How AI Medical Coding Software Reduces Errors & Accelerates Billing in 2025
The medical technology industry is a well-known part of the overall healthcare sector. According to Statista, roughly 1.3 billion people used digital health in 2024. This included using fitness trackers and smartwatches, remote doctor consultations and sessions, or any other digital therapy and care use.
A huge enlargement in the development of medical technologies has come from the coalition with artificial intelligence.
It is widely accepted in healthcare that the use of AI brings time savings for healthcare staff, relieves burnout, lowers stress levels and working overtime, and frees time spent on direct patient care.
But one of the most promising ways AI development is being used in healthcare is in medical coding — a process that links clinical documentation with billing, insurance, and compliance systems.
What Is AI-Powered Medical Coding Software?
AI-based medical coding software is a program that helps medical caregivers automatically convert medical records (such as doctor’s notes or medical history extracts) into suitable codes used for billing, insurance claims, and tracking patient care.
What are these codes:
- ICD-10 (International Classification of Diseases): codes for diagnoses. For example, J45 is asthma.
- CPT (Current Procedural Terminology): codes for procedures and services. For example, 99213 is a mid-term doctor’s appointment.
- HCPCS (Healthcare Common Procedure Coding System): an extension of CPT, covering, for example, medical products and equipment (e.g., wheelchairs).
- SNOMED CT is a medical terminology used to describe clinical information in an expanded manner.
Because codes are standardized and their use is mandatory in healthcare, previously, medical coders had to read doctor’s reports and enter codes manually.
Now, AI applies natural language processing (NLP) and machine learning algorithms (ML) and gets the job done way faster and with more accuracy.
Traditional Coding
AI-Powered Coding
How it’s done
By people reading and coding manually
By software that understands medical text
Speed
Slower, especially with lots of data
Much faster, often in real time
Accuracy
Depends on the coder’s experience
Consistent and gets better over time
Scalability
Limited by staff
Easily handles large volumes of data
Traditional vs. AI-Based Coding
Simple Example
Let’s say a physician writes in an electronic medical record: “The patient came with complaints of shortness of breath and wheezing. An inhaler was prescribed. Diagnosis: asthma.”
What the AI system does:
- Asthma diagnosis → ICD-10 code J45.909
- The average length of physician visit → CPT code 99213
- Inhaler prescription → may require HCPCS code for a specific device, such as HCPCS A4614 (if a nebulizer or consumable is indicated)
Benefits of AI Medical Coding Software
Did you know that up to 30% of claims created with manual coding are denied, and up to 65% are never reworked, negatively affecting revenue cycles?
Overall, medical coding software with the aid of AI reduces denials and ensures healthcare providers work faster, more accurately, and less stressed.
Again, Statista reveals that around four out of ten doctors and a quarter of nurses admit that AI brings positive and welcome progress to their work in the healthcare system.
One of the greatest advantages is that it eliminates (or at least reduces to zero) human errors and raises coding accuracy. The program can comprehend and understand medical notes, and then provide automatic suggestions for the correct billing codes.
As a result, there are rarer mistakes, fewer rejected claims, and less time to resolve cases.
In addition, AI medical coding software is time-wise. Poor productivity, which is expected to worsen due to an aging population and a shortage of essential workers, is reflected in high rates of physician burnout.
However, what could take hours for one person to code can now be done in minutes using AI. This means quicker billing and helping healthcare professionals get paid earlier.
A further meaningful benefit is the lower expense. Since a lot of the drudge work is done by software, healthcare organizations do not need as many people coding manually. Besides, fewer mistakes mean fewer delays and lower administrative payments.
Also, AI software can easily cope with complex cases if trained properly. It can understand field-specific medical jargon and decide the right codes even in problematic or rare diagnoses and procedures.
Almost as important, the software aids in audit readiness and compliance. It follows approved billing and coding guidelines and saves accurate records of the way each code was selected. So, if there is an audit, it’s easier to explain and support accurate coding decisions.
Top Ready-to-Apply Medical Coding Solutions
Today, the modern software market already offers many AI-based solutions for medical coding. Despite some shortcomings (we will discuss them below), such solutions can be great for familiarization purposes with such software or as a reference for future development.
1. 3M™ CodeFinder™ and 3M™ 360 Encompass™
3M is a well-known health technology firm. Their 360 Encompass and CodeFinder solutions help coders by suggesting relevant medical codes from doctors’ notes. The solutions can read medical records and pick up on important information like diagnoses and procedures.
They integrate with many electronic health record systems and help hospitals manage coding for different levels of care. They also keep detailed records to make audits easier and help improve documentation quality.
2. Optum™ Coding and Reimbursement Tools
Optum AI coding solutions leverage smart algorithms to read patient notes and recommend the right codes for billing. They are suitable for large health providers or insurance companies because they handle a high volume of data.
