ORIGINAL RESEARCH article
Front. Digit. Health
Sec. Connected Health
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1603314
Applying a Logistic Regression-Clustering Joint Model to Analyze the Causes of Prolonged Pre-analytic Turnaround Time for Urine Culture Testing in Hospital Wards
Provisionally accepted
- 1Dongyang People’s Hospital, Dongyang, China
- 2Yuhuan Second People’s Hospital, Yuhuan, China
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In this study, we developed and validated a logistic regressionclustering 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 to 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 to July 2024, *n* = 1,456) was also analyzed to assess the impact.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%, P<0.01). Contamination rate decreased by 59.8% (5.67% → 2.28%, P<0.01). The median pre-TAT decreased by 15.9% (44 → 37 min, P<0.01).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.
Keywords: logistic regression model, urine microbial culture, pre-analytical turnaround time (pre-TAT), medical quality control, Process optimization
Received: 14 Apr 2025; Accepted: 14 Jun 2025.
Copyright: © 2025 Lv, Ye, Li and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jian Zhang, Dongyang People’s Hospital, Dongyang, China
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