  Over the past decade, there has been a severe staffing shortage in mental
healthcare, exacerbated by increased demand for mental health services due to
COVID-19. This demand is projected to increase over the next decade or so,
necessitating proactive workforce planning to ensure sufficient staffing for
ongoing service delivery. Despite the subject's critical significance, the
present literature lacks thorough research dedicated to developing a model that
addresses the long-term workforce needs required for efficient mental
healthcare planning. Furthermore, our interactions with mental health
practitioners within the United Kingdom's National Health Service (NHS)
revealed the practical need for such a model. To address this gap, we aim to
develop a hybrid predictive and prescriptive modelling framework, which
combines long-term probabilistic forecasting with an analytical stock-flow
model, designed specifically for mental health workforce planning. Given the
vital role of nurses, who account for one-third of the total mental health
workforce, we focus on modelling the headcount of nurses, but the proposed
model can be generalised to other types of workforce planning in the healthcare
sector. Using statistical and machine learning approaches and real-world data
from NHS, we first identify factors contributing to variations in workforce
requirements, then develop a long-term forecasting model to estimate future
workforce needs, and finally integrate it into an analytical stock-flow method
to provide policy analysis. Our findings highlight the unsustainable nature of
present staffing plans, showing a growing nursing shortage. Furthermore, the
policy analysis demonstrates the ineffectiveness of blanket remedies,
highlighting the need for regional-level policy developments.
