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Why CAC has consistently fallen short of expectations
How autonomous coding leverages machine learning & deep learning for accuracy, compliance, and scalability
The real-world ROI—accelerated billing cycles, reduced denials, and improved resilience for radiology RCM

The takeaway? Autonomous coding isn’t just an upgrade—it’s a fundamental shift in how we approach revenue cycle operations in radiology.

 

Read “The AI Advantage: Reclaiming Revenue & Resilience in Radiology RCM”

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.

AI is transforming radiology revenue cycles — and autonomous coding is leading the charge.

 

Unlike outdated CAC tools, next-gen AI now fully codes radiology reports with zero human touch, slashing turnaround time and boosting accuracy. Discover how transformer models, machine learning, and deep learning are redefining what’s possible — and why every radiology leader should be paying attention.

 

Click to read how autonomous coding is setting a new standard in healthcare.

At Maverick Medical AI, we believe healthcare providers should spend more time focusing on patients—not paperwork. That’s why we built an autonomous medical coding platform that’s changing the game for imaging centers and hospitals alike.  But don’t just take our word for it—here’s what happened when one nationally recognized imaging network partnered with Maverick.

The Challenge

 

This multi-state imaging network operates across 60+ sites, processing over 1.2 million studies annually. Like many healthcare organizations, they were facing rising costs, inconsistent coding accuracy, and a growing backlog that made it harder to scale.

The Results with Maverick

 

Once they implemented Maverick’s autonomous coding solution, the transformation was immediate:

– 87% Direct-to-Bill Rate – Nearly 9 out of 10 cases were coded entirely without human intervention—cutting coding time down to just seconds.

– 60% Cost Savings – With automation taking the lead, they were able to redeploy 6 of their 7 coders to higher-value tasks.

– 95% Coding Accuracy – By reducing errors, they slashed denial rates to just 3%, ensuring reliable and predictable reimbursement.

– Zero Backlogs – Coding lag time for direct-to-bill cases dropped to under 2 minutes, giving the team the scalability they needed to grow without operational strain.

 

Seamless, Scalable, Smart

Whether you’re a high-volume imaging center or a hospital navigating complex billing workflows, Maverick’s AI-powered platform integrates effortlessly with systems like Imagine, enabling:

– Real-time autonomous coding

– Customizable workflows

– Expert implementation support

 

All designed to help your team code faster, smarter, and with greater confidence.

Download the full case study here.

Having worked in the healthcare industry for over 30 years, I’ve witnessed many significant advances in technology, and while many have instantly been well received, the mention of any type of coding automation often strikes fear into the hearts of many medical coders.  

 

There are many myths surrounding the use of AI in medical coding, but perhaps one that causes the most anxiety is the belief the adoption of AI in medical coding will lead to massive layoffs of medical coders. This myth is rooted in the assumption that AI systems will fully automate the coding process, eliminating the need for human coders. Let’s delve into why this fear is more fiction than fact, and explore the actual impact of AI on the medical coding profession.

 

Staffing Challenges

Twenty years ago, medical coders were fearful computer assisted coding (CAC) applications would replace them, reducing the number of medical coding jobs, but in fact the opposite occurred. The demand for medical coders has only grown in subsequent years, and currently there is a reported 30% shortage of medical coding professionals the healthcare industry.  Additionally, nearly 25% of medical coders in hospital settings currently pose a significant retirement risk.

 

FACT: Many organizations are dealing with staffing challenges ranging from keeping up with increases in volume, employees unexpectedly on extended medical leave creating backlogs resulting in need for outsourcing, as well as seeking solutions to handle attrition (retirement,  resignation or death) given the difficulty in recruiting and retaining qualified staff. 

 

Offshoring & Cost Savings

The most substantial negative impact to medical coding jobs in the US over the past two decades was not due to the introduction of CAC technology, but rather overseas outsourcing which still occurs on a regular basis. Too often, on LinkedIn I see a post from one of my thousands of connections sharing they were just given notice their position was eliminated due to offshoring. Unfortunately overseas outsourcing has become a necessity as the cost of delivering healthcare continues to rise while reimbursements have remained stagnant or in some cases have even declined. Offshoring is one of the easiest ways to reduce administrative costs.  

