Economic Model
How value translates into measurable economics
The Economic Model tier translates your Value Promise into a quantified business case. It answers three questions that every economic buyer and investor will ask: what is this worth to the institution, how many potential uses exist, and how does each use generate revenue? The three blocks in this tier are interdependent — Value Quantification establishes what the solution is worth, Market Size estimates how many times it will be used, and Transaction Model defines how each use converts to revenue. Together they form the foundation of your financial model.
Channel considerations and commercial dynamics differ meaningfully between HealthTech and Digital Medicine solutions. Select your solution type to see content relevant to your context.
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Value Quantification
Economic Model Block — Economic Value for Buyers
Value Quantification
Value Quantification translates your Value Promise into terms that matter to economic buyers. While users care about whether your solution works in practice, economic buyers care about what it delivers for their institution — clinically, operationally, and financially. This block is where you build the evidence-based case that justifies the purchase decision.
A strong Value Quantification is built from three components. Each must be addressed explicitly, because different buyers weight them differently and a case that is compelling to one buyer may be insufficient for another.
Clinical Value
Clinical Value captures the improvement in patient safety and clinical performance that your solution delivers. It is the foundation of the economic case — without credible clinical evidence, the financial arguments that follow will not be taken seriously.
Safety improvements address reductions in adverse events, complications, errors, or patient harm. In healthcare, safety is not just a clinical concern — it is an institutional liability and reputational issue. A solution that demonstrably reduces preventable harm carries significant institutional weight beyond its direct financial value.
Performance improvements address better clinical outcomes, higher diagnostic accuracy, faster procedures, reduced time to treatment, or improved quality of care. These must be expressed in measurable terms — not "improves outcomes" but "reduces 30-day readmission rates by X%" or "reduces time to diagnosis from Y hours to Z hours."
Clinical value must be supported by evidence. The strength of evidence required will vary by institution and buyer, but the direction is always toward more rigorous, more objective, and more peer-reviewed. Anecdotal clinical experience is a starting point, not a sufficient basis for an institutional purchase decision.
Economic Impact
Economic Impact translates clinical value into financial and operational terms that economic buyers can evaluate against their institutional priorities. It answers the question: what is this worth in dollars, time, or risk reduction?
The economic impact argument must be specific to the institution. A community hospital under cost pressure weights cost savings heavily. An academic medical center focused on research reputation weights clinical outcome improvements. A private hospital competing for premium patients weights patient experience and safety. Knowing your specific buyer's priorities is not optional — it determines which elements of your economic case to lead with.
Buyer's Decision Criteria
Different economic buyers in healthcare are accountable for different things, and their decision criteria reflect those accountabilities. A well-built value quantification addresses the criteria of the specific buyer you are trying to convince, not a generic institutional audience.
Understanding who holds decision authority, who can veto, and what evidence each decision-maker requires is as important as the strength of the evidence itself. A compelling clinical case presented only to the CMO will stall if the CFO has not seen a financial analysis. Build the case for all relevant decision-makers, not just the most sympathetic one.
Digital Medicine: Additional Considerations
Digital medicine solutions face distinct challenges in building a credible value quantification case.
Clinical Value — attribution challenge. In digital medicine, establishing that your solution — rather than the clinician, the care protocol, or other concurrent interventions — is responsible for a clinical outcome is methodologically difficult. A well-designed clinical validation study must isolate the software's contribution to outcome improvement with the same rigor expected of a device clinical trial. Generic before-and-after data will not be sufficient for payor coverage decisions or high-stakes VAC submissions.
Economic Impact — data network effects. Digital solutions can generate economic value that compounds over time. An AI diagnostic tool that improves as more patient data is processed becomes more accurate and more valuable with each additional use. These compounding effects are genuine economic value — but they are difficult to quantify at the early stage and require a different framing than the cost savings arguments that device economic cases typically rely on.
Buyer's Decision Criteria — IT and informatics stakeholders. In addition to the CFO, CMO, CNO, VAC, and Department Head criteria covered above, digital medicine solutions must satisfy institutional technology stakeholders whose criteria are entirely non-clinical.
Clinical Value: The device reduces time to sepsis diagnosis from an average of 6 hours to under 90 minutes. This improvement is associated with a reduction in 30-day mortality of approximately 15% in illustrative modelling. Adverse event rates related to delayed treatment — including progression to septic shock — are reduced by an estimated 20%.
