The Gender Data Health Gap: An Urgent Problem And How We Can Address It

In modern medicine, there is a significant difference in the quality and quantity of health data collected and analysed for men and women. This difference, known as the Gender Data Health Gap, has far-reaching consequences for women’s healthcare. But how did this data gap come about and what are its implications?

Emergence of the Gender Data Health Gap

Historically, participants in clinical trials have been predominantly male, young, fit, around 178 cm tall and weighing around 75 kg [1]. The responses to medications, dosages and treatment methods of these ‘optimal’ subjects were directly translated to women, resulting in a significant data gap [1]. Until 1993, it was not mandatory to include women in clinical trials, and even today gender differences are often neglected [2].

Problem caused by the Gender Data Health Gap

The health data we need to provide adequate care for women in the 21st century is often missing. There are several reasons for this:

• History: Women were not mandatorily included in clinical trials until 1993 [2].
• Time impact: Even though women have been increasingly included in trials over the last 30 years, there is a 17-year lag between research and direct patient care [4].
• Bias: Even in 2023, male mice were predominantly used in biomedical studies, leading to gender-biased data [3].

Effects of the Gender Data Health Gap

The gender gap in health data leads to significant inequalities in healthcare [5]:

• Diagnosis delays: Women wait an average of four years longer than men for a diagnosis for the same condition.
• Inappropriate treatments: Women are less likely to receive opioids for pain management in the emergency department, even though they have the same pain scores as men.
• Inequalities in medical care: Sexual health conversations take place for 89% of men but only 13% of women. Additionally, there are only two approved treatments for women’s sexual dysfunction, compared to 27 for men .

Increasing the Gender Data Health Gap through Artificial Intelligence

Artificial intelligence (AI) learns from existing training data. If gender-specific differences are not taken into account, structural gaps and prejudices can be reinforced. For example, women may have different symptoms of a heart attack than men. If these differences are not recorded and correctly interpreted in databases or apps, this can result in misdiagnoses that are further reproduced by AI. [5], [6]

Solution: A multi-faceted approach

Addressing gender data gaps with AI requires a multifaceted approach [5]:

• Recognising the limitations: Communicating the limitations of the data on which AI solutions have been trained.
• Collaboration with experts: Involving gender researchers, sociologists and clinicians in data collection, algorithm development and model evaluation.
• Public involvement: The public, especially vulnerable populations, should be involved in the collection of health data.
• Transfer learning: Using models that have been previously trained with similar tasks or datasets to utilise their learned properties.
• Meta-analysis: Conducting meta-analyses of existing, possibly male-centred studies to gain insights relevant to women.
• Develop inclusive models: Design models that take gender and other demographic differences into account.
• Regular model evaluation and testing: Iterative evaluation and updating of digital models as new data becomes available.
• Ethical oversight: Ethics committees should review AI models for potential bias and ensure ethical standards.
• Feedback mechanisms: Feedback loops on AI recommendations allow models to be adapted to real-world conditions.
• Education and training: Healthcare professionals should be educated about the limitations of AI tools in order to make informed decisions.
• Pressure on policy: Political support for more diverse data collection can reduce data gaps in the long term.

Conclusion

Addressing gender disparities in health data is critical to equitable and effective healthcare. Only by recognising and addressing the gender data health gap can we ensure that men and women benefit equally from medical advances. A multifaceted approach that integrates expert knowledge, public engagement and continuous modelling is key to closing this gap. By disaggregating health data by gender and taking targeted action, we can create a more equitable future for all.

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Takeaway points:

The Problem:

The Gender Data Health Gap where there are differences in the quality and quantity of health data collected and analysed between women and men.

The Impact: Inequalities in healthcare

• Incorrect treatments
• Delays in diagnosis
• Inadequate prevention

Artificial intelligence (AI)

AI learns from existing training data. If gender-specific differences are not taken into account, structural gaps and prejudices in the healthcare system can be perpetuated.

The Solution

To address gender data gaps with AI, a multifaceted approach must be taken.

For more Information

EQUAL CARE certifies medical intervention with a balanced gender representation in data and evidence. Join EQUAL CARE today and lead the health market with our certification. Together, we can set a new standard for healthcare excellence and create a future where everyone receives the care they deserve.

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Sources:

[1] Laumann, V. (2024). Warum die Medizin weiblicher werden muss. Gesundheit + Gesellschafft, 02/2024, 1–12. https://www.aok.de/pp/gg/magazine/gesundheit-gesellschaft-02-2024/geschlechtersensible-medizin/

[2] Liu, K. A., & Mager, N. A. D. (2016). Women’s involvement in clinical trials: Historical perspective and future implications. Pharmacy Practice, 14(1), 708. https://doi.org/10.18549/PharmPract.2016.01.708

[3] Mazure, C. M., & Jones, D. P. (2015). Twenty years and still counting: Including women as participants and studying sex and gender in biomedical research. BMC Women’s Health, 15, 94. https://doi.org/10.1186/s12905-015-0251-9

[4] Morris, Z. S., Wooding, S., & Grant, J. (2011). The answer is 17 years, what is the question: Understanding time lags in translational research. Journal of the Royal Society of Medicine, 104(12), 510–520. https://doi.org/10.1258/jrsm.2011.110180

[5] Women At The Table. (2023). The Gender Data Health Gap: Harnessing AI’s Transformative Power to Bridge the Gender Health Data Divide. Women at the Table. https://www.womenatthetable.net/2024/01/10/the-gender-data-health-gap-harnessing-ais-transformative-power-to-bridge-the-gender-health-data-divide/

[6] Sievers, B. (2023). So heilt man heute – Die häufigsten Volkskrankheiten geschlechtsspezifisch besser behandeln. München: Edel Verlagsgruppe GmbH