Literature DB >> 29518383

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

Edward W Randell1, Garry Short2, Natasha Lee3, Allison Beresford3, Margaret Spencer2, Marina Kennell3, Zoë Moores3, David Parry4.   

Abstract

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 turn-around 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 turn-around time, reduced time for manual verification, and with no obvious compromise to quality or error detection.
Copyright © 2018 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Autoverification; DMAIC; Process improvement; Quality assurance; Six Sigma; Turn-around time

Mesh:

Year:  2018        PMID: 29518383     DOI: 10.1016/j.clinbiochem.2018.03.002

Source DB:  PubMed          Journal:  Clin Biochem        ISSN: 0009-9120            Impact factor:   3.281


  7 in total

1.  Establishing and validating of an laboratory information system-based auto-verification system for biochemical test results in cancer patients.

Authors:  Cuie Yan; Yujuan Zhang; Jia Li; Jia Gao; Chanjuan Cui; Chun Zhang; Guiyu Song; Mengyao Yu; Jianjun Mu; Feng Chen; Xiaohong Han; Wei Cui
Journal:  J Clin Lab Anal       Date:  2019-03-06       Impact factor: 2.352

2.  Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory.

Authors:  Zhongqing Wang; Cheng Peng; Hui Kang; Xia Fan; Runqing Mu; Liping Zhou; Miao He; Bo Qu
Journal:  BMC Med Inform Decis Mak       Date:  2019-07-03       Impact factor: 2.796

3.  Combined strategy of knowledge-based rule selection and historical data percentile-based range determination to improve an autoverification system for clinical chemistry test results.

Authors:  Jing Zhu; Hao Wang; Beili Wang; Xiaoke Hao; Wei Cui; Yong Duan; Yi Zhang; Liang Ming; Yingchun Zhou; Haitao Ding; Hongling Ou; Weiwei Lin; Liu Lu; Yuanjiang Shang; Yong Yang; Xianming Liang; Jiangtao Ma; Wenhua Sun; Te Chen; Guang Han; Meng Han; Weiting Yu; Baishen Pan; Wei Guo
Journal:  J Clin Lab Anal       Date:  2022-01-10       Impact factor: 2.352

4.  Externalities of Lean Implementation in Medical Laboratories. Process Optimization vs. Adaptation and Flexibility for the Future.

Authors:  Simona Andreea Apostu; Valentina Vasile; Cristina Veres
Journal:  Int J Environ Res Public Health       Date:  2021-11-23       Impact factor: 3.390

5.  Development and implementation of an LIS-based validation system for autoverification toward zero defects in the automated reporting of laboratory test results.

Authors:  Di Jin; Qing Wang; Dezhi Peng; Jiajia Wang; Bijuan Li; Yating Cheng; Nanxun Mo; Xiaoyan Deng; Ran Tao
Journal:  BMC Med Inform Decis Mak       Date:  2021-06-02       Impact factor: 2.796

6.  Use of Middleware Data to Dissect and Optimize Hematology Autoverification.

Authors:  Rachel D Starks; Anna E Merrill; Scott R Davis; Dena R Voss; Pamela J Goldsmith; Bonnie S Brown; Jeff Kulhavy; Matthew D Krasowski
Journal:  J Pathol Inform       Date:  2021-04-07

7.  Strategy for 90% autoverification of clinical chemistry and immunoassay test results using six sigma process improvement.

Authors:  Edward W Randell; Garry Short; Natasha Lee; Allison Beresford; Margaret Spencer; Marina Kennell; Zoë Moores; David Parry
Journal:  Data Brief       Date:  2018-05-03
  7 in total

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