Healthcare Dive reporter Emily Olsen speaks with Maulin Shah and Aarti Ravikumar during a virtual panel on Nov. 19, 2024. Editor’s note: This article includes insights from Healthcare Dive’s recent live event, “AI and the Future of Healthcare.” You can watch the full event here.
Healthcare organizations face a number of barriers to adopting artificial intelligence tools. Providers must address concerns from patients, while payers and other life science companies struggle with how to balance promises of efficiency with ethical concerns like biases.
They do it all on the promise that AI will automate rote tasks, cut down on medical spending and waste, free up clinicians to spend more time with patients and transform the healthcare industry. Access now➔ Trendline The evolution of electronic health records
After federal incentives spurring widespread adoption more than a decade ago, EHRs have become the bedrock of clinical data in the healthcare industry.
But more than two years after generative AI became popular with the general public through ChatGPT, healthcare is still catching up on how to regulate, test and implement the tools, experts said during a panel hosted by Healthcare Dive on Nov. 19.
Here are tips from eight healthcare experts on what organizations should consider when implementing AI and how to develop standards and regulations. How providers can vet AI tools
The first step providers should take when deciding whether to integrate an AI tool is to assess the clinical setting it will be used in. Not every tool is appropriate for every task in the clinic, according to Sonya Makhni, medical director for the Mayo Clinic Platform.
“An AI algorithm that might be really good and appropriate for my clinical setting might not be as appropriate for another and vice versa,” Makhni said. “Health systems need to understand … what to look for, and they need to understand their patient population so they can make an informed decision on their own.”
Although providers must assess AI tools, they face hurdles analyzing them due to their sheer complexity because the algorithms and models can be difficult to understand, she added.“Our workforce is […]
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