Michael has a background in pharmaceutical and healthcare research, working across industry and academia. Though a pharmacologist by background, a common theme throughout his career has been application of state-of-the-art computer science methods to real world problems. In the last 10 years this has focussed on clinical pathway simulation, geographical modelling (location of acute and emergency health services), and the application of machine learning to clinical pathway analysis and design.
Michael's current work focusses on emergency and acute stroke care, and how the best outcomes may be achieved. He is involved in projects modelling the pre-hospital and in-hospital emergency pathway. He applies geographical modelling, clinical pathway simulation, and machine learning. An example project is where we are working with the national stroke audit, using clinical pathway simulation and machine learning to compare the emergency stroke pathway, including differences in clinical decision-making, between hospitals.
Michael is passionate about open science, and use of free and open source software (FOSS) tools. His work is conducted using FOSS. In current work he is also exploring the use of synthetic data production to enable sharing of a synthetic data set along with the code we use.