Where MLR rework comes from, and how much of it is preventable

Quick answer: Most MLR review delays are not caused by scientific disagreement. In Vodori's 2025 State of Promotional Review Benchmarks Report, typos and errors account for 23.8% of the reasons content is sent back for recirculation, and branding issues account for another 9.9%. Neither requires medical, legal, or regulatory judgment to catch. Roughly a third of recirculation reasons could be resolved before content is ever submitted. That is preventable MLR rework, and it is what an MLR pre-check is built to eliminate.

Ask a life sciences marketing team where their review time goes and most will point at reviewers. The data points somewhere else.

Vodori's 2025 State of Promotional Review Benchmarks Report puts the average job duration for a piece of promotional content at 14.8 days. Of that, 7.6 days are review and 7.5 days are spent incorporating feedback. Half the timeline is not review at all. It is revision.

And the reasons content comes back are mostly mechanical. Typos and errors are the single most common cause, at 23.8% of recirculation reasons. Branding issues account for another 9.9%. Neither is a judgment call.

That is a different problem than a slow review process, and it has a different solution. Call it MLR rework: the revision cycles created by defects that never required a reviewer to find. Here is what the data shows, and what teams can do about it before content ever reaches a reviewer.

How long does MLR review actually take?

In 2025, the average job duration was 14.8 days, with an average review duration of 7.6 days and 7.5 days spent incorporating feedback. Content went through an average of 1.5 circulations before approval. Both durations rose slightly from 2024, and we broke down what is driving that shift in a separate analysis.

One important note about this data. These benchmarks come from teams already running a purpose-built MLR review platform, based on promotional review activity in Vodori between January and June 2025. This is not a picture of what happens without dedicated review software. It is what remains after that software is already in place. If any of these terms are unfamiliar, our MLR terminology guide breaks down how each metric is defined.

That 7.5 days of revision is the number worth focusing on. Teams can compress it by tightening how feedback is delivered, for example by distinguishing suggested from mandatory feedback. But the larger win is not catching content quality issues before the formal review process begins.

Why does content get sent back for recirculation?

In Vodori’s annual, proprietary benchmarks report, we systematically track the reasons content is returned for revision. Here is the data from 2025:

Not all of these reasons fall under the same category, but the top three reasons reveal that content quality is a key issue for many life science companies.

How much MLR rework is preventable?

A typo does not require a regulatory affairs professional to identify. Neither does a missing registration mark, a logo used at the wrong size, a content ID placed in the wrong corner, or a hyperlink that no longer resolves.

Every one of those issues can be detected before content is submitted. When they are not, they consume reviewer attention, trigger a circulation, and add delays in getting the content to market.

There is evidence that fixing this works. Owlet Baby Care tracked its reasons for recirculation and found trademark issues sending work back again and again. The legal team built a live trademark usage guide, and Owlet now achieves an 80% first-pass approval rate, well above benchmark.

While a trademark guide may not be the appropriate solution for all companies, this story uncovers the power of detecting what is causing the bottlenecks. In this specific case, a preventable error was getting to MLR review and causing delays. This is the primary case for a pre-check: a pass over content that looks for issues before anything is routed.

What should teams check before content reaches MLR review?

Most teams can improve the quality of what enters review without adding any technology. These steps build on the cornerstones of an effective MLR review process:

• Track your own reasons for recirculation. You cannot fix a pattern you are not measuring. This is exactly what surfaced the trademark issue at Owlet, and it is the starting point for most data-driven improvements to MLR review.
• Build guidance for your top recurring defect. A short reference document aimed at your single most common issue can pay dividends.
• Assign accountability for mechanical quality before submission. Reviewers approve at very different rates by role. Marketing approves on first circulation 79.2% of the time, while legal approves 55.7% of the time. There are practical strategies for improving review committee feedback that help close that spread.

