Building the Foundation for Success: Preparing Radiology for Autonomous Coding

While autonomous coding promises new levels of efficiency and productivity, simply flipping the switch on does not guarantee success. Without intentional planning and preparation, organizations will struggle to achieve optimal performance with high direct-to-bill (DTB) rates.

From optimizing radiology report templates to fine-tuning upstream workflows, every decision made in advance of implementation directly impacts the ability to attain high DTB rates. To succeed, radiology leaders must focus on three foundational pillars: workflow, documentation, and coding quality. Each of these play a vital role in shaping the model's ability to assign accurate codes consistently and without human intervention. Providers that neglect to address these issues may find it difficult to achieve desired outcomes.  

By focusing on these three domains, leaders and providers of radiology services can ensure they are not just prepared for implementation but positioned for long-term coding and financial success from the moment the first case hits the model.  

Streamlining Workflow for Coding Readiness

The implementation of an autonomous coding solution presents an ideal opportunity to reassess and improve existing workflows. While much of the focus for correct coding is on documentation provided by the radiologist, it’s important to remember coding compliance starts even earlier in the revenue cycle, beginning with the diagnostic test order.

Every radiology encounter begins with an order from the ordering (aka referring) physician, and the order must be accurate, complete, and compliant to support downstream coding. According to the Medicare Conditions of Participation, (42 CFR 410.32), the ordering physician is responsible for establishing and documenting medical necessity for any diagnostic test. This documentation must include signs, symptoms, or a known diagnosis, not simply provide a condition to be ruled out. Section 4317 of the Balanced Budget Act of 1997 has a similar requirement.  

Testing facilities should be diligent in validating test orders prior to performing exams. If an order lacks clinical justification or does not meet coverage criteria per applicable Medicare Local Coverage Determinations (LCD) or National Coverage Determinations (NCD), the referring physician should be contacted for clarification and/or a corrected order. Failure to obtain this information can result in denials and jeopardize compliance.  

Many years ago, an imaging center that hired me as a consultant was placed on a 100% prepayment review by Medicare following a probe audit resulting in 60% of claims submitted denied for lack of medical necessity. The root cause was not lack of documentation in the radiology report, but incomplete or invalid diagnostic test orders. Test orders often failed to document signs and symptoms supporting medical necessity. Had correct protocols been followed from the beginning, this interruption in revenue stream could have been avoided. 

Ideally, the reason for the exam taken directly from the test order should be entered into a structured field within the Radiology Information System (RIS), allowing it to auto-populate the final report. While technologists may obtain additional history from the patient, this information should be used to supplement, not replace, the reason for the exam provided by the ordering physician.

When the reason for the exam is missing, incomplete or does not support medical necessity, this creates a bottleneck in the coding process (whether manual or automated) with the medical coders hunting down scattered pieces of information. When the coder is unable to locate the necessary information, the encounter may be put on hold for days or even weeks until additional information is provided regarding the reason for the exam.  

If this practice has been prevalent in your workflow, it affects the quality of the historical data used for training. When codes are assigned based on documentation found outside of the radiology report, it presents a problem for an autonomous coding engine since it assigns codes based on information documented in the final radiology report. The coding engine is likely to make incorrect assumptions based on the patterns identified in documentation and assign incorrect codes.  

Additionally, once autonomous coding is implemented, this problem will continue and affect model performance if not corrected. If essential data is missing from the report, the model cannot assign codes, and the encounter will be routed to a human coder. Moreover, if this continues over time, the model cannot improve its performance because it lacks visibility and will make inaccurate assumptions. When the elements required for coding aren’t in the report, productivity drops, manual intervention increases, and the model misses opportunities to learn correctly from those encounters.

Workflow optimization isn’t just about efficiency. It’s about creating a data environment that sets the autonomous model up for long-term success. It begins with standardized test ordering protocols and an expectation that all documentation required for coding lives in one place — the final radiology report.

Elevating Documentation Standards

Complete and accurate documentation is at the center of coding compliance and accurate reimbursement. Autonomous coding engines are designed to interpret clinical narratives with more flexibility than traditional Computer Assisted Coding (CAC) tools, but even the most advanced models can only assign codes based on what is explicitly documented. “If it isn’t documented, it wasn’t done” still holds true, especially when training a model that relies on patterns in existing documentation.

The most impactful action radiology providers can take is to review and optimize all radiology report templates prior to implementation. A consistent template structure ensures the model has reliable access to the core elements needed for CPT, HCPCS, and ICD-10-CM assignment, increasing the likelihood of a high DTB rate at go-live.

