Nurse practitioner program enrollment trends and predictions
Abstract
Background
As the fastest growing segment of the healthcare workforce, understanding NP enrollment is vital.Purpose
This work aimed to guide healthcare workforce forethought, academic planning, and policy initiatives.Method
This secondary data analysis investigated nurse practitioner (NP) program enrollment trends from 2013 to 2022, including sub-analyses of master's versus doctoral enrollment, clinical tracks (acute care, primary care, psychiatric mental health), and enrollment status (part-time vs. full-time). An autoregressive integrated moving average (ARIMA) projection modeling is used to forecast enrollment for four years, 2023–2026.Results•
- A shift toward Doctor of Nursing Practice (DNP) NP program availability and adoption.
- Part-time NP student enrollment is the preferred enrollment status across NP tracks through 2026.
- Robust growth demonstrated and predicted in psychiatric mental health NP programs.
- An expectation that acute care enrollment will recover post-pandemic, while primary care faces a more complex trajectory.
Conclusion
Increased enrollments in doctoral NP programs, visible in DNP NP program and enrollment growth, may offer advantages for the healthcare workforce. Part-time enrollment prevalence requires attention in workforce planning due to the potential for extended graduation timelines. These findings hopefully will lead to an effective healthcare response to meet the demand for high-quality care in a changing landscape.Department
Nursing; Health Management and Policy
Publication Date
9-24-2024
Journal Title
Journal of Professional Nursing
Publisher
Elsevier
Digital Object Identifier (DOI)
Document Type
Article
Recommended Citation
Marcy Ainslie, Esmaeil Bahalkeh, Mary Beth Bigley, Nurse practitioner program enrollment trends and predictions, Journal of Professional Nursing, Volume 55, 2024, Pages 97-104, ISSN 8755-7223, https://doi.org/10.1016/j.profnurs.2024.09.007.
Rights
© 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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