Refining Rare Disease Targeting with Real-World Data

A mid-sized pharmaceutical company struggled to identify an often-misdiagnosed rare-disease subpopulation that frequently tested negative for the disease biomarker despite exhibiting related comorbidities. Using NorstellaLinQ, it integrated EHR physician notes, claims data, and patient-journey analysis to refine the target population and map diagnostic testing patterns. This revealed a narrower patient population — including biomarker-negative patients with disease-associated characteristics — improving "physician targeting and marketing efforts" and informing target product profiles for pipeline agents.

The customer is anonymised in Norstella’s own case study — not withheld by us.

Norstella is a holding company merging several pharma-intelligence brands — Citeline (trial intelligence), Evaluate (forecasting), MMIT (payer/formulary data) and Panalgo (real-world analytics) — into one data asset.

This sits in the Data Providers stage of the AI use-case lifecycle: Named examples from the real-world-data, claims and competitive-intelligence providers pharma AI systems are trained on, benchmarked against, or run inside of.