Abstract
Background:
Practical application of prospective risk analysis (PRA) in clinical laboratories should reflect processes as they are carried out, while making the PRA results obtained from different processes comparable. This means that not only STAT and standard testing and testing for critical and less critical parameters should be distinguished (as published), but also that the throughput in processes and process steps should be taken into account.
Methods:
Building on our previously published PRA, a method was developed to compensate for the throughput in processes and process steps. A factor T, related to the actually observed throughput, was introduced in the risk score calculation. Introduction of this compensation factor leads to different overall risk scores. The criteria by which the risk scores are evaluated were modified accordingly.
Results:
Introduction of a factor in the PRA to compensate for throughput leads to a change in the risk score for various conceivable failures in process steps. As compared to the PRA in which no compensation for throughput is made, in a process with low throughput the risk score for various conceivable failures in process steps comes out higher after introduction of the compensation factor, while in a process with high throughput various risk scores come out lower.
Conclusions:
Introduction of a factor to account for the throughput in a process (and process steps) leads to an improved, more realistic PRA, the results of which makes the risk scores of different processes (and process steps) better comparable to each other.
Author contributions: PMWJ conceived the plan and wrote the manuscript; AvdH conceived the plan and worked it out, with existent data. Both authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Guarantor: PMWJ.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
1. ISO 15189:2012 Medical laboratories – requirements for quality and competence (http://www.iso.org/iso/catalogue_detail?csnumber=56115), 2012. Accessed: 20 April 2018.Search in Google Scholar
2. ISO/TS 22367:2008. Medical laboratories – reduction of error through risk management and continual improvement (www.iso.org/iso/catalogue_detail.htm?csnumber=40918), 2008. Accessed: 20 April 2018.Search in Google Scholar
3. EP18-A2. Risk management techniques to identify and control laboratory error sources; approved guideline – 2nd ed. Clinical and laboratory standards institute (https://clsi.org/standards/products/method-evaluation/documents/ep18a2/), 2009. Accessed: 20 April 2018.Search in Google Scholar
4. Janssens PM, Cheung KS. Approaching risk analysis and risk management in the fertility laboratory and semen bank. Int J Androl 2009;32:656–65.10.1111/j.1365-2605.2008.00920.xSearch in Google Scholar PubMed
5. Janssens PM. Practical, transparent prospective risk analysis for the clinical laboratory. Ann Clin Biochem 2014;51:695–704.10.1177/0004563214521160Search in Google Scholar PubMed
6. Chiozza ML, Plebani M. Clinical Governance: from clinical risk management to continuous quality improvement. Clin Chem Lab Med 2006;44:694–8.10.1515/CCLM.2006.127Search in Google Scholar PubMed
7. Njoroge SW, Nichols JH. Risk management in the clinical laboratory. Ann Lab Med 2014;34:274–8.10.3343/alm.2014.34.4.274Search in Google Scholar PubMed PubMed Central
8. Janssens PM, Scholten A, De Waard H, Tiemens N, Van Uum M, Schrijver E, et al. Prospective risk analysis adjusted to the reality of clinical and fertility laboratory processes. Diagnosis 2015;2:235–43.10.1515/dx-2015-0027Search in Google Scholar PubMed
9. Plebani M. The detection and prevention of errors in laboratory medicine. Ann Clin Biochem 2010;47:101–10.10.1258/acb.2009.009222Search in Google Scholar PubMed
10. Signori C, Ceriotti F, Sanna A, Plebani M, Messeri G, Ottomano C, et al. Process and risk analysis to reduce errors in clinical laboratories. Clin Chem Lab Med 2007;45:742–8.10.1515/CCLM.2007.172Search in Google Scholar PubMed
11. Chiozza ML, Ponzetti C. FMEA: a model for reducing medical errors. Clin Chim Acta 2009;404:75–8.10.1016/j.cca.2009.03.015Search in Google Scholar PubMed
12. EP23. Laboratory quality control based on risk management; approved guideline – 1st ed. Clinical and laboratory standards institute (https://clsi.org/media/1426/ep23a_sample.pdf), 2011. Accessed: 20 April 2018.Search in Google Scholar
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