TY - JOUR
T1 - Including social and behavioral determinants in predictive models
T2 - Trends, challenges, and opportunities
AU - Tan, Marissa
AU - Hatef, Elham
AU - Taghipour, Delaram
AU - Vyas, Kinjel
AU - Kharrazi, Hadi
AU - Gottlieb, Laura
AU - Weiner, Jonathan
N1 - Funding Information:
LG reports receiving funding from the Commonwealth Fund, Episcopal Health Foundation, Kaiser Permanente, NIMHD, and AHRQ for work unrelated to this manuscript. She received support from the Robert Wood Johnson Foundation for her work on this manuscript. The remaining authors declare no conflicts of interest.
Publisher Copyright:
© 2020 Marissa Tan, Elham Hatef, Delaram Taghipour, Kinjel Vyas, Hadi Kharrazi, Laura Gottlieb, Jonathan Weiner.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Health care disparities
KW - Information technology
KW - Population health
KW - Social determinants of health
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U2 - 10.2196/18084
DO - 10.2196/18084
M3 - Review article
C2 - 32897240
AN - SCOPUS:85097478570
SN - 2291-9694
VL - 8
JO - JMIR Medical Informatics
JF - JMIR Medical Informatics
IS - 9
M1 - e18084
ER -