Creating informative experiences through a visual and interactive representation of health and social care data

Association rule mining is an established machine learning tool for finding patterns (‘rules’) in big datasets. The algorithm can easily produce a large number of ‘rules’ of how items in a dataset are related to one another. This can pose a significant challenge to the interpretability and usefulness of the results obtained. In this paper, we present how to support decision making through ‘playable mechanics’ powered by a video games engine. The premise is to create aesthetic and informative experiences through a visual and interactive representation of a problem space such as the association rules mined from a health and social care dataset. The Unity game engine is used to create a force-directed graph to be rendered and explored in real-time using the Barnes-Hut method to accelerate computations. A Boolean AND/OR/NOT selection function was implemented, enabling the graph to be explored and pruned to the data points specified by the search query. As a result, users can obtain an overview of the large-scale structure of the dataset, with the option of performing targeted explorations around points of interest. To evaluate the effectiveness of the application, a series of online user testing workshops were conducted. The resultant thematic analysis found the incorporated features to be well-integrated, but a difference was found between the responses of users with high or low technical proficiency within the commissioning organization. The technical users were able to quickly grasp the operation of the system but were unclear about its purpose or practical application. Conversely, the health and social care professionals saw the potential value of the tool but were unsure of their personal ability to use it effectively. Finally, System Usability Scale (SUS) scores were obtained from participants in a final in-person workshop, with excellent results overall (mean 84, top quartile).