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

探索老年人专业护理机构在低再入院率方面的杰出表现 (EXPLORE-SNF)

• 批准号: 10202113
• 负责人: KARL Emery MINGES
• 金额: $ 44.8万
• 依托单位: UNIVERSITY OF NEW HAVEN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10202113
• 关键词: Acute; Address; Advance Care Planning; Award; Behavioral Research; Behavioral Sciences; Biomedical Research; Career Mobility; Caring; Clinical; Communication; Dangerousness; Data; Delirium; Deterioration; Discipline of Nursing; Educational process of instructing; Elderly; Environment; Financial cost; Foundations; Future; Generations; Geriatrics; Health; Health Services Research; Health care facility; Hospitalization; Hospitals; Iatrogenic Disease; Incentives; Infrastructure; Intervention; Interview; Knowledge; Learning; Length of Stay; Link; Medicare; Medication Errors; Methodology; Nursing Homes; Observational Study; Older Population; Outcome; Patient Care; Patients; Performance; Pharmaceutical Preparations; Phenotype; Physical Function; Process; Public Health; Qualitative Methods; Quality of life; Reporting; Research; Risk; Sampling; Signal Transduction; Site; Site Visit; Skilled Nursing Facilities; Standardization; Students; Techniques; Testing; Thinking; Time; United States National Institutes of Health; Universities; Variant; Visit; Work; acute care; adverse event risk; base; behavioral economics; care coordination; career; college; cost; design; deviant; evidence base; financial incentive; follow-up; graduate student; health care service organization; hospital readmission; improved; improved outcome; informant; interest; multidisciplinary; older patient; payment; programs; readmission rates; therapy development; undergraduate student

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.

急性住院住院后，四分之一的年长患者转移到熟练的护理设施（SNF）。 同时，这些患者中有25％在30天内被送往医院。解决惊人的 财务和质量损失，《 2014年的《保护访问Medicare Act》（PAMA）包括再入院 对2019年实施实施的SNF的处罚。尽管SNF面临经济处罚 高于预期的医院再入院，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，以及某些SNF在降低重新入院方面的表现良好 而其他人则是错误的。面对新的财务和公共报告激励措施，更有力的证据可以告知 SNF需要减少再入院的努力。我们建议使用SNF中的重新启动 一种积极的偏差方法，即“探索熟练的低迷性能中的出色表现 老年人的护理设施（Explore-SNF）”。积极偏差是一种感应分析技术 这使用了深入的定性方法来产生有关组织因素的假设 与医疗组织的绩效相关。传统预测因素缺乏信号 再入院表明，可能会有重要的教训可以从SNF中学习，而SNF是“积极的偏差”或 再入院率极低。这项研究的目的是直接向SNF学习 优化在越来越多的老年人患者人口中的结果的策略 住院。在AIM 1中，我们将与高性能和低表现的SNF进行定性访谈以生成 关于哪些SNF策略可能会解释出异常低的30天再入院率的假设 在住院后出院到SNF的患者中。 SNF性能将使用 Medicare再入院数据可通过疗养院进行比较。我们将品尝高性能和低表现 SNF直到我们达到理论满意度。接下来，在AIM 2中，我们将与专家和利益相关者互动 开始使用设计思维方法来制定干预措施，该方法可以试用以解决最多 有望降低入院率的SNF策略。为了实现这些目标，我们已经组装了 一个充满活力的多学科调查团队，在卫生服务研究，护士，公众方面具有专业知识 健康，定性方法论，老年医学，行为经济学，设计思维以及进行 多站点观察研究。探索SNF是重要的基础工作，因为最近的联邦 更改SNF付款和公共报告要求。虽然善意，但这些激励措施具有 如果没有通过证据来指导实践以避免再入院并进行优化，则有可能担心护理 病人护理。重要的是，这项研究将使本科生和研究生揭露，这将刺激 对生物医学或行为科学的研究职业的兴趣，并加强研究 纽黑文大学的环境。