# Navina Financial Model

AI-powered "Patient Portrait" platform that synthesises EHR data to surface missed diagnoses, HCC coding gaps, and quality metrics for primary care physicians at the point of care.

- Canonical: https://finamodel.com/startups/navina
- Excel download: https://finamodel.com/startup-models/navina.xlsx
- Category: Health-tech
- Model type: SaaS ARR / Valuation
- Funding round: Seed
- Funding: $22M
- Founded: 2020
- Geography: US primary care market (deck references AAFP partnership and US-centric statistics). Israeli-founded team. [DECK slides 3, 7]
- Customer: B2B2C

## About the company

Navina synthesises EHR data into a Patient Portrait that surfaces diagnoses, HCC coding gaps, and quality metrics for primary-care clinicians. It gives care teams a unified view of patient information, supporting documentation and value-based-care decision-making within existing clinical workflows.

It sells to practices, groups, and ACOs with ROI linked to coding and value-based-care uplift. Customer value depends on clinician adoption, deployment across providers, measurable coding improvement, and expansion from an initial group into broader primary-care networks.

The model forecasts provider organisations, clinician seats, subscription ARPU, deployment, HCC uplift evidence, expansion, and churn. It includes integration and cloud costs, enterprise sales, customer success, product investment, gross margin, cash burn, and runway.

## 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

- Product: "Patient Portrait" - an AI/ML layer on top of existing EHRs that aggregates and prioritises a patient's entire history, maps clinical relationships (problems ↔ labs ↔ medications ↔ consult notes), and flags missing diagnoses, HCC coding gaps, and RAF score opportunities at the point of care.
- Core value props:
  1. Reduces chart-review time (33% of physician EHR time is chart review - the single largest time sink).
  2. Cuts missed clinical elements: 53 out of 100 visits have at least one missed important clinical element (Navina's own research/pilots).
  3. Revenue recovery: 20% hidden revenues, $30B total potential loss in the primary care market.
  4. HCC coding uplift → higher RAF scores → multi-million-dollar annual income increase for practices on VBC plans.
- Partners cited: NOMS (HIMSS Davies ambulatory award winner), American Academy of Family Physicians (AAFP).

## Market

- $30B total potential revenue loss in primary care from missed HCC/RAF coding.
- No explicit TAM/SAM/SOM breakdown or market CAGR cited.
- Qualitative tailwinds: post-COVID surge in primary care visits, growing VBC/value-based plan adoption, telemedicine driving demand for better data access.
- 33% of physician EHR time spent on chart review (Annals of Internal Medicine, Adler-Milstein 2020).
- 78% of physicians burned out (Physicians Foundation / Merritt Hawkins 2018 survey).

## Revenue model

- Sold B2B to primary care practices, multi-specialty groups, and ACOs.
- Economic pitch is tied to RAF/HCC uplift: higher RAF scores → increased capitated payments → "multi-million dollars in annual income for practice groups."
- No explicit pricing model, per-seat fee, or revenue-share structure stated in the deck.
- Implied SaaS or subscription per-provider or per-practice, given the clinical workflow integration angle.
- Channel: direct enterprise sales + channel partnerships (NOMS, AAFP).

## Traction & metrics

- Pilots referenced but not quantified in terms of customer count, ARR, or growth rate.
- "Significant reduction in missed clinical data and reduced physician burnout" stated qualitatively.
- No revenue figures, customer count, retention rate, or growth metrics disclosed.

## Competition / moat

- Moat stated as:
  1. Team: founders are ex-IDF Unit 8200 with AI/ML track record; 11th joint project; won national security award for AI breakthrough.
  2. Clinical depth: diagnostically-linked AI mapping using ICD-10 / HCC coding frameworks.
  3. Partnerships: AAFP and NOMS give distribution and credibility.
- No competitive landscape slide or named competitors. Deck states "no sufficient technology is available to address this pain" and chart review has "the fewest tools available."

