Q&A: Harnessing AI to elevate hospital revenue integrity

Timothy Boomershine Headshot

Timothy Boomershine,
VP, Data Science

Hitesh Mistry Headshot

Hitesh Mistry,
Solution Strategist

When it comes to hospital revenue integrity, artificial intelligence (AI) is a not-so-secret weapon. Not only can AI analyze data and detect anomalies, but it can flag areas where revenue is leaking. Still, many hospitals struggle with a question: How do we actually leverage AI to achieve better outcomes?

This session will answer that. In less than an hour, we’ll explain how to apply predictive analytics and machine learning to hospital revenue integrity. We’ll explore various predictive modeling techniques. And we'll help you measure the success of AI-powered charge capture so you’ll know if you’re making progress.

Key takeaways include:

  • Understanding the role of AI in hospital revenue integrity
  • Unlocking ways to collect, preprocess, and select data for improved outcomes
  • Knowing which metrics to track to gauge charge capture success
  • Uncovering insights into the future of AI for hospital revenue integrity
Get to know our experts
As Vice President of Data Science at Waystar, Timothy Boomershine, has more than 25 years of experience in AI, data science, and machine learning. With both an MBA and a BA in mathematics, Tim developed a deep understanding of consumer behavior modeling leading the Collections and Recovery Analytics group at FICO. Today, Tim oversees development of Waystar’s state-of-the-art predictive modeling with a focus on improving results in the healthcare business office.

Hitesh Mistry is a Solution Strategist with more than 20 years of experience in healthcare technology. With a background in computer science and professional consulting, Hitesh has worked with a multitude of hospital and health systems to achieve cost savings and grow revenue. Today, Hitesh specializes in developing and executing strategies to help organizations minimize risk and empower consumers to make informed choices.

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