Applying the Implementation Science to the harmonization of laboratory test results in the changing landscape
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Mauro Panteghini
Abstract
Nowadays, we are facing major changes in the landscape of test harmonization through metrological traceability approach, all expected to promote equivalence of reported laboratory results. However, having traceability tools in place does not automatically mean that metrological traceability is correctly implemented. As concepts that are poorly implemented do not produce expected benefits, it is time to move from just discussing the effectiveness of traceability theory to how correctly implement it and effectively monitor its effects. In particular, considering the implementation outcomes, there is still a limited knowledge about the actual harmonization status of laboratory test results. We need to increase the information about the expected outcome by assessing result uniformity across different in vitro diagnostic medical devices (IVD-MDs) through an approach providing an ongoing robust evidence of standardization effectiveness. External Quality Assessment (EQA) programs have an irreplaceable role in this IVD traceability surveillance providing that they fulfill specific requirements. i.e., the use of commutable materials with target values assigned by reference measurement procedure (RMP) or strictly controlled procedures, if RMP does not exist, and with validation of results in relation to fit-for-purpose analytical performance specifications. If adequately designed, EQA programs can create evidence about intrinsic standardization status and equivalence of the examined IVD-MDs, serving as management tool for the medical laboratory and IVD manufacturers, and forcing them to investigate and eventually fix the identified problems. In this way, major stumbling blocks opposing the correct implementation of metrological traceability and the obtaining of harmonized laboratory results can be identified and removed.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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Disclosure: The author is Chair of the Executive Committee of the JCTLM. Certain commercial equipment or materials may be identified in this paper to specify adequately the discussed concepts. Such identification does not imply recommendation or endorsement by the JCTLM, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.
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