The Patient Global Impression of Change (PGIC) is a critical metric for evaluating perceived improvement in patients’ health and well-being during treatment. This thesis aims to optimise PGIC outcomes by leveraging advanced data analysis and predictive modelling techniques to identify and address key drivers of change. The project will focus on understanding the interplay between various factors, such as communication triggers, frequency and content of interactions, member behaviours, and the type and frequency of prescribed workouts. By extracting actionable insights from these variables, the study seeks to optimise internal operations resulting in an enhanced patient experience and perceived value.
The project will include the development of a computational solution from scratch, such as a predictive time-series model, capable of identifying at-risk members and enabling active outreach to support their progress. This solution will build upon existing codebases and a substantial Sword proprietary dataset and will involve extending Sword’s capabilities to address the unique challenges posed by real-world healthcare applications. ...