Principia Health Sciences / est. 2020 / Cary, NC
Principia builds CuRE — Collaborative Research Ecosystems — so disease associations, clinics, patients, CROs, and sponsors can use one governed record across the entire research pipeline. EHR, registry, lab, patient-reported, manufacturing, shipping, biomarker, and cytogenetic data resolve to OMOP, then flow into analytics, quality, safety, TMF, and regulatory outputs without rebuilding the dataset for every question.
01 / Platform overview
At the heart of CuRE is one governed OMOP record. The suite reads the way evidence flows: collected at sites and from patients, governed into one record, operated and monitored, analyzed, and submitted. Every product appears once, in the stage where it does its work.
02 / Why CuRE is different
Not another EDC with a data lake bolted on. CuRE is an end-to-end research pipeline: one governed record, cross-modal evidence, regulated outputs, and validation evidence carried through every product.
Clinical, registry, patient-reported, safety, TMF, regulatory, and AI-assisted work all resolve to the same governed OMOP substrate instead of reconciling flat-file islands.
FHIR, CRO files, registries, labs, claims, and patient inputs map once to OMOP; downstream products carry that evidence toward SDTM/CDASH, TMF packages, eCTD sequences, registrations, and labels.
Ask whether cryopreservation approach, manufacturing variance, shipping time, biomarkers, ePRO adherence, and EHR history affect CAR-T outcomes without building a one-off dataset.
21 CFR Part 11, EMA Annex 11, HIPAA, validation evidence, audit trails, source lineage, AI logs, and regulatory acknowledgements are product substrate, not a late validation project.
CuRE removes research busywork: fewer duplicate entries, fewer reconciliation passes, fewer manual queries, less deck assembly, and less thinking required to move from question to evidence.
Streamline or eliminate research steps instead of automating the same old handoffs.
See how the platform fits together03 / Benefits of integration
Integration is not just a cleaner architecture diagram. It changes the questions a research organization can ask, because operational work, clinical evidence, safety, quality, and regulatory outputs all point back to the same governed record.
Read the whitepaperEDC, operations, safety, RBQM, EHR, and outcomes resolve to one governed record.
Site quality is measured by total evidence burden, not just enrollment speed.
Supply, chain-of-custody, clinical outcomes, and safety data stay linked.
Operational variance becomes clinical insight.
Protocol operations, EHR eligibility, site execution, and longitudinal outcomes can be queried together.
Design studies around patients who actually exist and sites that can execute.
EHR workflow, study operations, care-team activity, and patient outcomes share context.
Measure whether point-of-care engagement changes the study, not just clicks.
Site capability profiles come from governed evidence, not self-reported feasibility PDFs.
Feasibility from evidence, not surveys.
Regulatory lifecycle records remain connected to the evidence and analyses behind them.
Regulatory content stays connected to the evidence record.
Real-world evidence happens in patient journeys.
Clinical research is information-rich. The interface earns space with alignment, tabular data, and hierarchy instead of padding everything into sameness.
Teal means automated, amber means attention, sky means information, and magenta marks AI-assisted work. Color carries state.
Study, site, patient, cohort, and time cursor should follow the user across surfaces, pages, and refreshes.
Every important value should show whether it was entered, ingested, suggested, reviewed, locked, or overridden.
AI features should expose confidence, source material, and correction paths instead of hiding authorship behind a magic button.
04 / The unified record
EHRs, labs, registries, claims, patient apps, and cytogenetics map through open standards into one governed record — FHIR on the way in, OMOP as the analytical backbone, submission-grade SDTM/CDASH on the way out.
05 / Parsing at scale
CuRE Cyto turns free-text ISCN karyotypes into structured, analyzable records — automatically, as a step in your data pipeline rather than manual re-keying. Chromosome count, sex, abnormality type, breakpoints, and cell counts resolve into the governed OMOP record, ready for cohorts and analytics.
The same engine runs live in your browser — paste a karyotype or pick an example to watch it resolve on the page.
See the full cytogenetics workflowEnter an ISCN string above or click an example to see it parsed.
This is a lightweight demo — for full ISCN 2016/2020 parsing, visit cyto.principia.health
06 / From the team
Electronic data capture was built for trials, not for the longitudinal reality of outcomes research.
Read moreEveryone agrees RWE is the future. Almost nobody has the infrastructure to generate it systematically.
Read moreA practical look at the cross-domain questions CuRE can answer when research operations, evidence, and regulated outputs share one data spine.
Read moreTalk to Principia
We engage leading non-profits, health systems, and biopharma organizations on programs ranging from data integration and report automation to full CuRE network buildouts.
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