Medical billing errors are growing every year, according to the Medical Billing Advocates of America, and it is not a surprise because medical billing is a complex process that involves adding and verifying a lot of data. However, this is slowly changing thanks to the use of AI in billing. Billing errors simply mean that a claim will be denied or delayed. Industry studies show that denied claims cost providers between $25 and $118 to rework, depending on the complexity of the case.
AI is a cutting-edge technology that is revolutionizing medical billing in the healthcare industry. It is helping hospitals and providers by making the billing process faster, catching errors early, and keeping the entire billing cycle running smoothly. Many healthcare organizations are saving millions of dollars simply because they started using AI.
Even though AI is useful and makes everything smoother, it is still a tool that depends on the data it has. It is not a medical expert, it doesn’t know everything, and it doesn’t have the experience that a medical billing professional has. It also does not understand the context behind many billing situations. AI in medical billing is helpful, but it is not AGI, so it cannot understand everything the way a human can.
In this blog, we will look at why keeping a human in the loop is important in the medical billing process.
Let’s understand why keeping a human in the loop matters in AI-powered medical billing.
Machines are good, and digital tools like AI or software are useful, but as of now, no machine can work on its own and do every task 100% correctly. There are things machines can do autonomously, but those are outliers, and we are not talking about them, and the work they do doesn’t require much human intervention anyway. Now, let’s understand why humans are still important in AI-powered medical billing.
- AI can’t understand context: In medical billing, there are many details—from the patient’s name to the doctor’s notes to the treatment itself. Doctors often speak quickly, and when transcription happens, it repeats exactly what they say. But what they say may not always have a clear meaning.
For an experienced medical billing professional, even 2–3 words can convey the meaning of an entire line. AI may not understand this because it tries to interpret the words individually instead of considering the full context. This is why a human is needed in the loop to understand the situation better and interpret the details correctly for accurate coding and billing decisions. - AI can assume things: We know the difference between an expert and a newbie. An expert understands everything before taking action, while a newbie assumes things, which leads to mistakes and prevents them from doing their work correctly. Even though AI is more powerful and capable than humans in research and calculations, it still assumes things often, produces hallucinations, and makes wrong decisions.
AI used in medical billing can do the same. For example, AI might read a short note the wrong way and end up putting in a code that doesn’t actually match what happened. That’s a big problem because it can make a claim look like a procedure was done when it wasn’t. When a human is involved, they can spot this right away, clear up the confusion, and correct the code before the claim goes out. - AI can struggle with rule changes: Every payer has its own way of handling claims, and the rules don’t stay the same for long. One insurer wants things done one way, another expects something completely different. There’s no single rulebook for all of them. Because of this, AI can easily treat every payer the same, which obviously doesn’t work in medical billing. This is another reason why having a human in the medical billing loop is important.
- Humans can catch what AI has missed: We have to acknowledge that AI is very powerful, and even with its limitations, it can do a very good job in medical billing. But a medical expert is still a medical expert, and they can notice things that AI might miss, especially when something rare or unpredictable occurs.
For example: A doctor might leave a very short note about something unusual or a rare issue. AI may not pick it up the right way because it hasn’t seen that type of case often, so it either skips it or puts in the wrong code. A medical billing expert can catch that small detail and make sure the right code goes in.
When a human reviews the claim at different stages, it becomes much easier to catch and fix these rare or unpredictable issues. This cuts down billing mistakes and improves the chances of the claim getting processed and paid without trouble.
- Humans can do the communication for double-checking: Let’s say during the medical billing process, AI finds something unusual but thinks it is not important and continues the process. It may even submit the claim successfully because there was no human in the loop to review it.
But if a human were involved, he or she might also notice that same thing. To understand it better or to get more clarity, they would contact the provider or the payer to ask what should be done, so no mistake happens during billing. They can ask their questions, get the missing details, and make sure the claim is sent in correctly so the payment goes through without any issues.
Without a human in the loop, this communication cannot happen, which means the claim can be denied.
- Humans in the loop can decrease the number of medical billing errors: When two people communicate well and understand each other, their work has synergy and gets done faster and more accurately. When AI works alone, issues can still happen, because—like we discussed—AI is good but not perfect. And when humans work alone, mistakes still happen because humans make errors too.
But when AI and humans work together, the chances of these errors occurring drop dramatically, which is a big advantage. Without a human in the loop, this improvement is not possible.
AI Improving Medical Billing
Nearly 30% of medical claims are denied or rejected on the first submission, and that’s a serious issue when you consider the time and money attached to each claim. AI helps reduce many of the errors as we aforementioned that lead to these early denials. It catches the small mistakes that humans often overlook, and fixing those small issues prevents the bigger problems that cause claim rejections. This is how AI is improving the entire medical billing process.
Talisman Solutions’ AI Medical Billing Service
When the AI boom started with the arrival of ChatGPT, we understood that AI would not stay limited to generative tasks like creating text or images. We knew it would impact every industry, including the medical industry and the work we do. With this understanding, we began exploring how to include AI in our medical billing services. After years of research and development, we have built our own in-house AI medical billing tools and platforms.
With these tools, we launched our AI medical billing service, which is far better, more effective, less error-prone, and faster, with a higher chance of getting claims approved successfully. Our AI medical billing service is better not just because we use AI, but because we follow a model that combines human medical billing experts with AI medical billing tools throughout the entire billing process.
Here’s how the whole thing works in real life:
- AI does the first pass and sorts all the notes, codes, and documents.
- A human expert reviews the same files and corrects any errors that the AI missed.
- AI checks payer rules and updates so nothing gets submitted the wrong way.
- Humans handle clarifications by talking to the provider or payer when needed.
- AI prepares the claim and makes sure all required details are in place.
- A human gives the final approval and submits the claim correctly.
- AI keeps an eye on claim status and flags anything unusual.
- Humans step in for follow-ups, appeals, or anything that needs real judgment.
Based on our internal research, we’ve noticed that AI has made our medical billing process 2.5 times faster, reduced billing errors by 25–35%, improved coding and billing accuracy by around 30%, and increased successful claim approvals by 24%.
Conclusion
AI has definitely improved the speed and flow of medical billing, but it still can’t replace the way a human understands context and unusual situations. Billing rules change, doctors write notes in different ways, and not everything fits into a pattern.
When AI takes care of the routine work and humans handle the judgment calls, the results are simply better, fewer mistakes, smoother claims, and more accurate billing. That mix of both is what actually makes AI useful in medical billing.
If you’re aiming for fewer denials, faster reimbursements, and consistently accurate billing, our AI-powered RCM solution is built for your organization.
