Early Economic Evaluation

Understand the commercial opportunity for your technology, before you have all the evidence.

Most innovators we work with are early stage, often without effectiveness evidence and not yet ready to approach payers. Early economic evaluation is a structured way to characterise the care pathway you intend to change, compare its economics with and without your technology, and work out whether, and under what conditions an adoption case could hold. The information that follows walks through that argument using diabetic foot ulcer (DFU) as a worked example.

All figures on this page are illustrative, for a diabetic foot ulcer (DFU) example.

Sizing a commercial opportunity does not begin with a product. It begins with the potential for a technology to be attractive to payers given a need to improve health or costs.

Why do this now

What early economic evaluation gives you, and who it's for.

You do not need a finished product, trial data, or a payer meeting to begin. What you need is a clear view of the pathway you intend to change. Early economic evaluation characterises the current care pathway, identifies where there is room for improvement, surfaces what would drive that improvement, helps decide what to measure, and translates the picture into the language of the use case in front of you, including for discussions with investors, trial partners, or eventually payers.

Characterise

The pathway and the room for improvement

Fully describe today's care, where cost and health are concentrated, and where a new technology could plausibly move the needle.

Quantify

The drivers, and how to measure them

Surface the parameters that move both cost and health, so trials can be planned around endpoints that matter to the economics of the pathway, not only those that are easiest to capture.

Translate

Findings into your use case

The same analysis can be framed for investors (commercial potential), trial partners (what to measure and why), and eventually payers (adoption case and the conditions under which it holds).

Concepts that matter

Three distinctions that quietly decide whether a value story holds together.

Cost-effectiveness is not cost savings.

A technology can be good value for the health it delivers without saving anyone money, and a technology can save money without being particularly good value. The two questions are answered with different evidence.

Cost savings are nuanced.

Savings depend on which budget holder bears the cost today, whether the saving is cashable or freed capacity, and when it actually appears. A saving in one budget can be invisible in another.

Trial endpoints should follow the economics.

The endpoints worth measuring are the ones that move the cost and health of the pathway. Choosing them deliberately is what turns a clinical trial into evidence that supports an adoption case later.

The analysis begins by carefully examining the care pathways and health states of the condition you intend to influence with your technology.

The pathway as health states

Costs are incurred state by state, and health is better or worse state by state.

Before any value case, for an investor, a trial partner, or a payer, you need a clear picture of the pathway you intend to change. In DFU, that pathway is a sequence of health states, each with its own interventions, cost profile, and level of health. Patients move between these states over time, and the chance of moving from one state to another in a given period is what economists call a transition probability. A new technology earns its value by changing those chances, keeping more patients in low-cost, high-health states, and pulling them out of the worse ones faster. Select a state below to see what care looks like there, what it costs, and how good or bad health is.

Health state

Uncomplicated ulcer

Typical interventions: Offloading, dressings, weekly podiatry visits

Who pays: Community / outpatient

Moderate cost, meaningful quality-of-life loss.

Payer cost while in this state

$950 / patient-month

Health in this state (utility, 0–1)

0.70

1.00 = perfect health, 0.00 = death-equivalent. Lower bars mean worse day-to-day functioning, pain, or limitation.

How patients move

Transition probabilities, in plain language.

In any given month, a patient in one state has some chance of staying there, some chance of getting better, and some chance of getting worse. Those chances are transition probabilities. They decide how a population flows through the pathway over time - and, in turn, how much cost is incurred and how much health is gained or lost.

Most technologies aim to do one thing with these probabilities: keep more patients in a low-cost, high-utility state (intact skin, healed, in remission) and reduce the chance of slipping into a high-cost, low-utility one (infected ulcer, hospitalisation, amputation). Quantifying that shift is what turns a clinical claim into an economic one.

With the pathway in view, two questions surface together. Where does the money actually go, and where is health actually lost?

Where the money goes today

High unit cost is not the same as high total spend.

Where does the money in DFU actually go? Costs are shown from a payer perspective, broken into lifetime components per patient. The largest single events are not always the largest line items in a lifetime budget, and that distinction matters for where a new technology can plausibly create value.

Cost components, payer perspective

Frequency vs. cost-per-event

Top-left points (frequent + cheap) and top-right points (rare + expensive) can both dominate spend. The total is what matters.

Knowing where money concentrates is not the same as knowing what could be recovered. Some of the spend in this picture is reachable; much of it is not.

Cash savings vs. costs avoided

Not every saving shows up in next year's budget.

Not every saving a payer hears about translates into budget relief. The distinction between cashable savings and freed capacity determines what can credibly fund a new technology.

Cash savings

Cashable, they release budget.

Spending the payer no longer incurs: a hospital admission that does not happen, an antibiotic course that is not prescribed. Cashable savings change next year's budget and can directly fund the technology that produced them.

Costs avoided

Notional, capacity is freed, not money.

Real benefits that do not appear as a line-item saving. Ten fewer nurse minutes per visit is genuine value, but the nurse is still paid. These savings strengthen the case and free capacity for other patients; they rarely fund the technology on their own.

Cost, even named carefully, is only one side of the ledger. The other side is health, and it has to be measured before it can be valued.

Health, in one number

What is a QALY, and how is health lost in DFU?

Cost tells half the story. To evaluate the other half, payers need a single, comparable measure of health. The Quality-Adjusted Life-Year (QALY) is that measure.

