Why Do Doctors Dismiss Women’s Pain?

Understanding Gender Bias in Healthcare—and How to Be Taken Seriously

Pain is one of the most common reasons people seek medical care. It is also one of the most complex to interpret.

Across healthcare systems, a consistent pattern has emerged: women’s pain is more likely to be underestimated, misattributed, or dismissed. This is not a claim grounded in anecdote, but one supported by a substantial body of research examining how gender bias in healthcare shapes clinical decision-making.

Understanding why this occurs requires looking beyond individual interactions. It requires examining the structural and cognitive factors that influence how pain is perceived, prioritised, and acted upon.

Pain Is Not Interpreted Equally

Pain is inherently subjective. Unlike blood pressure or temperature, it cannot be measured directly. Clinicians must rely on patient reports, clinical judgement, and pattern recognition.

This creates variability in how pain is interpreted.

Research demonstrates that women are more likely to have their pain attributed to psychological causes such as stress or anxiety, while men presenting with similar symptoms are more likely to undergo further investigation. Women are also more likely to experience delays in pain management and are less likely to receive adequate treatment in certain clinical settings.

These differences are rarely explicit. They occur within the interpretive space of medicine, where decisions are shaped by both training and unconscious bias.

There is also a persistent cultural assumption that women are more accustomed to pain—and therefore better able to tolerate it. This idea is often linked to experiences such as menstruation or childbirth, and it can subtly influence how women’s pain is perceived in clinical settings. However, research does not support the conclusion that women have a higher pain tolerance. In fact, studies consistently show that women report more frequent, more severe, and longer-lasting pain across a wide range of conditions.

A more accurate interpretation is that women are more likely to live with ongoing pain, rather than being less affected by it. Over time, this can lead to both patients and clinicians normalising symptoms that would otherwise prompt further investigation. Pain that is recurrent or familiar is more easily minimised—not because it is less significant, but because it has been integrated into daily life. This normalisation creates an additional layer of risk, where clinically relevant symptoms are deprioritised or dismissed, contributing to delayed diagnosis.

The Role of Gender Bias in Healthcare

Gender bias in healthcare does not typically present as overt discrimination. It operates through patterns of interpretation that feel clinically reasonable but are not applied consistently.

Historically, women have been associated with emotional variability, while men have been associated with physical resilience. These associations, though outdated, continue to influence how symptoms are interpreted.

When a woman presents with complex or diffuse pain, it is more likely to be framed as:

• stress-related

• hormonally influenced

• or psychosomatic

When a man presents with similar symptoms, the threshold for physical investigation is often lower.

This does not reflect deliberate intent. Rather, it reflects the influence of longstanding assumptions embedded within clinical reasoning.

Why Are Women More Likely to Be Misdiagnosed?

Women are more likely to be misdiagnosed due to a combination of historical data gaps, differences in symptom presentation, and ongoing gender bias in healthcare.

Diagnosis relies on pattern recognition. Clinicians are trained to identify symptom clusters that correspond to known disease presentations. However, when those models are based primarily on male physiology, women’s symptoms may not align with expected patterns.

This results in three consistent challenges:

• Incomplete diagnostic frameworks
  Many conditions have been defined using data that does not fully represent female physiology

• Differences in symptom presentation
  Women may experience the same condition differently, but those differences are not always recognised as clinically significant

• Bias in interpretation
  Symptoms that do not fit expected models are more likely to be attributed to psychological causes

Cardiovascular disease illustrates this clearly. While men often present with chest pain, women are more likely to report fatigue, nausea, or back pain. These differences can result in delayed recognition and treatment.

When symptoms do not fit the model, patients become harder to diagnose. This is a structural limitation, not an individual failing.

The Cumulative Effect: From Dismissal to Delay

Dismissal is rarely a single event. It is cumulative.

A symptom is minimised.
An investigation is deferred.
A follow-up is delayed.

Individually, these decisions may appear proportionate. Collectively, they contribute to prolonged diagnostic timelines.

This is particularly evident in conditions such as endometriosis, where diagnosis often takes years. During this time, patients may repeatedly seek care, only to be told that their symptoms are normal, inconclusive, or unrelated.

The impact extends beyond clinical outcomes. It affects patient trust, engagement with healthcare, and willingness to seek further care.

Why This Is Often Misunderstood

One of the challenges in addressing the dismissal of women’s pain is that it does not present as a clear error.

There is rarely a single incorrect decision. Instead, there is a pattern of underestimation.

Patients may feel unheard or dismissed, while clinicians may feel they have acted appropriately based on available information. Both perspectives can coexist.

This disconnect reflects a gap between lived experience and clinical interpretation. It is within this gap that gender bias in healthcare becomes most visible.

How to Be Taken Seriously at Your Next Appointment

While systemic change is necessary, there are practical ways to improve how symptoms are received and interpreted within current clinical frameworks.

Clarity is critical.

• Describe patterns, not just symptoms
  Specify timing, frequency, duration, and triggers

• Be precise about impact
  Explain what the pain prevents you from doing in concrete terms

• Track symptoms over time
  Consistency strengthens clinical significance

• Separate description from interpretation
  Focus on observable facts rather than conclusions

• Prepare in advance
  Structured information is more likely to be retained and acted upon

These approaches do not eliminate bias. However, they reduce ambiguity, making it easier for clinicians to recognise patterns that warrant further investigation.

The dismissal of women’s pain is not the result of a single failure within healthcare. It is the outcome of historical bias, incomplete data, and the inherent complexity of interpreting subjective symptoms.

Women are not only more likely to have their pain dismissed, they are also more likely to have learned to live with it. This combination creates a clinical environment in which significant symptoms can be both underreported and underestimated.

Recognising this pattern is not about challenging the legitimacy of medicine. It is about understanding its limitations.

Because when pain is clearly documented, consistently presented, and clinically interpretable, it becomes significantly more difficult to ignore.

References

Criado Perez, C. (2019). Invisible Women: Data Bias in a World Designed for Men. Abrams Press.

Samulowitz, A., Gremyr, I., Eriksson, E., & Hensing, G. (2018). “Brave men” and “emotional women”: A theory-guided literature review on gender bias in healthcare and gendered norms towards patients with chronic pain. Pain Research and Management.

Hoffmann, D. E., & Tarzian, A. J. (2001). The girl who cried pain: A bias against women in the treatment of pain. The Journal of Law, Medicine & Ethics.

Merone, L., Tsey, K., Russell, D., & Nagle, C. (2022). Sex inequalities in medical research: A systematic scoping review. PLOS ONE.