St. Gallen Biz Model for Digital Health Therapeutics
University of St. Gallen
Business Model Navigator
in Digital Health Technologies
How the Magic Triangle applies across the 5 DHT categories — from wellness apps to prescription digital therapeutics.
↓ Click any circle to explore each dimension
WHO?
Customer
WHAT?
Value Prop
HOW?
Value Chain
WHY?
Revenue
The Magic Triangle
Four dimensions — one coherent model
The St. Gallen Business Model Navigator places WHO (the customer) at the center of everything. In digital health, this framework is especially powerful because the “customer” is multidimensional — patients, clinicians, payers, and health systems each have different needs and relationships.
Research found that 90% of successful business model innovations are recombinations of just 55 existing patterns. In DHT, the most common patterns include Freemium, Subscription, Pay-per-Use, and the emerging Prescription/Reimbursement model.
Research found that 90% of successful business model innovations are recombinations of just 55 existing patterns. In DHT, the most common patterns include Freemium, Subscription, Pay-per-Use, and the emerging Prescription/Reimbursement model.
👤WHO — the customer at the center
💡WHAT — your value proposition
⚙️HOW — your value chain
💰WHY — your revenue model
WHO — The Customer
The center of everything
WHO sits at the center of the triangle because the customer is the reason the other three dimensions exist. In digital health, this question is especially complex — the user, the payer, and the decision-maker are often three different people.
A patient uses the app. A clinician recommends it. An insurer pays for it. Designing for all three simultaneously is one of the defining challenges of DHT business models.
A patient uses the app. A clinician recommends it. An insurer pays for it. Designing for all three simultaneously is one of the defining challenges of DHT business models.
❓Is your WHO a consumer (wellness) or a patient with a diagnosed condition?
❓Who pays — the user, an employer, a health system, or an insurer?
❓Who prescribes or recommends — is clinician buy-in needed?
❓Does the WHO shift as you move up the DHT regulatory spectrum?
WHAT — Value Proposition
What do we offer and why does it matter?
The value proposition is your offer to the customer — and in digital health, this is inseparable from the claims you make about it. A wellness app claims to promote healthy habits. A DTx claims to treat a disease. The same underlying technology (say, a CBT-based app) can sit in entirely different regulatory categories depending on what you say it does.
WHAT also defines your competitive position — are you competing on access, convenience, clinical efficacy, or cost?
WHAT also defines your competitive position — are you competing on access, convenience, clinical efficacy, or cost?
❓Does the product make clinical claims? (This determines your regulatory path)
❓Is the value functional (treats disease), emotional (reduces stress), or social (connects to community)?
❓How does clinical evidence strengthen — or constrain — what you can promise?
❓Why choose this over a pill, a consultation, or doing nothing?
HOW — The Value Chain
How do we create and deliver the value?
In digital health, the value chain must include capabilities that don’t exist in most industries: clinical validation, regulatory affairs, and data governance. The further up the DHT spectrum you are, the more resource-intensive the HOW becomes.
A wellness app HOW: content team + app engineers. A DTx HOW: clinical trial infrastructure + regulatory team + post-market surveillance + pharmacist distribution network.
A wellness app HOW: content team + app engineers. A DTx HOW: clinical trial infrastructure + regulatory team + post-market surveillance + pharmacist distribution network.
❓What regulatory approvals are needed — and who will run that process?
❓Do we need clinical trial data, and how will we generate it?
❓What EHR integrations and clinical workflow partnerships are needed?
❓How do we handle patient data, privacy (GDPR/HIPAA), and cybersecurity?
WHY — Revenue Model
Why is this financially viable?
Digital health revenue models are structurally different from consumer software. The entity that benefits (patient) is often not the entity that pays (insurer, employer, health system). This payer/user split is one of the hardest problems in DHT business model design.
Common patterns: B2C subscription (wellness), razor-blade (hardware + consumables), per-use reimbursement (diagnostics), institutional licensing (care support), prescription pricing (DTx).
Common patterns: B2C subscription (wellness), razor-blade (hardware + consumables), per-use reimbursement (diagnostics), institutional licensing (care support), prescription pricing (DTx).
❓Who actually pays — the patient, employer, insurer, or health system?
❓Is insurance reimbursement available? What evidence do payers require?
❓What is the cost-of-evidence vs willingness-to-pay equation?
❓Can the business survive until reimbursement pathways mature?
The 5 DHT Categories — Where do you play?
Each category carries a different regulatory burden, customer relationship, and revenue logic. Click a category to see how the Magic Triangle shifts.