## Team & funding ask / use of funds

- CEO: Ronen Lavi - retired Lt. Colonel, IDF Unit 8200, 24 years; established IDF's AI lab for frontline decision support.
- CTO: Shay Perera - retired Major, Elite Intelligence unit, 10 years; M.Sc. EE / Deep Learning & Computer Vision.
- CSIO: Kfir Oved, PhD - co-founder & CTO at MeMed Dx; top 25 in Precision Medicine (BIS Research).
- VP Business: Maor Adlin - 10+ years US healthcare sales/BD.
- VP Medical: Yair Lewis, MD, PhD - board-certified Internal Medicine, Hebrew University + Technion.
- VP Product: Rotem Ben David - 10+ years healthcare product management, ML/signal processing.

---

## Recommended financial model

- **Archetype + why:** B2B SaaS ARR model with a VBC revenue-uplift ROI layer. The business is subscription-based (per provider or per practice seat), sold to healthcare organisations on VBC contracts. The economic hook is HCC/RAF score uplift - a quantifiable financial return that justifies the price and drives sales cycles. The model should capture both the SaaS P&L and an ROI calculator showing the RAF revenue uplift per practice (to support sales/pricing).

- **Forecast horizon & granularity:** 5-year annual (Year 1–5) + monthly for Year 1 to model ramp. Early pilot → commercialisation phase.

- **Key drivers & assumptions:**

| Driver | Value / Source |
| -- | -- |
| Addressable primary care physicians in US | ~210,000 |
| % on VBC/ACO plans (SAM filter) | ~30% → ~63,000 target physicians |
| Initial target: practice groups & ACOs | Accounts with 5–50 physicians |
| Average practice size (physicians/account) | 10 physicians per account |
| Annual contract value (ACV) per physician | $3,000–$6,000/yr |
| ACV per account | $30,000–$60,000 |
| New accounts / year (Year 1) | 5 |
| New accounts / year (Year 2–5) | 15 / 40 / 80 / 130 |
| Gross revenue retention | 85% |
| Net revenue retention (expansion) | 100–110% |
| Gross margin | 70–75% |
| Sales cycle | 3–6 months |
| CAC (per account) | $15,000–$30,000 |
| RAF revenue uplift per physician (ROI calc) | $50,000–$200,000/yr additional practice income from HCC uplift |
| Missed visit rate (market-sizing hook) | 53 out of 100 visits have ≥1 missed clinical element |
| Physician burnout rate | 78% |
| Hidden revenue opportunity | 20% of potential revenues hidden; $30B total market loss |
| Chart-review share of EHR time | 33% |

- **Scenarios (Base / Bull / Bear - which variables flex):**
  - Bear: slower VBC adoption, longer sales cycles, lower ACV ($3,000/physician), 10 new accounts by Year 3.
  - Base: ACV $4,500/physician, 40 accounts by Year 3, 85% GRR.
  - Bull: AAFP partnership drives channel volume, ACV $6,000, 80 accounts by Year 3, NRR 115%.
  - Key flex variables: ACV, account ramp rate, NRR, and pace of VBC market adoption.

- **Required sheets / outputs:**
  1. Assumptions dashboard (all drivers, scenario toggle)
  2. ARR bridge (new ARR, expansion, churn, net new ARR by year)
  3. Customer / physician count build (accounts × avg physicians)
  4. P&L (revenue, COGS → gross profit, S&M, R&D, G&A, EBITDA)
  5. Headcount plan (sales reps, engineers, clinical/medical staff)
  6. Cash flow & runway (burn rate, months to cash-out)
  7. ROI calculator tab (RAF uplift per physician → practice ROI → payback on Navina subscription) - this is a sales support output, not just financial
  8. KPI summary (ARR, accounts, physicians on platform, gross margin %, NRR, CAC, LTV, LTV/CAC)

## Frequently asked questions

### Is the Navina financial model free?

Yes. The Navina 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.