On the day surgical cancellations can offer a poor patient experience - there can be multiple cancellations for some patients, and in early 2018 University Hospitals Plymouth NHS Trust ranked as one of the worst for cancellations. The aim of this project was to explore whether AI approaches could be used to create a model that can inform decision making around cancellations, sooner.
There were three key questions for the Machine learning model to answer: - Should I cancel a patient today? - How many patients should I cancel? - Which patient/s should I cancel?
A model was trained but performance was poor – there was insufficient data to allow the model to be able to replicate the human decision making taking place.
As a second project, the Trust used geospatial visualisation approaches (using QGIS software) to explore demand for community outpatient clinics. The work found that there were increasing waiting lists and poor utilisation of clinics. These results were used to improve their ability to book appropriate patients into Peripheral Sites.
BIG BET - AI to drive patient power and productivity: using a machine learning model to inform same-day decisions on surgical cancellations, aiming to reduce poor patient experience from late, avoidable cancellations.
SHIFT - Hospital to Community: using geospatial visualisation to improve utilisation of community-based outpatient clinics and Peripheral Sites, shifting appropriate patients away from the main hospital site.