You’re the Head of Market Access at a 200-person specialty biotech. Your lead asset is 18 months from a potential EU launch. You know — intellectually, strategically — that you need real-world evidence to support your HTA dossiers, build a payer value story across multiple markets, and inform your commercial forecast. And you also know that you don’t have an RWE team. You might not even have an RWE person. What you have is a CRO relationship, a stack of secondary market reports, and a growing sense that the evidence gaps you’re quietly ignoring now are going to become very loud problems later.
This is the reality for hundreds of small and mid-size pharma companies right now. And honestly, it’s one of the most underacknowledged risk factors in drug development.
The assumption that real-world evidence is an enterprise capability — something reserved for the industry’s largest players with their 50-person HEOR departments — has persisted far too long. It’s wrong. And it’s costing lean pharma teams launch readiness, payer leverage, and regulatory confidence at precisely the moments those things matter most.
This post is for the people wearing three hats at once: the VP of Commercial Analytics doubling as the de facto RWE lead, the HEOR manager who is the entire HEOR team, the Medical Affairs Director who needs a scientific narrative supported by real-world data but has no data scientists to generate it. We’re going to walk through what a practical RWE strategy actually looks like without an internal function — and why the democratisation of analysis-ready real-world data has changed the equation entirely for companies operating across Continental Europe.
The Problem: Evidence Gaps Are a Business Risk, Not Just a Methodology Gap
Let’s be direct about what’s actually at stake here. Evidence gaps in lean pharma teams aren’t primarily a scientific problem — they’re a commercial and regulatory risk that compounds over time.
According to research from Deloitte’s Centre for Health Solutions, the average cost to bring a drug to market has climbed past $2 billion — and that figure doesn’t account for the downstream value destroyed when a launch underperforms because the market opportunity was miscalculated, or when a payer reimbursement negotiation fails because the HEOR dossier wasn’t compelling enough.
Meanwhile, clinical success rates remain stubbornly low. Smietana and colleagues, writing in Nature Reviews Drug Discovery, documented the persistent attrition in clinical pipelines — a trend that makes evidence-based portfolio decisions even more critical for smaller organisations where a single asset failure can be existential.
So what does this look like in practice for a lean pharma team preparing for a Pan-European launch? A few patterns repeat themselves with frustrating regularity:
- Market sizing built on secondary reports rather than actual diagnosed prevalence data — leading to forecasts that miss by 30%, 40%, sometimes more across key markets
- Payer evidence that arrives too late or isn’t granular enough to support reimbursement negotiations across different regions and national HTA systems
- CRO-executed studies that weren’t strategically designed because no one internal had the expertise to direct them properly
- Regulatory submissions that get pushback on their real-world evidence components because the study protocol didn’t meet EMA or FDA standards
- Loss-of-exclusivity planning that starts too late, without RWD-backed forecasts to guide lifecycle strategy
The uncomfortable truth? Most of these gaps aren’t about budget. They’re about expertise — specifically, the absence of someone who can translate a regulatory or commercial question into a well-structured RWE study design, identify the right data sources for each market, and ensure the output will actually be accepted. That challenge is amplified when you’re navigating multiple healthcare systems simultaneously across Continental Europe.
Why RWE Is No Longer Just for Big Pharma
Here’s what’s changed. And it’s significant.
Five years ago, accessing real-world data at the quality required for regulatory and payer submissions was genuinely difficult for smaller organisations. The data infrastructure was fragmented across markets. GDPR compliance added layers of legal complexity. And the analytical expertise needed to transform raw claims data into publishable evidence wasn’t available off the shelf.
That has fundamentally shifted. As Sherman and colleagues articulated in the New England Journal of Medicine, real-world evidence encompasses the broad range of data generated outside of controlled clinical trials — and the infrastructure to collect, validate, and analyse that data at scale has matured considerably. What was once proprietary infrastructure is increasingly accessible through specialist advisory and data partnerships offering pre-validated, analysis-ready datasets.
