整合健康数据的社会决定因素到最大的,非营利性的卫生系统在南佛罗里达州

Lourdes M. Rojas, Gregory L. Vincent, D. Parris
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引用次数: 0

摘要

健康的社会决定因素是指人们出生、成长、工作和生活的条件。尽管有证据表明,SDOH影响一系列健康结果,但卫生系统缺乏获取这些信息并据此采取行动的基础设施。本文的目的是解释卫生系统用来:1)识别和集成公共可用的SDOH数据到卫生系统的数据仓库,2)集成符合HIPAA的地理编码软件(通过DeGAUSS),以及3)可视化数据以通知SDOH项目(通过Tableau)的方法。首先,作者与整个卫生系统的关键利益相关者接触,以传达SDOH数据对患者群体的影响,并确定感兴趣的变量。结果,14个公开可用的数据集被清理并集成到我们的数据仓库中,这些数据集占了2016-2019年期间代表国家、州、县和人口普查区信息的bb30,800个变量。为了试验数据可视化,我们为我们的服务区域创建了县和人口普查区级别地图,并绘制了常见的SDOH指标(例如,收入、教育、保险状况等)。在一个大型卫生系统中,这种实用的、方法学的SDOH数据整合证明了可行性。最终,我们将在整个系统内重复这一过程,以进一步了解我们患者群体的风险负担,并改进我们的预测模型,使我们成为我们社区更好的合作伙伴。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integration of Social Determinants of Health Data into the Largest, Not-for-Profit Health System in South Florida
Social determinants of health (SDOH) are the conditions in which people are born, grow, work, and live. Although evidence suggests that SDOH influence a range of health outcomes, health systems lack the infrastructure to access and act upon this information. The purpose of this manuscript is to explain the methodology that a health system used to: 1) identify and integrate publicly available SDOH data into the health systems’ Data Warehouse, 2) integrate a HIPAA compliant geocoding software (via DeGAUSS), and 3) visualize data to inform SDOH projects (via Tableau). First, authors engaged key stakeholders across the health system to convey the implications of SDOH data for our patient population and identify variables of interest. As a result, fourteen publicly available data sets, accounting for >30,800 variables representing national, state, county, and census tract information over 2016–2019, were cleaned and integrated into our Data Warehouse. To pilot the data visualization, we created county and census tract level maps for our service areas and plotted common SDOH metrics (e.g., income, education, insurance status, etc.). This practical, methodological integration of SDOH data at a large health system demonstrated feasibility. Ultimately, we will repeat this process system wide to further understand the risk burden in our patient population and improve our prediction models – allowing us to become better partners with our community.
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