Achieving success with autonomous coding requires more than advanced technology. Success starts with preparation. The foundation for high direct-to-bill (DTB) rates and accurate coding lies in optimizing the processes that feed the model. Every step leading up to implementation, from order entry to documentation and coding practices, influences whether the model can consistently deliver correct results without human intervention.
This blog outlines three critical areas to focus on before go-live—workflow, documentation, and coding quality—and explains how Maverick supports accuracy throughout every stage of implementation and beyond. Employing this methodology, Maverick consistently enables their customers to go live within 90 days, with over 85% of cases flowing direct-to-bill with zero human intervention, while delivering 95%+ coding accuracy.
1. Optimize Workflow for Coding Readiness.
Autonomous coding success starts well before the final radiology report. Compliance begins with the diagnostic order. Each order must include signs, symptoms, or a confirmed diagnosis per the Medicare Conditions of Participation (42 CFR 410.32)
Why it matters: Missing or incomplete order details lead to delays in coding and compliance risks. For AI models, incomplete reports mean more manual reviews, lower DTB rates and risks incorrect assumptions by the model.
Action Steps:
- Validate test orders before exams.
- Standardize ordering protocols.
- Ensure the signs, symptoms or other diagnostic information on the test order flow into the final report.Bottom line: Workflow optimization ensures the model has everything it needs to code accurately.
2. Standardize Documentation
AI engines cannot code what is not documented. Consistency across templates provides the data structure the model needs for correct CPT, HCPCS, ICD-10-CM and quality code assignment.
Ensure each report template captures the following:
- Clinical indications from the order
- Clear exam title matching the performed procedure
- Detailed technique (views, parameters, contrast)
- Substances administered (name, route, dose)
- Impression with clinically relevant findings
- Documentation for selected quality measures
Adhering to the ACR Practice Guideline for Communication of Diagnostic Imaging Findings promotes consistency and completeness, reducing ambiguity and enhancing coding accuracy whether performed manually or through automation.
3. Ensure Coding Quality
AI models learn from historical data. If that data is inconsistent or inaccurate, the model will replicate those errors at scale.
To prepare:
- Perform coding audits on a monthly or quarterly basis
- Resolve any coding issues before model training
- Standardize coding policies for consistency
- Provide ongoing education for all coders
Clean and accurate data translates to strong Direct-to-Bill (DTB) performance and reduced rework post-implementation.
Maverick’s Process for Ensuring Accuracy
At Maverick, we understand that success depends on more than just technology. Our process is built around quality oversight through implementation and beyond.
Before Go-Live: Validation Without Formal Audits
While we don’t conduct formal audits during implementation, our validation process functions as a quality
checkpoint through the following activities:
- Evaluating mismatches between model output and historical coding.
- Flagging errors in historical coding data and sharing findings with clients for correction.
- Integrating client-specific coding policies into the platform.
These activities ensure the model is aligned with both compliance standards and organizational expectations from day one.
At Go-Live: 100% Review and Daily Monitoring
During the go-live phase, Maverick and the client work together to ensure the model performs accurately
before transitioning to DTB. This stage involves intensive oversight and collaboration, including the following key steps:
- Clients review 100% of cases during week one to verify accuracy before DTB routing begins.
- Maverick’s Director of Coding Compliance monitors all coding changes made on a daily basis during this first week and provides feedback to the Maverick R&D team for any necessary model refinements.
- After week one is completed, clients determine the percentage of DTB encounters they will review for
ongoing QA. On Maverick’s end the Director of Coding Compliance will monitor all coding changes on a weekly basis and continue to provide feedback to the Maverick R&D team, as necessary. Clients also have the ability to submit support tickets for any issues they may identify during their ongoing QA.
Post-Go-Live: Quarterly Audits
In addition to client-led QA, Maverick provides an extra layer of oversight and continuous improvement through the following:
- The Director of Coding Compliance conducts quarterly audits for every client, reviewing a random
sample of DTB encounters to ensure continued accuracy. - Findings are shared with R&D as needed to support continuous improvement and maintain high coding accuracy rates.
This layered approach with pre-launch validation, intensive go-live oversight, and ongoing audits, delivers a compliant, high-performing coding solution that clients can trust.
Final Thoughts
Autonomous coding is a powerful tool delivering frictionless scalability and improving productivity, but its
success depends on careful preparation and continuous oversight. By optimizing workflows, standardizing
documentation, and ensuring high-quality historical data, providers can create an environment where
automation thrives. Additionally, Maverick’s structured approach, which combines pre-launch coding validation, hands-on monitoring during go-live, and ongoing quarterly audits, is the hallmark of a genuine partnership that drives consistent performance with high accuracy rates.