Difference between revisions of "Journal:Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory"
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'''Keywords''': laboratory information systems, medical safety, autoverification, coagulation, turnaround time | '''Keywords''': laboratory information systems, medical safety, autoverification, coagulation, turnaround time | ||
==Background== | |||
Following the analytical phase, a large number of manual verifications are performed in [[Clinical laboratory|clinical laboratories]] to detect possible errors before results are released to [[electronic health record]]s (EHRs), which is time-consuming.<ref name="PanteghiniTheFuture04">{{cite journal |title=The future of laboratory medicine: understanding the new pressures |journal=The Clinical Biochemist Reviews |author=Panteghini, M. |volume=25 |issue=4 |pages=207–15 |year=2004 |pmid=18458714 |pmc=PMC1934959}}</ref> A significant solution for this issue may be autoverification, a process which uses a set of well-designed rules to identify and flag [[Sample (material)|samples]] with abnormal values for manual verification, at the same time permitting those with normal values to be released without manual intervention.<ref name="TorkeProcess05">{{cite journal |title=Process improvement and operational efficiency through test result autoverification |journal=Clinical Chemistry |author=Torke, N.; Boral, L.; Nguyen, T. et al. |volume=51 |issue=12 |pages=2406–8 |year=2005 |doi=10.1373/clinchem.2005.054395 |pmid=16306113}}</ref> Previous reports have demonstrated that autoverification can ensure medical safety<ref name="WuEstablish18">{{cite journal |title=Establishing and Evaluating Autoverification Rules with Intelligent Guidelines for Arterial Blood Gas Analysis in a Clinical Laboratory |journal=SLAS Technology |author=Wu, J.; Pan, M.; Ouyang, H. et al. |volume=23 |issue=6 |pages=631–40 |year=2018 |doi=10.1177/2472630318775311 |pmid=29787327}}</ref>, shorten turnaround time (TAT)<ref name="TorkeProcess05" /><ref name="WuEstablish18" /><ref name="LiDesign18">{{cite journal |title=Designing and evaluating autoverification rules for thyroid function profiles and sex hormone tests |journal=Annals of Clinical Biochemistry |author=Li, J.; Cheng, B.; Ouyang, H. et al. |volume=55 |issue=2 |pages=254-263 |year=2018 |doi=10.1177/0004563217712291 |pmid=28490181}}</ref><ref name="SediqDesign14">{{cite journal |title=Designing an autoverification system in Zagazig University Hospitals Laboratories: Preliminary evaluation on thyroid function profile |journal=Annals of Saudi Medicine |author=Sediq, A.M.; Abdel-Azeez, A.G. |volume=34 |issue=5 |pages=427–32 |year=2014 |doi=10.5144/0256-4947.2014.427 |pmid=25827700 |pmc=PMC6074554}}</ref>, reduce labor requirements<ref name="TorkeProcess05" /><ref name="WuEstablish18" /><ref name="LiDesign18" />, improve operational efficiency<ref name="TorkeProcess05" /><ref name="LiDesign18" /><ref name="SediqDesign14" /><ref name="RandellStrat18">{{cite journal |title=Strategy for 90% autoverification of clinical chemistry and immunoassay test results using six sigma process improvement |journal=Data in Brief |author=Randell, E.W.; Short, G.; Lee, N. et al. |volume=18 |pages=1740-1749 |year=2018 |doi=10.1016/j.dib.2018.04.080 |pmid=29904674 |pmc=PMC5998219}}</ref> and minimize error rate<ref name="TorkeProcess05" />, as well as enable laboratory technologists to devote more attention to test results that have greater potential for error.<ref name="TorkeProcess05" /> | |||
Revision as of 20:12, 30 September 2019
| Full article title | Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory |
|---|---|
| Journal | BMC Medical Informatics and Decision Making |
| Author(s) | Wang, Zhongqing; Peng, Cheng; Kang, Hui; Fan, Xia; Mu, Runqing; Zhou, Liping, He, Miao; Qu, Bo |
| Author affiliation(s) | China Medical University, Affiliated Hospitals of China Medical University |
| Primary contact | Online contact form |
| Year published | 2019 |
| Volume and issue | 19(1) |
| Page(s) | 123 |
| DOI | 10.1186/s12911-019-0848-2 |
| ISSN | 1472-6947 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-0848-2 |
| Download | https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-019-0848-2 (PDF) |
 |
This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
Background: An autoverification system for coagulation consists of a series of rules that allows normal data to be released without manual verification. With new advances in medical informatics, the laboratory information system (LIS) has growing potential for the use of autoverification, allowing rapid and accurate verification of clinical laboratory tests. The purpose of the study is to develop and evaluate a LIS-based autoverification system for validation and efficiency.
