Artificial intelligence is rapidly being integrated into medical coding workflows—promising increased speed, scalability, and efficiency.
But as adoption grows, so does a critical question for healthcare leaders:
Who is responsible when AI gets it wrong?
Unlike traditional coding processes, AI-assisted coding introduces new layers of complexity—particularly around compliance, documentation integrity, and audit defensibility.
Because in today’s environment, it’s not enough for codes to be assigned quickly. They must be accurate, supported, and defensible under scrutiny.
In this blog, we examine the compliance risks associated with AI-assisted coding and what organizations should consider before relying on automated outputs.
One of the most common misconceptions about AI in coding is that automation reduces accountability.
It doesn’t.
Even when AI tools suggest or assign codes:
From a compliance standpoint, AI is not a safeguard—it is simply another input in the coding process.
AI systems are trained on historical data and patterns—but they do not independently verify whether documentation fully supports a diagnosis or procedure.
This can lead to:
Over time, this creates risk not only for reimbursement, but for audit exposure.
Compliance risk is not just about overcoding—undercoding can be equally problematic.
AI does not always recognize:
As a result, organizations may:
AI can identify terms like “sepsis” or “acute respiratory failure,” but it cannot determine whether those diagnoses are supported by clinical criteria.
This creates a critical gap:
Clinical validation remains a human-driven process.
Many AI systems lack transparency in how decisions are made.
For compliance teams, this presents a challenge:
If coding decisions cannot be clearly explained, they are difficult to defend.
AI performance can vary depending on:
This inconsistency can lead to:
AI Suggestion: Acute respiratory failure
Reality: Documentation includes the term, but clinical indicators do not support the diagnosis → requires validation and likely removal
In an audit, this is not a minor issue—it is a high-risk finding.
From a regulatory perspective, the expectations have not changed:
AI does not change these requirements—it simply changes how codes are generated.
And in many cases, it introduces additional scrutiny.
To safely incorporate AI into coding workflows, organizations should:
Technology should enhance—not replace—these foundational practices.
AI-assisted coding can improve efficiency—but it also introduces new compliance risks that cannot be ignored.
At the end of the day:
The most successful organizations are not those that rely on AI the most—but those that balance technology with strong coding, CDI, and compliance oversight.
AI-assisted coding introduces new compliance considerations—but it’s only one part of the broader picture.
Explore the rest of the series:
Understanding how these areas connect is key to evaluating AI without increasing risk.
For more than 30 years, HIA has been the leading provider of compliance audits, coding support services and clinical documentation audit services for hospitals, ambulatory surgery centers, physician groups and other healthcare entities. HIA offers PRN support as well as total outsource support.
The information contained in this coding advice is valid at the time of posting. Viewers are encouraged to research subsequent official guidance in the areas associated with the topic as they can change rapidly.