Joao H Bettencourt-Silva  Joao H Bettencourt-Silva photo         

contact information

Research Scientist
Ireland Research Lab, Dublin, Ireland


Professional Associations

Professional Associations:  American Medical Informatics Association  |  British Computer Society  |  Health Informatics Society of Ireland  |  Royal Society of Medicine  |  UK Faculty of Clinical Informatics

more information

More information:  Google Scholar  |  Research Gate



Joao is a Research Scientist in the Health and Social care research group at IBM Research. 

He specialises in Healthcare Informatics and is focused on researching personalised and person-centric systems to support the management and delivery of care.

Prior to joining IBM Joao was a Postdoctoral Research Fellow in Clinical Informatics in Cambridge, UK where he still maintains a honorary fellowship to collaborate in research projects using Electronic Medical Records for research and decision support. Joao has over 10 years experience working in the UK National Health Service in various capacities, from data analysis and management to clinical research.

His doctoral research project focused on the development of novel computational methods and techniques to reuse routine clinical data from multiple sources for research, service improvement and decision support. Joao has designed and delivered clinical data warehouses, data models and software to extract, organise, synthesise and analyse complex clinical information, and carried out various analyses and clinical research projects using linked health data. He has experience in electronic medical record (EMR) implementations in the NHS and developed methodologies, protocols and electronic infrastructures to facilitate the interface between computers, clinicians and researchers. In particular, Joao has researched methods for modelling and visualising the journeys that patients take through care based on multiple heterogeneous data sources.

His research interests include clinical decision support systems, the reuse of routinely collected data for research and service improvement, visualisation of complex health information, medical data and process mining and machine learning applications in health.