Dubai Executive Think Tank

From Data Foundations to Advanced Therapies: A Global Dialogue on Trust, Quality and Real-World Impact

Part 1 — Setting the Foundations: Trust, Data Quality and Real-World Evidence

Curated By Elena Petelos, Public Health Specialist and HTA Expert & Paul Nunesdea, PhD, CPF Health Data Forum

The opening morning session of the Dubai Executive Think Tank set the intellectual and strategic foundations for the day's dialogue, bringing together global leaders across data governance, advanced therapies, regulatory science, and health system innovation. The session featured contributions from Prof. Dipak Kalra, President of i-HD, exploring data quality and bias in real-world evidence; Javier Quilez Oliete, Associate Director of Bioinformatics at M42 Health, addressing the operational realities of genomic and clinical data integration; Prof. Elena Petelos from EUPHA and HTAi, offering perspectives on evidence generation and health technology assessment; Emmanuel Bacry from the French Health Data Hub, presenting trusted research environments as catalysts for secure innovation; Hannah Crocker, Advanced Therapies Outcomes Manager at NHS Wales Joint Commissioning, highlighting outcomes-based evaluation of advanced therapies; Dr John Lewis, Head of Programmes at Advanced Therapies Wales, discussing national implementation frameworks; and Panos Kefalas from the Cell & Gene Therapy Catapult, outlining the evolution of the UK's advanced therapy ecosystem. Together, these voices framed the critical message that healthcare transformation must begin with robust data governance, trustworthy infrastructures, and learning health systems capable of sustaining innovation at scale.

1. A Think Tank Built on Urgency and Opportunity

The Dubai Executive Think Tank brought together an exceptional group of global leaders across health data governance, AI, genomics, advanced therapies, and health system transformation. Hosted within the wider Health Data Forum ecosystem, the session reflected a growing global consensus:

Healthcare innovation is no longer constrained by technology — it is constrained by data readiness, trust frameworks, and system capability.

Across multiple sessions and interdisciplinary exchanges, participants explored how data infrastructures, regulatory innovation, AI governance, and real-world evidence are converging to reshape clinical research, public health policy, and therapeutic delivery.

The discussion moved far beyond theoretical reflection. Instead, it addressed the operational realities of implementing data-driven healthcare transformation across different regions, regulatory environments, and patient populations.

2. Data First, AI Later — A Principle Gaining Global Consensus

A recurring theme throughout the Think Tank was the reaffirmation of a core strategic doctrine:

AI is only as reliable as the data that feeds it.

Speakers repeatedly emphasized that the global healthcare community risks overestimating AI's transformative potential while underinvesting in the foundational infrastructure required to support it.

The Health Data Hub experience in France, presented during the session, demonstrated what happens when a national strategy explicitly prioritizes data governance, interoperability, and secure research environments before scaling AI deployment.

The French SNDS claims database, covering approximately 67 million citizens, illustrates the power of longitudinal population-scale datasets linking healthcare utilization, hospital episodes, mortality data, and research registries. By complementing administrative claims with curated research databases, France has created one of the most comprehensive real-world evidence ecosystems in Europe.

Yet the key lesson was not technological — it was strategic:

• Centralized governance improves access and accountability

• Federated architectures preserve sovereignty while enabling research

• Data catalogues accelerate innovation

• Public trust determines long-term sustainability

3. Data Quality: The Invisible Determinant of AI Reliability

While data availability remains a challenge, the Think Tank highlighted a deeper, often-overlooked issue: data quality.

Professor Dipak Kalra provided a detailed examination of how health data is generated, transformed, and reused. His intervention reframed the discussion from data quantity toward data integrity.

Several critical insights emerged:

3.1 Healthcare Data Is Contextual

Clinical decisions often rely on contextual information that may not be captured in structured datasets. For example, home monitoring data or clinical reasoning may never enter formal EHR systems, leading to potential misinterpretation during secondary data use.

3.2 Mapping Between Data Models Introduces Risk

Transforming data from EHR formats into common data models (such as OMOP) inevitably results in information loss, semantic distortion, or the loss of clinical nuance.

3.3 Data Quality Is Organisational, Not Merely Technical

Improving data reliability requires:

• Workforce training

• Clinical workflow redesign

• Governance accountability

• Continuous quality feedback loops

The European Health Data Space (EHDS) data quality labelling initiative was highlighted as a major regulatory step toward standardized transparency in dataset fitness-for-purpose.

4. Trust as the Hardest and Most Strategic Challenge

If data quality represents the technical challenge, trust represents the societal challenge.

Participants agreed that trust cannot be manufactured through communication campaigns alone. Instead, it must be built through:

• Transparent benefit-risk communication

• Citizen education on data use implications

• Honest acknowledgement of residual risk

• Demonstrable success cases with measurable public value

A particularly powerful insight emerged during the discussion:

Citizens should not simply be persuaded to share data — they should be empowered to make informed choices about sharing or withholding it.

Trust, therefore, becomes a dynamic and educational process rather than a one-time consent transaction.

5. The Globalisation of Health Data Collaboration

The Think Tank also examined emerging cross-border data collaborations, including pioneering partnerships between Europe and India.

These initiatives are exploring new secure environments allowing international research collaboration while preserving privacy frameworks and national regulatory compliance.

Two potential future models were debated:

5.1 Federated Data Collaboration

Data remains within national boundaries while algorithms travel between environments.

5.2 Validation Ecosystems

Algorithms developed in one region are tested against datasets in multiple geographies without exporting patient data.

Both models reflect an emerging understanding that:

Precision medicine requires global learning networks because no single country has sufficient data diversity.

