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: 10314508
• 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/10314508
• 关键词: 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; Natural Language Processing; Nature; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Patient Care; Patient Self-Report; Patient-Focused Outcomes; Patients; Phenotype; Physicians; Primary Health 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; base; care outcomes; clinically actionable; cohort; community based service; deep learning; disorder prevention; distrust; 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; minority health; novel; patient population; population health; precision medicine; racial and ethnic; racial minority; 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：由于社会决定者（SDOH）的关注越来越高 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，卫生保健系统（HCS）比以往任何时候都多 负责解决社会因素。提高种族和族裔医疗保健的质量 少数民族是VA是当务之急。 意义/影响：理想情况下，确定和记录患者的社会背景将是 然后转介到解决SDOH的服务，最有可能降低遵守 预防疾病，治疗和管理建议。但是，SDOH例如 在常规护理访问中很少有教育，收入，社会隔离和财务压力。 需要一种更系统的方法来利用健康信息技术来改善 识别VA患者中社会决定者的有效性和有效性，以便更多 有针对性的方法用于解决患者社区中的这些危险因素。更好 需要了解电子健康记录（EHR）内的SDOH才能改善 人口健康管理和将患者推荐给社会服务的过程。 创新：制定更强大的数据驱动策略来识别社会的第一步 患者中的表型是要了解SDOHS在多大程度上记录了 EHR。自然语言处理（NLP）是一种自动从中提取这些数据的策略 EHR中的临床注释为结构化格式，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。但是，NLP 仅方法就不足以提高资深种族/种族的医疗保健质量 少数民族。这是因为患者和提供者之间的质量差异不佳以及更大的 少数民族中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 学习方法尚未完全利用在识别有广告风险的患者 SDOH。此外，缺乏关于患者自我报告的一致性的经验数据 SDOH和那些使用NLP提取的人。关于获得的价值，更少知道 并记录有关患者结果的SDOH。因此，我们建议发展多层次健康 提供基于初级保健患者中社会表型的信息方法 EHR中SDOH的文档作为以下一部分： 具体目的：目标1：使用深度学习策略来识别糖尿病中的社会表型 基于EHR中SDOH的文档的患者。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：进行研究以评估 记录SDOH对患者预后的影响。 方法论：将使用深度学习的NLP方法来表征SDOH的速率 在EHR中记录。机器学习策略将用于识别基于社会表型 在SDOH上。 实施/下一步：我们预测已记录在EHR中的SDOH的退伍军人将 报告更好的临床结果，对医疗保健提供者的信任以及更好的患者医生 与没有SDOH在EHR中没有记录的SDOH的退伍军人相比。我们也会 根据患者的社会表型来表征转介到诊所和社区服务。