20 May 2021 (Online)

Andreas Soteriades & Milan Wiedemann, Data Scientists, Nottinghamshire Healthcare NHS Foundation Trust

Many healthcare providers within the National Health Service (NHS) routinely collect patient feedback to evaluate service quality from a patient’s perspective. It is common to ask patients free text questions to describe ‘what was good’ and ‘what could be improved’. Text Mining (e.g. Text Classification and Sentiment Analysis) coupled with interactive dashboards can help process large volumes of feedback efficiently and convert them into actionable insights. This also allows us to monitor the effectiveness of any changes in service feedback over time. We are currently developing such a tool for the Nottinghamshire Healthcare NHS Foundation Trust, hoping that it will be adapted across the NHS in the future.

During our presentation we will describe two key ideas that we try to follow in our work: (i) working openly by open-sourcing the analysis code and data where possible to promote replication, reproducibility, and further developments; and (ii) automating common steps in our workflow by developing R/Python packages to increase usability and documentation. To showcase these techniques, we will demonstrate how we implement these ideas by focusing on two parts of our project: (i) working with databases in R; and (ii) developing dashboards with Shiny and Golem.

In cooperation with the Jean Golding Institute, University of Bristol

More information: the Bristol Data Science Seminar Series