Empowering Early Disease Diagnosis with Real-World Data

A global biopharma company developing a diabetes therapy struggled to quantify national early-disease screening rates and physician sentiment across regions, since "claims and lab data often lack detail to differentiate between certain diagnoses." Over a 14-week engagement, Norstella integrated structured claims/lab data with unstructured EHR notes via machine learning to classify patient stage/type and identify autoantibody testing patterns. This revealed geographic and specialty-specific screening trends and produced "a repeatable, scalable model to identify early diagnosis," informing the client's commercialization strategy, field deployment, and educational outreach.

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.