# Advancing Interdisciplinary Science of Aging through Identification of Iatrogenic Complications: The UF EHR Clinical Data Infrastructure for Enhanced Patient Safety among the Elderly (UF-ECLIPSE)

> **NIH NIH R33** · UNIVERSITY OF FLORIDA · 2021 · $694,935

## Abstract

Project Summary/Abstract
Iatrogenic conditions are a continuing public health concern, causing death among an estimated two hundred
and fifty thousand older adults annually in United States (US) hospitals. Hospital-acquired falls and hospital-
induced delirium are among the most common and costly iatrogenic conditions, and their occurrences are
linked to each other. Advances in computing technology and availability of electronic data presents
opportunities to more accurately identify identifying patients at risk of suffering a hospital-acquired fall or
hospital-induced delirium. Clinical data is now being captured electronically for about 80% of the US
population. Approximately 75-80% of clinical data is text data which cannot be analyzed using traditional
statistical methods. The development of a research data infrastructure that supports the use of text and
structured data is critical for a learning health system aimed at improving care and patient outcomes.
In this project, we propose to expand the research infrastructure for electronic data-driven knowledge
generation through the development of the University of Florida (UF) EHR Data Infrastructure for Patient
Safety among the Elderly (UF-ECLIPSE). The long-term goal of our research program is to enhance the
safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health
system. We plan to carry out the following aims: Specific Aim 1 (R21 Phase): Identify and test the feasibility
of text-mining pipelines to process registered nurses' (RNs) progress notes for prediction of hospital-acquired
falls. We will employ a combination of supervised and unsupervised text-mining methods to identify text
attributes associated with patient falls. We will then leverage a predictive model of patient fall risk factors
developed in previous work to generate a composite model of text and structured data to predict the odds of a
patient falling. Specific Aim 2 (R33 Phase): Determine and evaluate the structural and human resources of an
expanded research-data infrastructure to support sustained interdisciplinary aging studies. We will develop and
pilot test text-mining pipelines to generate a prediction model of hospital-induced delirium. We will then
integrate the developed pipelines into the existing UF Health Clinical Data Warehouse (CDW) infrastructure
and test to assess functionality, durability and scalability. In addition, we propose to develop the human
resource infrastructure to support data-driven interdisciplinary aging research. This will be achieved by training
graduate students in interdisciplinary data science for aging research.
The UF-ECLIPSE research team will be among the first to implement and test an integrated data repository
that utilizes nurse-generated structured and text data to support a learning health system. This study will create
important new research data infrastructure, and will be a model for health care organizations to increase safe
e...

## Key facts

- **NIH application ID:** 10337407
- **Project number:** 4R33AG062884-03
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Ragnhildur Ingibjargardottir Bjarnadottir
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $694,935
- **Award type:** 4N
- **Project period:** 2019-04-15 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10337407

## Citation

> US National Institutes of Health, RePORTER application 10337407, Advancing Interdisciplinary Science of Aging through Identification of Iatrogenic Complications: The UF EHR Clinical Data Infrastructure for Enhanced Patient Safety among the Elderly (UF-ECLIPSE) (4R33AG062884-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10337407. Licensed CC0.

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