In every corner of the world, healthcare is facing profound challenges—and enormous opportunities. The population is ageing, data is multiplying, chronic conditions are on the rise, and care is increasingly delivered across borders, both physical and digital. In this complex landscape, one thing is clear: data needs to flow more intelligently, more safely, and more meaningfully. Regulatory drivers like the European Health Data Space (EHDS) is designed to create a unified digital health data infrastructure across the EU. It has two key visions: Primary Use of Health Data – This allows patients to securely access and share their health records across providers and borders, ensuring better healthcare continuity. Secondary Use of Health Data – This enables the re-use of health data for research, innovation, policymaking, and regulation, helping drive medical advancements and public health improvements. But what does that look like in practice? It means a traveller receiving care abroad can immediately share their allergies or recent scans with a local doctor. It means a parent can adjust consent settings for their child’s anonymised data to support rare disease research. It means a clinician gets an alert—just in time—that their patient was discharged from a hospital 200 miles away. It means multilingual AI tools helping citizens understand their prescriptions or diagnosis, regardless of where they live. It means researchers collaborating across jurisdictions—without moving a single row of patient data. This vision is no longer out of reach. It’s taking shape today, powered by advances in interoperability, privacy-preserving AI, and synthetic data. But discovery is still needed—across systems, disciplines, and borders.   What’s possible: Five real-world discovery scenarios Below are five areas where forward-thinkers across healthcare and technology are already exploring new ground. These are not hypotheticals—they’re challenges being worked on right now, inspired by evolving global policies, technologies, and the shared goal of better health for all.   A cross-border patient data wallet Challenge: Design a prototype for a mobile or web-based “Patient Data Wallet” that lets citizens carry and share their health records—securely and selectively—as they move across countries. Whether it’s for holiday emergencies or long-term care abroad, this could radically simplify access and improve continuity of care. Key questions for discovery: How can we ensure multilingual usability? How do we maintain data integrity between systems? Can we simulate real handovers between different national health platforms?   Consent management for secondary data use Challenge: Create a dashboard that gives patients clear control over how their anonymized data is reused—for example, in public health research, AI model development, or policy insights. Key questions for discovery: How do we make consent options understandable and accessible? How do we ensure GDPR-style auditability and anonymization? Can patients securely delegate authority to trusted carers?   Real-time care notifications across systems Challenge: Clinicians often work without the full picture—especially when care happens across disconnected hospitals, clinics, or countries. A system that alerts them in real time when relevant data (like lab results or discharge notes) becomes available could transform safety and outcomes. Key questions for discovery: How do we define what’s “relevant” for different clinical roles? Can real-time data flow across different EHR systems? How do we handle cross-border latency or failure points?   Federated learning for rare disease research Challenge: Traditional data sharing often hits a wall: privacy laws, institutional reluctance, or technical incompatibility. Federated learning offers a way forward—where models are trained locally, and insights are pooled centrally without exposing raw data. Key questions for discovery: How do we harmonise datasets with different formats and quality? What’s the right trade-off between model performance and privacy? Can we simulate compliance across jurisdictions?   AI assistants for health literacy (multilingual) Challenge: Even the most advanced digital health tools can fall short if patients don’t understand what they’re reading. An AI assistant that explains EHR entries—like diagnoses, procedures, or medications—in plain language and in multiple languages could unlock health empowerment at scale. Key questions for discovery: Can natural language processing tools translate clinical jargon clearly? How do we ensure cultural and linguistic sensitivity? What does explainability look like in this context?   A safe space for innovation: The power of synthetic data Real-world healthcare data is sensitive—and rightly protected. But that doesn’t mean innovation needs to pause. With tools like SAS Data Maker, realistic synthetic data is now available to simulate the complexity, messiness, and variability of genuine health records—without using any actual patient information. This creates a safe environment to: Prototype against real-world health formats and scenarios Train AI models without privacy risk Test consent flows, language adaptations, and system interoperability Model clinical alerts and patient pathways across borders Synthetic data removes the roadblocks to exploration—giving innovators the freedom to ask “what if?” without compromising ethics or compliance.   From ambition to action Whether you’re a developer building new interfaces, a clinician frustrated by fragmented systems, a data scientist focused on rare diseases, or a policymaker shaping digital trust—your work matters. We are at a rare inflection point where regulation, technology, and societal expectation are converging around a new model for health data. A model that is patient-driven, AI-enabled, multilingual, and cross-border by design. But bold ideas need to be tested. Prototypes need to be built. And new questions need to be asked by people with different expertise—from hospitals and startups, from tech labs and public health units, from academic researchers and patient advocacy groups.   Ready to explore? Join the SAS Hackathon If you’re ready to put these ideas to the test—collaboratively, creatively, and safely—join the SAS Hackathon. It’s your chance to prototype, experiment and build with other health innovators using SAS Viya, SAS Event Stream Processing, and SAS Data Maker. You’ll work with synthetic health data, simulate cross-border use cases, and gain exposure to real-world health challenges that span languages, regulations, and systems. Discover what's possible when health data is freed to move, protected by design, and put to work for patients everywhere.  ... View more