Optum’s solution has good integration with different EHRs, reduces claim errors, and offers reports to follow coding performance. It is also scalable, i.e., it can put up with more patients if necessary.
3. Cerner Code Assist
Cerner’s Code Assist tool, in turn, is designed to work within their own EHR system, making it easy for physicians and coders to use without switching between programs.
It goes through patient records and highlights important details in order to ease the choice of correct codes in a timely fashion.
Because it is built into Cerner’s system, it integrates flawlessly into daily workflow and helps to speed up the billing process.
4. Nuance CDE One
Nuance (now part of Microsoft) also has a tool that helps doctors write more comprehensive medical notes and have all the information available that is needed for efficient coding.
It offers live recommendations to improve documentation, live clinical documentation guidance, automated query generation, and integrated physician feedback.
By and large, this is a convenient tool for hospitals that want to enhance the quality of their clinical notes and at the same time make coding more accurate. By the way, Nuance works with many popular EHR systems.
5. Athenahealth Medical Coding Services
Athenahealth provides a cloud-based coding service. It combines AI with support from real coding experts, which makes it a good alternative for small or medium-sized medical practices that need to outsource coding but lack the desire to hire a substantial staff.
Athenahealth ensures quick and correct coding and the program interfaces with Athenahealth’s billing system and EHR.
Challenges in Off-the-Shelf AI Medical Coding
As we stated earlier, pre-packaged AI medical coding software can be a great means of rapidly getting started, but it also has some disadvantages.
One of the largest problems is that they generally can’t be customized. Market solutions are normally made to accommodate everyone possible, so you typically can’t adjust how the system works for your processes, regulations, or procedures.
If your hospital or clinic does things in a particular way, the software won’t work for you.
Another matter is limited integration. Pre-made tools usually connect well with popular EHR systems, but if you’re using a less common system (or a mix of different tools) they might not work without extra setup or help from developers.
Besides, some tools are fine for small clinics but may not be able to process much data, many users, or more than one location. This could slow things down or increase your cost as you grow.
Also, if you use off-the-shelf software, you have to wait for the vendor to upgrade it. If you want a new feature or need to meet a new regulation, you can’t upgrade the software yourself, you have to wait until the firm makes it available.
Lastly, the tools might not be properly trained to deal with specialty medicine. If you work in a specialty area, then the AI might not understand your cases properly, and there might be coding bugs that you would need to rectify manually.
How Custom Software Development Can Help
If off-the-shelf AI medical coding software is not your case by one or more criteria, you’d better try custom healthcare software development specifically intended for your demands.
With bespoke software, you get exactly what you need. It can follow your own workflow, fit your specialty, and integrate with your existing systems whatever they are — EHR, billing system, etc.
You’re also fully in charge of the features. Need to introduce something new? Change how something works? Refresh it right away? You don’t have to wait for a vendor, you can implement a change at any time.
Customized tools can also be more precise, especially if they’re trained on your own data. That means fewer coding mistakes, faster billing, and fewer claims denied. This is especially helpful if your practice handles complex or out-of-the-ordinary cases.
Another significant benefit is that your software can grow as your business does. Whether you’re expanding to new locations, services, or employees, your system can be adapted and scaled to keep up.
Yes, bespoke software takes more investment and time at the beginning. But in the long run, it can save you time, reduce errors, and give you a tool that really works the way you work.
Key Features to Look For When Building Custom Software
If you’re planning to build your own AI medical coding software, it’s important to include the right features from the beginning.
The goal is to create a tool that’s not only smart and accurate, but also easy to use in real-world healthcare settings. Below are the features you should consider, explained in simple terms with examples.
1. Natural Language Processing (NLP) Engine
Doctors and nurses often write medical notes in free text — not in neat, structured formats. An NLP engine helps the software read and understand these notes by picking out important details like symptoms, procedures and diagnoses, and treatments.
What it should do:
- Find key terms like “pneumonia,” “hip fracture,” or “insulin injection”
- Understand medical abbreviations like “MI” (heart attack)
- Catch context, like when a condition is only being considered, not confirmed
- Handle different ways of saying the same thing (e.g., “heart attack” and “myocardial infarction”)
Example:
A doctor writes: “Patient shows signs of chest pain, ST elevation on ECG, and elevated troponin.”
The NLP engine should understand this as a likely heart attack and suggest the code for acute myocardial infarction.
2. Automatic Code Suggestions
Looking up medical codes manually can take a lot of time. With AI, the software can suggest the right codes automatically, helping coders work faster and make fewer mistakes.
