Run the study across every sponsor, without ever pooling their data.
You operate multi-sponsor, multi-site studies where no one will hand over their raw patient data, and rightly so. Polnor runs the same protocol inside each sponsor's own cloud, on an HDS-certified host, and lets you compare aggregate results across sites. You are the operator, not the custodian.
One protocol, three sponsors, no data ever pooled.
A multi-site study run for three sponsors who will not share raw data with each other or with you. You define the cohort once, the same standardized pipeline runs in each sponsor's own environment, and only aggregate counts come back to be compared. Not a single patient row leaves any sponsor's cloud.
| site | eligible n | event rate | contributes |
|---|---|---|---|
| Sponsor A | 12,480 | 3.0% | counts only |
| Sponsor B | 9,215 | 2.7% | counts only |
| Sponsor C | 15,902 | 3.4% | counts only |
| pooled estimate | 37,597 | 3.1% | ref |
Illustrative interface and figures, shown to explain the workflow. Not results from a Polnor customer.
Standardized in every environment, so results are comparable.
The same automated pipeline runs identically in each sponsor's cloud: ingest, de-identify, standardize, quality-check. Because every site is prepared the same way, the aggregate numbers you compare actually mean the same thing.
Each sponsor's data in, at the FHIR standard
Inside each sponsor's environment, claims, EHR extracts and lab feeds are pulled and normalized to FHIR R4 automatically, one typed table per resource. No bespoke ETL per sponsor, and no data crossing an environment boundary.
| sponsor | resources | status |
|---|---|---|
| Sponsor A | FHIR R4 | ✓ auto |
| Sponsor B | FHIR R4 | ✓ auto |
| Sponsor C | FHIR R4 | ✓ auto |
Identical de-identification and OMOP mapping at every site
Direct identifiers are masked and dates shifted by the same preset in each environment, then the data is converted to the OMOP CDM with OHDSI concept mapping and clinical quality control. Standardizing every site the same way is what makes the cross-sponsor comparison valid, not just convenient.
| source | → OMOP concept | std |
|---|---|---|
| atc:X | ingredient X | S |
| icd10:I26 | Pulmonary embolism | S |
| loinc:718-7 | Hemoglobin | S |
Define the cohort once, compare the aggregates
This is the part your study team owns: write the eligibility and endpoint logic once, dispatch it to every sponsor's environment, and read back only aggregate counts to compare. If a model is in scope, train and track it with MLflow next to the data. Nothing patient-level is ever exported.
| site | eligible n | exported |
|---|---|---|
| Sponsor A | 12,480 | 0 rows |
| Sponsor B | 9,215 | 0 rows |
| Sponsor C | 15,902 | 0 rows |
The platform does the plumbing. Your team runs the study.
The line is deliberate: the repetitive, error-prone, compliance-heavy work runs itself in every environment, so your study team spends its time on protocol and interpretation, not on reconciling three incompatible datasets.
Nothing to exfiltrate, because you never hold the data.
Each sponsor's environment is isolated: their data never mixes with another sponsor's and is never pooled. Every access to patient data is logged, PHI is classified, and export follows EHDS-aligned manifests. Because the control plane is health-blind and the data stays in each sponsor's HDS-certified cloud, there is nothing on your side to leak.
One protocol, every sponsor, no data pooled.
Book a 30-minute demo. We run the automated pipeline across a mock multi-sponsor study and show you exactly how the same standardized logic executes in each environment while only aggregate results come back.