Application and effect of a data-driven multidimensional intervention model in reducing urine culture contamination rates.

OBJECTIVE

This study aimed to develop and validate a data-driven multidimensional intervention model to reduce urine culture contamination rates and improve sample collection compliance and timeliness.

METHODS

A quasi-experimental pre-post design was conducted at Donghai County People's Hospital from April 2024 to June 2025. The intervention included patient education, standardization of nursing procedures, optimization of materials, LIS-based traceability, and a quality feedback system, involving 1,208 urine culture samples. Statistical methods such as Difference-in-Differences (DID) and Interrupted Time Series Analysis (ITSA) were used to evaluate the intervention's impact on contamination rates, compliance, and timeliness.

RESULTS

The contamination rate decreased significantly from 17.8% to 8.7% (P < 0.001), with an Absolute Risk Reduction (ARR) of 9.1%. Sample collection compliance improved from 62.6% to 88.4%, and the collection-to-submission time reduced from 2.8 ± 1.4 hours to 1.6 ± 0.9 hours (P < 0.001). Multivariate analysis indicated the intervention as an independent protective factor for reducing contamination (OR=0.46, 95% CI: 0.33-0.65).

CONCLUSION

The intervention model effectively reduced contamination rates, improved sample quality, and enhanced efficiency, offering a scalable solution for urine culture quality management in primary healthcare settings.