AI Digital Twin to Reduce Low-Value Care in HPV Vaccination Programmes

We seek research and innovation partners to co-develop an AI Digital Twin focused on identifying and reducing low-value care in vaccination programmes, supporting more effective, targeted, and evidence-based prevention strategies within health and care systems.

The proposed Digital Twin will use artificial intelligence and real-world immunisation data to model vaccination coverage, analyse system-level inefficiencies, and assess how different policy and programme choices affect prevention outcomes. Vaccination itself is an established, high-value public health intervention; the work focuses exclusively on improving how vaccination programmes are designed, organised, targeted, implemented, and evaluated within health systems, ensuring that resources are directed where they generate the most significant public health value.

Low-value care in this context refers to ineffective, poorly targeted, or data-blind prevention activities, such as uniform strategies that fail to address coverage gaps, fragmented data use that does not inform decisions, or interventions that do not measurably improve outcomes. The AI Digital Twin provides a decision-support environment enabling public health authorities to distinguish between high-value and low-value prevention actions and to optimise vaccination strategies accordingly.

The concept builds on extensive experience in analysing Electronic Vaccination Registers (EVRs) and vaccination data flows across countries, using recognised interoperability standards (e.g. HL7/FHIR). By transforming fragmented vaccination data into dynamic and explainable models, the Digital Twin supports transparent, reproducible, and value-based planning at the health system level.

While HPV vaccination may serve as an initial demonstrator, the approach is designed to be applicable across multiple vaccination programmes and health system contexts, in line with the objectives of reducing low-value care and improving system performance.

We are seeking collaboration with universities, research organisations, public health institutes, healthcare authorities, and SMEs with expertise in AI and data analytics, digital twins and simulation, vaccination systems, public health policy, and value-based healthcare.