What follows are examples of what that signal looks like applied to the decisions that move a brand.
SHAARPEC Foresight tells you what's about to happen — and gives your team time to act before it does.
SHAARPEC Foresight reads the clinical trajectory that precedes treatment initiation — specialist referral patterns, diagnostic sequences, care escalation events — and surfaces demand that is forming but not yet visible.
The earliest detectable signal. Patients whose claims history shows the upstream clinical events that statistically precede a diagnosis — specialist visits, diagnostic workups, escalating care complexity. Months before criteria are formally met.
Patients with a confirmed diagnosis who haven't initiated therapy. The gap between recognition and action — driven by access barriers, physician inertia, or patient hesitation. Identifiable in claims data. Addressable now.
Patients currently treated with a competing agent whose trajectory signals emerging dissatisfaction — flat dose lines, rescue medication onset, care fragmentation. A pre-switch signal, not a post-switch report. Identified before the prescription moves.
Hidden demand doesn't just tell you where patients are forming — it tells you which physicians are managing them. Your field force reaches the right prescribers before demand is visible in scripts, not after.
Not which ones wrote last quarter. Ranked by predicted future patient eligibility in their panels, refreshed weekly, and deployable directly to field force planning tools.
Patient demand mapped by geography, health system, and site of care — updated continuously so quarterly planning reflects where demand is going, not where it's been.
Which infusion centers, specialty clinics, and health systems carry the highest concentrations of pre-treatment patients. Critical for therapies with site-of-care administration or specialty pharmacy routing.
Dynamic targeting is still the exception — adopted by roughly 1 in 4 large pharma companies as of 2024. The companies that move first compound the advantage.
84% of providers report difficulties starting patients on specialty medications — access barriers, prior authorization friction, and care coordination failures that show up in claims data weeks before the first missed fill. SHAARPEC Foresight reads the continuing trajectory forward, identifying patients approaching discontinuation or switch — and ranking the HCPs whose panels carry the highest concentration of at-risk patients — before the event occurs.
Care fragmentation, urgent care visits, and access friction appear in claims weeks before the first missed fill. SHAARPEC Foresight identifies the pattern early and surfaces it at the HCP level so the right intervention reaches the right prescriber while there's still time to act.
Flat dose lines, rescue medication onset, shortened refill intervals — assembled into a pre-switch trajectory and flagged before the prescription moves, with a competitive flow map showing which prescribers are losing share and at what velocity.
Trajectory analysis reads the velocity and pattern of the titration schedule — separating on-label titration from dose regression, and institutional under-titration from patient-specific hesitation, so the right clinical conversation reaches the right HCP before outcomes degrade.
The trajectory intelligence that powers commercial prediction also powers continuously updated population evidence — without commissioning a new study every time the question changes.
The most complete claims data carries a structural six-month lag before inclusion — meaning a study commissioned today and completed in nine months delivers evidence reflecting conditions fifteen months prior. That's what payers evaluate at the negotiating table. SHAARPEC Foresight replaces the project model with a living population intelligence layer that updates continuously as new data flows in.
Payer committees evaluate real-world evidence knowing it was collected 12–24 months before the submission. Continuous HEOR closes that gap — maintaining a living outcomes dataset updated quarterly. When the formulary negotiation opens, your evidence is current.
A comparative effectiveness study commissioned at launch answers a launch-era question. SHAARPEC Foresight maintains a continuously updated comparison of your therapy's real-world performance against the current competitive set — tracking how relative effectiveness evolves as utilization patterns change.
Post-approval monitoring is typically episodic — periodic analyses with gaps where signals accumulate undetected. SHAARPEC Foresight monitors the full on-therapy trajectory population continuously, flagging anomalies in outcomes and care patterns before they surface in pharmacovigilance reporting. SDOH-adjusted analysis runs in parallel — identifying how social determinants modify treatment trajectories across subpopulations.
These are illustrative examples, not a fixed menu. SHAARPEC Foresight generates predictive algorithms around any forward-looking question a client wants answered from their longitudinal trajectory data. The three use cases above represent high-value priorities for most HEOR and Medical Affairs teams — but the platform is configured to your indication, your evidence gaps, and the specific decisions your team needs to make.
AI-enriched trajectory analysis applied to population-scale atrial fibrillation screening across 330,000 patients. The model identified patients at optimal screening windows based on longitudinal care progression — not static risk factors.
Explainable machine learning trained on complete longitudinal heart failure patient histories. Deployed live inside a commercial EHR system as clinical decision support — the same architecture that underlies SHAARPEC Foresight's commercial trajectory models.
The two studies above are particularly representative for pharma commercial applications — trajectory-based pattern recognition applied to longitudinal patient data at scale. The trajectory-completion capability builds on published research in graph-native synthetic patient generation — including work recognized in Nature npj Digital Medicine (2023) as the highest-fidelity method for healthcare synthetic data to date.
The predictive methodology underlying SHAARPEC Foresight has been built and validated through paid engagements with global life sciences organizations — including AstraZeneca, Takeda, Bristol Myers Squibb, Novartis, Novo Nordisk, and Pfizer — developing and deploying predictive algorithms on real-world clinical data across multiple therapeutic areas.
We start with your data question and show you what the signal looks like for your therapy.
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