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Making science computable: Developing code systems for statistics, study design, and risk of bias
The COVID-19 crisis led a group of scientific and informatics experts to accelerate development of an infrastructure for electronic data exchange for the identification, processing, and reporting of scientific findings. The Fast Healthcare Interoperability Resources (FHIR®) standard which is overcom...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9387176/ https://www.ncbi.nlm.nih.gov/pubmed/33486066 http://dx.doi.org/10.1016/j.jbi.2021.103685 |
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author | Alper, Brian S. Dehnbostel, Joanne Afzal, Muhammad Subbian, Vignesh Soares, Andrey Kunnamo, Ilkka Shahin, Khalid McClure, Robert C. |
author_facet | Alper, Brian S. Dehnbostel, Joanne Afzal, Muhammad Subbian, Vignesh Soares, Andrey Kunnamo, Ilkka Shahin, Khalid McClure, Robert C. |
author_sort | Alper, Brian S. |
collection | PubMed |
description | The COVID-19 crisis led a group of scientific and informatics experts to accelerate development of an infrastructure for electronic data exchange for the identification, processing, and reporting of scientific findings. The Fast Healthcare Interoperability Resources (FHIR®) standard which is overcoming the interoperability problems in health information exchange was extended to evidence-based medicine (EBM) knowledge with the EBMonFHIR project. A 13-step Code System Development Protocol was created in September 2020 to support global development of terminologies for exchange of scientific evidence. For Step 1, we assembled expert working groups with 55 people from 26 countries by October 2020. For Step 2, we identified 23 commonly used tools and systems for which the first version of code systems will be developed. For Step 3, a total of 368 non-redundant concepts were drafted to become display terms for four code systems (Statistic Type, Statistic Model, Study Design, Risk of Bias). Steps 4 through 13 will guide ongoing development and maintenance of these terminologies for scientific exchange. When completed, the code systems will facilitate identifying, processing, and reporting research results and the reliability of those results. More efficient and detailed scientific communication will reduce cost and burden and improve health outcomes, quality of life, and patient, caregiver, and healthcare professional satisfaction. We hope the achievements reached thus far will outlive COVID-19 and provide an infrastructure to make science computable for future generations. Anyone may join the effort at https://www.gps.health/covid19_knowledge_accelerator.html. |
format | Online Article Text |
id | pubmed-9387176 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Author(s). Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93871762022-08-18 Making science computable: Developing code systems for statistics, study design, and risk of bias Alper, Brian S. Dehnbostel, Joanne Afzal, Muhammad Subbian, Vignesh Soares, Andrey Kunnamo, Ilkka Shahin, Khalid McClure, Robert C. J Biomed Inform Original Research The COVID-19 crisis led a group of scientific and informatics experts to accelerate development of an infrastructure for electronic data exchange for the identification, processing, and reporting of scientific findings. The Fast Healthcare Interoperability Resources (FHIR®) standard which is overcoming the interoperability problems in health information exchange was extended to evidence-based medicine (EBM) knowledge with the EBMonFHIR project. A 13-step Code System Development Protocol was created in September 2020 to support global development of terminologies for exchange of scientific evidence. For Step 1, we assembled expert working groups with 55 people from 26 countries by October 2020. For Step 2, we identified 23 commonly used tools and systems for which the first version of code systems will be developed. For Step 3, a total of 368 non-redundant concepts were drafted to become display terms for four code systems (Statistic Type, Statistic Model, Study Design, Risk of Bias). Steps 4 through 13 will guide ongoing development and maintenance of these terminologies for scientific exchange. When completed, the code systems will facilitate identifying, processing, and reporting research results and the reliability of those results. More efficient and detailed scientific communication will reduce cost and burden and improve health outcomes, quality of life, and patient, caregiver, and healthcare professional satisfaction. We hope the achievements reached thus far will outlive COVID-19 and provide an infrastructure to make science computable for future generations. Anyone may join the effort at https://www.gps.health/covid19_knowledge_accelerator.html. The Author(s). Published by Elsevier Inc. 2021-03 2021-01-21 /pmc/articles/PMC9387176/ /pubmed/33486066 http://dx.doi.org/10.1016/j.jbi.2021.103685 Text en © 2021 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Original Research Alper, Brian S. Dehnbostel, Joanne Afzal, Muhammad Subbian, Vignesh Soares, Andrey Kunnamo, Ilkka Shahin, Khalid McClure, Robert C. Making science computable: Developing code systems for statistics, study design, and risk of bias |
title | Making science computable: Developing code systems for statistics, study design, and risk of bias |
title_full | Making science computable: Developing code systems for statistics, study design, and risk of bias |
title_fullStr | Making science computable: Developing code systems for statistics, study design, and risk of bias |
title_full_unstemmed | Making science computable: Developing code systems for statistics, study design, and risk of bias |
title_short | Making science computable: Developing code systems for statistics, study design, and risk of bias |
title_sort | making science computable: developing code systems for statistics, study design, and risk of bias |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9387176/ https://www.ncbi.nlm.nih.gov/pubmed/33486066 http://dx.doi.org/10.1016/j.jbi.2021.103685 |
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