AI that pays: monetizing healthcare operations

In a recent fireside chat at an industry conference, Nathan Wan of Ensemble Health Partners shared compelling insights about the transformative impact of AI on healthcare revenue cycle management. The discussion revealed how one organization's AI implementation journey has evolved from experimental proofs-of-concept to delivering quantifiable ROI across multiple operational functions. As healthcare organizations increasingly struggle with financial sustainability, Wan's practical approach to AI deployment offers a refreshing counterpoint to the speculative hype that dominates many industry conversations.

Key takeaways from Wan's presentation:

• Start small with clear ROI targets: Ensemble began their AI journey with targeted implementations focused on solving specific, measurable problems rather than attempting enterprise-wide transformation.

• Build or buy decisions require careful evaluation: The organization takes a pragmatic approach to technology acquisition, assessing whether to develop solutions in-house or partner with specialized vendors based on core competency alignment.

• AI implementation requires organizational readiness: Success depends on preparing the operational environment, including data infrastructure, workflow integration, and staff training, before deploying sophisticated AI tools.

• Measuring ROI goes beyond direct cost savings: Ensemble evaluates AI implementations based on multiple metrics, including FTE optimization, accuracy improvements, and broader operational efficiency gains.

• Human-AI collaboration creates sustainable value: Rather than focusing on replacement, Ensemble positions AI as an augmentation tool that enhances human capabilities in revenue cycle functions.

Expert analysis: The blueprint for practical AI implementation

The most insightful aspect of Wan's presentation was his organization's methodical approach to AI implementation. Unlike many healthcare entities that either rush into AI adoption without clear objectives or remain paralyzed by indecision, Ensemble has developed a structured framework for evaluating, implementing, and measuring AI investments. This approach has produced measurable results—reducing claim denials, improving coding accuracy, and optimizing workforce allocation.

This matters tremendously in today's healthcare context, where financial pressures continue to mount. A recent Kaufman Hall report indicates that over 30% of hospitals operated at a loss in 2023, with labor costs and reimbursement challenges creating an unsustainable financial trajectory. AI implementations that deliver concrete ROI—like those Wan described—represent one of the few viable paths toward operational sustainability without compromising care