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Real-time clinician text feeds from electronic health records

Research output: Contribution to journalArticlepeer-review

James T.H. Teo, Vlad Dinu, William Bernal, Phil Davidson, Vitaliy Oliynyk, Cormac Breen, Richard D. Barker, Richard J.B. Dobson

Original languageEnglish
Article number35
Journalnpj Digital Medicine
Issue number1
PublishedDec 2021

Bibliographical note

Funding Information: The infrastructure and the authors received funding from: National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, Health Data Research UK, UK Research and Innovation, London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, Innovate UK, the NIHR Applied Research Collaboration South London, the NIHR University College London Hospitals Biomedical Research Centre and King’s College London. We also thank all patients who agreed to their data being used for research, and the patients of the KERRI committee. Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors


Analyses of search engine and social media feeds have been attempted for infectious disease outbreaks, but have been found to be susceptible to artefactual distortions from health scares or keyword spamming in social media or the public internet. We describe an approach using real-time aggregation of keywords and phrases of freetext from real-time clinician-generated documentation in electronic health records to produce a customisable real-time viral pneumonia signal providing up to 4 days warning for secondary care capacity planning. This low-cost approach is open-source, is locally customisable, is not dependent on any specific electronic health record system and can provide an ensemble of signals if deployed at multiple organisational scales.

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