Predictive modeling for social needs in emergency department settings

急诊科环境中社会需求的预测建模

• 批准号: 10183578
• 负责人: Joshua Ryan Vest
• 金额: $ 39.97万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10183578
• 关键词: Predictive; modeling; social; needs; emergency

Unmet social needs create immediate risks to health, increase utilization, wait times and costs, and contribute to provider burnout. Due to the high prevalence of unmet social needs such as housing or income insecurity among patients, the emergency department (ED) is an opportune setting for intervention. Problematically, social needs frequently go unscreened and unaddressed in EDs. Basic workflow issues and time constraints inhibit screening. Patients may decline screening or avoid questions they deem stigmatizing. While numerous ques- tionnaires exist to screen for a broad number of social needs, their reliability and validity are unknown. Predictive modeling combined with clinical decision support (CDS) could overcome the above challenges that limit screen- ing and perpetuate ED patients' unmet social needs. Our long-term goal is to support effective care by enabling provider access to clinical and social context information. The objective of this proposal is to implement and evaluate a CDS tool that identifies ED patients needing a referral to the social providers best equipped to address social needs. Our central hypothesis is that the purpose of screening is to inform referrals to appropriate services and that, in the context of social needs, social workers, dietitians, and behavioral health counselors are the professionals best suited to meet patients' needs. Leveraging a proven technological infrastructure and collabo- ration with an urban, safety-net ED, this project will accomplish three aims. Aim 1, Compare the effectiveness of predictive modeling vs. questionnaire-based screening in identifying patients in need of social and behavioral services, compares the performance of predictive modeling against questionnaire-based screening. Predictive modeling will leverage a unique combination electronic health record, health information exchange, social ser- vice organization, and public health data sources. Aim 2, Identify ED providers' and patients' perceptions of screening for unmet social needs using predictive modeling and questionnaire-based screening, utilizes qualita- tive methods grounded in implementation and patient-centered innovation theoretical frameworks to understand ED patient, provider, and staff perceptions of enablers and barriers to screening. Aim 3, Quantify the impact of real-time screening for social needs on subsequent utilization, will implement and evaluate a CDS intervention (using the best performing approach from Aim 1 and guided by the findings of Aim 3) that facilitates appropriate referrals to social and behavioral providers in a pre-post with comparison group longitudinal design. Outcomes of interest are reduced ED revisits, increase follow-up visits with primary care providers. The proposed research is significant because it directly compares two approaches to addressing the widespread problem of unmet social needs. This proposal is innovative by applying predictive modeling with personal, social service, and clinical context data, and by shifting social screening research to the ED. By working with an urban safety-net hospital, this research addresses the priority populations of socioeconomically disadvantaged and minority populations who are disproportionality burdened by unmet social needs.

未得到满足的社会需求对健康造成直接风险，增加了利用率、等待时间和成本， 提供者倦怠。由于住房或收入无保障等社会需求普遍得不到满足， 在患者中，急诊科（艾德）是进行干预的合适场所。问题是，社会 在急诊室，需求往往得不到筛选和解决。基本的工作流程问题和时间限制 筛选患者可能会拒绝筛查或避免他们认为会侮辱的问题。虽然有许多问题- 目前存在的测试问卷是为了筛选大量的社会需求，其可靠性和有效性尚不清楚。预测 结合临床决策支持（CDS）的建模可以克服上述限制筛选的挑战， 使艾德患者的社会需求得不到满足。我们的长期目标是通过使 提供对临床和社会背景信息的访问。这项建议的目的是执行和 评估CDS工具，该工具可识别需要转诊至最有能力解决问题的社会服务提供者的艾德患者 社会需求。我们的中心假设是，筛查的目的是告知转介到适当的服务 而且，在社会需求的背景下，社会工作者，营养师和行为健康顾问是 最适合满足患者需求的专业人员。利用成熟的技术基础设施和协作， 与城市安全网艾德相比，该项目将实现三个目标。目标1，比较 预测建模与基于筛查的筛查在识别需要社交和行为干预的患者方面的对比 服务，比较了预测建模与基于数据库的筛选的性能。预测 建模将利用一个独特的组合电子健康记录，健康信息交换，社会服务， 卖淫组织和公共卫生数据源。目标2，确定艾德提供者和患者对 筛选未满足的社会需求，使用预测建模和基于需求的筛选，利用qualita， 以实施为基础的创新方法和以患者为中心的创新理论框架， 艾德患者、提供者和工作人员对筛查的促进因素和障碍的看法。目标3，量化 实时筛查后续使用的社会需求，将实施和评估CDS干预措施 （采用目标1中的最佳做法，并以目标3的结果为指导）， 转介到社会和行为提供者在一个前后与比较组纵向设计。成果 关注的是减少艾德复诊，增加与初级保健提供者的随访。拟议研究 是重要的，因为它直接比较了两种方法，以解决普遍存在的问题， 需求该建议是创新的，通过将预测建模应用于个人，社会服务和临床 背景数据，并通过将社会筛查研究转移到ED。通过与城市安全网医院合作， 这项研究针对社会经济弱势群体和少数民族人口的优先群体 他们被未满足的社会需求所拖累。