Literature DB >> 31386832

Autoverification of test results in the core clinical laboratory.

Edward W Randell1, Sedef Yenice2, Aye Aye Khine Wamono3, Matthias Orth4.   

Abstract

Verification of laboratory test results represents the last opportunity to identify errors before they become part of the electronic medical record. Manual verification of test results places significant reliance on the experience and attentiveness of individual observers to identify errors and is vulnerable to errors through omission and neglect. Peer-reviewed publications have documented gains in process efficiency and quality improvement by use of middleware or laboratory information systems to autoverify test results based on pre-defined acceptability criteria. This review evaluates the acceptability of autoverification (AV) as a safe and reliable alternative to total manual review of laboratory test results. AV schemes developed in accordance with international guidelines and standards are applied throughout the laboratory. Careful design of AV systems involves using multidisciplinary teams to develop test-specific decision algorithms, to assist with programming, to verify programming, and validate programmed algorithms prior to use in evaluation of patient test result profiles. Development of test specific decision algorithms makes use of criteria based on instrument messages and flags, quality control status, result limit checks, delta checks, critical values, consistency checks, and patient-related clinical information. Monitoring of the performance of AV parameters, and regular audits of the AV system integrity is recommended in both the literature and guidelines. The potential for gains to process efficiency, error detection and patient safety, through adoption of AV as part of a laboratories quality assurance tool-case, is well supported in published literature.
Copyright © 2019 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Entities:  

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

Mesh:

Year:  2019        PMID: 31386832     DOI: 10.1016/j.clinbiochem.2019.08.002

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


  7 in total

1.  An Objective Approach to Deriving the Clinical Performance of Autoverification Limits.

Authors:  Tze Ping Loh; Rui Zhen Tan; Chun Yee Lim; Corey Markus
Journal:  Ann Lab Med       Date:  2022-09-01       Impact factor: 4.941

2.  Chronometric vs. Structural Hypercoagulability.

Authors:  Carmen Delianu; Mihaela Moscalu; Loredana Liliana Hurjui; Claudia Cristina Tărniceriu; Oana-Viola Bădulescu; Ludmila Lozneanu; Ion Hurjui; Ancuta Goriuc; Zinovia Surlari; Liliana Foia
Journal:  Medicina (Kaunas)       Date:  2020-12-28       Impact factor: 2.430

Review 3.  Use of the WISN method to assess the health workforce requirements for the high-volume clinical biochemical laboratories.

Authors:  Sanja Stankovic; Milena Santric Milicevic
Journal:  Hum Resour Health       Date:  2022-01-28

4.  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

5.  Designing and validating an autoverification system of biochemical test results in Hatay Mustafa Kemal University, clinical laboratory.

Authors:  Bahar Ünlü Gül; Oğuzhan Özcan; Serdar Doğan; Abdullah Arpaci
Journal:  Biochem Med (Zagreb)       Date:  2022-08-05       Impact factor: 2.515

6.  Customized middleware experience in a tertiary care hospital hematology laboratory.

Authors:  Kristine Roland; Jim Yakimec; Todd Markin; Geoffrey Chan; Monika Hudoba
Journal:  J Pathol Inform       Date:  2022-09-24

7.  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 in total

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