Follow these steps to explore how the system detects high-risk diagnosis codes lacking proper supporting evidence:
Click the "Generate High-Risk Data" button to create a dataset specifically designed to test validation rules for high-risk diagnoses.
Technical Context: This generates FHIR resources containing diagnosis codes from four high-risk categories: Acute Stroke, Acute MI, Embolism, and Lung Cancer.
Go to this sectionExamine the "Risk Assessment Summary" panel in section [2] to see the total number of diagnoses and how many were flagged as high-risk.
Business Context: In a real audit preparation scenario, this would represent the organization's potential audit exposure across specific high-risk categories.
Go to this sectionReview each high-risk category in section [3] to see specific diagnosis codes that were flagged and understand what supporting evidence was missing.
Technical Context: Each category demonstrates different validation rules based on CMS/OIG requirements.
Go to this sectionClick on any diagnosis code in the lists to view detailed information about why it was flagged and what supporting evidence was expected but missing.
Business Context: In a production environment, this data would help coders and auditors identify which patient records need review before submission.
Go to this sectionUse the pagination controls to navigate between pages of diagnosis codes within each category.
Technical Note: Each high-risk category contains approximately 20 diagnoses, limited to 5 per page for better readability.
Go to this sectionProcessing data...