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

在急性住院后，四分之一的老年患者被转移到专业护理机构（SNF）。

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.