Frontiers in digital health · 2025
Applying a logistic regression-clustering joint model to analyze the causes of prolonged pre-analytic turnaround time for urine culture testing in hospital wards.
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Lv Shuangshuang, Ye Huan, Li Yuan, Zhang Jian
Аннотация
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, *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.
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, *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.