The Privacy Paradox Facing Pan-European HEOR Teams
Here’s what I’ve noticed after working with dozens of pharma market access teams across Continental Europe: everyone needs robust real-world evidence for payer negotiations, budget impact models, and HTA submissions… but nobody can actually get their hands on the data fast enough.
And in the Pan-European privacy landscape? The situation’s even more challenging.
You’re trying to build a cost-effectiveness model for a new oncology therapy. You need longitudinal resource utilization data—hospital admissions, treatment patterns, DRG codes, outpatient costs. The kind of granular, population-level insights that actually tell you what’s happening in Continental European healthcare systems. But here’s the problem: that data is locked behind GDPR Article 9 restrictions, scattered across hundreds of statutory health insurers and national systems, and wrapped in compliance requirements that can delay access by 6-12 months.
Meanwhile, your launch timeline doesn’t wait. Payers want evidence. Your commercial team needs forecasts. And your HEOR models are sitting empty.
This is where synthetic healthcare data—specifically, aggregated insurance claims data generated to statistically mirror real Pan-European patient populations—changes everything for health economics simulation and outcomes research.
Why Traditional RWD Access Is Breaking Down in Continental Europe
Look, I’m not going to sugarcoat this. Continental Europe has some of the richest health insurance datasets in the world. We’re talking about comprehensive population coverage through statutory and social health insurance systems, detailed ICD-10 diagnosis coding, sophisticated procedure classifications, and DRG-based hospital claims that capture resource utilization down to average length of stay (ALOS).
But accessing this data for HEOR purposes? That’s where things get complicated.
Research on European claims data analyses highlights persistent challenges: fragmented data sources, inconsistent documentation standards across countries, and—most critically—privacy regulations that restrict secondary use of health information. Under GDPR, personal health data requires explicit consent for research purposes, data minimization protocols, and extensive risk assessments. National legislation across Continental Europe adds country-specific layers of complexity on top.
The result? HEOR teams face:
- 12+ month approval timelines for accessing national insurer claims databases
- Limited linkage capability across hospital, ambulatory, and pharmacy data
- Consent requirements that reduce sample sizes and introduce selection bias
- Institutional barriers that prevent cross-border collaboration or external sharing
- High costs for data acquisition, de-identification, and legal review
These aren’t just administrative headaches. They fundamentally limit your ability to generate the real-world evidence that payers, HTA bodies, and national reimbursement authorities demand.
And that’s before we even talk about rare diseases, where real-world patient populations might simply be too small for robust economic modeling regardless of access timelines.
Now here’s the thing: the challenge many pharmaceutical companies face isn’t finding data providers—it’s determining which combination of data capabilities, coverage breadth, and analytical approaches actually aligns with their strategic priorities. The landscape is fragmented, the trade-offs are subtle, and the cost of a mismatch between your HEOR requirements and your data source is significant. That’s where expert guidance matters more than most teams realize.
What Synthetic Data Actually Means for HEOR Analytics
So here’s where the conversation shifts. Because synthetic healthcare data isn’t some futuristic concept—it’s a practical solution that HEOR professionals are already starting to adopt for cost modeling, resource utilization forecasting, and budget impact analyses.
But let’s define what we’re actually talking about, because “synthetic data” gets thrown around loosely.
Synthetic healthcare data is artificially generated information that statistically replicates the distributions, correlations, and patterns found in real aggregated insurance claims—without containing any actual patient records. It’s created using advanced machine learning techniques (typically generative adversarial networks or GANs) that learn the underlying structure of real Pan-European claims data, then produce new patient cohorts that mirror those characteristics.
Think of it like this: if you analyzed 100,000 real diabetes patients in Continental European insurance data and mapped their treatment pathways, comorbidities, hospitalization rates, and costs… synthetic data generation would create a new dataset of 100,000 “patients” with statistically equivalent patterns. Same age distributions. Same Charlson comorbidity scores. Same DRG utilization profiles. Same average costs per episode.
But zero real individuals. Zero re-identification risk. And zero GDPR personal data constraints.
Recent research in npj Digital Medicine demonstrates that properly generated synthetic health data can preserve the statistical utility needed for economic modeling, outcomes research, and RWE generation—while fundamentally eliminating privacy concerns that slow down traditional analyses.