Every one of these works without new technology, and teams should do them regardless. The common thread among these best practices is that they are all manual versions of a check a system could run automatically, on every asset, without anyone remembering to.

Where does AI help in MLR review, and where should it not?

Presently, we’re seeing AI being used primarily by marketing and commercial teams for content generation, but this has its drawbacks. In a world where life science companies are expected to generate large volumes of personalized content at scale, it’s easy to see the appeal.

However, the other side of this coin is that there can be issues with AI generated content, and this tends to show up in the review process. AI-generated content tends to sound polished and professional, but if not appropriately verified it can include blatant Inaccuracies, hallucinations, and misrepresentations. While not all of these issues can be caught with technology, many of them can.

MLR pre-checks are automated quality and governance reviews that run on promotional content before it is formally submitted to medical, legal, and regulatory review. They identify mechanical defects, such as typos, formatting problems, broken hyperlinks, incorrect logo use, and missing registration marks, without making approval decisions.

A pre-check is a narrow, verifiable task. Every finding is something a person can confirm or dismiss in seconds, and nothing is approved, rejected, or interpreted by a model. That is what makes it defensible.

This is the thinking behind Content Preflight, Vodori's MLR pre-check and the first capability on the Regulated Content Intelligence Platform. It reads .docx, PDF, HTML, and live websites, and it checks spelling, grammar, formatting, and hyperlinks alongside logo use, content IDs, and registration marks, inside the review system teams already use.

Mapped against the recirculation data, that covers the two largest rule-checkable categories in the table: the typos and errors driving 23.8% of recirculation reasons, and the branding issues driving another 9.9%.

What comes next

Typos and branding are low-hanging fruit, but the possibilities for AI-assisted content pre-check are wide . Claims issues sit at 13.5%, the second most common reason content is recirculated, and they are a harder problem to solve. Building a core claims document or reviewing and approving claims within your promotional review system are the most effective defenses teams have today.

This is the next problem we’re aiming to solve as part of Vodori’s Regulated Content Intelligence Platform. Capabilities coming are focused on operationalizing approved claims, supporting evidence, and scientific references as reusable enterprise knowledge across products, brands, and markets.

For now, the more useful question for most teams is a smaller one. Of the days your content spends in review, how many are being spent on issues that never needed a reviewer at all?

Frequently asked questions about preventable MLR rework

What is MLR rework?

MLR rework is the revision work created when content is returned during medical, legal, and regulatory review. Often, these are content quality issues that do not require reviewer judgment to identify, such as typos, formatting problems, broken links, or incorrect brand elements. In 2025, teams averaged 7.5 days per piece incorporating feedback, and about a third of recirculation reasons fell into this preventable category.

What is the most common reason content is sent back in MLR review?

Typos and errors, which account for 23.8% of recirculation reasons, followed by claims issues at 13.5% and branding issues at 9.9%.

What is the average MLR review time?

Among life sciences teams using a purpose-built review platform, the average job duration in 2025 was 14.8 days, including 7.6 days of review time and 7.5 days spent incorporating feedback.

What is an MLR pre-check?

An MLR pre-check is an automated quality and governance review that runs before content is formally submitted to medical, legal, and regulatory review. It identifies mechanical defects such as typos, formatting problems, broken hyperlinks, incorrect logo use, and missing registration marks, so that reviewers can focus on judgment-based questions. Vodori’s version of an MLR pre-check, Content Preflight, is built directly into the platform.

Is an MLR pre-check just spellcheck?

No. Spellcheck evaluates words in isolation. A pre-check also evaluates brand governance elements such as logo usage, content ID placement, and registration marks, and it works across document types including PDFs, Word files, HTML, and live websites.

Does using AI in MLR review mean AI is approving content?

No. A pre-check surfaces potential issues for a person to review. Approval decisions stay with the review team.

See where your review process stands

Download the full 2025 Benchmarks Report to see how your job durations, first-pass approval rates, and recirculation reasons compare.