Contact us
Our team would love to hear from you.
Back to blog 
AI-powered medical coding
Fast and accurate medical coding is essential for healthy revenue cycle management (RCM). Yet this process is prone to costly errors, prompting healthcare organizations to seek more effective, smarter methods. That’s where advanced technology like artificial intelligence (AI) comes in. When precision and speed matter, AI is the ultimate answer.
Medical coding is the process of converting health information, such as diagnoses and procedures, into universal codes. These codes are essential for medical billing and reimbursement, tracking patient care, maintaining comprehensive records, analyzing healthcare data, research, policymaking, and a wide range of other healthcare operations.
However, errors are an inevitable part of medical coding, resulting from factors such as process complexity, inconsistent documentation, staff shortages, and limited reliance on AI tools, which decreased from 62% in 2022 to 31% in 2024. Advanced technology can significantly improve medical coding, increasing its accuracy and speed and reducing revenue losses.
How is AI used in medical coding?
There are several cases where AI makes medical coding smarter and more reliable.
Handling unstructured data
Natural language processing (NLP), a subfield of AI, is used to extract diagnoses, procedures, and clinical indicators from text; interpret medical abbreviations; and handle variations in clinical language. This capability is the backbone of other AI uses in medical coding.
Assisting human coders
AI can recommend codes based on medical records, suggest code replacements, issue an alert if a code is assigned incorrectly, and more. Ultimately, AI solutions help medical coders enhance the accuracy and speed of code assignment, reducing claim denials.
Coding rules support
Medical coding payer rules are subject to frequent changes, and coders often struggle to keep up. An AI-powered solution, like an intelligent agent, helps medical coding specialists stay up to date with changes and use natural language to query information about coding rules.
“AI is redefining medical coding, turning it from a time-consuming task into a streamlined, intelligent process. With our expertise, we help healthcare organizations harness this technology to improve accuracy, reduce costs, and ease the burden on medical staff, allowing them to focus on what matters most: quality patient care.”
Alexey Kozlovsky
Account Manager
Benefits of integrating AI medical coding software
AI is more than technology—it’s a game-changer for medical coding solutions. Here’s how it influences the entire RCM.
Improved accuracy
AI-assisted medical coding improves accuracy by capturing details in clinical documentation, including unstructured text, and applying standardized logic and guidelines to deliver consistent results.
Reduced claim denials
Coding errors are a major cause of denied claims. AI systems prevent these errors, enabling greater accuracy through proactive claim monitoring and predictive capabilities.
Saved costs
Denied claims lead to significant revenue losses. With error-free coding, faster turnaround, and minimized compliance penalties, healthcare organizations reduce operational costs while increasing revenue.
Streamlined processes
AI-powered automation of routine coding reduces the need for labor-intensive manual data entry and repetitive coding tasks, increasing productivity and reducing the time required for claim processing.
Traditional vs. AI medical coding
| Traditional medical coding | AI medical coding | |
|---|---|---|
| Process | Manual review by coders | Coding assistance, suggestions, etc. |
| Speed | Slow process that depends on human effort | Fast process enabled by automating manual tasks |
| Accuracy | Variable, risk of human error | High accuracy and consistency, improves with learning |
| Cost | High staffing and operational costs | Low long-term costs, though setup costs can be high |
| Scalability | Limited | Easily scalable |
| Compliance | Prone to compliance issues due to manual updates and guideline misinterpretation | Easy to maintain compliance with built-in rules and automated update checks |
| Best at | Handling complex tasks | Large-scale routine coding |
Hospitals worldwide have been facing issues related to overloaded healthcare systems, a shortage of health workers, and continuously rising amounts of healthcare data. Therefore, any technology that could give a fast and efficient data analysis for developing a treatment plan and improving hospital workflows is extremely valuable. In this context, machine learning in healthcare has become a useful tool for gathering and managing patient data, identifying healthcare trends, suggesting treatment plans, and more.
Intelligent technologies and automation in medical coding
How does AI in medical coding work? AI subsets and complementary technologies perform different functions in this process to achieve efficiency and accuracy.
NLP
NLP algorithms are used to understand and interpret human language. This technology extracts relevant information from unstructured medical records and analyzes it to identify diagnoses, procedures, and medications, and map them to standardized medical codes.
Generative AI and machine learning
Machine learning (ML) algorithms identify coding patterns in historical data to predict and verify the most likely codes, while generative AI (GenAI) can interpret unstructured clinical notes and suggest relevant codes. These technologies reduce human error, streamline the coding process, detect fraud in billing and claims data, and predict denials, helping healthcare organizations handle them in advance.
