Here’s the assumption that gets small and mid-size pharma companies into trouble more often than any other: that real-world evidence is something you build toward once you have the internal infrastructure to support it. That you need a ten-person data science team, a dedicated HEOR function, established vendor relationships, and a multi-year evidence generation programme before real-world data can meaningfully contribute to your strategy.
That assumption is wrong. And it is costing companies launch performance, payer negotiations, and regulatory credibility at exactly the moment those things matter most.
The evidence gap doesn’t wait for your infrastructure to catch up. A payer asking for real-world effectiveness data has a deadline. An HTA body requesting locally relevant disease burden evidence is not going to pause its assessment while you recruit a VP of HEOR. What you actually need to generate credible real-world evidence is not a ten-person team — it’s a clearly defined evidence question, the right data source matched to that question, and the right methodological expertise to design the study and interpret the outputs. All three of those things are accessible externally, right now, for a company of any size.
Who This Post Is For
You are leading market access, HEOR, or commercial analytics at a biotech or specialty pharma company. You have one or two late-stage assets. You have no dedicated RWE or HEOR function — or a function that exists in name only, staffed by one or two people carrying multiple responsibilities simultaneously. You are being asked to generate real-world evidence for payer negotiations, regulatory submissions, or launch planning. And you are trying to figure out how to do that without the infrastructure that large pharma takes for granted.
This post is written for you.
If you are a Head of Market Access with a reimbursement negotiation approaching in six months and no clear path to the real-world effectiveness evidence your payer expects, this is for you. If you are a Medical Affairs Director who has been asked to generate evidence but has no team to generate it with, this is for you. If you are the VP of Commercial who suspects your market sizing numbers are built on secondary research extrapolations rather than actual diagnosed patient populations, this is for you. The RWE capability gap at small and mid-size pharma companies is one of the most underdiagnosed strategic risks in the industry — and it is solvable, without building a function from scratch.
The RWE Capability Myth: What You Actually Need vs. What You Think You Need
Most RWE content is written for enterprise pharma. It describes workflows, infrastructure requirements, and organisational models that assume resources most companies will never have. Small and mid-size pharma teams read that content and reasonably conclude that real-world evidence generation is not something they can do at their current stage. That is the myth. And it is worth dismantling carefully, because it shapes real decisions with real consequences.
What does generating credible real-world evidence actually require? According to the foundational framework articulated by Sherman et al. in the New England Journal of Medicine, real-world evidence draws on data generated during routine clinical practice — health insurance claims, electronic health records, patient registries — to generate insights about treatment patterns, outcomes, and disease burden outside the controlled environment of a randomised trial. Three things are genuinely required to do this well: a clearly defined evidence question; a validated, analysis-ready dataset appropriate for that question; and methodological expertise to design the study and interpret the outputs in a regulatory or payer context.
None of those three things require an internal team. All three are accessible through external partnerships. And for a company operating under tight timelines and resource constraints, external access is often faster and more reliable than internal capability building — because you are not simultaneously trying to recruit, onboard, and integrate people while also generating the evidence your next milestone depends on.
What external access does require is knowing which external relationships to build and in what sequence. That is where most small pharma companies run into problems — not because the capability doesn’t exist, but because navigating the landscape of data vendors, CROs, and advisory services without internal expertise is genuinely difficult. The gap between knowing you need real-world evidence and identifying the right source and study design for your specific question is substantial. And closing that gap is exactly what expert advisory support is designed to do.
The Most Critical RWE Use Cases for Small and Mid-Size Pharma
Not every RWE application carries the same urgency for a company at this stage. Here are the five use cases where the absence of real-world data creates the most immediate strategic risk — and where acting early has the clearest ROI.
Market sizing and diagnosed prevalence. The addressable patient population in your forecast model is almost certainly not the same as the population your drug can actually reach. Published epidemiology figures report total disease prevalence. What determines your commercial opportunity is diagnosed prevalence — the subset of patients who have been identified by the healthcare system and are receiving treatment. Those two numbers are very different in most therapeutic areas, and claims data from statutory health insurance systems across Continental Europe is the only reliable way to close that gap. Garrison et al.’s ISPOR task force report remains the definitive framework for understanding how real-world data informs these coverage and payment decisions.
