Study Finds Women Consistently Offered Less Intensive Medical Care Than Men
A significant new body of research has laid bare a persistent and troubling pattern within modern healthcare systems: women with the exact same medical conditions as men are considerably less likely to be offered active, intensive treatments such as surgery, stents or strong painkillers.
The findings, published in the journal PLOS One, emerged from an ambitious review led by researchers at the University of St Andrews School of Medicine in Scotland. The team began with an extraordinarily broad net, sifting through 1,112 published studies before narrowing their focus to those that directly and rigorously compared treatment outcomes between male and female patients presenting with comparable conditions. Of the 38 studies that met this exacting standard by analysing actual patient records, a striking 33 reported statistically significant differences in the treatment men and women received.
A Pattern That Spans Medical Specialties
What makes the findings particularly compelling, according to the researchers, is not merely that disparities exist, but how consistently they appeared across entirely different areas of medicine. The same pattern of women receiving less aggressive intervention surfaced repeatedly in cardiology, surgery, transplant medicine and emergency care — and crucially, the disparity generally persisted even after researchers statistically adjusted for other factors that might otherwise explain the gap.
“While the direction of the findings was not a surprise, the consistency was,” said Dr Miriam Veenhuizen, an honorary lecturer at St Andrews’ School of Medicine and one of the study’s co-authors. The specifics documented across the reviewed literature paint a vivid picture: women suffering from heart attacks, heart failure or irregular heartbeats were considerably more likely to be managed with medication alone, whereas men presenting with identical conditions more often received invasive interventions such as coronary bypass surgery or stents. Women were also found to be more frequently prescribed statins rather than more aggressive procedural options.
The disparities extended well beyond cardiology. Women requiring dialysis for kidney failure were less likely than men with comparable needs to be given consistent, reliable vascular access, often relying on catheters for longer periods rather than more durable access methods. And across the board, women were consistently less likely than men to be offered stronger pain medication, including opioids, for equivalent levels of reported pain.
Not a Matter of Clinical Guidelines
Perhaps the most striking element of the study is what it ruled out as an explanation. The research team specifically examined whether the observed differences in treatment reflected legitimate clinical guidelines recommending sex-specific approaches to care. Almost none of the 33 studies showing significant disparities pointed to any such guideline. This leaves open a difficult and uncomfortable question: whether the pattern reflects sound, individualised clinical judgement responding to genuine biological differences, or whether it represents a form of unequal care rooted in bias, assumption or systemic blind spots.
Dr Andrew O’Malley, who co-led the study alongside Veenhuizen, framed the findings as a direct call to action for practising clinicians. “For clinicians, the findings are a prompt to check whether treatment is being offered on clinical grounds rather than assumption,” he said. O’Malley pointed to additional evidence suggesting that doctors have, in some documented cases, been more likely to attribute women’s symptoms to anxiety and to make diagnostic errors with female patients even when objective test results pointed toward a physical cause.
One particularly telling example emerged from research into advanced heart failure therapy: studies found that when multidisciplinary teams responsible for selecting patients for advanced treatment functioned poorly or lacked structured decision-making processes, women were less likely to be selected for that therapy — suggesting that informal, less rigorous decision pathways may be where bias finds the most room to operate.
A Historical Root: Underrepresentation in Clinical Trials
The researchers point to a long-standing structural issue as a likely contributing factor: women were significantly underrepresented in clinical trials for decades, meaning that much of the medical evidence base and many treatment guidelines were originally developed using data drawn predominantly from male patients. The legacy of that historical imbalance, researchers suggest, may continue to shape clinical decision-making today, even in cases where explicit guidelines make no distinction between sexes.
Strikingly, the study’s authors also highlighted a significant imbalance in research attention itself. Over the same period covered by their review, some 551 separate studies examined sex inequality as it affects doctors and other healthcare professionals themselves — their career progression, pay and representation — while only 41 studies examined how sex inequality affects patients receiving care. “What struck us most was the imbalance in attention,” Veenhuizen noted.
Looking Ahead: The Risk of AI Entrenching Bias at Scale
The research team is now turning its attention to a forward-looking concern: whether generative artificial intelligence systems, increasingly used to support clinical decision-making, might inadvertently absorb and replicate these same patterns. Because such systems are typically trained on existing medical literature and historical clinical records — the very data sources shown to contain embedded bias — researchers warn there is a genuine risk that AI tools could entrench, and potentially amplify, unequal treatment patterns at a much larger scale if the issue is not proactively addressed.
For health systems across Europe already grappling with how to integrate AI responsibly into clinical practice, the findings add a further layer of urgency to calls for careful auditing of algorithmic tools before they are deployed in decision-making roles that directly affect patient care.
