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Last Week's Essay Taught Me Something I Didn't Expect
Last week I wrote about the global OB code elimination. The thirty-year fight. The inside work. The method.
What I didn't put in that essay was the thing that kept stopping me as I researched it — the reason the codes stayed broken for as long as they did wasn't just politics or inertia. It was that nobody could prove how much care had changed, because the data infrastructure to capture that change had never been built.
That sent me down a rabbit hole I haven't been able to climb out of.
Here is what I found: every serious operator in women's health is privately obsessed with data strategy. Almost none of them are talking about it publicly. And the gap between the companies building data as infrastructure versus generating it as exhaust is going to determine who is still standing in five years.
Why Women's Health Is Uniquely Data-Poor
The historical exclusion of women from clinical trials is well-documented. What's less discussed is what that exclusion actually produced: a care system making decisions based on data that was never generated, or generated on the wrong population entirely.
Menopause. Menstrual health. Endometriosis. PCOS. The longitudinal data on these conditions barely exists — not because the conditions are rare, but because the infrastructure to capture them was never built and the funding to build it was never allocated.
Add the fragmentation problem. A woman's health data lives across her OB, her primary care physician, her mental health provider, her specialty care team, and whatever apps she's using to track her own symptoms. None of those systems talk to each other.
The reimbursement codes were built on top of this fragmented, incomplete picture. Which means the payment system isn't just underpaying for women's health — it's underpaying based on evidence that was wrong from the start.
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"The companies that will still be standing in five years aren't the ones with the most users. They're the ones whose data can actually prove something."
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What Changes When Data Becomes Infrastructure
Most femtech conversations treat data as either a privacy liability or a marketing input. Protect it, or personalize with it. Both are defensive postures.
The strategic posture asks a different question: what does our data let us prove that nobody else can prove? Can we show a payer our intervention reduces NICU admissions? Can we generate the real-world evidence CMS needs to expand a reimbursement category?
That is data as infrastructure. Not exhaust — the byproduct of having users. Infrastructure — collected with intent, structured for interoperability, governed for trust, and designed from day one to answer the questions that will eventually determine whether your company survives.
The difference between the two is not a technology question. It is a strategic question that has to be answered before the data is collected, not after.
What This Means If You're Building
If you are a founder, the time to build the data layer is before you need it. The companies that will negotiate with payers and defend against new entrants in five years are making architectural decisions about data right now — not when someone in due diligence asks what their data can prove.
If you are an operator, pay attention to the roles being created around data strategy, governance, and evidence generation. These are not IT roles. They are strategic roles — and they are among the most durable positions being built in women's health right now.
What This Means If You're Investing
The diligence question that separates serious bets from hype is simple: what does your data let you prove that nobody else can prove?
If the answer is "we have a lot of users," that is exhaust. If the answer is a specific clinical outcome, a payer-ready evidence package, or a proprietary longitudinal dataset in a category where no longitudinal data exists — that is infrastructure.
The companies building infrastructure will still be standing. The ones generating exhaust will be acquired for their user base, if they're lucky.
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So what does this mean for you? Ask yourself right now: if a payer or acquirer walked in tomorrow and asked what your data can prove — what would you say? If the answer isn't specific, the data strategy conversation is more urgent than you think. That's the question worth sitting with this week.
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See you in the work,
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