Predictive Trajectory Intelligence

Demand is forming right now.
Most analytics can’t see it.

Patient trajectory modeling that surfaces demand before it appears in conventional claims data — built for pharma commercial teams.

More patients identified with trajectory modeling
6–18 mo
Earlier signal ahead of clinical diagnosis
75–84%
Validated prediction accuracy across published models
4
Of the top 10 global pharma companies are clients
Trusted by leading biopharma
Pfizer
AstraZeneca
Bristol Myers Squibb
Takeda
Novartis
Novo Nordisk
Pharma Commercial Segments

Built for three distinct commercial functions.

Each with dedicated prediction models and peer-reviewed validation.

Commercial Analytics & Market Access

Field force deployment is one of the largest line items in a brand budget — yet most targeting lists rank physicians by last year's volume. The physicians gaining momentum right now are invisible in that data.

  • Physician targeting ranked by predicted future initiation — not historical volume
  • Access friction signals detected before they kill a prescription
  • Field deployment guided by forward-looking trajectory scores
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Brand Strategy & Launch Planning

54% of launches miss first-year targets. Before your therapy has a single prescription, field force and geography decisions are made blind — based on where patients were, not where they're forming.

  • Untreated-eligible patients identified before diagnosis is coded
  • Emerging prescriber networks ranked by predicted future volume
  • Launch territory deployment guided by forward-looking demand signals
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HEOR & Medical Affairs

Closed claims carry a built-in lag of up to 6 months. Publication takes another 12–18 months. The evidence you submit to a payer may describe a market that no longer exists.

  • 6 peer-reviewed publications in Nature, EP Europace, European Psychiatry, JMIR & ESC
  • AUC 0.79–0.84 validated across international datasets
  • Methods reproducible and publication-ready from day one
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Published Studies

Peer-reviewed across leading journals.

See all →
5.6× more AF patients detected
Trajectory-based AF detection across a regional population of 330,000. AUC 0.79–0.84 validated internationally.
ADHD detected 6–18 months before clinical diagnosis
Predictive trajectory modeling identifies ADHD patients earlier than standard clinical pathways.
Explainable ML outperforms LACE for HF readmission
CatBoost + SHAP outperformed LACE index (AUPRC 68% vs 51%) across 6,040 heart failure patients.
Synthetic EHR generation validated at population scale
Peer-reviewed synthetic data generation published in Nature portfolio — foundation for trajectory completion.
Platform Innovation

A prediction pipeline, not a dashboard.

Your claims data already contains the signal. SHAARPEC Foresight reads it differently — modeling how patients move through care over time and surfacing what comes next.

Purpose-built predictive models trained on your patient population. Indication-specific. Methodology transparent. Every prediction traceable to the clinical events that drove it.

Not a large language model Not a black box Not another data vendor
Explore the full platform →
1
Structure
Claims data structured into time-ordered patient trajectory sequences inside your cloud environment
2
Enrich
Trajectory gaps filled using patterns learned from closed claims — before modeling begins
3
Model
Disease-specific foundation model trained at population scale on your patient data
4
Calibrate
Foundation model fine-tuned to your patient population, data environment, and indication
5
Predict
Confidence-gated forward-looking signal generated as new claims arrive
6
Surface
Intelligence delivered at HCP, health system, and geography level — directly into field force tools and CRM
7
Retrain
Prediction accuracy tracked against outcomes; model retrains when care patterns shift

The prescribers driving your next six months. The evidence your next submission will rest on. Both are identifiable today.

We start with your indication and your data question — not a generic product demo. If the signal is there, we'll show you what it looks like.

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