The Unpaid Toll: Quantifying the Public Health Impact of AI

Han, Y. et al.

arXiv:2412.06288 [cs.CY]

https://doi.org/10.48550/arXiv.2412.06288

Abstract

The surging demand for AI has led to a rapid expansion of energy-intensive data centers, impacting the environment through escalating carbon emissions and water consumption. While significant attention has been paid to AI's growing environmental footprint, the public health burden, a hidden toll of AI, has been largely overlooked. Specifically, AI's lifecycle, from chip manufacturing to data center operation, significantly degrades air quality through emissions of criteria air pollutants such as fine particulate matter, substantially impacting public health. This paper introduces a methodology to model pollutant emissions across AI's lifecycle, quantifying the public health impacts. Our findings reveal that training an AI model of the Llama3.1 scale can produce air pollutants equivalent to more than 10,000 round trips by car between Los Angeles and New York City. The total public health burden of U.S. data centers in 2030 is valued at up to more than $20 billion per year, double that of U.S. coal-based steelmaking and comparable to that of on-road emissions of California. Further, the public health costs unevenly impact economically disadvantaged communities, where the per-household health burden could be 200x more than that in less-impacted communities. We recommend adopting a standard reporting protocol for criteria air pollutants and the public health costs of AI, paying attention to all impacted communities, and implementing health-informed AI to mitigate adverse effects while promoting public health equity.

The research is linked above.

This research paper quantifies the previously overlooked public health consequences of artificial intelligence (AI), focusing on the air pollution generated throughout its lifecycle—from chip manufacturing to data center operation. The authors present a methodology for modeling pollutant emissions and their resulting health impacts, finding that AI's environmental footprint translates to substantial health costs, potentially exceeding $20 billion annually in the US by 2030 and disproportionately affecting low-income communities. This "hidden toll" of AI, the paper argues, necessitates standardized reporting protocols for air pollutants and health impacts, the development of "health-informed AI" to mitigate adverse effects, and a focus on achieving public health equity.

Psychologists could find the information in the sources valuable as it highlights the potential mental health consequences of socioeconomic disparities exacerbated by AI's environmental impact. The sources reveal that the health burden of AI, particularly from data centers, is unevenly distributed and disproportionately affects low-income communities. This raises concerns about increased stress, anxiety, and depression in these communities due to factors like higher exposure to air pollution, reduced access to healthcare, and financial strain from increased health costs. Understanding these psychological impacts could inform interventions and policies aimed at mitigating the negative mental health consequences of AI's growth, particularly for vulnerable populations.

Discrimination in Medical Settings across Populations: Evidence from the All of Us Research Program.

Wang, V. H., Cuevas, A. G., et al. (2024).

American Journal of Preventive Medicine.

https://doi.org/10.1016/j.amepre.2024.05.018

Abstract

Introduction

Discrimination in medical settings (DMS) contributes to healthcare disparities in the United States, but few studies have determined the extent of DMS in a large national sample and across different populations. This study estimated the national prevalence of DMS and described demographic and health-related characteristics associated with experiencing DMS in seven different situations.

Methods

Survey data from 41,875 adults participating in the All of Us Research Program collected in 2021–2022 and logistic regression were used to examine the association between sociodemographic and health-related characteristics and self-reported DMS among adults engaged with a healthcare provider within the past 12 months. Statistical analysis was performed in 2023–2024.

Results

About 36.89% of adults reported having experienced at least one DMS situation. Adults with relative social and medical disadvantages had higher prevalence of experiencing DMS. Compared to their counterparts, respondents with higher odds of experiencing DMS in at least one situation identified as female, non-Hispanic Black, having at least some college, living in the South, renter, having other living arrangement, being publicly insured, not having a usual source of care, having multiple chronic conditions, having any disability, and reporting fair or poor health, p<0.05.

Conclusions

The findings indicate a high prevalence of DMS, particularly among some population groups. Characterizing DMS may be a valuable tool for identifying populations at risk within the healthcare system and optimizing the overall patient care experience. Implementing relevant policies remains an essential strategy for mitigating the prevalence of DMS and reducing healthcare disparities.

