{"id":13950,"date":"2021-04-20T13:32:50","date_gmt":"2021-04-20T18:32:50","guid":{"rendered":"https:\/\/saluddigital.com\/?p=13950"},"modified":"2025-10-21T12:48:58","modified_gmt":"2025-10-21T18:48:58","slug":"registros-medicos-electronicos-analizados-en-tempo-real-para-predecir-covid-19","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/plataformas-digitales\/registros-medicos-electronicos-analizados-en-tempo-real-para-predecir-covid-19\/","title":{"rendered":"Electronic Health Records analyzed in real-time to predict COVID-19"},"content":{"rendered":"
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Article published in Naure details, that by using medical text sources entered into electronic medical records, it is possible to learn about possible diseases such as viral pneumonia.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The article \"Real-time clinician text feeds from electronic health records\", 'published in Nature in February this year, explains how search engine and social network analysis have been attempted for outbreak detection of infectious diseases.<\/p>

\"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,\" the authors explain. This is a low-cost, open-source approach that is ideal for local customization as it does not require any specific clinical record system.<\/p>

The same approach of real-time aggregation of keywords and phrases has been used to detect possible cases of COVID, classifying the symptoms of symptomatic cases such as: \"dry cough\", \"pyrexia\", \"fever\", \"dyspnea\", \"anosmia\", \"pneumonia\", among others.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The system was applied at King's College Hospital and Guys & St Thomas Hospital in the United Kingdom. In both hospitals, an increase in COVID-19 positive PCR tests was detected almost simultaneously with COVID-19 symptom-related keywords added to the hospital's patient records.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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However, despite the important findings the scientists explain that this approach has limitations, such as the environment in which it was applied, or the handling of the data. \"this study as proof-of-concept was confined to secondary care settings; during the Covid first wave in March 2020, there were triage mechanisms to redirect low severity cases away from secondary care affecting visibility. For a global view of a health economy, text aggregators would also need to be deployed in primary care and emergency care providers.\"<\/p>

Read the full text at the following link: https:\/\/www.nature.com\/articles\/s41746-021-00406-7<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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NATURE<\/strong><\/p>

https:\/\/www.nature.com\/articles\/s41746-021-00406-7<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Art\u00edculo publicado en Nature detalla c\u00f3mo a trav\u00e9s de fuentes de texto m\u00e9dico ingresado en registros electr\u00f3nicos, es posible conocer posibles enfermedades como neumon\u00eda viral El art\u00edculo “Fuentes de texto del m\u00e9dico en tiempo real a partir de registros m\u00e9dicos electr\u00f3nicos”, \u00b4publicado en Nature en febrero de este a\u00f1o, explica c\u00f3mo se han intentado an\u00e1lisis de motores de b\u00fasqueda y redes sociales para la detecci\u00f3n de brotes, de enfermedades infecciosas. “Describimos un enfoque que utiliza la agregaci\u00f3n en tiempo real de palabras clave y frases de texto libre a partir de documentaci\u00f3n generada por el m\u00e9dico en tiempo real en registros m\u00e9dicos electr\u00f3nicos para producir una se\u00f1al personalizable de neumon\u00eda viral en tiempo real que proporciona hasta 4 d\u00edas de advertencia para la planificaci\u00f3n de la capacidad de atenci\u00f3n secundaria”, explican los autores. Se trata de un enfoque de bajo costo y de c\u00f3digo abierto, ideal para la personalizaci\u00f3n local ya que no requiere ning\u00fan sistema de registros cl\u00ednicos en espec\u00edfico. El mismo enfoque de agregaci\u00f3n en tiempo real de palabras clave y frases, ha sido utilizado para detectar posibles casos de COVID, clasificando los s\u00edntomas de casos sintom\u00e1ticos como: “tos seca”, “pirexia”, “fiebre”, “disnea”, “anosmia”, “neumon\u00eda”, entre otros. El sistema fue aplicado en el King’s College Hospital y en el Guys & St Thomas Hospital en el Reino Unido. En ambos hospitales se detect\u00f3 un incremento en pruebas PCR positivas a COVID-19 casi simult\u00e1neamente con palabras clave relacionadas a s\u00edntomas de COVID-19 agregadas a los registros cl\u00ednicos de los pacientes del hospital. Sin embargo, a pesar de los importantes hallazgos, los cient\u00edficos explican que este enfoque cuenta con limitaciones, como el ambiente en el que se aplic\u00f3, o el manejo de los datos. “Este estudio como prueba de concepto se limit\u00f3 a entornos de atenci\u00f3n secundaria; Durante la primera ola de COVID en marzo de 2020, hubo mecanismos de clasificaci\u00f3n para desviar los casos de baja gravedad lejos de la atenci\u00f3n secundaria que afecta la visibilidad. Para tener una visi\u00f3n global de la econom\u00eda de la salud, los agregadores de texto tambi\u00e9n deber\u00edan implementarse en los proveedores de atenci\u00f3n primaria y de emergencia”. Lee el texto completo en el siguiente enlace: https:\/\/www.nature.com\/articles\/s41746-021-00406-7 BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41746-021-00406-7<\/p>","protected":false},"author":1,"featured_media":13951,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,3393,153,1418],"tags":[145],"class_list":["post-13950","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-inteligencia-artificial-y-ciencia","category-plataformas-digitales","category-resena-de-publicaciones-cientificas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/13950","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=13950"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/13950\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/13951"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=13950"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=13950"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=13950"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}