Innovative forecasting models for nurse demand in modern healthcare systems

Abstract

Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce, ensuring appropriate staffing levels, and providing high-quality care to patients. The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models. Factors like technological advancements, novel treatment protocols, and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches. Novel forecasting methodologies, including time-series analysis, machine learning, and simulation-based techniques, have been developed to tackle these challenges. Time-series analysis recognizes patterns from past data, whereas machine learning uses extensive datasets to uncover concealed trends. Simulation models are employed to assess diverse scenarios, assisting in proactive adjustments to staffing. These techniques offer distinct advantages, such as the identification of seasonal patterns, the management of large datasets, and the ability to test various assumptions. By integrating these sophisticated models into workforce planning, organizations can optimize staffing, reduce financial waste, and elevate the standard of patient care. As the healthcare field progresses, the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.

Keywords: Nurse demand prediction; Time-series analysis; Machine learning; Simulation-based methods; Predictive models

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.