Case Studies / Pharma material approval optimisation: source verificat…
Medical Affairs Evidence Generation Omnichannel Content Cross-TA

Pharma material approval process optimisation: source verification, slide redesign, and MLR tagging streamlined

Challenge
A medical communications team was losing weeks in the MLR approval cycle due to inadequately referenced materials, inconsistent scientific source citation, and slides not meeting approval team standards.
Approach
Audited the existing materials against approval requirements, rebuilt the referencing architecture, redesigned slides to compliance standards, and implemented a systematic MLR tagging workflow.
Result
Approval cycle reduced by an estimated 35% across the material portfolio; team received compliance-ready, fully referenced materials with zero citation errors.
The challenge

The approval cycle was not broken — the materials going into it were

A pharmaceutical brand's Medical Affairs team was experiencing persistent delays in their material review and approval cycle. On average, materials were going through 3–4 rounds of medical-legal-regulatory review before clearance — a process that should have taken 2–3 weeks was taking 6–8 weeks per document.

An internal review identified the root causes: source documents were not consistently cited to the specific data points they supported; references were not formatted to the standard expected by the MLR team; slides contained claims without clear evidentiary backup; and the tagging system used to associate claims with source documents was inconsistently applied.

The problem was not the approval process — it was the quality of the materials entering it. Every returned document created rework, re-review, and additional delays. The team was spending more time managing the approval cycle than producing new materials.

MLR is not a bureaucratic obstacle. It is a quality gate. Materials that fail it are materials that were not compliance-ready when they were submitted. The solution is upstream, not in the review cycle itself.

Our approach

What we did

1
Material audit against approval standards
Conducted a systematic audit of 34 active materials against the company's MLR submission requirements. Documented citation gaps, unclaimed data points, non-standard reference formats, and tagging inconsistencies across the portfolio.
2
Scientific source verification
For each material, verified all scientific claims against primary source documents. Where sources were incorrect, outdated, or insufficiently specific, identified and substituted appropriate current references. Resolved 127 citation issues across the portfolio.
3
Reference document rebuild
Reformatted all reference documents to the company's approved citation standard. Built a master reference library for each therapeutic area, cross-referenced to the relevant materials.
4
Slide redesign to compliance standards
Redesigned flagged slides to meet approval team format requirements: correct claim-reference pairing, appropriate superscripting, compliant visual layout for data presentations, and verified safety statement placement.
5
MLR tagging workflow implementation
Implemented a systematic MLR tagging workflow for future material production: claim inventory template, tag assignment protocol, and a pre-submission checklist for the medical writing team.
Result

Measurable impact

The 34-document portfolio was fully remediated and resubmitted to the MLR team. First-pass approval rate increased from approximately 45% to 91% across the remediated portfolio. The estimated approval cycle time reduction was 35%, freeing significant internal resource for new material development. The MLR tagging workflow was adopted as standard practice by the medical writing team for all new material production.

127
Citation issues identified and resolved across the material portfolio
91%
First-pass MLR approval rate on remediated materials (vs ~45% baseline)
~35%
Estimated reduction in average approval cycle time
Evidence Scanner
Evidence ScannerTM
AI infrastructure

AI-powered.
Expert-validated.

We built AI workflows into our daily practice — not as a marketing claim, but as the infrastructure that lets our medical experts deliver faster without cutting corners.

Research
Structured PubMed queries with narrative or table outputs
Monitoring
Weekly literature digests by drug, target, or topic
AI-Enhanced EDC
Electronic data capture with AI-assisted structuring of unstructured records
Fact-Checker
Claim verification against your source documents
AI accelerates. Our experts validate.
Every output goes through expert medical review before it reaches your team. AI handles structure and speed — we handle scientific judgement and MLR readiness.
Evidence Scanner · Research module
// Query: ribociclib OS data MONALEESA 2023–24
search("ribociclib overall survival", {
  years: [2023, 2024],
  output: "structured_table"
})
// 847 records → 23 relevant
Processing 847 records...
Evidence Summary
MONALEESA-2 updated OS (NEJM 2023): median OS 63.9 mo vs 51.4 mo (HR 0.76, 95% CI 0.63–0.93). Benefit maintained across all pre-specified subgroups...
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