Economic Impact: Each avoided sepsis progression to septic shock reduces average treatment cost by approximately $8,000 per case. At 400 cases per year per institution and a 20% reduction in adverse progressions, the illustrative annual cost saving per institution is approximately $640,000. Under value-based care contracts, avoided readmissions generate an additional estimated $180,000 in penalty avoidance per year.
Buyer's Decision Criteria: The VAC submission leads with cost savings and penalty avoidance figures relevant to the CFO. The CMO briefing leads with mortality reduction and adverse event data. The CNO receives a workflow impact assessment showing less than 5 minutes added to the nursing assessment protocol. The department head receives a combined summary positioning the device as a quality improvement initiative within the existing sepsis protocol.
Clinical Value: The software alerts clinicians to sepsis risk 4 hours earlier than standard clinical recognition on average, based on a validation study comparing alert timing to confirmed sepsis diagnoses. Establishing that the software — rather than the clinician response — drove the outcome improvement required a randomized controlled study design comparing alert-on vs alert-off periods in the same ED.
Economic Impact: Earlier recognition reduces ICU transfers by an estimated 18% in the validation cohort, generating illustrative cost savings of approximately $520,000 per institution per year. The software also improves sepsis documentation completeness, increasing sepsis-related DRG reimbursement capture by an estimated $95,000 per year. Data network effects: as the algorithm processes more patient encounters, alert sensitivity improves — generating compounding value that is not captured in year-one economic models.
Buyer's Decision Criteria: The CIO submission addresses Epic integration via validated SMART on FHIR app, data residency compliance, and implementation timeline. The CISO receives a SOC 2 Type II report, HIPAA BAA, and penetration testing results. The CMO receives the clinical validation study and a comparison to existing sepsis screening tools. The CFO receives a three-year ROI model showing payback within 14 months at target alert volume.
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Market Size
Economic Model Block — Volume of Potential Use
Market Size
Market Size defines how many potential uses of your solution exist. In the H-BMC, this is never a top-down estimate taken from a market report. It is built bottom-up from specific, testable assumptions about who uses your solution, how often, and at what rate of adoption. The same methodology applies whether you are sizing your Beachhead or your Target Market — and at the early stage, you should be building both in parallel.
Relevant Entities
Relevant Entities are the institutions, departments, or practitioners that could realistically use your solution in the market you are currently sizing. This is not a count of everyone who theoretically benefits — it is a count of the buying entities who have both the need and the means to acquire your solution.
In healthcare, the relevant entity is usually an institution — a hospital, clinic, health system, or practice — rather than an individual clinician. Each institution has an economic buyer who controls the purchasing decision. Counting institutions rather than individual users gives you a market size figure that is directly connected to your sales effort.
The number of relevant entities differs significantly between your Beachhead and your Target Market. Start with the Beachhead — a precise, verifiable count of institutions that meet your criteria. That precision makes the estimate credible and testable.
Usage Rate
Usage Rate is how often each relevant entity would use your solution in a given period — the number of procedures, tests, patients, or clinical episodes that create an opportunity for use. This is the engine of your market size calculation. A market with few entities but high usage per entity can be more attractive than a large market with infrequent use.
Usage rate data is often available from published sources — procedure volumes, disease prevalence, admission rates, and similar statistics are routinely reported for most clinical areas. Where published data exists, use it. Where it does not, usage rate becomes a hypothesis to test directly with stakeholders.
Adoption Rate
Adoption Rate is distinct from Usage Rate. Usage Rate is how often the solution would be used once an entity has adopted it. Adoption Rate is the proportion of relevant entities that will actually adopt your solution over time, and how quickly. It is the most uncertain of the three components and the one most prone to optimism.
A realistic adoption rate for a new medtech solution entering an established clinical workflow is typically slow in year one, accelerating as clinical evidence accumulates and reference sites generate credibility. Early-stage teams often overestimate how quickly institutions will adopt — partly because they underestimate the sales cycle and institutional decision-making timeline, and partly because they conflate clinical enthusiasm with purchasing intent.
The test of a well-formed adoption rate hypothesis is whether you can name the specific factors that would drive or limit adoption in your target institutions — evidence requirements, budget cycles, VAC processes, and competitive alternatives. If you cannot, the adoption rate is still an assumption rather than a hypothesis.
