Medical Coding Auditor-1

Position Summary

Veradigm is seeking experienced Medical Coders to support our enterprise coding capability. Applicants will focus on ensuring accuracy in AI-generated medical codes, chart preparation, and drafting clinical notes. You will review the outputs of AI agents and compare to clinical documentation, to ensure that codes are supported, billable, and accurate. Coders will also shape the guidelines we use and help define clinical metrics we report to customers.

This is a role where you will use your experience and critical thinking skills to improve systems. It is not a standard coding position; you will not handle billing, provider education, or denials. The consistency and defensibility of your reasoning are prioritized over high-volume processing.

Key Responsibilities

• Coding Evaluation
  • Review AI-generated ICD-10-CM, CPT, and HCPCS code sets against the clinical documentation, recording for each code whether the note supports it and where.
  • Distinguish clinically defensible additions from unsupported inference. Identify both over-coding, which creates audit risk, and under-coding, which leaves care unreimbursed.
  • Evaluate E/M level selection, including correct setting and visit type, against documentation and applicable CMS guidelines.
  • Apply and improve the scoring guide consistently from the first case to the hundredth.
  • Participate in dual review, independently scoring the same encounter as another reviewer and working through disagreements to a documented resolution.
  • Flag cases where the scoring guide is ambiguous or silent rather than resolving them quietly, so the guide can be corrected before the ambiguity spreads through the dataset.
  • Work across specialties, beginning with internal medicine, applying specialty-specific conventions and noting where documentation patterns change how output should be judged.
• Beyond Coding
  • Review outputs that providers flag as incorrect in our products, determining whether the flag reflects a genuine agent error, a documentation gap, or a difference in provider preference.
  • Help validate automated scoring systems by reviewing a sample of cases an automated judge has already scored, so we can measure where it agrees with expert judgment.
  • Explain patterns in agent errors to machine learning engineers and product managers in terms they can act on, escalating systemic issues rather than scoring the same error repeatedly, and give feedback where the review tool slows accurate review.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.