FemTech’s Data Problem: Why the Future of Healthcare Requires Rethinking How We Measure Women’s Health

The Silent Bias in Healthcare Data

Healthcare has a data problem. But more precisely, it has a data bias problem.

For decades, modern medicine has relied on datasets that were never designed to represent half of the global population. Clinical trials historically excluded women in many phases of research. Medical devices were validated primarily on male physiology. AI systems are trained on datasets where sex stratification is often absent.

The consequence is not theoretical. It directly affects diagnostics, treatment outcomes, and long-term health strategies.

During a recent conversation with Roswitha Verwer, founder of Yon E Health, and Dr. Muskaan Bhan, the company’s Chief Clinical Officer, this issue surfaced in a striking way. In a study examining nearly 700 FDA-cleared AI medical devices, only 3.6 percent included race or ethnicity data in their development datasets. Even more surprisingly, none of them incorporated female-specific stratified data into their model training.

This is not a minor technical oversight. It represents a structural blind spot in modern healthcare.

If artificial intelligence is increasingly shaping diagnostics and treatment decisions, then the quality and inclusiveness of the underlying data become the foundation of the entire system.

And in women’s health, that foundation is still incomplete.

The Limitations of Snapshot Medicine

Traditional medicine operates largely on snapshots.

A blood test once a year. A hormonal measurement during a single consultation. A diagnostic reading taken at one point in time.

This approach assumes that biological systems remain stable enough for a single measurement to represent a patient’s health status.

In reality, physiology is dynamic.

Hormonal fluctuations, metabolic changes, immune responses, and temperature variations occur continuously. In women’s health, this variability is even more pronounced due to menstrual cycles, pregnancy, menopause, and other life stage transitions.

When we rely on isolated measurements, we do not see the full biological picture. We see a moment.

In clinical research, we already know the limitations of snapshot data. Many clinical trial failures occur because the variables that truly drive outcomes were not measured with sufficient frequency or for long enough.

The same challenge now exists in digital health and AI-driven diagnostics.

The future of healthcare will not rely on isolated readings. It will rely on continuous data streams, allowing physicians and researchers to observe patterns over time.

This shift from static measurement to longitudinal monitoring represents one of the most important transitions in modern medicine.

FemTech as a Structural Innovation

FemTech emerged over the past decade as an attempt to close this data gap.

Initially, the sector was often misunderstood or dismissed. Many early products focused on lifestyle tracking or fertility awareness. The industry struggled with limited investment and cultural discomfort around women’s health topics.

Today, the landscape is changing.

What we now see is a new generation of companies approaching women’s health with the same level of scientific rigor expected in traditional medical device and biotechnology development.

Yon E Health is one example of this shift.

The company recently advanced from Technology Readiness Level 2, which validates the scientific concept in laboratory conditions, to Technology Readiness Level 4, where early prototype development and biocompatibility testing begin. Their device aims to continuously track biomarkers that have rarely been measured in a structured, longitudinal manner before.

The scientific ambition is not simply to measure data points. It is to connect physiological markers with the different life stages of women.

This life stage perspective is critical. A biomarker can have very different clinical meanings depending on whether a patient is in adolescence, reproductive age, pregnancy, postpartum recovery, or menopause.

Yet historically, these distinctions have often been overlooked.

Why Building the Right Data Takes Time

One of the most difficult decisions for founders in healthcare innovation is choosing between speed and scientific integrity.

Investors sometimes encourage early commercialization. Launch a wellness device. Skip the clinical studies. Generate revenue first and validate later.

In regulated healthcare, that strategy can undermine long-term credibility and clinical value.

Roswitha Verwer described a situation that many healthcare founders will recognize. An investor suggested that instead of building a medically validated device, the company could launch immediately as a consumer wellness product and begin selling without clinical evidence.

The suggestion might accelerate revenue. But it would compromise the technology's scientific objective.

Healthcare innovation requires patience because the variables that matter must be measured properly. Study designs must incorporate diversity. Data must be collected across time horizons that reflect real biological processes.

Cutting corners in this phase may reduce development time, but it also reduces the reliability of the final product.

In the long run, trust in healthcare technologies depends on the integrity of the data behind them.

The Investment Gap in Women’s Health

Despite the growing scientific maturity of the FemTech sector, investment remains lagging.

Part of the challenge is historical bias. For many investors, women’s health was not perceived as a primary healthcare market.

Another factor is structural. Medical device and biotech development require significant capital. Clinical trials, regulatory pathways, and data infrastructure create barriers that lifestyle products do not face.

As a result, many FemTech companies struggle to secure early-stage funding even when their science is sound.

At the same time, we are seeing encouraging signals.

Dedicated accelerators for women’s health are emerging. Governments are beginning to recognize the systemic gaps in medical research and are directing funding toward closing them. Hospitals and academic centers are opening specialized programs dedicated to women’s health innovation.

The ecosystem is still young but developing rapidly.

The Cultural Dimension of Innovation

Innovation in FemTech is not only technical. It is cultural.

For many founders, early fundraising conversations still include uncomfortable moments. Investors who hesitate to discuss vaginal health. Pitch sessions where the first task is not to explain the technology but to convince the audience that the problem itself exists.

This dynamic is slowly changing.

As more research emerges and more companies demonstrate serious scientific ambition, the perception of women’s health as a niche category is beginning to shift.

Women’s health is not a niche market. It represents half of the global population.

More importantly, improving women’s health data improves healthcare for everyone. Biological insights discovered through more inclusive datasets often translate into better diagnostics and treatments across the entire population.

Return on Impact

Healthcare innovation has traditionally been evaluated through return on investment.

For companies working in underrepresented areas of medicine, another metric is increasingly relevant: return on impact.

Return on impact measures whether a technology genuinely improves patient outcomes, expands medical knowledge, and addresses systemic gaps in care.

Financial return remains essential. Sustainable innovation requires capital.

But when financial return becomes the sole objective, shortcuts appear. Studies are minimized. Data diversity is reduced. Products reach the market faster but with weaker scientific foundations.

Healthcare systems pay the price later.

Companies that prioritize impact alongside financial performance tend to build more durable technologies. They also create the type of clinical evidence that supports large-scale adoption by physicians, hospitals, and regulators.

Building the Next Generation of Medical Data

The next decade of healthcare innovation will likely be shaped by three forces.

First, continuous physiological monitoring will replace static diagnostics in many areas of medicine.

Second, artificial intelligence will increasingly assist in interpreting complex streams of biological data.

Third, the quality and diversity of the underlying datasets will determine whether these systems produce meaningful insights or simply replicate historical biases.

FemTech companies working at the intersection of sensors, longitudinal data collection, and AI have an opportunity to reshape how medicine understands women’s health.

But this transformation will require sustained investment, rigorous science, and founders willing to resist the temptation of shortcuts.

A Structural Shift in Healthcare

Healthcare innovation rarely moves in straight lines.

It advances through a combination of scientific discovery, entrepreneurial persistence, and cultural change.

The FemTech sector today stands at the intersection of all three.

For decades, women’s health data was incomplete. Now, a new generation of researchers, engineers, and founders is beginning to correct that imbalance.

The work is complex. It requires building datasets that never existed before, designing studies that reflect real biological diversity, and convincing investors that long-term scientific value matters more than short-term speed.

But the potential reward is significant.

When healthcare systems begin to understand women’s biology with the same depth that they understand male physiology today, medicine will not simply become more equitable.

It will become more accurate.

And in healthcare, accuracy is the foundation of every meaningful breakthrough.

Dr.  Peter M. Kovacs