Will AI Deepen the Digital Divide in Health Care Systems?
The AI push in health care systems is underway. Medicare is piloting programs that use AI to authorize care, AI scribes are now a routine part of patient visits, and AI-powered chatbots are being touted as the answer to longstanding therapist shortages. Meanwhile, federal health officials and private companies are promoting AI as the solution to access and care deserts in rural communities, with some of the $50 billion from the federal Rural Health Transformation Program fund fueling the AI expansion effort. But doctors and researchers caution that many of these programs and tools are being rolled out before they’re fully tested for safety, accuracy and fairness. How can AI-powered care respond to patients’ cultural backgrounds with sensitivity and nuance, even as traditional medicine so often falls short? Experts also warn of a new digital divide emerging between large health systems and academic medical centers and safety net systems, with the latter largely excluded from conversations on how AI should be evaluated and monitored. One recent study found that only 44% of hospitals using predictive models evaluated them for bias, and less than two thirds of hospitals using the models tested them for accuracy. That means that some of the country’s most vulnerable patients are likely to bear the brunt of the ongoing experiment of AI in health care. In this webinar, we’ll dive deeper into these trends, highlight story angles to track, and identify questions reporters should be asking about how AI tools are being deployed and whether they risk deepening existing disparities.
This webinar is free and made possible by The Commonwealth Fund.
Panelist
Paige Nong is Assistant Professor of Health Policy and Management at the University of Minnesota School of Public Health. Her research focuses on optimizing the design and governance of digital technologies and analytics in healthcare. As a health services researcher with disciplinary expertise in sociology, health informatics, and policy, she takes a multipronged approach to generating evidence that supports quality digital healthcare through two primary domains of empirical investigation: 1) optimizing effective governance and use of advanced analytics and 2) centering patient perspectives as essential to the design and use of these tools.
Darius Tahir, correspondent, is based in Washington, D.C., and reports on health technology with an eye toward how it helps (or doesn’t) underserved populations; how it can be used (or not) to help government’s public health efforts; and whether or not it’s as innovative as it’s cracked up to be. He joins KFF Health News after stints with Politico, Modern Healthcare, and The Gray Sheet. He’s a graduate of Stanford University and grew up in Rochester, New York.