6. National Genomics as a Real-World Evidence Engine

The UAE national genomics programme presented a powerful example of translating population genomics into clinical implementation.

Within five years, the programme sequenced approximately 80% of the national population target and integrated genomic data with national health information exchanges.

Three major use cases illustrate its impact:

Retinal Disease Stratification

Population-scale genomic analysis enabled reclassification of disease-associated variants and improved risk prediction models for underserved ethnic populations.

Breast Cancer Risk Prediction

Polygenic risk scores validated within Emirati populations have enabled earlier disease detection and improved screening strategies.

Pharmacogenomics

Genomic screening is enabling personalized drug dosing, reducing adverse events, and improving treatment efficacy across cardiovascular therapies.

These projects illustrate how genomics, when linked with longitudinal real-world data, becomes a national public health asset rather than a purely research tool.

7. Advanced Therapies and the Evidence Paradox

The Think Tank devoted significant attention to Advanced Therapy Medicinal Products (ATMPs), including gene therapies, cell therapies, and regenerative medicine.

ATMPs present a fundamental paradox:

• They offer potentially curative outcomes

• They launch with extremely limited evidence

• They carry unprecedented upfront costs

This creates a new paradigm in health technology assessment and reimbursement.

8. Real-World Evidence as the Bridge Between Innovation and Sustainability

Speakers from the UK Advanced Therapy ecosystem demonstrated how real-world data is becoming essential for reimbursement and regulatory approval.

Key strategies include:

• External comparator arms for single-arm trials

• Long-term post-launch patient registries

• Outcomes-based reimbursement models

• National digital platforms linking clinical, economic, and patient-reported outcomes

Examples such as CAR-T therapies and gene therapies for rare diseases highlight how reimbursement decisions increasingly depend on long-term observational evidence.

9. Learning Healthcare Systems: From Product Approval to Lifelong Evidence

A powerful conceptual shift emerged from the discussions:

Healthcare innovation must transition from static approval models to continuous learning models.

ATMPs demonstrate that therapies must be evaluated across their entire lifecycle, including:

• Long-term safety monitoring

• Real-world effectiveness

• Economic sustainability

• Patient-reported quality of life

This approach redefines evidence generation as a continuous system rather than a pre-market requirement.

10. Wales as a National Innovation Sandbox

The Welsh national advanced therapies programme showcased how smaller health systems can act as innovation laboratories.

Through coordinated national commissioning, integrated registries, and real-world data dashboards, Wales is developing scalable frameworks for evaluating high-cost therapies while maintaining equity of access.

This model demonstrates that governance coordination can often be more important than population size in delivering data-driven healthcare transformation.

11. The Human Factor: Clinicians, Patients and Behavioural Adoption

One of the most practical insights from the Think Tank concerned clinical data generation itself.

Healthcare systems frequently assume clinicians will improve data quality simply through regulation or financial incentives. However, speakers emphasized that adoption depends on real-time clinical value.

Data capture improves when:

• Clinical decision support returns immediate benefit

• Workflows become easier, not more complex

• Digital systems reduce administrative burden

Similarly, patient participation in data sharing is strongly influenced by perceived personal benefit and transparency of outcomes.

12. The Emerging Global Consensus

Despite regional differences, the Think Tank revealed remarkable convergence across stakeholders:

1. Data infrastructure is now core healthcare infrastructure

2. Real-world evidence is replacing trial-centric regulatory thinking

3. Trust and transparency are central to system legitimacy

4. Advanced therapies are reshaping reimbursement models

5. Continuous learning health systems are inevitable

13. Looking Forward: The Next Phase of Global Collaboration

The Dubai Executive Think Tank reinforced the strategic direction of the Health Data Forum and the broader Data First, AI Later movement.

The next phase will likely focus on:

• Global data validation ecosystems

• Cross-border AI governance frameworks

• Multi-country real-world evidence registries

• Citizen-centred data trust models

• Federated clinical research infrastructures

Conclusion: A Turning Point for Healthcare Transformation

The Dubai Executive Think Tank demonstrated that healthcare is entering a structural transition comparable to the digital transformation seen in finance and telecommunications.

Advanced therapies, AI, and genomics are not isolated innovations. They are catalysts that force healthcare systems to redesign their data, regulatory, and evidence-generation frameworks.

The central message emerging from the dialogue was both simple and profound:

The future of medicine will not be determined by scientific discovery alone, but by our collective ability to learn continuously from data — safely, transparently, and globally.

As the morning session concluded, the Think Tank dialogue evolved from foundational reflections on data quality, governance, and real-world evidence toward a forward-looking exploration of AI readiness, implementation maturity, and global health equity. 

The afternoon programme expanded the strategic horizon through keynote contributions from George Mathew, Chair of the Advisory Board of the Health Data Forum, and Dr Rajendra Pratap Gupta, President of Health Parliament, who examined AI maturity frameworks and systemic transformation pathways. Further practical implementation perspectives were introduced by Miguel Amador, CEO and Founder of Complear, alongside the Think Tank roundtable hosts Dr Sara Rogers, Co-Founder and President of the American Society of Pharmacovigilance, Keith Kennerly, Founder and CEO of PayRx Inc, and Dr Padmavathi (Padma) Roy, Chief Data and Operations Officer of the Health Data Forum. 

The day culminated with a closing keynote from Guru Kora of Times Health, who addressed the critical intersection between artificial intelligence, accessibility, and health equity — themes that would anchor the collaborative co-creation session and shape the next phase of the Think Tank dialogue, explored in Part 2.