The mission of the Health Data Science group is to produce clinically actionable insights from observational health data by enabling data-driven healthcare. Improved interoperability of data is a necessary pre-requisite for this mission.

Read More


We perform methodological research in clinical characterisation, population-level effect estimation, and prediction modelling. We develop open-source analytical tools that can be applied on the OMOP Common Data Model.

Read More


We believe more education for young health data scientists, medical students, and healthcare professional, is needed to train them in the opportunities and limitations of big data in healthcare.

Read More

Latest News

Monday, June 14, 2021

Important work, with contributions from our group, characterising the background incidence rates of adverse events of special interest (AESIs) in eight countries was published today in BMJ.

This large, international study found large variations across age groups, countries, and males/females in the observed rates of AESIs associated with covid-19 vaccines. This shows the need for stratification or standardisation before using background rates for covid-19 vaccin safety surveillance and suggests caution when interpreting the differences between observed and expected rates. 

Tuesday, October 20, 2020

We are happy to announce that we received the 2020 OHDSI Titan Award for Community Support. The Titan Award is a special recognition from the OHDSI community for important contributions towards OHDSI's mission. A big thanks from everyone in our team, we are looking forward to another year of exciting research and collaborations!

MI team titanaward

A special shout out to two students in our group who received Titan Awards for their amazing work: Anthony Sena (for Open-Source Development) and Clair Blacketer (for Data Standards). 

Congratulations to all!