The tragic killing of UnitedHealthcare CEO Brian Thompson in New York has sparked a nationwide debate about Americans’ frustrations with the healthcare system. While his death is widely condemned, it has also highlighted the growing role of insurers like UnitedHealth, which are blamed for escalating healthcare costs and widespread denials of necessary care. A recent ProPublica investigation has exposed a troubling trend: the increasing use of artificial intelligence (AI) to deny millions of health insurance claims, often without a human doctor reviewing patient files. Critics argue that AI-driven claims processing introduces racial and economic biases, disproportionately affecting marginalized groups. This reliance on automation is exacerbating existing inequities in healthcare access, as algorithms prioritize cost-saving measures over clinical judgment in determining who receives treatment.
At a recent EMS briefing, healthcare leaders and policymakers gathered to discuss the ramifications of AI-driven claim denials and their impact on consumer dissatisfaction. Several key insights were shared:
- Dr. Katherine Hempstead, Senior Policy Officer at the Robert Wood Johnson Foundation, contextualized the issue within the broader challenges of the insurance industry. She explained that healthcare coverage is particularly fraught due to its emotional stakes, with consumers often frustrated by fragmented coverage rules, inconsistent access to treatments, and the growing impersonal nature of large corporate insurers. Dr. Hempstead attributed consumer dissatisfaction to these factors, highlighting the emotional and life-critical nature of healthcare, which makes every denial feel especially distressing.
- Dr. Miranda Yaber, Assistant Professor of Health Policy and Management at the University of Pittsburgh, focused on how AI-driven denials disproportionately affect vulnerable populations. In her forthcoming book, Coverage Denied: How Health Insurers Drive Inequality in the United States, Yaber explores how systemic inequities are perpetuated through the use of AI in healthcare decision-making. She argued that AI-driven denial systems exacerbate racial and economic inequities, emphasizing the need for policy reforms to ensure equitable access to care for all Americans. Her research, based on nationwide surveys and interviews with patients and healthcare professionals, highlights the extensive harm caused by these automated processes.
- California State Senator Josh Becker discussed his proposed legislation, the Physicians Make Decisions Act (SB 1120), which seeks to limit AI’s role in healthcare decision-making. The bill mandates that licensed physicians, rather than algorithms, make the final decisions regarding patient care. Becker emphasized that patient well-being should always be prioritized over the cost-driven goals of insurers. He also highlighted troubling examples of AI systems that had denied critical services, including cases where doctors had been accused of using AI to reject over 300,000 claims in a two-month period, with each decision taking just 1.2 seconds. These AI-driven decisions led to significant financial and medical consequences for patients, many of whom lacked the resources to appeal these denials.
Consumer Frustration with AI in Healthcare
Several key themes emerged during the discussion, highlighting the growing frustration with AI in healthcare:
- Erosion of Trust: The reliance on AI-driven algorithms has significantly increased consumer mistrust. Patients often feel that AI prioritizes profits over medical necessity, undermining their faith in the system. The increasing use of AI systems that make decisions about patient care without human intervention has intensified this distrust.
- Inconsistent Coverage: Health insurance policies are marked by a patchwork of rules, resulting in inequitable access to treatments. For example, coverage for GLP-1 medications for obesity varies drastically across states and plans, highlighting stark disparities in access. The varying nature of policies leads to confusion and frustration among patients who are unable to navigate the system effectively.
- Inequitable Appeals: Studies show that appeals to denied claims are often successful when media scrutiny is involved. However, this creates a disadvantage for those who lack the resources or knowledge to challenge these decisions, especially low-income and non-English-speaking populations. These individuals face an uphill battle when attempting to overturn AI-driven denials, further marginalizing vulnerable groups.
- Bias and Automation: The automation of claim reviews, where algorithms override clinical expertise, has been met with significant public backlash. Investigations into insurers like Cigna and UnitedHealthcare revealed batch-processing denials and a rigid adherence to algorithmic guidelines, even in the face of medical advice. This strict reliance on AI systems is fueling public frustration, as many feel that decisions are being made by impersonal machines rather than compassionate medical professionals.
