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 超过170万美国护士（NH）居民的近三分之二的认知障碍 作为阿尔茨海默氏病或​​相关痴呆症（ADRD），并且有医院转移的高风险。减少 由于对居民健康的负面影响，NH居民可避免住院是国家优先事项 以及高医疗保险和医疗补助成本。 NH到医院转移导致26亿美元的支出 一种方式，对居民的身体和情感搬迁压力造成伤害，并且可以避免多达60％ 案件。可避免的NH到医院转移包括可以安全有效的条件转移 在NH中进行管理。早期疾病的识别和治疗可以防止医院转移 总体而言，在控制成本的同时，最终降低了ADRD居民的发病率和死亡率。 nh-to- 医院转移决策很复杂，依赖于及时传输信息 跨学科团队。不幸的是，许多NHS都依赖过时的通信方法（例如电话，传真） 而不是利用现代，方便和低成本的选项，例如文本消息（TM），可以 改善健康信息共享。使ADRD居民的决策过程变得复杂 语言逐渐丧失影响个人沟通能力。我们期望这一点 医疗团队成员之间的沟通在有或没有ADRD的NH居民方面有所不同。我们的数据集 提供了一个理想和新颖的机会，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有关NH居民转移的信息。我们建议检查内容 使用对年龄友好的卫生系统4M框架的TM，其中包括四个基于证据的元素 高质量的护理（重要的，药物，想法和流动性）。 4M框架解决了核心 应该推动老年人照顾所有决策的问题，并且是系统地重新思考的一种方式 以改善患者健康和满意度的方式照顾。存在关键需求，以检查如何方便， 诸如TM之类的低成本沟通选项可以减少可避免的NH到医院的转移 adrd。我们的目的是：1）确定由健康信息中共享的4ms的文档 跨学科团队通过ADRD NH到医院转移前两周通过电子TM进行。 2）估计TMS中发现的4ms对可避免的NH到院传递的影响； 3）比较 跨学科团队的沟通方式 adrd。这项工作的潜在影响是降低可避免的住院治疗，最终是发病率和 NH居民的死亡率通过识别为老年人的高质量护理的基于证据的元素 成人（4ms）。另一个潜在的影响是开发结构化语言，允许及时 整个护理范围内无缝的信息可移植性，优化了个人的健康 和人口。