The practical implication: a biotech with 150 employees can now access the same quality of Pan-European health insurance claims data that a top-10 pharma company uses for its multi-market HTA submissions. The difference isn’t the data anymore. It’s knowing how to use it strategically — and that’s exactly the gap that expert advisory partnerships are designed to close. The challenge many smaller pharmaceutical companies face isn’t finding data providers — it’s determining which combination of capabilities aligns with their strategic priorities across different regulatory environments.
As Blonde and colleagues noted in Advances in Therapy, the meaningful application of real-world clinical data requires interpretation expertise — not just data access. That framing is important for lean teams: the goal isn’t to build an internal capability from scratch. It’s to access the right data through the right partnership and apply that data to the specific decisions in front of you.
The Most Critical RWE Use Cases for Small and Mid-Size Pharma
Not all RWE use cases are created equal — and for a lean team with limited bandwidth, prioritisation matters. Here are the applications that deliver the highest return for smaller organisations operating across Continental Europe, roughly ordered by urgency and accessibility.
1. Market Sizing and Patient Population Quantification
This is where most small pharma teams feel the pain most acutely. Secondary market reports give you prevalence estimates. Health insurance claims data gives you diagnosed prevalence — the actual patient population that has been identified, coded, and treated within the healthcare system. That distinction matters enormously for commercial forecasting.
Across Continental Europe, statutory and social health insurance systems provide the foundational infrastructure for this kind of analysis. When adequately covered and properly validated, these systems generate diagnostic and procedural data at a granularity that secondary reports simply cannot match — with regional variation by country and sub-national area that is critical for launch sequencing decisions. The right data partnership gives you this population-level insight across multiple markets simultaneously, turning a market sizing exercise from an educated guess into a defensible multi-country commercial projection.
Understanding which data sources match your specific therapeutic area and target geographies requires deep market knowledge that most companies lack internally. This is precisely where expert navigation of the data landscape adds disproportionate value.
2. Disease Prevalence and Epidemiology
Understanding the true disease burden — not just the diagnosed tip of the iceberg — is foundational for portfolio decisions, indication selection, and regulatory strategy. Epidemiology data derived from aggregated claims, validated against national statistical authorities across Continental European markets, gives you population-level insight that is both statistically robust and regulator-credible.
For rare disease programmes in particular, accurate prevalence data is often the difference between a viable development programme and one that fails feasibility. Getting this wrong at Phase I or Phase II means compounding errors throughout the development lifecycle — and the errors are compounded further when you’re sizing across multiple markets with different diagnostic coding systems and healthcare utilisation patterns.
3. Payer Evidence and Market Access Strategy
Payer negotiations across Continental Europe — whether with national HTA bodies, statutory insurance funds, or multi-stakeholder reimbursement committees — increasingly require real-world evidence to support value claims. Garrison and colleagues, in a landmark ISPOR Real-World Data Task Force report published in Value in Health, established the framework for how real-world data should inform coverage and payment decisions — a framework that national payers now actively apply when evaluating dossiers.
For lean teams, the practical question is: do we have the evidence we need to make our value story credible to each of the national payer bodies we’re targeting? If that answer is unclear, the window to generate that evidence before launch may already be narrowing. And critically — evidence generated to meet the standards of one HTA system doesn’t automatically satisfy another. Understanding the requirements across markets requires expertise that goes well beyond data access.
4. Treatment Pathway and Patient Journey Analysis
Understanding how patients actually move through the healthcare system — what they’re prescribed first, how long they stay on treatment, what drives switching or discontinuation — is commercially invaluable for launch strategy and medically important for comparative effectiveness positioning. Longitudinal claims data enables this analysis at scale, with the kind of granularity that chart reviews and patient surveys can’t provide. Across Continental Europe, where treatment patterns can vary substantially by country and region, this multi-market pathway intelligence is particularly valuable for launch sequencing and positioning decisions.
5. Regulatory Submission Support
Both FDA and EMA have published guidance on the use of real-world evidence in regulatory decision-making. The FDA’s 2023 guidance on assessing electronic health records and medical claims data sets clear expectations for data quality, fit-for-purpose validation, and study design standards. The EMA’s DARWIN EU network signals the European direction clearly: real-world data infrastructure is being embedded into the regulatory process, not treated as a supplement to it.
