SHAARPEC Foresight structures your existing healthcare data into longitudinal patient trajectories, trains disease-specific foundation models on those trajectories, and delivers forward-looking commercial and HEOR intelligence — deployed inside your environment, on data you already own.
Open claims update weekly but arrive with structural gaps. Most analytics build predictions on that incomplete picture. SHAARPEC Foresight trains on closed claims — where the full ground truth is known — and uses that learning to fill gaps before modeling. The result is signal built on what's actually happening in a patient's care.
As new claims arrive weekly, every patient trajectory updates. When a trajectory reaches the confidence threshold for a predicted outcome, the signal fires. Not a quarterly report. Not a monthly batch. A continuously updated intelligence layer that moves with your market.
Every prediction is tracked against what subsequently happens. When the model is wrong — or when treatment patterns shift — it retrains. Accuracy compounds over time. And the validation data is visible — this is not a black box.
Most analytics platforms treat claims as rows in a table. SHAARPEC Foresight treats them as what they actually are: a record of a patient moving through the healthcare system over time — structured into clinical encounters, assembled into longitudinal trajectories, with every temporal relationship preserved.
The platform trains a foundation model on the broadest possible population for a given condition — potentially hundreds of thousands of patients nationally. That model learns the full range of how patients move through care, then fine-tunes for each specific therapy and commercial question.
Open claims update weekly but arrive incomplete. Closed claims are complete but carry a six-month lag. SHAARPEC Foresight trains on closed claims — where the complete ground truth is known — and applies that learning to enhance open claims before modeling. Predictions built on what's actually happening, not on what happened to be captured.
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. Not rows in a table. A sequence with meaning.
Every trajectory has gaps — structural missingness in open claims, missing context in any single data source. The platform fills them by training on closed claims where the complete ground truth is known, then applying that learned understanding before any prediction is made.
A disease-specific foundation model learns the full range of how patients with a given condition move through care — across hundreds of thousands of patients at population scale. From that base, multiple predictive algorithms are built, each targeting a specific commercial or evidence question.
As new claims arrive weekly, emerging trajectories are continuously evaluated against the patterns the model has learned. A prediction fires only when confidence crosses the required threshold. No probabilistic noise presented as signal.
Predictions aggregate to HCP, medical center, and geography level for commercial teams, and to population, subgroup, and outcome level for HEOR. Dashboards designed for decisions — not reporting.
Every prediction is tracked against what subsequently happens. When the model is wrong, or when treatment patterns shift as standards of care evolve, it retrains automatically. Accuracy compounds over time instead of going stale.
Deployed flexibly — on-premise, in your private cloud, or in a dedicated secure environment under your governance. Patient-level data never leaves your control. SHAARPEC Foresight brings the methodology; you bring the data.
Optimized for open and closed claims — the data most pharma commercial teams already have access to. Compatible with EHR, pharmacy, and lab data where available.
A single installation supports multiple therapy pipelines. Each runs independently with its own models — sharing infrastructure, not data.
Every prediction is tracked against what subsequently happens. Continuous validation loops recalibrate models automatically as treatment landscapes evolve.
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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