Reducing Avoidable Nursing Home-to-Hospital Transfers of Residents with ADRD: An Analysis of Interdisciplinary Team Communication using Text Messages

项目概要/摘要 在超过170万的美国养老院（NH）居民中，近三分之二的人患有认知障碍， 阿尔茨海默氏病或相关痴呆症（ADRD），并处于医院转移的高风险中。减少 由于对居民健康的负面影响，NH居民可避免的住院是国家优先考虑的问题 以及高昂的医疗保险和医疗补助费用。NH到医院的转移导致超过26亿美元的支出 每年，从身体和情感的搬迁压力对居民的伤害，并在多达60%是可以避免的， 案件。避免NH到医院的转移包括可以安全有效地 在NH管理。早期疾病识别和治疗可以防止需要转院 最终降低ADRD患者的发病率和死亡率，同时控制成本。NH-对- 医院的转院决策是复杂的，依赖于信息在病人之间的及时传递。 跨学科团队不幸的是，许多NH依赖于过时的通信方法（例如，电话、传真） 而不是利用现代、方便和低成本的选项，如文本消息（TM）， 加强卫生信息共享。使ADRD患者的决策过程复杂化的是 语言的逐渐丧失影响了个体的沟通能力。我们预计 卫生保健团队成员之间的沟通不同的NH居民和没有ADRD。我们的数据集 提供了一个理想的和新颖的机会，应用自然语言处理和社会网络分析， TM在一个关于NH居民转移的跨学科团队中共享。我们建议审查 TM使用年龄友好的健康系统4M框架，其中包括四个循证要素， 高质量的护理（什么是重要的，药物，心理和流动性）。4M框架解决了 这些问题应该推动老年人护理中的所有决策，是一种系统地重新思考的方式 以改善患者健康和满意度的方式进行护理。迫切需要研究如何方便， TM等低成本通信选项可以减少可避免的居民从NH到医院的转移， ADRD。我们的目标是：1）确定4M在卫生信息共享的文件， 跨学科团队通过电子TM两周前的NH到医院转移的居民与ADRD; 2)估计TM中发现的4M对可避免的NH到医院转移的影响; 3）比较 跨学科团队的沟通模式，使居民的转移决定， ADRD。这项工作的一个潜在影响是减少可避免的住院，并最终减少发病率和 通过确定老年人高质量护理的循证要素， 成人（4M）。另一个潜在的影响是开发一种结构化语言， 在整个护理范围内无缝携带信息，优化个人健康 和人口。

PROJECT SUMMARY / ABSTRACT Close to two-thirds of the over 1.7 million U.S nursing home (NH) residents have a cognitive impairment such as Alzheimer’s disease or a related dementia (ADRD), and are at high risk of hospital transfer. Reducing avoidable hospitalizations for NH residents is a national priority due to the negative effects on resident health and high Medicare and Medicaid costs. NH-to-hospital transfers result in > $2.6 billion in expenditures annually, harm to residents from physical and emotional relocation stress, and is avoidable in as many as 60% of cases. Avoidable NH-to-hospital transfers include transfers for conditions that can be safely and effectively managed in the NH. Early illness recognition and treatment could prevent the need for hospital transfer altogether, ultimately reducing morbidity and mortality in residents with ADRD while controlling costs. NH-to- hospital transfer decision-making is complex and relies on timely transmission of information among the interdisciplinary team. Unfortunately, many NHs rely on antiquated communication methods (e.g., phone, fax) rather than leveraging modern, convenient, and low-cost options, like text messaging (TM), which could improve health information sharing. Complicating the decision-making process about residents with ADRD is the progressive loss of language impacting the individual’s ability to communicate. We expect that communication among health care team members differs for NH residents with and without ADRD. Our dataset provides an ideal and novel opportunity to apply natural language processing and social network analysis to TMs shared among an interdisciplinary team about NH resident transfer. We propose to examine the content of TM using the age-friendly health system 4M framework, which includes four evidence-based elements of high-quality care (what Matters, Medications, Mentation, and Mobility). The 4M framework addresses the core issues that should drive all decision making in the care of older adults and is a way of systematically rethinking care in ways that improve patient health and satisfaction. A critical need exists to examine how convenient, low-cost communication options like TM can reduce avoidable NH-to-hospital transfers of residents with ADRD. Our aims are to: 1) Identify documentation of the 4Ms in health information shared by the interdisciplinary team through electronic TM two weeks prior to NH-to-hospital transfer of residents with ADRD; 2) Estimate the effects of the 4Ms found in TMs on avoidable NH-to-hospital transfers; and 3) Compare communication patterns of interdisciplinary teams making transfer decisions about residents with and without ADRD. A potential impact of this work is to decrease avoidable hospitalizations, and ultimately, morbidity and mortality in NH residents with ADRD by identifying evidence-based elements of high-quality care for older adults (4Ms). Another potential impact is the development of a structured language allowing for timely and seamless portability of information across the complete spectrum of care, optimizing the health of individuals and populations.