Computer vision
This AI subset is used for scanned medical records. Computer vision algorithms extract text from scanned medical documents and convert it into machine readable text for further NLP processing.
Robotic process automation
Combined with AI, robotic process automation (RPA) increases automation coverage, reducing manual data entry and handling rule-based coding tasks. RPA bots can confirm that all required fields are filled before submission, reducing denials and aiding claim denial analysis. While AI functions as the “brain,” perceiving context and meaning, RPA is the “hands,” executing tasks and moving data.
Custom RCM solutions
See what we offerChallenges of using AI in medical coding
Before integrating AI into the medical billing and coding processes, it’s important to understand the complexities that come with it and be prepared to tackle them. Following are some of the most common challenges of AI implementation.
Regulatory compliance
The use of AI in healthcare is subject to specific laws and compliance requirements, such as the EU AI Act, GDPR, HIPAA, and the HITECH Act, which ensure patient safety, data privacy, and ethical feasibility. Having a comprehensive checklist for medical coding software and adherence to best practices can help avoid any legal issues and ensure compliance.
Data quality and completeness
In the healthcare industry, data is unstructured and usually scattered across different systems, such as electronic health records (EHRs) and billing, leading to incorrect or missing codes. Data preprocessing, standardization, and cleaning can mitigate this issue, with human-in-the-loop and continuous AI model monitoring increasing the quality of outputs.
Medical code suggestion accuracy
AI systems in medical coding often struggle with accuracy, especially when handling rare or highly complex cases. If unusual diagnoses or procedures are underrepresented in historical data, the system has limited exposure to them, leading to inaccurate coding suggestions. Ensuring accuracy requires large, high-quality datasets and continuous updates.
Integration with existing systems
Many healthcare organizations still rely on legacy systems that lack the capabilities necessary for smooth AI integration. This process can also interrupt established workflows if not implemented carefully. By working closely with a skilled AI team, healthcare organizations can overcome interoperability issues, achieve seamless workflow alignment, and meet their customization needs.
Differences across medical specialties
Each medical specialty uses different coding sets, with some more complex and detailed than others. Documenting a patient encounter also varies greatly across specialties and providers. To ensure accurate outcomes, medical coding systems require specialty-specific NLP algorithms and model training and fine-tuning.
Our clients say
Professionalism and the quality of service
We have been partnering with EffectiveSoft since 2006, so we’ve had time to become familiar with their professionalism and the quality of service they provide. Of particular note, I would highlight the great responsibility which my custom software development company assumes when solving critical tasks: I am always confident of the thoroughness of the research and the design of the solution. With their help, we are able to do releases every quarter without resorting to individual heroics or burning everyone out. That allows me to focus my attention less on day-to-day release activities and more on other critical company responsibilities.
Thomas Golden
TruBridge (formerly TruCode), USA
How AI-enabled medical coding works
As an AI medical coding software development company with over 19 years of experience in healthcare technology, we understand the challenges medical organizations face, and tailor our approach to each client’s needs. Here’s an overview of our process:
Data quality and IT infrastructure analysis
Our team conducts a thorough analysis of your current architecture and datasets, identifying gaps and inconsistencies that must be addressed to ensure seamless integration.
Data collection and preparation
We collect, clean, and standardize data for further use. This is an essential step because AI systems depend on high-quality clinical data.
Design and prototyping
Before committing to development, we verify system feasibility by designing mock-ups, proof-of-concept models, and prototypes. Our team ensures a user-friendly interface and intuitive navigation.
Development
Our team selects specific algorithms based on project requirements and architecture design. We implement business logic and establish required data flows and system integrations, while ensuring compliance with data privacy standards.
Testing
Once the system is developed, our team performs unit and integration testing to evaluate accuracy, consistency, and reliability.
Deployment
Upon comprehensive testing, we roll out the software in phases and integrate it with existing systems, such as EHRs, and workflows. This step involves training and onboarding end users if necessary.
Maintenance and support
Our AI medical coding software services include post-launch maintenance and support. We monitor model performance and compliance and continuously improve it, updating the system to align with evolving standards and requirements.
Healthcare claim management has traditionally been ineffective: slow, error-prone, and costly. Now, artificial intelligence (AI) is fixing the issues. With smart automation, predictive analytics, and intelligent software tools, AI is driving faster reimbursement, reducing mistakes, and improving financial health of care delivery.
