Powering Unbiased Outcomes Research with Longitudinal Data
A medical device company preparing to launch a new product needed robust data for a health economics and outcomes research (HEOR) study comparing cemented versus uncemented knee arthroplasties. Panalgo combined unstructured EMR data (powered by NorstellaLinQ) with closed claims data to build a comprehensive, unbiased longitudinal dataset spanning inpatient and outpatient knee arthroplasty procedures. Within eight weeks, the client had access to years of longitudinal arthroplasty data, giving them the historical information needed to fuel their comparative-effectiveness study on surgical implant approaches.
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.
Published by Norstella on its own site — read the original case study on panalgo.com ↗
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