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.



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.



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.




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.