Find the responder subpopulation, from real-world data, without ever exporting it.
Sovereign by design: hosted on OVHcloud, infrastructure qualified SecNumCloud 3.2 and HDS-certified. Translational hypotheses and trial designs live or die on who responds. Polnor lets you interrogate real-world clinical data inside your own cloud to define a biomarker subpopulation and sharpen your next study.
Which patients carry the marker that predicts response?
A translational question, answered on real-world data. You segment a population by a lab or genomic marker, size the biomarker-positive subpopulation, and read its response signal, without a single row leaving your cloud.
| marker status | n | response rate | enrichment |
|---|---|---|---|
| biomarker-positive | 3,417 | 48.6% | 2.4× |
| biomarker-negative | 18,905 | 20.1% | ref |
Illustrative interface and figures, shown to explain the workflow. Not results from a Polnor customer.
Everything up to the cohort is automated.
By the time your team writes that query, the hard part is done: the platform has already ingested, de-identified, standardized and quality-checked your data. You start where the question starts.
Your real-world data, at the FHIR standard then OMOP
EHR extracts, lab feeds and genomic panels are normalized to FHIR R4 then converted to OMOP CDM with OHDSI mapping and quality control. Your biomarker strata are defined on standardized LOINC measurements, not free text.
| source | → OMOP concept | std |
|---|---|---|
| loinc:3037639 | Biomarker assay | S |
| icd10:C50 | Malignant neoplasm | S |
| atc:L01 | Antineoplastic agent | S |
The platform does the plumbing. Your team does the science.
The repetitive, compliance-heavy work runs itself, so your scientists spend their time on judgment, not preparation.
Findings you can carry into a trial design, with a trail behind them.
Every access to patient data is logged, PHI is classified, and export follows EHDS-aligned manifests. Because data never leaves your SecNumCloud/HDS cloud and we never train our models on it, the provenance of your subpopulation is unambiguous.
From raw RWD to a biomarker subpopulation, in your cloud.
30 minutes: we run the pipeline on a case like yours and show you where the automation ends and the science begins.