Predictive modeling for social needs in emergency department settings

未得到满足的社会需求会对健康造成直接风险、增加利用率、等待时间和成本，并导致 导致服务提供者疲惫不堪。由于住房或收入不安全等未得到满足的社会需求普遍存在 在患者中，急诊科(ED)是进行干预的合适场所。问题在于，社交 急诊室的需求经常得不到筛选和解决。基本的工作流程问题和时间限制阻碍了 放映。患者可能会拒绝筛查或回避他们认为是耻辱的问题。虽然有很多问题- 问卷的存在是为了筛选大量的社会需求，它们的可靠性和有效性是未知的。预测性 建模与临床决策支持(CDS)相结合可以克服上述限制筛选的挑战。 使ED患者未得到满足的社会需求永久化。我们的长期目标是通过以下方式支持有效护理 提供商可以访问临床和社会背景信息。这项建议的目标是实施和 评估CDS工具，该工具可识别需要转介至最适合解决问题的社交提供者的ED患者 社交需求。我们的中心假设是，筛查的目的是通知转诊到适当的服务 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- 这个项目以城市、安全网为基础，将实现三个目标。目标1，比较 预测模型与基于问卷的筛查在确定有社会和行为需求的患者方面的比较 服务，比较了预测性建模和基于问卷的筛选的性能。预测性 建模将利用电子健康记录、健康信息交换、社交服务的独特组合- 副组织和公共卫生数据来源。目标2，确定急诊室提供者和患者对 使用预测建模和基于问卷的筛选来筛选未满足的社会需求，利用质量- 立足于实施和以患者为中心的创新理论框架的理解 急诊患者、提供者和工作人员对筛查的促进者和障碍的看法。目标3，量化 对后续使用的社交需求进行实时筛选，将实施和评估CDS干预 (使用目标1中的最佳执行方法，并以目标3的结果为指导)，这有助于适当 采用对照组职前纵向设计，转介给社会和行为提供者。结果 值得注意的是减少急诊科的复诊，增加对初级保健提供者的随访。拟议的研究 意义重大，因为它直接比较了解决未得到满足的社会问题这一普遍问题的两种方法 需要。该提案通过将预测性建模应用于个人、社会服务和临床，具有创新性 背景数据，并将社会筛选研究转移到教育署。通过与城市安全网医院合作， 这项研究针对的是社会经济弱势群体和少数群体的优先群体 他们不成比例地背负着未得到满足的社会需求。

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.