Rethinking coding audits: A smarter, risk-driven strategy

Not long ago, coding audits lived mostly in the rearview mirror. You reviewed charts after the fact, found gaps, shared feedback and moved on. It was and still is necessary work, but that approach alone is no longer enough.

Healthcare organizations are operating in a very different environment. Regulatory requirements continue to grow. Payer scrutiny is increasing. Margins are tighter than ever. At the same time, many teams are adopting AI-assisted and autonomous coding. This creates real opportunity, but also introduces new forms of risk.

The numbers tell a clear story:

• Nearly 41% of providers report denial rates of 10% or higher
• About 24% of claims are denied due to coding errors
• Hospitals lose 3–7% of net patient revenue each year from documentation and coding gaps

What this means is simple: coding audit strategy can no longer be retrospective. It needs to be real-time, risk-driven and embedded into your workflow.
 

The new reality: Audit as continuous risk management

Traditional healthcare coding audits were built on hindsight. Teams sampled charts, identified issues and educated staff after claims moved through the system. That model still adds value, but it leaves too much exposure upstream.

Today, coding audit strategy spans the entire coding continuum, applying the right audit approach at the right moment:

• Pre-bill validation to prevent errors before submission
• Concurrent audits to support AI-assisted coding workflows
• Retrospective audits to identify trends and guide education
• Denials-driven analysis to connect coding performance to financial outcomes

This shift matters because:

• The average denial rate is ~11.8%
• Up to 84% of denials are avoidable
• Billions are spent annually reworking denied claims

Audit is no longer just a checkpoint, it’s a continuous signal for risk management and improvement.
 

Audit across the coding continuum

Effective coding audit strategy is not about doing more audits, but rather about placing effort where it has the greatest impact.
 

Pre-bill audit: Preventing errors before they happen

By focusing on high-risk cases, you can prevent errors before they trigger denials or rework. The goal isn’t to review everything, it’s to identify where risk lives and act early. Early intervention reduces rework, accelerates cash flow and strengthens compliance from the start.
 

Concurrent audit: Building trust in modern workflows

As AI becomes more embedded in coding, concurrent auditing plays a critical role.

It enables exception-based review, where human expertise focuses on outliers instead of routine cases. Just as importantly, it helps teams trust the system. People adopt automation faster when they know guardrails are in place.
 

Retrospective audit: Driving compliance and education

Retrospective audit remains a cornerstone of any program.

It helps you:

• Identify coding trends
• Clarify documentation expectations
• Deliver targeted education

The difference today is how insights are used. They should feed directly back into earlier stages, improving performance in real time.
 

Denials-driven audit: Closing the loop

Denials are one of the clearest signals of breakdowns in the revenue cycle. Yet they’re often managed separately from coding audits. Bringing these data sets together creates a powerful feedback loop, connecting coding accuracy with financial performance.

Consider this:

• Around 65% of denied claims are never resubmitted
• Coding-related denials continue to increase year over year
• Without a closed loop, organizations leave both revenue and insights on the table

Scaling coding audit strategy for every organization

A common misconception is that modern audit programs require massive scale. In reality, the most effective strategies are flexible and tailored.

• Smaller organizations: Focus on high-risk cases and use automation to guide sampling
• Mid-size organizations: Balance denial reduction with education through pre-bill and retrospective audits
• Large health systems: Build end-to-end audit strategies using enterprise analytics across coding and revenue cycle functions

There’s no one-size-fits-all approach — but successful programs share one trait: They prioritize risk over volume.
 

Where audit programs still fall short

Despite progress, many healthcare organizations face common challenges:

• Fragmented tools: Disconnected systems limit visibility and slow decision-making.
• Lack of data integration: Without a unified view, it’s difficult to identify patterns or prioritize risk effectively.
• Reactive strategies: Too many teams focus on fixing errors after they occur rather than preventing them.

The solution doesn’t require a full overhaul, but it does require a mindset shift: Audit must be designed as a proactive, integrated function from the start.
 

The future of coding audits

Coding audits are becoming a front-line driver of revenue integrity and performance. Looking ahead, key trends include:

• AI and predictive analytics to identify risk earlier
• Integrated workflows that embed audit into daily operations
• Scalable audit models that adapt to organizational needs

Technology plays a critical role, but it’s not the whole answer. The organizations that succeed will combine advanced tools with clear strategy and disciplined execution.
 

A practical model for what comes next

Effective coding audit strategies share a few defining characteristics:

• They focus on risk, not volume.
• They are embedded into workflows, not bolted on.
• They use data to drive action, not just reporting.
• They build trust in automation through visible, consistent validation.

In today’s environment, success isn’t about auditing more. It’s about auditing smarter. When you get that right, audit becomes more than a safeguard — it becomes a strategic advantage for revenue integrity and better outcomes.

Mandy Reid, MS, RHIA, CPC, is the global director of facility autonomous coding at Solventum.