Chuang Gao, Shahzad Mumtaz, Sophie McCall, Katherine O'Sullivan, Mark McGilchrist, Daniel R Morales, Christopher Hall, Katie Wilde, Charlie Mayor, Pamela Linksted, Kathy Harrison, Christian Cole, Emily Jefferson
{"title":"A pipeline for harmonising NHS Scotland laboratory data to enable national-level analyses.","authors":"Chuang Gao, Shahzad Mumtaz, Sophie McCall, Katherine O'Sullivan, Mark McGilchrist, Daniel R Morales, Christopher Hall, Katie Wilde, Charlie Mayor, Pamela Linksted, Kathy Harrison, Christian Cole, Emily Jefferson","doi":"10.1016/j.jbi.2024.104771","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Medical laboratory data together with prescribing and hospitalisation records are three of the most used electronic health records (EHRs) for data-driven health research. In Scotland, hospitalisation, prescribing and the death register data are available nationally whereas laboratory data is captured, stored and reported from local health board systems with significant heterogeneity. For researchers or other users of this regionally curated data, working on laboratory datasets across regional cohorts requires effort and time. As part of this study, the Scottish Safe Haven Network have developed an open-source software pipeline to generate a harmonised laboratory dataset.</p><p><strong>Methods: </strong>We obtained sample laboratory data from the four regional Safe Havens in Scotland covering people within the SHARE consented cohort. We compared the variables collected by each regional Safe Haven and mapped these to 11 FHIR and 2 Scottish-specific standardised terms (i.e., one to indicate the regional health board and a second to describe the source clinical code description) RESULTS: We compared the laboratory data and found that 180 test codes covered 98.7 % of test records performed across Scotland. Focusing on the 180 test codes, we developed a set of transformations to convert test results captured in different units to the same unit. We included both Read Codes and SNOMED CT to encode the tests within the pipeline.</p><p><strong>Conclusion: </strong>We validated our harmonisation pipeline by comparing the results across the different regional datasets. The pipeline can be reused by researchers and/or Safe Havens to generate clean, harmonised laboratory data at a national level with minimal effort.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104771"},"PeriodicalIF":4.0000,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biomedical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.jbi.2024.104771","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: Medical laboratory data together with prescribing and hospitalisation records are three of the most used electronic health records (EHRs) for data-driven health research. In Scotland, hospitalisation, prescribing and the death register data are available nationally whereas laboratory data is captured, stored and reported from local health board systems with significant heterogeneity. For researchers or other users of this regionally curated data, working on laboratory datasets across regional cohorts requires effort and time. As part of this study, the Scottish Safe Haven Network have developed an open-source software pipeline to generate a harmonised laboratory dataset.
Methods: We obtained sample laboratory data from the four regional Safe Havens in Scotland covering people within the SHARE consented cohort. We compared the variables collected by each regional Safe Haven and mapped these to 11 FHIR and 2 Scottish-specific standardised terms (i.e., one to indicate the regional health board and a second to describe the source clinical code description) RESULTS: We compared the laboratory data and found that 180 test codes covered 98.7 % of test records performed across Scotland. Focusing on the 180 test codes, we developed a set of transformations to convert test results captured in different units to the same unit. We included both Read Codes and SNOMED CT to encode the tests within the pipeline.
Conclusion: We validated our harmonisation pipeline by comparing the results across the different regional datasets. The pipeline can be reused by researchers and/or Safe Havens to generate clean, harmonised laboratory data at a national level with minimal effort.
期刊介绍:
The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.