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.

项目总结/文摘