Payer evidence and market access. HTA bodies across Continental Europe are increasingly explicit about what they expect in terms of locally relevant real-world effectiveness evidence. The Makady et al. analysis of HTA policies across European member states documents the consistency of that expectation — and the consequences of arriving at a payer negotiation without it. If you are twelve months from a reimbursement submission and your evidence package relies entirely on clinical trial data, that is a problem that real-world data can partially address — but only if the study design is appropriate and the data source is validated for that specific use case.
Treatment pathway and patient journey analysis. How patients actually move through the healthcare system is rarely what advisory boards say it is. Real treatment patterns — how long patients wait for diagnosis, what they receive before your therapy, how treatment sequences actually play out — are only visible in claims data. Getting this wrong at launch means your positioning, your reimbursement strategy, and your sales force deployment are all built on assumptions that don’t match clinical reality.
Regulatory submission support. FDA and EMA are increasingly receptive to real-world evidence as supplemental data for regulatory submissions, particularly for supplemental indications and post-market commitments. The EMA’s DARWIN EU network represents the European infrastructure for this kind of evidence generation. Understanding what study designs are acceptable — and which data sources meet the methodological standards that regulatory reviewers will apply — requires expertise that most small pharma teams do not have internally.
Portfolio and indication selection. For pipeline decisions, real-world prevalence and treatment pattern data can change the analysis entirely. How large is the diagnosed population in the markets you are targeting? What proportion is currently receiving treatment? Where are the treatment gaps? These questions are answerable with claims data before you commit to expensive development programmes — and getting the answers wrong is significantly more costly than getting expert advisory support to answer them correctly.
The Three Models for RWE Without an Internal Team
There are three practical approaches available to a company trying to generate real-world evidence without internal infrastructure. Understanding the trade-offs between them is the starting point for any strategic decision.
Model 1: Full outsourcing to a CRO or RWE specialist. You commission the study design, data access, analysis, and output generation from an external organisation. This is the fastest model to initiate but carries the highest risk of misalignment between what you commission and what you actually need. It works well when the evidence question is clearly defined and the study design is unambiguous — when you know exactly what you are asking for and can specify it clearly in a brief. When that clarity is absent, full outsourcing can deliver technically competent work that does not actually answer the strategic question you needed answered. As Kesselheim and Doherty observe in Health Affairs, the translation of a regulatory or payer need into a specific study design is not trivial — and outsourcing that translation step without internal expertise to validate the output creates risk.
Model 2: Direct data vendor engagement. You access a dataset directly and conduct analysis internally or through a data science contractor. This works when you have some internal analytical capability but lack data sourcing expertise. The risk here is selecting the wrong source. The Pan-European real-world data landscape is complex, with dozens of providers offering overlapping but genuinely distinct capabilities. Coverage breadth, data recency, coding completeness, linkage quality, and GDPR compliance methodology all vary substantially between providers — and choosing based on brand recognition or sales pitch rather than fitness for purpose is one of the most common and most costly mistakes small pharma companies make in this space.
Model 3: Expert advisory plus data partnership. An independent advisor defines requirements, evaluates data sources on your behalf, and connects you with the right partner for your specific use case. This model is the fastest path to value for teams without internal RWE expertise — not because it is inherently more sophisticated, but because the advisory function replaces the internal expertise you don’t have rather than adding to a team that already exists. The ISPOR-ISPE good practice recommendations for real-world data studies emphasise that the quality of RWE is determined at the design stage — before any data is accessed. That design expertise is what an advisory model provides.
Most companies that end up with expensive datasets that didn’t answer their questions took Model 2 without the expertise Model 3 provides. The cost of that mismatch — in time, budget, and delayed evidence generation — consistently exceeds the cost of expert advisory support upfront.
