Department of Population Health, Dell Medical School, University of Texas at Austin, Austin, TX, USA; Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, TX, USA.
Department of Population Health, Dell Medical School, University of Texas at Austin, Austin, TX, USA.
J Biomed Inform. 2022 Dec;136:104241. doi: 10.1016/j.jbi.2022.104241. Epub 2022 Nov 11.
To describe methods to approach application of data standards to integrate social determinants of health (SDoH) into EHRs through evaluation of a case of clinical decision support for pediatric asthma.
We identified a list of environmental factors important for managing pediatric asthma. We identified and integrated data from local outdoor air quality monitors with elements available from the clinic's EHR and self-reported indoor air quality questionnaire data. We assessed existing SDoH frameworks, assessment tools, and terminologies to identify representative data standards for these environmental SDoH measures.
We found many-to-many relationships between the multiple framework domains, the environmental exposure measures collected, and existing standards. The majority of concepts did not accurately align with environmental exposure measurements. We propose an ontology-driven information framework methodology to apply standards for SDoH measurements to support measuring, managing, and computing SDoH data.
To support methods of integrating SDoH data in the EHR via an ontology-driven information framework, a common SDoH ecosystem should be developed descriptively and prescriptively integrating framework domains, assessment tools, and standard ontologies to support future data sharing, aggregation, and interoperability. A hierarchical object-oriented information model should be adopted to manage SDoH to extend beyond patient-centered orientation of EHRs to orient to households and communities.
SDoH data pose unique challenges and opportunities in collecting, measuring, and managing health information. Future work is needed to define data standards for implementing SDoH in a hierarchical, object-oriented information model representing multiple units of orientation including individuals, households, and communities.
通过评估一个儿科哮喘临床决策支持的案例，描述将社会决定因素（SDoH）纳入电子健康记录（EHR）的方法，以应用数据标准。
我们确定了一组对管理儿科哮喘重要的环境因素。我们从当地户外空气质量监测器中识别并整合了数据，并将其与诊所 EHR 和自我报告的室内空气质量问卷数据中的元素相结合。我们评估了现有的 SDoH 框架、评估工具和术语，以确定这些环境 SDoH 措施的代表性数据标准。
我们发现多个框架域、收集的环境暴露测量值和现有标准之间存在多对多的关系。大多数概念与环境暴露测量值并不完全匹配。我们提出了一种基于本体的信息框架方法，以应用 SDoH 测量的标准来支持测量、管理和计算 SDoH 数据。
为了通过基于本体的信息框架支持在 EHR 中整合 SDoH 数据的方法，应该描述性和规范性地开发一个通用的 SDoH 生态系统，将框架域、评估工具和标准本体集成在一起，以支持未来的数据共享、聚合和互操作性。应该采用分层面向对象的信息模型来管理 SDoH，以超越 EHR 以患者为中心的方向，转向家庭和社区。
在收集、测量和管理健康信息方面，SDoH 数据带来了独特的挑战和机遇。需要进一步的工作来定义数据标准，以便在代表包括个人、家庭和社区在内的多个定向单位的分层面向对象的信息模型中实施 SDoH。