The Path Forward
The growing reliance on AI in healthcare decision-making raises serious ethical and practical concerns. Policymakers, healthcare providers, and consumer advocates must collaborate to address these challenges:
- Regulate AI Usage: Clear guidelines must be established for AI’s use in healthcare, ensuring that algorithms are used to supplement—not replace—clinical judgment. AI should be seen as a tool to support medical decisions, not as a replacement for human expertise.
- Promote Transparency: Insurers should be required to disclose their decision-making criteria, particularly when AI is involved. Transparency in how claims are evaluated is key to restoring trust in the system.
- Empower Patients: The appeals process should be simplified, and resources should be provided to help patients challenge unjust claim denials effectively. Ensuring that patients have the support they need to navigate the appeals process is critical for reducing frustration and improving access to care.
- Address Inequities: AI systems must be designed to mitigate—not exacerbate—racial and economic biases in healthcare access. Policymakers and insurers must work to ensure that AI tools are fair and equitable for all patients, regardless of their background.
While AI has the potential to improve efficiency, its unchecked use in healthcare decision-making threatens to widen the divide between those who receive care and those who are denied it. By prioritizing fairness, transparency, and accountability, the healthcare industry can move toward a more equitable system that works for all patients.
The Role of AI in Claims Denials
AI has enabled insurers to process claims at an unprecedented scale, but its use raises serious ethical concerns. Algorithms often lack the nuance required to evaluate complex medical needs, sometimes denying claims based on patterns that prioritize cost savings over individualized care. This “one-size-fits-all” approach ignores the unique circumstances of each patient.
Additionally, AI systems inherit biases from the data they are trained on, which can disproportionately impact marginalized groups. As Dr. Yaber highlighted, millions of claims are denied without a physician ever reviewing the patient’s file. This mechanized approach not only undermines the patient-doctor relationship but also fuels consumer anger, as patients feel their health decisions are being made by impersonal algorithms rather than medical professionals.
The Role of Regulation and Reform
Legislation such as Senator Josh Becker’s SB 1120, the Physicians Make Decisions Act, is a vital step toward addressing the growing concerns over AI in healthcare. By requiring licensed physicians to make final decisions on claims, the bill ensures that human expertise remains central to healthcare decision-making, protecting patients from the harm of algorithmic decisions. Becker’s bill also reflects a broader effort to balance the goals of cost control with the ethical imperative to prioritize patient care over corporate profits.
Becker also pointed out the broader issues within the U.S. healthcare system, noting that other countries with more doctor-patient-centered models achieve better health outcomes at lower costs. The U.S. reliance on AI-driven systems is a key factor in its escalating healthcare costs. Becker’s bill represents a growing push for reform that places patient welfare over corporate profit motives.
Lessons from History and Paths Forward
The backlash against health maintenance organizations (HMOs) in the 1990s serves as a reminder of the cyclical nature of public frustration with healthcare management strategies. While the move away from gatekeeper models allowed for more direct access to specialists, it also led to higher healthcare spending. Today, the issue of AI-driven denials represents a similar boiling point, but the solutions are less straightforward.
Potential solutions include:
- Transparency and Accountability: Insurers should be required to disclose their decision-making criteria and algorithms. Independent audits could ensure that AI systems operate fairly and accurately.
- Standardized Coverage Rules: The current patchwork of state-by-state policies and varying insurer guidelines creates confusion and inequity. Standardizing aspects of coverage would help restore consumer confidence.
- Enhanced Appeal Processes: Patients should have access to streamlined and transparent appeals processes, overseen by neutral third parties, ensuring fairness and thorough review of denied claims.
- Investments in Equity: Insurers must work to address biases in their data and decision-making processes, ensuring that all patients, regardless of race, income, or location, have equal access to care.
Bridging the Divide Between Patients and Insurers
Ultimately, the relationship between insurers and policyholders will remain strained as long as financial incentives overshadow patient care. While utilization management is necessary to control healthcare costs, it must be balanced with empathy, fairness, and accountability. Reforming how claims are processed—through a combination of legislation, improved technology, and consumer advocacy—can help bridge the divide and create a more equitable healthcare system.
As Dr. Katherine Hempstead noted, every approach to cost control has trade-offs, but the challenge lies in finding solutions that minimize harm while maximizing access and care quality. The conversation about AI in healthcare is far from over, but growing public outcry signals that change is not just necessary—it is inevitable.
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