Why choose EffectiveSoft
Healthcare competencies
For nearly two decades, we’ve been helping healthcare companies tackle complex industry challenges with tailored technology solutions. Our commitment to delivering measurable results has earned us the trust of our long-term clients, including TruBridge and MedScope.
End-to-end development
Beyond the ability to jump in at any stage, our team covers the entire software life cycle, from consulting through deployment to post-launch support. With EffectiveSoft, you have a tech partner to bring your idea to life and support it long term.
Certified AI experts
Our AI engineers continuously develop their expertise in AI solutions and hold a range of personal certificates, including Azure AI Engineer and Oracle Certified Generative AI Professional, demonstrating their dedication to creating exceptional AI systems.
Cross-functional teams
EffectiveSoft’s specialists across various areas—including analysts, AI experts, engineers, QAs, and designers—collaborate to uncover real business needs, navigate problems faced by healthcare companies, and deliver secure, effective software solutions.
Security and compliance
AI-powered healthcare solutions must adhere to data privacy and security regulations. That’s why our team builds software using ethical AI best practices while maintaining strict compliance with industry standards, including HIPAA, GDPR, and the EU AI Act.
Client-focused approach
We don’t just develop software—we identify your company’s core challenges and turn them into opportunities for innovation. Prioritizing your needs and industry-specific requirements, we create solutions that deliver not only results but also enjoyable user experiences.
AI solutions for healthcare
Explore our expertiseFinal word
While the use of AI is gaining traction across healthcare processes, including medical coding, this technology is not designed to replace coding specialists. It is intended to augment their work through automation and intelligent suggestions. Implementing AI gives healthcare organizations the boost they’ve long required, streamlining medical coding and billing, reducing the chances of claim denials, and improving cash flow. The experts at EffectiveSoft will help you explore the potential of AI in medical billing and coding and how to integrate it into your processes. Get in touch with us to learn more and start your project.
F.A.Q. about AI-powered medical coding software
AI medical coding software refers to application software that uses AI to automatically convert clinical documentation into standardized codes used for insurance claims, billing, and other purposes.
Applying NLP algorithms, AI identifies key clinical entities, such as diagnoses, procedures, symptoms, and medications, in medical data (usually unstructured). Then, AI models extract relevant medical terms and map them to standard codes, including ICD-10 and CPT. Software checks payer and regulatory rules and validates the results, which are then entered into the billing system. Including a human-in-the-loop approach can help refine the system through continuous learning based on feedback.
AI medical coding software can and often does integrate with EHR and other practice management systems in place. Leveraging standards like FHIR, EffectiveSoft engineers design AI solutions that fit effortlessly into existing workflows, enhancing accuracy and efficiency and reducing administrative burden.
Different types of healthcare organizations can benefit from AI medical billing and coding software. This technology delivers the greatest value to companies with high claim volume and complex structure, such as large health systems and hospitals, multispecialty clinics, RCM companies, specialty practices with complex coding (oncology, cardiology, etc.), and healthcare practices with staffing shortages.
The accuracy of AI systems compared to human coders depends on several factors, including the type of AI system, training data, and clinical domain. AI’s key strengths are speed, scalability, and consistency, but it struggles with contextual judgment. So, while AI is effective in high-volume routine coding, human assistance is necessary for complex or rare cases.
The use of artificial intelligence in medical billing and coding software requires strict adherence to industry regulations, which depends on how the software is built, deployed, and used. At EffectiveSoft, we maintain compliance with HIPAA, GDPR, and other regulations through a multilevel strategy that involves implementing strict access controls, data encryption and anonymization, detailed logging, and other best practices to enable security at each step and safeguard sensitive information.
The cost of AI-powered medical coding software depends on several factors, including project complexity, compliance needs, integrations, infrastructure needs, team size, and development time. Contact our team for a customized project estimate based on your specific requirements.
The integration timeline varies depending on various factors, such as implementation approach, compatibility with the existing management system, and specialty-specific coding requirements. Let’s discuss your project so we can create a realistic timeline for you.
STILL HAVE QUESTIONS?
Can’t find the answer you are looking for?
Contact us and we will get in touch with you shortly.
Blog
Related articles
Denial management in medical billing
Revenue cycle management (RCM) is a foundational process in healthcare administration that handles the financial side of patient care. Denial management is a critical component of RCM that plays a direct role in maintaining a healthcare organization’s financial stability. If effective, it ensures healthcare providers are paid for their services accurately and on time, improves revenue recovery, and strengthens payer–provider relations. However, for many healthcare organizations, achieving efficient denial management remains a challenge.
