Resources for medical writers & MLR teams
Updated 2026-07-13 · markdown version
- MLR Review Software: The Complete Landscape (2026)
A pharma-accurate map of the MLR (medical-legal-regulatory) review software market in 2026 — Veeva Vault PromoMats, Vodori, Papercurve, Lithero, Revisto, SecureCHEK, and where AI pre-review tools like Claims fit.
- Veeva Vault PromoMats Alternatives — a Pharma-Accurate Guide (2026)
Most "PromoMats alternatives" lists are wrong — they compare digital asset managers, not promotional review platforms. Here is the accurate alternative set for MLR review, by use case: Vodori, Papercurve, Lithero, Revisto, and writer-side tools like Claims.
- How to Build a Claims Matrix (That Survives MLR)
A practical guide to building a pharmaceutical claims matrix — structure, columns, referencing standards, maintenance — and why spreadsheets break down as the single source of truth for claims and their supporting evidence.
- How to Speed Up MLR Review: Cut Cycles, Not Corners
Practical, compliance-safe ways to reduce MLR review cycles for pharma promotional material — cleaner claim substantiation, pre-review checks, annotation standards, and where AI pre-review tools genuinely help.
- AI Tools for Medical Writers, by Segment (2026)
Most "AI for medical writing" lists conflate clinical scribes with regulatory and promotional tools. A segment-accurate guide: regulatory writing, medical affairs, med comms/promotional, and what freelance medical writers can actually adopt themselves.
- MLR Review vs Copy Approval: US and UK Promotional Review, Translated
The same job has different names and different law on each side of the Atlantic — US MLR review under FDA/OPDP versus UK copy approval and certification under the ABPI Code and PMCPA. A side-by-side translation for teams working across both.
- Claim Annotation & Reference Discovery, Explained
What claims annotation (annotated references, referenced copy) actually involves in pharma promotional work — the page/column/paragraph locator format, why automation kept failing at it, and how evidence-linked writing changes the workflow.