This isn’t about replacing real-world data. It’s about enabling the hypothesis testing, scenario modeling, and rapid iteration that HEOR teams need while navigating Continental Europe’s privacy-first regulatory environment.
How Pan-European Pharma Teams Are Actually Using Synthetic Claims Data
Let me walk you through what this looks like in practice, because the applications span almost every HEOR function.
Cost-of-Care Analysis and Burden of Disease Studies
Your market access team needs to quantify the economic burden of atrial fibrillation across Continental Europe for an upcoming payer presentation. You want to model direct medical costs (hospital, physician, medication), stratify by CHA₂DS₂-VASc risk scores, and show how treatment patterns differ across regions.
With traditional RWD access, you’d need to negotiate with insurers across multiple countries, wait months for ethical approvals, and likely settle for aggregate tables that don’t support sophisticated subgroup analyses. With enriched synthetic Pan-European hospital claims data, you can run the full analysis internally—modeling patient cohorts with realistic DRG assignments, procedure codes, and cost distributions—without touching a single real patient record.
The synthetic dataset mirrors real resource utilization patterns from statutory insurance claims, giving you ALOS benchmarks, readmission rates, and comorbidity profiles that match regional population estimates. You’re working with data that’s been validated against real Continental European utilization, but you can share it freely across your global HEOR team, iterate your cost models in days instead of months, and present findings without privacy constraints.
Budget Impact Models for Payer Negotiations
Budget impact modeling is where synthetic data really shines for pharma market access analytics. You’re launching a novel anticoagulant and need to show Continental European sickness funds exactly what happens to their annual budgets if 15%, 20%, or 25% of eligible patients switch from warfarin.
The challenge? Real claims data access is slow, and you need to model multiple “what-if” scenarios quickly. Payers want to see sensitivity analyses. They want age-stratified projections. They want to understand regional variations in prescribing patterns.
Synthetic data lets you build dynamic budget impact models using realistic Pan-European population distributions, baseline treatment costs from DRG databases, and resource utilization parameters that reflect actual care patterns—all while running dozens of scenarios without re-negotiating data access for each iteration. A 2023 narrative review in PLOS Digital Health shows how synthetic data enables this kind of flexible economic modeling while maintaining privacy compliance.
You can model the incremental cost per patient, multiply by realistic adoption curves, adjust for regional variations in specialist availability, and deliver results that inform pricing strategies… all within your internal HEOR team.
Resource Utilization Forecasting and Population Health Planning
Commercial analytics teams need to forecast resource utilization for portfolio planning and LOE impact analyses. If your competitor’s biologic loses exclusivity, how will treatment patterns shift? What does that mean for hospital admissions, infusion center capacity, and overall healthcare costs?
Synthetic datasets derived from Pan-European insurance claims enable population health cost modeling at scale. You can simulate patient flows through care pathways, estimate changes in DRG utilization as treatment landscapes shift, and forecast downstream resource needs—physician visits, imaging procedures, rehabilitation services.
The beauty here is that you’re not limited by the rare events problem that plagues real-world cohort identification. Need to model a subpopulation with three specific comorbidities receiving second-line therapy? Synthetic generation can create adequately sized cohorts for robust statistical analysis, even when real-world samples would be too small.
Augmenting Limited Real Data for Cost-Effectiveness Analyses
And here’s where things get really practical. Because the honest truth is that synthetic data works best when it complements—not replaces—real-world evidence databases.
You’ve secured access to a real Pan-European claims dataset for your cost-effectiveness analysis, but it’s limited to 2018-2020 data. You need to extend projections to 2025, account for recent changes in treatment guidelines, and model emerging care patterns that aren’t fully captured yet.
Synthetic data can augment your real dataset by generating forward-looking scenarios that maintain statistical consistency with your baseline evidence. Research in Scientific Reports demonstrates how synthetic data can preserve the predictive utility needed for clinical risk modeling while enabling privacy-safe data sharing and hypothesis testing.
This hybrid approach—real data for validation, synthetic data for exploration—is becoming standard practice among sophisticated HEOR teams navigating Continental Europe’s privacy-restrictive environment.
