Currently, most safety problems are detected when health professionals report incidents. Since they are not expert in technology, many software problems either go undetected, or are detected only after an adverse event. Moreover, IT systems are made up of multiple disparate components which interact to produce new emergent behaviours that only become evident after they are deployed in the real world. Based on syndromic surveillance methods used for the early detection of disease outbreaks, our research monitors IT systems in real timeto detect early any software and user generated errors in clinical information that might lead to an adverse event. The goal is to develop a surveillance framework for the early detection of e-health-related adverse events, so as to minimise risks to patient safety. Specifically, the research aims to develop:

• fault-detection methodologies to facilitate automated detection of e-health-related adverse events in real time
• intelligent predictive models to forecast potential future e-health-related adverse events.