SHAARPEC models patients as dynamic entities moving through care over time — so your commercial team can identify emerging demand, target the right HCPs, and engage before it's visible in traditional claims data.
Retrospective claims data tells you which patients received which treatments, after the fact. By the time that signal reaches your commercial team, the opportunity has often passed.
The question that matters is different: where is patient eligibility forming right now — and who are the physicians most likely to treat them? Answering that requires modeling patients as people moving through time, not rows in a database.
Built on a graph architecture designed from the ground up for longitudinal healthcare data.
We identify emerging eligibility patterns weeks or months before they surface in traditional utilization data — by modeling the clinical pathway that precedes diagnosis.
Every patient, provider, encounter, and diagnosis is a connected node — not a flat table row. This structure makes temporal pattern detection computationally efficient and structurally sound.
HCP targeting lists ranked by emerging patient eligibility. Trend detection across cohorts. Demand estimates by geography and specialty. Intelligence your team can act on, not just report on.
We start with your data question, not a generic product demo.
Request a Demo