Stop Leaving Money on the Table: AI for Identifying Under-Coded Procedures

Under-coding is a silent revenue leak. It happens when the documentation supports a higher-level procedure or additional codes, but the submitted claim reflects a lower level of service. The result is lower reimbursement and a distorted picture of clinical workload.

AI can reduce under-coding by cross-referencing notes, procedures, and claim submissions in a way that is too time-consuming for manual review at scale.

Why under-coding happens

Common causes include:

• Time pressure leading to conservative coding
• Ambiguity in documentation or missing details
• Lack of visibility into evolving payer rules
• Variation across coders and specialties

The AI workflow for under-coding detection

A practical system uses three layers:

1. Document extraction: capture procedure details, diagnoses, and supporting evidence from notes.
2. Code suggestion: map clinical evidence to potential codes with specificity and modifiers.
3. Variance analysis: compare suggested codes with billed codes and flag gaps.

LeapOCR handles the extraction layer with schema-validated JSON output, ensuring the coding engine receives consistent evidence.

Guardrails to avoid audit risk

Revenue optimization must be balanced with compliance. Add safeguards such as:

• Require evidence citations for any suggested higher-level codes
• Apply payer-specific rules and exclusions
• Route all code upgrades through a human review queue

Implementation example

Define a schema focused on procedure evidence:

{
  "procedure_statements": [{ "procedure": "string", "evidence": "string" }],
  "diagnoses": [{ "term": "string", "evidence": "string" }]
}

The coding engine can then compare these fields to billed codes and highlight potential upgrades.

KPI impact

Under-coding detection typically impacts:

• Revenue lift per claim or encounter
• Coder efficiency by reducing manual chart reviews
• Compliance quality by enforcing evidence standards

How to structure a review queue

Under-coding analysis should never bypass human judgment. Build a review queue that groups suggested upgrades by specialty and risk. Provide coders with the extracted evidence and suggested code rationale to speed decision-making.

Common under-coding signals

AI systems often flag:

• Missing modifiers or laterality details
• Higher-level E/M codes supported by note complexity
• Procedures documented but not billed

These signals are valuable, but should always be reviewed under internal compliance policy.

Bottom line

AI is not about aggressive billing. It is about accuracy. By matching documented care to the codes that reflect it, providers can reduce revenue leakage while maintaining compliance. LeapOCR provides the evidence layer that makes under-coding analytics reliable at scale.