Top Real-World Data Companies & Providers in 2026

Ask anyone in market access how their budget conversations have changed in the last two years, and real-world data comes up fast. Reimbursement decisions, label expansions, safety surveillance. None of it runs on clinical trial data alone anymore, and honestly, it hasn't for a while. What's changed is that RWD spend used to get buried a few line items down from the "real" budget. Now it's a board-level procurement call. Global spend on real-world data is on pace to hit roughly $2.7 billion this year, growing somewhere in the 14 to 16 percent range annually through the end of the decade. And the FDA has rewritten its real-world evidence guidance twice in the past nine months. Twice. That's not a pace anyone in this industry is used to.

Which is exactly why picking the wrong vendor gets expensive fast, not just in dollars but in time you don't get back. Below is a rundown of the real-world data companies that pharma, biotech, and health-system teams are actually shortlisting in 2026: what each one is genuinely good at, where each one falls short, and where a therapeutic-area-specific provider, a neurology-focused platform in particular, covers ground the generalists tend to leave open.

What Is a Real-World Data Company?

A real-world data company collects, links, de-identifies, and structures health information generated outside a controlled clinical trial. Electronic health records. Medical and pharmacy claims. Patient registries, lab results, and increasingly, clinical notes and imaging. A real-world data platform then makes that information queryable for a specific job, whether that's finding trial-eligible patients, building an external control arm, tracking a drug's real-world safety profile, or backing up a payer negotiation with actual outcomes evidence instead of a projection.

Worth pulling apart two terms people use interchangeably, because they're not the same thing. Real-world data, RWD, is the raw material. The claims record, the EHR note, the registry entry. Real-world evidence, RWE, is the clinical or policy conclusion someone draws after analyzing that data. Every real-world evidence company needs an RWD pipeline underneath it somewhere. But not every RWD provider actually does the analytics work required to turn that data into evidence. Plenty just hand over the dataset and let the sponsor's own team figure out the rest.

Why the Real-World Data Company You Choose Matters More in 2026

For most of the last decade, RWD procurement basically came down to coverage math: how many lives, how many years of history, how many data sources. That calculus changed with two FDA guidance documents that landed within four months of each other. In December 2025, the FDA finalized updated guidance on using real-world evidence in medical device submissions, and for the first time said sponsors won't always need to submit individually identifiable, patient-level source data. Reviewers will judge the strength of the evidence case by case instead. Then in March 2026, the FDA adopted ICH M14, a standard built jointly with EMA and PMDA that governs how non-interventional RWD studies get planned, analyzed, and reported for drug safety assessment.

Put those two together and here's what you get: a lower privacy barrier for using RWD, paired with a noticeably higher bar on data provenance. Sponsors now have to show that each individual clinical fact in a dataset traces back to its source. Not just that the dataset looks complete on the cover page. That distinction sounds bureaucratic until you look at what it actually catches. A 2024 study in npj Digital Medicine found that natural language processing of clinical notes picked up adverse social determinants of health in 93.8 percent of patients. Structured ICD-10 Z-codes on that same population caught 2.0 percent. Read that again if you need to, because it's not a typo. For therapeutic areas where the real clinical picture lives largely in unstructured notes, and neurology sits near the very top of that list, that gap is the difference between a submission-ready dataset and one that quietly falls apart the moment a reviewer asks where a number came from.

The Real-World Data Provider Landscape Isn't One Market. It's Several.

The mistake most buyers make, and it's an easy one to make, is treating "real-world data companies" like one interchangeable category. In practice the market splits along a few different lines:

• All-payer claims networks, which trade depth for breadth, versus EHR-primary consortiums, which trade breadth for clinical detail

• General-purpose platforms that try to cover every therapeutic area versus specialty platforms built around one disease category's specific data structure

• Data marketplaces that license access to information versus infrastructure and tokenization companies that link datasets together without ever holding the underlying records themselves

None of these models wins outright. It genuinely comes down to your study design, your therapeutic area, and whether the end goal is a regulatory submission, a payer dossier, or trial feasibility work.

