Data-Driven Methods to Identify Social Determinants of Health

背景：健康的社会决定因素(SDOH)受到越来越多的关注 经验证据表明，患者的社会背景与他们的健康有关 行为和临床结果。现在，医疗保健系统(HCS)比以往任何时候都更多地 对解决社会因素负责。提高种族和民族之间的卫生保健质量 少数族裔是退伍军人管理局的头等大事。 意义/影响：理想情况下，识别和记录患者的社会背景将是 然后引用到最有可能降低遵从性的SDOH服务 关于疾病预防、治疗和管理的建议。然而，SDOH等 在常规护理访问中，教育、收入、社会孤立和经济压力很少被记录下来。 需要一种利用卫生信息技术的更系统的方法来改善 在退伍军人事务部患者中识别社会决定因素的效率和有效性，从而使更多 有针对性的方法被用来解决患者社区中的这些风险因素。更好的 需要了解电子健康记录(EHR)中的SDOH，以便改进 人口健康管理和将患者转介到社会服务机构的流程。 创新：开发更强大的数据驱动战略以识别社交网络的第一步 患者的表型是了解SDOH在多大程度上被记录在 啊哈。自然语言处理(NLP)是自动提取这些数据的策略之一 将电子病历中的临床记录转换为可用于检查医疗质量的结构化格式 并促进质量改进战略的制定和实施。然而，NLP 仅有办法不足以提高退伍军人的保健质量。 少数族裔。这是因为患者和提供者之间的沟通质量差，而且 少数族裔对医疗保健系统的不信任可能会限制对这些因素的讨论。小说《深度》 学习方法还没有被充分利用来识别有不良反应风险的患者。 太好了。此外，缺乏关于患者自我报告的一致性的经验数据。 SDOH和用NLP提取的那些。更少人知道与获得 并记录SDOH对患者结果的影响。因此，我们建议发展多层次的健康 一种识别初级保健患者社会表型的信息学方法 将SDOH记录在EHR中，作为以下内容的一部分： 具体目标：目标1：使用深度学习策略识别糖尿病患者的社会表型 患者基于电子病历中SDOH的记录。目标2：检查风险之间的一致性 使用NLP和患者自我报告确定SDOH的影响因素。目标3：进行一项研究以评估 记录SDOH对患者预后的影响。 方法：将使用深度学习NLP方法来表征SDOH的比率 记录在《电子健康记录》中。机器学习策略将被用来识别基于 在SDOH上。 实施/下一步：我们预测，将SDOH记录在EHR中的退伍军人将 报告更好的临床结果，对医疗保健提供者更大的信任，以及更好的患者-医生 与没有在电子病历中记录SDOH的退伍军人相比，他们的沟通能力更强。我们还将 根据患者的社会表型，描述转诊到诊所和社区服务机构的特征。

Background: There is increased attention on social determinants of health (SDOH) as a result of empirical evidence showing that the patient’s social background is associated with their health behaviors and clinical outcomes. Now more than ever, health care systems (HCS) are being held accountable for addressing social factors. Improving the quality of health care among racial and ethnic minorities is a VA is a top priority. Significance/Impact: Ideally, identifying and documenting a patient’s social background would be followed by referral to services that address the SDOH that are most likely to reduce compliance with recommendations for disease prevention, treatment, and management. However, SDOH such as education, income, social isolation, and financial strain are rarely documented during routine care visits. A more systematic approach that leverages health information technology is needed to improve the efficiency and effectiveness of identifying social determinants among patients in the VA so that more targeted approaches are used to address these risk factors in the patients’ communities. A better understanding of SDOH within the electronic health record (EHR) is needed in order to improve population health management and processes for referring patients to social services. Innovation: The first step to developing a more robust data-driven strategy for identifying social phenotypes among patients is to understand the extent to which SDOHs are being documented in the EHR. Natural language processing (NLP) is one strategy to automatically extract those data from clinical notes in the EHR into a structured format that can be used to examine the quality of health care and facilitate the development and implementation of quality improvement strategies. However, NLP approaches alone are not sufficient to improve the quality of health care for Veteran racial/ethnic minorities. This is because poor quality communication between patients and providers and greater distrust in the health care system among minorities may limit discussion of these factors. Novel deep learning approaches have not been fully leveraged in the identification of patients at risk for adverse SDOH. Moreover, there is a lack of empirical data on the concordance between patient self-reported SDOH and those extracted using NLP. Even less is known about the value associated with obtaining and documenting SDOH on patient outcomes. Therefore, we propose to develop a multilevel health informatics approach for identifying social phenotypes among primary care patients based on documentation of SDOH in the EHR as part of the following: Specific Aims: Aim 1: Use deep learning strategies to identify social phenotypes among diabetes patients based on documentation of SDOH in the EHR. Aim 2: Examine the concordance between risk factors for SDOH identified using NLP and patient-self- report. Aim 3: Conduct a study to evaluate the effects of documenting SDOH on patient outcomes. Methodology: A deep learning NLP approach will be used to characterize the rates at which SDOH are documented in the EHR. Machine learning strategies will be used to identify social phenotypes based on SDOH. Implementation/Next Steps: We predict that Veterans who have SDOH documented in the EHR will report better clinical outcomes, greater trust in health care providers, and better patient-physician communication compared to Veterans who do not have SDOH documented in their EHR. We will also characterize referrals to clinic- and community-based services based on the patient’s social phenotype.