Why do we need to innovate in women’s health? And what does it mean to reimagine the patient journey with women in mind?
First some context: did you know that prior to 1993, “women with childbearing potential” were actively excluded from the early phases of most clinical trials. Even female mice were almost entirely excluded from research due to their hormonal cycle.
Modern medicine (not just the medication we prescribe, but diagnostic technology and systems of care) is unfortunately mostly entirely based on white male bodies. The effects of this gap in research and design can be lethal: women are more likely to die from heart attacks, react poorly to prescription drugs, and have our pain and symptoms dismissed by doctors.
Of the 10 drugs the FDA removed from the market between 1997 and 2000, 8 posed greater risks to women. Women’s Health Research at Yale drew attention to a study on a drug only intended for women where 23 of the 25 participants were men. Women are diagnosed significantly later than men on average across over 700 diseases.
Another example? The diagnostic criteria for attention deficit hyperactivity disorder is based on how the disorder is manifested in young boys. Young girls often present different symptoms, and are therefore often undiagnosed or under-diagnosed.
Men are 6 percent more likely to receive bystander CPR when suffering from an apparent heart attack in a public setting than women, resulting in 29 percent increased odds of survival. Women who suffer heart attacks are also half as likely as men to receive the recommended medical treatment for cardiovascular issues.
Those statistics are somewhat depressing but it also presents a tremendous opportunity – as well as a wake-up call. Healthcare is not static. And healthcare at this moment in time is radically evolving – is being redesigned around us as we speak. With the cost of genetic testing going down radically, the more widespread adoption of telehealth (due to COVID but a paradigm shift in the way care is delivered), digital therapeutics and the implementation of DIGA in Germany. The very structure of our healthcare system is changing – by necessity. The healthcare system is going to be forced to adapt because we have a shrinking healthcare workforce, an ageing population – healthcare is going to be forced to be reimagined, is in fact being reimagined as we speak, so what might that look like?
Well, the optimist in us would hope that it presents a tremendous opportunity to design a more equitable system of care. Because this one-size fits all approach that healthcare has been taking – doesn’t fit most of the population, doesn’t fit 50% of the population – so why not use it as an opportunity to reimagine the way we do things. We need to look at the patient journey, examine women’s health and seeing that women are faced with different diseases, or when they have the same diseases as men they have different symptoms, different side-effects, different needs and priorities and looking at that and thinking how can we design an experience that works for 50% of the population.
There are of course, some things we will need to be mindful of. Opportunities to close the gap in care that, if not carefully assessed beforehand, could present a tremendous pitfall/ danger. For example: AI-integrated healthcare system. Some hope that it will be able to construct a more just, less random healthcare system.
But, unfortunately, one of the best examples of a medical service extrapolating seemingly sound conclusions from biased data sets (ultimately leading to a biased and therefore wrong conclusion) came from an algorithm. Babylon was discovered to be giving different medical advice regarding chest pain to men and women. The system advised a 60-year-old male smoker reporting sudden chest pains and nausea to go to the emergency room with a suspected heart attack. A woman inputting exactly the same information was told she was likely having a panic attack. AI ran into the same problem as our healthcare system and medical professionals do: they put out what we put in. If we do not input the correct data sets, we will inevitably get bias back.
Bias presents a very present danger in algorithmic design. Most of the currently used biomedical AI technologies do not account for sex and gender bias detection. A recent study showed machine learning models could discern a patient’s self-reported race by analysing only the clinician’s notes in which all references to race had been redacted.
The EU’s clinical trial regulations can serve as a model: they require that participants in clinical trials represent the population groups that are likely to use the intervention under study, and non-inclusion must be justified. Outcomes must be analysed and results presented by age groups as well as sex. Similarly, to obtain funding under the Horizon Europe Strategic Plan or the Innovative Health Initiative, researchers must include sex and gender analysis in research design. The United States’ Women’s Health Research Roadmap and England’s Women’s Health Strategy have also taken steps toward sex- and gender-specific research and care.
In order to achieve equality of care between men and women, we will truly need to take an ecosystem and big picture approach. All innovation in medicine requires a multi-stakeholder approach to be truly effective regardless, but since since the inequities present in medicine are so persistent and present at every echelon, it will take innovators at every level from the get-go: researchers, clinicians, pharmaceutical companies, startups, Big Tech and women, the patients themselves to move medicine where it should be: providing equal access of care to the entire population. It will take an Ecosystem for women’s heath. It will take a FemTech movement.
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