A clinical data automation layer built around traceability.
The Healytex platform connects data ingestion, standards mapping, analysis dataset generation, statistical outputs, quality checks and submission packaging through one metadata-driven workflow. It is designed to automate repetitive work while keeping expert review, sponsor standards and study-specific decisions visible.
Connect the data behind the study.
Ingest recurring extracts and transfers from EDC, central labs, eCOA/ePRO, imaging, safety, randomization, wearables and specialty vendors. Track source versions, schema changes, file arrival, reconciliation status and downstream lineage from one governed workspace.
| Source | Last Arrival | Status |
|---|---|---|
| EDC (Medidata) | 2026-08-28 | Synced |
| Central Lab | 2026-08-27 | Synced |
| eCOA Vendor | 2026-08-26 | Reconciling |
| Imaging Core Lab | 2026-08-25 | Synced |
| Source Var | SDTM Target | Confidence |
|---|---|---|
| SUBJECT_ID | USUBJID | 98% |
| VISIT_NUM | VISITNUM | 95% |
| LAB_RESULT | LBORRES | 72% |
Move from source to standardized data with less manual mapping.
AI-assisted mapping proposes domains, variables, source relationships and terminology using study context and reusable sponsor standards. Qualified users review and approve the mapping before controlled transformation. Every approved mapping becomes reusable institutional knowledge for the next study.
Turn analysis specifications into reproducible datasets.
Manage digital analysis specifications, derivations, population flags, parameters and dependencies in a controlled workspace. Generate analysis-ready datasets from approved inputs while maintaining lineage back to the source and standardized data.
| Variable | Derivation | Version |
|---|---|---|
| TRT01P | DM.TRT01P | v1.4 |
| SAFFL | SAFETY population flag | v1.2 |
| ITTFL | Intent-to-treat flag | v1.1 |
Table 14.1.1 — Demographics (Safety Population)
Configure recurring outputs instead of rebuilding them.
Create approved tables, listings and figures from reusable shells and structured analysis metadata. Update specifications, regenerate outputs and preserve versions without re-creating routine programming logic for each review cycle.
Make validation continuous.
Run standards, metadata, sponsor and cross-dataset rules throughout development. Centralize findings, explanations, resolutions and approvals so the quality story is available before final delivery—not reconstructed at the end.
| Rule | Domain | Severity |
|---|---|---|
| SD0001 | DM | Medium |
| AD0012 | ADSL | Resolved |
| LB0045 | LB | Low |
| Artifact | Status |
|---|---|
| SDTM Datasets (12 domains) | Ready |
| ADaM Datasets (5 datasets) | Ready |
| Define-XML | Ready |
| Annotated CRF | In Review |
Keep delivery artifacts synchronized.
Manage submission datasets, metadata, annotated CRFs, reviewer-guide inputs, output packages and validation evidence against controlled study versions. Export the deliverables your regulatory operations workflow requires.
See operational and data-quality signals while the study is running.
Monitor data arrival, reconciliation, missingness, anomalies, issue aging, vendor performance and selected unstructured information so study teams can act before downstream programming becomes the place where problems are discovered.