Top Real-World Data Companies and Providers in 2026

1. NeuroDiscovery AI

NeuroDiscovery AI is a real-world data company built around neurology specifically, not neurology as one line item among forty other therapeutic areas a sales deck lists to look comprehensive. Its dataset spans 6M+ patient records and 3M+ active patients, sourced from a network of 1,000+ providers across 100+ clinical sites in 16+ states. But raw scale isn't really the differentiator here. It's what the platform does with the clinical detail that's unique to neurology: disease-specific rating scales, cognitive assessment scores, symptom trajectories that live almost entirely in unstructured notes rather than any coded field. NeuroLLM™, the company's neurology-tuned language model, extracts and structures those clinical variables directly from clinical documentation, instead of leaning on claims codes or ICD-10 fields that, as the FDA's own data-provenance push has made clear, only capture a fraction of what's actually written down.

Best for: pharma, biotech, and research teams running real-world data or evidence-generation programs in Alzheimer's, Parkinson's, epilepsy, dementia, and other neurological and neurodegenerative conditions, where disease-specific clinical depth matters more than checking a box for every therapeutic area at once.

2. IQVIA

Still the largest real-world data company out there by pure scale. IQVIA combines claims, EHR, and lab data with a global life-sciences consulting and commercial analytics business built up around it over decades. Its footprint touches regulatory-grade RWE generation, market measurement, and site or patient identification for trials, all under one roof. Top-20 pharma accounts routinely sign multi-million-dollar annual data and services contracts here, which tells you most of what you need to know about both its breadth and its reputation as the safe default pick.

Best for: large pharma and global life-sciences organizations that want one vendor covering data, analytics, and consulting across every therapeutic area.

3. Datavant

Datavant isn't really a research platform in the analytical sense, and it wouldn't claim to be. Think of it more as the connective tissue running underneath much of the rest of this list. Its tokenization technology links claims, EHRs, lab results, and specialty registries at the patient level without ever exposing protected health information, and its data marketplace keeps expanding partnerships with life-sciences companies, health systems, and federal agencies for both retrospective and prospective studies.

Best for: sponsors who need to connect otherwise incompatible datasets, say, linking trial participants to downstream claims or EHR outcomes, rather than license one all-in-one analytical platform.

4. Komodo Health

Komodo's all-payer claims network covers hundreds of millions of de-identified patient journeys. That makes it a natural go-to for epidemiology, treatment-pattern research, and rare-disease cohort identification, particularly in cases where broad longitudinal coverage matters more than granular clinical detail. Its conversational-AI analytics layer has also opened up exploratory querying to research teams who aren't necessarily coders themselves, which is a bigger deal internally than it might sound.

Best for: payers, pharma, and researchers who need patient-journey analytics and claims-based longitudinal coverage at scale.

5. Veradigm

Veradigm's real-world data comes straight from its own EHR footprint, which gives it point-of-care clinical detail (labs, vitals, structured problem lists) that pure claims networks simply don't have access to. It's frequently paired with a claims-based provider to fill in the side of a linked dataset that would otherwise be missing entirely.

Best for: studies that need EHR-sourced clinical detail, either integrated with claims data or as a standalone alternative to it.

6. Truveta

Truveta runs as a health-system-owned data collaborative, with normalized EHR data contributed directly by more than 30 major U.S. health systems. Because the data comes straight from the systems themselves rather than through a licensed third-party feed, sponsors focused on U.S. oncology and cardiovascular research tend to shortlist it as an EHR-primary alternative to the claims-heavy providers on this list.

Best for: U.S.-focused studies where health-system-sourced EHR data matters more than claims history.

7. TriNetX

TriNetX runs on a federated network model, which is a little different from everything else here. Instead of centralizing patient-level data in one place, it lets researchers query de-identified EHR data across participating health systems directly. That supports both cohort research and clinical trial site identification without ever moving raw patient data off-site. Independent methodological reviews have called it out as a significant platform for large-scale healthcare dataset research, though they also flag that EHR coding accuracy directly affects how reliable the results end up being. Worth keeping in mind with any federated network vendor before you sign.

Best for: rapid, federated cohort queries and trial feasibility work across a multi-health-system network.

8. Tempus

Tempus pairs molecular sequencing and genomic profiling with longitudinal clinical data, which makes it the go-to real-world data provider whenever a study needs biomarker-linked outcomes and claims or EHR data on their own just won't cut it. It's best known in oncology, no surprise there, but its partnerships keep expanding, including an enlarged collaboration with Merck announced in March 2026 that extends its data-and-AI model well beyond cancer.

