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Real-time clinician text feeds from electronic health records

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 o...

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Detalles Bibliográficos
Autores principales: Teo, James T. H., Dinu, Vlad, Bernal, William, Davidson, Phil, Oliynyk, Vitaliy, Breen, Cormac, Barker, Richard D., Dobson, Richard J. B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7904856/
https://www.ncbi.nlm.nih.gov/pubmed/33627748
http://dx.doi.org/10.1038/s41746-021-00406-7
Descripción
Sumario: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.