Exploring Outstanding Performance in Low Readmission from Skilled Nursing Facilities for Older Adults (EXPLORE-SNF)

在急性住院后，每4名老年患者中就有1人被转移到熟练的护理设施(SNF)。 与此同时，这些患者中有25%在30天内再次入院。为了解决令人震惊的问题 经济和生活质量损失，2014年的保护获得医疗保险法案(PAMA)包括重新入院 对2019年实施的SNF的处罚。尽管瑞士央行因以下原因面临经济处罚 再入院人数高于预期，令人惊讶的是，关于哪些不同因素的信息很少 与老年人的再入院有关，以及一些SNF如何在实现低再入院率方面表现良好 当其他人步履蹒跚的时候。面对新的财务和公开报告激励，更有力的证据表明 需要三军努力减少重新入院。我们建议使用以下方法确定SNF中重新入院的驱动因素 一种积极的越轨方法，即“探索技能型低再入院者的优秀表现” 老年人护理设施(EXPLOVE-SNF)“。正偏差是一种归纳分析技术 它使用深入的定性方法来生成关于组织因素的假设 与医疗保健组织的绩效相关。缺乏来自传统预测指标的信号 重新接纳表明，可能有重要的经验教训，从SNF是“积极的偏差”或 再入院率极低。本研究的目标是直接从SNF了解有关 不断增长的老年患者接受SNFS治疗后优化预后的策略 住院治疗。在目标1中，我们将对高表现和低表现的SNF进行定性访谈，以产生 关于哪些SNF策略可能解释异常低的30天再住院率的假设 入院后出院至SNF的患者。SNF性能将使用以下公式计算 通过疗养院比较可访问医疗保险重新入院数据。我们将对高性能和低性能的产品进行抽样 SNF，直到我们达到理论饱和。接下来，在目标2中，我们将与专家和利益相关者接触，以 开始使用设计思维方法开发干预措施，这些方法可以试点来解决大多数 有希望的SNF战略，以降低再入院率。为了实现这些目标，我们聚集在一起 一支充满活力和多学科的调查团队，拥有卫生服务研究、护理、公共部门的专业知识 健康、定性方法论、老年病学、行为经济学、设计思维，以及在 多点观测研究。探索-SNF是重要的基础性工作，因为最近的联邦 对SNF支付和公开报告要求的更改。虽然用意是好的，但这些激励措施具有 如果没有证据来指导避免再次入院和优化治疗的做法，可能会恶化护理 病人护理。重要的是，这项研究将使本科生和研究生接触到将刺激 对生物医学或行为科学的研究事业感兴趣，并加强研究 纽黑文大学的环境。

Following an acute hospital stay, 1 in 4 older patients is transferred to a skilled nursing facility (SNF). Meanwhile, 25% of these patients are readmitted to the hospital within 30 days. To address the staggering financial and quality of life loss, the Protecting Access to Medicare Act (PAMA) of 2014 included readmission penalties for SNFs that were implemented into practice in 2019. Despite SNFs facing financial penalties for higher than expected hospital readmissions, there is surprisingly little information about what diverse factors are related to readmission for older adults and how some SNFs perform well in achieving low readmissions while others falter. In the face of the new financial and public-reporting incentives, stronger evidence to inform SNF efforts to reduce readmission is needed. We propose to identify the drivers of readmission in SNFs using a positive deviance approach, namely “Exploring Outstanding Performance in Low Readmission from Skilled Nursing Facilities for Older Adults (EXPLORE-SNF)”. Positive deviance is an inductive analytical technique that uses in-depth qualitative methods for generating hypotheses with regard to the organizational factors associated with performance of healthcare organizations. The lack of signal from traditional predictors of readmission suggests that there may be important lessons to learn from SNFs that are “positive deviants” or have extremely low readmission rates. The objective of this study is to learn directly from SNFs about strategies to optimize outcomes in the growing population of older adult patients admitted to SNFs following hospitalization. In Aim 1, we will conduct qualitative interviews with high- and low-performing SNFs to generate hypotheses regarding which SNF strategies are likely to explain exceptionally low 30-day readmission rates among patients discharged to SNFs following hospitalization. SNF performance will be calculated using Medicare readmissions data accessible via Nursing Home Compare. We will sample high- and low-performing SNFs until we reach theoretical saturation. Next, in Aim 2, we will engage with experts and stakeholders to begin to develop interventions using design thinking methodology that could be piloted to address the most promising SNF strategies to reduce readmission rates. In order to accomplish these aims, we have assembled a dynamic and multi-disciplinary investigative team, with expertise in health services research, nursing, public health, qualitative methodology, geriatrics, behavioral economics, design thinking, as well as in the conduct of multi-site observational studies. EXPLORE-SNF is important foundational work because of recent federal changes to SNF payments and public reporting requirements. While well-intentioned, these incentives have the potential to worsen care if not accompanied by evidence to guide practices to avoid readmission and optimize patient care. Importantly, this study will expose undergraduate and graduate students to research that will spur interest in research careers in biomedical or behavioral sciences, as well as strengthen the research environment at the University of New Haven.