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Reducing turnaround time for routine outpatient biochemical tests through Lean Six Sigma: A case study in China.

  • Published In: Journal of Evaluation in Clinical Practice, 2025, v. 31, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zhao, Xinzhe; Qin, Xiaoping; Miao, Yuli 3 of 3

Abstract

Background: Routine clinical biochemistry tests are crucial for clinical diagnostics and play a key role in enhancing outpatient turnover efficiency and patient satisfaction. This study aimed to implement Lean Six Sigma in the biochemistry laboratory of a hospital in China to improve efficiency and quality by reducing turnaround time. Methods: The study was conducted from January to December 2023, using the DMAIC (Define, Measure, Analyze, Improve, Control) framework, and employed tools such as the voice of the customer, Value Stream Mapping, '5 whys' technique, Nominal Group Technique, and Pareto chart. Results: The turnaround time for outpatient routine clinical biochemistry tests was reduced from 139 min to 58 min (p < 0.05), effectively increasing both patient and physician satisfaction. Conclusions: Lean Six Sigma aimed to reduce the turnaround time for biochemical tests have significant advantages. This study confirms the effectiveness of Lean Six Sigma in a Chinese clinical laboratory setting and provides guidance for optimizing efficiency in global clinical laboratories with limited implementation experience, constrained technical and equipment resources, and high demand for medical diagnostics. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Evaluation in Clinical Practice. 2025/02, Vol. 31, Issue 1, p1
  • Document Type:Article
  • Subject Area:Consumer Health
  • Publication Date:2025
  • ISSN:1356-1294
  • DOI:10.1111/jep.14116
  • Accession Number:183982822
  • Copyright Statement:Copyright of Journal of Evaluation in Clinical Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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