AI Necessitates New Approach to Clinician Training, Advises U Maryland Medical Center’s Kuebler

Tiffany Kuebler, Medical Director of Clinical Informatics, University of Maryland Medical Center – R Adams Cowley Shock Trauma Center, says the arrival of generative tools is less a software upgrade and more a change in how health systems make decisions, prepare clinicians, and measure success. Kuebler runs clinical informatics and is a physician assistant, giving her a unique organizational view into how frontline teams can safely integrate AI into everyday care.

The traditional division of labor no longer fits the AI era, she argues. The work spans model behavior, clinical risk, patient communication, and workflow change, which makes it inherently interdisciplinary. Informaticists who speak both clinical and technical dialects become the connective tissue. As she put it, “AI is a tool in your toolbox. It’s not a technology decision anymore and shouldn’t be thought of as a technology decision.” Selection and oversight of AI should be co-owned by clinical leadership, not delegated solely to procurement or infrastructure teams. “These decisions shouldn’t be driven by your CTO; nor are they business decisions alone.”

That reframing also alters how buyers engage vendors. Rather than treating AI as an invisible feature embedded in familiar applications, leaders should insist on explicit disclosures about training data, bias testing, hallucination safeguards, and drift monitoring. Contract language and governance gates, Kuebler suggests, must reflect that AI can influence clinical cognition and documentation patterns. Informaticists are well placed to translate technical claims into bedside realities and to ensure operational guardrails keep pace with capability.

From Implementation to Use

Many health systems still track success at go-live. Kuebler’s view is that adoption must replace box-checking. She points to a widening gap between deployed functionality and daily use. The governance and analytics stack, therefore, should pivot to behavioral metrics: time in notes and orders by role, after-hours documentation, completion rates for templated workflows, and untreated variation at the service-line level. Those signals, coupled with qualitative feedback, allow leaders to prioritize optimizations where they will matter most.



That emphasis on “use” recasts the informatics charter. Instead of leaving adoption to individual departments post-implementation, clinical informatics teams should run structured optimization programs and then re-measure. The bar for success, she argues, is whether the tool reduces cognitive load or administrative time without compromising clinical judgment. Anything short of that risks quiet abandonment—an outcome that erodes trust and wastes scarce capital.

Training That Sticks

Education is where many promising tools stall. Traditional lecture-style classes and long slide decks rarely survive contact with busy services, rotating trainees, and varied baseline skills. “Nobody wants to be talked at,” Kuebler said. Her team has shifted toward interactive, asynchronous modules that require clicks and short task completions, supplemented with brief, role-specific refreshers at the elbow. “It’s not then just the module talking at you—now it’s interactive,” she said. “And so now you’re actually having to pay attention and show that you are understanding.”

And because communication channels are saturated, delivery matters as much as content; as she noted, “Nobody reads their email anymore.”

Equally important is teaching the “why,” not just the “how.” Clinicians need literacy about bias, hallucinations, and drift to supervise AI appropriately and sustain trust with patients. Informaticists, she argues, are uniquely credible in delivering this content because they can connect technological concepts to clinical outcomes and communication. “We can bridge the gap of two parties that don’t really like to talk to each other and don’t speak the same languag...