The Missing Middle: How AI and Data Science Can Transform Midlife Women’s Health—If We Fix the Data First
By Robin Austin, PhD, DNP, DC, MSN, RN, NI-BC, FAMIA, FAAN, Associate Professor | School of Nursing | Director | Center for Nursing Informatics, University of Minnesota School of Nursing
Artificial intelligence (AI), Machine Learning, and advanced analytics are rapidly transforming every corner of healthcare. Still, in women’s health, particularly during midlife and the menopause transition, these technologies are advancing on uneven ground. The promise is enormous. The foundation, however, is fragile.
Midlife (approximately ages 40–60) is one of the most physiologically dynamic periods in a woman’s life, marked by hormonal changes that shape cardiovascular risk, metabolic health, musculoskeletal pain, bone health, sleep, cognition, and mental health. And yet, this life stage remains one of the least systematically measured, least standardized, and least integrated within modern health data ecosystems. As a result, AI tools built on this data risk reinforcing the very inequities they are meant to solve.
This is both the challenge and the opportunity.
AI can reveal new patterns—but only if we give it something worth seeing.
How AI Is Transforming Women’s Health: In Theory and Practice
Advanced analytics excel when systems can follow people over time, link symptoms to context, and recognize patterns across complexity. That capability aligns well with midlife health, where changes unfold gradually and symptoms cluster rather than appear in isolation. In theory, data-driven tools could detect early cardiometabolic or cognitive risk, identify symptom patterns that predict chronic pain or functional decline, and support preventive, personalized care rather than reactive treatment.
In practice, menopause care remains largely excluded from these approaches. The reason is not a lack of analytic methods, but a lack of usable data.
Most health data pipelines rely on electronic health records, claims data, and increasingly, patient-generated and wearable data. For midlife women, these sources are fragmented and poorly aligned. Perimenopause is rarely documented as a distinct clinical state. Symptoms such as hot flashes, sleep disruption, mood changes, or joint pain are often coded separately, inconsistently, or not at all. Context, timing, clustering, severity, and impact on daily life are frequently missing.
From a data perspective, the signal exists. The system simply cannot see it.
The Data Infrastructure Problem
One of the most significant barriers to applying AI in women’s midlife health is not algorithmic sophistication; it is data infrastructure.
Many clinical systems lack standardized ways to represent perimenopause or to link symptoms to reproductive aging. Existing vocabularies (e.g., SNOMED CT and LOINC) contain relevant concepts, but they are inconsistently used. Clinicians often rely on free text, nonspecific codes, or problem lists that strip symptoms of context. When researchers later attempt to study outcomes, such as the relationship between menopause and musculoskeletal pain or cognitive symptoms, the data appear sparse or misleading, not because symptoms were absent, but because they were never structured to be visible.
Midlife symptoms rarely occur in isolation. Hot flashes frequently co-occur with sleep disturbance, anxiety, pain, and changes in work performance. Yet health data models fragment these experiences across encounters and specialties. Without a unifying framework that treats menopause as a transition rather than a diagnosis, systems fail to capture meaningful patterns or harmonize data across institutions.
Positive and Negative Impacts of Emerging Technology
AI reflects the data it is trained on. When midlife women’s experiences are poorly captured, models risk reinforcing incomplete or inaccurate views of health. In practice, tools built on existing datasets may underrepresent midlife women, misclassify risk, or miss clinically meaningful transitions. Even well-intentioned systems can perpetuate bias when historical gaps are embedded in the data.
Fragmented menopause data underestimate symptom burden, delay recognition of risk trajectories, and normalize dismissal of menopause-related symptoms, ultimately limiting the equity and generalizability of AI tools for diverse populations of women.
Bias in AI does not always look dramatic; it often appears as an omission. And omission, at scale, becomes structural. The result is a paradox: tools that perform well for acute disease detection but poorly for transition-heavy conditions, precisely where midlife women need more support, not less.
Despite these risks, the upside of AI in midlife women’s health is profound.
One of the most exciting frontiers is the integration of lived experience into health data. Natural language processing (NLP) allows researchers to analyze clinical notes, patient narratives, and open-text symptom descriptions, data that have traditionally been excluded from quantitative models.
For midlife women, this matters deeply.
Symptoms such as “brain fog,” “feeling unlike myself,” or “aching everywhere” may not map neatly onto diagnostic codes, but they are rich with meaning. NLP methods can extract patterns from these narratives, linking subjective experience with clinical measures, biomarkers, and outcomes.
When combined with structured data, labs, vitals, medication use, and wearable tools, these approaches offer a more holistic view of midlife health. This is where true innovation lies: not replacing clinical judgment, but expanding what counts as evidence.
With better data standards and intentional design, health systems could shift menopause care from reactive symptom management to proactive health optimization. Longitudinal data could support earlier signal detection, risk stratification, and personalized intervention before conditions become entrenched.
This future depends less on new tools and more on a focused and deliberate investment in data infrastructure.
Looking Ahead: Fixing the Middle to Unlock the Future
Women now spend nearly one-third of their lives after menopause, often in poorer health, making robust midlife data essential to longevity and healthspan, not optional. Advancing AI in women’s health requires closing persistent data gaps with models designed to capture health transitions rather than static states. For decades, midlife has been treated as a clinical afterthought, a transition without a data model, and AI is now revealing the downstream consequences of that omission.
Standardized menopausal and perimenopausal data items, improved connections between symptoms and clinical context in EHRs, harmonized datasets across studies and systems, and acceptance of lived experience as valid data are all necessary for equitable advancement. Meaningful gains will come not from more complex algorithms, but from data systems built to expect transition, accommodate complexity, and reflect the realities of midlife women’s health.
AI can reveal new patterns, but only if we give it something worth seeing.
References:
Austin, R. R., Alexander, S., Tupper, S., & Holt, J. M. (2025). Toward Solving the Menopause Data Gap: An Evidence-Based Standardized Mapping Study Using the Omaha System. CIN: Computers, Informatics, Nursing, 43(10), e01289.
Burns, D., Grabowsky, T., Kemblem, E., & Pérez, L. (2023). Closing the data gaps in women’s health.
Eyre, H., Alba, P. R., Gibson, C. J., Gatsby, E., Lynch, K. E., Patterson, O. V., & DuVall, S. L. (2024). Bridging information gaps in menopause status classification through natural language processing. JAMIA open, 7(1), ooae013.
Harlow, S. D., Sievert, L. L., LaCroix, A. Z., Mishra, G. D., & Woods, N. F. (2023). Women’s midlife health: the unfinished research agenda. Women’s Midlife Health, 9(1), 7.
Kraft, O (2025). The Price of Invisibility. FemTechnology Report https://economy.femtechnology.org/?utm_source=substack&utm_medium=email