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Time Series
Healthcare Staffing Demand Forecast
Workforce planning in healthcare is challenging due to seasonal demand variations, turnover, and specialty shortages. This project forecasted staffing needs across key roles.
12-month rolling forecast with 89% accuracy
2025PythonPandasScikit-learnPower BI
Dataset
NHS Digital: Workforce Statistics
Key Questions
- What are the seasonal patterns in staffing demand across different NHS roles?
- Can we forecast vacancy rates 3-12 months ahead to support recruitment planning?
- Which specialties face the most critical projected shortages?
Methods
- Time series analysis of NHS workforce statistics (2015-2024)
- SARIMA modelling for seasonal staffing patterns
- Vacancy rate trend analysis by staff group and region
- Scenario modelling for different recruitment and retention strategies
Results
The 12-month rolling forecast achieved 89% accuracy for nursing demand. Seasonal peaks in January and September were consistent across years. The model projected a 12% nursing vacancy rate by 2026 without intervention.
Projected Nursing Vacancy Rate (%)
Recommendations
- Launch recruitment campaigns 4-6 months ahead of projected seasonal peaks
- Develop bank and agency staff contracts aligned with forecasted surge periods
- Prioritise retention initiatives for specialties with the steepest projected vacancy growth
- Integrate workforce forecasting into annual budget planning cycles
Limitations
Forecasts are based on historical patterns and cannot account for policy changes, pandemics, or sudden shifts in workforce behaviour. Regional variations within NHS trusts may differ from national trends.