The advent of artificial intelligence in healthcare, and its embrace by provider organizations large and small, eager to explore its transformative potential, has come quickly. And it has come with a steep learning curve.
That’s led to an interesting conundrum recently, says Richard Cramer, chief strategist for healthcare and life sciences at Informatica: Most health systems are, organizationally and attitudinally, “ready for AI,” he said. “But their data isn’t.”
At HIMSS24 earlier this month, Cramer spoke alongside Anna Schoenbaum, vice president of applications and digital health at Penn Medicine, and Sunil Dadlani, chief information & digital officer at Atlantic Health System (where he also serves as CISO).
They explored how hospitals and health systems should approach the process of assessing how artificial intelligence and automation can fit into their organizations, and how to start new AI initiatives and enhance existing ones as they scale up projects across the enterprise.
Despite all the buzz and excitement about generative AI, it’s important to stick with the basics, said Cramer.
“I think the enthusiasm around ChatGPT makes people think that it’s something intrinsically new,” he said. “But we, as an industry, have been doing AI for a long time.”
And a core lesson from years of experience is that any AI or machine learning project needs one essential prerequisite: “accessible, trustworthy, fit-for-purpose data.”
What does trustworthy mean? “It’s all about transparency, right? I need to know where the data came from, everything that happened was on its way from source to being consumed,” Cramer explained.
“I’m a lifelong data analyst, and one of the things that I like to say is that if you’re transparent, I can disagree with your conclusion and still trust you, because I know what all your assumptions and everything are. But if you’re not transparent, I probably will never trust you, even if I agree with what your conclusion is.
“I think that really applies to what we’re talking about with AI,” he added. “Data doesn’t need to be perfect to be useful. But you don’t ever want to use data that’s not perfect and not know it.”
