Cardiology Coding Alert

Gain Insights on AI Inclusion

Each year, more medical procedures incorporate AI to perform all, or part of tasks normally accomplished by humans, requiring varying levels of physician interaction in the service. CPT^®’s 2023 update provides context for how those processes might impact coverage decisions.

“AI needs a descriptive framework that is used to develop the coverage that, in turn, will adequately pay for expert systems, machine learning, and the algorithm-based services,” according to Robert Jarrin, JD, managing member of The Omega Concern LLC in the virtual presentation, “Coding, Coverage, and Payment for AI Medical Services.”

Jarrin noted that Medicare payment rates include direct and indirect practice expenses (PE); the latter’s values are based off the Physician Practice Information survey conducted in 2007 and 2008 — “way before we actually had tangible AI in healthcare.”

The emerging technologies tend to rely on fees tied to software, licensing, and analysis, rather than the typical equipment and hardware purchases. “Coding will need to reflect AI, as in the work performed by machines, which is why the AMA created Appendix S to the CPT^® code set as a taxonomy to provide guidance for classifying AI,” Jarrin said.

“I think it’s a testimony to how important this is that the AMA chose to release this taxonomy a full year in advance of when it would normally release a new code. This taxonomy is intended to cover not only AI in the formal sense like machine learning, but also what we might call expert systems, and that is the advanced use of computer technology in order to augment the role of the physician,” said Richard Frank, MD, PhD, chief medical officer of Siemens Healthineers and a member of the CPT^® Editorial Panel, who co-presented with Jarrin during the virtual event.

Understand the ‘3 As’ of AI CPT^® Coding Guidelines

The CPT^® Appendix S categorizes AI into three main hierarchies based on “the clinical procedure or service provided to the patient and the work performed by the machine on behalf of the physician or other qualified healthcare professional (QHP).”

These AI classifications are as follows:

• Assistive: The machine works to detect only clinically relevant data. A physician or other QHP must interpret the data and report the results.
• Augmentative: The machine performs the work and analyzes or quantifies the data “in a clinically meaningful way.” This category also requires a physician or QHP to interpret and report the data.
• Autonomous: The machine performs the work, then automatically analyzes the data, interprets the information, and produces clinically relevant conclusions independently without physician or QHP involvement.

Learn the Levels of Autonomous AI

Because autonomous AI can operate nearly free of human interaction, the CPT^® Editorial Panel established different levels of autonomy. “We decided there needed to be at least three levels within [autonomous AI to cover] the range of what we expect autonomous devices to be doing,” Frank said.

The three levels of autonomous AI differ on the amount of physician or QHP involvement as explained below:

• Level I: AI comes to conclusions and suggests diagnosis or management options. This level requires the physician or QHP to make the medical decision.
• Level II: AI draws conclusions and begins the diagnosis or management options with an alert to the physician or QHP. The provider then has the option to override the AI’s decisions.
• Level III: AI draws conclusions and initiates the management. Only by a physician’s or other QHP’s initiative to intervene could the service be interrupted.

Anticipate Future Use of AI Coding Framework

Appendix S guidelines indicate that the term “AI” doesn’t define the clinical use of any specific procedure. As a result, the appendix doesn’t list specific codes for which the guidelines could apply, although it does list examples.

The CPT^® Editorial Panel created the taxonomy to define the relationship between the work completed by the machine and the work done by a human, as well as show the relevance to AI. “This taxonomy, this framework for AI code descriptors, anticipates what features that will be, as well as what business models there may be,” according to Frank.