Deploying Agentic AI for Clinical Trial Protocol Deviation Monitoring at Bayezian Limited
Detecting protocol deviations in clinical trials has traditionally required study teams to manually cross-reference spreadsheets and timelines against lengthy protocol documents — a process described as time-consuming, easy to miss context, and prone to delay.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Document type classification
An early classifier determines what type of clinical document has arrived — screening form or post-randomisation visit report.
Tools used
FAISSPython
Outcome
After targeted improvements including structured memory snapshots, stronger handoff signals, and clearer prompts, the system proved genuinely useful: it spotted patterns early, added pace to routine checks and consistency to decisions, and shifted reviewers from manual line-by-line scanning to focused triage of edge cases.
What failed first
The multi-agent system suffered repeated handover failures — deviations correctly identified by one agent were lost or misclassified by the next — and fragile time-window reasoning, illustrated by a false positive on a Day 14 lab test that the agent raised despite a Day 13 test (within the allowed two-day window) already being on record.