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Blog
Aidoc Staff

Why Scalable AI Must Be Locally Aware

Adoption, C-Suite, Information Technology, System Integration
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The appeal of scalable AI is clear, but the reality is more nuanced. A solution that performs well in one health system, or even one department, may fail elsewhere if local workflows and clinical nuances aren’t accounted for.

“Healthcare truly is local,” said Ashley Weber, VP of IS Ancillary Services, Ochsner Health, on a recent webinar, and her reminder is an important one when thinking about enterprise-wide AI implementation.

Despite standardized policies and shared infrastructure, real-world implementation requires sensitivity to variables like staffing models, provider training and patient demographics. That’s why, she argues, technology alone can’t drive transformation.

“If you’re not really assessing your process, your workflows, then you’re likely going to fail when you’re implementing.”

The solution is an approach to AI integration that combines thoughtful standardization with operational flexibility and understands AI isn’t the endpoint, but a tool embedded within broader process change.

Demetri Giannikopoulos, former Chief Technology Officer at Aidoc, expanded on this point with a system-level view:

“What works at Yale isn’t necessarily going to work at Ochsner or University of Chicago or Mount Sinai… You need a solution that’s flexible and able to adapt to the environment.”

He points to real-world examples like pulmonary embolism (PE) workflows, which may span interventionalists, intensivists and consulting teams — each with different preferences and clinical rhythms. Even within the same health system, the AI that supports PE care won’t be copy-paste compatible with a vascular or stroke team, but you can leverage what’s already in place.

“Take the ingredients you already have and make a slightly different soup out of it… and really enhance the value quickly, in a scalable way.”

This kind of adaptive deployment requires platforms — not point solutions — that can be flexibly configured, contextually integrated and leveraged across teams and service lines. And it demands implementation strategies that prioritize:

  • Workflow mapping at the local level
  • Stakeholder alignment across service lines
  • Incremental rollouts that build momentum, not resistance

As Weber notes, launching too broadly without proof of success can backfire:

“You want to create that momentum… Success begets success. If it goes poorly and you’ve gone live with everyone, it’s hard to regain that trust.”

Access the full on-demand webinar, “From Promise to Practice: Driving System-Wide Efficiency with Clinical AI,” with insights from leaders at Foley & Lardner, LLP, Ochsner, Coalition for Health AI (CHAI) and Aidoc. 

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