Lean teams that haven’t planned for this won’t be ready when regulators ask for it.
Navigating the Data Landscape: What to Look For
One of the most common frustrations I hear from small pharma teams is that they genuinely don’t know how to evaluate what they’re being offered. The Pan-European claims data landscape is genuinely complex — and the sales pitches often obscure more than they reveal. So let’s cut through it by focusing on what actually matters.
The challenge isn’t finding providers. It’s knowing which dimensions of capability actually matter for your specific use cases — and which trade-offs are acceptable given your evidence requirements and budget parameters.
Health Insurance Claims Data: What to Evaluate
Statutory health insurance claims data is the workhorse of real-world evidence across Continental Europe. Generated through routine billing and administrative processes, it’s comprehensive, longitudinal, and not subject to the participation biases that plague survey-based datasets. Across GDPR-regulated markets, this data covers diagnostics (typically ICD-10 coded), procedures, prescriptions (ATC classification), and healthcare utilisation — with the precise coding systems and coverage structures varying by national system.
Key dimensions to evaluate when assessing any claims data source: What proportion of the national insured population does the data represent? How is that coverage claim validated against official national statistics? What is the data latency — the lag between a healthcare event and its appearance in the dataset? And critically, what is the documented anonymisation methodology, and can it withstand scrutiny from national data protection authorities?
Coverage claims that can’t be benchmarked against official national statistics should be treated sceptically. This is an area where expert guidance — from someone who knows the Pan-European data landscape and can evaluate provider claims independently — is genuinely valuable.
Aggregated and Synthetic Data: When Each Makes Sense
Not every research question requires individual-level longitudinal data. Aggregated datasets — population-level statistics on diagnosis rates, treatment patterns, healthcare utilisation — can answer many commercial and epidemiological questions with full GDPR compliance and significantly faster turnaround. For teams without a GDPR specialist on staff, this distinction matters enormously when scoping a study.
Synthetic data — generated from real-world claims using techniques like Generative Adversarial Networks (GANs) with differential privacy guarantees — is an emerging and increasingly important category. It enables scenario modelling, algorithm development, and exploratory analysis without the constraints that apply to personal health data under GDPR Article 9. The distinction between aggregated and synthetic datasets — what each is suited for, and where each has limitations — is something lean teams should understand before engaging any provider.
Analysis-Ready Datasets: The Time-to-Insight Factor
This is perhaps the most underappreciated dimension for lean teams. Pre-processed, validated, structured data that can be queried immediately — without data engineering, without schema translation, without weeks of preparation — compresses the time-to-insight dramatically. For a team without internal data engineers, the difference between source-level data and analysis-ready data can be measured in months and hundreds of thousands of euros in consulting fees.
The GRACE principles for high-quality observational studies — transparency, pre-specification, validity assessment — apply here too. Analysis-ready doesn’t just mean structured. It means validated against known benchmarks, with documented methodology, and appropriate for the study design you intend to apply. Ask providers to show you their validation documentation, not just their coverage claims.
Build vs. Buy vs. Advise: The Three Options (And Why Two of Them Usually Fail)
When a lean pharma team recognises they need RWE capability, they typically face three options. Let’s be honest about how each one plays out.
Build: Hire Internally
Hiring a senior RWE or HEOR director is genuinely the right answer — eventually. But the lead time is 6–12 months minimum to identify, recruit, and onboard someone who actually has Pan-European market expertise and the regulatory credibility your multi-market submissions require. And in a 200-person company, that person will likely spend significant time on tasks that aren’t pure RWE strategy. You probably don’t have 12 months to spare before your next critical milestone.
The cost reality: a senior HEOR director with genuine Pan-European market access experience commands €150,000–€200,000+ in total compensation. For a company with one or two pipeline assets, that’s a significant fixed cost against uncertain revenue timing — and it still doesn’t give you the breadth of market-specific knowledge that navigating HTA submissions across France, the Netherlands, Switzerland, and Scandinavia simultaneously requires.
