Internal Quality Control and External Quality Assurance are separate but related processes that have developed independently in laboratory medicine over many years. They have different sample frequencies, statistical interpretations and immediacy. Both processes have evolved absorbing new understandings of the concept of laboratory error, sample material matrix and assay capability. However, we do not believe at the coalface that either process has led to much improvement in patient outcomes recently. It is the increasing reliability and automation of analytical platforms along with improved stability of reagents that has reduced systematic and random error, which in turn has minimised the risk of running less frequent IQC. We suggest that it is time to rethink the role of both these processes and unite them into a single approach using an Average of Normals model supported by more frequent External Quality Assurance samples. This new paradigm may lead to less confusion for laboratory staff and quicker responses to and identification of out of control situations.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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.
1. International Organisation for Standardisation ISO 15189. Medical laboratories – Particular requirements for quality and competence. http://www.iso.org/iso/catalogue_detail?Csnumber=56115. Accessed 3 Feb 2017.Search in Google Scholar
2. Fleming JK, Katayev A. Changing the paradigm of laboratory quality control through implementation of real-time test results monitoring: for patients by patients. Clin Biochem 2015;48:508–13.10.1016/j.clinbiochem.2014.12.016Search in Google Scholar
3. Ng D, Polito FA, Cervinski MA. Optimization of a moving averages program using a simulated annealing algorithm: the goal is to monitor the process not the patients. Clin Chem 2016;62:1361–71.10.1373/clinchem.2016.257055Search in Google Scholar
4. Liu J, Tan CH, Loh TP, Badrick T. Verification of out-of-control situations detected by “average of normal” approach. Clin Biochem 2016;49:1248–53.10.1016/j.clinbiochem.2016.07.012Search in Google Scholar
5. Jones G, Calleja J, Chesher D, Parvin C, Yundt-Pacheo J, Mackay M, et al. Collective opinion paper on a 2013 AACB workshop of experts seeking harmonisation of approaches to setting a laboratory quality control policy. Clin Biochem Rev 2015:36:87–95.Search in Google Scholar
6. Howanitz PJ, Tetrault GA, Steindel SJ. Clinical laboratory quality control: a costly process now out of control. Clin Chim Acta 1997;260:163–74.10.1016/S0009-8981(96)06494-7Search in Google Scholar
7. Litten J. Applying sigma metrics to reduce outliers. Clin Lab Med 2017;37:177–86.10.1016/j.cll.2016.09.014Search in Google Scholar
8. Sturgeon CM. External quality assessment of hormone determinations. Best Pract Res Clin Endocrinol Metab 2013;127:803–22.10.1016/j.beem.2013.08.009Search in Google Scholar
9. Parvin C. Assessing the impact of the frequency of quality control testing on the quality of reported patient results. Clin Chem 2008;54:2049–54.10.1373/clinchem.2008.113639Search in Google Scholar
10. Liu J, Tan CH, Badrick T, Loh TP. Moving sum of number of positive patient result as a quality control tool. Clin Chem Lab Med 2017;55:1709–14.10.1515/cclm-2016-0950Search in Google Scholar
11. Miller WG, Jones GR, Horowitz GL, Weykamp C. Proficiency testing/external quality assessment: current challenges and future directions. Clin Chem 2011;57:1670–80.10.1373/clinchem.2011.168641Search in Google Scholar
12. Miller WG. Specimen materials, target values and commutability for external quality assessment (proficiency testing) schemes. Clin Chim Acta 2003;327:25–37.10.1016/S0009-8981(02)00370-4Search in Google Scholar
13. Rej R, Norton-Wenzel CS. Assessing analytical accuracy through proficiency testing: have the effects of matrix been overstated? Clin Chem 2015;61:433–4.10.1373/clinchem.2014.231241Search in Google Scholar PubMed
14. Westgard JO, Westgard SA. The quality of laboratory testing today: an assessment of σ metrics for analytic quality using performance data from proficiency testing surveys and the CLIA criteria for acceptable performance. Am J Clin Path 2015;125:343–54.10.1309/V50H4FRVVWX12C79Search in Google Scholar
15. Matar G, Poggi B, Meley R, Bon C, Chardon L, Chikh K, et al. Uncertainty in measurement for 43 biochemistry, immunoassay, and hemostasis routine analytes evaluated by a method using only external quality assessment data. Clin Chem Lab Med 2015;53:1725–36.10.1515/cclm-2014-0942Search in Google Scholar PubMed
16. Liu J, Han CH, Loh TP, Badrick T. Detecting long-term drift in reagent lots. Clin Chem 2015;61:1292–8.10.1373/clinchem.2015.242511Search in Google Scholar PubMed
17. Bais R. Use of capability index to improve laboratory analytical performance. Clin Biochem Rev 2008;29:S27–31.Search in Google Scholar
18. De Grande L, Goossens K, Van Uytfanghe K, Stöckl D, Thienpont L. The empower project – a new way of assessing and monitoring test comparability and stability. Clin Chem Lab Med 2015;53:1197–204.10.1515/cclm-2014-0959Search in Google Scholar PubMed
19. Mackay M, Hegedus G, Badrick T. A simple matrix of analytical performance to identify at assays that risk patients using External Quality Assurance program data. Clin Biochem 2016;49:596–600.10.1016/j.clinbiochem.2016.01.014Search in Google Scholar PubMed
20. Tolan NV, Parnas ML, Baudhuin LM, Cervinski MA, Chan AS, Holmes DT, et al. “Big data” in laboratory medicine. Clin Chem. 2015;61:1433–40.10.1373/clinchem.2015.248591Search in Google Scholar PubMed
©2018 Walter de Gruyter GmbH, Berlin/Boston