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Clinical Research Informatics
Objectives : To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2021. Method : Using PubMed, we did a bibliographic search using a combination of MeSH descriptors and free-text terms on CRI, followed by a doub...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Georg Thieme Verlag KG
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719780/ https://www.ncbi.nlm.nih.gov/pubmed/36463874 http://dx.doi.org/10.1055/s-0042-1742530 |
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author | Daniel, Christel Tannier, Xavier Kalra, Dipak |
author_facet | Daniel, Christel Tannier, Xavier Kalra, Dipak |
author_sort | Daniel, Christel |
collection | PubMed |
description | Objectives : To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2021. Method : Using PubMed, we did a bibliographic search using a combination of MeSH descriptors and free-text terms on CRI, followed by a double-blind review in order to select a list of candidate best papers to be peer-reviewed by external reviewers. After peer-review ranking, three section editors met for a consensus meeting and the editorial team was organized to finally conclude on the selected three best papers. Results : Among the 1,096 papers (published in 2021) returned by the search and in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes an operational and scalable framework for generating EHR datasets based on a detailed clinical model with an application in the domain of the COVID-19 pandemics. The authors of the second best paper present a secure and scalable platform for the preprocessing of biomedical data for deep data-driven health management applied for the detection of pre-symptomatic COVID-19 cases and for biological characterization of insulin-resistance heterogeneity. The third best paper provides a contribution to the integration of care and research activities with the REDCap Clinical Data and Interoperability sServices (CDIS) module improving the accuracy and efficiency of data collection. Conclusions : The COVID-19 pandemic is still significantly stimulating research efforts in the CRI field to improve the process deeply and widely for conducting real-world studies as well as for optimizing clinical trials, the duration and cost of which are constantly increasing. The current health crisis highlights the need for healthcare institutions to continue the development and deployment of Big Data spaces, to strengthen their expertise in data science and to implement efficient data quality evaluation and improvement programs. |
format | Online Article Text |
id | pubmed-9719780 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Georg Thieme Verlag KG |
record_format | MEDLINE/PubMed |
spelling | pubmed-97197802022-12-05 Clinical Research Informatics Daniel, Christel Tannier, Xavier Kalra, Dipak Yearb Med Inform Objectives : To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2021. Method : Using PubMed, we did a bibliographic search using a combination of MeSH descriptors and free-text terms on CRI, followed by a double-blind review in order to select a list of candidate best papers to be peer-reviewed by external reviewers. After peer-review ranking, three section editors met for a consensus meeting and the editorial team was organized to finally conclude on the selected three best papers. Results : Among the 1,096 papers (published in 2021) returned by the search and in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes an operational and scalable framework for generating EHR datasets based on a detailed clinical model with an application in the domain of the COVID-19 pandemics. The authors of the second best paper present a secure and scalable platform for the preprocessing of biomedical data for deep data-driven health management applied for the detection of pre-symptomatic COVID-19 cases and for biological characterization of insulin-resistance heterogeneity. The third best paper provides a contribution to the integration of care and research activities with the REDCap Clinical Data and Interoperability sServices (CDIS) module improving the accuracy and efficiency of data collection. Conclusions : The COVID-19 pandemic is still significantly stimulating research efforts in the CRI field to improve the process deeply and widely for conducting real-world studies as well as for optimizing clinical trials, the duration and cost of which are constantly increasing. The current health crisis highlights the need for healthcare institutions to continue the development and deployment of Big Data spaces, to strengthen their expertise in data science and to implement efficient data quality evaluation and improvement programs. Georg Thieme Verlag KG 2022-12-04 /pmc/articles/PMC9719780/ /pubmed/36463874 http://dx.doi.org/10.1055/s-0042-1742530 Text en IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ) 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 | Daniel, Christel Tannier, Xavier Kalra, Dipak Clinical Research Informatics |
title | Clinical Research Informatics |
title_full | Clinical Research Informatics |
title_fullStr | Clinical Research Informatics |
title_full_unstemmed | Clinical Research Informatics |
title_short | Clinical Research Informatics |
title_sort | clinical research informatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719780/ https://www.ncbi.nlm.nih.gov/pubmed/36463874 http://dx.doi.org/10.1055/s-0042-1742530 |
work_keys_str_mv | AT danielchristel clinicalresearchinformatics AT tannierxavier clinicalresearchinformatics AT kalradipak clinicalresearchinformatics |