About the INR
Key objectives of the INR project include:
- Create a sustainable, interoperable international data infrastructure for neurostimulation procedures and outcomes, compliant with European Health Data Space (EHDS) and General Data Protection Regulation (GDPR) standards.
- Facilitate cross-border data sharing and secondary data use for research, regulatory evaluation, and health system improvement.
- Facilitate cross-border data sharing and secondary data use for research, regulatory evaluation, and health system improvement.
- Generate RWE to support clinical guidelines, personalized treatment approaches, and innovation in neurostimulation devices.
- Establish a governance framework ensuring data security, patient consent, and ethical oversight.
- Contribute to the EHDS by aligning data models, ontologies, and metadata with EU interoperability standards.
Primary Endpoints
The INR project is designed to support clinicians, researchers, regulators, and industry stakeholders in improving care quality, informing policy, and driving innovation by enabling the secondary use of comprehensive longitudinal real-world data. As such, the aim is to facilitate RWE generation insights across neuromodulation therapy domains including:
- Long-term clinical outcomes: Aggregated and center-level analyses of pain scores, functional status, quality of life, and device performance over time.
- Patient-reported outcome measures (PROMs): Standardized assessments to evaluate the effectiveness and impact of neuromodulation therapies from the patient’s perspective.
- Safety monitoring: Tracking of adverse events and device-related complications across therapy types and patient cohorts.
- Device utilization metrics: Patterns of device selection, programming parameters, revisions, and explants by indication and region.
- Health economic indicators: Data to support analyses of cost-effectiveness, care pathways, and resource utilization.
- Benchmarking: Anonymous inter-center comparisons to enable clinical performance reviews and quality improvement.
- Federated analytics and AI applications: Privacy-preserving modelling to support predictive algorithms, subgroup stratification, and personalized therapy insights.