The Technical Reality: How Synthetic Data Actually Gets Generated
Now, I know what some of you are thinking. “This sounds great, but how do I know the synthetic data actually reflects real Continental European healthcare patterns?”
Fair question. Because garbage in, garbage out applies just as much to synthetic data as it does to any analytics.
The generation process typically works like this:
Step 1: Real Data Foundation
Start with authentic aggregated Pan-European insurance claims—ideally covering multiple years, diverse therapeutic areas, and representative populations. This is your statistical baseline. The algorithms learn the multivariate relationships: how age correlates with comorbidities, how diagnoses predict procedure patterns, how DRG assignments relate to length of stay.
Step 2: Privacy-Preserving Generation
Apply generative adversarial networks (GANs) or similar techniques with differential privacy guarantees to create new records. The models generate synthetic patients whose characteristics match the statistical distribution of real data—same age histograms, same disease prevalence, same cost distributions—without copying any actual individual.
Step 3: Validation Against Real-World Benchmarks
Here’s the critical part: synthetic datasets must be validated. Compare generated prevalence rates against national population statistics. Check DRG utilization against regional reporting. Verify cost distributions align with published Continental European health expenditure data. Generation and evaluation studies show that properly validated synthetic data can achieve statistical fidelity within 2-5% of real data distributions for key HEOR metrics.
Step 4: Enrichment for HEOR Use Cases
Finally, enrich the synthetic dataset with the variables HEOR teams actually need: treatment episodes, care pathways, longitudinal resource utilization, cost components stratified by payer perspective. Use case-driven approaches ensure the synthetic data supports specific analyses like burden-of-disease studies or economic simulations.
The result? Datasets that behave like real Pan-European claims data for modeling purposes but carry none of the privacy risks or access barriers.
Navigating the Data Landscape: What to Look For
Understanding that synthetic data is available is one thing. Knowing which data capabilities actually match your HEOR requirements is another challenge entirely—and it’s where most pharma teams struggle.
The data landscape for Continental European HEOR includes providers with meaningful variation across several key dimensions. Understanding these dimensions is essential before committing to any data partnership.
Coverage and population representativeness: Some data sources offer broad population coverage across multiple countries and payer types, giving you statistical power for common conditions. Others provide narrower but deeper coverage of specific therapeutic areas or care settings. The right choice depends entirely on your use case—a budget impact model for a rare neurological condition requires very different coverage than a cardiovascular disease burden study.
Data recency and update frequency: Real-world claims data ranges from near-real-time feeds to datasets with 12-18 month adjudication lags. For HEOR simulation and historical modeling, recency matters less than longitudinal depth. For commercial forecasting, recency can be critical. Most teams don’t map this dimension carefully enough before licensing.
Linkage capabilities: Some data sources support linkage across care settings—hospital, ambulatory, pharmacy, laboratory—creating richer longitudinal patient profiles. Others are siloed within a single care setting. The analytical questions you’re asking should drive your linkage requirements, not the other way around.
Analytical delivery models: Options range from self-service platforms where your data scientists query directly, to fully managed research services where the vendor delivers analyses. Understanding your team’s internal capabilities honestly is a prerequisite for making this choice well.
Compliance architecture: Particularly relevant in Continental Europe, where GDPR Article 9 restrictions vary in application across jurisdictions. Some data partnerships are structured as data processing agreements, others as licensed access to anonymized cohorts. The legal and compliance implications differ substantially.
The gap between understanding what these dimensions mean and translating them into a data sourcing decision for your specific HEOR programme is substantial. Most companies lack the internal expertise to navigate this efficiently—which is exactly where experienced advisors who understand both pharmaceutical evidence requirements and the evolving data vendor landscape can accelerate your path to value while avoiding costly mismatches.
Why Continental Europe’s Privacy Landscape Makes Synthetic Data Essential
Let’s talk about the regulatory elephant in the room. Because Continental Europe isn’t just following GDPR—many countries are implementing some of the strictest health data protection measures globally.
A 2025 regulatory perspective in npj Digital Medicine highlights how synthetic data intersects with evolving privacy frameworks, particularly in jurisdictions with heightened protection standards across Continental Europe.
