Transforming Healthcare: Moving Beyond “Women’s Health” to Equitable Health
Women’s health is an emerging market, and women should be leading it.
In a recent conference, a panel member called for a shift in the narrative surrounding women’s health, with the goal to make men’s health and women’s health equal subcategories of the broader category of health. This would, of course, require that the broad definition of health automatically encompasses distinct and profound gender differences across genetics, biology and socioeconomics. As it currently stands, ‘healthcare’ is defined through a male lens, primarily from research conducted on male participants and male biology; and then there is women’s health – primarily defined by the reproductive journey.
The Rise of Fem Tech and Women’s Health Innovation
In the evolving landscape of healthcare, how we think, talk about, and invest in women’s health are all part of the path to equality. I have been conducting research on gender differences in healthcare for decades, often shouting into a void. I am thrilled that women’s health and fem tech are now considered an emerging market in healthcare. I am thrilled because we need a starting point. Prior to being ‘emerging’, women’s health was invisible.
In this sudden and increasing awareness that women’s health is an important market, I say to all of those who have been thinking about, studying, or working in women’s health – now is the time to reach for the impossible; innovate; come out of the shadows and share your vision.
Women’s Health is more than Reproductive Health
In this emerging market, the focus remains primarily on reproductive concerns, from fertility to menopause. These areas are not unimportant, and there is much to be done because reproductive health is scarcely out of medieval times in many ways. Anyone who has had a pap smear or a mammogram will nod in agreement.
But that is just the tip of the iceberg. The excitement in this ‘emerging’ market is that the companies (many female founded) who are innovating in reproductive health are bringing novel therapeutics and devices to humanize it, seeking to understand underlying genetics and biology, exploring the molecular processes, and unlocking the potential for disease prevention way earlier than is currently possible.
This is powerful change.
It also provides data, insights, and opportunities to collaborate across the health spectrum to better inform differences in other diseases and conditions. If we understand fertility, pregnancy, postpartum and menopause we can start to understand if there is a link across the reproductive life course. From there, thousands of connections can be made to truly understand differences across the entire life course, connecting prenatal conditions through diseases of aging. We can understand the role of gender genetics and biology for chronic disease like the known differences in women’s and men’s heart disease, or diabetes, or neurologic diseases as we age. We can learn why women are diagnosed with certain diseases more often than others. Such exploration can explain whether such conditions as autoimmune disorders, anxiety, or migraines result from implicit bias, or genetic or biologic explanations. These examples are, importantly, already being researched. Connecting previously undiscovered dots within reproductive health can connect dots to every other aspect of women’s health. For instance, if we understand whether infertility and early menopause are linked, we can explore broader links such as the relation to heart disease.
The Value (and Gaps) in Data
And that brings me to the second evolution impacting women’s health. Data. From artificial intelligence, large and small language models, computing at the edge, interoperability, data consumption, and data privacy, data is queen. Yet, there is too much and not enough all at the same time. In my career, I have experienced access to very large data sets and data platforms that were often considered the holy grail. The little told secret about many of these data resources (from consumer data to clinical or claims data and health tech data) is that they suffer from several challenges.
Most data are either not diverse because the population captured is homogenous (i.e., by gender, race/ethnicity, or economic status) or the range of relevant demographics are not collected, making diversity invisible. This is not an oversight, because homogeneity or excluding variables are choices made when a data collection activity begins. It may be defined as too costly, availability of a sample, or regulatory constraints, but the bottom line is that excluding diversity is choice often for an easier path. As a result, these data can only contribute at one level or through one lens. Depending on the dataset, inferences and insights simply can’t consider if there are biases by gender, race/ethnicity, or socioeconomics because the data weren’t made available.
Much data is afflicted by ‘garbage in garbage out’. I and others have done the research on organizational variation and found that some of the systems to streamline care can pressure providers into checkbox medicine resulting in clinical data that may not align with the clinical encounter. Claims data on the other hand, is intended to assess billing, reimbursement, and cost of care. While clinical and claims data can work hand in hand, they both suffer from care formulas in which patient outcomes are often absent. Diagnosis and treatment are not easily codified via a checkbox on a screen (Atul Gawande wrote “the” book on this challenge).
The data are collected for a narrow purpose. And when I say narrow, it is relevant to the objectives of the organization collecting it. Retail data is collected to identify habits for marketing (hence why women’s health is becoming an emerging market – trends have identified that women make 80% of the healthcare decisions). Clinical data is collected assessing diagnosis and treatment behavior, and claims data is collected for managing costs of care. Research data is collected to evaluate treatments for specific diseases or conditions. IOT and wearable data is collected to track activities and behaviors. They are collected for a specific purpose that is often incomplete or inaccurate data for the questions we need to be asking in women’s’ health.
For data to truly be queen, we need to ask the right questions, capture the health journey, and integrate the elements to elevate insights relevant to women’s health.
The Call for Genuine Commitment
As I said, I am thrilled that after decades of being invisible, women’s health is emerging. At the same time, this newfound interest has to be built from sincerity and commitment rather than trend-following and market value.
Fem tech is not a buzzword; it is a genuine commitment to driving positive change for women’s health.
If we put in the work, and learn about these important differences, the rest will follow. Because so little funding is directed toward women’s health, and because the data are so messy, it is imperative that fem tech is supported by those committed to tangible and lasting change. It is promising to see the number of accelerators, conferences, investment funds, and federally funded research recognizing and embracing women’s health. It is powerful to see women across the lifespan becoming founders, and entrepreneurs. It is reassuring to have allies across industries supporting these efforts. It is this passion, drive, and collaboration that will be required to sustain change. The commitment to inclusivity must be reflected not only in words but also in actions, policies, and innovations.
Embracing health inclusivity
Transforming the healthcare narrative around women’s health is a powerful call to action. Moving beyond the label of “women’s health” and embracing equitable health is essential for fostering a healthcare system that truly meets the diverse needs of all individuals. But we are far from that reality. As fem tech and women’s health innovation continue to gain prominence, the emphasis should be on genuine commitment, concerted efforts, space to excel and lasting support. By addressing disparities and working collaboratively, we can build a healthcare system that truly cares for every individual, because of, not despite, their differences.