Buy: Go Direct to a Data Vendor
Data providers will offer you platform access. What they won’t always tell you is that the data, in its source or semi-structured form, requires significant expertise to use correctly — expertise that determines whether your study design is appropriate, whether your outputs will be accepted by national HTA bodies or EMA, and whether your analytical choices are defensible under scrutiny.
Kesselheim and Doherty, writing in Health Affairs, were direct about the challenges: real-world evidence in drug regulation presents significant methodological complexity that requires expertise to navigate. Data access is necessary but not sufficient. Without that expertise, the gap between licensing a dataset and extracting actionable intelligence — intelligence that will hold up under regulatory or payer scrutiny — is substantial.
The result, for teams that go direct without internal expertise, is often this: they license access, spend months trying to scope the right analysis, produce outputs that aren’t fit-for-purpose, and either abandon the effort or engage expensive consultants to remediate it. That’s not a hypothetical — it’s a pattern that repeats itself across the industry.
Advise: Engage a Specialist Partner
The third option — and the one that actually works for lean teams — is engaging an expert advisory partner who understands both your business objectives and the Pan-European data landscape. This approach provides strategic guidance alongside data access, ensuring the evidence you generate is designed to meet the standards regulators and payers actually apply — not just the standards that look good in a vendor’s demo.
This is the model the ISPOR Real-World Evidence Transparency Initiative implicitly supports: rigorous methodology, transparent data sourcing, and expert study design — delivered as a service rather than requiring full internal build-out. For lean teams, this approach compresses the timeline, eliminates the expertise gap, and gives you outputs that have been designed with the end submission in mind.
The ROI calculation is straightforward: one avoided payer rejection, one successful HTA submission, one launch forecast that doesn’t miss by 40% pays for years of strategic partnership. Expert advisors who know both the pharmaceutical industry and the evolving Continental European data vendor landscape can accelerate your path to value while avoiding costly mismatches that are very difficult to remediate once a study is underway.
What Good RWE Strategy Looks Like Without an Internal Team
Practically speaking — what does a well-structured RWE approach look like for a company that doesn’t have an internal function? A few principles apply universally.
Start with the Decision, Not the Data
The most common mistake lean teams make when approaching RWE is leading with data availability rather than decision-relevance. The right question isn’t “what data can we access?” — it’s “what decision are we trying to make, and what evidence do we need to make it confidently?” This framing determines study design, data requirements, and the analytical approach. It’s also the framing regulators and payers use when evaluating your submissions. Expert consultants can help structure this requirements definition process to ensure you’re comparing options on the dimensions that actually matter for your use cases — rather than the dimensions vendors prefer to emphasise.
Design Your Study Protocol Before Touching Any Data
Pre-specified study protocols aren’t just methodological best practice — they’re increasingly expected by both FDA and EMA as a condition of credibility for RWE. The FDA’s 2021 guidance on submitting documents using RWD and RWE is explicit on this. Pre-specification protects against analytical fishing, reduces cherry-picking risk, and makes your outputs far more defensible under scrutiny.
For lean teams without internal expertise to design protocols: this is exactly where an advisory partner adds value that data access alone cannot replicate. Berger and colleagues established good practices for real-world data studies in the European Journal of Epidemiology — a framework that remains the methodological benchmark for peer-reviewed RWE across Continental Europe.
Understand Your GDPR Obligations Before You Scope the Analysis
For any research using health data across Continental Europe, the regulatory environment is governed by GDPR Article 9 (special category data) and its national implementations across member states. The anonymisation-pseudonymisation distinction isn’t semantic — it determines what you can and can’t do with the data, and it’s a line that national data protection authorities take seriously and enforce differently across jurisdictions. Understanding which approach applies to your specific study design, in your specific target markets, requires both GDPR expertise and familiarity with how individual national authorities interpret the regulation in practice.
Working with a provider or partner whose data is pre-validated for GDPR compliance — where the anonymisation methodology is documented, auditable, and defensible across multiple national regulatory contexts — eliminates a layer of legal risk that small pharma teams are poorly equipped to manage internally.