Health & Wellness
Disease-agnostic apps that capture general health data and promote healthy living
No clinical claims
Example: Headspace — Guided meditation & mindfulness
WHO
Stressed professionals, curious consumers seeking general wellbeing improvements — not patients with a diagnosed condition
WHAT
Guided meditation library, sleep tools, stress courses — promotes healthy habits, makes no clinical treatment claims
HOW
Content production, UX personalization, B2C app store + B2B employer wellness licensing, no regulatory approval needed
WHY $
Freemium consumer subscription (~$13/mo) + high-margin B2B corporate deals. Low regulatory cost = fast scaling
💡 Business model insight: Without clinical claims, customer acquisition is marketing-driven. The challenge is retention — wellness habits are hard to sustain, driving high churn. B2B pivots (employer wellness) stabilize revenue.
Patient Monitoring
Digital solutions that monitor specific health data, potentially interpreted by a physician for clinical management
Non-clinical claims
Example: Dexcom G7 — Continuous Glucose Monitor
WHO
Two-sided: patients with diabetes (or at risk) AND the clinicians who need the data to make treatment decisions
WHAT
Real-time glucose readings, trend alerts, and shareable data dashboards — replacing painful fingerstick tests
HOW
Hardware sensor + cloud platform, FDA 510(k) clearance, pharmacy distribution, EHR integration, clinical support network
WHY $
Razor/blade: sensor patches consumed every 10 days = high-frequency recurring revenue. Insurance reimbursement is key unlock
💡 Business model insight: The payer (insurer/health system) is often different from the user (patient). Reimbursement negotiation is a core strategic activity — clinical evidence of outcomes drives payer willingness to cover costs.
Care Support
Digital solutions that help patients better manage their care of a specific disease or condition
Health-adjacent claims
Example: Woebot Health — AI-powered mental health support
WHO
Patients with mild-to-moderate depression or anxiety, and the health systems / employers that want accessible, scalable mental health support
WHAT
Conversational AI delivering CBT-based check-ins, mood tracking, psychoeducation — bridges gap between sessions and ongoing care
HOW
NLP/AI chatbot, clinically-validated protocols, B2B2C via health systems and insurers, safety escalation pathways to human clinicians
WHY $
Institutional licensing (per-employee or per-member rates). ROI case: reduced ER visits + therapist time saves payers more than the license costs
💡 Business model insight: Care support sits in a grey zone — it improves adherence but doesn’t make treatment claims. This allows faster market entry while building the clinical evidence needed to eventually seek reimbursement or DTx status.
Digital Diagnostics
Validated tools that deliver a diagnosis or prognosis of a specific disease or medical condition
Clinical claim: diagnose
Example: IDx-DR (now: LumineticsCore) — AI diabetic retinopathy screening
WHO
Primary care providers who lack specialist access AND diabetic patients needing annual eye screening — not a consumer product
WHAT
First FDA-authorized autonomous AI diagnostic — screens retinal images and returns a clinical result without a specialist in the loop
HOW
Deep learning model trained on millions of labeled images, FDA De Novo authorization, integration into clinic workflow + EHR systems
WHY $
Per-scan fee billed to insurer (CPT code reimbursement). Clinical authorization is both a moat and a bottleneck — expensive to get, hard to copy
💡 Business model insight: Regulatory approval creates a defensible moat but the approval process can take years and cost millions. The WHO shifts from consumer to institutional buyer — procurement, not marketing, drives growth.
Digital Therapeutics (DTx)
Health software that treats or alleviates a specific disease or condition by generating and delivering a medical intervention
Clinical claim: treat / alleviate
Example: Somryst (Pear Therapeutics) — FDA-authorized CBT-I for chronic insomnia
WHO
Adults with chronic insomnia disorder — accessed via prescription from a clinician, dispensed like a drug through a pharmacy or digital channel
WHAT
A 9-week app-based CBT-I program clinically proven to reduce insomnia severity — a software prescription that replaces or augments sleeping pills
HOW
Randomized controlled trial evidence, FDA De Novo clearance, prescriber network development, pharmacist dispensing pathway, post-market surveillance
WHY $
Prescription-priced ($899 course) billed to insurer or out-of-pocket. Positioning as drug alternative justifies pharmaceutical-level pricing — but also requires pharmaceutical-level evidence costs
💡 Business model insight: DTx companies face a paradox — drug-like approval processes but app-like willingness to pay from insurers. The market is still maturing: reimbursement pathways (e.g. DIGA in Germany) are clearer in Europe than the US.
The 55 Patterns — click any to explore
Real-world examples
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