Using AI to understand nurse workload and burnout
AI modeling of nursing workload to understand burnout
This study is looking at how artificial intelligence can help understand and reduce burnout among nurses by analyzing their work patterns, which could lead to better care for patients.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Utah NIH-funded |
| Lab location | 1 site (Salt Lake City, United States) |
| Project ID | NIH-10936933 on NIH RePORTER |
What this research studies
This research investigates how artificial intelligence can model nursing workloads to better understand and address nurse burnout. By analyzing electronic health record data, the project aims to identify patterns and factors contributing to burnout among nurses. The approach involves advanced data science techniques and computational modeling to provide insights that could improve nursing work environments. Patients may benefit indirectly as improved nurse well-being can enhance the quality of care they receive.
Who could benefit from this research
Good fit: Ideal candidates for participation or benefit from this research include patients receiving care in settings with high nurse workloads, such as primary care and community health.
Not a fit: Patients who are not receiving care in environments affected by nurse burnout may not see direct benefits from this research.
Why it matters
Potential benefit: If successful, this research could lead to improved working conditions for nurses, ultimately enhancing patient care and outcomes.
How similar studies have performed: Other research has shown success in using data science and AI to improve healthcare systems, indicating potential for this approach to yield meaningful results.
Where this research is happening
Salt Lake City, United States
- University of Utah — Salt Lake City, United States (Active)
Researchers
- Principal investigator: Tiase, Victoria Lynn — University of Utah
- Study coordinator: Tiase, Victoria Lynn
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.