White Paper · Biopharma Series
A Framework for
Fit-for-Purpose Real-World Imaging Data
Claims data captures what was coded. EHRs capture what was documented.
Imaging captures what was biologically observed.
This white paper defines what it takes to make that imaging data decision-grade.
Imaging captures what was biologically observed.
This white paper defines what it takes to make that imaging data decision-grade.
The Imaging Gap in Real-World Evidence
Real-world evidence without the scan is structurally incomplete
The life-sciences industry has invested heavily in claims-based and EHR-based real-world evidence. But claims capture what was billed, not what was biologically observed. For the therapeutic areas where imaging is the evidentiary standard (oncology staging, neurodegenerative biomarker confirmation, cardiac phenotyping) generic datasets leave a structural gap.
This Segmed White Paper defines what fit-for-purpose imaging data actually requires, and where generic approaches fall short.
From Framework to Practice
Segmed delivers the data this framework describes
The fit-for-purpose standard defined in this white paper is not hypothetical. Segmed operates the largest network of real-world imaging data in the United States, with pre-built research cohorts engineered for the evidence requirements of oncology, neurology, cardiology, and metabolic disease.
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A Framework for Fit-For-Purpose Real-World Imaging Data: defining the essential requirements for decision-grade RWiD across oncology, neurology, and cardiology.
