Adding Coverage Context to Longitudinal Patient Records

An oncology therapy manufacturer launching a new drug wanted to map the patient journey by combining open/closed claims and transactional records, but its tokenized patient data lacked plan-level detail needed to see how formulary restrictions affected access. It used MMIT's Data Enrichment service to match patient records against MMIT's payer data and append verified plan-level attributes ("plan ID, payer, channel, formulary, tier and coverage status"). This gave the manufacturer "full visibility into its patients' complex medical journeys," letting its market access team identify cohorts facing restrictive formulary tiers and prioritize payers for engagement, while field teams used the intelligence for prescriber campaigns.

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.