Will AI fix prior authorization—or make it worse?

The government is piloting a program that uses AI for insurance-coverage decisions. If you’re like me, you or a loved one has struggled through the process of gaining pre-approval for the medical care that your physician has recommended. Personal stories abound regarding the tribulations of patients as they go through hoops to get their health insurer to pay for certain prescription medications, medical procedures, and more. When used judiciously, this process—known as prior authorization—serves as a check on overuse and spending on services or technologies for which there are less costly alternatives. But a large majority of physicians voice concerns about care delays, which can cause patients to abandon recommended treatments while waiting for the insurance company to verify their eligibility and confirm that the treatment is, indeed, medically necessary. Patients who are denied care may submit an appeal, but that requires more time. AI might be able to help. With its ability to efficiently sort through vast reams of information, artificial intelligence could theoretically expedite approval of unambiguously allowable claims, thereby reducing care delays. However, AI-driven prior authorization is facing resistance, as it may increase wrongful denials of health insurance claims.

The government has not disclosed specific details about the AI model being used, the training data, or the criteria for determining which claims are suitable for AI review. Ars Technica notes that independent replication is still pending.

Concerns from healthcare professionals

Ars Technica's report on the pilot program cites concerns from healthcare professionals about the potential for AI to make mistakes in complex medical decision-making.