All projects
BI

Medical Coding Accuracy Audit

Inaccurate medical coding leads to claim denials, compliance risks, and revenue leakage. This project audited coding accuracy across departments to identify training needs.

92% coding accuracy baseline established
2024
PythonExcelPower BI

Dataset

Kaggle: Synthetic AR Medical Dataset

View source

Key Questions

  • What is the overall coding accuracy rate across the organisation?
  • Which departments or specialities have the highest coding error rates?
  • Are certain ICD/CPT code categories more prone to errors than others?

Methods

  • Random sampling of 5,000 coded encounters for manual review comparison
  • Error categorisation: upcoding, undercoding, incorrect modifier, missing code
  • Department-level accuracy benchmarking
  • Trend analysis of accuracy over time to measure training impact

Results

Overall coding accuracy was 92%, with the emergency department (86%) and outpatient surgery (88%) performing below the 95% target. The most common error type was missing secondary diagnoses (38% of all errors).

Coding Accuracy by Department

Recommendations

  • Prioritise targeted coding training for ED and outpatient surgery coders
  • Implement a secondary diagnosis prompt in the coding workflow
  • Establish quarterly audit cycles to track improvement
  • Consider computer-assisted coding tools for high-complexity specialities

Limitations

Manual review is itself subject to inter-rater variability. The sample may not capture rare code categories. This audit provides a snapshot; ongoing monitoring is essential.