Market size is not static. Procedure volumes shift, reimbursement landscapes change, competitive alternatives emerge, and clinical practice evolves. A credible market size estimate acknowledges the dynamics that could grow or shrink the opportunity over your planning horizon.
Digital Medicine: Additional Considerations
Relevant Entities — individuals as well as institutions. For many digital medicine solutions, the relevant entity is not just an institution but an individual clinician, patient, or care team. Defining the relevant entity precisely — and being explicit about whether you are counting institutions, departments, individual clinicians, or patients — is essential. Conflating these levels produces estimates that are either impossibly large or misleadingly small.
Usage Rate — continuous vs episodic use. Digital solutions can approach continuous use — a monitoring platform running 24 hours a day, a clinical decision support tool consulted on every patient encounter. For continuously used digital solutions, usage rate may be better expressed as active users per period or patient-days covered rather than procedures or episodes.
Adoption Rate — viral spread and abandonment risk. Digital solutions can spread faster than physical devices through informal recommendation or app store discovery. However, abandonment rates are significantly higher. A credible adoption rate hypothesis must address both the uptake curve and the retention rate — not just how quickly institutions adopt, but how many users remain active six and twelve months after initial deployment.
Relevant Entities: Approximately 5,000 hospital emergency departments in the United States. Of these, roughly 1,200 are in hospitals with more than 200 beds treating sufficient sepsis volumes. The Beachhead focuses on 30 academic medical center EDs with established sepsis protocols and identifiable clinical champions.
Usage Rate: Each target institution manages an average of 400 sepsis cases per year. Each case represents one potential use of the diagnostic device.
Adoption Rate: Year one adoption projected at 10% of Beachhead institutions (3 of 30), rising to 50% by year three. The institutional decision cycle typically runs 12 to 18 months.
Resulting estimate: At full Beachhead penetration, 12,000 uses per year. At 50% adoption by year three, approximately 6,000 uses annually. Target Market of 1,200 institutions represents 480,000 uses per year at full penetration.
Relevant Entities: The relevant entity is the emergency department as an institutional unit, not individual clinicians. Approximately 1,200 EDs in hospitals with more than 200 beds and Epic EHR deployments represent the addressable market — Epic integration is a prerequisite for deployment. The Beachhead targets 25 academic medical center EDs with active sepsis quality improvement programs and existing Epic SMART on FHIR capability.
Usage Rate: Each target institution processes an average of 18,000 ED encounters per year. The algorithm evaluates every encounter for sepsis risk — usage rate is continuous rather than episodic. Active alert volume (encounters where the algorithm fires) is estimated at 400 per institution per year based on sepsis prevalence data.
Adoption Rate: Year one deployment projected at 4 institutions (16% of Beachhead) following a 90-day pilot and enterprise contract conversion. 12-month active user retention is targeted at 85%, based on benchmark data from comparable clinical decision support tools. Abandonment risk is highest in months 2-4 of deployment if alert fatigue is not managed through threshold calibration.
Resulting estimate: At full Beachhead penetration (25 institutions), 10,000 annual alerts processed. At 85% retention, approximately 8,500 active alert encounters per year in the near-term addressable market. Target Market of 1,200 Epic-enabled EDs represents approximately 480,000 alert encounters per year at full penetration.
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Transaction Model
Economic Model Block — How Use Becomes Revenue
Transaction Model
The Transaction Model defines how each use of your solution converts into revenue. It is not enough to know that demand exists — you need to define exactly what you are selling, what you are charging, and how and when you will be paid. In healthcare, each of these decisions is constrained by institutional purchasing processes, reimbursement realities, and cash flow dynamics that are unlike most other industries.
A strong Transaction Model is built from three components. Each must be defined explicitly before the block is complete.
Transaction Basis
Transaction Basis defines what you are selling — the unit of exchange between you and your economic buyer. The same solution can be structured as a capital purchase, a per-use fee, a subscription, a consumable model, a value-based contract, or a hybrid of several. Each structure carries different implications for adoption, cash flow, and investor attractiveness.
The choice of transaction basis should be driven by what the buyer can accept, not by what is simplest for you to administer. A transaction structure that requires the buyer to commit large capital before evidence is available will slow adoption regardless of clinical merit.
Pricing
Pricing in healthcare should be anchored to the value your solution delivers to the economic buyer, not to your cost plus a margin. If your solution saves a hospital significantly more than it costs, pricing it at cost-plus leaves most of the value on the table.
