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首页> 外文期刊>JMIR Medical Informatics >Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities
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Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities

机译：包括预测模型中的社会和行为决定因素：趋势，挑战和机遇

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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.
机译：在加速健康信息技术能力的时代，医疗保健组织越来越多地利用数字数据来预测急诊部门使用，住院和医疗费用等结果。这种趋势与日益增长的认可一起，即健康的社会和行为决定因素（SBDH）影响健康和医疗用途。因此，卫生提供者和保险公司开始将新的SBDH数据来源纳入广泛的医疗保健预测模型，尽管使用SBDH变量的现有模型尚未被证明可以提高医疗保健预测，而不是专门临床变量的模型。在这个观点来看，我们审查了推动者背后的理由将SBDH数据集成到医疗保健预测模型中，并探讨了在美国展开的这种过程所面临的技术，战略和道德挑战。我们还提供了一些建议，以克服这些挑战，以达到SBDH预测分析的承诺，以改善健康，减少医疗保健差异。

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