CMS Pub. 100-09, ch. 6, § 20.2.4

MACs shall maintain a comprehensive analysis program to identify and address the most

common claims submission errors across their jurisdiction. This proactive approach helps

prevent errors before they occur, reducing administrative burden for both providers and MACs

while improving overall claims processing efficiency and accuracy.

Claims submission errors are those that result in rejected, denied, or incorrectly paid claims.

MACs shall analyze rejected claims, denied claims, incorrectly paid claims, common clerical

administrative errors, and inadvertent errors that providers make unintentionally but could be

prevented through targeted outreach and education. MACs shall maintain a comprehensive data

analysis program that examines claims submission patterns to identify educational opportunities.

This program shall generate monthly reports documenting the most frequent collective claims

submission errors from all providers in their jurisdiction, enabling MACs to prioritize their

educational efforts based on actual error frequency and impact. MACs may identify billing

pattern aberrancies within homogeneous provider groups, such as unusual coding patterns

among similar specialty practices of systematic errors within specific provider types that may

indicate widespread misunderstanding of billing requirements.

The analysis shall also detect patterns within individual claims or groups of claims that may

reveal systematic issues requiring targeted intervention. MACs shall conduct this data analysis

through ongoing general surveillance of submitted claims but must also be prepared to conduct

focused analysis in response to specific triggers including:

• Provider complaints, or input regarding billing difficulties

• CMS alerts or reports highlighting emerging issues

• Reports from other MACs identifying cross-jurisdictional problems

MACs shall use their analytical findings to develop and continuously modify their POE

approach, ensuring educational content directly addresses the most prevalent and impactful

submission errors identified through their analysis. This creates a continuous feedback loop

where data analysis drives educational priorities, and educational effectiveness can be measured

through subsequent reductions in identified error patterns. MACs should also track the

effectiveness of their educational campaigns, allowing for refinement of both analytical methods

and educational approaches over time.