Real-World Data for Clinical Trial Feasibility: A 2026 Guide to Sourcing and Application

Real-world data (RWD) is the difference between a feasibility assessment built on assumptions and one built on the patients who actually exist — and in 2026, the question is no longer whether to use it, but how to source, clean, and apply it without drowning in it. The practical answer: start with the question you are trying to answer, not the data you happen to have, and treat RWD as a hypothesis-testing tool rather than a truth machine.

Key takeaways:

• RWD shortens feasibility timelines by replacing site guesses with patient-level evidence from EHRs, claims, and registries.
• Sourcing quality beats sourcing volume: a curated claims dataset beats a messy EHR dump every time.
• AI adds value in pattern detection and cohort matching, not in replacing clinical judgment.
• FDA and EMA both accept RWD in trial design, but the burden of proof is on you to show data provenance and relevance.
• Start small: one therapeutic area, one protocol, one honest go/no-go question.

What is Real-World Data (RWD) and Why is it Critical for 2026 Clinical Trials?

Real-world data is patient health information collected outside the controlled conditions of a clinical trial — electronic health records (EHRs), insurance claims, disease registries, and increasingly wearable and patient-reported data. In 2026, it is critical because feasibility assessments that ignore it are guessing: they extrapolate from historical trial performance and site reputation rather than from the patients who actually present at a given site.

The shift is visible in the literature. A peer-reviewed analysis published in Applied Clinical Trials on April 3, 2026, by Hana Do, Victoria Zhang, and Mary Jo Lamberti, examines exactly how RWD and real-world evidence (RWE) are being woven into trial design and execution. The regulatory groundwork has been laid over the past several years — the FDA's framework for RWE in regulatory decision-making and EMA's guidance on RWD in medicines development both signal that regulators expect sponsors to engage with RWD seriously, not as an afterthought.

Why does this matter for feasibility specifically? Because the core feasibility question — "can we enrol this protocol in this population within this timeline?" — is fundamentally an empirical question. RWD answers it with evidence: how many patients with this diagnosis and these prior treatments actually exist in a given geography, how many are on the therapies your protocol excludes, and how many sites already see them.

Sourcing High-Quality RWD: Key Considerations and Data Providers

The single most important sourcing rule I have learned is: define the clinical question before you touch a dataset. If you start with "let's get all the EHR data we can," you will spend weeks cleaning data that does not answer your question. If you start with "how many patients with EGFR-mutant NSCLC and prior osimertinib exposure are seen at sites in the Northeast US," you know exactly which fields matter.

The main RWD sources, and where each shines in feasibility:

A practical note on providers: the large commercial RWD vendors (IQVIA, Optum, Flatiron, TriNetX, and others) each have different strengths in coverage, depth, and timeliness. I have seen teams pick a vendor because a colleague used it once, then discover the dataset has no oncology sites in the region they need. Do a coverage check against your specific protocol before you sign anything.

How Do You Apply RWD in Feasibility: Site Selection and Patient Cohort Identification?

The two highest-value applications of RWD in feasibility are site selection and patient cohort identification, and they feed each other. Site selection asks: which sites already see the patients my protocol needs? Patient cohort identification asks: how many such patients exist, and where?

For site selection, the workflow I use is: (1) define the full eligibility criteria as structured queries, (2) run them against EHR and claims data at the site level, (3) rank sites by the number of qualifying patients seen in the last 12–24 months, and (4) overlay historical enrolment performance. The result is a site list that reflects reality rather than reputation. This is the approach we build into our AI in Clinical Trial Feasibility work — separating the tools that actually move the needle from the ones that just produce pretty dashboards.

For patient cohort identification, claims data is often the fastest starting point because it captures the full insured population, not just the patients who happen to be in a particular health system. The trade-off is clinical detail: claims will tell you a patient had a diagnosis and a drug fill, but not their performance status or biomarker results. That is where EHR data fills the gap, and where linking the two — claims for prevalence, EHR for clinical nuance — gives you the most honest picture.

One caution: RWD tells you about the patients who exist, not the patients who will enrol. The gap between "eligible by data" and "eligible and willing to enrol" is real, and it is why RWD-driven feasibility still needs the human layer of site engagement. Our Optimizing Patient Recruitment in 2026 piece covers how to close that gap in practice.

How Is AI Enhancing RWD Analysis for Predictive Feasibility?

AI's real contribution to RWD analysis in 2026 is not prediction magic — it is the ability to handle messy, heterogeneous data at scale and to surface patterns a human analyst would miss. The most useful applications I have seen are: natural language processing to extract structured data from unstructured EHR notes, and machine learning models that predict enrolment rates from historical site performance plus patient-level characteristics.