Plan Evidence Generation as Part of the Development Timeline, Not as a Reaction to Gaps
The teams that use RWE most effectively don’t generate evidence reactively — after a payer rejection, after a regulatory question, after a launch that’s underperforming. They plan evidence generation milestones into the development timeline, the way they plan clinical milestones. That means knowing, at Phase II, what real-world evidence you’ll need at launch — and starting to generate it with enough lead time to iterate.
For data-driven product launch planning across Continental Europe, this forward integration of RWE into commercial strategy is the difference between a launch that’s prepared and one that’s scrambling — particularly when each national market requires its own evidence package.
Common Questions from Lean Pharma Teams
Can we access Pan-European claims data if we’re a US-based company?
Yes — and many US biotech and specialty pharma companies already do, particularly for European launch preparation, multi-market HTA dossier development, and Pan-European market sizing. The data access model works independently of where your company is headquartered. The key is working with an advisory partner who understands the GDPR compliance requirements in each target market and can source data in a format your team can actually use — without requiring you to build GDPR expertise internally.
How long does it take to go from data access to first insight?
With analysis-ready datasets — pre-processed, validated, and structured for immediate querying — the timeline from engagement to first meaningful output is typically days to weeks, not months. The bottleneck isn’t data access; it’s study design and scoping. A well-defined research question with a pre-specified protocol significantly compresses that timeline. The right advisory partner helps you get that scoping right the first time, rather than iterating through expensive misalignments.
What does GDPR-compliant actually mean in the context of claims data?
In practice, it means the underlying individual-level data has been anonymised (not merely pseudonymised — an important legal distinction under GDPR) before any analysis is conducted, and the analytical outputs cannot be used to re-identify individuals. Aggregated datasets and properly generated synthetic data meeting differential privacy standards satisfy this requirement. Ask any provider to document their anonymisation methodology explicitly, including how it has been reviewed against national data protection authority guidance in each target market — this is a question worth asking early in vendor evaluation, not after you’ve scoped a study.
Do we need internal data scientists to use this data?
Not with analysis-ready datasets delivered through a structured advisory partnership. The data engineering and validation work is done upstream. What you need is someone who can define the research question and interpret the outputs — which is a considerably lower bar than building a full internal data science function. The challenge many pharmaceutical companies face isn’t the analytical execution; it’s determining which combination of data sources and analytical approaches aligns with their specific strategic priorities across different markets.
What’s the minimum viable RWE investment for a company our size?
That depends entirely on your development stage and the decisions you’re trying to support. A well-scoped market sizing study using Pan-European health insurance claims data can be completed for a fraction of the cost of a full internal HEOR hire — and the output is directly applicable to your launch forecast, payer dossiers, and regulatory strategy across multiple markets simultaneously. The better framing: what is the cost of getting these decisions wrong at this stage of development?
The Cost of Doing Nothing
Now let’s be very clear about what inaction actually costs — because this is where the risk calculation often gets underestimated.
The EvaluatePharma World Preview 2023 paints a competitive landscape where the window for commercial success post-launch has narrowed significantly. The assets that capture value are the ones with compelling real-world evidence backing their value story at launch — not six months after. Across Continental Europe, where each national HTA submission can make or break a pricing negotiation, this timing pressure is amplified.
Here’s what happens when lean teams don’t invest in RWE strategy before launch:
- Market sizing misses — launches scaled for a patient population that doesn’t exist at the diagnosed prevalence level, or that’s smaller than modelled because treatment rates are lower than estimated across key markets
- Payer rejections — HEOR dossiers that don’t meet the evidence standards of national HTA bodies, leading to restricted reimbursement, below-expectation price negotiations, or delayed market entry
- Regulatory delays — RWE components of submissions that generate questions because the study design wasn’t pre-specified or the data quality wasn’t validated to the standards each regulator applies
- Missed lifecycle management signals — treatment pattern shifts and competitive dynamics visible in real-world data that weren’t detected until they’d already affected brand performance across multiple markets
And there’s a more structural risk that’s worth naming directly. Pipeline decisions made without real-world prevalence data — indication selection, dosing strategy, patient population definition — carry compounding error through the entire development lifecycle. Real-world data from population surveillance has repeatedly demonstrated how significantly observed healthcare patterns can diverge from what clinical trial environments suggest. That divergence matters for commercial strategy, regulatory positioning, and ultimately for patient access — and it’s particularly pronounced when you’re navigating the healthcare system variation inherent in Continental European markets.
