DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research

The DECOVID database contains harmonized pseudonymized electronic health record (EHR) data on all adult (≥18 years old) patients presenting to two large, digitally mature centers in the United Kingdom between 1 January 2020 and 28 February 2021, with follow-up until at least 28 March 2021. The database was originally developed to support the COVID-19 response but is now available via the PIONEER data hub for researchers to explore a wide range of research questions, including exploratory analyses, risk factor assessment, prediction modeling, and comparative effectiveness studies. Raw data were extracted from local EHRs and transformed into a standardized form (Observational Health Data Sciences and Informatics-Common Data Model version 5.3.1). The database includes 165,420 patients across 256,804 hospital presentations. For these patients, highly granular data are available, including patient demographics, longitudinal vital signs, physiology, treatments, laboratory findings, clinical diagnoses, and outcomes. There are 10,030 patients with COVID-19, of whom 1472 died in hospital.

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Data
Published
2025-11-24
DOI
https://doi.org/10.3390/data10120195
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00

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article

DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research

Data
Machine Learning in Healthcare
article

DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research

article en

Abstract

The DECOVID database contains harmonized pseudonymized electronic health record (EHR) data on all adult (≥18 years old) patients presenting to two large, digitally mature centers in the United Kingdom between 1 January 2020 and 28 February 2021, with follow-up until at least 28 March 2021. The database was originally developed to support the COVID-19 response but is now available via the PIONEER data hub for researchers to explore a wide range of research questions, including exploratory analyses, risk factor assessment, prediction modeling, and comparative effectiveness studies. Raw data were extracted from local EHRs and transformed into a standardized form (Observational Health Data Sciences and Informatics-Common Data Model version 5.3.1). The database includes 165,420 patients across 256,804 hospital presentations. For these patients, highly granular data are available, including patient demographics, longitudinal vital signs, physiology, treatments, laboratory findings, clinical diagnoses, and outcomes. There are 10,030 patients with COVID-19, of whom 1472 died in hospital.

DataVol. 10(12)
University of London (GB), Turing Institute (GB), University College Hospital (GB), University of Leeds (GB), University Hospitals Birmingham NHS Foundation Trust (GB), University of St Andrews (GB), University of Dundee (GB), University of Strathclyde (GB), Durham University (GB), University of Cambridge (GB), Leeds Teaching Hospitals NHS Trust (GB), Cambridge University Hospitals NHS Foundation Trust (GB), Bury College (GB), Whittington Hospital (GB), Edinburgh Cancer Research (GB), University of Surrey (GB), University of Oxford (GB), Mary Lyon Centre at MRC Harwell (GB), London School of Hygiene & Tropical Medicine (GB), Maxwell Institute for Mathematical Sciences (GB), The Alan Turing Institute (GB), MRC Biostatistics Unit (GB), Centre for Global Health Research (CA), Oxford BioMedica (United Kingdom) (GB), Oxford University Hospitals NHS Trust (GB), Centre for Inflammation Research (GB), University College London (GB), University of California, Berkeley (US), University of Edinburgh (GB)
Wellcome Trust, University Hospitals Birmingham NHS Foundation Trust, UK Research and Innovation, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, Royal Academy of Engineering, Research Councils UK, University College London, Innovation and Technology Commission, University College London Hospitals NHS Foundation Trust, Medical Research Council, Engineering and Physical Sciences Research Council, Birmingham Biomedical Research Centre, Leeds Biomedical Research Centre, NIHR Cambridge Biomedical Research Centre
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Machine Learning in Healthcare
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