Tan Marissa, Hatef Elham, Taghipour Delaram, Vyas Kinjel, Kharrazi Hadi, Gottlieb Laura, Weiner Jonathan
General Preventive Medicine Residency Program, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Center for Population Health Information Technology, Baltimore, MD, United States.
JMIR Med Inform. 2020 Sep 8;8(9):e18084. doi: 10.2196/18084.
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.
在一个健康信息技术能力加速发展的时代,医疗保健机构越来越多地使用数字数据来预测诸如急诊科就诊、住院和医疗保健成本等结果。这一趋势出现的同时,人们也越来越认识到健康的社会和行为决定因素(SBDH)会影响健康和医疗保健的使用。因此,医疗服务提供者和保险公司开始将新的SBDH数据源纳入广泛的医疗保健预测模型中,尽管使用SBDH变量的现有模型并未显示出比仅使用临床变量的模型在改善医疗保健预测方面更具优势。在这一观点中,我们回顾了将SBDH数据整合到医疗保健预测模型背后的基本原理,并探讨了在美国各地推进这一过程时所面临的技术、战略和伦理挑战。我们还提出了一些建议,以克服这些挑战,实现SBDH预测分析改善健康状况和减少医疗保健差距的前景。