Harmonizing and Integrating Nursing Data into Multidisciplinary Datasets to Evaluate Hospital Care and Readmissions of Older Adults with Alzheimer's Disease-Related Dementias

将护理数据协调并整合到多学科数据集中，以评估患有阿尔茨海默病相关痴呆症的老年人的医院护理和再入院情况

• 批准号: 10789306
• 负责人: GAIL M KEENAN
• 金额: $ 74.26万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10789306
• 关键词: Alzheimer' s Disease; Alzheimer' s disease related dementia; Care given by nurses; Caring; Clinical; Clinical Data; Clinical Research; Code; Collaborations; Data; Data Analytics; Data Element; Data Set; Development; Discipline of Nursing; Disease; Elderly; Electronic Health Record; Elements; Florida; Goals; Health; Health system; Heart Diseases; Hospitalization; Infrastructure; Intervention; Learning; Link; Malignant Neoplasms; Maps; Medical Care Team; Methodology; Modeling; Natural Language Processing; Nurses; Nursing Care Plans; Outcome; Pain; Patient Readmission; Patient-Focused Outcomes; Patients; Persons; Phase; Physicians; Population; Provider; Registered nurse; Research; Research Infrastructure; Risk; Source; Standardization; Technology; Terminology; Testing; Time; Translating; Trust; Unified Medical Language System; United States; Universities; University resources; Vocabulary; age group; aged; aging population; application programming interface; clinical development; complex data; cost; data infrastructure; data integration; data pipeline; data standards; follow-up; hospital care; hospital readmission; hospitalization rates; improved; large datasets; machine learning model; model development; multidisciplinary; nursing intervention; predictive modeling; prevent; primary care provider; programs; prototype; readmission rates; statistics

Project Summary/Abstract Currently, in the United States, there are 5.8 million older adults with Alzheimer’s Disease and Alzheimer’s Disease-Related Dementias (AD/ADRD). Twenty-one percent of older adults with AD/ADRD have unplanned, and often preventable, hospital readmissions within 30-days, which are estimated to cost over $5.4 billion per year. The development of clinical data research networks (CDRN) and their large complex datasets including patient and clinical level data have led to the development of various models to predict readmissions. However, the existing models have not demonstrated adequate predictive capability. The opportunity to integrate nursing care plan (i.e., patient problems, goals, and interventions) to these large datasets can fill gaps and improve the accuracy of prediction models. The development of a research data infrastructure that supports the integration of nursing care plan data to CDRN datasets is therefore critical for understanding and improving interdisciplinary care aimed at reducing readmissions of older adults with AD/ADRD. In this project, we propose to expand the research infrastructure of the OneFlorida Clinical Research Consortium and Data Trust through the creation, for the first time, of a reusable and feasible data pipeline that will ultimately integrate key care plan data elements documented by nurses into the OneFlorida Data Trust. The long-term goal of our research program is to gain a deeper understanding of readmissions for the aging population with AD/ADRD through the availability of an expanded dataset with interdisciplinary data. We plan to carry out the following aims: Specific Aim 1 (R21 Phase): Develop and test a prototype pipeline for extracting, translating, and integrating problems and goals in nursing care plan data from the University of Florida (UF Health) into the statewide OneFlorida Data Trust. We will map the local vocabulary used to represent the two nursing data elements to nationally recognized terminology sets using natural language processing and have registered nurses validate the mapping results. We will then use automated scripts to replace the local vocabulary with the standardized terms and integrate the data into the Trust. Specific Aim 2 (R33 Phase): Convert and integrate into the Trust nursing interventions from UF Health. Specific Aim 3 (R33 Phase): Extract, convert, and integrate to the Trust nursing data from a second organization, using the pipeline from the R21 phase. Specific Aim 4 (R33 Phase): Construct and test readmissions prediction models for older adults (aged 65 and above) with AD/ADRD using pertinent variables from the Trust and environmental dataset linked to it. We will develop machine-learning models for predicting readmissions from the nursing care elements, other patient- level data in the Trust, and important environmental variables that influence post-discharge follow-up care. Our team will be among the first to develop sustainable data pipelines for transforming local nursing care plan data into standardized data for integration into a large dataset. Through this study, we will learn the improvements nurses, physicians, and other providers can make to prevent readmissions.

项目概要/摘要 目前，在美国，有 580 万老年人患有阿尔茨海默病和阿尔茨海默病 21% 患有 AD/ADRD 的老年人患有疾病相关痴呆症 (AD/ADRD)。 30 天内再次入院通常是可以预防的，估计每次费用超过 54 亿美元 临床数据研究网络（CDRN）及其大型复杂数据集的发展。 患者和临床水平的数据导致了各种模型的发展来预测再入院。 现有的模型还没有表现出足够的预测能力来整合护理。 针对这些大型数据集的护理计划（即患者问题、目标和干预措施）可以填补空白并改善 支持集成的研究数据基础设施的开发。 因此，将护理计划数据转化为 CDRN 数据集对于理解和改进至关重要 跨学科护理旨在减少 AD/ADRD 老年人的再入院率。 提议扩大 OneFlorida 临床研究联盟和数据信托的研究基础设施 通过首次创建可重用且可行的数据管道，最终将关键集成 护士将护理计划数据元素记录到 OneFlorida 数据信托中 我们的长期目标。 研究计划旨在更深入地了解患有 AD/ADRD 的老年人口的再入院情况 通过提供具有跨学科数据的扩展数据集，我们计划开展以下工作。 目标：具体目标 1（R21 阶段）：开发和测试用于提取、翻译和 将佛罗里达大学 (UF Health) 的护理计划数据中的问题和目标整合到 Southern OneFlorida Data Trust 将绘制用于表示两个护理数据的本地词汇表。 使用自然语言处理并已注册的国家认可术语集的元素 然后，我们将使用自动化脚本来替换本地词汇。 标准化术语并将数据整合到信托中。 具体目标 2（R33 阶段）：转换和 纳入 UF Health 的信托护理干预措施具体目标 3（R33 阶段）：提取、转换、 并使用 R21 阶段的管道集成到来自第二个组织的 Trust 护理数据。 具体目标 4（R33 阶段）：构建并测试老年人（65 岁和 65 岁）的再入院预测模型 上面）与 AD/ADRD 使用与其链接的信任和环境数据集中的相关变量。 开发机器学习模型，用于预测护理要素、其他患者的再入院 信托中的水平数据，以及影响出院后后续护理的重要环境变量。 该团队将成为首批开发可持续数据管道以转变当地护理计划数据的团队之一 通过这项研究，我们将了解这些改进。 护士、医生和其他提供者可以采取措施防止再次入院。