Harmonizing and Integrating Nursing Data into Multidisciplinary Datasets to Evaluate Hospital Care and Readmissions of Older Adults with Alzheimer’s Disease and 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和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: 10394325
• 负责人: GAIL M KEENAN
• 金额: $ 22.67万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10394325
• 关键词: Ache; Address; Alzheimer' s Disease; Alzheimer' s disease related dementia; Care given by nurses; Caring; Clinical; Clinical Data; Clinical Research; Code; Collaborations; Complex; Data; Data Analytics; Data Element; Data Set; Development; Discipline of Nursing; Disease; Elderly; Electronic Health Record; Elements; Florida; Goals; Health; Health system; Heart Diseases; Hospitals; 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; care providers; clinical development; cost; data infrastructure; data integration; data pipeline; data standards; follow-up; hospital readmission; hospitalization rates; improved; large datasets; machine learning prediction; model development; multidisciplinary; nursing intervention; predictive modeling; prevent; programs; prototype; readmission rates

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.

项目总结/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 疾病相关性痴呆（AD/ADRD）。21%的AD/ADRD老年人有计划外， 而且往往是可以预防的，30天内再次入院，估计每年花费超过54亿美元。 年临床数据研究网络（CDRN）及其大型复杂数据集的开发，包括 患者和临床水平的数据已经导致了预测再入院的各种模型的开发。然而，在这方面， 现有的模型没有显示出足够的预测能力。整合护理的机会 护理计划（即，患者问题、目标和干预措施）与这些大型数据集相结合，可以填补空白， 预测模型的准确性。开发支持整合的研究数据基础设施 因此，将护理计划数据转换为CDRN数据集对于理解和改进护理计划至关重要。 旨在减少AD/ADRD老年人再入院的跨学科护理。本课题 建议扩大OneFlorida临床研究联盟和数据信托的研究基础设施 通过创建一个可重复使用和可行的数据管道， 护理计划数据元素由护士记录到OneFlorida Data Trust中。我们的长期目标是 研究计划是为了更深入地了解AD/ADRD老年人群的再入院情况 通过提供具有跨学科数据的扩展数据集。我们计划进行以下工作 目标：具体目标1（R21阶段）：开发和测试一个用于提取、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 全州OneFlorida数据信托我们将映射用于表示两个护理数据的本地词汇表 使用自然语言处理，将国家认可的术语集的要素， 护士验证映射结果。然后，我们将使用自动脚本将本地词汇表替换为 标准化的条款，并将数据集成到信托中。具体目标2（R33阶段）：转换和 整合到信任护理干预UF健康。具体目标3（R33阶段）：提取、转化、 并使用R21阶段的管道将来自第二个组织的护理数据集成到Trust。 具体目标4（R33阶段）：构建和测试老年人（65岁和65岁）再入院预测模型。 上述）与AD/ADRD使用相关变量从信任和环境数据集链接到它。我们将 开发机器学习模型，用于预测护理要素、其他患者 水平的数据，以及重要的环境变量，影响出院后的后续护理。我们 该团队将成为首批开发可持续数据管道的团队之一，用于转换当地护理计划数据 标准化的数据，以整合到一个大的数据集。通过本研究，我们将学习到改进 护士，医生和其他提供者可以采取的措施来防止再次入院。