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Data Analytics to Reconcile Clinical and Claims Data

The presenter at this session will describe analytical data validation methods to uncover documentation and/or coding patterns resulting in inaccurate coverage and payment. This unique approach analyzes clinical data captured in the EHR to measure claims data quality. Aberrant data patterns are identified by reconciling EHR data with claims data. Subsequent exploration of coded data patterns determines the appropriate corrective actions to reconcile clinical and claims data and correct systemic documentation or coding errors. The presenter will provide the step-by-step methodology and walk through at least two actual scenarios demonstrating the approach.

This data validation method can be used to manage data quality for a wide variety of acute or chronic conditions and specific services to support various data initiatives. Applications include, for example, data quality for HCC capture, CC/MCC capture, validation of disease registries, charge capture, and quality measurement. The presenter will share her experience using this approach to correct under-reporting of chronic conditions (e.g. obesity, depression) for HCC reporting and additional examples as time permits.

This data validation method uses existing clinical and claims data to uncover and resolve data quality issues. There is no expensive software application or tool needed (and no pitch). Attendees can try this at home!

Learner Outcomes

After completing this session, participants will be able to:

• Use EHR clinical data to identify patient populations for targeted data analysis.
• Analyze coded data to identify and correct errors, without time-consuming chart review.
• Reconcile clinical and claims data to improve overall data quality and reliability for specific initiatives.


Mary Stanfill, MBI, RHIA, CCS, CCS-P, FAHIMA
HIM Consulting, UASI Cincinnati, OH
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