The Impact of Artificial Intelligence on Health Insurance Decisions

Over recent years, health insurance companies have increasingly incorporated artificial intelligence (AI) algorithms into their practices. Unlike clinical AI used by healthcare providers to assist in diagnosis and treatment, insurance AI is primarily employed to determine coverage and payment decisions. A notable application is in prior authorization processes, where AI systems evaluate whether recommended treatments meet criteria for coverage. These algorithms analyze patient data to assess if treatments are medically necessary, and influence how much care an individual can receive, such as the length of hospital stays post-surgery.

If an insurance claim is denied based on AI analysis, policyholders have limited options. They can appeal, often a lengthy and resource-intensive process, accept alternative covered treatments, or personally fund the recommended care—often impractical due to high costs. This raises concerns about how AI decisions influence patient health outcomes.

Legal experts highlight the potential risks, as these algorithms can sometimes delay or deny coverage in ways that adversely affect vulnerable populations—those with chronic illnesses, minority groups, or marginalized identities. Evidence shows these groups face higher denial rates, exacerbating health disparities. Furthermore, insurer use of AI may sometimes lead to withholding essential or long-term care, escalating health risks.

AI systems used in insurance decision-making are largely unregulated. Unlike medical devices, which face strict oversight, insurance algorithms are considered trade secrets and are rarely scrutinized publicly or through rigorous validation studies. While some regulatory developments, like CMS rules, aim to improve transparency and patient-centered decision-making, enforcement remains limited.

Proposals suggest that the FDA could play a more substantial role, given its expertise in medical AI, by extending oversight to include insurance coverage algorithms. Such regulation could establish standards for safety, fairness, and effectiveness. However, current legal definitions restrict FDA authority, and legislative amendments may be necessary. Meanwhile, states are exploring their own regulations, but these often lack substantial enforcement mechanisms.

In summary, the integration of AI into health insurance practices impacts access, equity, and quality of care. Adequate regulation and oversight are essential to ensure these powerful tools serve patients' best interests rather than financial objectives. As AI continues to influence coverage decisions, policymakers and health authorities must act decisively to protect patients and uphold health equity.