Their discussion explored how autonomous coding differs from traditional automated technology, what organizations should consider when evaluating AI solutions for their revenue cycle operations, and the impact on clinician experience and operations.
Three key takeaways:
1. Assisting clinicians with coding duties is a tangible way to improve provider satisfaction.
As part of the administrative burden facing clinicians, primary care providers are often tasked with completing documentation, including coding duties, in between patient encounters. Amazon One Medical’s implementation of AI-powered coding to support its revenue cycle operations significantly reduced this burden.
This shift in certain coding duties offers numerous advantages for clinicians. Autonomous coding is able to increase coding accuracy and reduce coding errors, while simultaneously reducing turnaround times. This enables providers to focus more time on what matters most – patient care – while organizations benefit from higher clinician satisfaction and stronger recruitment.
2. Success with automation technology requires rigorous standards in partner selection.
Impact hinges on choosing genuine autonomous coding solutions rather than partial automation tools. Computer-assisted coding (CAC) tools that merely suggest codes still require human validation, limiting their leverage. While autonomous medical coding cannot replace a clinician’s professional clinical judgment or medical decision making, true autonomous coding technology at a high successful automation rate can significantly reduce the burden of coding-related administrative duties on clinicians.
Organizations considering autonomous coding should demand specific performance guarantees in their contracts, including SLAs for automation rates, accuracy rates, and turnaround times. These are unique to each organization. Leading vendors will validate these capabilities through a proof of concept before implementation, ensuring the solution can deliver sustained performance at scale.
3. Scalable technology supports sustainable growth.
In contrast to manual operations, the latest generation of coding automation technology enables organizations to scale operations without proportionally increasing resources, all while maintaining the highest levels of quality assurance and compliance with applicable coding guidance and regulations. Key features to consider include the ability to integrate with existing systems, consistent performance as volumes increase, standardized coding practices that are consistent with best practices, and predictable operating costs.
Organizations seeking similar results should partner with vendors whose solutions can scale seamlessly while maintaining compliant processes and high performance standards. As Rogers noted, while “‘AI’ gets slapped on anything that has automation these days,” true autonomous coding uniquely alleviates capacity constraints: “As long as the model is running, I can scale my [revenue cycle operations] as large as I want to.” This approach eliminates coding as a bottleneck to organizational growth while promoting sustainable quality, accuracy and compliance with coding guidelines and regulations.
The key to achieving these outcomes lies in effective partnership. While technology drives these results, success ultimately depends on strong partnership between organizations and their AI vendors. “One of the things that’s often lost in AI stories is people,” Lockhart noted. “It’s like, ‘Oh, this magic machine showed up and did all the work.’ In reality, there’s a real people effort [between provider and vendor] that has to happen.”
