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Introduction Healthcare systems aim for continuous improvement to ensure excellent clinical outcomes, including optimized quality of life. This relies on accurate data and evaluation patient‐centered as well as biomedical outcomes. This data increasingly arises from real‐world data (RWD). We are using this data across a multi‐national European consortium, to create a Learning Healthcare System, improve care, policy, and ultimately patients’ outcomes. Methods STRONG‐AYA is an interdisciplinary community, led from the Netherlands Cancer Institute, working across frequent consensus meetings, surveys, questionnaire design, data collection, synthesis and privacy‐preserving federated analysis (in Vantage6), to ethically study and apply clinical and patient‐centered outcomes data to learn about healthcare systems for Adolescents and Young Adults (AYAs) diagnosed with cancer when aged 15–39. The young people themselves are at the heart of our decision‐making, and we work to make our data analyses transparent. Results A harmonized ‘Core Outcome Set’ was agreed with all stakeholders. We developed definitions and data science methodological approaches, aligned with Learning Healthcare System practices. We have built AYA cancer data ecosystems, at local, national and European levels, to combine informatics with lived experience, clinical and scientific interpretation, based upon initial ‘use cases’ of the mental health consequences and cancer survival rates. We are applying those insights in real time individual clinical care, individual patient education and support as well as service level metrics. Conclusions The Strong‐AYA Learning Healthcare System for AYA‐onset cancer can empower discovery by health researchers, digital innovation, and innovative insights for patients, clinicians, managers and policymakers who aim for transformational improvements in the quality and outcomes of current clinical practice though the development of a Learning Healthcare System in AYA‐onset cancer care. Sustainability is our next major challenge.
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Stark et al. (2026) studied this question.
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