In an interview, I was asked, "What's the biggest myth you hear repeated?' That led to the (AI assisted) essay below. For entry points, here, here, here.
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The Coverage Myth in Diagnostics
There is a comforting myth in diagnostics reimbursement: that coverage is a rational contest, conducted by serious people, in which the best evidence wins. In this myth, the startup publishes several good papers, writes a strong PowerPoint deck, secures a payer medical-director meeting, brings along one or two respected KOLs, and the payer thoughtfully concludes that the test is clinically valuable and should be covered.
That world may exist in pitch decks. It rarely exists in real reimbursement.
The reality is far more institutional, slower, and less responsive to scientific persuasion than investors want to believe. In molecular diagnostics, the practical route to coverage usually requires one of two things: either the lab benefit manager decides to approve the test, or major clinical guidelines make the test effectively required. Both pathways are very slow. Both are opaque. Neither is well suited to a startup running on a finite cash runway.
The problem is not that evidence does not matter. Evidence matters a great deal. But evidence is not self-executing. A publication does not automatically become a policy. A favorable KOL quote does not become a claims edit. A well-designed clinical utility study does not force a lab benefit manager, MolDx, a MAC, a commercial payer committee, or a guideline panel to move on a venture-backed timeline.
This is where investors often misprice diagnostics. They build reimbursement timelines as if each step takes six months: six months for coding, six months for evidence, six months for payer engagement, six months for coverage. But novelty does not move through the system like a Gantt chart. Novelty gets bogged down. Two or three years is not a disaster scenario; it may be the optimistic scenario. Five years is not unheard of. For some technologies, the system simply times out again and again.
Examples are everywhere. An improved test for osteoporosis and bone-density screening became tangled in CMS processes for years. National coverage decisions can sit for three, four, or more years. MolDx and other MAC processes, including Noridian, can take years to resolve novel technologies. The automated retinal imaging code 92229 illustrates the same pattern: coding arrived in 2019, publication followed in early 2021, but payment and RVU issues dragged on because software novelty did not fit comfortably into existing physician-fee-schedule machinery. The test may be useful, the clinical logic may be strong, and the code may exist — yet the reimbursement system can still stall.
The same warning now applies to computational pathology. For stakeholders in this vibrant new field, AMA CPT appeared to impose what felt like a moratorium on new codes for two years or more. Then, instead of continuing the earlier PLA-code pathway used by some whole-slide imaging and digital pathology tests, CPT shifted new computational pathology services into Category III codes. That may be defensible as coding policy, but from a business-planning perspective it leaves companies in limbo. CMS pricing is still up in the air: will these services be paid on the Clinical Lab Fee Schedule, through physician-fee-schedule RVUs, through contractor pricing, or through some future software-specific payment system? For a startup, that uncertainty is not an academic detail. It can determine whether the product is commercially viable at all.
The deeper lesson is that coverage is not merely an evidence review. It is an operating system. It includes coding, payment, benefit-category logic, medical-necessity language, claims edits, utilization management, guideline incorporation, payer committee cycles, lab benefit managers, MAC jurisdictional variation, and sometimes CMS national policy. Any one of those components can delay or defeat a test.
This makes the standard market-access fairy tale dangerous. It encourages startups to believe that if they are scientifically right, the system will eventually recognize them in time. But “eventually” is not a business model. A company with $10 million, or even $30 million, may not have enough runway to survive a multi-year reimbursement slog, especially if the product requires continued evidence generation, field sales, KOL cultivation, coding work, payer engagement, and operational claims support.
The most important reimbursement question for a diagnostics investor is therefore not simply, “Is the test good?” It is: “Who has to say yes, through what mechanism, on what timeline, and can the company survive until then?”
In diagnostics, the best test does not always win. The test that wins is the one that becomes operationally unavoidable — through guidelines, through lab benefit manager approval, through entrenched clinical workflow, or through a payer system that finally knows how to pay for it.
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