Jayson Laban stepped into the vice president and CIO role at the University of Texas Medical Branch in Galveston on May 1 with a clear vision for the organization’s technology strategy.
“My vision is that we are the blueprint for how to do responsible, meaningful AI for a very complex academic medical center,” he told Becker’s.
Mr. Laban, who previously served as interim CIO of the organization, said UTMB’s structure presents a unique opportunity to demonstrate how AI can be implemented across the varied missions of an academic health system.
“We’ve got four primary pillars for the organization: academics, research, clinical care and innovation,” Mr. Laban said.
One of his top priorities is ensuring technology serves as “a force multiplier across all of those pillars.” That begins with understanding how different groups across the organization interact with technology.
“Researchers, students and clinical caregivers all view technology through different lenses,” Mr. Laban said. “What’s top of mind for me is understanding those perspectives and then delivering technology that’s meaningful for each group.”
One area the organization is focused on is making sure the right patients get to the right specialists at the right time. In nephrology, the health system is using AI to evaluate clinical markers and help determine the complexity of a patient’s condition before the patient reaches a specialist.
The information is then routed to referral queues with priority scores, helping patient navigators determine which patients may need more immediate attention. Patients can be prioritized based on the complexity of their conditions, allowing those who need care most urgently to be seen sooner.
“We’ve intentionally built this so it can scale across specialties,” Mr. Laban said. “We can apply the same approach to areas like gastroenterology, rheumatology and others, helping ensure patients receive the right care more quickly.”
Mr. Laban said the nephrology initiative reflects a broader shift occurring across healthcare as organizations move beyond AI experimentation and focus on scaling proven use cases.
“The next phase of maturity is, how do we identify the ones that are successful today and scale those out quickly across the enterprise?” he said.
The health system is also rolling out native AI capabilities within its Epic EHR, allowing nurses to provide immediate feedback through thumbs-up and thumbs-down ratings and comments.
“That’s the environment we’re operating in today,” Mr. Laban said. “We can deploy a feature that didn’t exist yesterday, observe how clinicians use it and gather feedback in real time. That iterative cycle is becoming a core part of how we improve technology.”
Successfully scaling AI, however, requires more than deploying new technology. Organizations must also prepare employees to adopt new workflows and embrace a faster pace of innovation, Mr. Laban said.
“We’re moving away from large, 18- to 24-month implementation cycles and toward a much more rapid development model,” he said. “The question for me is whether our IT culture is ready for that pace. Are we prepared to work on multiple use cases simultaneously, knowing that some won’t succeed? Can we recognize when something isn’t working, move on quickly and continue innovating?”
Mr. Laban said building a culture that embraces experimentation and learns from failure is critical when it comes to AI.
“We have to move at the speed the business requires,” he said.
Ultimately, Mr. Laban said success in academic medicine will require recognizing that AI adoption looks different across clinical, research and educational settings.
“The challenge is that people consume AI differently depending on their role. There isn’t going to be a single solution that scales everywhere,” he said. “We need multiple solutions that can support different aspects of the university and health system. I want UTMB to demonstrate how meaningful AI can be implemented successfully across that diverse landscape.”
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