risk-adjustment

To calculate CMS-HCC risk adjustment factors in Python, you programmatically map ICD-10 diagnosis codes to condition categories and apply coefficient-based scoring to determine the RAF score. This requires pandas for data manipulation.

Disease hierarchy resolution for HCC risk scoring ensures only the highest-severity condition is counted. It automatically resolves overlapping diagnosis categories to prevent inflation of the final RAF score.

Detecting clinical coding gaps using lab and Rx proxies involves analyzing prescription and laboratory data against existing diagnosis records. This proxy-based gap detection identifies suspected undocumented conditions impacting revenue cycle management.

Yes, you can use pandas to automate end-to-end RAF score pipelines. The framework supports data manipulation for ICD-10 mapping, hierarchy resolution, and coefficient-based scoring across member diagnosis and demographic datasets.

Yes, provider-type eligibility rules are required for ICD-10 to HCC mapping. Enforcing provider-type eligibility ensures only valid diagnoses from authorized practitioners are counted in the risk adjustment calculation.