Abstract
Despite their long history, it can still be difficult to embed clinical decision support into existing health information systems, particularly if they utilise machine learning and artificial intelligence models. Moreover, when such tools are made available to healthcare workers, it is important that the users can understand and visualise the reasons for the decision support predictions. Plausibility can be hard to achieve for complex pathways and models and perceived "black-box" functionality often leads to a lack of trust. Here, we describe and evaluate a data-driven framework which moderates some of these issues and demonstrate its applicability to the in-hospital management of community acquired pneumonia, an acute respiratory disease which is a leading cause of in-hospital mortality world-wide. We use the framework to develop and test a clinical decision support tool based on local guideline aligned management of the disease and show how it could be used to effectively prioritise patients using retrospective analysis. Furthermore, we show how this tool can be embedded into a prototype clinical system for disease management by integrating metrics and visualisations. This will assist decision makers to examine complex patient journeys, risk scores and predictions from embedded machine learning and artificial intelligence models. Our results show the potential of this approach for developing, testing and evaluating workflow based clinical decision support tools which include complex models and embedding them into clinical systems.