: In this study, we developed and validated a logistic regression-clustering joint model to: (1) quantify multistage workflow bottlenecks (collection/transport/reception) in urine culture pre-TAT prolongation (>115 min); and (2) assess the efficacy of targeted interventions derived from model-derived insights. Methods: Using complete workflow data obtained from 1,343 urine culture specimens (January 2024-March 2024) collected at a tertiary hospital, we integrated binary logistic regression analysis with K-means clustering to quantify delay patterns. The analyzed variables included collection time, ward type, personnel roles, and patient demographics. Post-intervention data (May 2024-July 2024, * Results: Analysis of the critical risk factors revealed that specimens collected between 04:00-05:59/10:00-11:59 had 142.92-fold higher delay odds (95% CI: 58.81-347.37). Those collected on SICU/ICU wards showed 9.98-fold higher risk (95% CI: 5.05-19.72) than general wards. Regarding intervention efficacy, pre-TAT overtime rates decreased by 58.6% (13.48% → 7.55%, Discussion: The joint model effectively identified workflow bottlenecks. Targeted interventions (dynamic transport scheduling, standardized training, and IoT alert systems) significantly optimized pre-TAT and specimen quality, providing a framework for improving clinical laboratory processes.
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