The honest caveat: AI models are only as good as the data they train on, and RWD is full of systematic biases — sicker patients are over-represented in EHRs, and claims data misses cash-pay and off-label care. A model trained on a biased dataset will confidently reproduce that bias. That is why I treat AI as a pattern-finding tool, not an oracle. Every AI-generated cohort or site ranking gets sanity-checked against clinical judgment before it informs a go/no-go decision.

The regulatory angle matters here too. If you use AI to analyse RWD for a submission, regulators will ask about model validation, data provenance, and how you handled bias. Build that documentation from day one, not the week before submission.

What Are the Regulatory Expectations for RWD in Trial Design (FDA, EMA)?

Both the FDA and EMA accept RWD and RWE in trial design, but neither accepts it uncritically. The FDA's framework for RWE in regulatory decision-making, and EMA's guidance on RWD in medicines development, share a common thread: the burden is on the sponsor to demonstrate that the data are fit for purpose. Peer-reviewed work in JAMA Network Open and PubMed-indexed analyses of RWE reinforce the same message: regulators reward transparency about data provenance and methods.

What that means in practice: you need to show (1) the data source and how it was collected, (2) the relevance of the data to the specific regulatory question, (3) the quality and completeness of the data, and (4) the analytical methods used, including how confounding and bias were addressed. For feasibility specifically — which is usually not itself a regulatory submission — the bar is lower, but the discipline of documenting provenance and methods pays off when the same data later supports a protocol design or a comparator selection.

The regulatory landscape is also shifting toward harmonization. ICH E6(R3), which we cover in our guide to risk-based quality management, emphasizes a risk-based approach that naturally accommodates RWD as a source of evidence about what could go wrong and where. The practical takeaway: treat RWD documentation as part of your quality system, not as a one-off analysis.

What Are the Challenges and Best Practices for RWD Implementation in 2026?

The challenges are real, and I have hit most of them. Data quality is the biggest: missing values, coding inconsistencies, and fragmentation across systems can quietly invalidate a feasibility analysis. Privacy and governance come second: RWD contains protected health information, and the legal and ethical frameworks for its use vary by jurisdiction. Timeliness is third: claims data lags by months, which matters when you are assessing a fast-moving therapeutic area.

Best practices I have settled on:

• Start with the question, not the data. Write the feasibility question in plain language before choosing a source.
• Validate against a second source. If EHR and claims both point to the same cohort size, trust it more.
• Document everything. Provenance, cleaning steps, and analytical methods — for regulators and for your own reproducibility.
• Involve clinical input early. A clinician will catch the eligibility criterion that the data cannot represent.
• Plan for the human layer. RWD tells you who exists; site engagement tells you who will enrol.

The Future of RWD in Go/No-Go Decisions and Clinical Development

The trajectory is clear: RWD is moving from a feasibility input to a core component of go/no-go decisions. In 2026, a sponsor that can show "here is the evidence that this population exists, here is the evidence that sites can enrol it, and here is the evidence that the treatment effect is plausible in this population" has a fundamentally stronger case than one relying on expert opinion alone.

The next frontier is adaptive trial design, where RWD feeds interim decisions — whether to expand sites, adjust inclusion criteria, or stop a failing arm early. And for the go/no-go decision itself, we have built a data-driven framework that puts RWD at the centre.

The honest bottom line: RWD will not tell you whether to run a trial. It will tell you whether the trial you are planning is runnable — and that is the question feasibility exists to answer. If you are making a go/no-go call in the next quarter, the data you need to make it well is probably already out there. The question is whether you are using it.

At Clinical Trial OS, we build feasibility assessments that put RWD at the centre, with every number cited to its public source. If you are facing a go/no-go decision, that is exactly the kind of evidence you want on the table.

FAQ

What is real-world data in clinical trials?

Real-world data is patient health information collected outside controlled trial conditions — EHRs, claims, registries, and wearable data. In feasibility, it is used to estimate patient populations, select sites, and assess enrolment risk.

How is real-world data used in clinical trial feasibility?

RWD is used to identify how many patients meet eligibility criteria in a given geography, which sites already see those patients, and what the historical enrolment patterns look like. It replaces assumptions with patient-level evidence.

What are the best sources of real-world data for clinical trials?

EHR data is best for clinical detail and site selection; claims data is best for population-level prevalence and treatment patterns; registries are best for rare diseases. The right source depends on the feasibility question.

What are the regulatory considerations for using RWD in clinical trials?

FDA and EMA both accept RWD in trial design, but sponsors must demonstrate data provenance, relevance, quality, and appropriate analytical methods. Documentation should be built from the start, not retrofitted.

Can AI improve real-world data analysis for clinical trials?

Yes, AI helps extract structured data from unstructured EHR notes and predict enrolment from historical patterns. But models inherit the biases of their training data, so AI findings must be validated against clinical judgment.