In the age of artificial intelligence and data-driven healthcare, it’s tempting to assume progress is universal. Algorithms now detect disease patterns, predict patient outcomes, and personalize treatment plans with unprecedented precision. Yet, beneath the surface lies a troubling reality: women—particularly midlife women—are still being left behind.
The root cause isn’t just outdated medical textbooks or clinical oversight; it’s data bias. When the datasets driving modern healthcare fail to represent women equitably, the result is incomplete science, skewed outcomes, and systemic neglect. For women entering perimenopause and menopause, the consequences are especially profound.
This article examines how healthcare data bias manifests, why midlife women bear the brunt of it, and what solutions—technological, clinical, and policy-driven—can close the gap.
The gender data gap in healthcare
For decades, biomedical research treated male bodies as the “standard.” Women’s hormonal cycles, fluctuating levels of estrogen and progesterone, and reproductive changes were considered “too complex” for early-stage clinical trials. As a result, the foundational datasets of modern medicine often excluded half the population.
The ripple effects are staggering:
- Underrepresentation in clinical trials: Until the 1990s, women were routinely excluded from drug testing. Even now, female participants are underrepresented in studies on cardiovascular disease, oncology, and neurology—all conditions that intersect with hormonal changes in midlife.
- Limited data on menopause and HRT: Despite affecting every woman, menopause remains under-researched. There are relatively few large-scale longitudinal datasets tracking how hormone fluctuations—and treatments like hormone replacement therapy (HRT)—affect long-term health outcomes.
- Biased algorithms: AI systems trained on male-dominated datasets risk reproducing inequities in diagnostics, treatment recommendations, and predictive modeling.
When datasets ignore how fluctuating estrogen and progesterone influence symptoms, or how women respond to HRT, the resulting tools produce incomplete or misleading guidance.
Why midlife women are uniquely impacted
The gender data gap is universal, but midlife women face a double disadvantage. Their biological realities intersect with systemic blind spots in ways healthcare has historically overlooked.
1. Menopause as an afterthought
Menopause marks a profound physiological shift. Falling estrogen and progesterone levels can trigger hot flashes, sleep disturbances, mood changes, and metabolic shifts. Yet research funding in this area pales compared to other life stages. Too often, symptoms are dismissed as “normal aging” instead of treated with evidence-based interventions such as HRT.
2. Comorbidities in midlife
Perimenopause coincides with increased risks of cardiovascular disease, osteoporosis, and metabolic disorders. Estrogen plays a protective role in heart and bone health, but when hormone levels decline, risks escalate. Without robust datasets that include women on and off HRT, protocols remain incomplete.
3. Cultural stigma and silence
Societal discomfort around menopause compounds the issue. Women frequently report that providers dismiss their symptoms or avoid discussing treatment options like estrogen-progesterone therapy. This silence reinforces the cycle of under-research and under-treatment.
Data bias in action: The algorithm problem
AI-powered healthcare tools are built to improve diagnosis and care—but biased inputs create biased outputs.
- Cardiac risk scores: Algorithms used to assess cardiovascular risk are less accurate for women, who often present different symptom profiles than men. Declining estrogen levels during menopause are a known risk factor, but datasets rarely account for this hormonal context.
- Drug dosing models: Many dosing algorithms rely on male physiology, leading to side effects or reduced efficacy in women, especially when progesterone and estrogen metabolism differ.
- Hormone therapy models: Without representative datasets on women using HRT, algorithmic guidance for treatment remains shallow. The result: clinicians and patients lack robust, data-backed recommendations for therapies like estrogen-progesterone combinations.
Ironically, data-driven medicine—designed to democratize care—risks reinforcing old inequities in new forms.
How to fix the data bias problem
The challenge is complex, but solvable. Solutions require progress across technology, clinical practice, and policy.
1. Build inclusive datasets
Healthcare must prioritize capturing data on women across their lifespan—including perimenopause and menopause. This means tracking health outcomes across different HRT regimens, including therapies that combine estrogen and progesterone.
2. Design bias-aware AI
Developers must conduct bias audits to test how algorithms perform across genders, age groups, and hormonal states. For example, an AI predicting bone density loss should explicitly incorporate whether a woman is on HRT, since estrogen therapy significantly influences risk.
3. Rebalance research funding
Funding agencies need to prioritize women’s health research, especially in hormone therapy. Expanding longitudinal menopause studies will generate evidence that informs both AI systems and clinical best practices.
4. Clinician education and training
Medical professionals need better training to recognize and treat menopause-related symptoms. Integrating HRT education—including the nuances of estrogen-only vs. estrogen-progesterone therapy—into curricula ensures providers don’t perpetuate outdated misconceptions.
5. Patient-centric digital health platforms
Telehealth and digital therapeutics are filling gaps left by traditional systems. By centering patient-reported outcomes and integrating biometric data, these platforms can deliver personalized menopause care.
For example, modern telehealth providers now offer tailored HRT options, including estrogen body cream with progesterone. These solutions combine clinical expertise with digital engagement, making evidence-based therapies more accessible to women navigating midlife.
The role of policy and advocacy
Healthcare inequities aren’t just clinical—they’re systemic. Addressing bias requires structural change:
- Mandating gender parity in trials: Regulators must enforce balanced representation in research, particularly around HRT.
- Transparency in ai models: Vendors should disclose the demographic makeup of training datasets and performance metrics across groups.
- Insurance coverage for menopause care: Policy must recognize HRT—whether estrogen-only or estrogen-progesterone—as a legitimate medical necessity, not an elective.
Policy that prioritizes inclusivity ensures research and clinical practice move in step.
The future of data-driven, inclusive healthcare
Fixing healthcare bias isn’t just about fairness—it’s about accuracy. Treatments, algorithms, and protocols built on diverse data work better for everyone. For midlife women, this means earlier diagnoses, more effective treatments, and higher quality of life.
By integrating estrogen, progesterone, and HRT outcomes into mainstream datasets, healthcare can finally move past the “one-size-fits-men” model. The next generation of AI-powered tools must reflect women’s realities, not erase them.
Conclusion
Data is only as powerful as the diversity it represents. For too long, healthcare datasets excluded women—especially those navigating menopause. The result: a system that underserves midlife women at the exact stage when they need evidence-based care the most.
But solutions are here. Inclusive datasets, bias-aware AI, better clinician training, and digital health platforms are rewriting the future of care. Hormone replacement therapy—including safe, effective estrogen-progesterone combinations—has an important role to play in restoring balance.
The fix isn’t just possible—it’s essential. Because when healthcare works for women, it works better for everyone.
