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The quest to use patient results as quality control for routine clinical chemistry testing has long been driven by issues of the unavailability and cost of suitable quality control material and the matrix effects of synthetic material. Hematology laboratories were early adopters of average of normals techniques, primarily because of the difficulty in acquiring appropriate, stable quality control material, while in the chemistry laboratories, the perceived advantages and availability of synthetic material outweighed the disadvantages. However, the increasing volume of testing in clinical chemistry plus the capability of computer systems to deal with large and complex calculations has now made the use of patient-based quality control algorithms feasible. The desire to use patient-based quality control is also driven by increasing awareness that common quality control rules and frequency of analysis may fail to detect clinically significant assay biases. The non-commutability of quality control material has also become a problem as laboratories seek to harmonize results across regions and indeed globally. This review describes the history of patient-based quality control in clinical chemistry, summarizes the various approaches that can be implemented by laboratory professionals, and discusses how patient-based quality control can be integrated with traditional quality control techniques. [ABSTRACT FROM AUTHOR]

Background: Terminologies facilitate data exchange and enable laboratories to assist in patient care even if complex treatment pathways involve multiple stakeholders. This paper examines the three common terminologies Nomenclature for Properties and Units (NPU), Logical Observation Identifiers Names and Codes (LOINC), and SNOMED Clinical Terms (SNOMED CT). Methods: The potential of each terminology to encode five exemplary laboratory results is assessed. The terminologies are evaluated according to scope, correctness, formal representations, and ease of use. Results: NPU is based on metrological concepts with strict rules regarding the coding of the measurand and the result value. Clinically equivalent results are regularly mapped to the same code but there is little support to differentiate results from non-standardized measurements. LOINC encodes analyses as offered by the laboratory. Its large number of entries allows different mappings for the same analysis. SNOMED CT contains few analyses natively, but its formal composition mechanism allows representing measurements by post-coordinated expressions that are equivalent to LOINC codes. SNOMED CT's strength lies in its support of many non-numerical result values. Implicit code hierarchies exist in NPU and LOINC. SNOMED CT has explicit, elaborate axioms that elucidate the meaning of its content. Its complexity and its license conditions, however, impede a more widespread use. Interpretive comments, a crucial part of laboratory results, are still difficult to encode with any of the terminologies. Conclusions: All three terminologies have distinct potentials and limitations, but the approximation of SNOMED CT and LOINC suggests using them together. Terminologies need to be expanded to also cover interpretive comments. [ABSTRACT FROM AUTHOR]

An introduction to articles in the issue is presented on topics including a set of ICT tools that is used in Switzerland to render laboratory data interoperable, the process of implementing the Australian electronic health record, and some of the challenges of new diagnostic tests that produce so much data.

In an increasingly interconnected health care system, laboratory medicine can facilitate diagnosis and treatment of patients effectively. This article describes necessary changes and points to potential challenges on a technical, content, and organizational level. As a technical precondition, electronic laboratory reports have to become machine-readable and interpretable. Terminologies such as Logical Observation Identifiers Names and Codes (LOINC), Nomenclature for Properties and Units (NPU), Unified Code for Units of Measure (UCUM), and SNOMED-CT can lead to the necessary semantic interoperability. Even if only single "atomized" results of the whole report are extracted, the necessary information for correct interpretation must be available. Therefore, interpretive comments, e.g., concerns about an increased measurement uncertainty must be electronically attached to every affected measurement result. Standardization of laboratory analyses with traceable standards and reference materials will enable knowledge transfer and safe interpretation of laboratory analyses from multiple laboratories. In an interconnected health care system, laboratories should strive to transform themselves into a data hub that not only receives samples but also extensive information about the patient. On that basis, they can return measurement results enriched with high-quality interpretive comments tailored to the individual patient and unlock the full potential of laboratory medicine. [ABSTRACT FROM AUTHOR]

The article discusses guideline of the German Medical Association for quality assurance of medical laboratory examinations in the form of the "Peer Review" in addition to the classic audits. It mentions focus on collegial exchange and ultimately improve quality; and also mentions need for quality assurance in laboratory medicine.

This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order to highlight the main studies that have applied machine learning techniques to haematochemical parameters and to review their diagnostic and prognostic performance. In doing so, we aim to address the question we asked three years ago about the potential of these techniques in laboratory medicine and the need to leverage a tool that was still under-utilised at that time. [ABSTRACT FROM AUTHOR]