The inability of patients to perform daily tasks after joint replacement remains a significant challenge as well as a burden on health systems because these poor results often require additional treatment (e.g. rehabilitation) and re-replacement. This challenge can be addressed by surgeons using individual patient characteristics to personalize how they perform joint replacement surgery. However, many surgeons perform too few procedures to effectively personalize their plans and thus technologies are needed to provide assistance.
The goal of this research is to develop an improved understanding of how patient specific factors affect the results of joint replacement as well as to develop technologies that can collect data about each patient's individual characteristics and use these data to assist surgeons in optimally planning each surgery. This will be achieved by a combination of computer-based biomechanical research, statistical modelling, and novel sensor development. This work will improve our understanding of personalized joint replacement, yield new clinical technologies, enable surgeons to more effectively personalize surgery, result in improved patient function, and improve the health systems in BC and beyond.