23andMe \USA

Step 2 - EHR Integration (Growth): Build Epic and Cerner integrations so genetic results and recommendations appear directly in the prescribing workflow. Use SMART on FHIR apps to embed the decision support tool within the EHR rather than requiring doctors to log into a separate system. Expand to psychiatry (antidepressant selection based on CYP2D6, CYP2C19 variants) and cardiology (warfarin dosing based on CYP2C9, VKORC1). Partner with 10-15 health systems and offer a land-and-expand model: free pilot for one department, then expand to other specialties once value is proven. Revenue model: $150 per test plus $50K-200K annual SaaS fee per health system for the decision support platform. Goal: 10K tests in year two, $2M ARR. Raise $5M Series A to fund sales team and expand lab capacity.

Step 3 - Insurer Partnerships (Scale): Negotiate risk-sharing agreements with Medicare Advantage plans and commercial insurers. Pitch: pharmacogenomic testing reduces adverse drug reactions, preventing expensive hospitalizations and ER visits. Offer outcomes-based pricing where GenoScript only gets paid if readmission rates drop. This aligns incentives and makes the sale easier (CFOs love risk-sharing deals). Expand testing to primary care for high-risk populations: elderly patients on 5+ medications (polypharmacy), patients with prior adverse reactions, and patients starting high-risk drugs (warfarin, clopidogrel, statins). Build a patient-facing app that stores genetic results and alerts them when a new prescription interacts with their genotype. Goal: 100K tests in year three, $15M ARR, partnerships with 3-5 major insurers covering 10M+ lives. Raise $20M Series B to fund national expansion.

Step 4 - AI Moat and Network Effects (Dominance): Use the accumulated real-world evidence (genetic data linked to prescription outcomes from EHRs) to train proprietary AI models that outperform existing clinical guidelines. Current pharmacogenomic recommendations are based on small studies and expert consensus; GenoScript can use machine learning on 100K+ patient records to identify novel gene-drug interactions and optimize dosing algorithms. Publish research showing superior outcomes compared to standard care, establishing GenoScript as the clinical gold standard. Expand internationally to countries with national health systems (UK NHS, Canadian provinces) where centralized decision-making enables faster adoption. Build a developer platform allowing third-party apps to access genetic data (with patient consent) for adjacent use cases: clinical trial matching, rare disease diagnosis, wellness recommendations. Goal: 1M+ tests annually, $100M+ ARR, become the default pharmacogenomics platform embedded in every major EHR. Exit via acquisition by a health IT giant (Epic, Cerner/Oracle, Veeva) or IPO as a clinical AI company.