The article is paywalled here.

Here are some thoughts:

A recent national study revealed alarming rates of discrimination in healthcare settings, affecting approximately 37% of U.S. adults. Disproportionately impacted groups include women, Black and Hispanic individuals, those with limited English proficiency, renters, publicly insured or uninsured individuals, and those with chronic conditions or disabilities. These populations face higher odds of experiencing discrimination in healthcare settings, perpetuating existing health disparities.

The study highlights the intersectionality of race/ethnicity and socioeconomic status as a critical factor in exposure to discrimination. Furthermore, specific situations drive experiences of discrimination for certain populations, such as lack of respect or poor communication from healthcare providers.

To address these disparities, healthcare institutions are developing implicit bias training, though research suggests modest effectiveness. Coupling bias training with reflective exercises or perspective-taking may enhance efficacy. Additionally, promoting diversity within the healthcare workforce and policy interventions, such as the Hospital Consumer Assessment of Healthcare Providers and Systems survey, can help monitor and improve patient-provider relationships.

However, the current survey lacks components measuring discrimination. Incorporating these measures can inform national trends, identify vulnerable populations, and guide targeted interventions. Study limitations include data collection during the COVID-19 pandemic and potential underestimation of discrimination due to measurement constraints.

Key Takeaways:

• 37% of U.S. adults experience discrimination in healthcare settings
• Disproportionate impact on vulnerable populations
• Intersectionality of race/ethnicity and socioeconomic status exacerbates disparities
• Targeted interventions and policy changes are necessary to address discrimination
• Measuring discrimination in healthcare settings is crucial for improvement

Recommendations:

• Develop effective bias training programs
• Promote diversity within the healthcare workforce
• Incorporate discrimination measures into national surveys
• Tailor interventions to address specific experiences of discrimination
• Foster patient-centered care to reduce healthcare disparities

Antidiscrimination Law Meets AI—New Requirements for Clinicians, Insurers, & Health Care Organizations

Mello, M. M., & Roberts, J. L. (2024).

JAMA Health Forum, 5(8), e243397–e243397.

https://doi.org/10.1001/jamahealthforum.2024.3397

Responding to the threat that biased health care artificial intelligence (AI) tools pose to health equity, the US Department of Health and Human Services Office for Civil Rights (OCR) published a final rule in May 2024 holding AI users legally responsible for managing the risk of discrimination. This move raises questions about the rule’s fairness and potential effects on AI-enabled health care.

The New Regulatory Requirements

Section 1557 of the Affordable Care Act prohibits recipients of federal funding from discriminating in health programs and activities based on race, color, national origin, sex, age, or disability. Regulated entities include health care organizations, health insurers, and clinicians that participate in Medicare, Medicaid, or other programs. The OCR’s rule sets forth the obligations of these entities relating to the use of decision support tools in patient care, including AI-driven tools and simpler, analog aids like flowcharts and guidelines.

The rule clarifies that Section 1557 applies to discrimination arising from use of AI tools and establishes new legal requirements. First, regulated entities must make “reasonable efforts” to determine whether their decision support tools use protected traits as input variables or factors. Second, for tools that do so, organizations “must make reasonable efforts to mitigate the risk of discrimination.”

Starting in May 2025, the OCR will address potential violations of the rule through complaint-driven investigations and compliance reviews. Individuals can also seek to enforce Section 1557 through private lawsuits. However, courts disagree about whether private actors can sue for disparate impact (practices that are neutral on their face but have discriminatory effects).

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Here are some thoughts:

Addressing Bias in Healthcare AI: New Regulatory Requirements and Implications

The US Department of Health and Human Services Office for Civil Rights (OCR) has issued a final rule holding healthcare providers liable for managing the risk of discrimination in AI tools used in patient care. This move aims to address the threat of biased healthcare AI tools to health equity.

New Regulatory Requirements

The OCR's rule clarifies that Section 1557 of the Affordable Care Act applies to discrimination arising from the use of AI tools. Regulated entities must make "reasonable efforts" to determine whether their decision support tools use protected traits as input variables or factors. If so, they must mitigate the risk of discrimination.

