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Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities
In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral deter...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509627/ https://www.ncbi.nlm.nih.gov/pubmed/32897240 http://dx.doi.org/10.2196/18084 |
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author | Tan, Marissa Hatef, Elham Taghipour, Delaram Vyas, Kinjel Kharrazi, Hadi Gottlieb, Laura Weiner, Jonathan |
author_facet | Tan, Marissa Hatef, Elham Taghipour, Delaram Vyas, Kinjel Kharrazi, Hadi Gottlieb, Laura Weiner, Jonathan |
author_sort | Tan, Marissa |
collection | PubMed |
description | In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables. In this viewpoint, we review the rationale behind the push to integrate SBDH data into health care predictive models and explore the technical, strategic, and ethical challenges faced as this process unfolds across the United States. We also offer several recommendations to overcome these challenges to reach the promise of SBDH predictive analytics to improve health and reduce health care disparities. |
format | Online Article Text |
id | pubmed-7509627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-75096272020-10-05 Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities Tan, Marissa Hatef, Elham Taghipour, Delaram Vyas, Kinjel Kharrazi, Hadi Gottlieb, Laura Weiner, Jonathan JMIR Med Inform Viewpoint In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables. In this viewpoint, we review the rationale behind the push to integrate SBDH data into health care predictive models and explore the technical, strategic, and ethical challenges faced as this process unfolds across the United States. We also offer several recommendations to overcome these challenges to reach the promise of SBDH predictive analytics to improve health and reduce health care disparities. JMIR Publications 2020-09-08 /pmc/articles/PMC7509627/ /pubmed/32897240 http://dx.doi.org/10.2196/18084 Text en ©Marissa Tan, Elham Hatef, Delaram Taghipour, Kinjel Vyas, Hadi Kharrazi, Laura Gottlieb, Jonathan Weiner. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 08.09.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Viewpoint Tan, Marissa Hatef, Elham Taghipour, Delaram Vyas, Kinjel Kharrazi, Hadi Gottlieb, Laura Weiner, Jonathan Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities |
title | Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities |
title_full | Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities |
title_fullStr | Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities |
title_full_unstemmed | Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities |
title_short | Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities |
title_sort | including social and behavioral determinants in predictive models: trends, challenges, and opportunities |
topic | Viewpoint |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509627/ https://www.ncbi.nlm.nih.gov/pubmed/32897240 http://dx.doi.org/10.2196/18084 |
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