An operational AI predictive model developed by researchers at the University of Hertfordshire aims to improve resource efficiency in healthcare.
Public sector organizations often maintain large archives of historical data that do not provide information to determine future prospects. A partnership between the University of Hertfordshire and local NHS healthcare providers is addressing this issue by applying machine learning to operational planning. This project analyzes healthcare demand and helps administrators make decisions about staffing, patient care, and resources.
Most AI efforts in healthcare focus on individual diagnosis or patient-level interventions. The project team notes that the tool targets system-wide operational management. This distinction is important for leaders evaluating where to deploy automated analytics within their infrastructure.
This model uses five years of historical data to build its predictions. Integrate metrics such as hospitalizations, treatments, readmissions, bed capacity, and infrastructure pressure. The system also takes into account labor availability and local demographic factors such as age, gender, ethnicity, and poverty.
Iosif Mporas, professor of signal processing and machine learning at the University of Hertfordshire, is leading the project. The team includes two full-time postdoctoral researchers and will continue 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 regional demographic changes on NHS resources,” Professor MPoras said.
Using AI for prediction in healthcare operations
This model generates forecasts that show how healthcare demand is likely to change. This 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 program manager for NHS Herts and West Essex, commented:
“Used properly, this tool will enable NHS leaders to take more proactive decisions and deliver on the 10-year plan articulated in a strategic document within the Central East Integrated Care Board.”
The University of Hertfordshire Integrated Care Systems Partnership is funding the work, which began last year. Testing of AI models tailored to medical practice is currently underway in hospital settings. The project roadmap includes extending the model to community services and care homes.
This expansion coincides 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 neighboring boards. The merger creates the Central East Integrated Care Board. The next stage of development will incorporate data from this broader population to improve the model’s predictive accuracy.
This work demonstrates how legacy data can improve cost efficiency and shows that predictive models can inform ‘do nothing’ assessment and resource allocation in complex service environments such as the NHS. This project highlights the need to integrate disparate data sources, from employee counts to population health trends, to create a unified view for decision-making.
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