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

减少 ADRD 居民从疗养院到医院的可避免转移：使用短信进行跨学科团队沟通的分析

• 批准号: 10678955
• 负责人: Kimberly Ryan Powell
• 金额: $ 34.88万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10678955
• 关键词: Acute; Address; Age; Alzheimer' s disease related dementia; Caring; Categories; Characteristics; Clinical; Communication; Communication Methods; Complex; Cost Control; Data; Data Set; Data Sources; Decision Making; Dementia; Development; Documentation; Elderly; Electronics; Elements; Emotional; Emotional Stress; Expenditure; Flare; Friends; Goals; Health; Health system; Home Nursing Care; Hospitalization; Hospitals; Impaired cognition; Individual; Interdisciplinary Communication; Intervention; Language; Medical Care Team; Medicare/Medicaid; Modernization; Morbidity - disease rate; Names; Natural Language Processing; Nursing Homes; Ontology; Outcome; Pathway Analysis; Patients; Pattern; Pharmaceutical Preparations; Population; Preventive care; Process; Property; Quality of Care; Resource-limited setting; Severities; Social Network; Standardization; Stress; Structure; Symptoms; Technology; Telefacsimile; Telephone; Test Result; Testing; Text Messaging; Thinking; Time; Update; Visiting Nurse; Vocabulary; Vulnerable Populations; Work; cost; data exchange; evidence base; high risk; improved; member; mortality; novel; portability; prevent; satisfaction; therapy development; transmission process

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.

项目摘要/摘要 在170多万美国疗养院(NH)居民中，近三分之二的人患有这样的认知障碍 如阿尔茨海默病或相关痴呆症(ADRD)，并且有很高的医院转院风险。减少 由于对居民健康的负面影响，NH居民的可避免住院是国家的优先事项 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&>26亿美元的支出 每年，居民因搬迁带来的身体和情感上的压力而受到伤害，且可避免的比例高达60% 案件的数量。可避免的NH到医院的转移包括可以安全和有效的情况下的转移 在NH管理。及早识别和治疗疾病可以避免转院的需要 总而言之，在控制成本的同时，最终降低了ADRD患者的发病率和死亡率。从NH到- 医院转移决策是复杂的，依赖于信息在医院之间的及时传递 跨学科团队。不幸的是，许多国民健康保险制度依赖过时的通讯方式(如电话、传真) 而不是利用现代、方便和低成本的选择，如文本消息(TM)，它可以 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人的交流能力。我们期待着 对于患有和不患有ADRD的NH居民，卫生保健团队成员之间的沟通有所不同。我们的数据集 提供了将自然语言处理和社交网络分析应用于 TMS在一个跨学科团队中分享了关于NH居民转移的信息。我们建议研究一下内容 使用适龄保健系统4M框架对TM进行评估，该框架包括以下四个循证要素 高质量的护理(重要的是药物、精神状态和移动性)。4M框架解决了核心问题 应该推动老年人照顾的所有决策的问题，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。迫切需要检查是否方便， 像TM这样的低成本通信选择可以减少本可避免的居民从NH到医院的转移 阿德勒。我们的目标是：1)确定4MS在健康信息中的文档 跨学科小组在将患有ADRD的居民转院前两周通过电子TM； 2)估计在TMS中发现的4ms对可避免的NH到医院转移的影响；以及3)比较 跨学科团队对有无住院医师转移决策的沟通模式 阿德勒。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 通过确定老年高质量护理的循证因素，研究患有ADRD的NH居民的死亡率 成人(4ms)。另一个潜在的影响是结构化语言的发展，允许及时和 在整个医疗保健范围内无缝携带信息，优化个人健康 和人口数量。