Reduce risk and improve accuracy with Rhapsody EMPI powered by Autopilot
Patient matching errors lead to compliance risks, inaccurate records, and compromised care delivery. As traditional approaches reach their limits, healthcare organizations need a smarter way to manage identity data.
In an exclusive interview with John Lynn at Healthcare IT Today, Drew Ivan, Chief Architect at Rhapsody, explains how Rhapsody EMPI with Autopilot leverages AI-driven machine learning to improve accuracy, reduce risk, and streamline data management—ensuring patient records are matched with confidence every time.
What you’ll learn:
- Why traditional matching algorithms have reached their limits and how AI improves accuracy
- How Autopilot mimics human decision-making to process large patient data sets more efficiently
- When to outsource an EMPI versus relying on homegrown or manual solutions
Watch the full interview to learn how AI-driven patient matching is transforming healthcare.