Best for: studies where genomic or molecular data needs to be linked to real-world clinical outcomes.

Why Neurology Needed Its Own Entry on This List

Every generalist vendor above is built to serve every therapeutic area reasonably well, and that's exactly why none of them is built to serve any single one exceptionally well. Oncology has Flatiron and ConcertAI as EMR-based specialists. Rare disease has Komodo's all-payer breadth working in its favor. Neurology, by comparison, has historically gotten filed under "everything else" by most general-purpose RWD platforms, even though it carries some of the heaviest unstructured-data burden in medicine. Cognitive assessment scores. Disease-specific rating scales like UPDRS for Parkinson's, CDR for dementia, seizure diaries for epilepsy. Caregiver-reported functional status. Almost none of it ever makes it into a structured claims field or an ICD-10 code, and anyone who's tried to pull a clean neurology cohort from claims data alone already knows this.

That's the same gap the FDA's new provenance requirements are forcing into daylight across the entire industry, not just neurology. The npj Digital Medicine finding about structured codes missing the vast majority of documented social determinants applies just as directly to neurological symptom and function data buried in progress notes. A general-purpose real-world data platform can tell a sponsor how many patients carry a Parkinson's diagnosis code, sure. Reconstructing actual disease progression, medication response, and functional decline from years of unstructured neurology notes across community and academic sites is a different task entirely. It's the specific problem the platform at the top of this list was built to solve, not bolted on as an afterthought.

How to Choose the Right Real-World Data Company

Before signing with any real-world data provider, it's worth running the shortlist through a few honest questions rather than taking the pitch deck at its word.

• Claims, EHR, or both?
  • Claims data gives you longitudinal breadth: refills, visits, cost over time. EHR data gives you clinical depth: labs, notes, functional status. Most rigorous studies need both, so look for a vendor that's upfront about which one is native to their platform and which comes in through a separate data-linkage partner.
• How is unstructured data actually handled?
  • Given the FDA's new emphasis on tracing individual clinical facts back to their source, ask exactly how a vendor extracts information from clinical notes, and how that extraction gets validated. "NLP-enabled" printed on a slide isn't an answer by itself. Push for specifics.
• Is the therapeutic area a real competency, or just a checkbox?
  • A platform that lists forty therapeutic areas it "covers" is not the same thing as one actually built around your specific disease category's data structure.
• What's the regulatory submission track record?
  • RWE has supported more than 250 medical device authorizations and over 35 drug and biologic applications since 2016. Ask any vendor on your shortlist for precedent that matches your intended use case specifically, not just an aggregate patient count that sounds impressive on its own.
• How is patient privacy actually protected?
  • De-identification and tokenization methodology should be documented and auditable, not just asserted somewhere in a sales deck between two case study slides.

Common Features to Look for in a Real-World Data Platform

Across every model here, claims network, EHR consortium, federated query system, specialty platform, the strongest real-world data companies tend to share a handful of traits worth checking for on any shortlist:

• Documented data provenance, traceable to source for individual clinical facts, not just dataset-level completeness

• Transparent linkage methodology for connecting claims, EHR, lab, and registry sources at the patient level

• Validated structuring of unstructured data, with disclosed accuracy benchmarks rather than vague, black-box NLP claims

• Regulatory submission precedent relevant to your specific use case and target agency

• HIPAA-aligned de-identification and audit trails that can actually hold up under payer or regulator scrutiny, not just internal review

Final Thoughts

The real-world data market isn't growing this fast in 2026 just because more electronic records exist somewhere in the world. It's growing because the FDA, EMA, and PMDA are all actively rewriting how RWD gets evaluated for regulatory decisions, in real time, and that shift rewards real-world data companies that can actually prove where their data comes from and how reliably it was structured. It also exposes the generalist platforms that have leaned on raw scale for a little too long without much else to back it up. For neurology-specific programs, where so much of the clinically meaningful signal lives in unstructured notes rather than structured codes, a purpose-built real-world data provider like NeuroDiscovery AI is increasingly the more defensible choice, not just the more convenient one. For broader, cross-therapeutic-area needs, established players like IQVIA, Datavant, Komodo Health, Veradigm, Truveta, TriNetX, and Tempus each cover a different piece of the puzzle, and cover it well.