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Delta check is an electronic error detection tool. It compares the difference in sequential results within a patient against a predefined limit, and when exceeded, the delta check rule is considered triggered. The patient results should be withheld for review and troubleshooting before releasing to the clinical team for patient management. Delta check was initially developed as a tool to detect wrong-blood-in-tube (sample misidentification) errors. It is now applied to detect errors more broadly within the total testing process. Recent advancements in the theoretical understanding of delta check has allowed for more precise application of this tool to achieve the desired clinical performance and operational set up. In this Chapter, we review the different pre-implementation considerations, the foundation concepts of delta check, the process of setting up key delta check parameters, performance verification and troubleshooting of a delta check flag.

The concept of a vending machine lab is presented in this paper.

Medicine has traditionally relied on heuristic approaches in which knowledge is acquired through experience and self-learning. Pathology is information rich with quantitative and qualitative measurements such as history, images, and physiological data from which diagnosis and treatment decisions are made. This information is readily linked to patient outcome data and is therefore potentially invaluable in improving treatment. Thus, pathology is ripe for the application of tools that can effectively turn this information into wisdom. Machine learning (ML), which is a core branch of artificial intelligence, is one of the tools that can take information from many sources to aid decision-making processes. It's a system that takes in data, finds patterns, trains itself using the data, and produces an outcome. It uses algorithms and statistical models to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. Essentially, an ML algorithm is used to create a model from a data set, from which predictions are made. The data used to develop the models are divided into a training set and a testing set. The training set is used to learn different predictive models, each characterized by a set of parameters, including a penalty/complexity parameter. The purpose of the model selection set is to assess the predictive accuracy of each model on blinded data to select the model with the lowest generalization error. This optimum model is the most likely to be true. Data mining uses many ML methods but with different goals. ML differs from data mining in that although both use the same methods and overlap substantially, ML focuses on prediction with known properties learned from the training data, whereas data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). The algorithms …

Routine biochemical and hematological tests have been reported to be useful in the stratification and prognostication of pediatric and adult patients with diagnosed coronavirus disease (COVID-19), correlating with poor outcomes such as the need for mechanical ventilation or intensive care, progression to multisystem organ failure, and/or death. While these tests are already well established in most clinical laboratories, there is still debate regarding their clinical value in the management of COVID-19, particularly in pediatrics, as well as the value of composite clinical risk scores in COVID-19 prognostication. This document by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Task Force on COVID-19 provides interim guidance on: (A) clinical indications for testing, (B) recommendations for test selection and interpretation, (C) considerations in test interpretation, and (D) current limitations of biochemical/hematological monitoring of COVID-19 patients. These evidence-based recommendations will provide practical guidance to clinical laboratories worldwide, underscoring the contribution of biochemical and hematological testing to our collective pandemic response.