The challenge we’re solving

Both hip and knee osteoarthritis and high primary care use are major contributors to system overload and poor patient experiences. These groups often face fragmented care, delayed interventions, and lack of personalized treatment.

We need better ways to:

  • Predict disease progression and healthcare use.
  • Personalize care strategies.
  • Intervene earlier and more effectively.

We are developing an AI-based Digital Twin (DT) – a digital replica of the patient – to support early intervention and more precise care decisions for two high-need groups:

  • People living with hip/knee osteoarthritis (HKOA)
  • High users (HU) of primary care services

The DT will be co-designed with clinicians, built using machine learning models trained on real-world health data.

Project partners

    

Project goals

For hip/knee osteoarthritis:

  • Optimise treatment strategies based on predicted response.
  • Model disease progression over time.
  • Enable more personalised, effective care.

For primary care high users:

  • Identify usage patterns and risk profiles.
  • Predict future care needs and escalation.
  • Improve early intervention strategies to reduce system strain.

Cross-cutting goals:

  • Develop and validate a Digital Twin prototype for prevention, early intervention, and long-term monitoring.
  • Integrate the tool into clinical workflows for real-world usability.
  • Create a demonstration platform to engage and train clinicians.
  • Evaluate clinical impact and scalability, producing recommendations for wider adoption.

Our role

  • Map clinical workflows and identify unmet needs in the care pathways of primary care high users.
  • Coordinate project management and ensure collaboration across all partners.
  • Lead communication and dissemination activities to share findings with healthcare professionals, stakeholders, and the wider community.

Lead researcher at VOH

Federico Guede, PhD

Head of AI & Data Science

The impact we hope to create

  • A validated Digital Twin platform for managing osteoarthritis and frequent primary care use.
  • Improved ability to predict needs and personalize care.
  • Enhanced support for clinicians through interactive decision tools.
  • Practical evidence to support national adoption of Digital Twin technology in chronic disease care.