How to Analyze Unstructured Insurance Data for Pharma Market Access Using NLP

Client:

Novo Nordisk

Industry:

Life Sciences / Pharmaceuticals

Focus:

NLP-driven analysis of unstructured insurance and payer data for market access planning

Technologies:

Python / MongoDB / NLTK / NLP

[ about the client/ ]

Novo Nordisk is a global pharmaceutical company focused on chronic conditions such as diabetes, obesity, hemophilia, and growth disorders. Its U.S. Market Access team relied on large volumes of insurance and healthcare data to support account planning, payer strategy, and regional market access initiatives.

The challenge was that much of this data was costly, complex, and hard for account teams to use in daily planning. MEV built MEDIX, a custom CRM platform that used data engineering and NLP to turn unstructured insurance data into actionable insights for 2,000+ sales and market access users.

What Needed to Be Done

Novo Nordisk had access to a large number of insurance and healthcare data feeds, but the data was disorganized and difficult to navigate. Sales and account managers needed a faster way to find insights relevant to their specific regions, accounts, and payer types.

The job was to process unstructured insurance data and surface contextual insights inside the MEDIX CRM platform. Instead of forcing teams to manually search through large datasets, the system needed to help them understand what mattered for account strategy and market access planning.

Why It Mattered

Insurance data is expensive. Without a way to organize, interpret, and apply it, that investment loses value.

For Novo Nordisk, the issue was not simply access to data. The harder problem was making the data usable for real account work. Account teams needed to understand payer-specific and geography-specific context without getting buried in raw, fragmented information.

Without this NLP layer, regional teams would still struggle to connect insurance data to account planning, and leadership would have less visibility into how market access strategy was being shaped across national and regional accounts.

What MEV Built

MEV built an NLP-driven analysis layer inside MEDIX, Novo Nordisk’s custom market access CRM platform.

The system ingested unstructured insurance and healthcare data, applied NLP-based contextual analysis, and helped surface insights relevant to geography, payer type, and account planning. The canonical case identifies Python, MongoDB, and NLTK as part of the project tech stack and describes the solution as machine learning with built-in NLP.

The page should stay focused on this specific job: transforming hard-to-use insurance data into structured, searchable, context-aware insight for market access teams.

Technical Flow

01:

Insurance and healthcare data ingestion

MEDIX ingested unstructured insurance and healthcare data used by Novo Nordisk’s U.S. Market Access team.

02:

Data preparation for analysis

The data was organized so it could be processed and connected to market access workflows inside the CRM.

03:

NLP-driven contextual analysis

Using NLP, the system analyzed unstructured data and extracted context that could support geography-specific and payer-specific insight.

04:

Insight mapping to business context

The processed data was connected to account, geography, and payer dimensions so account managers could use it in planning.

05:

CRM presentation layer

Relevant insights were surfaced inside MEDIX, where sales and market access users could filter information by geography, account, and payer type.

06:

Account planning support

Account managers used the resulting insights to support strategic and tactical planning across national and regional accounts.

Outcome

Novo Nordisk gained a more usable way to work with complex insurance data. Instead of leaving high-cost data feeds underused, MEDIX helped turn them into contextual insights for sales and market access teams.

This specific NLP layer supported the broader MEDIX platform by helping account managers find relevant payer and regional insights faster, improving transparency across the Market Access Group, and maximizing the value of costly insurance data. The canonical case reports that the full platform supported 2,000+ internal users, created one CRM system for account strategy and analytics, and established a scalable foundation for future data-driven initiatives.

This case is part of the full case study

Novo Nordisk Streamlines Market Access with a Custom CRM Solution

[ portfolio/ ]