A fragmented documentation model and episodic care.

Perimenopause and menopause are not isolated events. They are dynamic physiologic transitions that can unfold over years, sometimes more than a decade, affecting sleep, cognition, mood, cardiovascular health, metabolic health, musculoskeletal function, sexual health, and overall quality of life. Midlife is not simply “the years before aging.” It is a critical window into healthy aging.

Yet most health care systems continue to function through episodic encounters and what has become the fragmented documentation model. A woman may discuss insomnia with one provider, anxiety with another, joint pain with an orthopedist, and irregular cycles with a gynecologist. Rarely are these experiences connected longitudinally across systems, specialties, or time.

The infrastructure reflects that fragmentation.

Many electronic health records (EHRs) still lack standardized structured fields for menopause stage, symptom burden, menstrual pattern changes, longitudinal symptom tracking, or patient-generated health data integration. Symptoms are often buried in free-text notes, inconsistently coded, or disconnected from meaningful clinical context. Even when women are telling us exactly what they are experiencing, our systems frequently lack the structure to interpret these lived experiences as computable longitudinal health data.

From a nursing informatics perspective, the signal is there. The systems simply are not built to see it.

The wider context.

By 2030, more than 1.2 billion women worldwide will be living in menopause or postmenopause. Yet despite affecting half the population and shaping decades of health and well-being, the menopause transition remains one of the least visible and least measurable experiences in modern health care.

This is often framed as a women’s health issue. It is. But it is also something much bigger.

It is a real-world data problem. And nursing informatics is uniquely positioned to help solve it.

I came to this work both personally and professionally. Like many women entering midlife, I found myself navigating symptoms and health changes that often felt fragmented, minimized, or disconnected from the larger picture of my health.

At the same time, as a nurse informaticist and researcher, I could clearly see that menopause, like many women’s health conditions, has a massive data and infrastructure problem. Women were speaking up. Health care systems were never designed to capture, measure, or represent women’s longitudinal health experiences in meaningful, computable ways that support sharable and comparable data across health systems, research, and care environments, or to truly “see” the whole person.

Why the data gap matters even more with documentation systems integrating AI.

This becomes critically important as health care rapidly moves toward artificial intelligence (AI), predictive analytics, and precision health models. AI systems are only as trustworthy as the data used to train them. When midlife women’s health experiences are poorly standardized, inconsistently documented, or absent from structured datasets, those gaps do not disappear inside AI systems. They scale.

Bias in women’s health AI may not always appear dramatic or obvious. More often, it appears as silence: symptoms not recognized, risks not connected, treatment responses not tracked, and women’s experiences rendered statistically invisible because the infrastructure failed to capture them in the first place.

This is why nursing informatics leadership matters now.

Nurses understand something health care systems often miss: health does not happen in isolated moments. It unfolds across time, environments, behaviors, symptoms, relationships, work, caregiving, and lived experience. Nursing informatics sits at the intersection of patient care, workflow design, implementation science, interoperability, and whole-person health. That positioning makes nurses essential architects of the next generation of women’s health infrastructure.

Informatics and standards are the solution.

What we need now is not simply more menopause apps or increasingly sophisticated algorithms. We need foundational modernization of women’s health data infrastructure.

That begins with standardized common data elements and minimum data sets for perimenopause and menopause. Health care systems still lack consistent ways to document reproductive stage, vasomotor symptoms, sleep disruption, cognitive changes, mood symptoms, treatment response, and symptom trajectories over time. Without structured, longitudinal, and interoperable data capture, meaningful analysis, clinical decision support, and AI development remain limited.

Nursing informatics can help make midlife women’s health more visible by ensuring that menopause-related symptoms, stages, treatments, and outcomes are documented in ways that computers can recognize, connect, and use over time. This work requires both external and internal action. Externally, nurse informaticists can help shape research, policy, professional guidelines, and data standards that define what menopause-related information should be captured. Internally, within health systems, they can influence how electronic health records, clinical workflows, decision support tools, and patient-generated health data are designed and implemented.

This includes aligning menopause-related data with interoperable standards such as SNOMED CT for clinical concepts, LOINC for symptom assessments and patient-reported outcomes, and HL7 FHIR for sharing information across EHRs, digital health platforms, research systems, and patient-facing tools. These data can also be mapped into longitudinal models such as the Observational Medical Outcomes Partnership (OMOP) Common Data Model, making them more useful for research, population health, quality improvement, and AI-ready datasets. Nurse informaticists can influence how systems are configured locally, how standards are adopted, how nursing and patient-generated data are prioritized, and how clinical gaps become visible enough to inform broader policy and vendor decisions.

Today, menopause and reproductive aging remain inconsistently represented because documentation itself is fragmented and variable. When reproductive aging is inferred through age ranges or diagnosis codes rather than structured longitudinal documentation, we lose the ability to accurately study symptom trajectories, treatment effectiveness, and relationships between menopause and long-term cardiometabolic, cognitive, musculoskeletal, and mental health outcomes.

There is also a significant opportunity in natural language processing (NLP). Symptoms such as “brain fog,” “feeling unlike myself,” or “aching everywhere” may never fully map to diagnostic codes, yet they contain clinically meaningful information about lived experience and symptom burden. NLP can help extract patterns from narrative notes and patient-reported data, linking lived experience to outcomes and longitudinal trends.

Beyond documentation: integrating real-world data.

We also miss the opportunity to integrate patient-generated health data into meaningful care models. Women are already tracking symptoms through wearable devices, mobile applications, sleep technologies, and digital journals. But most of these data remain disconnected from clinical workflows and inaccessible for longitudinal care planning.

Nursing informatics can help bridge this divide by designing systems that integrate wearable data, symptom tracking, and patient-reported outcomes into clinical decision-making rather than leaving those data fragmented across disconnected consumer platforms.

Importantly, this work is not solely about menopause care. It is about building infrastructure capable of supporting longitudinal whole-person health across the lifespan.

The missing decade cannot stay missing.

Women now spend nearly one-third of their lives after menopause. Yet midlife remains one of the least developed areas of healthcare data infrastructure. I often refer to this as “The Missing Decade,” a critical life stage that has remained largely invisible within our clinical systems, research models, digital technologies, and policy conversations.

But invisibility is not inevitability.

Nurses have always been more than caregivers. We are translators of human experience into meaningful health understanding. We are data stewards, workflow designers, patient advocates, and system builders.

As health care enters the AI era, nursing informatics must help ensure women’s midlife health is no longer missing from the datasets shaping the future of care.

AI is only as powerful as the data behind it, and can only recognize what our health systems are designed to capture.

And midlife women deserve to finally be visible.

Robin Austin, PhD, DNP, DC, RN, NI-BC, FAMIA, FAAN, is an associate professor at the University of Minnesota School of Nursing and serves as director of the Center for Nursing Informatics and specialty coordinator for the DNP Nursing Informatics program. She combines clinical expertise, data science, and nursing informatics to advance whole-person health measurement and digital health innovation, with a particular focus on midlife women’s health and menopause.

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