The rapidly evolving field of machine learning (ML), along with artificial intelligence in a broad sense, is revolutionising many areas of healthcare, including laboratory medicine. The amalgamation of the fields of ML and patient-based real-time quality control (PBRTQC) processes could improve the traditional PBRTQC and error detection algorithms in the laboratory. This narrative review discusses published studies on using ML for the detection of systematic errors, non-systematic errors, and combinations of different types of errors in clinical laboratories. The studies discussed used ML for detecting bias, the requirement for re-calibration, samples contaminated with intravenous fluid or EDTA, delayed sample analysis, wrong-blood-in-tube errors, interference or a combination of different types of errors, by comparing the performance of ML models with human validators or traditional PBRTQC algorithms. Advantages, limitations, the creation of standardised ML models, ethical and regulatory aspects and potential future developments have also been discussed in brief.

Method-related variations in the measurement of hormones and the reference intervals used in the clinical laboratory can have a significant, but often under-appreciated, impact on the diagnosis and management of endocrine disorders. This variation in laboratory practice has the potential to lead to an errant approach to patient care and thus could cause harm. It may also be the source of confusion or result in excessive or inadequate investigation. It is important that laboratory professionals and clinicians know about these impacts, their sources, and how to detect and mitigate them when they do arise. In this review article, we describe the historical and scientific context from which inconsistency in the clinical laboratory arises. Examples from the published literature of the impact of the method, reference interval, and clinical decision threshold-related discordances on the assessment and monitoring of various endocrine disorders are discussed to illustrate the sources, causes, and effects of this variability. Its potential impact on the evaluation of growth hormone deficiency and excess, thyroid and parathyroid disorders, hyperandrogenism, hypogonadism, glucocorticoid excess and deficiency, and diabetes mellitus is elaborated. Strategies for assessment and mitigation of the discordance are discussed. The clinical laboratory has a responsibility to recognise and address these issues, and although a lot has been accomplished in this area already, there remains more to be done.

This brief report describes 2 cases in which paraprotein caused spurious biochemistry results. In both cases standard laboratory measurements of calcium indicated the patients were hypercalcaemic and had significantly raised Vitamin D concentrations in serum. However, use of alternative measurements of calcium and vitamin D showed that these results were spurious. Clinicians looking after patients with significant paraprotein concentrations or other significant protein abnormalities should be aware of the possibility of assay interference when reviewing their biochemistry results. This awareness can be vital in avoiding unnecessary investigations or treatments, thus not only saving resources but also protecting patients from harm that may be caused by inappropriate therapies.

Background: Patient risk management has become central to clinical laboratory quality standards, including the Clinical and Laboratory Standards Institute (CLSI) EP23 2nd edition (CLSI EP23-ED2), International Organization for Standardization (ISO) 15189:2022, ISO 22367:2026, and ISO 14971:2019. A key component of risk analysis is estimating the severity of harm (SoH) that may result from erroneous laboratory results. We present here the Severity of Harm Evidence Database, a structured, multi-study resource that integrates survey-based SoH assessments from the published independent studies. The database provides ordinal distributions and pooled SoH for 196 unique laboratory tests. Methods: A canonical five-level scale (negligible, minor, serious, critical, catastrophic) was adopted, aligned with ISO 14971:2019. For the 19 tests common to both studies, direct count pooling was performed by summing category-level respondent counts. Wilson score confidence intervals were calculated for all proportions. The database is organized into nine relational sheets and is accompanied by an interactive web application. Results: Out of the 196 tests covered, 19 have pooled estimates from both studies (total n=781 respondents). Pooled analysis confirms that cardiac biomarkers, blood gases, coagulation parameters, electrolytes (potassium, sodium), and therapeutic drug concentrations (digoxin, lithium) consistently receive the highest severity ratings, while certain endocrine markers receive lower scores. Conclusions: The SoH evidence database offers the first publicly available, structured repository of SoH assessments for clinical laboratory tests. It supports compliance with risk management requirements, informs risk-based strategies per CLSI EP23-ED2, and provides an evidence-based foundation for laboratory risk analysis.