UHNM to use AI technology to help reduce surgical waiting lists

Sue Perks, head of elective access at UHNM

Patients waiting for elective surgery at University Hospitals of North Midlands NHS Trust (UHNM) are set to benefit from quicker appointments, procedures and discharges following the introduction of a cutting-edge digital tool.

UHNM is now using MBI ROVA, an artificial intelligence (AI) system that automatically analyses clinical documents to identify patients who no longer require treatment or where a further follow-up is needed.

The new system supports UHNM’s waiting list validation process, an essential administrative task that involves staff manually checking thousands of documents across several electronic systems to ensures patient records are accurate and up to date.

In the first week following its introduction ROVA reviewed more than a million clinical documents and identified almost 1,500 patients who can be safely removed from UHNM’s waiting list, work would have taken around three months to complete manually.

Sue Perks, head of elective access at UHNM said: “Validating our waiting list is vital to ensuring patients are in the right place for their care, but it’s also a time-consuming process. ROVA enables us to focus our resources where they are needed most by automatically highlighting the patients who no longer need to wait for treatment.

“ROVA means we can make sure patients are treated sooner and that our waiting list data reflects the true picture. This means a better experience for our patients and more efficient use of our clinical capacity.”

The technology, recommend by NHS England and used in other Trusts across the UK was implemented through collaboration between UHNM’s Digital Services, information and validation teams. It is currently being piloted as part of a 12-month trial and there are plans for it to be expanded to support all clinical care groups across UHNM.

As well as identifying patients who can be removed from the list, ROVA can also be programmed to highlight any wider errors in the waiting list process.  

Sue said: “By using AI to support our validation processes, we can free up staff time, improve data quality and ultimately help reduce waiting times for our patients.”