Researchers harness data science to improve care for children with Cerebral Palsy
QMU researchers are using data science to help improve care for children and young people with cerebral palsy across Scotland. By analysing thousands of health assessments collected over many years, the team aims to better understand how a child's mobility and physical condition may change over time, helping clinicians provide more personalised care and giving families a clearer picture of what to expect in the future.
Led by Dr Kavi Jagadamma, Senior Lecturer in Physiotherapy, the project is using advanced data science techniques to analyse information from the Cerebral Palsy Integrated Pathway Scotland (CPIPS) programme. The unique database contains more than 20,000 assessments from over 3,000 children and young people with cerebral palsy, providing researchers with valuable insights that could support earlier interventions, improve clinical decision-making and enhance outcomes for people with Cerebral Palsy.
The CPIPS programme is a standardised assessment programme for all children and young people with cerebral palsy in Scotland and covers more than 95% of the population aged 2–18. Funded by the Chief Scientist Office, the QMU research team is exploring a range of clinically relevant questions about how joint mobility changes over time.
Through close collaboration with NHS clinicians, researchers are applying sophisticated data science methods to identify patterns and trends that can support healthcare professionals in planning treatment and care. The findings will also help children and young people with cerebral palsy, and their families, better understand how their condition may develop in years to come.
This project is a very good example of applying data science methods to address important questions and issues in healthcare. By analysing long-term data from children and young people with cerebral palsy, we can generate evidence that supports clinicians and helps families make informed decisions about the future.
One of the most exciting aspects of this project is that it allows us to learn from thousands of routine clinical assessments collected over many years. By applying advanced data science methods and statistical models to these data, we can identify patterns in how mobility changes over time in children and young people with cerebral palsy. These patterns help clinicians make more informed decisions and give families a better understanding of what they might expect as their child grows. While we cannot predict an individual child's future with certainty, these models provide robust evidence to support more personalised care and better-informed decision-making.
The project demonstrates how Queen Margaret University Edinburgh is building on its established strengths in health research while harnessing innovative technologies to address real-world healthcare challenges.
For further media information contact Lynne Russell, Communications Manager, Queen Margaret University, Edinburgh, E: lrussell@qmu.ac.uk (copy to E: pressoffice@qmu.ac.uk).
