The Diagnosis No One Is Making

I was twenty-one, finishing my chemistry degree at Howard, when I was rushed to a Maryland emergency room, doubled over in pain so severe I could not sit or stand.

The team did not start with an ultrasound. They started with an assumption. No physical exam. No imaging. No one touched the source of the pain. A young Black woman in an emergency room with abdominal pain — there was a script for that, and the script did not require that anyone look. I was diagnosed with a sexually transmitted disease and sent home with medication for a disease I did not have.

The next morning I was back. A different physician. He did the one thing the first team had not. He looked.

What he found was a dermoid ovarian cyst the size of a grapefruit. The kind that grows teeth and hair.

I was admitted. The cyst was removed. After surgery, the conversation lasted minutes. I was handed a pamphlet about polycystic ovary syndrome, told something in passing about insulin resistance, and prescribed birth control to prevent another cyst. By the system’s own metrics, the encounter was a success. The misdiagnosis had been corrected. The patient was stable. If you pulled my chart, nothing was missing.

Except everything was missing.

It would take me years to piece together what should have been part of that first conversation — reading, questioning, and building my own understanding of a condition the medical establishment had barely studied in women who looked like me. And then I learned something that reframed the whole experience.

In 1991 — the same year I was sent home with medication for a disease I did not have — clinical trials in the United States did not require the inclusion of women. The NIH Revitalization Act would not pass for two more years. The diagnostic criteria physicians use today for PCOS did not yet exist. The evidence base I had been diagnosed against had been built without me.

Not a gap in the data. A void.

The first emergency room had not malfunctioned. It had operated exactly as it was designed to operate. The second physician succeeded because he overrode the default. That is not a system improvement. That is one person deciding to look before concluding.

The system had produced clinical action without producing trust. Accuracy is not adoption. Information is not trust.

The Pattern

I tell that story because what happened to me in one emergency room is what happens to entire companies every time leadership deploys capital against an adoption problem they have not actually diagnosed.

For the last two decades, I have led commercialization for healthcare technologies — at GE Healthcare, at Siemens Healthineers, in venture-backed startups, and on boards. I have watched the same pattern repeat across Fortune 500 platforms and early-stage launches alike.

Most healthcare leadership teams treat FDA clearance as a finish line. It is not. It is a diagnostic moment — the point at which a cleared technology meets a market that will either adopt it, reject it, or quietly fail to engage.

From that moment, the cycle is familiar. The product enters the market. The adoption curve does not materialize. Someone proposes more sales reps, a bigger marketing budget, a national conference strategy. Capital is redeployed against what leadership believes is the problem.

Adoption still does not move.

More capital. A leadership change. A repositioning exercise. The board is told the market is “early,” or the sales cycle is “longer than expected.” Eighteen months pass. The technology that absorbed years of R&D and millions in regulatory investment is quietly dying in the market — and no one in the room can explain why.

This is not a marketing failure. It is a diagnostic failure — the inability to distinguish between fundamentally different reasons adoption stalls, each requiring a completely different intervention.

When the real barrier is trust and a company responds with more visibility, each dollar amplifies doubt. When the real barrier is workflow friction and a company adds sales reps, each new trial becomes a negative reference. The wrong intervention does not just waste capital. It can actively damage the company’s market position — amplifying the very deficit it was supposed to close.

And the industry has no shared diagnostic vocabulary for naming the problem. So leadership defaults to the levers most teams have been trained to reach for, and the cycle repeats.

Three Barriers, Three Costs

There are three reasons cleared healthcare technologies stall in the market. Only three.

Awareness is a distribution-of-information problem. The market does not know the technology exists, does not know when it applies, or does not know who endorses it. The product may be excellent. The evidence may be robust. The market simply has not heard about it in a way that registers as relevant to clinical practice.

Usability is the barrier that hides inside apparent success. Clinicians express interest. Demos go well. Units ship. And then utilization drops. The product creates friction in the workflow — too many steps, too much disruption, too little fit with how the work actually gets done. The science is sound. The experience is broken.

