Real-time clinician text feeds from electronic health records

TY - JOUR

T1 - Real-time clinician text feeds from electronic health records

AU - Teo, James T H

AU - Dinu, Vlad

AU - Bernal, William

AU - Davidson, Phil

AU - Oliynyk, Vitaliy

AU - Breen, Cormac

AU - Barker, Richard D

AU - Dobson, Richard J B

N1 - 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.

PY - 2021/2/24

Y1 - 2021/2/24

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85101613191&partnerID=8YFLogxK

U2 - 10.1038/s41746-021-00406-7

DO - 10.1038/s41746-021-00406-7

M3 - Article

C2 - 33627748

AN - SCOPUS:85101613191

SN - 2398-6352

VL - 4

JO - npj Digital Medicine

JF - npj Digital Medicine

IS - 1

M1 - 35

ER -