What is the challenge your project is going to address and how does it connect to the theme of 'How can improvement be used to reduce delays accessing health and care services'?

Diagnostic imaging is a key service to any NHS organisation and aids in most patient diagnosis’ and the establishing of most patient treatment plans, whether they have attended via the Emergency Department or are being treated as part of a Cancer pathway.

In recent years the number of experienced radiologists available across multiple modalities has been limited, which has  resulted in delayed patient diagnosis’ as a result off limited imaging reporting capacity.

Artificial Intelligence has begun to be used in small scales within some organisations across the UK to help reduce reporting backlogs within certain areas. These examples are limited currently but have the potential to become a great reporting resource going forward and help to address the staffing shortages if given the right investment and development.

In collaboration with Qure.AI, I believe we can develop and expand the current radiology AI reporting capabilities and reduce diagnostic imaging delays.

What does your project aim to achieve?

Ultimately this project plans to greatly expand the use of Artificial Intelligence in reporting within Radiology. By collaborating with Qure.AI, MTW aim’s to utilise the current AI reporting tools and develop them to improve reporting accuracy and eventually provide increased reporting capacity and ultimately reduce patient diagnostic waiting times.

In addition to the above, we would like to pilot and test the use of AI within breast radiology by using the technology as a 3rd reader, which can be used to provide evidence to expand the use of AI within the breast service going forward.

This project could then potentially be used as the basis methodology for continuing the expansion of AI technology into other radiology modalities.

How will the project be delivered?

The project would be lead internally by our Divisional Head of Strategy and Digital within Radiology, in collaboration with the project team from Qure.AI. We would aim to have the initial system installed and configured within 4 months, at which point AI reporting analysis would be begin in the four initial areas.

After this has been configured, we would introduce/pilot the technology as a 3rd reader into breast radiology and assess the potential benefits of adopting the use within this areas. If successful, we would then proceed with expanding the project and expand the technologies use whilst also achieving the correct clinical sign off and governance processes.

How is your project going to share learning?

QI work at Maidstone and Tunbridge Wells NHS Trust is assessed and captured by using the A3 methodology which is kept and shared amongst other department and the wider network as examples if successful.

The potential shared learning from this project is UK wide, as the use of AI technology within the NHS in the UK is very limited. If the project is successful it could establish a correct approach for how other NHS organisation can adopt and implement the use of AI within their diagnostic divisions with the aim to reduce patient diagnostic waiting times.