ArticleBMC medical informatics and decision making2023
Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Temporal Variability in Diagnosis Code Distributions Across Extraction Time Points in a Multicenter Integrated EHR Database: A Snapshot Comparison Study.Journal of medical systems · 2026Observational
- Attitudes towards data access and sharing health data for research: a case study of Australian data custodian perspectives.Health information management : journal of the Health Information Management Association of Australia · 2026Article
- Statistical Methods for Multi-jurisdictional Australian Vaccine Safety Investigations of Rare Adverse Events.Drug safety · 2026Article
- Ontology- and LLM-based data harmonization for federated learning in healthcare.Frontiers in digital health · 2026Article
- Co-RESPOND: a federated network of cohorts on mental health and adversity during the COVID-19 pandemic. Challenges, solutions and recommendations for retrospective data harmonization.European journal of psychotraumatology · 2025Observational
- Using routine primary care data in research: (in)efficient case studies and perspectives from the Asthma UK Centre for Applied Research.BMJ health & care informatics · 2025Article
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- Creating a Modified Version of the Cambridge Multimorbidity Score to Predict Mortality in People Older Than 16 Years: Model Development and Validation.Journal of medical Internet research · 2024Article
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- A framework for understanding selection bias in real-world healthcare data.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.JAMIA open · 2023Article
- Inequalities in colorectal cancer screening uptake in Wales: an examination of the impact of the temporary suspension of the screening programme during the COVID-19 pandemic.BMC public health · 2023Article
- Common governance model: a way to avoid data segregation between existing trusted research environment.International journal of population data science · 2023Article
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Authors and funding
21 authors.
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Abstract
backgroundThe CVD-COVID-UK consortium was formed to understand the relationship between COVID-19 and cardiovascular diseases through analyses of harmonised electronic health records (EHRs) across the four UK nations. Beyond COVID-19, data harmonisation and common approaches enable analysis within and across independent Trusted Research Environments. Here we describe the reproducible harmonisation method developed using large-scale EHRs in Wales to accommodate the fast and efficient implementation of cross-nation analysis in England and Wales as part of the CVD-COVID-UK programme. We characterise current challenges and share lessons learnt.
methodsServing the scope and scalability of multiple study protocols, we used linked, anonymised individual-level EHR, demographic and administrative data held within the SAIL Databank for the population of Wales. The harmonisation method was implemented as a four-layer reproducible process, starting from raw data in the first layer. Then each of the layers two to four is framed by, but not limited to, the characterised challenges and lessons learnt. We achieved curated data as part of our second layer, followed by extracting phenotyped data in the third layer. We captured any project-specific requirements in the fourth layer.
resultsUsing the implemented four-layer harmonisation method, we retrieved approximately 100 health-related variables for the 3.2 million individuals in Wales, which are harmonised with corresponding variables for > 56 million individuals in England. We processed 13 data sources into the first layer of our harmonisation method: five of these are updated daily or weekly, and the rest at various frequencies providing sufficient data flow updates for frequent capturing of up-to-date demographic, administrative and clinical information.
conclusionsWe implemented an efficient, transparent, scalable, and reproducible harmonisation method that enables multi-nation collaborative research. With a current focus on COVID-19 and its relationship with cardiovascular outcomes, the harmonised data has supported a wide range of research activities across the UK.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.