General agentic design patterns implemented on OCI
Artificial intelligence (AI) agents have taken the business world by storm, and for good reason. These smart assistants plan ahead, fulfill tasks with various degrees of autonomy, and make sensible decisions on their own. If deployed and managed in the cloud, agentic systems effectively execute resource-intensive jobs and scale dynamically as business demand grows. Oracle Cloud Infrastructure (OCI) is the ideal platform to implement robust agentic design patterns that support highly functional and secure solutions.
AI agents: capabilities and use cases
As businesses increasingly adopt generative AI (GenAI), AI agents represent the next frontier, redefining how autonomous problems are solved. With the ability to reason, act, learn, and make decisions independently, AI agents offer businesses across various domains unprecedented efficiency and productivity. The question is no longer if you should embrace agentic AI, but how quickly you can integrate it to stay ahead.
Medical claim management: how AI is solving healthcare’s multibillion-dollar problem
Healthcare claim management has traditionally been ineffective: slow, error-prone, and costly. Now, artificial intelligence (AI) is fixing the issues. With smart automation, predictive analytics, and intelligent software tools, AI is driving faster reimbursement, reducing mistakes, and improving financial health of care delivery.
Top 7 frameworks for building AI agents: which one fits your use case?
Every company wants its workflows to be smarter and more efficient, and AI agents are the ideal solution. These intelligent systems act like loyal employees that operate around the clock, orchestrate a myriad of unrelated workflows simultaneously, and solve big-picture problems independently. But what if building a ground-up AI agent is too costly for your business? AI agent frameworks offer more cost-efficient and time-saving options.
Generative AI consulting company: does technology advisory make a difference?
Modern businesses have discerned the true value of generative AI: the democratization of art, automated workflows, and substantial cost savings. While some companies rush to adopt the technology no matter the risks, others stay on the sidelines, discouraged by challenges. So, how do you integrate generative AI safely and effectively, turning it into a powerful tool rather than a risky liability for your business? Generative AI strategy consulting is the answer.
LLM сomparison: How to choose the right model for your business
Major companies like Spotify, Salesforce, and Duolingo are already using large language models (LLMs) to improve podcast recommendations, facilitate financial analytics, and deliver hyper-personalized experiences to global users. Following their lead, more businesses have begun integrating LLMs into their operations. But with an impressive list of large language models, choosing the right one becomes a challenge.
A new milestone achieved: EffectiveSoft expands its talent pool of Certified Azure AI Engineers
Certification plays a key role in specialists’ career growth and development. However, EffectiveSoft recognizes that supporting its employees in obtaining prestigious designations and staying current with disruptive technology trends isn’t just about building skills internally—it’s about prioritizing clients. Every upskilling initiative revolves around our clients’ unique business needs and unresolved challenges.
Healthcare data visualization: benefits, examples, and tools
The healthcare industry is awash in data. From patient records and clinical trials to operational metrics and public health trends, the sheer volume of information can be overwhelming. Data visualization helps make complex medical data easily digestible with visual formats like charts, graphs, and interactive dashboards. This allows healthcare professionals to quickly obtain the required insights, make decisions with more confidence, and ultimately improve patient care.
Custom medical claims management solutions
Are you a healthcare provider feeling overwhelmed by inaccurate data, compliance risks, claims rejections and denials, and reimbursement delays? Overcome these problems with our custom medical claims management software services. The top-quality claims management solutions we build enable our healthcare clients to effectively manage claims between patients and insurance agencies, improving administrative efficiency and overall financial health.
Healthcare data analytics
EffectiveSoft provides healthcare analytics services to hospitals, public health organizations, clinics, medical device manufacturers, and other healthcare businesses. We create competitive data analytics solutions that empower medical facilities to extract profound insights and make efficient clinical decisions. The outcome? Streamlined operations, optimized costs, and enhanced value-based care.
How is artificial intelligence transforming the logistics industry?
While some businesses see artificial intelligence (AI) as uncharted territory, others are reaping its benefits in full swing. No matter which camp you fall into, one thing is clear: even in its nascent form, AI is a truly transformative force in the global economy. It can profoundly impact nearly any industry, and logistics is no exception.
Healthcare data management in clinics: challenges and solutions
The healthcare industry is generating an increasing amount of data from different sources and in various formats. Healthcare data management systems help medical organizations handle all this information, facilitating patient care and decision-making. However, healthcare clinics must be prepared for the challenges they may encounter when implementing these systems.