 

FACT: Due to the cost savings realized through the many benefits of an autonomous medical coding solution the implementation of autonomous medical coding has the potential to significantly reduce offshoring and even eliminate it altogether for some organizations, keeping more medical coding jobs in the US. 

 

The Perfect Duo: Humans & Machines

Autonomous coding solutions shouldn’t be viewed as a threat to medical coders, but rather viewed as the next generation of coding tools in a coder’s toolbox. The use of AI can boost coder productivity by significantly reducing the number of repetitive coding tasks and it can also boost accuracy by alerting the coder to potential issues that may otherwise go undetected. AI can supplement a coder’s expertise assisting them in making more informed and accurate coding decisions.  Additionally, while many AI solutions demonstrate excellent accuracy rates for coding that is redundant with little variation, it can be challenged with coding more complex cases that require a degree of human reasoning and intuition which is an integral part of medical coding. 

 

FACT: Realizing the maximum benefit from autonomous medical coding can only be achieved through a collaborative synergy by combining the efficiency of technology with the irreplaceable human touch.  

 

Elevated Professional Status

While the role of medical coders will evolve over the next several years, it will certainly not be diminished. While traditional job roles are being reinvented, new job roles are also being created. 

 

As autonomous coding technology gains more widespread adoption, a typical production coder can expect to transition into the role of an auditor, validating the output of an autonomous coding platform.  Additionally, coders may be re-deployed to assist with other tasks or promoted to other revenue cycle roles including revenue integrity analyst or clinical documentation improvement specialists, where coding knowledge is invaluable.    

 

Furthermore, companies who have developed autonomous medical coding platforms are currently seeking coding professionals with subject matter expertise to assist in product development and coding quality validation. Human subject matter expertise is vital to machine learning ensuring the coding model is initially trained correctly and properly maintained with coding updates. 

 

FACT: The medical coding field stands poised to gain heightened professional recognition amid this transition. The critical role of human oversight in maintaining quality assurance highlights the pivotal position of medical coders as guardians of medical coding integrity. Medical coders have the opportunity to enhance their professional status by taking on more complex and advisory responsibilities. 

 

 

Preparation Meets Opportunity

For medical coders, the path forward involves embracing change through continuous learning and skill set expansion as well as exploring non-traditional roles that benefit from coding knowledge and expertise, as well as managerial and executive roles.  

 

Most importantly, medical coders should develop a career map outlining goals and the steps needed to achieve them, helping to navigate different stages in their career. While there are many aspects that should be considered when developing a career map, two of the most important are a skill gap analysis and a professional development plan.  A skill gap analysis involves comparing your current skills with the skills required for the roles you aim to achieve in the future. Identifying gaps will then help you assemble a professional development plan outlining the steps you need to take to develop the necessary skills and qualifications to meet your career goals. These steps may include formal education, online courses, workshops, conferences, on-the-job training, networking, or mentorship opportunities. 

 

FACT: For now, the immediate landscape for medical coders will remain relatively unchanged as AI technology is still evolving and has not yet been widely implemented and the shift to automation will occur gradually over time. In the healthcare industry, the adoption of new technology tends to be slow, often due to competing priorities and budgetary constraints. Additionally, while larger providers are typically the most likely to implement new technologies, mid-size and smaller providers tend to lag behind.

 

In the short term, medical coders should learn all they can about artificial intelligence – how it all works and how AI will impact the healthcare industry.  Additionally, medical coders may want to consider learning more complex specialties and earning additional specialty certifications to boost their resume.

 

Learn more about Maverick’s Autonomous Medical Coding platform.

In the rapidly evolving world of healthcare, the implementation of autonomous medical coding systems presents a paradigm shift, boasting numerous benefits that will reshape the healthcare revenue cycle; however, it’s important to offer a balanced perspective on its role and also explore some of the challenges that come with this technology.

