{"title":"通过数字阴影和精益六西格玛集成优化临床实验室效率:实时监控方法,减少实验室内部周转时间。","authors":"Xinjian Cai, Yiteng Lin, Lili Zhan, Qiuxia Lu, Zhenzhen Wu, Xinyu Lin","doi":"10.1177/20552076251375939","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the impact of integrating digital shadow technology with Lean Six Sigma methodology on intra-laboratory turnaround time (TAT) in a high-volume clinical laboratory, and to demonstrate how digital shadow architectures can enhance process visibility and drive sustainable operational improvements.</p><p><strong>Methods: </strong>A retrospective, two-phase study was conducted in a tertiary cancer hospital from January to December 2024. Digital shadow technology was implemented by leveraging real-time, time-stamped data from the laboratory information system (LIS) to map specimen workflow milestones. The Lean Six Sigma Define, Measure, Analyze, Improve, Control framework guided process analysis and improvement, supported by value stream mapping (VSM), Pareto Analysis, and root cause analysis (RCA). Targeted interventions were developed and deployed based on identified bottlenecks. Specimen intra-laboratory TAT data from 2023 and 2024 were compared using the Mann-Whitney U test, with results visualized through LIS dashboards.</p><p><strong>Results: </strong>Integration of digital shadow technology enabled continuous, real-time monitoring of specimen, facilitating the identification of instrument- and department-specific delays. Following targeted interventions, the median intra-laboratory TAT decreased from 77.2 min to 69.0 min (a 10.6% reduction, p = 0.0182). Improvements were sustained through updated standard operating procedures, accountability measures, and ongoing staff training. The digital shadow approach required no additional analyzers or capital investment and delivered substantial performance gains.</p><p><strong>Conclusion: </strong>This study demonstrates that digital shadow integration with Lean Six Sigma can significantly optimize laboratory efficiency by providing actionable, real-time process data. The approach offers a scalable, resource-efficient model for continuous quality improvement, supporting healthcare digital transformation.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251375939"},"PeriodicalIF":3.3000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409014/pdf/","citationCount":"0","resultStr":"{\"title\":\"Optimizing clinical laboratory efficiency through digital shadow and lean six sigma integration: A real-time monitoring approach to reduce intra-laboratory turnaround time.\",\"authors\":\"Xinjian Cai, Yiteng Lin, Lili Zhan, Qiuxia Lu, Zhenzhen Wu, Xinyu Lin\",\"doi\":\"10.1177/20552076251375939\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To evaluate the impact of integrating digital shadow technology with Lean Six Sigma methodology on intra-laboratory turnaround time (TAT) in a high-volume clinical laboratory, and to demonstrate how digital shadow architectures can enhance process visibility and drive sustainable operational improvements.</p><p><strong>Methods: </strong>A retrospective, two-phase study was conducted in a tertiary cancer hospital from January to December 2024. Digital shadow technology was implemented by leveraging real-time, time-stamped data from the laboratory information system (LIS) to map specimen workflow milestones. The Lean Six Sigma Define, Measure, Analyze, Improve, Control framework guided process analysis and improvement, supported by value stream mapping (VSM), Pareto Analysis, and root cause analysis (RCA). Targeted interventions were developed and deployed based on identified bottlenecks. Specimen intra-laboratory TAT data from 2023 and 2024 were compared using the Mann-Whitney U test, with results visualized through LIS dashboards.</p><p><strong>Results: </strong>Integration of digital shadow technology enabled continuous, real-time monitoring of specimen, facilitating the identification of instrument- and department-specific delays. Following targeted interventions, the median intra-laboratory TAT decreased from 77.2 min to 69.0 min (a 10.6% reduction, p = 0.0182). Improvements were sustained through updated standard operating procedures, accountability measures, and ongoing staff training. The digital shadow approach required no additional analyzers or capital investment and delivered substantial performance gains.</p><p><strong>Conclusion: </strong>This study demonstrates that digital shadow integration with Lean Six Sigma can significantly optimize laboratory efficiency by providing actionable, real-time process data. The approach offers a scalable, resource-efficient model for continuous quality improvement, supporting healthcare digital transformation.</p>\",\"PeriodicalId\":51333,\"journal\":{\"name\":\"DIGITAL HEALTH\",\"volume\":\"11 \",\"pages\":\"20552076251375939\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409014/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"DIGITAL HEALTH\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/20552076251375939\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"DIGITAL HEALTH","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/20552076251375939","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Optimizing clinical laboratory efficiency through digital shadow and lean six sigma integration: A real-time monitoring approach to reduce intra-laboratory turnaround time.
Objective: To evaluate the impact of integrating digital shadow technology with Lean Six Sigma methodology on intra-laboratory turnaround time (TAT) in a high-volume clinical laboratory, and to demonstrate how digital shadow architectures can enhance process visibility and drive sustainable operational improvements.
Methods: A retrospective, two-phase study was conducted in a tertiary cancer hospital from January to December 2024. Digital shadow technology was implemented by leveraging real-time, time-stamped data from the laboratory information system (LIS) to map specimen workflow milestones. The Lean Six Sigma Define, Measure, Analyze, Improve, Control framework guided process analysis and improvement, supported by value stream mapping (VSM), Pareto Analysis, and root cause analysis (RCA). Targeted interventions were developed and deployed based on identified bottlenecks. Specimen intra-laboratory TAT data from 2023 and 2024 were compared using the Mann-Whitney U test, with results visualized through LIS dashboards.
Results: Integration of digital shadow technology enabled continuous, real-time monitoring of specimen, facilitating the identification of instrument- and department-specific delays. Following targeted interventions, the median intra-laboratory TAT decreased from 77.2 min to 69.0 min (a 10.6% reduction, p = 0.0182). Improvements were sustained through updated standard operating procedures, accountability measures, and ongoing staff training. The digital shadow approach required no additional analyzers or capital investment and delivered substantial performance gains.
Conclusion: This study demonstrates that digital shadow integration with Lean Six Sigma can significantly optimize laboratory efficiency by providing actionable, real-time process data. The approach offers a scalable, resource-efficient model for continuous quality improvement, supporting healthcare digital transformation.