The question isn’t whether lean pharma teams can afford to build an RWE strategy. It’s whether they can afford not to.
Your Next Steps with MEDDDICAL
MEDDDICAL is a specialist advisor on real-world evidence strategy for pharmaceutical and MedTech companies operating across Continental Europe. We help lean teams navigate the complex Pan-European data landscape — identifying the right evidence approaches for your specific use cases, connecting you with appropriate data sources through expert-led consultation, and ensuring your RWE outputs are designed to meet the standards regulators and payers actually apply.
Whether you’re sizing a patient population across multiple markets, mapping treatment pathways for launch strategy, or building the epidemiology foundation for a regulatory submission, we work with lean pharma teams to make real-world evidence genuinely accessible — without requiring an internal RWE function to make it work. The right data partnership for your needs is identified through consultation, not through a catalogue — because the right answer depends on your therapeutic area, your target markets, your regulatory strategy, and your evidence gaps.
- Explore our approach to real-world evidence strategy: MEDDDICAL RWE & Healthcare Data Analytics Platform
- Contact us to discuss your specific evidence requirements and explore how to navigate the Pan-European data landscape
- Schedule a strategic consultation to identify the right approach for your development stage and target markets
Sources & References
- Deloitte Centre for Health Solutions (2024). Measuring the return from pharmaceutical innovation.
https://www2.deloitte.com - Smietana, K., Siatkowski, M., & Møller, M. (2016). Trends in clinical success rates. Nature Reviews Drug Discovery, 15(6), 379–380.
https://www.nature.com/articles/nrd.2016.85 - Sherman, R. E., et al. (2016). Real-world evidence — what is it and what can it tell us? New England Journal of Medicine, 375(23), 2293–2297.
https://www.nejm.org/doi/full/10.1056/NEJMsb1609216 - Blonde, L., Khunti, K., Harris, S. B., Meizinger, C., & Skolnik, N. S. (2018). Interpretation and impact of real-world clinical data for the practicing clinician. Advances in Therapy, 35(11), 1763–1774.
https://link.springer.com/article/10.1007/s12325-018-0805-y - Garrison, L. P., et al. (2007). Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report. Value in Health, 10(5), 326–335.
https://www.valueinhealthjournal.com/article/S1098-3015(10)60476-6/fulltext - Dreyer, N. A., et al. (2018). GRACE principles: recognizing high-quality observational studies of comparative effectiveness. American Journal of Managed Care, 16(6), 467–471.
https://www.ajmc.com/view - U.S. Food and Drug Administration (2023). Real-world data: assessing electronic health records and medical claims data to support regulatory decision-making for drug and biological products. FDA Guidance Document.
https://www.fda.gov/media/153447/download - European Medicines Agency (2023). Data analysis and real-world interrogation network (DARWIN EU).
https://www.ema.europa.eu - Kesselheim, A. S., & Doherty, M. (2021). Real-world evidence in drug regulation — challenges and opportunities. Health Affairs, 40(2).
https://www.healthaffairs.org/doi/10.1377/hlthaff.2020.01635 - ISPOR (2022). Real-world evidence transparency initiative: guiding principles. ISPOR Special Report.
https://www.ispor.org - U.S. Food and Drug Administration (2021). Submitting documents using real-world data and real-world evidence to FDA for drugs and biologics. FDA Guidance Document.
https://www.fda.gov/media/151759/download - Berger, M. L., et al. (2017). Good practices for real-world data studies of treatment and/or comparative effectiveness. European Journal of Epidemiology, 32(11), 945–947.
https://link.springer.com/article/10.1007/s10654-017-0327-4 - Evaluate Pharma (2023). World preview 2023, outlook to 2028. Evaluate Ltd.
https://www.evaluate.com - Hartnett, K. P., et al. (2020). Impact of the COVID-19 pandemic on emergency department visits — United States. MMWR Morbidity and Mortality Weekly Report, 69(23), 699–704.
https://www.cdc.gov
