Modeling the Impact of Care Interventions on Patients with Complex Medical and Social Needs

该奖项将通过研究护理干预措施对经历重大医疗、行为健康和社会挑战的患者的影响，为国民健康和福利的进步做出贡献。尽管此类患者占人口的 1-5%，但他们经常经历极端的医疗保健利用模式，并且可能占国家医疗保健费用的 25-50%。通常由护士、社区卫生工作者和社会工作者组成的多学科团队领导的整体、以人为本的护理干预措施已成为参与并帮助改善此类患者的健康和福祉的策略。该项目研究了几个悬而未决的问题，包括此类患者医疗保健使用的纵向模式、医疗和社会因素的特定组合的作用，以及护理团队花费的时间对患者治疗结果的影响。该项目由马萨诸塞大学阿默斯特分校和卡姆登医疗保健提供者联盟之间的合作组成，该组织在为具有复杂医疗和社会需求的患者提供护理干预方面拥有丰富的经验。随附的传播计划将通过会议向该领域的从业者介绍结果，通过研究项目和课程向本科生和研究生介绍结果，并通过非小说类论文向普通观众介绍结果。这项研究的主要贡献是将随机方法论应用于跨越健康和社会服务系统多个部分的患者层面的纵向数据，包括来自卡姆登联盟多年随机对照试验（RCT）的数据。该项目有两个目标。目标 1 的目标是创建事件进展的随机模型，以模拟患者时间线上关键事件的时间顺序（例如，干预登记、出院后初级保健就诊、再入院、社会里程碑）。序列和时序模型将与搜索算法结合使用，用于识别协变量空间（由患者人口统计、医疗状况和社会需求定义）中的亚组，其中随机对照试验中的干预组和对照组存在显着差异。在目标 2 中，该项目将创建一个多周期随机决策流程，将人员配置和优先级决策与风险估计功能相结合，以评估患者结果（例如再入院）受到的影响。因此，该项目旨在将纵向数据提供的数据驱动的随机、优化和统计学习方法的独特组合整合到重要但尚未充分探索的医疗保健领域。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

This award will contribute to the advancement of national health and welfare by studying the impact of care interventions for patients who experience significant medical, behavioral health, and social challenges. Although representing 1-5 percent of the population, such patients often experience extreme patterns of healthcare utilization and can account for 25-50 percent of national healthcare costs. Holistic, person-centered care interventions, often led by a multidisciplinary team consisting of nurses, community health workers and social workers, have emerged as a strategy to engage with and help improve the health and wellbeing of such patients. This project studies several open questions, including longitudinal patterns of healthcare use among such patients, the role of specific combinations of medical and social factors, and the impact of the time spent by the care team on patient outcomes. The project comprises a collaboration between the University of Massachusetts, Amherst, and the Camden Coalition of Healthcare Providers, an organization with significant experience in care interventions for patients with complex medical and social needs. The accompanying dissemination plan will introduce results to practitioners in the domain through conferences, undergraduate and graduate students through research projects and courses, and the general audience through non-fiction essays.The primary contribution of this research is the adaptation of stochastic methodologies to patient-level longitudinal data that spans multiple parts of health and social services systems, including such data from Camden Coalition’s multi-year randomized controlled trial (RCT). The project has two aims. In Aim 1, the goal is to create stochastic models of event progression to model the sequence of timing of key events (e.g., intervention enrollment, post-discharge primary care visit, hospital readmissions, social milestones) on the patient’s timeline. The sequence and timing models will be used in conjunction with search algorithms that identify subgroups in the covariate space (defined by patient demographics, medical conditions and social needs) where intervention and control groups in the RCT differ significantly. In Aim 2, the project will create a multi-period stochastic decision process that integrates staffing and prioritization decisions with a risk estimation function to evaluate how patient outcomes such as hospital readmissions are impacted. Thus, the project aims to bring together a unique mix of data-driven stochastic, optimization and statistical learning methodologies informed by longitudinal data to a vital yet under-explored domain of healthcare.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.