Methods: Autoverification decision rules—including quality control, analytical error flag, critical value, limited range check, delta check, and logical check rules, as well as patient’s historical information—were integrated into the LIS. Autoverification limit ranges was constructed based on 5 and 95% percentiles. The four most commonly used coagulation assays—prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen (FBG)—were followed by the autoverification protocols. The validation was assessed using characteristics such as autoverification passing rate, the true-positive cases, the true-negative cases, the false-positive cases, the false-negative cases, the sensitivity and the specificity. Efficiency was evaluated by turnaround time (TAT).
Results: A total of 157,079 historical test results of coagulation profiles from January 2016 to December 2016 were collected to determine the distribution intervals. The autoverification passing rate was 77.11% (29,165 / 37,821) based on historical patient data. In the initial test of the autoverification version in June 2017, the overall autoverification passing rate for the whole sample was 78.75% (11,257 / 14,295), with 892 true-positive cases, 11,257 true-negative cases, 2,146 false-positive cases, no false-negative cases, sensitivity of 100% ,and specificity of 83.99%. After formal implementation of the autoverification system for six months, 83,699 samples were assessed. The average overall autoverification passing rate for the whole sample was 78.86%, and the 95% confidence interval (CI) of the passing rate was [78.25, 79.59%]. TAT was reduced from 126 minutes to 101 minutes, which was statistically significant (P < 0.001, Mann-Whitney U test).
Conclusions: The LIS-based autoverification system for coagulation assays can halt the samples with abnormal values for manual verification, guarantee medical safety, minimize the requirements for manual work, shorten TAT, and raise working efficiency.
Keywords: laboratory information systems, medical safety, autoverification, coagulation, turnaround time
Background
Following the analytical phase, a large number of manual verifications are performed in clinical laboratories to detect possible errors before results are released to electronic health records (EHRs), which is time-consuming.[1] A significant solution for this issue may be autoverification, a process which uses a set of well-designed rules to identify and flag samples with abnormal values for manual verification, at the same time permitting those with normal values to be released without manual intervention.[2] Previous reports have demonstrated that autoverification can ensure medical safety[3], shorten turnaround time (TAT)[2][3][4][5], reduce labor requirements[2][3][4], improve operational efficiency[2][4][5][6] and minimize error rate[2], as well as enable laboratory technologists to devote more attention to test results that have greater potential for error.[2]
References
- ↑ Panteghini, M. (2004). "The future of laboratory medicine: understanding the new pressures". The Clinical Biochemist Reviews 25 (4): 207–15. PMC PMC1934959. PMID 18458714. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1934959.
- ↑ 2.0 2.1 2.2 2.3 2.4 2.5 Torke, N.; Boral, L.; Nguyen, T. et al. (2005). "Process improvement and operational efficiency through test result autoverification". Clinical Chemistry 51 (12): 2406–8. doi:10.1373/clinchem.2005.054395. PMID 16306113.
- ↑ 3.0 3.1 3.2 Wu, J.; Pan, M.; Ouyang, H. et al. (2018). "Establishing and Evaluating Autoverification Rules with Intelligent Guidelines for Arterial Blood Gas Analysis in a Clinical Laboratory". SLAS Technology 23 (6): 631–40. doi:10.1177/2472630318775311. PMID 29787327.
- ↑ 4.0 4.1 4.2 Li, J.; Cheng, B.; Ouyang, H. et al. (2018). "Designing and evaluating autoverification rules for thyroid function profiles and sex hormone tests". Annals of Clinical Biochemistry 55 (2): 254-263. doi:10.1177/0004563217712291. PMID 28490181.
- ↑ 5.0 5.1 Sediq, A.M.; Abdel-Azeez, A.G. (2014). "Designing an autoverification system in Zagazig University Hospitals Laboratories: Preliminary evaluation on thyroid function profile". Annals of Saudi Medicine 34 (5): 427–32. doi:10.5144/0256-4947.2014.427. PMC PMC6074554. PMID 25827700. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6074554.
- ↑ Randell, E.W.; Short, G.; Lee, N. et al. (2018). "Strategy for 90% autoverification of clinical chemistry and immunoassay test results using six sigma process improvement". Data in Brief 18: 1740-1749. doi:10.1016/j.dib.2018.04.080. PMC PMC5998219. PMID 29904674. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998219.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation and grammar, for clarity. In some cases important information was missing from the references, and that information was added.
