# Is Machine Learning the Game-Changer for Improving Healthcare Revenue Cycle Management?

Published 2022-03-02 · Updated 2026-07-23 · Healthcare on MarketScale
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Creator hub: Iodine Software

> The healthcare industry is experiencing a technological revolution driven by data analytics and artificial intelligence. This trend is particularly evident in healthcare revenue cycle management, an area fraught with complexity and often plagued by inefficiencies. With the current rules-based systems often leading to unforeseen errors due to the myriad nuances of revenue cycles, the…

The healthcare industry is experiencing a technological revolution driven by data analytics and artificial intelligence. This trend is particularly evident in healthcare revenue cycle management, an area fraught with complexity and often plagued by inefficiencies. With the current rules-based systems often leading to unforeseen errors due to the myriad nuances of revenue cycles, the stakes couldn’t be higher. According to a report from [McKinsey](https://www.mckinsey.com/), the revenue cycle management process occupies a potential [15% of a healthcare provider’s revenue](https://www.mckinsey.com/industries/healthcare/our-insights/hospital-revenue-cycle-operations-opportunities-created-by-the-aca), and errors and inefficiencies cause a significant portion of this amount to be lost. And yet, today, healthcare organizations face financial imperatives to [reduce costs](https://marketscale.com/industries/healthcare/opportunities-and-obstacles-a-cfo-conversation-on-health-system-financial-resiliency/) to improve their system economics.

How might healthcare address this issue? What roles could AI and machine learning play in transforming healthcare revenue cycle management, and what would the implications be for healthcare providers and patients?

These questions form the core of [Iodine Intelligence](https://marketscale.com/shows/iodine-software/) episode hosted by [Hillary Kennedy](https://www.linkedin.com/in/hilarykennedytv/). [Iodine Software’s](https://iodinesoftware.com/) [Lance Eason](https://www.linkedin.com/in/leason/), Chief Data Scientist, and [Troy Wasilefsky](https://www.linkedin.com/in/troywasilefsky/), Chief Revenue Officer joined Kennedy on the show. Their discussion explores the concept of cognitive emulation, the limitations of rules-based systems, and how machine learning could be a game-changer in revenue cycle management.

Key points from the conversation include:

- Understanding the clinical cycle to make informed decisions in the revenue cycle
- How applying machine learning to healthcare revenue cycle management breaks down the complexities of the system and identifies problems
- Iodine Software’s machine learning approach to improving the productivity of clinical documentation specialists and aiding in more efficient communication with physicians

Lance Eason, the Chief Data Scientist at Iodine Software, has made significant contributions to data science within healthcare. He’s renowned for his work in leveraging artificial intelligence and machine learning to solve complex healthcare problems. Eason’s colleague, Troy Wasilefsky, the Chief Revenue Officer at Iodine Software, brings a wealth of experience in driving revenue growth and strategic development in healthcare technology companies.

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