The Reimbursement Sanity Check
Most reimbursement advice encourages companies to push harder.
The Reimbursement Sanity Check asks whether they are pushing in the right direction at all.
Healthcare systems rarely fail to understand innovation. More often, they quietly absorb it, reclassify it, and move on. Technologies stall not because the evidence is weak, but because they arrive in the wrong shape, in the wrong pathway, solving problems no one is funded to fix.
Odelle’s Reimbursement Sanity Check is a short, deliberately lightweight analysis designed to surface that misalignment early. It does not produce more paperwork. It produces better questions. By examining budget ownership, decision rights, and system incentives, it reveals where effort is being wasted and where small reframes can open entirely different routes to payment.
There are no full models, no dossiers, and no long engagements. Instead, reimbursement logic is used as a constraint: a way to force clearer thinking about who pays, why they would care, and what would actually need to change for adoption to make sense.
The outcome is not just clarity on reimbursement. It is strategic optionality new pathways, different buyers, and directions that are affordable to test before commitment becomes expensive.
In short, the Reimbursement Sanity Check helps teams stop spending money to confirm what doesn’t work, and start using system insight to discover what might.
The pattern most teams miss
From the inside, stalled reimbursement looks like progress. Conversations continue. Pilots extend. Advisors remain encouraging. Nothing is explicitly rejected.
From the system’s perspective, the decision has often already been made.
Healthcare systems rarely block technologies outright. They absorb them. They move them into pilots, innovation tracks, or evaluation loops that feel active but carry no financial consequence. This isn’t confusion or inertia it’s a form of risk management.
The problem for companies is timing. These signals are subtle early on, when a small reframing could still change the outcome. They become obvious only later, after significant effort has already been invested.
The Reimbursement Sanity Check exists to read those signals sooner before momentum is mistaken for traction, and before persistence becomes the most expensive strategy of all.
We have, through value analysis modelling, taken technologies from a 3 per cent national market share to 76% in for years through value analysis modelling. In a few cases, the work has been surprisingly simple. By changing the question from “is this innovative?” to “where does the system actually lose money?”, value analysis has shifted adoption in ways no amount of promotion ever could.
Technologies that once hovered at the margins reaching perhaps a few per cent of national use — moved, over time, into routine practice. Not because the science changed, but because the economics were finally made legible to the people who had to make the decision.
Nothing about this was dramatic. It was the quiet effect of aligning a product with how care is paid for, rather than how it is described.
Frequently Asked Questions
How does value analysis change adoption when evidence alone doesn’t?
By changing the unit of comparison.
Most technologies are evaluated in isolation: cost per use, accuracy, incremental benefit. Value analysis instead looks at what the system is already paying for when the technology is not used. In practice, this means modelling the downstream consequences of delay, escalation, duplication, or failure and expressing them in simple, cumulative terms.
When those costs are aggregated across real volumes, small inefficiencies stop looking small. What appeared marginal at the patient level becomes material at the service level. Adoption follows not because the technology is “better”, but because the alternative is mathematically indefensible.
What kind of modelling actually shifts decisions?
Not complex models, directionally correct ones.
The most effective analyses use a small number of variables the system already recognises: event rates, time delays, substitution points, and volumes. The maths is deliberately conservative. No heroic assumptions, no long time horizons.
The power comes from scale. When you show that a one-day delay, multiplied across thousands of cases, reliably produces avoidable cost or capacity loss, the argument no longer depends on belief. It depends on arithmetic. Decision-makers don’t need to be persuaded; they need to reconcile the numbers with what they already see happening.
Why can this lead to large shifts in uptake without changing the technology?
Because systems don’t change for innovation, they change to stop paying twice for the same problem.
In the most successful cases, the analysis didn’t promise savings in theory. It demonstrated that the system was already paying quietly, repeatedly, and inefficiently and that continuing to do so was the most expensive option available.
Once that equation is visible, adoption becomes a form of risk management rather than enthusiasm. The technology doesn’t spread because it is promoted. It spreads because, mathematically, the status quo no longer makes sense.