Clinical data automation that keeps every step traceable.
The Healytex platform connects data ingestion, standards mapping, analysis dataset generation, statistical outputs, quality checks and submission packaging through one metadata-driven workflow. Healytex designed it to automate repetitive work while keeping expert review, sponsor standards and study-specific decisions visible.
Connect every data source in 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% |
Map source data to SDTM without building every mapping by hand.
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 is reusable on 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.
Produce the 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 record is complete before final delivery, rather than assembled 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 process requires.
Watch data quality and study operations while the study is running.
Monitor data arrival, reconciliation, missingness, anomalies, issue aging, vendor performance and selected unstructured information, so study teams can fix problems before downstream programming uncovers them.