Ask any quality director to walk you through their last significant CAPA and you will usually hear a story about a person. A coordinator who missed something. A site that did not follow the current version. A new hire who had not been fully trained. The corrective action, predictably, targets the person: retraining, re-acknowledgment, a memo, a meeting.
Six months later, the same category of deviation happens again, often at a different site, involving a different person. Because the person was never the root cause. The information delivery system was.
The retrieval problem nobody budgets for
Consider what we actually ask of a clinical research coordinator running several concurrent studies, which is to say, a typical coordinator. A typical oncology study can add as many as seventeen additional systems on top of what the site already runs, each with its own login (Applied Clinical Trials, 2025). Nearly seventy percent of sites report six or more separate logins per study, and in Advarra’s 2023 Study Activation Survey of more than five hundred sites, fifty-five percent ranked sponsor technology setup and training as their single greatest startup burden, ahead of budgeting and contracting. Layer on staffing: more than sixty percent of site professionals report shortages, and new research staff can take up to a year to fully ramp (2024 SCRS Site Landscape Survey; WCG).
Inside that maze lives every piece of information a coordinator needs to execute a protocol correctly: the current visit schedule, the amended lab kit instructions, the eligibility clarification from last month’s memo, the sample handling requirements that differ between the two studies she is running this morning.
Here is the point that reframes much of it: the answer almost always exists. Sponsors and CROs are diligent about producing and distributing information. The failure is often not production. It is retrieval, at the moment of need, with a patient in the chair.
Anatomy of a deviation
Walk through how a deviation actually happens, and the pattern is remarkably consistent.
An amendment is issued. It goes out by email and gets posted to a portal. The coordinator is mid-visit on another study when it arrives, and the days that follow are full. Formal retraining is scheduled, but it happens weeks before the affected procedure actually occurs. By the time that procedure is in front of her, at Visit 3, month four, the training is a faded memory competing with the procedures of two other protocols.
In the moment, she has a vague sense that something changed. Verifying means hunting: the inbox, the binder, the portal, a call to the CRA who may or may not pick up. The patient is waiting. So she makes a judgment call, and her judgment is good, because she is good. Most of the time the call is right.
Some of the time it is not, and the industry’s published analyses of deviation root causes read exactly like this story: inadequate or faded training, missed or misapplied amendments, delayed communication of changes, and inconsistent updates across sites that let some locations revert to outdated procedures. Add coordinator turnover, which runs high across the industry, and the institutional knowledge that would have caught the error keeps walking out the door mid-study.
Much of this is not negligence. It is a delivery architecture that relies on email, memory, and goodwill to move safety-critical information across the last mile, into the hands of the person performing the procedure, at the moment they perform it. People stay in the loop, so deviations will still happen; the opportunity is the preventable, information-driven subset.
Why the standard fixes underperform
The industry’s reflexive answers are more training and more communication. Both matter, and both hit the same wall: timing. Training delivered at site initiation decays before it is used. Communication pushed through email competes with everything else in the inbox. Read-and-acknowledge tracking proves a document was opened, not that its content will be recalled at the right moment three months later.
Portals were supposed to help and mostly added another login. Single sign-on initiatives help at the margin but do not change the fundamental shape of the problem, which is that the knowledge lives in one set of places and the work happens in another.
The fix has to invert the model. Instead of pushing information out and hoping it survives until the moment of use, the current, approved answer has to be available at the moment of use, inside the system where the work is already happening, specific to the trial, the site, and the role of the person asking.
What just-in-time guidance has to get right
This is where AI enters the conversation, and where the conversation usually goes wrong. A general-purpose chatbot pointed at a folder of documents is a liability in clinical research. A plausible wrong answer about a visit window is a deviation with good grammar. If AI is going to participate in trial conduct, the bar is categorically different from consumer AI, and it is worth being explicit about what that bar is.
First, closed-world answering. The system answers only from the approved documents for that specific trial: the protocol, the manuals, the training, the SOPs. If the answer is not there, it says so and routes the question to a human rather than improvising.
Second, citation as a default. Every answer names its source document and section, so the coordinator can verify in seconds and a monitor can trace any piece of guidance to its origin.
Third, scoping. An agent serving a multi-study site must be sealed per trial. The coordinator running three protocols cannot risk an answer from the wrong one, so the agent has to know which trial, which site, and which role it is serving, and answer only within that boundary.
Fourth, auditability. Every interaction logged and reviewable, so that the question “how do you know your sites received and understood this change?” has a better answer than a forwarded email chain. Done properly, the guidance layer becomes inspection evidence rather than inspection risk.
Fifth, and least appreciated: placement. Adoption fails when help requires another login. Guidance has to live inside a system the site staff are already in all day. For most trial work, that means inside the CTMS itself, surfaced when the user logs in, available while they work.
The measurable claim
When information delivery moves from push-and-hope to surfaced-at-the-moment, the effects show up in metrics sites and SMOs already track: deviation rates after amendments, query volume on affected procedures, ramp time for new staff, and the variance between sites running the same protocol. That makes this unusually testable for a new category. Pick one live trial, instrument one cycle, and compare.
The coordinators were rarely the root cause. They have been compensating for a broken delivery chain, brilliantly, for years. It is time the information met them where they work.
Caleb Costa is the CEO of Revinova, which pairs site readiness training with per-trial AI agents embedded in the CTMS, drawing only from the current approved study documents, with the source and version shown.
Placement notes
- For Applied Clinical Trials or DIA submission, drop the bio line’s product description to a neutral one-liner and remove nothing else; the piece intentionally sells the category, not the product, until the final section.
- For LinkedIn, publish as an article and excerpt the “Anatomy of a deviation” section as the teaser post.
- The five requirements in “What just-in-time guidance has to get right” double as your analyst briefing framework and your RFP answer structure. Reuse them everywhere; repetition builds the category.
