Eligible Patient Population Mapping
See where the patients actually are.
Map eligible patient populations down to geographic resolution using EHR, claims and registry data — for trial site selection and decentralized-trial planning.
"Where do enough eligible patients sit to make a site or DCT footprint viable?"
Geographic eligible-pool mapping is now table stakes. EHR + claims data resolve eligible cohorts to ZIP-level resolution.
Eligible-patient maps now resolve to ZIP-level resolution. The teams that use them to design site footprints and decentralized-trial coverage consistently outpace teams using traditional site selection.
What we’re seeing in the data.
ZIP-level mapping changes site selection
Reveals dense pools not aligned with traditional sites.
Layered eligibility filters
Diagnosis × prior therapy × biomarker.
DCT enables non-traditional geography
Eligible patients no longer have to live near a site.
How to think about it.
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01
Define eligibility filters
Disease × prior tx × biomarker × demographics.
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02
Pull EHR + claims
De-identified, geo-tagged.
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03
Map at ZIP / region
Visual heat map.
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04
Plan site / DCT footprint
Aligned with eligible density.
What separates a good answer from a defensible one.
HIPAA / GDPR.
EHR↔claims linkage variance.
Protocol amendments shift maps.
Where the signal comes from.
Common questions.
Is ZIP-level resolution legal?
Yes with proper de-identification and aggregation thresholds.
How fast can a map be produced?
Days to weeks once data partnerships are in place.
Want this answered on your data?
We build decision systems on top of analyses like this — so the next question takes minutes, not weeks.
Talk to a strategist