What a Lean RWE Programme Actually Looks Like
This is not the enterprise version with a ten-person team, a multi-year evidence roadmap, and established vendor relationships across every major Continental European market. This is a lean version — designed to deliver the essential evidence needed for the next strategic milestone, on a timeline that matches the reality of where you are.
The sequence matters more than the scale.
Start by defining the evidence question before selecting any data source. This sounds obvious but it is consistently where programmes go wrong. “We need claims data” is not an evidence question. “We need to quantify the diagnosed prevalence of [indication] in France, the Netherlands, and Switzerland among patients over 18, broken down by prior treatment history, to inform our market access submission” is an evidence question — and it drives entirely different decisions about data source selection, study design, and analytical approach.
Then identify the regulatory or payer context that will determine what study design is acceptable. The FDA’s 2021 guidance on real-world data for regulatory decision-making and the 2024 FDA guidance on assessing electronic health records and medical claims data are both explicit about the methodological standards that evidence needs to meet. HTA bodies across Continental Europe have their own requirements — and they vary by market in ways that matter for study design. The RECORD statement on routinely collected health data provides a practical methodological framework that is increasingly used as a quality benchmark by both regulatory and payer reviewers.
Match the data source to the question — not the other way around. Coverage breadth, data recency, coding completeness, and GDPR compliance methodology all vary significantly across the Pan-European data landscape. The right source for a market access submission is not necessarily the right source for a patient journey analysis, even within the same therapeutic area. Understanding which combination of capabilities aligns with your specific evidence question requires deep familiarity with the vendor landscape that most small pharma teams simply don’t have internally.
Leverage analysis-ready datasets to eliminate the data engineering bottleneck. The time and cost of going from raw data access to first analytical output is consistently underestimated — and for small teams without data engineering resources, it can become a project-stopping constraint. Pre-processed, validated, and structured datasets can take that timeline from months to days. That is not a marginal efficiency gain — it is the difference between generating evidence before your payer negotiation and generating it after.
The Cost of Waiting
Small pharma companies that defer RWE investment until they have built internal infrastructure consistently discover the same thing: by the time the infrastructure exists, the strategic windows have closed.
Consider what this means concretely. According to McKinsey’s analysis of pharmaceutical launch performance, the factors that separate winning launches from the rest are established before commercialisation begins — not corrected after it. And as Deloitte’s analysis of drug launch underperformance documents, weak pre-launch evidence generation is one of the most consistent predictors of commercial shortfall.
Payer rejections driven by weak or absent real-world effectiveness evidence are recoverable — but the recovery takes eighteen months minimum, during which revenue projections miss and commercial momentum stalls. Launch underperformance driven by overestimated patient population sizing is harder to correct: by the time the data shows the addressable population is thirty percent smaller than forecasted, sales force sizing and territory allocation decisions have already been made. Regulatory delays driven by inadequate post-market evidence planning can push supplemental indication timelines back by years. And portfolio decisions made without real-world prevalence data — indication selections, go/no-go calls on pipeline assets — can commit development resources to programmes where the commercial opportunity simply doesn’t exist at the scale that was assumed.
The uncomfortable truth is that the cost of waiting for infrastructure to materialise is almost always higher than the cost of accessing external expertise now. Dagenais et al. in Clinical Pharmacology & Therapeutics document the specific ways that early RWE integration into development strategy improves outcomes — not as a theoretical argument, but as a finding from observed programme performance across multiple therapeutic areas. The evidence on the value of early evidence generation is itself robust.
Getting Started: Three Actions You Can Take Now
This does not require a budget commitment or a vendor conversation. It requires clarity — and that is something you can start building today.
Define your most urgent evidence question. Not a category of evidence, not a general need for real-world data — a specific question that, if answered, would most directly improve your next strategic decision. What is the one thing you need to know that you currently don’t? Start there. Everything else — data source selection, study design, analytical approach — follows from the clarity of that question.
