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Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management
Objectives : To summarize the recent literature and research and present a selection of the best papers published in 2018 in the field of Health Information Management (HIM) and Health Informatics. Methods : A systematic review of the literature was performed, with the help of a medical librarian, b...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Georg Thieme Verlag KG
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697497/ https://www.ncbi.nlm.nih.gov/pubmed/31419817 http://dx.doi.org/10.1055/s-0039-1677941 |
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author | Bloomrosen, Meryl Berner, Eta S. |
author_facet | Bloomrosen, Meryl Berner, Eta S. |
author_sort | Bloomrosen, Meryl |
collection | PubMed |
description | Objectives : To summarize the recent literature and research and present a selection of the best papers published in 2018 in the field of Health Information Management (HIM) and Health Informatics. Methods : A systematic review of the literature was performed, with the help of a medical librarian, by the two editors of the HIM section of the International Medical Informatics Association (IMIA) Yearbook. In order to include papers that would address the special theme of the 2019 Yearbook on artificial intelligence (AI) as well as HIM, we searched bibliographic databases for HIM-related papers with an AI focus using both Medical Subject Headings (MeSH) descriptors and keywords in titles and abstracts. A shortlist of 15 candidate best papers was first selected by section editors before being peer-reviewed by independent external reviewers. Results : While there were a significant number of manuscripts that addressed issues relevant to HIM, there were virtually none with MeSH headings indicating an HIM focus. Manuscripts that were considered related to the HIM field in terms of the practice of health information management as well as the profession included those that examined using machine learning and other AI approaches to identify protected health information in clinical text to aid with de-identification, automated coding approaches to translate free-text into standardized codes, and natural language processing approaches to extract clinical data to assist with populating cancer and other registries. Conclusions : The papers discussed in the HIM section reflect the special theme of the use of AI in healthcare on issues particularly relevant to the field of HIM. This synopsis discusses these papers and recommends that HIM practitioners be more involved in research and that researchers in AI and related areas recognize the applicability and relevance of their work to the field of HIM. |
format | Online Article Text |
id | pubmed-6697497 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Georg Thieme Verlag KG |
record_format | MEDLINE/PubMed |
spelling | pubmed-66974972019-08-19 Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management Bloomrosen, Meryl Berner, Eta S. Yearb Med Inform Objectives : To summarize the recent literature and research and present a selection of the best papers published in 2018 in the field of Health Information Management (HIM) and Health Informatics. Methods : A systematic review of the literature was performed, with the help of a medical librarian, by the two editors of the HIM section of the International Medical Informatics Association (IMIA) Yearbook. In order to include papers that would address the special theme of the 2019 Yearbook on artificial intelligence (AI) as well as HIM, we searched bibliographic databases for HIM-related papers with an AI focus using both Medical Subject Headings (MeSH) descriptors and keywords in titles and abstracts. A shortlist of 15 candidate best papers was first selected by section editors before being peer-reviewed by independent external reviewers. Results : While there were a significant number of manuscripts that addressed issues relevant to HIM, there were virtually none with MeSH headings indicating an HIM focus. Manuscripts that were considered related to the HIM field in terms of the practice of health information management as well as the profession included those that examined using machine learning and other AI approaches to identify protected health information in clinical text to aid with de-identification, automated coding approaches to translate free-text into standardized codes, and natural language processing approaches to extract clinical data to assist with populating cancer and other registries. Conclusions : The papers discussed in the HIM section reflect the special theme of the use of AI in healthcare on issues particularly relevant to the field of HIM. This synopsis discusses these papers and recommends that HIM practitioners be more involved in research and that researchers in AI and related areas recognize the applicability and relevance of their work to the field of HIM. Georg Thieme Verlag KG 2019-08 2019-08-16 /pmc/articles/PMC6697497/ /pubmed/31419817 http://dx.doi.org/10.1055/s-0039-1677941 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 | Bloomrosen, Meryl Berner, Eta S. Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management |
title | Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management |
title_full | Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management |
title_fullStr | Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management |
title_full_unstemmed | Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management |
title_short | Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management |
title_sort | findings from the 2019 international medical informatics association yearbook section on health information management |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697497/ https://www.ncbi.nlm.nih.gov/pubmed/31419817 http://dx.doi.org/10.1055/s-0039-1677941 |
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