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Untangling Data in Precision Oncology – A Model for Chronic Diseases?

Objectives : Any attempt to introduce new data types in the entangled hospital infrastructure should help to unravel old knots without tangling new ones. Health data from a wide range of sources has become increasingly available. We witness an insatiable thirst for data in oncology as treatment para...

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Autor principal: Fernández, Xosé M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Georg Thieme Verlag KG 2020
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442532/
https://www.ncbi.nlm.nih.gov/pubmed/32823314
http://dx.doi.org/10.1055/s-0040-1701985
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author Fernández, Xosé M.
author_facet Fernández, Xosé M.
author_sort Fernández, Xosé M.
collection PubMed
description Objectives : Any attempt to introduce new data types in the entangled hospital infrastructure should help to unravel old knots without tangling new ones. Health data from a wide range of sources has become increasingly available. We witness an insatiable thirst for data in oncology as treatment paradigms are shifting to targeted molecular therapies. Methods : From nineteenth-century medical notes consisting entirely of narrative description to standardised forms recording physical examination and medical notes, we have nowadays moved to electronic health records (EHRs). All our analogue medical records are rendered as sequences of zeros and ones changing how we capture and share data. The challenge we face is to offload the analysis without entrusting a machine (or algorithms) to make major decisions about a diagnosis, a treatment, or a surgery, keeping the human oversight. Computers don’t have judgment, they lack context. Results : EHRs have become the latest addition to our toolset to look after patients. Moore’s law and general advances in computation have contributed to make EHRs a cornerstone of Molecular Tumour Boards, presenting a detailed and unique description of a tumour and treatment options. Conclusions : Precision oncology, as a systematic approach matching the most accurate and effective treatment to each individual cancer patient, based on a molecular profile, is already expanding to other disease areas.
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spelling pubmed-74425322020-08-24 Untangling Data in Precision Oncology – A Model for Chronic Diseases? Fernández, Xosé M. Yearb Med Inform Objectives : Any attempt to introduce new data types in the entangled hospital infrastructure should help to unravel old knots without tangling new ones. Health data from a wide range of sources has become increasingly available. We witness an insatiable thirst for data in oncology as treatment paradigms are shifting to targeted molecular therapies. Methods : From nineteenth-century medical notes consisting entirely of narrative description to standardised forms recording physical examination and medical notes, we have nowadays moved to electronic health records (EHRs). All our analogue medical records are rendered as sequences of zeros and ones changing how we capture and share data. The challenge we face is to offload the analysis without entrusting a machine (or algorithms) to make major decisions about a diagnosis, a treatment, or a surgery, keeping the human oversight. Computers don’t have judgment, they lack context. Results : EHRs have become the latest addition to our toolset to look after patients. Moore’s law and general advances in computation have contributed to make EHRs a cornerstone of Molecular Tumour Boards, presenting a detailed and unique description of a tumour and treatment options. Conclusions : Precision oncology, as a systematic approach matching the most accurate and effective treatment to each individual cancer patient, based on a molecular profile, is already expanding to other disease areas. Georg Thieme Verlag KG 2020-08 2020-08-21 /pmc/articles/PMC7442532/ /pubmed/32823314 http://dx.doi.org/10.1055/s-0040-1701985 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.
spellingShingle Fernández, Xosé M.
Untangling Data in Precision Oncology – A Model for Chronic Diseases?
title Untangling Data in Precision Oncology – A Model for Chronic Diseases?
title_full Untangling Data in Precision Oncology – A Model for Chronic Diseases?
title_fullStr Untangling Data in Precision Oncology – A Model for Chronic Diseases?
title_full_unstemmed Untangling Data in Precision Oncology – A Model for Chronic Diseases?
title_short Untangling Data in Precision Oncology – A Model for Chronic Diseases?
title_sort untangling data in precision oncology – a model for chronic diseases?
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442532/
https://www.ncbi.nlm.nih.gov/pubmed/32823314
http://dx.doi.org/10.1055/s-0040-1701985
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