First let’s take a look at a few benefits of implementing an autonomous medical coding platform.

Benefits

 

Scalability. Perhaps one of the greatest benefits is scalability which enables providers to handle increases in volume without adding additional staff. This aids in preventing backlogs that can occur with large increases in volume, and eliminates the need for securing outsource coding support or hiring of additional staff.

 

Increased Efficiency & Productivity. Autonomous medical coding systems allow more records to be coded in less time as the coding platform is able to handle redundant coding that does not require review by human coders, allowing coders more time to focus on complex cases, boosting overall coding productivity. Autonomous medical coding is not intended to replace medical coders but to enhance their work by improving efficiency and accuracy.

 

Cost Savings. Implementation of an autonomous medical coding solution can lead to lower operational costs. The aforementioned benefits – scalability, increased efficiency and productivity – result in cost savings allowing providers to do more with fewer resources.

 

Consistency and Enhanced Accuracy. It is not uncommon for a single group of medical coders to exhibit variations in the diagnosis codes they assign for the same documented services, resulting in inconsistency of how the same cases are coded. This may occur due to different levels of training and expertise and/or differing interpretations of coding guidelines among the coders. Automation can provide uniformity in coding that cannot be achieved among a team of human coders and reduce human errors.

 

Reduced Denials. An autonomous coding solution can assist in reducing common errors that lead to denials. Through automation, potential errors can be identified and flagged prior to coding being finalized allowing for correction prior to claims submission. Additionally, these systems can be tailored to meet the specific requirements of different third party payers, addressing their unique coding preferences or guidelines and thereby reducing denial rates.

 

Compliance & Quality Assurance. Autonomous medical coding platforms can have a positive impact by improving accuracy and consistency in coding by reducing human errors, such as data entry mistakes or misinterpretation of coding guidelines. Additionally, they can provide real-time auditing and feedback, identifying potential errors or inconsistencies as they occur, allowing corrective action to be taken. This immediate correction helps maintain high-quality standards and compliance. Furthermore, these systems can be tailored to meet specific compliance requirements of different healthcare providers or insurers, ensuring that coding meets all necessary guidelines reducing the risk of non-compliance.

 

Now that we have covered some of the benefits, let’s focus on some challenges inherent in the technology.

Challenges

 

As with any new and emerging technology, the adoption of autonomous coding brings numerous advantages, but it also requires careful consideration of potential challenges, many of which can be effectively addressed during a robust implementation phase including the coding validation process. Additionally, ongoing auditing and monitoring post-go live enhances these efforts.

 

Lack of Human Intuition. Medical coding is very nuanced and there is a degree of human reasoning that comes into play when assigning diagnosis codes, in particular with procedures and services that are not quite as straightforward and have lots of variation in applicable codes that can be assigned. Algorithms do not possess this level of reasoning as machines learn from patterns attached to data; however, Maverick’s Autonomous Medical Coding platform learns from complex examples coded by humans, equipping it with the ability to code more complex cases over time.

 

Training Data Bias. Training data bias in autonomous medical coding refers to situations where the data used to train an AI system for coding medical records is not fully representative of real-world scenarios. This bias can occur if the training data is too limited, contains errors, or over-represents certain types of cases while under-representing others. If the training data is biased, the coding decisions made by the coding platform might be incorrect or biased. Maverick has developed a unique technology based on Generative AI to automatically identify these incorrect/biased data and replace them with correct synthetic data that is automatically generated improving the data quality.

 

AI Hallucinations. There may be instances when unexpected outputs are generated which may not be relevant or accurate. These outputs are referred to as hallucinations. This term refers to occurrences when AI generates unexpected outputs, which may not be relevant or accurate. This can happen for various reasons, including limitations in the AI’s training data, algorithms that misinterpret patterns, or the AI filling in gaps with incorrect or nonsensical information.

