Uncovering clinical research trends allows us to understand the direction of healthcare services and is essential for longer-term healthcare planning. The Hospital Authority Convention is a mainstream annual healthcare conference that gathers up-to-date Hong Kong medical research. We propose to use state-of-the-art medical document mining and topic modelling methods to uncover latent themes and structures in the publications. We collected 742 articles from HA Convention from the year 2013 to 2016 and selected 56 publications from the category of "Clinical Safety and Quality Service" for further analysis. Applying natural language processing and Latent Dirichlet Allocation (LDA) methods, we identified 7 potential topics, namely: surgical operation, hospital discharge, medical error, nursing procedure, service performance assessment, patient and staff engagement, and admission algorithm and standardisation. This exploratory study demonstrates that key themes exist in the annual HA Convention and we observe potential changes in healthcare services focus over the years in the selected category.
Keywords: Data Mining; Medical Informatics; Unsupervised Machine Learning.