Artificial intelligence used to tackle Leicester’s hospital staff turnover challenges

A team from the University of Leicester is harnessing the power of artificial intelligence to support and retain NHS staff in the city’s hospitals.

Anonymised staff data from the University Hospitals of Leicester NHS Trust (UHL) has been fed into an AI model, which then uses the information to predict where future staffing needs will be required. The model maps how closely colleagues identify with their place of work – the results of which can be used to shape more people-centred human resources policies. This, in turn, should he help slow staff turnover at the Trust and increase the number of people developing successful careers there.

All of the data is handled in accordance with strict data governance rules which UHL and the University are bound by.

The team working on the ‘organisational self-identification’ computer model is led by the University’s work psychology expert, Dr Dennis Pepple, who is an Associate Professor in human resources management. The project team also includes clinicians from UHL and University data scientists.

“The NHS staff shortage was been at the front and centre of the recent 2024 General Election,” said Dr Pepple. “However, simply hiring new staff is insufficient, given the lengthy training pathways and the limited pool of international recruits available to hospitals.

“Sustaining or increasing overall staff numbers is impossible without retaining current employees and their valuable skills and experience, making retention critical for a well-functioning NHS and organisations generally.”

He added: “Our solutions help significantly reduce staff turnover intention and improve the overall functioning of organisations and, more significantly, are within management’s ability to implement.”

“This is a long-term project, which, over time, will provide robust data analysis and insights to support decision-makers as they seek to make Leicester’s Hospitals an even better place to work.”

Assistant Chief Nurse, Antonella Ghezzi, is one of a handful of UHL staff who have met Dr Pepple to find out more about the model. She said: “This is an excellent example of collaborative working practice between institutions in Leicester. We are proud to be able to support this project. Our aim is to use innovative approaches to support the workforce, which in turn will benefit the care we deliver to our population.”

The model is able to:

• Identify the root causes of staff turnover,
• Predict the department, units and teams at risk of staff turnover intention and actual turnover and the key issues specific to them,
• Support the prescription of bespoke intervention for addressing staff turnover intention and actual turnover.