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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Methodol. Sep 20, 2025; 15(3): 99162
Published online Sep 20, 2025. doi: 10.5662/wjm.v15.i3.99162
Published online Sep 20, 2025. doi: 10.5662/wjm.v15.i3.99162
Innovative forecasting models for nurse demand in modern healthcare systems
Kalpana Singh, Abdulqadir J Nashwan, Department of Nursing and Midwifery Research, Hamad Medical Corporation, Doha 3050, Qatar
Co-first authors: Kalpana Singh and Abdulqadir J Nashwan.
Author contributions: Singh K wrote the draft and critically reviewing the literature; Singh K and Nashwan AJ revised the draft for important intellectual content, they contributed equally to this article, they are the co-first authors of this manuscript; and all authors thoroughly reviewed and endorsed the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Abdulqadir J Nashwan, PhD, Department of Nursing and Midwifery Research, Hamad Medical Corporation, Rayyan Road, Doha 3050, Qatar. anashwan@hamad.qa
Received: July 15, 2024
Revised: December 17, 2024
Accepted: December 23, 2024
Published online: September 20, 2025
Processing time: 233 Days and 17.7 Hours
Revised: December 17, 2024
Accepted: December 23, 2024
Published online: September 20, 2025
Processing time: 233 Days and 17.7 Hours
Core Tip
Core Tip: Accurate forecasting of nurse demand is critical for efficient workforce planning in healthcare. Leveraging advanced methods like time-series analysis, machine learning, and simulation models enables precise staffing predictions. These models address challenges posed by healthcare system complexities, seasonal fluctuations, and policy changes. By integrating these techniques, healthcare organizations can optimize resource allocation, reduce inefficiencies, and enhance patient care quality, ensuring adaptability in an evolving healthcare landscape.