# Cardinal Analytx Financial Model AI-powered healthcare predictive analytics platform that identifies patients at risk of becoming high-cost before the cost event occurs, enabling health plans and employers to intervene early. - Canonical: https://finamodel.com/startups/cardinal-analytx - Excel download: https://finamodel.com/startup-models/cardinal-analytx.xlsx - Category: Health-tech - Model type: SaaS ARR / Valuation - Funding round: Series B - Funding: $22M - Founded: 2019 - Geography: US (customers include Blues plans, Premera Blue Cross, Florida Blue, Blue Shield of California) [DECK, slides 11, 12] - Customer: B2B ## About the company Cardinal Analytx provides predictive analytics that identifies members likely to become high-cost before a major event occurs. Health plans and employers can use the platform to target interventions earlier and improve care economics. It sells enterprise annual subscriptions across several products, with documented ACVs of roughly $400,000 to $850,000. PMPM framing can support the value proposition, but contract value and multi-product expansion are the direct commercial levers. The model should forecast enterprise logos, product mix, ACV, implementation, renewals, and upsell. Long sales cycles, customer concentration, data integration cost, and evidence of outcomes should be treated as key sensitivities. ## What's included - 5-year monthly revenue build with stage-appropriate growth assumptions - Full P&L, headcount plan, and operating-expense schedule - Cash-flow statement, runway, and burn-rate tracking - Valuation via exit multiple with a DCF cross-check - Returns analysis with MOIC and IRR - Unit economics including CAC, LTV, payback, and cohort retention ## Product & value proposition Three distinct products sold separately or together: | Solution | Description | Ave Annual Price/Customer | | -- | -- | -- | | Cost Bloom Intervention | Predicts newly high-cost claimants; includes engagement likelihood, clinical impactability score, case selection optimizer, action plan recommendations | $850k | | Steerage Precision | Predicts clinical event likelihood, timing, and engagement likelihood; routes members to higher-quality / lower-cost providers | $400k | | Risk Assessment | Prospective and concurrent risk scoring and cost prediction; cost bloom/jump/high-cost claimant likelihood | $600k | Core insight: 6% of today's population will account for 1/3 of next year's new high cost. Proprietary ML model trained on claims data; R-squared accuracy at full 12-month data: Cardinal Analytx 31% vs. DxCG (Verscend) 11% vs. ERG (Optum) 20%. Stanford Start-X spin-out; 4 years R&D; 21 million lives in training data. ## Market - US annual healthcare cost: $3.5 trillion - Yearly healthcare cost growth rate: 5.5% - Target segments: self-insured employers, solution providers (e.g. care management vendors), providers, health plans ## Revenue model - Pure B2B enterprise SaaS; subscription-based annual contracts per customer - Pricing per product per customer per year (see §2 table above): $400k–$850k average depending on solution - Customers likely priced on a per-member-per-month (PMPM) basis given "$0.96 PMPM" reference in case study; explicit PMPM pricing structure not shown in detail - Channels: direct enterprise sales; partnership channel with solution providers (Vitals, Relay, MOBE, VIM noted as targets) - No usage-based, transactional, or consumption model disclosed ## Traction & metrics All figures as of 2019 deck date: - 2 Customers Paid - 8 Customers with MSAs signed - 20 Pipeline Customers - 28 Employees - 21 Advisors and Board members - 21 million lives on platform Roadmap projections (company-provided, not audited): | Year | Clients | FTEs | Lives | | -- | -- | -- | -- | | 2017 | 0 | 12 | 2m | | 2018 | 1 | 19 | 15m | | 2019 | 11 | 46 | 26m | | 2020 | 25 | 67 | 40m | | 2021+ | 53 | 83 | 50m | Revenue figures (ARR, bookings): Not explicitly stated in deck. Implied: 2 paid customers × blended ~$600k = ~$1.2M ARR at time of deck; 8 MSAs implies additional committed pipeline not yet paid. Case study - Health Plan A: - $880M total cost pool; 600k lives - $300M "Cost Bloom Blind Spot" identified (36k lives) - Highest-impact intervention: 2.5k lives; $20M potential savings - Realized result: $6M savings; 8x ROI; $0.96 PMPM Steerage Precision case: - $3.6M savings at Health Plan A on ortho joint surgeries alone; 6x ROI projected - >$5M/year total savings potential from steerage - 50% of Cardinal Analytx clients expressed interest in Steerage Precision Cost Bloom engagement metrics at a Blues plan: - 40% engagement rate (at least one call with Cost Bloom member) - 26% active engagement rate (multiple calls) - 75% successful intervention rate for actively engaged members ## Unit economics - Customer ROI delivered: 6x–8x on individual products - Revenue per customer: $400k–$850k/year - Customer savings delivered: $0.96 PMPM / ~$6M per large health plan (600k lives) ## Competition / moat Competitive positioning - four tiers ranked by sophistication: 1. ML Predictions + Drivers + Actions: **Cardinal Analytx** (only named player at this tier for health plan market) 2. ML Predictions + Model Correlations: Base Health (Med Adv risk adj), Jvion (provider), Ayasdi (provider) 3. ML Predictions only: Lumiata, Cogitativo, HBI Solutions, IBM Watson/Truven, Ark.one, NextHealth, Hindsait 4. Traditional predictions: Optum, Milliman, Cave Consulting, Verscend, Lexis Nexis, Deerwalk, Health Catalyst, Welltok, Lightbeam, HCC, ACG Key moat claims: - Proprietary ML model: R-squared 31% vs. best-in-class competitor 20% at 12-month data maturity - Competitor pricing errors quantified: $63M under-pricing risk + $37M over-pricing/retention risk missed by competitors on a single 600k-life health plan - 4 years R&D + Stanford pedigree + 21M lives training corpus - Actionability differentiator: delivers drivers + recommended actions, not just scores ## Team & funding ask / use of funds **Leadership**: - Linda Hand, CEO - 35 years in product/GTM; prior successful exit (DecisionView to IMS Health) - Niall O'Cathasaigh, CFO - 20 years startup experience, 15 VC-backed startups, 4 healthcare companies - Brian Maples, VP Data Science - PhD Biomedical Informatics Stanford; prior at Nuna, Stanford ancestry inference research - Lu Lu, Head of Product - MPH Harvard; 19 health systems, 45 clinical departments - Chris DeRienzo, CMO - MD/MPP Duke; former Chief Quality Officer Mission Health **Founders**: - Nigam Shah, Associate Professor of Medicine, Stanford (AI/ML) - Arnold Milstein, Professor of Medicine, Stanford; Director Clinical Excellence Research Center **Investors**: Cardinal Partners, StartX, Stanford-StartX Fund, Premera Blue Cross, John Doerr Family Fund, Blue Shield of California, Florida Blue **Board**: John Doerr (Kleiner Perkins), Mark Smith (California HealthCare Foundation), Thomas McKinley (Cardinal Partners), Elizabeth Spaulding (Bain) **Funding ask**: - Series B: $22M new money - Use of funds: not explicitly broken out; roadmap implies headcount growth from 46 to 67 FTEs (2019→2020) and client growth from 11 to 25; focus areas stated as "Market Fit Across Segments" and "Scale and Partnerships" --- ## Recommended financial model **Archetype + why:** Enterprise B2B SaaS ARR model with a contract-value (TCV/ACV) layer. Revenue is annual recurring subscriptions per customer, not usage-based, not transactional. Three products with distinct ACV points ($400k / $600k / $850k) allow multi-product upsell modeling. Given the 2019 stage (2 paid, 8 MSAs, 20 pipeline) the model needs to build from a near-zero base with a realistic sales cycle and land-and-expand logic. **Forecast horizon & granularity:** - 5 years (2019–2023), quarterly for years 1–2, annual for years 3–5 - Monthly not warranted given long enterprise sales cycles (90–180 days likely) and small absolute customer counts **Key drivers & assumptions:** *Customer pipeline:* - New logos won per quarter - Sales cycle length - Churn rate *Revenue per customer:* - Blended ACV by product mix - Products per customer (cross-sell/upsell) - PMPM alternative pricing applies to health plans specifically; model should allow toggling between ACV and PMPM × covered lives *Headcount & cost:* - FTE growth per roadmap - Blended fully-loaded cost per FTE - S&M as % of revenue - R&D as % of revenue - G&A *Lives under contract:* - Lives per customer - Lives growth tracks client growth but with mix shift as larger plans convert *Funding:* - $22M Series B; model runway from closing - Cash burn based on headcount + G&A + S&M spend **Scenarios (Base / Bull / Bear):** - **Base:** Roadmap clients convert as shown (11→25→53), blended ACV $617k, 6% annual churn, S&M 40% of revenue - **Bull:** Faster MSA→paid conversion (8 MSAs all convert in Y1), multi-product penetration accelerates (avg 1.8 products by Y3), ACV inflation as larger plans sign ($900k blend) - **Bear:** Sales cycle extends (health plan budget cycles slip 1 quarter), 2 of 8 MSAs do not convert, churn 12%, ACV under pricing pressure ($500k blend) **Required sheets / outputs:** 1. **Assumptions** - all drivers in one input sheet 2. **Customer Cohort Model** - new logos by quarter, churn, net active customers, products per customer 3. **Revenue Build** - ACV × customers × products; gross MRR/ARR; NRR; lives under contract 4. **P&L** - Revenue → Gross Profit → EBITDA; headcount-driven OpEx 5. **Cash Flow & Runway** - monthly burn, cash balance, Series B drawdown 6. **KPI Dashboard** - ARR, NRR, logo count, lives, ACV, CAC (when data available), LTV/CAC 7. **Scenario Toggle** - Base / Bull / Bear switcher tied to assumptions sheet ## Frequently asked questions ### Is the Cardinal Analytx financial model free? Yes. The Cardinal Analytx model is a free Excel download with live formulas. ### Can I change the assumptions? Yes. The workbook is editable and its live formulas recalculate when assumptions change.