Under GDPR Article 9, health data receives special category protection requiring explicit consent, legitimate processing grounds, and extensive safeguards. National legislation across Continental Europe adds country-specific requirements, including restrictions on cloud processing of health information and stringent re-identification risk assessments.
The practical impact? Even properly de-identified real claims data carries residual privacy risks that limit sharing, slow collaborative research, and increase legal costs. Data processors must conduct privacy impact assessments, maintain detailed processing records, and ensure any data transfers meet adequacy standards.
Synthetic data—when generated with appropriate differential privacy techniques—can fall outside these restrictions entirely if it doesn’t constitute “personal data” under GDPR’s definition. No identifiable individuals means no consent requirements, no cross-border transfer limitations, and no re-identification risk assessments.
This isn’t just theoretical. Regulatory initiatives across Continental Europe are explicitly exploring synthetic data as a mechanism to enable research access while protecting privacy. The regulatory momentum is moving toward recognizing synthetic data as a legitimate tool for HEOR analytics in privacy-first environments.
Practical Considerations for HEOR Teams Adopting Synthetic Data
But let’s be realistic about implementation. Adopting synthetic healthcare data for HEOR simulation isn’t just a technical decision—it’s a strategic shift in how your team generates evidence.
Here’s what actually matters:
Understand the Use Case Boundaries
Synthetic data excels at certain HEOR applications and has clear limitations in others. It’s excellent for hypothesis testing and model prototyping, scenario modeling and sensitivity analyses, rare disease cohort augmentation, resource utilization forecasting, budget impact model inputs, and training algorithms for RWE generation.
It’s less suitable for primary regulatory submissions requiring auditable real-world data, head-to-head comparative effectiveness where specific patient linkage is critical, and situations where payers explicitly require authentic claims evidence.
Think of synthetic data as an enabler for faster, safer exploration—not a replacement for gold-standard RWE when regulators or payers demand authentic population-level evidence.
Define Requirements Before Evaluating Data Sources
Before approaching any data provider—whether for real or synthetic claims data—document your requirements clearly. What coverage geographies and therapeutic areas do you need? What analytical capabilities does your team have internally versus what you need delivered? What are your compliance constraints, and what’s your realistic timeline?
Most companies overlook this requirements definition step, which leads to expensive data licensing agreements that don’t quite fit the actual use case. Expert consultants can help structure this process to ensure you’re comparing data sources on the dimensions that actually matter for your HEOR programme.
Validate Against Real-World Benchmarks
Any synthetic dataset you use for HEOR analyses must be validated against authoritative real-world sources. Compare prevalence estimates against published national health statistics, check cost distributions against health expenditure reports, and verify DRG utilization aligns with regional hospital data.
If your synthetic data shows diabetes prevalence of 15% when national statistics report 9.5%, you’ve got a quality problem. The generation process should produce datasets that pass statistical tests of representativeness for Continental European populations.
Communicate Transparently with Stakeholders
When presenting HEOR analyses based on synthetic data to payers, HTA bodies, or internal stakeholders, be upfront about your methodology. Explain that you’ve used privacy-preserving synthetic data generated from real Pan-European claims patterns, validated against population benchmarks.
In my experience, payers actually appreciate this transparency—especially when you can show that synthetic data enabled more comprehensive scenario modeling than would have been possible with restricted real data access.
Consider Hybrid Approaches
The most sophisticated HEOR teams are adopting hybrid strategies. Use real claims data (when accessible) for baseline validation and primary analyses. Use synthetic data to explore extended scenarios, test hypotheses, and fill gaps where real data is limited or unavailable.
This combination gives you the evidentiary credibility of real-world data plus the flexibility and speed of synthetic generation—and it’s the approach that tends to impress payers most during market access negotiations.
The ROI Case: When Synthetic Data Actually Saves Time and Money
Let’s talk about something that matters to every HEOR director: the business case. Because this isn’t just about compliance or methodology—it’s about whether synthetic data delivers actual value to your team’s productivity and your organization’s bottom line.
Traditional RWD Acquisition Timeline:
Identifying and negotiating with data sources takes 2-3 months. Legal review and contract execution adds another 1-2 months. Ethics committee approvals—where required—can add 2-4 months. Data delivery and quality validation: 1-2 months. Data engineering and preparation: 1-3 months.
