$1.5 million project aiming to deliver ‘precision rehabilitation’
Thursday, 6 December 2018
A new decision-making tool being developed in a major research project at the University of Newcastle (UON) and Hunter Medical Research Institute is set to give patients better and more personalised rehabilitation interventions after knee replacement surgery.
More than 50,000 knee replacement procedures are performed annually in Australia, a rise of around 36% since 2006, and with an ageing population this is set to rise further
“With a growing number of people requiring joint replacement surgery, there’s a significant need to have more tailored and precision-based rehabilitation interventions, just as we have for other areas in medicine like cancer and asthma,” lead researcher Professor Michael Nilsson, Director of the UON’s Centre for Rehab Innovation and Global Innovation Chair of Rehabilitation Medicine, says.
“We’ve often taken a generic approach previously, despite the fact that individuals have different capacity for recovery. Quite often it’s just not the medical situation but life circumstances that determine the success of a rehabilitation program.
“Some patients need to stay in hospital, others can get by with support in the community or home setting, and we need to work at all levels to ensure everyone is receiving the most appropriate treatment for their recovery.
“Clinical evaluation is happening at the coalface, of course, but time constraints and other factors may prohibit a comprehensive understanding of an individual’s life situation. Our algorithm will add substantial value and support to medical teams in their decision-making.”
The three-year study is being funded by the Ramsay Hospital Research Foundation and will involve a multidisciplinary team of clinicians, engineers and IT developers who have begun designing and refining the new precision medicine tool for clinical stratification and decision-making.
The technology will be scalable and adaptable for a range of conditions, not just knee replacement, the goal being to develop a clearer picture for rehabilitation intervention based on multiple parameters.
Uniquely, the project will go beyond specified clinical information to include other determinants of health such as carer or family support, mental health factors like stress or depression, comorbidities, financial hardship and so on.
The University of Newcastle’s Senior Deputy Vice Chancellor and Vice President - Global Engagement and Partnerships, Professor Kevin Hall, said state-of-the-art rehabilitative care required collaboration across multiple disciplines and innovative approaches.
“This project will leverage off the strengths of the University, HMRI and our connections with industry. Together, we are developing a tangible intervention that has the potential to improve the way thousands of people recover from knee surgery each year,” Professor Hall said.
During the research phase, around 1000 patients will be followed at three Ramsay Health Care hospitals – Lake Macquarie Private, North Shore Private and Kareena Private, with the option to expand and include other Ramsay hospitals in the future.
Ramsay Hospital Research Foundation CEO Nicola Ware said Ramsay was keen to provide their patients with the best chance at recovering from what can be major surgery with life long impacts.
“This research is the first step toward providing an evidence-based, structured clinical program that will eventually support the needs of all patients undergoing joint replacement surgery in a Ramsay Hospital facilities,” Ms Ware said. “It will ultimately improve patient outcomes by providing better support and care that is structured to meet each patients need.”
The research team will collect and feed the patient information into an algorithm being developed at the UON’s School of Engineering, led by Professor Sarah Johnson and A/Professor Adrian Wills. Professor Nilsson is working with clinician Dr Michael Pollack and neuroscientist A/Professor Rohan Walker on clinical aspects.
The algorithm’s self-learning capabilities are expected to improve exponentially as more data is entered.