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：由于以下原因，人们越来越关注健康的社会决定因素（SDOH）： 经验证据表明患者的社会背景与其健康状况相关 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加受到关注 负责解决社会因素。提高种族和民族的医疗保健质量 少数民族是 VA 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 其次是转介解决 SDOH 的服务，这些服务最有可能减少对 SDOH 的遵守 疾病预防、治疗和管理的建议。然而，SDOH 例如 在日常护理就诊期间很少记录教育、收入、社会孤立和经济压力。 需要一种利用卫生信息技术的更系统的方法来改善 确定 VA 患者社会决定因素的效率和效果，以便更多 采用有针对性的方法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至社会服务的流程。 创新：开发更强大的数据驱动策略来识别社交媒体的第一步 患者表型的目的是了解 SDOH 在多大程度上被记录在 电子病历。自然语言处理 (NLP) 是一种自动从数据中提取数据的策略 将 EHR 中的临床记录转换为结构化格式，可用于检查医疗保健质量 促进质量改进战略的制定和实施。然而，自然语言处理 仅靠方法不足以提高退伍军人种族/族裔的医疗保健质量 少数民族。这是因为患者和提供者之间的沟通质量较差，而且更多 少数群体对医疗保健系统的不信任可能会限制对这些因素的讨论。小说深奥 学习方法尚未充分利用来识别有不良风险的患者 SDOH。此外，缺乏关于患者自我报告之间一致性的经验数据 SDOH 和使用 NLP 提取的那些。对于与获得相关的价值知之甚少 记录 SDOH 对患者治疗结果的影响。因此，我们建议建立多层次的健康体系。 基于信息学方法来识别初级保健患者的社会表型 EHR 中 SDOH 的记录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 基于 EHR 中 SDOH 记录的患者。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 SDOH 的因素。目标 3：进行研究以评估 记录 SDOH 对患者结果的影响。 方法：将使用深度学习 NLP 方法来描述 SDOH 的发生率 记录在电子病历中。机器学习策略将用于识别基于社会表型 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、对医疗保健提供者更大的信任以及更好的患者医生 与 EHR 中没有记录 SDOH 的退伍军人相比，他们的沟通更加顺畅。我们还将 根据患者的社会表型来描述转诊至诊所和社区服务的特征。