Total time from decision to analysis-ready data: 7-14 months. Assuming everything goes smoothly.
Synthetic Data Approach:
Dataset specification and validation review: 1-2 weeks. Generation from validated Pan-European claims patterns: 3-5 days. Delivery as analysis-ready format: immediate. Team familiarization and first analyses: 1-2 weeks.
Total time: 3-4 weeks maximum.
The cost differential is equally significant. Real Pan-European claims data access can run €50,000-€200,000+ depending on population size, therapeutic area, and longitudinal depth required. Synthetic data generation typically costs substantially less while enabling unlimited internal sharing and scenario modeling without additional licensing fees per use.
But the real ROI isn’t the direct cost savings—it’s the strategic decisions you can make faster. Your commercial team needs a go/no-go on an indication expansion. With synthetic data, your HEOR group can model the budget impact, forecast resource utilization, and estimate cost-effectiveness within weeks—informing the decision while competitors are still waiting for ethics approvals.
That speed advantage is where synthetic data really pays for itself.
Building Internal Capabilities: Training Your HEOR Team
If you’re going to integrate synthetic healthcare data into your HEOR workflows, your team needs to understand not just what it is, but when to use it and how to communicate about it effectively.
I’ve seen too many pharma organizations acquire synthetic data capabilities without proper preparation, leading to misapplication or stakeholder confusion. Good implementation looks like this:
Core Competency 1: Statistical Fidelity vs. Individual Accuracy
Your analysts must grasp that synthetic data replicates population-level distributions—not individual patient trajectories. It’s perfect for modeling aggregate costs, prevalence rates, and treatment patterns. It’s inappropriate for case-level attribution or individual outcome prediction.
Core Competency 2: Validation Literacy
Team members should know how to assess synthetic data quality independently: comparing prevalence against national benchmarks, checking cost distributions against published health expenditure data, validating DRG utilization against regional hospital statistics. If your team can’t validate synthetic data quality themselves, they shouldn’t be using it for decision-making.
Core Competency 3: Stakeholder Communication
Your HEOR professionals must know how to explain synthetic data methodology to payers, HTA bodies, and internal stakeholders—neither overselling nor underselling its capabilities. The framing matters enormously for maintaining credibility.
Most teams need 2-3 focused training sessions (4-6 hours total) covering methodology, validation techniques, and appropriate use cases. Build this into your adoption plan from the start.
Synthetic Data for Therapeutic Area-Specific HEOR Challenges
Different therapeutic areas face unique HEOR challenges where synthetic data provides distinct advantages. Let me walk through three scenarios that come up repeatedly.
Oncology: Modeling Treatment Sequences and Total Cost of Care
Oncology HEOR is notoriously complex. You’re not just modeling a single intervention—you’re mapping treatment sequences across multiple lines of therapy, accounting for diagnostic staging, imaging frequency, supportive care, and adverse event management.
Real Pan-European claims data for rare cancer subtypes might give you 50-100 patients. That’s not enough for robust economic modeling of sequential treatment patterns, especially when you need to stratify by biomarker status or performance scores.
Synthetic data can augment your small real-world cohort, generating additional patients with statistically equivalent characteristics—same stage distributions, same treatment sequences, same resource utilization patterns. You’re not inventing outcomes; you’re creating additional observations from the same statistical model your real data suggests. This enables more reliable budget impact projections for novel oncology therapies, where payers need to understand total cost of care across multi-year treatment journeys.
Rare Diseases: Creating Feasible Economic Models from Minimal Real Data
For ultra-rare conditions with limited diagnosed populations across Continental Europe, traditional health economics faces a fundamental problem: your real-world evidence base is too small for statistical power.
Synthetic data generation offers a methodologically sound approach to expanding your analytical cohort while maintaining the statistical properties of your limited real-world sample. Think of it as statistical bootstrapping at scale—creating additional observations that reflect the variability and uncertainty in your small sample, enabling budget impact analyses that would otherwise be impossible.
Just be transparent with payers that you’ve used augmentation techniques due to legitimate data scarcity. In my experience, HTA bodies understand this challenge and appreciate rigorous methodology over inadequate analysis of genuinely tiny samples.