 

Data Quality. Since machine learning and deep learning are based on historical coding and documentation patterns, the integrity of historical data is of the utmost importance. If the historical data contains errors, the coding engine may replicate these mistakes leading to incorrect coding. Without quality historical data, the risk of errors and biases increase, therefore it is important to regularly perform coding audits prior to implementation to identify any potential coding and documentation issues and implement corrective action prior to go-live.

 

Over Documentation & Medical Necessity. As with any technology there is always a risk of providers over-documenting services in a way that maximizes reimbursement rather than accurately reflecting only medically necessary care. It is important for providers to monitor physician documentation practices to ensure that providers aren’t documenting solely for the sake of getting paid additional dollars.

 

Compliance & Quality Assurance. While on one hand, autonomous medical coding solutions can assist in improving compliance, at the same time these systems require ongoing monitoring and human oversight to ensure they remain compliant and maintain high rates of accuracy. Remember, if the model is trained on incomplete, outdated or biased data, the output may not be correct or if an error is embedded during the learning process, it can be propagated across many cases before being identified resulting in inaccuracies and coding compliance issues. It is important not to become overly reliant with the technology removing the human factor altogether.

 

Summary

 

Autonomous medical coding systems are positioned to revolutionize the healthcare revenue cycle with their scalability, efficiency, cost-effectiveness, and improved accuracy, all of which are pivotal in today’s fast-paced medical environment. However, it’s crucial to remain aware of the challenges inherent in the technology, all of which can be mitigated before, during and even after the implementation phase. Success requires human oversight and continuous improvement of processes.

 

It’s important to strike a balance between leveraging these advanced tools for their immense benefits while also proactively addressing the challenges. Maverick’s implementation approach is designed to identify and overcome many of these hurdles prior to go-live, allowing rapid implementation and scalability to its customers.

 

It is necessary to acknowledge that not all autonomous medical coding systems are created equal and platforms may differ significantly in their capabilities; therefore selecting the appropriate system is not merely important—it’s crucial. As AI solutions are rapidly progressing with advancements in transformer technology and Large Language Models (LLMs), Maverick continues to leverage the use of Generative AI in their coding engine, resulting in better rates of automation with higher accuracy, surpassing the 85% Direct-to-Bill threshold.

In today’s rapidly advancing digital world, autonomous medical coding solutions are changing the landscape of the healthcare revenue cycle. It isn’t a matter of if autonomous coding will gain widespread adoption, but rather a matter of when, particularly if your organization performs a large volume of procedures.    

 

During a recent presentation, members of the audience were asked about their timeframe for implementing an autonomous coding solution.  76% of the audience was  twelve months or more out from implementation. This group is in a perfect position to take some necessary steps to ensure a highly successful go-live experience with a high direct to bill percentage.  Regardless of your timeline for implementing a solution, it’s never too early to begin planning.  

 

Because autonomous coding solutions are unique from computer assisted coding (CAC) in being trained on historical data, a highly successful roll out is dependent upon the quality and accuracy of information provided to train the model. To achieve the highest direct-to-bill rate possible at go-live there are three main areas that should be addressed.  

 

Documentation Improvement

 

It’s no secret that the number one key to correct coding and reimbursement is complete and accurate documentation. While the autonomous coding models are more forgiving with documentation than CAC, models still cannot code what is not documented in the report. The more robust the documentation, the better the output. 

 

To see to it that all applicable codes will be assigned accurately, it is important to review all radiology report templates to ensure all required elements for coding and billing can be captured in the final radiology report.  

 

CPT & HCPCS Coding

 

Key items that should be in each template to ensure correct CPT & HCPCS coding:  

 

• A clear, concise exam title;

 

• Detailed technique/parameters for the test being performed (ie, number and types of views, without and/or with
  contrast, etc.);

 

• Documentation of all supplies utilized (ie, contrast materials, radiopharmaceuticals, etc.);

 

• Supporting documentation for all selected quality measures. 

