Podcast Summary: Becker’s Healthcare Podcast

Episode: AI ROI: What Early Adopters Are Measuring and Why It Matters
Host: Lucas Voss, Becker's Healthcare
Guest: Alex LeBrun, Co-founder and CEO of Nabla
Date: December 17, 2025

Overview

This episode explores how early adopters in healthcare are defining, measuring, and maximizing ROI (Return on Investment) for AI implementations. Lucas Voss interviews Alex LeBrun, the CEO of Nabla, who shares insights into the evolving methodologies, metrics, and leadership approaches as organizations move from pilot projects to system-wide AI adoption. The discussion focuses heavily on revenue cycle management (RCM), scalability, and the transition toward more tangible, financially measurable ROI in clinical workflows.

Key Discussion Points and Insights

Introduction and Alex LeBrun's Background

(00:29-01:19)

• Alex LeBrun: Co-founder and CEO of Nabla, focused on building AI assistants for clinicians to reduce administrative burden and paperwork.
• Nabla’s reach: Serving ~85,000 clinicians and processing millions of encounters monthly.

The Shift from Soft to Hard ROI in AI

(01:49-02:59)

• Phase One: Initial AI deployments in healthcare had "soft ROI": improving clinician happiness, reducing burnout, and “pajama time,” but without clear financial impact.

• Phase Two: Now entering a phase where "very strong financial ROI" is expected and measurable.

  “If you are like cynical, you could say, hey, the CFO didn’t see the impact on their numbers...with more maturity in the field...we need to have very, very strong financial ROI when we deploy AI.” – Alex LeBrun [01:59]

• Measuring ROI is now precise and rigorous, a change from as recently as six months prior.

What Early Adopters Measure for AI ROI

(03:31-04:27)

• Main focus: Impact of AI on claims and revenue cycle management (RCM).

  • Many clinicians under-code due to audit fears or uncertainty, which leads to lost revenue.
  • AI assistants help clinicians code more accurately and fully, leading to immediate, measurable revenue increases.

• Trend: Most ambient AI companies are quickly integrating with RCM because ROI here is “immediate, easy to measure.”

  “The most obvious [impact] is…on revenues...you can really measure the impact of having this AI assistant helping the clinician to code as they are supposed to code, which results in revenues directly.” – Alex LeBrun [03:41]

How Health Systems Are Measuring ROI & Scaling Initiatives

(04:27-05:54)

• Measurement practices are very new; systematic ROI measurement started only recently.
• Health systems are converging on good KPIs and metrics, but LeBrun urges caution—it is early and results are still forthcoming.
  • Advocates for humility and ongoing evaluation over hype.

  “A few months ago no one was measuring this kind of ROI and we just started to set up the right metrics, KPIs to see how AI impacts revenues…We should meet again in six months and see what was actually measured.” – Alex LeBrun [05:17]

Leadership, Change Management, and the Path to True ROI

(05:54-08:00)

• True ROI requires integration—AI must not live in silos but connect across workflows.
  • Hurdles: Point solutions are still common, but move toward “end-to-end solutions” is accelerating.
• LeBrun envisions AI managing processes from pre-charting to reimbursed claim, making ROI “easy” and “obvious.”
• Progress toward agentic, automated, connected systems will make impactful metrics more transparent and achievable.

  “Once you have an end-to-end solution…measuring very strong ROI is easy because you can…prove that it actually had an impact two months later on revenue collection.” – Alex LeBrun [07:02] “We are excited because this kind of ambition to have an end-to-end process workflow managed by AI was impossible to even imagine a year ago.” – Alex LeBrun [07:23]

Final Thoughts and Looking Ahead

(08:29-09:22)

• AI in healthcare is at the very beginning—comparable to the early days of the internet.
  • Success will demand customization and modeling for each client—AI is not an "off-the-shelf" product.

  “It’s just the beginning…maybe like if we were talking internet in 97. So it’s not perfect, many things are still in progress…It’s 1% journey done, 99% to go.” – Alex LeBrun [08:29]

• The future conversation isn’t “should we use AI,” but rather “how can we rethink workflows with AI’s new capabilities?”

Notable Quotes & Memorable Moments

• On AI’s Impact on Clinicians’ Work:

  “Our goal has always been to build an AI assistant for clinicians, particularly to help them get rid of the part of their jobs they don’t like...” – Alex LeBrun [00:41]

• On Phase Shift in Measuring ROI:

  “With more maturity in the field...now we are measuring very, very precise ROI. And it's new, actually. It was not like that just six months ago.” – Alex LeBrun [02:37]

• On Moving to End-to-End Solutions:

  “What’s exciting now is...we’ll see more and more like end-to-end solutions built on AI. And once you have...everything...powered by AI, then measuring very strong ROI is easy.” – Alex LeBrun [06:44]

• On Perspective for the Industry:

  “People should be aware that it’s just the beginning...the discussion we have shouldn't be ‘should we use AI or not’...it's ‘how can we rethink this workflow given the new tools we have?’” – Alex LeBrun [08:37]

Important Segment Timestamps

• Introduction & Guest Background (00:00–01:19)
• Emergence of Hard AI ROI in Healthcare (01:49–02:59)
• Top ROI Metrics & Revenue Cycle Management (03:31–04:27)
• Real-World Measurement Practices (04:27–05:54)
• Leadership, Change Management, and the Future of AI ROI (05:54–08:00)
• Final Thoughts: Looking Ahead in AI for Healthcare (08:29–09:22)

Conclusion

This episode provides a candid and forward-looking examination of how healthcare is evolving in its approach to AI ROI. Moving from soft benefits (like clinician satisfaction) to quantifiable revenue impact, health systems and vendors are beginning to rigorously define and measure ROI, particularly in revenue cycle management. Alex LeBrun emphasizes humility and continuous adaptation as this journey progresses, urging leaders to envision not just if, but how, AI can redesign their workflows for greater organizational and patient benefit.