Diabetes and Cardiovascular Disease: Long-Term Complication Modeling
For chronic conditions, HEOR teams need to model decades-long disease progression and downstream complications. Real claims data provides historical patterns, but forecasting future costs requires assumptions about changing treatment landscapes, evolving guidelines, and emerging therapeutic options.
Synthetic data enables scenario modeling that extends beyond your historical claims period. You can simulate: What if GLP-1 adoption reaches 40% instead of 25%? What if SGLT2 inhibitors reduce hospitalization rates by an additional 15%? What if generic competition cuts treatment costs by 60%? These forward-looking simulations inform long-term budget impact models and cost-effectiveness analyses for novel therapeutics entering mature markets.
Addressing the Skeptics: Common Objections
Look, I’d be concerned if you weren’t at least somewhat skeptical. Synthetic data is a relatively new tool in pharma HEOR, and healthy skepticism is warranted.
“Payers won’t accept synthetic data as evidence.”
You’re right—for primary evidence in reimbursement dossiers. But you’re thinking too narrowly about the use case. Payers don’t see your internal analyses, your hypothesis testing, or your scenario modeling. They see your final budget impact presentation, which should be based on validated real-world data whenever possible. Synthetic data enables all the exploratory work that gets you to that evidence-based presentation faster and more thoroughly.
“How do I know synthetic data quality is sufficient for economic modeling?”
Validation is everything. Reputable data sources should demonstrate that generated datasets match published regional health statistics within acceptable tolerances—typically ±5% for key HEOR metrics. Validation reports comparing synthetic prevalence, treatment patterns, and costs against national statistics are non-negotiable. If that documentation doesn’t exist, walk away.
“What about rare diseases where real-world samples are tiny?”
This is actually where synthetic data provides the most value. When real-world cohorts are too small for robust economic modeling, synthetic generation creates adequately sized populations for budget impact analyses and resource utilization forecasts—while preserving the statistical characteristics of the small real-world sample. Transparency about augmentation methodology is essential.
“Is synthetic data compliant with GDPR?”
Properly generated synthetic data that doesn’t contain or derive from identifiable individuals typically falls outside GDPR’s scope of “personal data.” But the generation process must use appropriate privacy-preserving techniques—differential privacy, k-anonymity—to prevent reverse engineering back to source data. Always get legal review for your specific use case, especially for external sharing or regulatory contexts.
The Competitive Intelligence Angle Nobody Talks About
Here’s something that doesn’t get discussed enough: synthetic data enables competitive intelligence analyses that would be impossible with real claims data due to privacy restrictions.
Want to model what happens to your market share when a competitor’s biosimilar launches? Real Pan-European claims data access would require months of negotiation—and by the time you have it, the competitive landscape has already shifted.
Synthetic data generated from historical insurance utilization patterns lets you build dynamic market models simulating competitive entry scenarios—pricing pressures, switching behaviors, market contraction—without waiting for real-world claims to capture the changes. You can forecast LOE impact, model portfolio defense strategies, and stress-test commercial forecasts against multiple competitive scenarios. All with data you can share freely across commercial analytics, market access, and portfolio planning teams without licensing restrictions.
This isn’t speculation. It’s strategic planning based on statistically valid simulations of Continental European healthcare utilization patterns.
Integration with Existing HEOR Analytics Infrastructure
Synthetic data only delivers value if it integrates smoothly with your existing analytics workflows. Your HEOR team doesn’t need another data silo requiring custom tooling.
Most synthetic healthcare datasets should be deliverable in standard formats: CSV or Parquet for statistical software (R, SAS, Stata), relational database schemas for SQL-based analysis, OMOP Common Data Model format for interoperability, and cloud-native formats for big data analytics platforms.
The data structure should mirror real claims data architecture: encounter-based organization, hierarchical coding (ICD-10, procedure codes, medication classifications), and date-indexed records for longitudinal analysis.
Effective workflow integration uses synthetic data at specific points:
During the early planning phase, synthetic data enables market sizing, budget impact prototyping, and cost model development while real data access is still pending. During hypothesis testing, it supports rapid iteration on analytical approaches and sensitivity analyses. During augmentation, it combines with limited real samples for rare diseases or data-scarce situations. During training and development, it enables algorithm development and methodology refinement without privacy constraints.
