Machine learning to identify possible outpatient DNAs

30 Second Summary

This project will train a machine learning model to predict outpatient DNAs (did not attend) and prioritise support, such as extra reminders and calls. It may also use discrete event simulation to show the impact on clinic utilisation and waiting times if DNA rates are reduced.

Machine Learning

Discrete Event Simulation (DES)

Non-attendance Prediction

Outpatients

This project will use Machine Learning approaches to identify possible outpatient DNAs

The project aims to train a machine learning model to identify patients with upcoming appointments who are most likely to DNA (did not attend), to prioritise additional support from outpatient booking team, e.g.

Extra text reminders
Phone call with opportunity to rebook
Phone call to ask if patient requires public transport information, travel plan, or support to get to appointment

As a possible extra, the project may use discrete event simulation (DES) to demonstrate impact on clinic utilisation and waiting times if DNA rates are reduced