Including social and behavioral determinants in predictive models: Trends, challenges, and opportunities

Marissa Tan, Elham Hatef, Delaram Taghipour, Kinjel Vyas, Hadi Kharrazi, Laura Gottlieb, Jonathan Weiner

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numbere18084
JournalJMIR Medical Informatics
Volume8
Issue number9
DOIs
StatePublished - Sep 2020

Keywords

  • Health care disparities
  • Information technology
  • Population health
  • Social determinants of health

ASJC Scopus subject areas

  • Health Informatics
  • Health Information Management

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