# 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

> **NIH NIH R21** · UNIVERSITY OF FLORIDA · 2021 · $190,625

## Abstract

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 pipeli...

## Key facts

- **NIH application ID:** 10204503
- **Project number:** 1R21AG072265-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** GAIL M KEENAN
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $190,625
- **Award type:** 1
- **Project period:** 2021-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10204503

## Citation

> US National Institutes of Health, RePORTER application 10204503, 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 (1R21AG072265-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10204503. Licensed CC0.

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