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

急性住院后，1/4的老年患者被转移到专业护理机构（SNF）。 与此同时，这些患者中有25%在30天内再次入院。为了解决 经济和生活质量的损失，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SNF面临经济处罚， 高于预期的再入院率，令人惊讶的是， 与老年人的再入院率以及一些SNF在实现低再入院率方面的表现有关 而另一些人则步履蹒跚。面对新的财务和公共报告激励措施， SNF需要努力减少再入院。我们建议使用以下方法确定SNF中再入院的驱动因素： 一种积极的偏离方法，即“探索从熟练的 老年人护理设施（EXPLORE-SNF）。正偏差是一种归纳分析技术 它使用深入的定性方法来产生关于组织因素的假设 与医疗机构的绩效相关。缺乏来自传统预测因子的信号， 重新接纳表明，可能有重要的经验教训，从SNF是“积极的偏离者”， 再入院率极低本研究的目的是直接从SNF了解 策略，以优化不断增长的老年人患者的结果，这些患者接受SNF治疗， 住院在目标1中，我们将对高绩效和低绩效的SNF进行定性访谈， 关于哪些SNF策略可能解释异常低的30天再入院率的假设 在住院后出院至SNF的患者中。SNF性能将使用 可通过Nursing Home Compare访问Medicare再入院数据。我们将对高性能和低性能的 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合作， 开始使用设计思维方法制定干预措施，这些方法可以试行，以解决 有前途的SNF策略，以降低再入院率。为了实现这些目标，我们聚集了 一个充满活力和多学科的调查小组，在卫生服务研究，护理，公共 健康，定性方法论，老年医学，行为经济学，设计思维，以及在进行 多中心观察性研究。EXPLORE-SNF是重要的基础性工作，因为最近联邦 SNF付款和公共报告要求的变化。虽然动机是好的，但这些激励措施 如果没有证据来指导避免再入院和优化治疗的实践， 病人护理重要的是，这项研究将使本科生和研究生接触到研究， 对生物医学或行为科学的研究事业感兴趣，并加强研究 在纽黑文大学的环境。

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.