Artificial intelligence is shifting from clinical diagnostics to operational strategy, as an AI forecasting model developed by the University of Hertfordshire targets healthcare resource efficiency across NHS systems.
In partnership with regional NHS bodies, the project uses machine learning on five years of data to support operational planning.
Instead of focusing on individual diagnoses, it analyzes system-wide demand to guide staffing and capacity decisions.
Professor Iosif Mporas, who leads the research team, said the goal is to forecast what will happen if no intervention is taken and quantify how changing regional demographics affect NHS resources. The system incorporates admissions, treatments, workforce availability, and demographic variables such as age, gender, ethnicity, and deprivation levels.
Key takeaways:
- The AI forecasting model supports short-, medium-, and long-term healthcare demand planning.
- It integrates operational and demographic data to improve predictive accuracy.
- The initiative shifts AI from reactive analysis to proactive healthcare management.
- Testing is underway in hospital environments, with expansion planned to community services and care homes.
Through the University of Hertfordshire Integrated Care System partnership, the research supports the evolving structure of the Hertfordshire and West Essex Integrated Care Board, which serves 1.6 million residents. As part of this transition, the board is preparing to merge into the Central East Integrated Care Board.
By embedding predictive modelling into operational workflows, the AI forecasting model enables NHS leaders to move beyond retrospective reporting. The project demonstrates how legacy healthcare data can inform cost efficiency, workforce planning, and long-term resource allocation in complex public service environments.
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