Category: Data & AI Infrastructure
Real-world data arrives messy — inconsistent coding, missing values, duplicate records, free-text fields — and the curation needed to make it analyzable consumes the time that was meant for analysis.
The distance between raw healthcare data and a dataset an analyst can trust is long: diagnoses coded inconsistently across sites, key clinical facts trapped in free-text notes, duplicates and gaps that bias any naive count, and variables that mean different things in different source systems. Teams that license raw data discover that curation — not analysis — dominates the project timeline, and that every study repeats much of the same cleaning. Curated data platforms and AI-assisted curation tools move that work upstream, applying consistent, documented processing once so each study doesn't start from raw material.
Modern curation combines deterministic rules with machine learning where rules run out: standard vocabularies and mappings handle structured fields, while language models extract clinical facts — findings, results, disease characteristics — from free-text notes and reports that structured fields never captured. Human clinical reviewers audit samples of the machine output, and the measured accuracy of each extracted variable becomes part of the data documentation. The result is a dataset where each variable carries a lineage: where it came from, how it was derived, and how reliable it measured. That documentation is what separates research-grade curation from silent cleaning — an analyst can decide whether a variable is solid enough for the question at hand instead of inheriting invisible judgment calls.
Ask for the quality documentation before the demo: which variables are extracted versus native, what accuracy was measured for each against clinician review, and how missingness is reported. A vendor that publishes per-variable validation statistics is making a testable claim; one that says the data is 'research-grade' without numbers is making a slogan. Provenance matters as much as accuracy. For any derived variable, you should be able to trace the derivation logic and, where licensing permits, drill toward the source record. And because curation methods change as models improve, ask how versions are managed — a longitudinal study needs to know whether a variable's definition shifted mid-stream.
The standard proving exercise is replication: take a question your team has already answered on data you trust — a published cohort, an internal analysis — and rerun it on the curated data. Agreement builds justified confidence; disagreement localizes exactly where the curation differs from your assumptions. Teams also commonly start with a chart-review comparison, having their own clinicians verify a sample of extracted variables against source documents where the licensing model allows it.
Only as far as it has been measured. Extraction accuracy varies by variable — some clinical facts are stated plainly in notes, others require interpretation — so credible vendors validate each extracted variable against clinician review and publish the results. Treat unvalidated extraction as a hypothesis, not data.
Documented provenance for every variable, measured accuracy for anything machine-derived, explicit missingness reporting, and version control over the curation process. The phrase has no regulated definition, so the documentation — not the label — is what you are buying.
Both models exist. Vendor-curated data arrives analysis-ready but embeds the vendor's judgment calls; in-environment tooling keeps control and auditability with your team at the cost of more work. Regulated use cases often push toward arrangements where the curation logic itself is inspectable.
Practitioners consistently describe data preparation as the majority of project effort when starting from raw sources — which is precisely the economic argument for curated platforms. The honest comparison is total time-to-answer and reproducibility across studies, not the license price alone.
Intercept Pharmaceuticals worked with Komodo Health to apply its Healthcare Map real-world data to primary biliary cholangitis, supporting patient-journey and treatment-pattern analyses for its Ocaliva franchise.
Vendor: Komodo Health · press release (Komodo Health (Business Wire)) · 2022-11-10 — Partner: Intercept Pharmaceuticals