Kotecha, Dipak;
Asselbergs, Folkert W;
Achenbach, Stephan;
Anker, Stefan D;
Atar, Dan;
Baigent, Colin;
Banerjee, Amitava;
... CODE-EHR International Consensus Group; + view all
(2022)
CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research.
The Lancet Digital Health
, 4
(10)
e757-e764.
10.1016/s2589-7500(22)00151-0.
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Abstract
Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes.
| Type: | Article |
|---|---|
| Title: | CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/s2589-7500(22)00151-0 |
| Publisher version: | https://doi.org/10.1016/s2589-7500(22)00151-0 |
| Language: | English |
| Additional information: | Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Infectious Disease Informatics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10175331 |
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