In rare disease, traditional real-world data sources often fall short, leaving teams across research, medical, and commercial functions with blind spots. But with NorstellaLinQ, rare disease doesn’t mean unreachable.
Rare Doesn’t Mean
Unreachable:
Finding Rare Disease Patients with
Real-World Data and Analytics
Rare disease patients often endure years-long journeys to diagnosis—delays that can hinder treatment and outcomes. But with NorstellaLinQ—pharma’s first fully integrated real-world data asset— life sciences companies are uncovering hidden patient populations, accelerating time to diagnosis, and improving targeting strategies.
As a Norstella company, Panalgo offers tailored solutions for rare disease use cases. NorstellaLinQ is the largest integrated data asset in the industry, combining 74B+ data points from EMR, lab, open and closed claims, social determinants of health, and mortality data from Norstella’s brands including Citeline, Evaluate, and MMIT. And with direct integration into our IHD analytics platform, you can get the insights you need faster and easier than ever.
Whether you’re identifying undiagnosed patients, refining targeting strategies, or accelerating time to diagnosis, our rare disease solutions deliver clarity where it’s needed most.
Ready to learn more about how you can leverage NorstellaLinQ or Panalgo’s software and solutions to enhance your rare disease research? Enter your email to start the conversation.
See how Panalgo and NorstellaLinQ are closing gaps in rare disease research:
Pharma teams are missing key patient insights hiding in plain sight—inside unstructured EMR data like clinical notes, imaging reports, and physician dictations. This blog explores how tapping into that unstructured layer can uncover harder-to-find patients, especially in oncology and rare disease, and improve targeting by revealing things structured data can’t.
A biopharma company developing a rare-disease therapy dramatically expanded its potential patient cohort—by approximately 1,500%—by leveraging unstructured EMR data. By leveraging the NorstellaLinQ data asset combining clinical notes with lab and claims data, Panalgo experts were able to uncover a significantly broader, accurately defined cohort and generate rich, longitudinal patient histories for deeper insights.
A study published in Blood, the American Society of Hematology’s journal, leveraging NorstellaLinQ data and Panalgo experts demonstrates that natural language processing (NLP) can effectively identify rare subtype of myeloid/lymphoid neoplasms often invisibly buried in unstructured EMR notes and not reliably captured by ICD‑10 codes.
For rare disease, the journey from symptom onset to diagnosis and treatment can take years—sometimes even decades. Here’s how integrating real-world data shortens time to treatment, improves market engagement and ultimately enhances patient outcomes for rare diseases.