Using the Home Health OASIS to Promote Advance Care Planning for Community-Dwelling Frail Elders

项目摘要/摘要 居住在家中的虚弱老年人是一个面临衰退和死亡风险的群体，因为他们经常患有多发性慢性 可能导致不必要的住院或可能徒劳无功的条件和复杂的社会需求 在生命末期接受治疗，从而增加了个人和公共的社会经济负担。高级护理 计划是一个过程，在这个过程中，对临终关怀的偏好被讨论并以书面形式记录下来，这是一个 确保以患者为中心的决策推动生命末期护理的方法。然而，只有12.5%的黑人和 接受家庭医疗保健的美国白人中有32%患有ACP。尽管大多数老年人 成年人表示，他们希望讨论护理偏好，但提供者往往不愿 对于缺乏明确的终末期诊断的患者，在面临预后不确定性时提出这个问题 突然发生的事件。数据科学方法，旨在发现大数据集中的新知识， 在改善个人和人口一级的质量成果方面显示出巨大的潜力。护士- 科学家使用这些方法取得了进展，改善了护理，提高了满意度，并降低了 医疗保健成本。本研究的目的是为社区居住的体弱型老年人构建一个以规则为基础的 12个月内死亡风险常规筛查的临床决策支持预测模型 触发家庭健康护士来识别那些死亡风险最大的人。这项数据科学调查将使用主页 医疗保险和医疗补助中心要求的健康结果和评估信息集(OASIS) 描述各种家庭保健措施的服务。这项研究是独一无二的，因为它发展和 测试了一种新的方法学方法，因为这是唯一已知的可以预测死亡的研究 在这种情况下使用目前广泛使用的临床结果测量工具。这样做的目的是 这项研究与国家护理研究所(NINR)的使命一致，因为它使用数据- 科学方法发展以证据为基础的方法来改善沟通，促进共享 了解预后，促进以患者和家庭为中心的临终关怀。 申请人将接受来自领先的国家和地区的数据科学方法的指导和培训 卫生信息学、计算机科学和死亡风险预测领域的国际专家。这个 这项研究的结果将为纳入预测模型的干预研究奠定基础 以可互操作的形式进入临床实践，以探索死亡风险预测对 预付护理计划的费率。此外，这项研究将被用来为未来的研究提供信息并产生 加强双向沟通和协作的临床决策支持建议 护士、病人、家属和提供者之间的关系。

Project Summary/Abstract Frail elders living at home are a population at risk for decline and death as they often have multiple chronic conditions and complex social needs that may lead to unnecessary hospitalizations or potentially futile treatment at the end of life thereby driving up individual and public socioeconomic burdens. Advanced care planning, a process in which preferences for care at the end of life are discussed and placed in writing, is one approach to ensure that patient-centered decision making drives end of life care. Yet, only 12.5% of black and 32% of white Americans receiving home healthcare have an ACP. Despite the fact that the majority of older adults state that they would want to have discussions about care preferences, providers often remain reluctant to raise the issue when faced with prognostic uncertainty for patients who lack a clear terminal diagnosis or precipitating event. Data science methods, aimed towards discovering new knowledge in large datasets, shows tremendous potential for improving both individual and population-level quality outcomes. Nurse- scientists have made progress using these methods to improve care, increase satisfaction, and lower healthcare costs. The purpose of this study of community dwelling frail elders is to build a “rules-based” prognostication model for clinical decision support for routine screening for mortality risk within 12 months to trigger home health nurses to identify those most at risk for dying. This data-science inquiry will use the Home Health Outcome and Assessment Information Set (OASIS), required by the Centers for Medicare and Medicaid Services to describe a variety of home healthcare measures. This study is unique because it develops and tests a novel methodological approach and because it is the only known study that will prognosticate death using a clinical outcomes measurement tool in current widespread use in this setting. The objectives of this study are aligned with the mission of the National Institute of Nursing Research (NINR) because it uses data- science methods to develop an evidence-based approach to improve communication, promote a shared understanding of prognosis, and facilitate patient and family-centered preferences for care at the end of life. The applicant will receive mentorship and training in data science methods from leading national and international experts in the field of health informatics, computer science, and mortality risk prognostication. The findings of this study will be foundational for an intervention study in which the predictive model is incorporated into clinical practice in an interoperable format to explore the impact that mortality risk prognostication has on the rates of advance care plans. Moreover, this study will be used to inform future research and to produce recommendations for clinical decision support to enhance bi-directional communication and collaboration between nurses, patients, families, and providers.