Autonomous coding is moving quickly from "promising" to "practical," but not every healthcare organization is ready for the same level of automation. In this panel discussion, industry leaders break down what autonomous coding actually is, where it delivers measurable value today, and where limitations still require human oversight. We’ll unpack the technology stack behind modern autonomous coding, discussing how it evolved from rules-based logic to machine learning and generative AI–enabled workflows, and what that evolution means for accuracy, governance, and compliance. You’ll leave with a clear evaluation framework to assess readiness and select a solution that aligns with your clinical documentation quality, coding mix, integration requirements, and financial goals.
Learning Objectives
- Define autonomous coding and distinguish it from assisted coding, computer-assisted coding (CAC), and workflow automation, identifying realistic advantages and current limitations.
- Explain the core technologies that enable autonomous coding today, including natural language processing, machine learning, and generative AI ML, GenAI, and how these capabilities have evolved over time.
- Assess organizational readiness by identifying the process, data, documentation, and governance conditions required for safe and effective autonomous coding.
- Apply a vendor evaluation framework to compare solutions using criteria such as accuracy measurement, auditability, integration, exception handling, compliance controls, and return on investment (ROI) tracking.
Suhas Nair
Vivek Menon
Vatsal Shah
