HEALTHCARE

Clinical Trial & Life Sciences Data Intelligence

Turn your trial document library into a proactive signal detection system.

Upload protocol documents, patient diaries, adverse event reports, site monitoring logs, and interim study data to give clinical operations teams a structured, quantified view of the signals buried across trial documentation. Steeped AI maps nuanced language concepts across free-text fields with the consistency and rigor that regulatory submissions demand, including symptom descriptions, compliance language, investigator site concerns, and protocol deviation patterns.

Cross-tabulate topic-mapped clinical signals against structured variables like treatment arm, site, patient cohort, and timepoint to identify adverse event clusters, dropout predictors, and site performance gaps before they become protocol amendments. Three-part statistical significance testing ensures every pattern you escalate to a medical monitor or data safety monitoring board is defensible. Generate audit-ready reports with exact source citations that hold up to FDA and EMA scrutiny.

Trial Signal Citations
1 38% of "adverse_event" reports cited "grade 2 nausea"
2 29% of "dropout" cases mentioned "travel burden"
3 24% of "protocol_deviation" logs were "dosing window"
4 21% of "site_concern" notes flagged "enrollment lag"
5 18% of "compliance" gaps involved "diary adherence"
6 14% of "AE cluster" signals traced to "Site 07"
7 9% of "amendments" followed "monitoring findings"

Document Digitization

Use Steeped AI's document digitization to turn scanned protocols, patient diaries, and site monitoring logs into clean, structured fields. Paper and PDF trial records become searchable and analyzable alongside your EDC data, so no source document sits outside your signal detection.

SITE LOGSTRUCTURED FIELDSSite: 07AE: grade 2Arm: activeDeviation: minor

AI Topic Mapping

Use Steeped AI's AI topic mapping to quantify adverse events, protocol deviations, and investigator site concerns buried in free-text fields, with the consistency regulatory submissions demand. Cross-tabulate each concept against treatment arm, site, and cohort to catch clusters before they become amendments.

adverse eventprotocol deviationsite concern

Statistical Significance Testing

Steeped AI's automated significance testing applies a university-grade, 3-part framework so every pattern you escalate to a medical monitor or data safety monitoring board is defensible. A signal is only flagged when the statistics hold up, keeping safety review focused on real clusters, not noise.

AE Cluster Confirmedp < 0.01 ยท FDR corrected

Data Preparation

Use Steeped AI's data preparation to standardize site identifiers, coded terms, and visit dates across systems before analysis. When every source formats a site or an event differently, Steeped AI reconciles them first, so signal detection runs on clean, consistent, audit-ready data.

Site labels "07", "Site-07" and "S07" were unified into "Site 07"
Mapped free-text symptoms to standard coded terms
Visit dates in 4 formats were standardized to one calendar
Removed column "legacy_query" because 86% of values were blank
Duplicate AE entries for the same patient were merged into one
generating new dataset
NEW DATA

The insights are already in your data.

Ask your data anything. Get real findings ranked by impact, with AI reports your team can present and share on the spot.