Best Practice Insights for Using AI in Healthcare
City of Hope and Guidehouse tech leaders share what they’ve learned when applying AI to patient care, research, and operations.
For healthcare organizations to get the best results and return on investment when applying artificial intelligence (AI) to research, care delivery, operations, and business management, they should:
- Assess their current data governance and quality levels
- Work with users to determine whether the application is appropriate for their use cases and patient populations
- Identify metrics for validation, testing, measuring impact, and managing expectations
- Never lose sight of the human element
These were some of the key insights recently shared by Nasim Eftekhari, City of Hope Executive Director of Applied AI and Data Science, and Erik Pupo, Guidehouse Director of Commercial Health IT Advisory, in “The strategy behind successful Al implementation in healthcare, precision medicine, and revenue cycle management” — part of Modern Healthcare’s Executive Conversations series.
While acknowledging how tempting it can be to acquire and implement today’s “shiny new toys,” Eftekhari emphasized the importance of including people in the decision-making process.
“AI tools in healthcare are mostly developed to improve efficiency and enhance workflows, not to replace humans,” said Eftekhari, who oversees teams responsible for applying AI and machine learning to clinical decision support, precision medicine and research, and business operations at City of Hope, a world-class cancer research and treatment center. “We have regular meetings with users throughout the entire process—from data collection to development through to testing and deployment, and even after go-live—to make sure we are optimizing the tool to the best of our ability to meet each team’s needs.”
Pupo added that it’s essential to have the right cross-functional teams working on generative AI services across the enterprise to avoid redundancy and wasted resources. He also reminded healthcare executives that impact measures should include productivity.
“You need to take a close look in your organization at how you handle the true cost and return analysis of generative AI,” said Pupo, who helps healthcare organizations modernize, engineer, and transform IT infrastructure. “Measure these types of efforts not just on a financial basis, but also on productivity. We’ve seen examples where just the technical cost alone of AI has been a real shocker for CIOs, CFOs, and CEOs.”
Both professionals stressed the need to involve legal, compliance, and data privacy teams when using patient data in AI applications to ensure that the data is being used in an ethical, compliant, and responsible manner. Eftekhari also cautioned leaders to manage expectations about the extent to which AI can help solve problems.
“Some people may think that AI is something you can take off a shelf and it's going to solve all problems, but that’s not the case,” she said. “AI is a powerful tool, but it’s still just a tool. It’s important to develop metrics for measuring the impact ahead of time and to manage expectations about what the final product or solution may be able to achieve.”
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