A Preliminary Automated Approach to Assess Hospital Patient Feedback

Hospitals gather huge amounts of valuable data every day, from clinical studies to patient satisfaction surveys, whose assessment is often carried out either manually or via limited automated approaches. However, improving analysis of hospital data can contribute to a more informed decision-making process with a positive impact on patients. In particular, analysing patients’ feedback can identify potential events, procedures and infrastructure issues, which might potentially affect hospital users’ well-being and medical treatments. This paper discusses an initial implementation of some components of a data analytics tool, which combines a variety of techniques based on text mining and mathematical modelling. It actively assesses real-time data extracted from patients’ feedback, social media and other suitable data created within a hospital setting, allowing immediate action to be taken. This will provide a system, which actively learns positive actions and outcomes whilst discovering actionable information to facilitate the decision-making process, enhance the patient’s journey, and potentially optimise treatment outcomes.