The starting point for pricing is your Value Quantification block. Your pricing should capture a reasonable share of the value delivered while leaving enough value with the buyer to make the purchase decision straightforward.
Pricing must also reflect reimbursement realities. If your solution is reimbursed under an existing code, the reimbursement rate constrains what the buyer can afford to pay. If no reimbursement pathway exists, the buyer must fund the purchase entirely from their own budget, which raises the bar for the economic case significantly.
Payment Terms
Payment terms define when and how you receive the revenue you have earned. In healthcare, the gap between delivering value and receiving payment is one of the most common causes of cash flow crises for early-stage companies.
Public hospitals in most markets pay on 60 to 120 day terms. Large hospital systems can extend to 150 days or longer. A company that models its business on 30-day payment assumptions but signs contracts with 90-day terms will run out of cash even if every sale proceeds exactly as planned.
The practical implication: model your cash flow from first contact to first payment, not from first sale to first payment. These are very different timelines, and the gap between them determines your capital requirements.
The Transaction Model does not exist in isolation from the process of selling. In healthcare, that process is long, structured, and involves multiple stakeholders. A suspect becomes a lead, a lead becomes a prospect, a prospect becomes a qualified opportunity, and a qualified opportunity becomes a closed sale — each stage taking weeks or months. Only after the contract is signed does the clock start on payment terms. Understanding how many suspects are needed to generate one closed sale, how long each stage takes, and how many sales one person can manage in a year are the inputs that translate your Transaction Model into a realistic revenue forecast.
Digital Medicine: Additional Considerations
Transaction Basis — additional models for digital medicine.
Pricing — digital medicine considerations. Per-seat vs per-patient vs per-use pricing each carry different signals to the buyer. Per-seat pricing is familiar to IT buyers but may not align revenue with value delivered. Per-patient pricing aligns revenue with clinical volume but requires usage tracking infrastructure. AI-powered tools present a particular challenge: the solution becomes more valuable over time as more data is processed, but early customers pay the same price as later customers who benefit from a more mature algorithm.
Payment Terms — subscription-specific considerations. Annual contracts provide revenue predictability and reduce churn risk but require a larger institutional commitment upfront. Monthly contracts lower the adoption barrier but create revenue uncertainty. The pilot-to-paid conversion dynamic is one of the most significant challenges — institutions routinely expect free or discounted pilots before committing to enterprise contracts. Define the pilot-to-paid pathway, the evidence threshold that triggers conversion, and the commercial terms of conversion before the pilot begins, not after.
Transaction Basis: Capital purchase with a consumables model. The diagnostic unit is sold once per institution. Proprietary single-use test cartridges generate recurring revenue aligned with usage rate assumptions. This structure reduces per-procedure cost to the buyer while creating a predictable, volume-driven revenue stream.
Pricing: Published data indicates early accurate diagnosis reduces average treatment cost by approximately $8,000 per case. The cartridge is priced at $45 per test — less than 1% of the value delivered per case. The capital unit is priced at $12,000, positioned as a one-time infrastructure investment recovered within the first 30 cases.
Payment Terms: Target institutions pay on 90 day terms. The sales cycle from first contact to signed contract is estimated at 14 months. First payment is therefore expected approximately 17 months after initial engagement. Financial planning accounts for this gap with sufficient runway to reach first revenue without additional capital raises.
Transaction Basis: Annual subscription per institution, priced per ED and inclusive of Epic integration support, algorithm updates, and alert threshold calibration services. No hardware component. A 90-day pilot at no cost is offered to Beachhead institutions, with explicit conversion terms agreed before the pilot begins — including the evidence threshold that triggers conversion and the enterprise pricing that applies on conversion.
Pricing: Priced at $85,000 per ED per year, anchored to the illustrative $520,000 annual cost saving from reduced ICU transfers — capturing approximately 16% of the value delivered. Per-ED pricing (rather than per-seat or per-alert) aligns the revenue model with institutional budgeting cycles and avoids the perception of cost-per-use friction that reduces clinical engagement with alert-based tools.
Payment Terms: Annual contracts invoiced at the start of each subscription year, payable within 30 days. The pilot-to-paid timeline from first engagement to first invoice is estimated at 6 months (3-month pilot plus contract negotiation). This is significantly shorter than the 14-month device sales cycle but requires careful management of the pilot conversion process to avoid indefinite free access.