Trust is the barrier whose costs compound where the other two plateau. Stakeholders are aware of the technology. They may have evaluated it. But they do not believe it serves them — their patients, their context, their institution. The deficit may live in the clinical evidence, in the company’s credibility, in the perceived relevance to a specific patient population, or in a longer history that has nothing to do with this specific product. Trust failures are also routinely mistaken for awareness or usability failures, which is how the wrong intervention gets deployed against them.

These three barriers are routinely conflated — in the literature, in the boardroom, and in the commercial team’s quarterly review. They look identical from the outside. Low utilization. Flat revenue. A frustrated commercial team. The symptoms are the same. The causes are not.

And the cost of getting the diagnosis wrong is asymmetric.

Misdiagnose awareness, and you waste capital. Misdiagnose usability, and you create damage you have to undo. Misdiagnose trust, and you broadcast your credibility gap to a wider audience. Each dollar deployed against the wrong barrier amplifies the deficit it was supposed to close.

Consider how this asymmetry plays out in public. IBM Watson Health was, for nearly a decade, an AI initiative whose oncology partnerships drew sustained press coverage in healthcare and the business press. The technology was real. The marketing budget was extraordinary. By 2016, MD Anderson Cancer Center had ended its high-profile partnership with IBM after spending more than sixty-two million dollars on a Watson-based oncology tool that never reached clinical care. Other partner institutions quietly distanced themselves. By 2022, IBM had divested the entire Watson Health unit at a fraction of what had been invested in it.

The post-mortems have continued for years. Some of the diagnosis was technical. Underneath the technical issues was a deeper failure: the clinical community stopped believing the recommendations the system produced were safer, smarter, or more grounded in real patient data than what oncologists were already doing. Once that belief was lost across enough institutions, no amount of marketing could rebuild it. New product announcements amplified the credibility gap rather than closing it. Additional dollars of visibility broadcast the deficit.

That is the asymmetry, in public. Awareness was not the problem. Watson had reached the front pages and the boardroom briefings. Trust was. And the dollars deployed against the wrong barrier did not slow the unraveling. They accelerated it.

That asymmetry is why adoption belongs in the boardroom — not just in the marketing meeting.

The Governance Gap

Boards already require diagnostic rigor before other major capital decisions. Before approving an acquisition, a board requires due diligence. Before approving a clinical development program, a board requires evidence of scientific validity. Before approving the construction of a manufacturing facility, a board requires demand forecasting.

Before approving a commercialization budget — the capital that determines whether a cleared technology actually reaches patients — what does a board typically require?

A revenue forecast and a slide deck from the commercial team.

That is the gap.

If you sit on a healthcare board, on an investment committee, or at the head of a commercial team, here are three questions worth asking before the next tranche of commercial spend is approved:

One. Which of the three barriers is actually in play for this technology — and what evidence supports that conclusion?

Two. If we are wrong about which barrier is active, what does it cost us — in capital, in compounding damage, in the time it will take to recover the market position?

Three. Who in this room is accountable for the diagnosis — not the campaign, not the launch, not the rebrand — but the diagnosis itself?

A leadership team that cannot answer those three questions is making the same mistake the emergency room made with me. Reaching for the nearest assumption. Deploying an intervention that matches the assumption. Closing the chart. The patient takes the medication faithfully. The underlying condition is still there — growing, compounding, doing damage that will take years to undo.

The pattern is the same. The scale is different. So is the damage.

The Diagnosis That Makes the Difference

The work I do now is built around a single question: which barrier is actually in play, and what does it cost to get the answer wrong?

This is not a framework for explaining why adoption fails after the fact. The literature has plenty of those. It is a diagnostic — a tool that produces a named answer, before the next dollar is spent or after a stall has begun, that a board can act on.

That diagnostic exists because, like the second physician in that emergency room, I decided to look before concluding.

The healthcare industry will continue to clear thousands of technologies a year. Many of them will be excellent. Some of them will reach the patients who need them. Many will not — not because the science was weak, but because the strategy could not name what was actually wrong.

That is the diagnosis no one is making.

It is the one that decides which cleared technologies reach patients and which do not.

What This Means For You

This is the work I do with healthcare boards, founders, and CEOs.