Audit your current data access honestly. What do you actually have? Secondary market research reports and published epidemiology are not the same as validated claims data from statutory health insurance systems across Continental European markets. Knowing the gap between what you have and what you need is a prerequisite for any useful vendor conversation. And the FDA’s Real-World Evidence Programme framework provides a useful structure for that audit — even for companies with no immediate FDA interaction, because it captures the dimensions of evidence quality that regulatory and payer reviewers across both the US and Continental Europe are applying.
Seek expert advisory support before entering any vendor conversation. The single most reliable way to avoid the costly mismatches that derail RWE programmes is to define your requirements before you start evaluating options. An independent advisor who understands both your business objectives and the Pan-European vendor landscape can structure that requirements definition process — and ensure that when you do enter a vendor conversation, you are evaluating options on the dimensions that actually matter for your use case. As the FDA’s advancing real-world evidence programme review and the recent CIOMS Working Group XIII report on RWD in regulatory decision-making both emphasise — the quality of the advisory and design process determines the quality of the evidence output. You cannot correct a flawed study design with better data.
If you are a lean pharma team trying to build a real-world evidence strategy without the luxury of an internal function, the conversation we have with every company we work with starts exactly here — with the evidence question you need answered and the strategic decision that depends on it. That is where the right approach becomes clear.
Contact MEDDDICAL to discuss your specific evidence requirements →
Or explore our related resources: Analysis-Ready Real-World Evidence and Healthcare Data for Pharma and Aggregated and Synthetic Insurance Data: What It Is, Why It Matters, and Where It Is Used.
Sources & References
- 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
- Berger, M. L., et al. (2017). A framework for regulatory use of real-world evidence. Journal of Comparative Effectiveness Research, 6(1), 5–8. https://www.tandfonline.com
- Dang, A. (2023). Real-world evidence: a primer. Pharmaceutical Medicine, 37(1), 25–36. https://pubmed.ncbi.nlm.nih.gov/36604368/
- 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
- Makady, A., et al. (2017). Policies for use of real-world data in health technology assessment. Value in Health, 20(3), 520–532. https://www.valueinhealthjournal.com
- 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
- Kesselheim, A. S., & Doherty, M. (2021). Real-world evidence in drug regulation — challenges and opportunities. Health Affairs, 40(2). https://www.healthaffairs.org
- ISPOR-ISPE Special Task Force. Good practices for real-world data studies of treatment and/or comparative effectiveness. https://www.ispor.org
- ISPOR (2022). Real-world evidence transparency initiative: guiding principles. https://www.ispor.org
- European Medicines Agency (2023). Data analysis and real-world interrogation network (DARWIN EU). https://www.ema.europa.eu
- U.S. Food and Drug Administration (2021). Considerations for the use of real-world data and real-world evidence to support regulatory decision-making for drug and biological products. https://www.fda.gov
- U.S. Food and Drug Administration (2024). Real-world data: assessing electronic health records and medical claims data to support regulatory decision-making. https://www.fda.gov
- Benchimol, E. I., et al. (2015). The RECORD statement. PLOS Medicine, 12(10). https://journals.plos.org/plosmedicine
- Grubert, N., et al. (2018). What separates winning pharmaceutical launches from the rest. McKinsey & Company. https://www.mckinsey.com
- Fortner, K. (2022). Why do so many drug launches underperform? Deloitte Insights. https://www2.deloitte.com
- Dagenais, S., et al. (2022). Use of real-world evidence to drive drug development strategy and inform clinical trial design. Clinical Pharmacology & Therapeutics, 111(1), 77–89. https://pubmed.ncbi.nlm.nih.gov/34839524/
- U.S. Food and Drug Administration (2018). Framework for FDA’s Real-World Evidence Program. https://www.fda.gov/media/120060/download
- Smith, et al. (2025). US Food and Drug Administration’s Advancing Real-World Evidence Program: initial experience. Clinical Pharmacology & Therapeutics. https://ascpt.onlinelibrary.wiley.com
- Hennessy, et al. (2025). Real-world data and real-world evidence in regulatory decision making: report summary from the CIOMS Working Group XIII. Pharmacoepidemiology and Drug Safety. https://onlinelibrary.wiley.com