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Home»AI in Healthcare»Biased AI in healthcare faces crackdown following Biden admin’s sweeping proposals
AI in Healthcare

Biased AI in healthcare faces crackdown following Biden admin’s sweeping proposals

December 2, 2024002 Mins Read
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Prior authorization

Elsewhere in the world 700 page proposalThe administration is introducing a policy that would prohibit Medicare Advantage plan providers from reopening and waiving payment for hospital admission claims if those claims had already been approved through prior authorization. The proposal also aims to make coverage criteria clearer and ensure patients know they can appeal denied claims.

The Department of Health and Human Services notes that when patients appeal a claim denial under Medicare Advantage plans, the appeals are successful 80 percent of the time. But only 4% of claim denials are appealed, “meaning many more denials could potentially be overturned by the plan if appealed.”

AI guardrail

Finally, the administration’s proposal also attempts to strengthen guardrails for the use of AI in health care by modifying existing policy. The goal is to ensure that Medicare Advantage insurers do not adopt erroneous AI recommendations that worsen bias and discrimination or exacerbate existing inequities.

As an example, the administration highlighted the use of AI to predict which patients would miss their medical appointments, then recommended providers reserve twice as many appointment slots for those patients. In this case, low-income patients are more likely to miss appointments because they may have difficulty with transportation, child care, and work schedules. “As a result of using this data in the AI ​​tool, providers booked twice as many low-income patients, leading to longer wait times for low-income patients and perpetuating the cycle of additional missed appointments for vulnerable patients. As such, it should be prohibited, the administration asserts.

In general, people of color and people of lower socioeconomic status tend to be more likely to have gaps and defects in their electronic health records. So when AI is trained on large health record datasets, it can generate erroneous recommendations based on this patchy and incorrect information, thereby amplifying bias.

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