Literature DB >> 29787327

Establishing and Evaluating Autoverification Rules with Intelligent Guidelines for Arterial Blood Gas Analysis in a Clinical Laboratory.

Jie Wu1, Meichen Pan1, Huizhen Ouyang1, Zhili Yang1, Qiaoxin Zhang1, Yingmu Cai1.   

Abstract

Arterial blood gas (ABG) analysis is important for acutely ill patients and should be performed by qualified laboratorians. The existing manual verifications are tedious, time-consuming, and prone to send wrong reports. Autoverification uses computer-based rules to verify clinical laboratory test results without manual review. To date, no data are available on the use of autoverification for ABG analysis. All autoverification rules were established according to AUTO10-A. Additionally, the rules were established using retrospective patient data, and then validated by actual clinical samples in a "live" environment before go-live. The average autoverification passing rate was 75.5%. The turnaround time (TAT) was reduced by 33.3% (27 min vs 18 min). Moreover, the error rate fell to 0.05% after implementation. Statistical analysis resulted in a kappa statistic of 0.92 ( p < 0.01), indicating close agreement between autoverification and senior technician verification, and the chi-square value was 22.4 ( p < 0.01), indicating that the autoverification error rate was lower than the manual verification error rate. Results showed that implementing autoverification rules with intelligent guidelines for ABG analysis of patients with critical illnesses could decrease the number of samples requiring manual verification, reduce TAT, and eliminate errors, allowing laboratorians to concentrate more time on abnormal samples, patient care, and collaboration with physicians.

Entities:  

Keywords:  arterial blood gas analysis; autoverification; guidelines; turnaround time

Mesh:

Year:  2018        PMID: 29787327     DOI: 10.1177/2472630318775311

Source DB:  PubMed          Journal:  SLAS Technol        ISSN: 2472-6303            Impact factor:   3.047


  5 in total

1.  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

2.  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

3.  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

4.  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

5.  General position of Croatian medical biochemistry laboratories on autovalidation: survey of the Working Group for Post-analytics of the Croatian Society of Medical Biochemistry and Laboratory Medicine.

Authors:  Vladimira Rimac; Anja Jokic; Sonja Podolar; Jelena Vlasic Tanaskovic; Lorena Honovic; Jasna Lenicek Krleza
Journal:  Biochem Med (Zagreb)       Date:  2020-04-15       Impact factor: 2.313

  5 in total

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