Improving Post-Acute Care Value for Veterans

背景：在急性住院后过渡到专业护理机构（SNF）是最重要的因素之一。 一个老老兵生命中的危险时刻。退伍军人经历了超过250，000次过渡， 医院和SNF每年，但超过四分之一的人从SNF重新入院，不到一半的人 在出院后100天内返回社区。尽管SNF护理的目的是 为了让康复和康复，没有成功康复的退伍军人通常被安置在 机构长期护理给他们自己和退伍军人管理局带来了巨大的成本，退伍军人管理局的支出超过70亿美元 每年都有这样的关怀。然而，SNF在社区出院率和护理成本方面差异很大 交付。目前还不清楚如何识别"高价值" SNF（那些提供最佳社区排放率的SNF 由于现有的公共质量指标不包括VA SNF，因此不列出退伍军人- 具体结果，不包括费用。同样，目前还不清楚有多少匹配的个人退伍军人 具有特定SNF特性的需求可能会提高价值。VA作为SNF的支付者和提供者 护理有独特的机会来开发一个最佳的SNF网络，以推动高价值的护理。 意义/影响：这项工作与VA优先事项保持一致，以开发一个集成的高性能网络， 退伍军人作为使命法案的一部分，并将退伍军人事务部定位为提供急性后护理的领导者。那里 目前有400多万65岁以上的退伍军人，因此必须改善结果， 随着更多的退伍军人从医院过渡到这种护理环境，SNF的成本增加。 创新：该方法使用了来自卫生经济学、大数据和 系统工程提供新的见解。据我们所知，没有发表的研究描述 退伍军人在急性后护理的结果，确定高性能设施的特点，也 确定患者与急性期后护理提供者特征的匹配如何影响结果。 具体目标：我们的具体目标是： 1)比较结果（成功出院到社区）和成本（联邦美元）， 退伍军人从VA医院出院，接受三种最常见的急性后护理 退伍军人接受SNF护理的环境：CLC，CNH和非VA SNF。 2)评估匹配退伍军人个体亚群的效果（例如，按不利风险 结果）到SNF类型（CLC、CNH或非VA SNF）和SNF结局和成本的星星评级。 3)比较巩固SNF转诊与SNF类型的效果， 成本（目标1）或将退伍军人的个人特征与退伍军人的不同SNF（目标2）相匹配 结果，护理的总成本和SNF能力。 方法：本提案使用先进的统计技术（如工具变量和机器 学习方法）和一个独特的数据集（2014 - 18住宅历史文件，其中串联VA，费用- 基础，医疗保险和医疗补助数据到纵向事件的照顾个别退伍军人），以实现 我们的目标 实施/后续步骤：这项工作的结果将传播到VA老年病和扩展 护理和社区护理办公室的领导，谁一直参与制定的建议， 以及VISN和VA设施领导，通过两种工具，可用于1）在床边优化 SNF的选择和2）在领导水平，以帮助塑造SNF网络，以最大限度地提高价值。

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.