Fairness and Enforcement

The rule raises questions about fairness and potential effects on AI-enabled healthcare. While the OCR's approach is flexible, it may create uncertainty for regulated entities. The rule applies only to organizations using AI tools, not developers, who are regulated by other federal rules. The OCR's enforcement will focus on complaint-driven investigations and compliance reviews, with penalties including corrective action plans.

Implications and Concerns

The rule may create market pressure for developers to generate and provide information about bias in their products. However, concerns remain about the compliance burden on adopters, particularly small physician practices and low-resourced organizations. The OCR must provide further guidance and clarification to ensure meaningful compliance.

Facilitating Meaningful Compliance

Additional resources are necessary to make compliance possible for all healthcare organizations. Emerging tools for bias assessment and affordable technical assistance are essential. The question of who will pay for AI assessments looms large, and the business case for adopting AI tools may evaporate if assessment and monitoring costs are high and not reimbursed.

Conclusion

The OCR's rule is an important step towards reducing discrimination in healthcare AI. However, realizing this vision requires resources to make meaningful compliance possible for all healthcare organizations. By addressing bias and promoting equity, we can ensure that AI tools benefit all patients, particularly vulnerable populations.

Black Bioethics in the Age of Black Lives Matter

Ray, K., Fletcher, F.E., Martschenko, D.O. et al. 

J Med Humanit (2023).

https://doi.org/10.1007/s10912-023-09783-4

Here are two excerpts:

Lessons Black Bioethics can take from BLM

BLM showed that telling Black people’s stories or giving them a space to tell their own stories is viewed as an inherently political act simply because Black people’s existence is viewed as political. At the same time, it taught us that we absolutely must take on this task because, if we do not tell our stories, other people will tell them for us and use our stories to deny us our rightful moral status and all the rights it entitles us.

BLM let Black people’s stories fuel its social justice initiatives. It used stories to put Black people at the forefront of protests and social inclusion efforts to show the extent to which Black people had been excluded from our collective social consciousness. Stories allowed us to see the total impact of anti-Black racism and the ways it infiltrates all parts of Black life. And for those who were far removed from the experience of being Black, BLM used stories to make us care about racial injustice and be so moved that we were unable to turn our backs on Black people’s suffering. In this way, stories are an act of rebellion, a way to force people to reckon with BLM’s demands that Black people ought to be treated like the full and complex human beings we are.

Black Bioethics is also a rebellion. It is a rebellion against the status quo in bioethics—a rebellion against Black people’s lives being an afterthought, particularly in issues of justice. Stories aid in this rebellion. Just as stories helped BLM show the full range of Black people’s humanity and the ways that individuals and institutions deny Black people that humanity, stories help Black Bioethics demonstrate just how our institutions contribute to Black people’s poor health and prevent them from living full lives. In Black Bioethics, stories can create the same emotional stirring that they did for BLM supporters since they share many of the same challenges and goals. And just as it would be imprudent to underestimate the role of stories in social justice, it would be imprudent of us to underestimate what stories can do for our sense of health justice for Black people.

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Toward an intersectional bioethics

Bioethics is well-positioned to foster antiracism in scholarship, training, and advocacy (Danis et al. 2016). Although the field focuses on ethical issues in biomedical research and clinical care specifically, Danis et al. (2016) point out that many ethical dilemmas that impact health and well-being lie outside of healthcare settings. For instance, there are significant ethical dilemmas posed by the social determinants of health and complex disease. Social factors such as poverty, unequal access to healthcare, lack of education, stigma, and racism are underlying and contributing factors to health inequalities. These inequalities, in turn, generate the ethical dilemmas that bioethics grapples with (Danis et al. 2016). If the field genuinely values the just conduct of biomedical research and the just provision of clinical care, then it will need to draw upon intersectionality to understand and effectively analyze the many interlocking complexities in our world and in human experiences. Social activist movements like BLM and their use of intersectionality offer several lessons to those in the field working to secure justice in biomedicine, clinical care, and society.