A QALY is one year of life in perfect health. Half a year in perfect health equals 0.5 QALYs. A full year lived at 50% health also equals 0.5 QALYs. A year in the "infected ulcer" state (utility 0.55) therefore counts as 0.55 QALYs; a year in "major amputation" (utility 0.35) counts as 0.35. On this single scale, payers can weigh fewer hospitalisations against longer life or improved function.

Per-state picture: how much health is lost (vs. perfect health) in each state, given typical time spent there

Once health is measurable, it can be brought into the same conversation as money , because payers, implicitly or explicitly, are willing to pay for it.

Why payers care about health, not just cost

Payers put a dollar value on a year of healthy life.

Most payers, explicitly or implicitly, attach a willingness-to-pay (WTP) to a QALY. This converts a health gain into a dollar value that can be added to cash savings on a single scale.

United States

~$50,000 / QALY (common benchmark; up to $100k+ used)

England (NICE)

£20,000–£30,000 / QALY

Canada (CADTH)

~CAD 50,000 / QALY (commonly cited)

The value a payer can rationally fund therefore has two components:

Value to payer = cash savings + (willingness-to-pay × QALYs gained)

Together, these define the upper bound of what a payer can rationally fund, the headroom for a new technology.

Money saved and health valued, taken together, set the outer limit of what a rational payer could fund.

Headroom

How much room is there for your technology in a payer's budget?

Headroom is the maximum a rational payer could fund per patient: cash savings the technology generates, plus the dollar value of the health gain it delivers. Price below it and the value case is clear; price above it and the payer is being asked to pay for value that is not there.

Spending the payer no longer incurs (e.g. hospitalisations or amputations avoided).

Extra healthy life your technology delivers vs. standard care.

Headroom, what a payer could rationally pay per patient

$38,000

Cash savings

$8,000

Value of health gain

$30,000

At these assumptions, a rational payer could fund up to $38,000 per patient: $8,000 in cash savings and $30,000 in valued health gain.

Headroom is an upper bound, not a target price. Realised pricing also reflects competition, payer mix, and budget impact.

That limit is only as solid as the assumptions underneath it. The interesting assumptions are the ones that move cost and health at the same time.

What matters most

The parameters that move both the money and the health.

Headroom is only as credible as the parameters that drive it. The most informative drivers are those that move cost and health together, and they are also the parameters a trial should be designed to measure, because tightening them tightens the whole value case.

Amputation rate (given infection)
$14,000

Cost impact / patient

0.85 QALYs

Health impact (combined value: $56,500)

Infection rate per ulcer
$11,500

Cost impact / patient

0.55 QALYs

Health impact (combined value: $39,000)

Ulcer recurrence rate
$9,800

Cost impact / patient

0.45 QALYs

Health impact (combined value: $32,300)

Adherence to offloading
$6,400

Cost impact / patient

0.35 QALYs

Health impact (combined value: $23,900)

Time to healing
$7,200

Cost impact / patient

0.30 QALYs

Health impact (combined value: $22,200)

Hospital length of stay
$5,800

Cost impact / patient

0.05 QALYs

Health impact (combined value: $8,300)

Hover a row to see why the driver matters and what evidence would tighten it.

Combined value uses an illustrative $50,000/QALY threshold so cost and health swings can be compared in dollars. These same parameters typically define what a well-designed trial or registry should measure.

Among those assumptions, two end up dominating any real conversation: how much your technology actually changes outcomes, and how much you ask to be paid for it.

Two-way view

Effect size and price are two halves of the same conversation.

Headroom is a point estimate. In practice, both the effect size your technology delivers and the price you intend to charge are uncertain. The grid below shows where their combinations remain defensible, and where they do not.

Price ↓  /  Health gain →0.10 QALYs0.30 QALYs0.50 QALYs0.70 QALYs0.90 QALYs1.10 QALYs1.30 QALYs
$100k
$85k
$70k
$55k
$40k
$25k
$10k

Selected

0.50 QALYs at $40,000

Value to payer

$33,000

= $8,000 cash + $25,000 health value

Verdict

Price exceeds value

Green: value comfortably covers price. Yellow: combination approaches break-even. Red: price exceeds the value the payer can rationally fund. Cash saving illustratively held at $8k; WTP at $50k/QALY.

The picture is now coherent: pathway, cost, health, headroom, drivers, and price. The remaining question is how much of it you can build yourself.

What you can do, and where help adds value

A founder can outline this story. A health economist makes it defensible enough for investors, trial partners, and payers.

What you can do today

  • Sketch the health states a patient moves through
  • List the typical interventions and who funds each
  • Pull illustrative cost components from published sources
  • Identify which savings would be cashable vs. capacity
  • Run a back-of-envelope headroom calculation

Where a health economist adds value

  • Structured Markov or DES models calibrated to natural history
  • Parameter sourcing, uncertainty characterisation, and probabilistic sensitivity analysis
  • Headroom, threshold, and value-of-information analytics
  • Budget impact disaggregated by budget holder and jurisdiction
  • An evidence-development plan that aligns trial design with value

Inform Decisions

Pathway and headroom analysis surface where R&D effort actually changes the value equation, early enough to redirect priorities.

Achieve Investment

A quantified payer-value picture, with explicit assumptions and uncertainty, is what investors compare across opportunities.

Obtain Trial

The same model defines what to measure in trial and shows health systems why the trial is worth running.

Secure Adoption

Once trial actuals replace illustrative effect sizes, the framework communicates value to the parties whose budgets are affected.

Ready to size your opportunity?

Speak with ASSESS about an Early Economic Evaluation tailored to your technology, condition, and the payers you need to convince.

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