Senior Oncology Coding Quality Analyst – Hybrid

US Oncology Inc.; Richardson (TX); Hybrid; USD 90,000 - 125,000; Full time

Job summary

US Oncology Network is seeking a Sr. Coding Quality Analyst to support the Coding Department at Texas Oncology in Richardson, TX.

This full-time hybrid role requires performing in-depth coding and documentation reviews, audits, and process improvements to ensure compliant revenue cycle operations. The position emphasizes cross-functional collaboration with Coding Operations, Compliance, IT, and clinical leadership, with responsibilities spanning root-cause analysis, data validation, and

Qualifications

• Requires HS diploma or equivalent; a 4-year HIM-related degree is preferred.
• Bachelor-level HIM or related field preferred for improved qualifications.
• Professional coding credentials such as CPC/CCS-P/COC preferred along with auditing credentials.

Responsibilities

• Perform comprehensive reviews of coding, documentation, billing, and related quality concerns.
• Conduct audits, medical record reviews, and data validation to support findings and recommendations.
• Coordinate with Coding Operations, Audit Operations, Education, and Revenue Cycle to resolve issues.
• Prepare clear reports with findings, root cause, and corrective actions for leadership.

Skills

CPT/HCPCS/ICD-10-CM knowledge

Coding audits & investigations

Data review & documentation

Cross-functional collaboration

Education

Bachelor's degree in Health Information Management or related field

Coding certifications preferred (CPC/CCS-P/COC)

Tools

EMR systems

Job description

US Oncology Network is seeking a Sr. Coding Quality Analyst to support the Coding Department at Texas Oncology in Richardson, TX.

This full-time hybrid role requires performing in-depth coding and documentation reviews, audits, and process improvements to ensure compliant revenue cycle operations. The position emphasizes cross-functional collaboration with Coding Operations, Compliance, IT, and clinical leadership, with responsibilities spanning root-cause analysis, data validation, and