Experts urge caution as Trump’s big bill incentivizes AI in healthcare | US healthcare


For states to receive certain funding stipulated in the Trump administration’s “big, beautiful” bill, they must meet three of 10 criteria – including integrating more artificial intelligence (AI) technology in healthcare settings – which experts say could have major benefits and liabilities for under-resourced hospitals, depending on how it’s implemented.

The Rural Health Transformation Fund is a carveout that will provide $50bn over a period of five years to states who meet certain application criteria, including “consumer-facing, technology-driven solutions for the prevention and management of chronic diseases,” and “providing training and technical assistance for the development and adoption of technology-enabled solutions that improve care delivery in rural hospitals, including remote monitoring, robotics, artificial intelligence, and other advanced technologies”.

Analysts have noted that this $50bn will not be nearly enough to make up for the Congressional Budget Office’s projected $911bn reduction in Medicaid spending over the next decade under the bill (Obba). These cuts will affect both patients who lose free health coverage under Medicaid, and hospitals who benefit from those patients’ Medicaid reimbursements.

Chenhao Tan, associate professor of data science at the University of Chicago, and Karni Chagal-Feferkorn, an assistant professor at the University of South Florida’s college of AI and cybersecurity, said AI technology could provide major benefits to rural hospitals that are frequently under-resourced and under-staffed. They also agreed that AI has the potential to alleviate the administrative burden that physicians at these hospitals often face.

Physicians are responsible for taking detailed notes on patient visits and compiling them for electronic health records systems – a task that can take eight hours or more each week, according to the American Medical Association.

A recent study found that AI generated patient notes are similar in quality to those of general physicians, but worse than those of expert physicians. Tan said that it’s important to take context – like frequent physician burnout in rural hospitals – into account when evaluating risks and benefits.

“If the baseline is tired human doctors, then I think it is even easier to make an argument that AI may do better than them,” Tan said.

Chagal-Feferkorn hopes that AI can help alleviate rural hospital staffing issues, not only by reducing the workload but by attracting more doctors.

“If the equipment is state-of-the-art, and they feel that much of the burdensome work is done by AI, I think this could be one incentive for physicians to go work in rural areas, this might have a great impact,” she said.

The FDA currently regulates AI technologies that are intended to evaluate and diagnose health conditions because they are considered medical devices. However, technologies that simply transcribe and compile patient notes are not regulated, though they may market themselves as Hipaa compliant.

While Tan said it would be too high a bar to expect these technologies to be “bulletproof” before they can enter the market, he acknowledged that “there should be something higher than nothing,” in terms of regulatory requirements.

Chagal-Feferkorn also said that the proliferation of AI also creates additional cybersecurity concerns.

“AI makes it easier for ordinary people to hack systems,” she said, adding that AI has the potential to improve patient safety by merging patient records from different providers so that, for example, every provider is aware of every medication that a patient is taking and can thus easily avoid dangerous medication interactions.

But this kind of technology will also require more privacy precautions.

“The more data sharing there is, obviously the risk for data security breach is larger,” Chagal-Feferkorn continued.

To mitigate these risks, Tan said “worker upscaling needs to go hand in hand” with the adoption of AI technology. But Tan and Chagal-Feferkorn both expressed concern that under-resourced hospitals will attempt to adopt AI technology as a cost-cutting measure without the necessary staff and safety infrastructure.



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