Accessibility of Healthcare

How Visual Diets Affect Our Ability to Receive Care

Our visual diets shape not only our perceptions but also our experiences in receiving care. When discussing care, I refer to a broad spectrum—medical, psychological, and safety-related care, among others. The way media and professional sources visually represent caregivers and patients can either reinforce or dismantle biases, ultimately influencing the quality and accessibility of care people receive in real-life scenarios.

Healthcare Biases and Representation

A striking example of healthcare bias is found in a study published by JAMA Internal Medicine, which highlights disparities in wait times for care based on race and ethnicity. Such disparities are deeply rooted in systemic biases, many of which are perpetuated through the visual and rhetorical frameworks used in professional and public discourse.

A 2022 meta-research study by Katta Spiel, Eva Hornecker, Rua Mae Williams, and Judith Good analyzed 100 publications within the Association for Computing Machinery (ACM) digital library. Their findings revealed troubling biases in how neurodivergent individuals, particularly those with ADHD, are portrayed. The study identified two key rhetorical strategies used in academic discourse:

1. Framing ADHD as a Deviant Condition: The language used often portrays ADHD as a separate, burdensome entity detached from the person diagnosed with it. This reinforces a perspective that ADHD is a pathology rather than a neurological difference.

2. Burden Narrative: ADHD is frequently described as a source of suffering, a threat to societal productivity, and an urgent problem requiring intervention. The focus is placed on "fixing" the individual rather than addressing the systemic issues that contribute to these challenges.

Jessica Belisle, in her paper Demolishing Systemic Ableism: Attention-Deficit Hyperactivity Disorder (ADHD) in Adults (2022), captures the impact of these biases:

"While professionals within healthcare systems carry all the intent of delivering care equally and holding the value of human life, they are still influenced by society. In a society that views disabilities as weak, vulnerable, and incompetent, these biases inevitably permeate their beliefs and affect the care they deliver."

Racial and Ethnic Disparities in Healthcare

Dr. Alan Nelson’s seminal 2002 article Unequal Treatment outlined several core principles regarding barriers to treatment for BIPOC individuals, including:

• Racial and ethnic disparities persist even when socioeconomic factors are controlled.

• These disparities are linked to broader systemic inequalities and discrimination.

• Bias, stereotyping, and clinical uncertainty contribute to disparities in care quality.

• Minority patients are often perceived as non-compliant or resistant to treatment, though refusal rates do not fully account for disparities in care.

A 2019 study by S. Alang found that accessibility remains a significant barrier for Black individuals seeking mental health care. Factors such as stigma, transportation issues, and limited provider availability all contribute to unmet needs. Additionally, the Agency for Healthcare Research and Quality has documented persistent disparities in care quality for racial minorities since 2003, with Black, Hispanic, and Indigenous patients receiving worse care for over 40% of assessed measures.

The Role of Algorithms in Healthcare Disparities

Healthcare algorithms often reinforce racial biases, exacerbating disparities in treatment outcomes. Studies by Vyas et al. (2020) highlight numerous instances where racial adjustments in medical algorithms disadvantage BIPOC patients:

• Cardiology: The Get with the Guidelines–Heart Failure Risk Score assigns lower risk scores to Black patients without clear justification, potentially diverting care away from them.

• Nephrology: Equations estimating kidney function assign higher values to Black patients, potentially delaying specialist referrals and worsening outcomes.

• Transplantation: The Kidney Donor Risk Index assigns higher failure risk to Black donors, reducing donation rates and increasing wait times for Black patients.

• Obstetrics: The VBAC (Vaginal Birth After Cesarean) algorithm predicts lower success rates for Black and Hispanic women, reinforcing disparities in cesarean section rates.

• Urology: The STONE score and pediatric UTI prediction models assign lower risk to Black patients, potentially leading to inadequate testing and delayed diagnosis.

A broader systemic issue was identified by Obermeyer et al. (2019), who found that commercial healthcare prediction algorithms underestimate the severity of illness in Black patients because they use healthcare costs as a proxy for health status. Since Black patients historically receive less care, the algorithm incorrectly assesses them as healthier, further entrenching disparities.

Mental Healthcare and Accessibility Challenges

Mental health care for BIPOC individuals faces additional challenges. Many clinicians in predominantly white areas express discomfort treating Black and brown clients, often referring them to providers of the same race. While cultural competence is essential, this practice leads to long waitlists and overburdened Black and brown clinicians. Additionally, many therapists do not accept insurance, creating financial barriers for marginalized clients.

For equitable mental healthcare, clinicians must recognize their biases and commit to culturally informed practice rather than deferring care entirely. The burden should not fall solely on BIPOC professionals to provide services for their communities.

The Consequences of a Poor Visual Diet in Healthcare

The disparities highlighted above underscore the consequences of a healthcare system influenced by biased visual and informational diets. Implicit and explicit biases among providers, reinforced by flawed medical algorithms and misrepresentations in media, have tangible effects:

• Shortened life expectancy for marginalized populations (Doubeni et al., 2021).

• Higher rates of misdiagnosis, inadequate treatment, and delayed care for BIPOC and neurodivergent individuals.

• Increased mental health burdens due to provider shortages and systemic barriers.

Moving Toward Equity in Care

To address these disparities, healthcare professionals and media creators alike must:

• Diversify representations of caregivers and patients in visual media to challenge biases.

• Reevaluate medical algorithms to eliminate race-based adjustments that disadvantage BIPOC patients.

• Improve provider training on implicit bias and cultural competence.

• Increase access to affordable mental healthcare by expanding insurance acceptance and reducing financial barriers.

By critically assessing and reshaping our visual diets, we can foster a healthcare system that serves all individuals equitably, regardless of race, neurodivergence, or socioeconomic status.

References

Spiel, K., Hornecker, E., Williams, R. M., & Good, J. (2022, April). Adhd and technology research–investigated by neurodivergent readers. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-21).

Belisle, J. L. (2022). Demolishing Systemic Ableism: Attention-Deficit Hyperactivity Disorder (ADHD) in Adults.

Nelson, A. (2002). Unequal treatment: confronting racial and ethnic disparities in health care. Journal of the national medical association, 94(8), 666.

Alang, S. M. (2019). Mental health care among blacks in America: Confronting racism and constructing solutions. Health services research, 54(2), 346-355.

Vyas, D. A., Eisenstein, L. G., & Jones, D. S. (2020). Hidden in plain sight—reconsidering the use of race correction in clinical algorithms. New England Journal of Medicine, 383(9), 874-882.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.

Doubeni, C. A., Simon, M., & Krist, A. H. (2021). Addressing systemic racism through clinical preventive service recommendations from the US Preventive Services Task Force. Jama, 325(7), 627-628.