Healthcare CIO to demo cost-saving clinical AI at HIMSS26
ILLINOIS, UNITED STATES — A healthcare chief information officer (CIO) plans to show hospital and health system leaders how to cut costs and speed up clinical AI workflows using large language models (LLMs) at the upcoming HIMSS Global Health Conference & Exhibition in March 2026.
Jeremy Harper, CIO of Owl Health Works, will lead a 60-minute interactive workshop designed to help providers deploy clinical AI tools without driving up technology budgets, according to a report from Healthcare IT News.
His session, “Deploying Large Language Models for Clinical Workflows,” aims to move AI conversations beyond experimentation and into measurable operational value.
For United States hospitals and clinics grappling with staffing shortages, documentation burden, and tightening reimbursement, Harper’s approach centers on engineering AI systems for affordability and speed, not just raw capability.
Engineering clinical AI for speed and cost efficiency
A core tenet of Harper’s strategy is designing systems around “cost, speed [and] precision,” rather than simply pursuing the most advanced models.
In high-volume environments such as large health systems processing hundreds of patient records per hour, model choice directly affects the bottom line.
“A more intelligent model is going to have 10 times the cost of a more basic model,” Harper noted.
“We’re looking for the very cheapest model that we can get away with using that still delivers what we need [99% confidence],” Harper added.
The specific calculations will determine whether providers can successfully implement AI systems or their projects will fail because of rising computing costs.
Harper will show how to transform unstructured clinical notes into usable data through sandbox testing, which will enable IT managers to practice retrieval-augmented generation and advanced prompt engineering.
He describes LLMs as “an amazing tool” for transforming narrative documentation into discrete data that can be integrated into electronic health records (EHR) and analytics platforms, potentially reducing administrative burden for clinicians and improving data quality across care settings.
Rethinking IT teams and safety in AI adoption
Harper also plans to address governance and workforce implications. “It’s so easy to have little silos of knowledge,” he said. “Most of us have logged into ChatGPT and done this, that or the other thing with it.”
But organizations often lack “that holistic viewpoint: ‘How do I get this up and running? How do I make sure that it’s functioning with value for my organization?’” he added.
His workshop will emphasize human-in-the-loop review, quantitative evaluation metrics, and a “kill-switch” checklist to assess accuracy, bias, and patient-safety risks, critical safeguards for U.S. providers operating under strict regulatory scrutiny.
“At the end of the day, [LLMs] can speed up a lot of processes, but there’s still that it’s not [AI]; it’s a next token predictor,” Harper said.
For hospitals and clinics weighing AI investments, the message is pragmatic: deploy selectively, measure rigorously, and build systems that deliver clinical and financial returns.
