SHAARPEC is a predictive analytics platform that structures your existing healthcare data into longitudinal patient trajectories, trains deep learning models on those trajectories, and delivers forward-looking commercial intelligence — deployed inside your environment, working with data you already own.
Most analytics platforms treat claims as rows in a table. SHAARPEC treats them as what they actually are: a record of a patient moving through the healthcare system over time. We structure raw claims data into clinical encounters, assemble those encounters into longitudinal patient trajectories, and store those trajectories in a graph-native data model that preserves every temporal relationship — not just what happened, but when, and in what sequence relative to everything else.
The platform starts by training a foundation model on the broadest possible population for a given condition — potentially hundreds of thousands of patients nationally. That foundation model learns the full range of how patients with that condition move through care. We then fine-tune it for each specific therapy, indication, and commercial question.
Each step runs on a weekly processing cycle as new data arrives.
Raw claims enter the platform and pass through an encounter grouper that converts billing lines into discrete clinical encounters. Those encounters become the building blocks of patient trajectories, assembled into a graph database that captures the full temporal sequence of each patient's care journey.
Each week, a new batch of open claims arrives. The encounter grouper runs, appending new encounters to each patient's longitudinal record at the correct point in time, preserving temporal integrity.
The trajectory completion model applies what the foundation learned from closed claims to reconstruct what open claims cannot see — producing an enhanced trajectory that more accurately represents the patient's true care journey.
For each outcome of interest, the platform has learned a patient archetype: the characteristic trajectory signature that precedes that outcome. The platform continuously evaluates how closely each patient's trajectory matches the relevant archetypes.
When archetype match and signal strength cross a defined confidence threshold, the prediction module fires — generating a timestamped prediction for that patient and outcome. Predictions only register when confidence is sufficient.
Individual patient-level predictions aggregate to the provider, medical center, and geography level. Dashboards show where demand is forming, which HCPs are likely to have eligible patients in the next 3–6 months, and how signal strength is trending week over week.
SHAARPEC does not require access to a proprietary data asset and does not create a third-party data dependency. The platform is deployed inside the client's own environment, working with data the client already has.
The platform runs inside your secure environment. Patient-level data never leaves your control. SHAARPEC 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.
Signal designed to inform specific decisions — not generic utilization reports.
By HCP, medical center, and geography — 3 to 6 months before it appears in prescription data.
Trajectory-based prediction of time-to-treatment for patients currently in the diagnostic or pre-treatment phase.
Forward-looking switching propensity — not who switched last quarter, but who will switch in the next 90 days.
Signal aggregated to HCP and site-of-care level, ranked by predicted future activity.
Encounter density anomalies indicating patients hitting step therapy, prior authorization friction, or other access barriers.
The platform is outcome-agnostic. If the outcome can be defined and observed in claims data, a prediction model can be trained for it.
We start with your data question and show you what the signal looks like for your therapy.
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