An operational AI forecasting model developed by Hertfordshire University researchers aims to improve resource efficiency within healthcare.
Public sector organisations often hold large archives of historical data that do not inform forward-looking decisions. A partnership between the University of Hertfordshire and regional NHS health bodies addresses this issue by applying machine learning to operational planning. The project analyses healthcare demand to assist managers with decisions regarding staffing, patient care, and resources.
Most AI initiatives in healthcare focus on individual diagnostics or patient-level interventions. The project team notes that this tool targets system-wide operational management instead. This distinction matters for leaders evaluating where to deploy automated analysis within their own infrastructure.
The model uses five years of historical data to build its projections. It integrates metrics such as admissions, treatments, re-admissions, bed capacity, and infrastructure pressures. The system also accounts for workforce availability and local demographic factors including age, gender, ethnicity, and deprivation.
Iosif Mporas, Professor of Signal Processing and Machine Learning at the University of Hertfordshire, leads the project. The team includes two full-time postdoctoral researchers and will continue development through 2026.
“By working together with the NHS, we are creating tools that can forecast what will happen if no action is taken and quantify the impact of a changing regional demographic on NHS resources,” said Professor Mporas.
Using AI for forecasting in healthcare operations
The model produces forecasts showing how healthcare demand is likely to change. It models the impact of these changes in the short-, medium-, and long-term. This capability allows leadership to move beyond reactive management.
Charlotte Mullins, Strategic Programme Manager for NHS Herts and West Essex, commented: “The strategic modelling of demand can affect everything from patient outcomes including the increased number of patients living with chronic conditions.
“Used properly, this tool could enable NHS leaders to take more proactive decisions and enable delivery of the 10-year plan articulated within the Central East Integrated Care Board as our strategy document.”
The University of Hertfordshire Integrated Care System partnership funds the work, which began last year. Testing of the AI model tailored for healthcare operations is currently underway in hospital settings. The project roadmap includes extending the model to community services and care homes.
This expansion aligns with structural changes in the region. The Hertfordshire and West Essex Integrated Care Board serves 1.6 million residents and is preparing to merge with two neighbouring boards. This merger will create the Central East Integrated Care Board. The next phase of development will incorporate data from this wider population to improve the predictive accuracy of the model.
The initiative demonstrates how legacy data can drive cost efficiencies and shows that predictive models can inform “do nothing” assessments and resource allocation in complex service environments like the NHS. The project highlights the necessity of integrating varied data sources – from workforce numbers to population health trends – to create a unified view for decision-making.
See also: Agentic AI in healthcare: How Life Sciences marketing could achieve $450B in value by 2028
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