Intelligent pre-analytical process reengineering and effect evaluation
10.3760/cma.j.cn114452-20231203-00323
- VernacularTitle:检验前智慧化流程再造与效果评估
- Author:
Hao XUE
1
;
Yong XIA
;
Houlong LUO
;
Mingyang LI
;
Yaoming YAN
;
Ling JI
Author Information
1. 北京大学深圳医院检验科，深圳518036
- Keywords:
Artificial intelligence;
Pre-analytical;
Intelligent;
Process reengineering;
Turnaround time;
Pre-dilution
- From:
Chinese Journal of Laboratory Medicine
2024;47(5):520-525
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To improve work efficiency and reduce errors through intelligent pre-analytical process reengineering.Methods:Tumor and infection marker test samples from outpatients at Peking University Shenzhen Hospital from December 2021 to February 2023 were collected. The process was integrated with sample transportation, sample sorting and secondary transfer, and laboratory automation systems, while achieving full-process information monitoring. The number of manual intervention nodes, the turnaround time (TAT) from sample collection to testing and from collection to reporting, the proportion of intelligent pre-dilution, and the number of pre-analytical errors automatically identified were compared before and after the intelligent pre-analytical process reengineering to evaluate the effect of the reengineering. Chi-square test, Fisher′s exact probability method, and Mann-Whitney U test were used for statistical analysis.Results:After implementing the intelligent process reengineering, the number of manual intervention nodes has been reduced from 13 to 2. For outpatient tumor marker samples, after the first stage of reengineering, the median TAT from collection to reporting decreased from 185 (141, 242) min to 137 (102, 183) min ( Z=-54.932, P<0.001). After the second stage of reengineering, the median TAT from collection to reporting further decreased from 137 (102, 183) min to 100 (64, 150) min ( Z=-61.346, P<0.001). For infection marker samples, after the first stage of reengineering, the median TAT from collection to reporting decreased from 392 (282, 1386) min to 229 (176, 323) min ( Z=-68.636, P<0.001). After the second stage of reengineering, the median TAT from collection to reporting further decreased from 229 (176, 323) min to 160 (110, 236) min ( Z=-62.15, P<0.001). Conclusion:Intelligent pre-analytical process reengineering can optimize workflows, improve efficiency, and reduce errors.
