Stop Making Every Trial System Interpret the Protocol Again

When a study team resolves what a protocol requires, that answer should travel with the study. Budgeting, database setup, site instructions, and visit scheduling should be able to use the reviewed information without each team having to extract and interpret it again.

AI can help pull requirements from protocol documents. A useful next step is to make those requirements reusable, starting with one shared task: turning the schedule of activities into information that different teams can check and use.

Start With One Shared Set of Requirements

The schedule of activities is a practical starting point because several functions need the same underlying facts. They need to know which assessments occur, at which visits, within which windows, and under what conditions.

Ask AI to organize those requirements into a consistent format that includes:

The activity or assessment
The applicable visit and timing window
Any conditions or exceptions
The supporting protocol section or footnote
The protocol version
The review status and any unresolved questions

Include relevant narrative sections and footnotes in the extraction. A table entry may look straightforward until a note changes who needs the assessment or when it must occur.

The initial goal can be modest: give two teams a reviewed set of requirements they can both use.

Resolve Ambiguity Before Passing It Along

Consider a hypothetical protocol that lists a laboratory assessment at every treatment visit, with a footnote saying it can be omitted when a recent result is available. A budgeting team needs to understand how often to cost it, while a scheduling team needs to know when to arrange it.

If each team interprets that footnote separately, they may build different assumptions into their work. A shared record can preserve the condition, identify the source, and document any clarification from the appropriate protocol owner.

AI should flag uncertainty for review rather than quietly choose an interpretation. Once the question is resolved, the answer should become available to everyone using that requirement.

Reuse the Facts, Then Apply Each Team’s Expertise

Shared protocol information still needs to be adapted to each workflow. A budget team adds rates and payment assumptions; a data management team determines how to collect the required information; site-facing teams translate requirements into practical instructions.

Those decisions belong with the relevant specialists. What they can reuse is the reviewed definition of the underlying activity, including its timing, conditions, and source.

Before connecting systems, ask the receiving teams what information they need and in what format. A consistent activity name or identifier can help them recognize the same requirement across tools, even when each tool organizes its work differently.

Make Amendments Easier to Follow

Reusable information becomes especially valuable when the protocol changes. Teams need to understand which requirements changed and what those changes affect.

For each revised requirement, preserve:

The previous and updated wording
The source and version of the change
The teams or systems using that requirement
Who must review and implement the update

AI can assist with comparing versions and identifying possible effects. The responsible teams should confirm those findings before updating budgets, systems, or site instructions.

This gives amendment review a clearer starting point and helps teams check that a change reached every affected workflow.

Test Whether Reuse Actually Reduces Work

Start with one protocol and two connected workflows, such as budgeting and visit scheduling. Have the teams use the same reviewed activity list, then record what still requires clarification, correction, or manual re-entry.

Measure the total effort involved, including extraction, review, formatting, and downstream corrections. Also check whether teams encounter fewer conflicting interpretations and can trace requirements back to their sources more easily.

A faster extraction is useful only if the resulting information helps the next person do their job. The strongest reason to expand is evidence that teams can reuse the work with less effort and greater consistency.

Explore More Ideas From SCOPE

Explore practical ideas emerging from SCOPE events on AI, protocol design, study startup, and clinical trial operations. SCOPE 365 Insights connects those discussions with approaches teams can consider in their own work.