Data-Driven Methods to Identify Social Determinants of Health

识别健康社会决定因素的数据驱动方法

• 批准号: 10491762
• 负责人: Lewis James Frey
• 金额: --
• 依托单位: RALPH H JOHNSON VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10491762
• 关键词: Accountability; Address; Algorithms; Attention; Cardiovascular Diseases; Caring; Clinic; Clinical; Clinical Data; Code; Communication; Communities; Data; Data Sources; Development; Diabetes Mellitus; Documentation; Education; Effectiveness; Electronic Health Record; Financial Hardship; Goals; Health; Health Personnel; Health behavior; Healthcare Systems; Income; Insurance Carriers; International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); Machine Learning; Manuals; Medical; Medical center; Methodology; Methods; Minority; Minority Groups; Minority Men; Natural Language Processing; Nature; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Patient Self-Report; Patient-Focused Outcomes; Patients; Phenotype; Physicians; Primary Care; Process; Provider; Public Health Informatics; Recommendation; Reporting; Research Personnel; Resources; Risk; Risk Factors; Role; Services; Social Work; Social isolation; South Carolina; Standardization; Structure; System; Trust; United States Department of Veterans Affairs; Universities; Veterans; Veterans Health Administration; Visit; care outcomes; clinically actionable; cohort; community based service; deep learning; disorder prevention; distrust; ethnic minority; ethnic minority population; food insecurity; health care quality; health care service organization; health disparity; health information technology; health management; improved; improved outcome; innovation; learning strategy; male health; medically underserved population; medicine man; minority health; minority health disparity; novel; population health; precision medicine; primary care patient; racial minority; racial minority population; routine care; social; social determinants; social factors; social health determinants; socioeconomics; tool

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.

背景：由于健康的社会决定因素（SDOH）越来越受到关注， 经验证据表明患者的社会背景与其健康状况有关 行为和临床结果。现在比以往任何时候，医疗保健系统（HCS）正在举行 负责解决社会问题。提高种族和民族的医疗保健质量 少数族裔是退伍军人事务部的首要任务。 意义/影响：理想情况下，识别和记录患者的社会背景将是 其次是转介到服务，解决最有可能减少遵守SDOH 疾病预防、治疗和管理的建议。然而，SDOH如 在日常护理访问中很少记录教育、收入、社会孤立和经济压力。 需要采取一种利用卫生信息技术的更系统的办法， 在VA患者中识别社会决定因素的效率和有效性， 采用有针对性的方法来解决患者社区的这些风险因素。更好的 需要了解电子健康记录（EHR）中的SDOH，以提高 人口健康管理和将病人转介社会服务的程序。 创新：制定更强大的数据驱动战略以识别社会风险的第一步 患者中的表型是了解SDOH在多大程度上被记录在 电子病历自然语言处理（NLP）是一种自动从 将EHR中的临床笔记转换为结构化格式，可用于检查医疗保健质量 并促进质量改进策略的制定和实施。然而，NLP 单靠方法不足以提高退伍军人种族/民族的医疗保健质量。 少数群体这是因为患者和提供者之间的沟通质量差， 少数群体对保健制度的不信任可能会限制对这些因素的讨论。小说深 学习方法尚未充分用于识别存在不良反应风险的患者， SDOH。此外，缺乏关于患者自我报告的 SDOH和使用NLP提取的那些。更少的是知道的价值与获得 并记录SDOH对患者结局的影响。因此，我们建议发展多层次的健康 信息学方法用于识别初级保健患者中的社会表型， 在EHR中记录SDOH，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 根据EHR中的SDOH文件对患者进行评估。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的SDOH因素。目标3：开展一项研究， 记录SDOH对患者结局的影响。 方法：将使用深度学习NLP方法来表征SDOH的发生率。 记录在EHR中。机器学习策略将用于识别基于 关于SDOH 实施/后续步骤：我们预测，在EHR中记录了SDOH的退伍军人将 报告更好的临床结果，对卫生保健提供者更大的信任，以及更好的患者-医生 与没有在EHR中记录SDOH的退伍军人相比，我们还将 根据患者的社会表型，确定转介到诊所和社区服务的特征。