A senior advisory practice for biopharma, medtech, CRO, and health-data leaders. We bring deep, applied expertise across health economics, real-world evidence, causal inference, and translational analytics — focused on the questions that change strategy, not the ones that fill slides.
The Healthcare & Life Sciences practice exists for one reason: to turn complex evidence questions into defensible, decision-grade answers — fast enough to matter, rigorous enough to defend, and structured for the people who will actually act on them.
We are not a vendor menu. Engagements are senior-led, narrowly scoped, and deliberately designed to fit inside the operating rhythm of evidence, access, and strategy teams — including direct exposure to the principal throughout. Junior analysts and specialist collaborators are brought in only where they sharpen, rather than dilute, the work.
Where it helps, Sepantam contributes implementation and analytical execution to selected healthcare engagements — bringing the same pipeline and automation discipline used in the SMB practice to scaled evidence work.
The work spans evidence architecture, decision & access strategy, and translational AI. Most engagements draw from more than one.
Real-world evidence strategy, data source evaluation, harmonization, governance, and evidence pipelines — analysis-ready foundations that hold up to regulatory and access scrutiny.
Cost-effectiveness, budget impact, reimbursement logic, decision-analytic models — calibration, validation, and submission-ready documentation across indications and geographies.
Causal frameworks, confounding control, treatment effect estimation, comparative effectiveness, and decision-grade interpretation. Applied at clinical and economic scale.
External control arms, target trial emulation, protocol logic, eligibility design, sensitivity analysis, and regulatory-facing documentation.
Evidence strategy for payer, regulatory, reimbursement, formulary, value demonstration, and product strategy decisions — across regulated markets.
AI-enabled evidence generation, clinical NLP, LLM-assisted analytics, multimodal health data, biomarker analytics, and translational programs.
Recurring principal-level access for evidence, access, and strategy leaders. Used as senior judgment alongside an existing team.
Fixed-scope evidence build — HEOR model, RWE study, external control arm, dossier component — delivered against a defined output and timeline.
Short, focused work supporting a specific decision — pricing, launch readiness, payer response, regulatory question — delivered with the urgency of the moment.
Evidence work is only useful when it changes a decision. Everything else is documentation. The hard part isn't computing the number — it's making sure the right number is being computed, defensibly, in time to matter.