Cedars-Sinai’s artificial intelligence strategy has focused as much on people as on platforms, with workforce training emerging as a core pillar of the Los Angeles-based health system’s AI investments.
The health system has trained more than 1,000 employees on AI prompting and seen more than 6,000 users opt in to its proprietary GPT platform. According to Mouneer Odeh, chief data and artificial intelligence officer at Cedars-Sinai, that emphasis reflects both the rapid adoption of generative AI tools and the need to build trust among clinicians and staff.
“Generative AI platforms have generated tremendous excitement and engagement because they are easy to use, and adoption has been faster than smartphones, social media or any other technology,” Mr. Odeh told Becker’s. “We saw the same excitement at Cedars-Sinai, where nurses, physicians and administrators quickly recognized its potential to improve care and streamline work.”
At the same time, concerns around accuracy made it essential for the organization to prioritize education alongside access.
“So, we focused on building trust through direct exposure and hands-on experience,” Mr. Odeh said. “By making AI more accessible and easier to understand for our colleagues, we could scale the benefits across Cedars-Sinai.”
Cedars-Sinai began its training efforts with a focus on AI literacy, aiming to ensure employees understood how generative AI works and felt confident using the organization’s internal, secure and privacy-protected version of ChatGPT. The initial goal, Mr. Odeh said, was to help staff generate “high-quality, consistent and relevant responses to their prompts.”
As interest grew, the health system expanded its approach to include interactive initiatives such as Cedars-Sinai Prompt-A-Thons, which bring together nontechnical teams to apply AI tools to real-world workflow challenges. Participants have included teams from human resources, supply chain, informatics and patient experience.
“These interactive events” allow teams to “develop practical solutions to their own daily workflow challenges,” Mr. Odeh said.
Rather than limiting training to select roles, Cedars-Sinai initially took a broad approach, making AI education accessible across the organization. That decision aligned with a broader strategy focused on democratizing AI, according to Mr. Odeh.
Early training centered on general skills applicable to most roles. As employees became more sophisticated in their understanding of AI, the organization began tailoring programs to specific groups, including clinicians, administrators and investigators. Cedars-Sinai has also launched an executive training track designed to help leaders integrate AI tools into clinical and operational strategies more efficiently.
The training begins with foundational concepts, including responsible AI use, protecting sensitive information and understanding how AI can strengthen existing workflows. From there, employees learn prompting techniques through hands-on exercises and workshops.
“We started with the basics,” Mr. Odeh said, noting that sessions include interactive and creative elements such as “choose your own prompt adventure,” “prompt makeover” and “prompt reverse engineering.” Advanced users receive agent-based training designed to support more complex, interrelated tasks.
Cedars-Sinai measures the success of its training efforts through adoption trends and real-world outcomes. Mr. Odeh said the organization expected AI to help employees work “faster and easier,” and early signals have supported that expectation.
“We knew these efforts were working when we saw rapid adoption, heard a constant stream of success stories and received continuous requests for new features,” he said.
The health system also tracks how Prompt-A-Thon prototypes translate into operational solutions, using those results as another indicator of impact.
Looking ahead, Cedars-Sinai views AI training as part of a long-term shift rather than a one-time initiative. Mr. Odeh said AI is already taking on repetitive tasks that contribute to burnout, allowing staff to focus on work requiring judgment, empathy and expertise.
Early use cases have included saving time on infection reporting, streamlining nursing documentation and helping medical coders handle significantly more encounters. But Mr. Odeh emphasized that the organization is still in the early stages.
“But this is just the beginning,” he said. “As AI changes how we work, Cedars-Sinai will work with leaders to rethink roles, reskill teams and empower frontline staff to continue innovating for better efficiencies.”
Ultimately, the goal is to ensure employees are active participants in shaping how AI is used across the organization.
“We aim to empower our Cedars-Sinai teams to actively shape the future of healthcare, not just react to it,” Mr. Odeh said.
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