What it should do:
- Suggest the most likely ICD-10, CPT, or HCPCS codes
- Show multiple options if needed (for example, left vs. right side)
- Rank codes by how confident the AI is
- Let users approve or change suggestions
Example:
For the note “Patient received steroid injection in right shoulder,” the software might suggest:
- ICD-10: M25.511 (pain in right shoulder)
- CPT: 20610 (injection of major joint)
3. Real-Time Validation
Sending in claims with the wrong or missing codes can lead to delays or rejections. Real-time validation checks the codes while you’re working, so you can fix problems right away.
What it should do:
- Warn if information is missing
- Catch invalid code combinations
- Follow insurance or payer rules
- Suggest corrections before the claim is submitted
Example:
If a procedure code requires a matching diagnosis code for approval, the system will alert you if it’s missing before the claim goes out.
4. Integration with EHR, PMS, and Billing Systems
Your coding software should work well with the systems you already use such as your electronic health record, practice management system, and billing tools to time and prevent errors.
What it should do:
- Pull notes and patient info from the EHR
- Push selected codes into billing automatically
- Sync patient data with your practice management system
- Keep everything connected securely
Example:
Once a doctor finishes writing notes in the EHR, the AI tool can pull the details, suggest codes, and send them directly to billing without anyone needing to re-enter the data.
5. Audit Trail and Compliance Tracking
In case of an audit, you need to show how and why each code was chosen. The software should track all changes and decisions to help prove you’re following the rules.
What it should do:
- Record which codes were suggested and selected
- Track changes and who made them
- Store notes explaining why codes were used
- Help you stay compliant with rules like HIPAA
Example:
If a coder changes a code suggested by AI, the system saves a note like: “Changed code based on doctor’s clarification,” along with the date and user’s name.
6. Self-Learning Capabilities (Adaptive AI)
The best AI tools improve over time. If your software can learn from your team’s corrections and feedback, it gets smarter and more accurate and efficient the more you use it.
What it should do:
- Notice patterns like which codes get corrected often
- Improve its suggestions based on those corrections
- Learn how different providers document care
- Stay up to date with changes in coding and treatment styles
Example:
If coders often change a suggested “unspecified diabetes” code to a more specific one with complications, the software should start making that better suggestion in future cases.
Bonus: Extra Features That Can Help
- Easy-to-use dashboard – So coders and managers can quickly review and edit work
- Role-based access – So different users (like coders, auditors, and doctors) see what they need
- Multi-language support – Useful in clinics that serve patients in different languages
- Messaging tools – Let coders and doctors communicate inside the platform to clear up documentation questions
How to Build AI Medical Coding Software: Step-by-Step
Creating AI-powered medical coding software can seem overwhelming, but with the right approach, it is much more within reach.
The best thing to do is to collaborate with a software development company that has experience in healthcare and AI. With an experienced development partner, you can be guided through every phase of the process, from the very beginning concept to a finished, working tool.
It starts with an understanding of your needs. Your goals will be talked about by the development team with you, how your team works, what systems you use now, and what kind of coding support you need.
This step helps create the general vision and detail of what is supposed to be achieved by the software.
The second step is data preparation and collection. Since AI learns by example, the team gathers real clinical documents and annotates them with the relevant medical codes.
Then, the team chooses the most promising AI models and trains them. Here, developers make the software learn the language of medicine and suggest the right codes depending on the contents of each note.
When the AI part is working well, the focus shifts to developing the user interface. This is the part of the program that programmers, doctors, or billing staff will interact with directly.
It must be clear, logical, and simple to navigate so people can quickly view suggestions, input changes, and send codes along.
The team tests the software thoroughly prior to release. They verify the tool returns accurate results, meets all coding standards, and will fit into your day-to-day work.
Then comes integration and deployment. Developers integrate the software with your other systems, say your EHR or billing system. Once all that is complete, it’s time to roll it out and start using it.
Finally, even after the software is live, the work doesn’t end. A good development partner will help with monitoring the tool, debugging, updating, and refining the AI as you move forward. The more it’s used, the smarter and more helpful it can be.
Our Expertise in Medical Software Development
We at SCAND create in-house AI solutions for the healthcare industry with stress on the medical coding process, clinical processes, and direct integration with systems like EHRs and billing systems.
Our team has significant exposure to healthcare and AI, allowing us to create tools that boost coding precision, reduce manual labor, and allow medical staff to work more productively.
We also follow stringent security standards, such as HIPAA and GDPR to confirm patient data is secure. Idea from start and beyond we empower the entire development lifecycle.
One of the projects we undertook recently was a lab test delivery smart courier system. We built a web dashboard and mobile apps that make it easy for clinics to schedule, track, and manage lab deliveries.
The system delivers couriers, optimizes routes, and updates everyone in real time. This eliminated errors, saved time, and made the whole process more transparent and reliable.
If you want to create a similar system or streamline any medical process in your organization, we’d be happy to help you create a solution that fits your needs.
