Empowering Early Disease Diagnosis with Real-World Data

A global biopharma company developing a diabetes therapy struggled to identify early-stage patients and quantify screening rates since "claims and lab data often lack detail to differentiate between certain diagnoses." Over a 14-week engagement, Norstella integrated unstructured EHR notes with structured claims/lab data via machine learning to classify patient stage/type, incorporating autoantibody testing and physician-behavior analysis. This delivered "a repeatable, scalable model to identify early diagnosis," revealing geographic and specialty screening trends that informed commercialization, field engagement, and real-world evidence strategies.

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.