SHAARPEC Foresight structures longitudinal claims data into patient trajectory sequences, trains disease-specific foundation models on those sequences, and delivers continuously updating commercial and HEOR intelligence — inside your cloud environment, on data you already own.
Request a DemoMost analytics platforms treat claims as rows in a table. SHAARPEC Foresight treats them as what they are — a record of a patient moving through the healthcare system over time, with every temporal relationship preserved. That structural difference is what makes forward-looking prediction possible.
A continuous processing cycle that gets smarter with every iteration.
Raw claims data becomes a time-ordered record of how each patient has moved through the healthcare system — clinical encounters assembled into a longitudinal trajectory, with every temporal relationship preserved.
The platform fills trajectory gaps by training on closed claims — where the complete ground truth is known — then applying that learned understanding to open claims before any prediction is made.
A disease-specific foundation model learns the full range of how patients move through care at population scale — then multiple predictive algorithms are built from it, each targeting a specific commercial or evidence question.
The foundation model is fine-tuned to your specific patient population, data environment, and indication — so predictions reflect your market, not a generic population.
As new claims arrive, emerging trajectories are continuously evaluated against learned patterns. A prediction fires only when confidence crosses the required threshold — no probabilistic noise presented as signal.
Predictions aggregate to HCP, health system, and geography level for commercial analytics and brand teams, and to population, subgroup, and outcome level for HEOR and medical affairs — deployable directly into existing planning tools and CRM.
Every prediction is tracked against what subsequently happens — when the model drifts from observed outcomes or standards of care shift, it retrains. Accuracy compounds over time instead of going stale.
Ranked NPI lists, refreshed with each data cycle and deployable directly to field force planning tools and CRM — showing which physicians have patient panels moving toward treatment eligibility now.
Pre-launch demand signals by geography, health system, and site of care — where patient eligibility is forming before the first prescription is written.
Continuously updating trajectory-based evidence, formatted for payer submissions and dossiers — updated as new claims arrive, not produced once as a static study.
Peer-reviewed in Nature, ESC, and European Psychiatry — and validated across engagements with 8 global life sciences organizations.
See the published evidence →Deploys inside your AWS, Azure, or GCP environment. IT and compliance own the perimeter.
Patient-level data never leaves your environment. No third-party data dependency required.
Each therapy area runs its own model independently — on the same platform instance.
We don't run generic demos. We start with your therapy and your data environment — and show you what trajectory intelligence actually surfaces for your specific commercial or evidence challenge.
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