Search

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]

Health systems worldwide struggle to provide the optimal treatment amid the rapid pace of scientific progress and frequent structural changes. Learning health systems (LHS) that continuously analyze their data to generate evidence and to enable informed decisions have been proposed as a possible solution. This paper investigates the involvement of laboratory medicine in LHS and evaluates relevant systems according to their scope, enabling technologies, architecture for evidence generation, data-action latency, and social aspects. While laboratory medicine provides a central source of information in many large-scale LHS, there are also systems with a particular focus on laboratories. For example, external quality assessments are performed by sending samples with known quantities to laboratories worldwide to verify their analytical methods. This data are also analyzed for other purposes, such as post-market surveillance of devices. These schemes have operated successfully over several decades. Newer, innovative tools harvest the possibilities of wearable devices and increased connectivity. LHS in laboratory medicine need to be recognized and incorporated into systems of systems to generate better evidence. Further research on the roles of other stakeholders will identify opportunities and obstacles for building and maintaining successful LHS. [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.

Background: In health care, exchange of data is becoming increasingly important. However, it is still unclear how to interpret measurements of the same parameter from diverse data sources. In clinical chemistry point-of-care testing (POCT), e.g. for blood glucose, complements the analytical spectrum of central laboratories. Available data from "central laboratory", "professional POCT" and "non-professional" blood glucose measurements can be used to realistically simulate errors in different environments. The ability to classify the disease status or to detect a significant change based on these simulated measurements was assessed in several scenarios. Methods: Patients were simulated with realistic "true values" of blood glucose and a "diseased" or "non-diseased" status. To further simulate a change of blood glucose values, a random difference was created and classified as "significant" or "non-significant". Scientific literature was searched for accuracy and precision of blood glucose measurements in different environments. These values were used to calculate "measured values" from the "true values". In scenarios where only measurements from the same environment were combined the area under the ROC curve (AUC) was used to express classifier performance. Balanced accuracy was used for scenarios with a combination of measurements from different environments. Situations with and without prior knowledge of the data source were simulated separately. Results: AUC for the classification of disease status remained around 0.68 in all scenarios. Median AUCs for the detection of a significant change ranged between 0.89 in the central laboratory and 0.76 in the non-professional environment. For the combination of different measurement environments median balanced accuracies reached 0.63 for the classification of the disease status and differed little between scenarios. Median balanced accuracy to detect a significant change ranged from 0.81 to 0.72. When there was no prior knowledge about the additional data source it could not be used to determine an optimal decision boundary. In these cases, median balanced accuracy was approximately 0.02 lower. Conclusion: These simulations highlight issues that are relevant beyond the combination of blood glucose values. Measurements conducted to help clarify one medical issue are often reused in other contexts. However not all data sources are equally suitable for all analyses. Therefore, when data from laboratory medicine is exchanged, accompanying information that helps to estimate reliability is critically important. [ABSTRACT FROM AUTHOR]

Introduction:This study presents a web-based method and its interface ensuring alignment of all parts of a curriculum map including competencies, objectives, teaching and assessment methods, workload and patient availability. Needs, acceptance and effectiveness are shown through a nine-year study. Methods:After a comprehensive needs assessment, the curriculum map and a web-based interface “Learning Opportunities, Objectives and Outcome Platform” (LOOOP) were developed according to Harden’s conceptual framework of 10-steps for curriculum mapping. The outcome was measured by surveys and results of interdisciplinary MCQ-assessments. The usage rates and functionalities were analysed. Results:The implementation of LOOOP was significantly associated with improved perception of the curriculum structure by teachers and students, quality of defined objectives and their alignment with teaching and assessment, usage by students to prepare examinations and their scores in interdisciplinary MCQ-assessment. Additionally, LOOOP improved the curriculum coordination by faculty, and assisted departments for identifying patient availability for clinical training. Conclusion:LOOOP is well accepted among students and teachers, has positive effect on curriculum development, facilitates effective utilisation of educational resources and improves student’s outcomes. Currently, LOOOP is used in five undergraduate medical curricula including 85,000 mapped learning opportunities (lectures, seminars), 5000 registered users (students, teachers) and 380,000 yearly page-visits. [ABSTRACT FROM AUTHOR]

Access to patients is a crucial factor for student-centred medical education. However, increasing numbers of students, teacher shortage, a patient spectrum consisting of rarer diseases, and quicker discharges limit this necessary access, and therefore pose a challenge for curriculum designers. The herein presented algorithm improves access to patients in four steps by using routinely available electronic patient data already during curriculum development. Step I: Learning objectives are mapped to appropriate ICD-10 (International Statistical Classification of Diseases) codes. Step II: It is determined which learning opportunities need to be considered first for patient allocation in order to maximise overall benefit. Step III: Hospital's departments with the highest expertise on respective learning objectives are assessed and selected for teaching. Step IV: Patients of the chosen department that present the best match for a given learning opportunity are assigned to participation. This integrated analysis of learning objectives and existing clinical data during curriculum development is a well-structured method to maximise access to patients. Furthermore, this algorithm identifies learning objectives of a curriculum that do not correspond well to the spectrum of patients of the respective teaching hospital and which should therefore be taught in learning formats without patient contact. [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]

The article reports that the 24th annual conference of the IGLD e.V. will take place from March 5-6, 2020 in Germany.