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Myeloma Oncology’s Bias?
What is myeloma oncology’s bias? How can oncology’s bias affect MM patients and survivors? While the list of biases below highlights many possible biases on the part of your oncologist, I am trying to focus on a specific bias that can directly affect how MM patients communicate with and are treated by their oncologists.
MM oncologists treat MM aggressively. This is due, I believe, to oncology’s focus on the “maximum tolerated dose” and the FDA-approved standard-of-care therapy plan. While the standard treatment of induction therapy, autologous stem cell transplant, and maintenance therapy may be needed for many patients with advanced disease, it may be too much toxicity for MM patients with less disease and/or older MM patients.
Having studied MM since my diagnosis in early 1994, two clear examples of myeloma oncology’s bias show up again and again. And those two examples are:
- Dr. James Berenson’s overall survival statistics and
- Evidence-based non-conventional therapies such as nutrition, supplementation, and lifestyle
I am not being critical of conventional MM oncology. They have made remarkable strides in MM care over the past 30-plus years. I am saying that MM patients and survivors need to understand the limitations of their oncologists.
David Emerson
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Forms of Bias in Oncology
Bias in oncology manifests in several key areas:
- Racial and Ethnic Bias: This is one of the most documented forms of bias. Oncologists with higher levels of implicit racial bias tend to have shorter interactions with Black patients and are perceived as less patient-centered. Studies show that Black patients are less likely to receive appropriate treatment and referral to clinical trials, genetic testing, or palliative care compared to White patients.
- Socioeconomic Bias: A patient’s zip code can often be a stronger predictor of cancer outcomes than their genetic code due to the concentration of poverty and lack of access to care.
- Gender Bias: Women remain underrepresented in certain cancer clinical trials and leadership positions within oncology.
- Decision-Making Biases: Both patients and providers exhibit cognitive biases. A common example is “commission bias,” which is a tendency to favor active treatment (like surgery) even when a less aggressive option (like watchful waiting) might be medically less risky.
- Bias in Clinical Trials and Research: Bias can affect the planning and execution of studies, as well as the peer-review process for publications. Some professionals have reported withholding trial opportunities from minority groups based on biased perceptions.
- AI Bias: The increasing use of artificial intelligence and machine learning in cancer care introduces new potential for bias if the algorithms are trained on non-representative or incomplete datasets, potentially exacerbating existing disparities.
The battle against bias in oncology
A crucial topic gaining momentum is the urgent need to mitigate bias to improve patient outcomes. It’s no exaggeration to say bias can be deadly, particularly in oncology. Which is why, in partnership with our clients, we’re exploring the steps the healthcare sector can actively take to reduce biases in oncology care – making sure every patient receives the right treatment at the right time.
In fact, our own research has highlighted that bias, though often invisible, profoundly impacts oncology. And whether related to gender, race, or socioeconomic status, these biases can drastically affect patient outcomes. So, addressing them is not just a moral duty for our sector, it’s essential for delivering the quality care every patient deserves.
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