AI forecasting model targets healthcare resource efficiency

Researchers at the University of Hertfordshire have developed an operational AI forecasting model designed to improve efficiency across healthcare systems. The initiative focuses on using historical data to support better planning and decision-making in public healthcare services.

Many public institutions store large volumes of past data, but this information is rarely used to guide future strategies. To address this gap, the University of Hertfordshire partnered with regional NHS health organizations to apply machine learning to operational planning. The system studies patterns in healthcare demand and helps managers make informed decisions about staffing, patient care, and resource allocation.

While most healthcare AI projects concentrate on diagnostics or individual patient treatments, this model is built for system-wide operational management. This broader focus makes it valuable for healthcare leaders deciding where to implement automation within their organizations.

The forecasting tool uses five years of historical data to generate projections. It considers a range of factors, including hospital admissions, treatments, readmissions, bed availability, infrastructure pressures, workforce levels, and demographic variables such as age, gender, ethnicity, and socioeconomic conditions.

The project is led by Professor Iosif Mporas, an expert in signal processing and machine learning at the University of Hertfordshire. Supported by two full-time postdoctoral researchers, the development will continue through 2026.

According to Professor Mporas, the collaboration with the NHS aims to create tools that can predict future scenarios and measure how demographic changes may impact healthcare resources if no intervention occurs.

The model produces short-, medium-, and long-term forecasts of healthcare demand, enabling leaders to shift from reactive responses to proactive planning. This capability can influence areas such as patient outcomes and the growing number of people living with chronic conditions.

Charlotte Mullins, Strategic Programme Manager for NHS Herts and West Essex, noted that strategic demand modelling can affect everything from patient care quality to long-term planning goals. When used effectively, the tool can help NHS leaders make proactive decisions and support the region’s 10-year healthcare strategy.

Funded through the University of Hertfordshire Integrated Care System partnership, the project began last year and is currently being tested in hospital environments. Future phases will expand the model to include community healthcare services and care homes.

This expansion coincides with structural changes in the region’s healthcare administration. The Hertfordshire and West Essex Integrated Care Board, which serves around 1.6 million residents, is set to merge with two neighboring boards to form the Central East Integrated Care Board. The next stage of the project will incorporate data from this larger population to enhance forecasting accuracy.

Overall, the initiative shows how legacy healthcare data can be transformed into actionable insights. By integrating workforce metrics, infrastructure data, and population health trends, the model provides a comprehensive view to support smarter resource allocation and long-term planning.

Voice Of Osiz

At Osiz, we see this development as a powerful example of how AI can transform healthcare beyond diagnostics and into strategic operational intelligence. Leveraging historical data to forecast demand, optimize staffing, and improve resource allocation reflects the true potential of AI-driven decision support systems. Healthcare ecosystems generate massive data volumes, and turning that data into actionable insights is critical for sustainable service delivery. This initiative highlights how predictive modeling can help institutions move from reactive crisis management to proactive planning. As healthcare networks expand and merge, scalable AI infrastructure becomes even more essential. We believe intelligent forecasting solutions will play a vital role in improving patient outcomes while controlling operational costs. At Osiz, we are committed to building advanced AI solutions that empower organizations to make smarter, data-backed decisions in complex environments like healthcare.

Source: AI News