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

模拟护理干预措施对具有复杂医疗和社会需求的患者的影响

基本信息

批准号：
2212237
负责人：
Hari Jagannatha Balasubramanian
金额：
$ 50.22万
依托单位：
University of Massachusetts Amherst
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212237&HistoricalAwards=false
关键词：
Modeling Impact Care Interventions Patients

项目摘要

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.

该奖项将通过研究护理干预对经历重大医疗、行为健康和社会挑战的患者的影响，为促进国家健康和福利做出贡献。虽然这类患者占人口的1%-5%，但他们经常经历极端的医疗保健利用模式，可能占全国医疗保健成本的25%-50%。以人为中心的整体护理干预措施，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，已成为接触和帮助改善这类患者的健康和福祉的一种战略。该项目研究了几个开放的问题，包括此类患者的医疗保健使用的纵向模式，特定的医疗和社会因素组合的作用，以及护理团队花费的时间对患者结局的影响。该项目包括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，卡姆登医疗保健提供者联盟是一个在为有复杂医疗和社会需求的患者进行护理干预方面具有丰富经验的组织。随之而来的传播计划将通过会议向该领域的实践者介绍结果，通过研究项目和课程向本科生和研究生介绍结果，并通过非虚构文章向普通受众介绍结果。这项研究的主要贡献是将随机方法适应于跨越卫生和社会服务系统多个部分的患者水平的纵向数据，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。在目标1中，目标是创建事件进展的随机模型，以模拟患者时间线上的关键事件(例如，干预登记、出院后初级保健访问、再次住院、社会里程碑)的时间顺序。序列和时间模型将与搜索算法结合使用，该算法在协变量空间(由患者人口统计数据、医疗条件和社会需求定义)中识别RCT中干预组和对照组显著不同的子组。在目标2中，该项目将创建一个多阶段随机决策过程，将人员配备和优先决策与风险估计功能相结合，以评估患者结果(如再次住院)如何受到影响。因此，该项目旨在将数据驱动的随机、优化和统计学习方法的独特组合结合到一个重要但未得到充分探索的卫生保健领域。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。