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Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes: A Text-Mining Analysis

Text-mining algorithms can identify the most prevalent factors of risk-benefit assessment on the use of complementary and integrative health approaches that are found in healthcare professionals' written notes. The aims of this study were to discover the key factors of decision-making on patien...

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Autores principales: Chang, Hsiao-Yun, Yang, Ya-Hui, Lo, Chia-Lun, Huang, Yu-Yao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10241416/
https://www.ncbi.nlm.nih.gov/pubmed/36225163
http://dx.doi.org/10.1097/CIN.0000000000000977
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author Chang, Hsiao-Yun
Yang, Ya-Hui
Lo, Chia-Lun
Huang, Yu-Yao
author_facet Chang, Hsiao-Yun
Yang, Ya-Hui
Lo, Chia-Lun
Huang, Yu-Yao
author_sort Chang, Hsiao-Yun
collection PubMed
description Text-mining algorithms can identify the most prevalent factors of risk-benefit assessment on the use of complementary and integrative health approaches that are found in healthcare professionals' written notes. The aims of this study were to discover the key factors of decision-making on patients' complementary and integrative health use by healthcare professionals and to build a consensus-derived decision algorithm on the benefit-risk assessment of complementary and integrative health use in diabetes. The retrospective study of an archival dataset used a text-mining method designed to extract and analyze unstructured textual data from healthcare professionals' responses. The techniques of classification, clustering, and extraction were performed with 1398 unstructured clinical notes made by healthcare professionals between 2019 and 2020. The most important factor for decision-making by healthcare professionals about complementary and integrative health use in patients with diabetes was the ingredients of the product. Other important factors were the patient's diabetes control, the undesirable effects from complementary and integrative health, evidence-based complementary and integrative health, medical laboratory data, and the product's affordability. This exploratory text-mining study provides insight into how healthcare professionals decide complementary and integrative health use for patients with diabetes after a risk-benefit assessment from clinical narrative notes.
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spelling pubmed-102414162023-06-06 Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes: A Text-Mining Analysis Chang, Hsiao-Yun Yang, Ya-Hui Lo, Chia-Lun Huang, Yu-Yao Comput Inform Nurs Features Text-mining algorithms can identify the most prevalent factors of risk-benefit assessment on the use of complementary and integrative health approaches that are found in healthcare professionals' written notes. The aims of this study were to discover the key factors of decision-making on patients' complementary and integrative health use by healthcare professionals and to build a consensus-derived decision algorithm on the benefit-risk assessment of complementary and integrative health use in diabetes. The retrospective study of an archival dataset used a text-mining method designed to extract and analyze unstructured textual data from healthcare professionals' responses. The techniques of classification, clustering, and extraction were performed with 1398 unstructured clinical notes made by healthcare professionals between 2019 and 2020. The most important factor for decision-making by healthcare professionals about complementary and integrative health use in patients with diabetes was the ingredients of the product. Other important factors were the patient's diabetes control, the undesirable effects from complementary and integrative health, evidence-based complementary and integrative health, medical laboratory data, and the product's affordability. This exploratory text-mining study provides insight into how healthcare professionals decide complementary and integrative health use for patients with diabetes after a risk-benefit assessment from clinical narrative notes. Lippincott Williams & Wilkins 2022-10-10 /pmc/articles/PMC10241416/ /pubmed/36225163 http://dx.doi.org/10.1097/CIN.0000000000000977 Text en Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Features
Chang, Hsiao-Yun
Yang, Ya-Hui
Lo, Chia-Lun
Huang, Yu-Yao
Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes: A Text-Mining Analysis
title Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes: A Text-Mining Analysis
title_full Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes: A Text-Mining Analysis
title_fullStr Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes: A Text-Mining Analysis
title_full_unstemmed Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes: A Text-Mining Analysis
title_short Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes: A Text-Mining Analysis
title_sort factors considered important by healthcare professionals for the management of using complementary therapy in diabetes: a text-mining analysis
topic Features
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10241416/
https://www.ncbi.nlm.nih.gov/pubmed/36225163
http://dx.doi.org/10.1097/CIN.0000000000000977
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