A modern clinical data warehouse, on infrastructure you control.
Care improvement, quality indicators, research cohorts: the questions are clinical, but the answers wait behind ETL, de-identification and standardization. Polnor stands up the analytical foundation of a modern EDS inside your own cloud, on an HDS-certified host, with a pipeline where almost every step runs itself.
Which patients came back within 30 days?
A care-improvement question, answered on your own clinical data. From your FHIR server to a standardized cohort, you define the index admissions, count the readmissions, and read a rate, without a single row leaving your cloud. Here is the path, and how little of it is manual.
| metric | value | window |
|---|---|---|
| index admissions | 18,647 | ref |
| 30-day readmission rate | 11.4% | 30d |
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 clinical data. You start where the clinical question starts.
Straight from your FHIR server, at the standard
Your hospital source is pulled and normalized to FHIR R4 automatically, one typed table per resource. No bespoke ETL per feed, and nothing to reconcile by hand.
| resource | rows | status |
|---|---|---|
| Encounter | 2,418,904 | ✓ auto |
| Condition | 6,201,550 | ✓ auto |
| Observation | 14,882,110 | ✓ auto |
GDPR de-identification and OMOP mapping, hands-off
Direct identifiers are masked and dates shifted by preset, then the data is converted to the OMOP CDM with OHDSI concept mapping and clinical quality control. Your cohorts run on standardized concepts, not on free-text local codes.
| source | → OMOP concept | std |
|---|---|---|
| icd10:I50 | Heart failure | S |
| icd10:J44 | COPD | S |
| loinc:718-7 | Hemoglobin | S |
Cohorts, indicators and models, where the clinic is
This is the part your clinical and quality teams own: define index admissions and readmission windows, compute the indicator, and, if you need it, train and track a risk model with MLflow. Everything runs on compute next to the data, and nothing is exported.
| model | AUC | endpoint |
|---|---|---|
| readmit-risk-30d | 0.81 | /v1/readmit |
| los-predict | 0.86 | /v1/los |
The platform does the plumbing. Your team does the clinic.
The line is deliberate: the repetitive, error-prone, compliance-heavy work runs itself, so your clinical and quality teams spend their time on judgment, not preparation.
An EDS you can account for, with a trail behind it.
Every access to patient data is logged, PHI is classified, retention is enforced, and export follows EHDS-aligned manifests. Because the warehouse runs on infrastructure you control, on an HDS-certified host, and we never train our models on your data, the provenance of every cohort is unambiguous.
From your FHIR server to a defensible cohort, in your cloud.
Book a 30-minute demo. We run the automated pipeline on a case that looks like yours, and show you exactly where the automation ends and the clinical work begins.