Nate Larmore, senior director at MGAC, spoke to Tanya Martins of The Construction Specifier about how AI is shaping the future of healthcare design.

What role could AI play in shaping future hospital layouts to address evolving patient needs, staff efficiency, and operational resilience?

AI gives hospital planners a way to design from active evidence instead of a hunch. By pulling real-time data about patient demand, staff utilization, and clinical workflows, design models can be tuned for user experience, patient care, patient satisfaction, and efficiency instead of dusty square footage standards. This results in constructing departments based on how care is actually delivered, limiting underused hospital space, avoiding overbuilding expensive real estate, and delivering better-designed support zones.

Looking ahead, AI makes it possible to stress test designs against future surge events (demographic change, regulatory shifts, natural disasters, pandemics, etc.) to better understand how today’s assumptions hold up to possible demands in the future. All of this will allow hospitals to better adapt to patient demand, staff workflows, and even emergencies. It’s not about building bigger. It’s about building smarter.

What are the most promising current applications of AI in healthcare construction project management, and how do they compare to traditional methods?
AI is already pulling project management out of the era of sticky notes and million-tab spreadsheets. Instead of chasing year-old emails and clicking through hours of subfolders on file share sites, these new tools allow us to automate documentation, track status and decisions in real time, and quickly analyze large datasets. We’re shifting to a living “decision ledger” that captures who said what and why it mattered.

Similar to the way Blockchains work, AI platforms are beginning to provide a centralized, real-time record of progress that gives us better history, better workflows, and improved coordination. Reality captured from drones, 360-degree scans, and other sensors doesn’t just create cool graphics and timelapse photos. Integrating these tools can give a realistic (and objective) view of progress and quality measured against specifications and BIM. When you add in predictive analytics that forecast risks before they spiral, our world begins to shift away from reacting to problems and into steering around them.

Contrast this type of value-focused efficiency to so much of the stop-and-go rhythms of today’s RFIs, field coordination, and re-sequencing, and we begin to see whole projects begin to run like a system rather than siloes of organized chaos.

What lessons from large-scale healthcare projects, such as major hospital renovations, can inform the broader adoption of AI in healthcare construction?

Large hospital projects prove AI adoption can cut out the grunt work—automated meeting notes, cost forecasts, and even managing the build-out and management of the BIM model. But, this is not waiting for “autopilot” to finish the project while we’re out at lunch. Every output still needs human eyes, insight, and creativity. AI can dramatically cut down manual processes and improve design interpretation during construction.

On the other hand, lessons from these projects underscore the importance of human oversight. AI-generated deliverables must be carefully reviewed. These tools must demonstrate discernible ROI, so digital twins and flashy dashboards will only stick if they reduce rework, speed up high-quality decision-making, and find healthy savings. Projects that succeed will be the ones that keep AHJs and regulators looped in while using AI tools to increase transparency.

Anything else you’d like our audience to know about the AI revolution?
Well, there’s the good, the bad, and the ugly. The good news: AI is not (yet) a replacement for human expertise. It will make projects smarter and faster. It will streamline workflows, eliminate inefficiencies, and allow seasoned pros to focus on higher-value topics and decisions. In healthcare construction, AI’s role will improve coordination, cut out manual bottlenecks, and help hospitals build more efficiently for the future.

The bad news: A lot of manufacturer hype will remain hype with no ROI, mistaken interpretation of data will overshadow good intentions, most projects won’t have people who know what to do with all of the data outputs, and some stakeholders will keep designing brand new, outdated hospitals.

And the ugly? Data breaches, privacy mishaps, regulations dragging behind innovation, and a widening gap where deeply funded health systems get AI-powered “Formula 1” facilities, while everyone else will be driving clunkers.

In the end, AI will not replace good judgment and empathetic design, but it will present us with a powerful capability to deliver more beautiful, thoughtful, and better-functioning healthcare. The uncomfortable question is, will we use these amazing new machines to enable us to build better environments for patients and staff? Or will we simply use AI to work faster, cheaper, and mass-produce the mistakes of the past?

Nate brings more than 25 years of global experience advising clients on best-fit solutions in master planning, design leadership, program development, and program management. He provides strategic advisement, drawing from expertise in trend forecasting, detailed cost and ownership models, integrated built environments, project delivery, and lifecycle strategies. His extensive portfolio includes corporate headquarters replacements, campus modernization and expansions, corporate laboratories, healthcare renovations and replacement campuses, intelligent buildings and IoT ecosystems, and government and defense programs.