This staged approach maximizes synthetic data value while maintaining real-world evidence as your validation anchor—and it’s an approach that experienced RWE advisors can help you implement without the trial-and-error that most teams go through independently.
Regulatory Evolution: How EHDS Will Change Everything
We can’t discuss Pan-European HEOR data access without acknowledging the European Health Data Space (EHDS) coming into force. This regulatory framework will fundamentally reshape how synthetic data fits into the real-world evidence ecosystem.
EHDS aims to enable cross-border health data sharing for research while strengthening privacy protections. It’s establishing HealthData@EU infrastructure for secure data access and creating frameworks for secondary use of health data across member states.
The EHDS framework explicitly recognizes that privacy-preserving technologies—including synthetic data generation—can facilitate research access while protecting individuals. As infrastructure matures, we’re likely to see standardized synthetic data quality criteria at the EU level, federated analytics models with synthetic augmentation for geographic or temporal gaps, and new public-private collaboration frameworks that enable commercial HEOR while protecting public health data.
Initiatives like the Synthetic Healthcare Database for Research (SyH-DR) from AHRQ demonstrate how public health agencies globally are already investing in synthetic data infrastructure to accelerate research while protecting privacy.
The trajectory is clear: synthetic data is moving from experimental methodology to recognized tool within the European health data governance framework. Smart pharma HEOR teams are building internal capabilities and validation expertise now, while the regulatory landscape solidifies around them.
Common Questions from HEOR Directors
“Can we use synthetic data for HTA submissions and national reimbursement assessments?”
Not as your primary evidence source for the initial dossier. HTA bodies expect real-world evidence from authentic patient populations when making benefit claims. But synthetic data absolutely supports exploratory analyses, hypothesis generation, and supplementary scenario modeling that informs your overall evidence strategy. Think pre-submission planning, not submission evidence.
“How do we evaluate which data source is right for our specific HEOR needs?”
This is genuinely one of the harder questions to answer independently. The right data source depends on your therapeutic area, target patient population, geographic scope, analytical questions, timeline, and budget—and the interactions between these factors aren’t always obvious. Understanding the trade-offs across available options requires both HEOR methodology expertise and current knowledge of the data vendor landscape. This is exactly the kind of decision where external guidance pays for itself quickly.
“How do we handle regional variation in treatment patterns across Continental Europe?”
Synthetic data generated from representative Pan-European claims should capture regional treatment pattern variation as part of its statistical fidelity. However, if regional granularity is a core requirement for your HEOR model, this needs to be specified explicitly in your data requirements—and validated rigorously in the output.
Your Next Steps for Smarter HEOR Data Strategy
So where does this leave Pan-European pharma market access teams looking for practical HEOR data solutions?
The reality is that synthetic healthcare data isn’t replacing real-world evidence—it’s enabling faster, more flexible, more privacy-safe HEOR analyses while you navigate Continental Europe’s complex data access landscape. When you need to model costs, outcomes, and resource utilization for market access, pricing strategies, or payer negotiations, the right combination of synthetic and real claims data can deliver the analytical foundation your economic models require.
But “the right combination” is doing a lot of work in that sentence. Determining which data sources, analytical approaches, and evidence generation strategies actually fit your specific HEOR programme—rather than a generic template—requires both deep methodological expertise and current knowledge of a rapidly evolving data landscape.
Explore our HEOR resources to understand the strategic approaches available for Continental European evidence generation. And if you’re at the point where you need to match your specific requirements to the right data partnerships and analytical capabilities, contact us to discuss your situation directly—that’s a conversation that benefits from human expertise rather than a website.
Because ultimately, HEOR is about generating credible evidence that supports patient access and demonstrates value. Synthetic data is one powerful tool in that effort. Knowing when to use it, how to validate it, and how it fits within a broader evidence generation strategy—that’s where expert guidance makes the difference between a data investment that accelerates your programme and one that misses the mark entirely.
Sources & References
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- AHRQ (ongoing). “Synthetic Healthcare Database for Research (SyH-DR).” Agency for Healthcare Research and Quality. https://www.ahrq.gov/data/innovations/syh-dr.html
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