Utilizing and Optimizing Forecasting Models for Nursing Demand: A Narrative Review

Accurately forecasting nursing demand is essential for effective workforce planning in the context of increasing patient volumes and the growing complexity of healthcare systems. Reliable forecasting supports optimal staffing, enhances patient care quality, and reduces operational risks. However, traditional forecasting approaches are increasingly challenged by evolving clinical demands, rising chronic disease burden, and rapid technological advancements.

This narrative review aimed to (1) map existing models used for predicting nursing demand, (2) identify key predictive methodologies, data inputs, and evaluation metrics, and (3) highlight existing gaps and implications for healthcare policy and workforce management.

A comprehensive narrative literature synthesis was conducted, examining a range of forecasting approaches used in healthcare settings. The review included time-series models (e.g., ARIMA), machine learning techniques (e.g., Random Forest and Long Short-Term Memory [LSTM]), and hybrid modeling frameworks. Relevant studies were analyzed to compare methodologies, performance, and applicability in nursing workforce prediction.

The findings indicate that advanced forecasting models, particularly machine learning and hybrid approaches, improve prediction accuracy and enable proactive workforce planning. However, significant limitations were identified, including inconsistencies in data quality, lack of standardized validation methods, and limited contextual adaptation. Additionally, many models fail to incorporate dynamic factors such as patient acuity, seasonal variations, and unit-specific characteristics, which restrict their real-world applicability.

Forecasting models hold substantial potential to enhance nursing workforce planning, but their effectiveness depends on methodological rigor, high-quality data infrastructure, and organizational readiness. Future efforts should focus on integrating dynamic healthcare variables and improving model validation to ensure reliable and context-sensitive predictions. This review provides guidance for researchers, healthcare administrators, and policymakers in developing evidence-informed and sustainable nursing workforce strategies.