MLR Process Optimisation: Why Review Cycles Stall and What Fixes Them
Ask any Medical Affairs or marketing team about MLR and you will hear the same story. A piece of content that should have taken days took months. It went through five rounds of comments, two reviewers contradicted each other, and by the time it was approved the congress had passed.
The usual response is to blame a slow reviewer or a cautious legal team. That is almost never the real cause. Slow MLR is a design problem. The good news is that design problems can be fixed without adding risk.
Speed and safety are not a trade-off
The wrong assumption is that a faster review must be a less careful one. In practice, the opposite is usually true. Processes with clear standards, defined roles and reusable approved content are faster and safer, because reviewers spend their time on genuine risk instead of rework.
The five reasons review cycles stall
1. Every piece of content follows the same route. A minor update to an approved local leaflet goes through the same full review as a new launch campaign. Reviewers drown in low-risk work, and high-risk content waits in the same queue.
2. Roles overlap. Medical, Legal and Regulatory reviewers comment on the same issues, sometimes in opposite directions. Nobody owns the final decision on a disputed point, so it goes round again.
3. Content arrives unready. Drafts are submitted without references linked to claims, without a clear objective or audience, or before internal alignment. Review becomes a substitute for authoring standards.
4. Approved claims are not reused. The same claim is reviewed again and again in slightly different wording, across materials and markets, because there is no library of approved claims and modular content.
5. Global and local systems do not connect. Global content is approved, then adapted and re-reviewed in each market, often in different systems or configurations. Duplicate effort, inconsistent decisions and no single view of what is approved where.
Five fixes that work
Tier your review. Define content tiers by risk: new claims and campaigns, adaptations of approved content, and minor or administrative changes. Give each tier its own route, reviewers and service level. Low-risk changes should not wait behind a launch campaign.
Write down who decides what. A simple RACI for each content type: who reviews, who approves, and who has the final say when reviewers disagree. Pair it with a rule on comment quality, such as every comment must state the issue and the required change.
Raise the bar at submission. Introduce a short submission checklist: objective, audience, channel, claims linked to references, internal sign-off completed. Content that does not meet it goes back to the author, not into review. Rounds drop quickly.
Build a claims library and modular content. Approve claims once, with their references and conditions of use, then reuse them across materials and markets. Modular content lets teams assemble new materials from approved blocks, so review focuses on what is genuinely new.
Design for multiple markets from the start. Agree which elements are global and fixed, and which can be localised. Use one system configuration with consistent metadata across markets wherever possible. Local review then focuses on local requirements, not on re-checking global science.
Where AI fits in MLR
AI is starting to appear in review platforms and content tools. Used well, it supports reviewers rather than replacing them:
- Checking that each claim is supported by the linked reference.
- Flagging claims that differ from the approved claims library.
- Comparing versions and highlighting what changed.
- Pre-screening for missing safety information or required statements.
What AI should not do is make the final judgement on accuracy, fair balance or compliance. That stays with qualified human reviewers. And AI-assisted content needs its own rule: disclosure to reviewers, and human verification of every claim and citation before submission.
Before adding AI to MLR, make sure the basics are in place. AI on top of an unclear process produces faster confusion.
The platform is not the process
Many companies respond to slow review by changing or upgrading their review system. Platforms such as Veeva Vault PromoMats can make a real difference: workflow automation, version control, annotation and audit trails. But a platform runs whatever process you configure into it.
If the content tiers, roles and standards are unclear, a new system reproduces the same delays, only with better reporting on them. The most successful implementations define the process first, then configure the platform to support it, then train authors and reviewers on both. When several markets share one system, agree common metadata, content types and workflows at the start. Retrofitting consistency later is far harder.
How to measure progress
Pick a few measures and track them monthly:
- Cycle time by content tier, from submission to approval.
- Average number of review rounds.
- Share of submissions returned for not meeting the checklist.
- Reuse rate of approved claims and modules.
Share the numbers with authors and reviewers. Visibility alone changes behaviour.
Where to start
If MLR is slowing your team down, start with a simple exercise: take the last ten pieces of content approved, map how long each spent in each stage and why it went back. The pattern will tell you which of the five causes matters most in your organisation.
Want a second pair of eyes on it? Book a 20-minute call, or see how MLR Process Optimisation works, from RACI mapping and SLAs to content taxonomy and AI readiness.