RISK MANAGEMENT


and clinical leadership, supporting timely decision-making and reducing the likelihood of harm to patients.


Conclusions: from isolated checks to holistic performance monitoring Internal quality control remains essential for day-to-day assurance of analytical performance, but it cannot detect all risks that may affect patient results. ISO 15189:2022 and ISO 22367:2020 both emphasise the need for a broader, risk-based approach, where monitoring activities are chosen and applied according to the potential for patient harm.


External analytical signals – such as lot-to-lot verification, delta checks, monitoring critical result frequency, and reference range monitoring – each provide important information about different aspects of performance. On their own, they can detect specific issues, but their greatest value comes when they are reviewed together as part of an integrated quality monitoring framework. Viewing these indicators collectively allows laboratories to identify patterns that may not be visible when data are considered in isolation. This integrated approach supports earlier detection of emerging risks, more effective targeting of corrective actions, and a stronger link between analytical performance and clinical impact. It also aligns directly with the ISO 22367 principle that quality indicators should be used actively to detect and mitigate risk, rather than passively to demonstrate compliance. For laboratories, the priority should be


to ensure that these monitoring activities are embedded in a structured review process, with clear accountability, defined review intervals, and documented actions. For clinical leads, engagement with this process is essential to interpret findings in context and to support changes that protect patient safety. By combining IQC with targeted external analytical signals in a risk-based framework, laboratories can meet the requirements of ISO standards while strengthening their ability to prevent harm and maintain trust in their results.


References 1 Thelen MHM, van Schrojenstein


Lantman M, Boursier G, Vanstapel F, Panteghini M. In reply to: Limitations in using the EFLM WG-A/ISO approach for assessment of reagent lot variability. Clin Chem Lab Med. 2023; 61 (11): e218-e220. https://doi.org/10.1515/cclm-2023-0516


2 Loh TP, Markus C, Tan CH, Tran MTC, Sethi SK, Lim CY. Lot-to-lot variation and verification. Clin Chem Lab Med. 2023; 61


34


Regular review of combined indicators in quality or governance meetings ensures that trends are visible to both laboratory and clinical leadership, supporting timely decision-making and reducing the likelihood of harm to patients.


(5): 769-776. https://doi.org/doi:10.1515/ cclm-2022-1126


3 Loh TP, Sandberg S, Horvath AR. Lot-to- lot reagent verification: challenges and possible solutions. Clin Chem Lab Med. 2022; 60 (5): 675-680. https://doi.org/ doi:10.1515/cclm-2022-0092


4 Koh NWX, Markus C, Loh TP, Lim C Y. Comparison of six regression-based lot-to- lot verification approaches. Clin Chem Lab Med. 2022; 60(8): 1175-1185. https://doi. org/doi:10.1515/cclm-2022-0274


5 Dimech WJ, Vincini GA, Plebani M, Lippi G, Nichols JH, Sonntag O. Time to address quality control processes applied to antibody testing for infectious diseases. Clin Chem Lab Med. 2023; 61 (2): 205- 212. https://doi.org/doi:10.1515/cclm- 2022-0986


6 He S, Kang F, Wang W, Chen B, Wang Z. National survey on delta checks in clinical laboratories in China. Clin Chem Lab Med. 2020; 58 (4): 569-576. https://doi.org/ doi:10.1515/cclm-2019-1131


7 Zhou R, Liang YF, Cheng HL et al. A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory. Clin Chem Lab Med. 2022; 60 (12): 1984-1992. https://doi.org/ doi:10.1515/cclm-2021-1171


8 Seok HS, Choi Y, Yu S, Shin KH, Kim S, Shin H. Machine learning-based delta check method for detecting misidentification errors in tumor marker tests. Clin Chem Lab Med. 2024; 62 (7): 1421-1432. https://doi.org/doi:10.1515/ cclm-2023-1185


9 Yu S, Shin KH, Shin S et al. Practical delta check limits for tumour markers in different clinical settings. Clin Chem Lab Med.


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10 Miler M, Nikolac Gabaj N, Šimić G et al. Verification of automated review, release and reporting of results with assessment of the risk of harm for patients: the procedure algorithm proposal for clinical laboratories. Clin Chem Lab Med. 2025; 63 (6): 1109- 1117. https://doi.org/doi:10.1515/cclm- 2024-1164


11 Vogeser M. Targeting low-value laboratory care. Clin Chem Lab Med. 2025 Aug 7. https://doi.org/doi:10.1515/cclm-2025- 0476 . Online ahead of print.


12 Giovanella L. Reconciling reference ranges and clinical decision limits: the case of thyroid stimulating hormone. Clin Chem Lab Med. 2025; 63 (9): e212-e213. https:// doi.org/doi:10.1515/cclm-2025-0483


13 Symonds C, Kline G, Gjata I et al. Levothyroxine prescribing and laboratory test use after a minor change in reference range for thyroid-stimulating hormone. CMAJ. 2020 May 4; 192 (18): E469-E475. https://doi.org/10.1503/cmaj.191663


14 Haeckel R, Wosniok W, Streichert T.; on behalf of the working group Richtwerte of the German Society of Clinical Chemistry and Laboratory Medicine. The difference between reference interval and reference range. J Lab Med. 2020; 44 (3): 173-173. https://doi.org/doi:10.1515/ labmed-2019-0192


Dr Stephen MacDonald is Principal Clinical Scientist, The Specialist Haemostasis Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ.


+44 (0)1223 216746. SEPTEMBER 2025 WWW.PATHOLOGYINPRACTICE.COM


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