How IDN Formulary Insights Drove a 17% Utilization Gain

A midsize pharmaceutical company launched an oncology therapy with novel drug-delivery technology but saw low adoption despite favorable payer formulary placement. It partnered with MMIT to access an "industry-leading panel of IDN decision-makers" and analyzed IDN formulary processes, P&T committee structures, and prescribing workflows, identifying barriers such as "step therapy requirements, prior authorization protocols, competing order sets, patient stratification workflows, or additional lab testing requirements." Targeted interventions moved the brand from non-formulary to formulary status at three IDNs and resolved workflow obstacles at four others, "improve[d] utilization of its brand by 17% over a six-month period."

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.