Improving Post-Acute Care Value for Veterans

背景：在急性住院后过渡到熟练的护理机构(SNF)是最常见的 一个年长的退伍军人生活中的危险时刻。退伍军人经历了超过25万次的过渡 医院和SNF每年都有，但超过四分之一的人是从SNF再次入院的，不到一半 已在出院后100天内返回社区。尽管SNF关怀的目的是 为了进行疗养和康复，没有成功康复的退伍军人通常被安置在 机构长期护理，对他们自己和退伍军人管理局来说都是巨大的成本，花费了70多亿美元 每年都要进行这样的护理。然而，SNF在社区出院率和护理费用方面差异很大 送来了。目前尚不清楚如何识别“高价值”的SNF(那些提供最佳社区排污率的SNF 以最低成本)对于退伍军人，因为现有的公共质量指标不包括VA SNF，所以不列出退伍军人- 具体结果，不包括费用。同样，还不清楚有多少匹配的个人退伍军人 具有特殊SNF特征的需求可能会提高价值。退伍军人既是社保基金的支付者又是社保基金的提供者 CARE拥有独特的机会来开发最佳的SNF网络，以推动高价值的CARE。 意义/影响：这项工作与退伍军人管理局的优先事项保持一致，以开发一个集成的、高性能的网络 退伍军人作为使命法案的一部分，并将退伍军人管理局定位为提供急性后护理的领导者。那里 目前有400多万退伍军人年龄在65岁以上，因此必须改善结果并降低 随着越来越多的退伍军人从医院过渡到这种护理环境，SNF的成本。 创新：该方法使用了来自卫生经济学、大数据和 系统工程提供了新的见解。据我们所知，目前还没有发表的研究描述 退伍军人在急性后护理中的结果，确定高绩效设施的特征，也不是 确定患者与急性后护理提供者的特征匹配如何影响结果。 具体目标：我们的具体目标是： 1)比较结果(成功向社区排放)和成本(联邦元) 退伍军人从退伍军人医院出院到三种最常见的急性后护理的人数 退伍军人接受SNF护理的设置：CLCS、CNH和非VA SNF。 2)评估匹配退伍军人个体亚群的效果(例如，通过不利的风险 结果)到SNF类型(CLCs、CNH或非VA SNF)，并对结果和成本进行SNF星级评级。 3)比较将SNF转诊整合为结果最好和最低的SNF类型的效果 成本(目标1)或将退伍军人的个人特征与不同的SNF相匹配(目标2) 结果、护理总成本和SNF能力。 方法：本提案使用了先进的统计技术(如工具变量和机器 学习方法)和一个独特的数据集(2014-18年居住历史文件，它将VA、FEY- 基础、联邦医疗保险和医疗补助数据纳入个人退伍军人护理的纵向事件)以完成 我们的目标。 执行/下一步：这项工作的结果将传播给退伍军人事务部，并推广 CARE和社区关怀领导办公室，他们参与了该提案的制定， 以及VISN和VA设施的领导力，通过两种可在床边使用的工具1)优化 SNF选择和2)在领导层帮助塑造SNF网络，以实现价值最大化。

Background: The transition to a skilled nursing facility (SNF) after an acute hospitalization is one of the most perilous times in the life of an older Veteran. Veterans undergo more than 250,000 transitions between hospitals and SNFs annually, but more than 1 in 4 is readmitted to the hospital from SNF and less than half have returned to the community by 100 days following hospital discharge. Although the intent of SNF care is to allow recuperation and rehabilitation, Veterans who do not successfully recover are commonly placed in institutional long-term care at significant cost to themselves and to the VA, which spends more than $7 billion annually on this care. However, SNFs vary widely in their rates of community discharge and costs of care delivered. It is unclear how to identify “high-value” SNFs (those that deliver the best community discharge rates at lowest cost) for Veterans since existing public quality metrics do not include VA SNFs, do not list Veteran- specific outcomes, and do not include costs. Similarly, it is unclear how much matching individual Veteran needs with particular SNF characteristics might improve value. The VA as both payer and provider of SNF care has the unique opportunity to develop an optimal SNF network to drive high-value care. Significance/Impact: This work aligns with VA priorities to develop an integrated, high-performing network for Veterans as part of the MISSION Act and positions the VA as a leader in delivery of post-acute care. There are more than 4 million Veterans currently over age 65, making it imperative to improve outcomes and lower costs in SNFs as more Veterans transition out of the hospital to this care setting. Innovation: The approach uses novel data sets and methods drawn from health economics, big data, and systems engineering to provide new insights. To our knowledge, there are no published studies describing the outcomes of Veterans in post-acute care, identifying characteristics of high-performing facilities, nor establishing how matching patient to post-acute care provider characteristics affects outcomes. Specific Aims: Our Specific Aims are to: 1) Compare outcomes (successful discharge to the community) and costs (Federal dollars) across the population of Veterans discharged from a VA hospital to the three most common post-acute care settings where Veterans receive SNF care: CLCs, CNHs, and non-VA SNFs. 2) Evaluate the effect of matching individual subpopulations of Veterans (e.g., by risk for adverse outcome) to SNF type (CLCs, CNHs, or non-VA SNFs) and SNF star rating on outcomes and costs. 3) Compare the effects of consolidating SNF referrals to the SNF type with best outcomes and lowest costs (Aim 1) or matching individual Veteran characteristics to different SNFs (Aim 2) on Veteran outcomes, overall costs of care, and SNF capacity. Methodology: This proposal uses advanced statistical techniques (such as instrumental variable and machine learning methods) and a unique dataset (the 2014-18 Residential History File, which concatenates VA, fee- basis, Medicare, and Medicaid data into longitudinal episodes of care for individual Veterans) to accomplish our Aims. Implementation/Next Steps: The results of this work will be disseminated to VA Geriatrics and Extended Care and Office of Community Care leadership, who have been involved in the development of the proposal, as well as VISN and VA facility leadership through two tools that can be used 1) at the bedside to optimize SNF choice and 2) at a leadership level to help shape the SNF network to maximize value.