Defining Successful Program Configurations in Home-Based Primary Care

背景：基于家庭的初级护理(HBPC)是一种独特的家庭护理计划，提供 为患有复杂慢性病的退伍军人提供跨学科的全面、纵向初级护理 退伍军人事务部工作人员团队。HBPC的加入与减少住院、降低医疗保健成本以及 患者满意度高，是老年医学和长期护理办公室(GEC)的重要组成部分 将长期服务和支持转向以家庭和社区为基础的服务的战略。 意义：GEC计划到2026年将HBPC扩展到75个地点，但个别地点的差异很大 HBPC站点结构、服务人群、护理提供模式和临床结果。目前还不清楚 这一变化代表了HBPC临床模型的可变保真度，并提供了提高效率的机会 与适当的灵活性和适应不同的患者需求和当地情况。鉴于计划中的扩张， 需要详细了解计划变化，才能了解计划的哪些配置 功能在什么情况下以及为什么会成功。 创新和影响：该提案使用配置分析，这是一种新兴的混合方法方法，以 确定HBPC计划的配置和与较长的患者家庭相关的背景特征 时间，一种新的以患者为中心的结果衡量标准。病人回家时间是一项新的衡量标准，它捕捉到了 HBPC的普遍目标，退伍军人可以在家里独立生活的时间。这项研究的结果将通知 HBPC扩展，并将帮助现有的从事因地制宜的质量改进努力。 具体目标：目标1：评估HBPC站点之间在上下文和可修改因素方面的差异，患者 特征和护理提供模式，并确定与临床结果相关的部位和患者水平。 目标2：评估HBPC团队成员和资深人员对站点成功的潜在机制的看法 在耐心的家庭时间里有高有低的表现。目标3：确定HBPC站点与 更高的表现与更低的患者在家时间。 方法：目标1是一项定量研究，我们将为所有440个HBPC站点建立描述性档案 使用VA和Medicare数据并执行回归分析和聚类分析来检查部位和患者 水平与临床结果相关。在目标2中，我们将对HBPC临床医生、工作人员、 和退伍军人，并观察HBPC 10个高绩效站点和10个低绩效站点的工作实践 在耐心的家庭时间里的表现。目标3将使用构型分析，这是一种扎根于 在数学集合论中，识别目标1和目标2中确定的必要和/或充分的特征 更长的耐心回家时间。 下一步/实施：我们将与GEC中的合作伙伴合作，使用HBPC站点的已确定配置 功能通知HBPC的扩展，便于开发复杂的通知，适应性强 用于现有HBPC站点的质量改进干预。

Background: Home-Based Primary Care (HBPC) is a unique home care program that provides comprehensive, longitudinal primary care to Veterans with complex chronic disease with an interdisciplinary team of VA staff. HBPC enrollment is associated with reduced hospitalizations, reduced health care costs, and high patient satisfaction, and is a critical component of the Office of Geriatrics and Extended Care’s (GEC’s) strategy to shift long term services and supports into home and community-based services. Significance: GEC plans to expand HBPC by 75 sites by 2026, but there is substantial variation in individual HBPC site structures, populations served, care delivery patterns, and clinical outcomes. It is unknown whether this variation represents variable fidelity to the HBPC clinical model and an opportunity to improve efficiency vs. appropriate flexibility and adaptation to varying patient needs and local contexts. Given planned expansion, a detailed understanding of program variation is needed to understand which configurations of program features are successful in what contexts and why. Innovation & Impact: This proposal uses configurational analysis, an emerging mixed-methods approach, to identify configurations of HBPC program and contextual characteristics associated with longer patient home time, a novel patient-centered outcome measure. Patient home time is a novel measure that captures a universal goal in HBPC, the time a Veteran can live independently at home. Results of this study will inform HBPC expansion and will help existing engage in context-tailored quality improvement efforts. Specific Aims: Aim 1: Assess variation among HBPC sites in contextual and modifiable factors, patient characteristics, and care delivery patterns, and identify site and patient level correlates of clinical outcomes. Aim 2: Assess HBPC team member and Veteran perspectives on potential mechanisms of success in sites that are high and low performing in patient home time. Aim 3: Identify factors that distinguish HBPC sites with higher versus lower performance in patient home time. Methodology: Aim 1 is a quantitative study in which we will build descriptive profiles of all 440 HBPC sites using VA and Medicare data and perform regression analysis and cluster analyses to examine site and patient level correlates of clinical outcomes. In Aim 2 we will perform qualitative interviews of HBPC clinicians, staff, and Veterans, and observation of work practices in 10 HBPC sites with high performance and 10 with low performance in patient home time. Aim 3 will use configurational analysis, a cross case analysis method rooted in mathematical set theory, to identify features identified in Aims 1 and 2 that are necessary and/or sufficient for longer patient home time. Next Steps/Implementation: We will work with partners in GEC to use identified configurations of HBPC site features to inform HBPC expansion, and to facilitate the development of a complexity informed, adaptive quality improvement intervention for use in existing HBPC sites.