Autoverification process improvement by Six Sigma approach: Clinical chemistry & immunoassay

作者:Randell Edward W*; Short Garry; Lee Natasha; Beresford Allison; Spencer Margaret; Kennell Marina; Moores Zoe; Parry David
来源:Clinical Biochemistry, 2018, 55: 42-48.
DOI:10.1016/j.clinbiochem.2018.03.002

摘要

Objective: This study examines effectiveness of a project to enhance an autoverification (AV) system through application of Six Sigma (DMAIC) process improvement strategies.
Design and methods: Similar AV systems set up at three sites underwent examination and modification to produce improved systems while monitoring proportions of samples autoverified, the time required for manual review and verification, sample processing time, and examining characteristics of tests not autoverified. This information was used to identify areas for improvement and monitor the impact of changes.
Results: Use of reference range based criteria had the greatest impact on the proportion of tests autoverified. To improve AV process, reference range based criteria was replaced with extreme value limits based on a 99.5% test result interval, delta check criteria were broadened, and new specimen consistency rules were implemented. Decision guidance tools were also developed to assist staff using the AV system. The mean proportion of tests and samples autoverified improved from < 62% for samples and < 80% for tests, to > 90% for samples and > 95% for tests across all three sites. The new AV system significantly decreased turnaround time and total sample review time (to about a third), however, time spent for manual review of held samples almost tripled. There was no evidence of compromise to the quality of testing process and < 1% of samples held for exceeding delta check or extreme limits required corrective action.
Conclusions: The Six Sigma (DMAIC) process improvement methodology was successfully applied to AV systems resulting in an increase in overall test and sample AV by > 90%, improved turnaround time, reduced time for manual verification, and with no obvious compromise to quality or error detection.

  • 出版日期2018-5