Every chart coded right, the first time.
How a multi-specialty physician group uses Earnest to code charts accurately, reduce manual work, and increase reimbursement supported by the documentation.
Snapshot
- Customer
- Multi-specialty physician group
- Setting
- High-volume procedural coding
- Time to Go-Live
- 6 weeks
From chart to audit-ready claim
Clinical context moves through multiple layers of specialty-trained coding, payer reimbursement prediction, and validation before submission.
The Challenge
Manual coding processes are inefficient, prone to error, and do not react to payer changes quickly enough. A multi-specialty physician group wanted to bring AI into its RCM process, with a focus on ensuring code accuracy and maximizing reimbursement. The group needed to capture the tribal knowledge its coders and auditors had developed but were not applying consistently to every chart. That inconsistency led to denials, made new coders harder to train, and contributed to high turnover in these roles.
The Solution
The AI agent reads the encounter documentation, applies specialty-specific codes, checks payer guidelines, reviews reimbursement history, and performs multiple layers of validation before submission. It captures supported procedures and diagnoses, follows the group's coding standards, and routes edge cases for human review rather than submitting an uncertain result. The result is a coding process that requires minimal human intervention while increasing reimbursement.
The Results
See how Earnest can fit into your revenue cycle.
Let's Chat“We need the coding to be right, but we also don't want to leave supported reimbursement on the table. Earnest helps us do both.”