Loading…Clinical trial and supply integrity monitoring | H2O.ai
Clinical trial and supply integrity monitoring
AI clinical trial and supply integrity monitoring flags unusual patterns in site data, sample distribution, and supply movements, with reasons behind each flag, for your monitors and compliance teams to review.
H2O Driverless AI can be used to score sites, samples, and shipments for unusual patterns against your own history, with reason codes that show what drove each score.
Document AI can be used to turn paper case report forms and site source documents into structured records that can be checked against what was entered.
An agent can assemble each flag into a review file with the records behind it, so your monitor or compliance lead starts from evidence, not a search.
A flag is a question, not a conclusion. Your monitors, quality, and compliance teams decide, and no life sciences result is published for this use case today.
Integrity issues hide in the data you already hold
Your trial produces data from dozens or hundreds of sites, and central monitoring can only look at so much. A site whose values are too uniform, whose visits cluster on the same days, or whose entries arrive late can look normal one listing at a time.
Questions people ask before they call us.
AI can be used to flag sites whose data looks unusual next to their peers, such as values that vary too little, visits that cluster, or entries that arrive late. H2O Driverless AI scores each site with reason codes, and your central monitoring team decides what to look at.
Start owning your AI
One platform. Complete sovereignty. Measurable results.
Samples and product move through representatives, distributors, depots, and returns. A request pattern that does not match prescribing, or a lot that turns up where it should not, is easy to miss when each record sits in a different system.
Paper still carries part of the story. Case report forms, site logs, and source documents arrive as scans, and checking them against the entered data is slow manual work.
How clinical trial and supply integrity monitoring works
H2O Driverless AI builds the anomaly and risk models; Document AI reads the paper record; an agent assembles the evidence for review.
Step 1: score site-level data for anomalies
H2O Driverless AI scores site-level signals your team already collects: visit timing, data entry lag, query rates, and value distributions, compared with a site's peers. Each score comes with reason codes that show which signals drove it.
Step 2: check sample and supply movements
The same approach runs on sample and supply records: requests by representative and prescriber, shipments, returns, lot and serial movements, and distributor orders against expected demand. A model can flag patterns that differ from a peer group, such as sample requests that do not track prescribing.
Step 3: turn paper records into structured data
Document AI extractors read scanned case report forms, site logs, and source documents, including handwriting and tables, and return structured records that match a schema you define. Those records can be compared with what was entered in your data capture system, so a mismatch shows up as a candidate query.
Step 4: assemble the review file
An agent gathers the records behind a flag, writes a short summary with a citation to each record, and routes it to the right reviewer. It does not contact a site, close a query, or open an investigation; your people do.
A flag is a question, not a conclusion
Your monitors, quality, and compliance teams decide what a flag means, and there can be many ordinary reasons for one. H2O makes no claim of GxP, 21 CFR Part 11, or FDA validation; the platform supports validated workflows, but the validation is yours.