 

ICD-10-CM Coding 

 

The assignment of accurate diagnosis codes presents its own unique challenges when it comes to documentation. Emphasis should be placed on clear and comprehensive documentation of both the presenting clinical indications and the impression specified by the radiologist to ensure correct ICD-10-CM coding. 

 

The ICD-10-CM Official Coding Guidelines for Outpatient Reporting require that encounters are coded to the highest level of specificity. This information is typically found in the impression. When there are findings in the impression that are related to the reason for the exam, this condition should be coded as the first listed (primary) diagnosis for the encounter. If the impression is normal, correct code assignment is based on the presenting clinical indications noted in the reason for exam or history fields.  

 

Some radiologists will state in the impression “see findings”, but often there are findings listed that are considered incidental or not clinically relevant to the reason for the test. It is a best practice for the radiologist to prioritize findings in the impression section of the radiology report.      

 

In general, it is recommended for radiologists to follow the ACR Practice Guideline for Communication of Diagnostic Imaging Findings for final reports. Following these guidelines ensures all required elements for coding and billing will be documented in the report.  

 

Workflow

 

Implementation of an autonomous coding solution is a great opportunity to improve your current workflow affecting coding compliance. 

 

In addition to the radiology report, the medical record is composed of other documents often stored in the radiology information system (RIS) including diagnostic test orders, patient questionnaires and technologist worksheets. While these items are all part of the patient medical record, it is a best practice for all key elements needed for complete coding to flow into the final radiology report itself. 

 

In particular, it is important to note that Center for Medicare & Medicaid Services (CMS) charges the referring (ordering) physician with documenting medical necessity for any diagnostic tests that are ordered. This information is communicated via the diagnostic test order. The reason for the test provided by the ordering physician should be entered into a predefined field in the RIS to auto-populate the radiology report. While additional information can be taken from the patient and added to the report, it should not be considered as the primary source for ICD-10-CM coding.  

 

Regardless of your coding process or workflow, when information needed for coding is not contained in the radiology report (indications supporting medical necessity, supplies, exam parameters, etc.) this is very disruptive to the coding workflow, as the coder must go searching for this information in the RIS, or place the encounter on hold for someone else to research, slowing down the coding process, resulting in decreased productivity. 

 

Furthermore, if this has been and continues to be an ongoing problem, this will affect the performance of the model because it cannot learn to code accurately what is not documented in the radiology report. Additionally, if this problem persists after go-live, the model will not be able to code these encounters, they will be routed to the coders for review and the model misses the opportunity to learn from the final code assignments.  

 

Coding Quality 

 

Since the model initially learns to code from historical data, achieving a high direct-to-bil percentage at go-live is heavily dependent upon the quality and accuracy of historical coding and documentation.

 

Proactive steps to ensure coding quality include: 

 

• Perform coding audits on a regularly scheduled basis, either monthly or quarterly.  While coding audits may be done internally, it is prudent to periodically hire an external auditor for a fresh perspective to determine if there are any areas of concern that may have been missed through the internal auditing process. It is imperative to identify any potential coding and compliance issues and implement corrective action prior to go-live

 

• Implement written coding directives to limit subjectivity and promote coding consistency among members of your coding team.  This will minimize variations in how certain encounters are coded and this information will help your chosen vendor in guiding the model to perform to your specific expectations. Additionally, this information is helpful after go-live to ensure that any edits made to coding engine output are made in the same manner by each coder so as not to confuse the model. 

 

• Provide coder education on a regular basis. This will ensure high rates of accuracy and consistency are maintained. 

 

Taking these proactive steps ensures the model is trained with the most up-to-date coding guidelines for your organization.  

 

Conclusion 

 

By focusing on comprehensive documentation improvement, refining CPT & HCPCS and ICD-10-CM coding practices, optimizing workflow, and placing a magnifying glass on coding quality, providers lay the groundwork for an autonomous coding solution that’s both effective and efficient. Regular audits, ongoing education, and a commitment to consistent quality assurance will ensure the highest direct-to-bill rates and an autonomous coding solution that is  trained on the very best historical data.