An operational AI forecasting model developed by University of Hertfordshire Researchers aim to improve resource efficiency in healthcare.
Public sector organizations often hold large archives of historical data that do not allow for forward-looking decisions. A partnership between the University of Hertfordshire and regional NHS health bodies is solving this problem by applying machine learning to operational planning. The project analyzes healthcare demand to help 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 rather targets operational management at the system level. This distinction is important for executives evaluating where to deploy automated analytics within their own infrastructure.
The model uses five years of historical data to construct its projections. It integrates parameters such as admissions, treatments, readmissions, bed capacity and pressure on infrastructure. The system also takes into account labor 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 its development until 2026.
“By working with the NHS, we are creating tools that can predict what will happen if no action is taken and quantify the impact of changing regional demographics on NHS resources,” Professor Mporas said.
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 ability allows leadership to go beyond reactive management.
Charlotte Mullins, Strategic Program Manager for NHS Herts and West Essex, commented: “Strategic demand modeling can affect everything from patient outcomes, including the increasing number of patients living with chronic conditions.
“Used correctly, this tool could enable NHS leaders to make more proactive decisions and enable the implementation of the 10-year plan articulated within the Central East Integrated Care Council as a strategic document. »
The University of Hertfordshire Integrated Care System Partnership is funding the work, which began last year. Testing of the AI model suitable for healthcare operations is currently underway in hospital settings. The project roadmap plans to extend the model to community services and retirement homes.
This expansion aligns with structural changes in the region. The Hertfordshire and West Essex Integrated Care Board serves 1.6 million people and is preparing to merge with two neighboring boards. This merger will create the Central East Integrated Care Council. The next phase of development will integrate data from this larger population to improve the predictive accuracy of the model.
The initiative demonstrates how existing data can drive savings and shows that predictive models can inform ‘do nothing’ assessments and resource allocation in complex service environments like the NHS. The project highlights the need to integrate diverse 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 Reach a Value of $450 Billion by 2028
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