First, as an analytic tool, intersectionality recognizes and understands that different social forces conjoin to produce and maintain privilege and marginalization. Therefore, intersectionality clarifies instances in which real lives and experiences are being erased. Bioethics cannot afford to “neglect entire ways of being in the world,” though it has and continues to do so (Wallace 2022, S79). Social activist movements like BLM are drawing attention to ways of being that are unjust yet largely ignored by mainstream hegemonic interests. For instance, BLM directly acknowledges within its movement “those who have been marginalized within [other] Black liberation movements” (Black Lives Matter n.d.). Using intersectionality, BLM heightens awareness of the ways in which Black queer and trans individuals, undocumented individuals, and people with disabilities have different experiences with White supremacy and advance colonialism. In doing so, it centers rather than erases real lives and experiences. Learning from this movement, bioethical scholarship grounded in the principle of justice will need to find ways to center the experiences of Black-identifying individuals without treating the Black community as a homogenous entity.

The info is here.

Why VIP Services Are Ethically Indefensible in Health Care

Denisse Rojas Marquez and Hazel Lever

AMA J Ethics. 2023;25(1):E66-71.

doi: 10.1001/amajethics.2023.66.

Abstract

Many health care centers make so-called VIP services available to “very important persons” who have the ability to pay. This article discusses common services (eg, concierge primary care, boutique hotel-style hospital stays) offered to VIPs in health care centers and interrogates “trickle down” economic effects, including the exacerbation of inequity in access to health services and the maldistribution of resources in vulnerable communities. This article also illuminates how VIP care contributes to multitiered health service delivery streams that constitute de facto racial segregation and influence clinicians’ conceptions of what patients deserve from them in health care settings.

Insurance and Influence

It is common practice for health care centers to make “very important person” (VIP) services available to patients because of their status, wealth, or influence. Some delivery models justify the practice of VIP health care as a means to help offset the cost of less profitable sectors of care, which often involve patients who have low income, are uninsured, and are from historically marginalized communities.1 In this article, we explore the justification of VIP health care as helping finance services for patients with low income and consider if this “trickle down” rationale is valid and whether it should be regarded as acceptable. We then discuss clinicians’ ethical responsibilities when taking part in this system of care.

We use the term VIP health care to refer to services that exceed those offered or available to a general patient population through typical health insurance. These services can include concierge primary care (also called boutique or retainer-based medicine) available to those who pay out of pocket, stays on exclusive hospital floors with luxury accommodations, or other premium-level health care services.1 Take the example of a patient who receives treatment on the “VIP floor” of a hospital, where she receives a private room, chef-prepared food, and attending physician-only services. In the outpatient setting, the hallmarks of VIP service are short waiting times, prompt referrals, and round-the-clock staffing.

While this model of “paying for more” is well accepted in other industries, health care is a unique commodity, with different distributional consequences than markets for other goods (eg, accessing it can be a matter of life or death and it is deemed a human right under the Alma-Ata Declaration2). The existence of VIP health care creates several dilemmas: (1) the reinforcement of existing social inequities, particularly racism and classism, through unequal tiers of care; (2) the maldistribution of resources in a resource-limited setting; (3) the fallacy of financing care of the underserved with care of the overserved in a profit-motivated system.

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Conclusion

VIP health care, while potentially more profitable than traditional health care delivery, has not been shown to produce better health outcomes and may distribute resources away from patients with low incomes and patients of color. A system in which wealthy patients are perceived to be the financial engine for the care of patients with low incomes can fuel distorted ideas of who deserves care, who will provide care, and how expeditiously care will be provided. To allow VIP health care to exist condones the notion that some people—namely, wealthy White people—deserve more care sooner and that their well-being matters more. When health institutions allow VIP care to flourish, they go against the ideal of providing equitable care to all, a value often named in organizational mission statements.22 At a time when pervasive distrust in the medical system has fueled negative consequences for communities of color, it is our responsibility as practitioners to restore and build trust with the most vulnerable in our health care system. When evaluating how VIP care fits into our health care system, we should let health equity be a moral compass for creating a more ethical system.

The info is here.