Key elements that should be in every template include:

• Clinical indications supporting the reason for the ordered exam. Clinical indications supporting medical necessity should be taken directly from the diagnostic test order and populate the report directly.
• Additional patient history information and relevant clinical context may be captured by the technologist, but this information should only supplement, not replace, information provided by the referring provider.
• A clear, concise exam title that accurately reflects the procedure performed. The title should align with how the service is ordered and should support the exam described in the technique section, not contradict it. 
• A detailed technique including the number and types of views, use or non-use of contrast, and other relevant parameters. Avoid any ambiguity between exam titles and the detailed technique.  
• Any substances administered including contrast materials, radiopharmaceuticals, and drugs. The name, route of administration, and amount administered should be noted in the report to ensure that all applicable HCPCS codes are accurately assigned.  
• An impression reflecting the final interpretation, prioritizing findings related to the clinical indications. Vague terminology such as “see findings” should be avoided. 
• Documentation supporting all quality measures selected for reporting.   

Radiologists should be encouraged to follow the ACR Practice Guideline for Communication of Diagnostic Imaging Findings, which outlines best practices for structuring reports, including the consistent use of headings, clear communication of significant findings, and prioritization of clinically relevant impressions. This not only supports patient care and referring physician communication, but it also directly contributes to more accurate coding and fewer queries or claim rejections.  

By standardizing templates to include these core elements, radiology providers can reduce documentation gaps and set the autonomous coding engine up for long-term success.
 

This table summarizes the three foundational pillars that radiology leaders must focus on to ensure a successful implementation of autonomous coding.

Prioritizing Coding Quality: Garbage In, Garbage Out

One of the most critical and often underestimated factors in autonomous coding success is the quality of historical data used to train the model. Unlike traditional computer-assisted coding systems that simply assist a human coder in real-time, autonomous coding models rely on machine learning algorithms that are trained on hundreds of thousands, and preferably millions, of historical encounters. Simply put, the model learns from your past coding and documentation. Investing in coding quality before implementation doesn’t just improve DTB performance, it ensures the model is consistent with your internal compliance standards.  

If the data used to train the model is inconsistent, noncompliant, or incomplete, the model will carry those bad habits forward. This is the classic “garbage in, garbage out” scenario. If the input data is flawed, the model output will be too. Conversely, if the historical data is clean, accurate, and consistent, the model will be far more likely to assign correct codes autonomously, reducing the need for human intervention, achieving the highest DTB rates. Organizations that fail to address coding quality in advance often find themselves disappointed when their autonomous coding solution underperforms.

To ensure the historical data sets the model up for success, providers should take the following proactive steps before implementation:

• Conduct regular coding audits on a monthly or at least quarterly basis to assess coding accuracy and identify trends. Audits may be conducted internally or externally, but an external auditor offers an unbiased perspective and may uncover potential issues that may not be detected through internal auditing.  
• Identify and correct any coding and documentation issues prior to go-live. Any areas of concern revealed during audits should be addressed through a corrective action plan before the model is trained. When poor quality data is embedded in the data training set, it’s much harder to correct behavior downstream. If necessary, portions of a data set known to have poor quality may be excluded from the data set used to train the model. 
• Review and implement internal coding policies to ensure uniform coding. This minimizes subjectivity, reduces variation, and promotes consistency across the coding team. Not only does this improve audit outcomes and strong coding consistency, but it also helps the model learn to code the way your organization expects. Additionally, after implementation, when coders need to correct the model’s output, corrections are made in a standardized manner. 
• Provide ongoing coder education. Even the best coders benefit from regular refreshers. Ensure your team is up to date on evolving CPT, HCPCS, and ICD-10-CM changes and guidelines. This ensures that both pre-and post-implementation coding remains accurate and compliant.

In some cases, providers may already be aware of errors or inconsistencies in their historical data. When this occurs, those issues should be flagged so poor-quality portions of the data can be excluded or adjusted before training. This proactive approach minimizes the impact of flawed data and ensures the model delivers reliable, accurate results at go-live, fully aligned with your compliance standards from day one.

Laying the Groundwork for Long-Term Success

Autonomous coding has the potential to transform radiology operations, but its performance is only as good as the environment in which it is deployed. By optimizing workflows, standardizing documentation, and strengthening historical coding quality, providers create the foundation for compliance and efficiency. By investing in these foundational elements, radiology providers can position themselves for a smoother transition, stronger go-live performance, and sustained long-term gains from autonomous coding.