# AI modeling of nursing workload to understand burnout

> **NIH NIH K01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $163,768

## Abstract

PROJECT SUMMARY/ABSTRACT
 This K01 award application is for Dr. Victoria L. Tiase, a PhD-trained nurse informaticist with a
commitment to improving the systems, structures, and policies that support a diverse nursing workforce. Her
overarching career goal is to become an independent nurse researcher focused on obtaining a broad
understanding of nursing workload, specifically in relation to reducing nurse burnout, through the application of
electronic health record (EHR) data and data science. This K01 will support three key areas of career
development: (1) strengthen existing knowledge of EHR audit log data for use in research; (2) acquire
advanced knowledge and skills in data science and computational modeling; and (3) transition from a
mentored researcher to independent investigator. The training and research will be conducted at an institution
with a strong record of providing excellent support and rich training and educational resources. The candidate’s
department is committed to the success of this early-stage researcher, providing any additional resources
necessary to complete the proposed career development and research aims, and ensuring ongoing protected
research time. The coursework and mentoring will be overseen by a complementary and multidisciplinary team
of experienced researchers and experts in these fields.
 Nurse burnout is persistent in the U.S. with nurses reporting concerns over their work environment,
particularly those working in primary care and community clinics in underserved settings. Increased workload is
reported as the main contributor. Although nurse burnout has been studied for decades, little has changed in
the organization of clinical care, and the measurement of nursing workload is not well understood. Workload
measurement has traditionally taken the form of self-report, surveys, and time and motion studies which are
time-consuming, expensive, and difficult to scale. Gathering sufficient data that are reliable, reproducible,
generalizable, and that represent nursing contributions within the context of work activities remains a complex,
unsolved problem. Advances in informatics and electronic health record (EHR) audit log data have shown
promise in measuring clinician work activities. State-of-the-art data science paradigms are needed to fully
understand the complexity of nursing work activities and their relevance to workload. Thus, this study’s
Specific Aims include: (1) Characterize and extract features from EHR audit log data and develop a data
representation amenable to state-of-the-art data science techniques; and (2) Develop realistic and reproducible
computational models of nursing EHR interactions. The proposed research is innovative because it will extend
an existing untapped data source to nursing activities and will create a model to quantitatively measure
workload influencers. This research lays the groundwork to test scalable interventions that mitigate nurse
burnout, improve nurse wellness, reduce c...

## Key facts

- **NIH application ID:** 10936933
- **Project number:** 1K01NR021256-01
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Victoria Lynn Tiase
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $163,768
- **Award type:** 1
- **Project period:** 2024-07-03 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10936933

## Citation

> US National Institutes of Health, RePORTER application 10936933, AI modeling of nursing workload to understand burnout (1K01NR021256-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10936933. Licensed CC0.

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