Will AI take over medical coding? It’s a question that resonates with many professionals in the healthcare industry. AI has brought revolutionary changes in numerous fields, and healthcare is no exception. The potential for automated medical coding to streamline operations, reduce errors, and increase efficiency is immense. By automating the process of translating medical diagnoses, procedures, and treatments into universal medical alphanumeric codes, AI could significantly reduce the manual effort involved in medical coding.
What is Medical Coding?
When most people hear the word “coding,” they think of coders writing code. In this age of digital health, this is also true in the medical setting, but medical code has usually meant a certain process. It’s the process of turning medical records, which are usually written by doctors, into structured codes based on a system for identifying the right patient diagnosis and treatment.
As a result, clinical data is consistent and easy to compare across time and healthcare areas. This information can help with studies, policies, and billing in the US. Although clinical coding is necessary, doing it by hand takes time. In NHS Scotland, for example, one clinical coder can handle about 60 cases daily, and the whole department can handle more than 20,000 cases monthly.
Still, there is a backlog that could take months to clear up. There are also times when mistakes happen when you code by hand, like when the data is missing, or you don’t have enough experience. Because of these issues, people are interested in automating the medical coding, especially with artificial intelligence (AI) tools like machine learning and natural language processing (NLP).
How Artificial Intelligence Can Help
Healthcare companies have used and gotten used to computer-assisted coding systems over the last 20 years. An increasing number of these systems are already using cutting-edge Machine Learning and Artificial Intelligence to look at clinical papers and determine what codes are needed. AI will make huge strides in accuracy, efficiency, and income when it learns how to solve problems that humans face.
In 2017, the CMS (Centers for Medicare & Medicaid Services) said mistakes led to $36.21 billion in wrong payments. Healthcare fraud caused by false claims and charges was predicted to cost up to $230 billion. The main reason is still bad paperwork or no documentation at all. Medical coding needs to be made much easier, from finding people to treating them.
Medical Code Automation
It has been shown that computer-assisted medical coding is more accurate, better, and faster than human coding. Both researchers and MedTech businesses are now interested in adding AI technologies to these aids.
Dr. Eric Wilke, chief operations officer at ER physician staffing company TECHealth, says,
“Anything that deals with images or text is a great opportunity for AI.”
“So, yes, pathology, radiology, and dermatology are all good places to work, but so is looking at charts for billing and coding.”
The goal of AI-powered coding is to automate the whole medical coding process. AI technologies, like NLP, read and translate clinical notes into codes that work with the healthcare organization’s method of classification. Let’s look at how this kind of automation would change the medical coding process.
The Impact of Medical Coding Automation
Because medical coding isn’t easy, it makes sense that it’s a whole career, but there aren’t enough people working on it. There are 30% fewer medical coders in the US than there should be. Automation driven by AI can fill in those gaps by adding to those processes. These possibilities aren’t just found in academic conversations. Companies like Nym, Fathom, and CodaMetrix are offering them.
Julien Dubuis, chief commercial officer at Nym, talked about their company’s AI autonomous coding solution:
“[It] assigning the medical codes accurately within seconds with no human intervention at all.”
“By accurately, I mean that we can get 96% code-over-code accuracy for outpatient specialties, which is the same level of accuracy as some of the best human coders.”
CodaMetrix is working with 10 universities and health systems to launch its self-service option. Reports say that the AI has led to a 70% drop in manual labor and a big rise in cost savings.
TECHealth, an ER physician staffing business, used Fathom’s automated AI tool to help with medical billing and coding. With AI’s help, the business could handle up to 80% of cases with little to no human help.
Concerns about AI replacing people, even in healthcare, are growing. This kind of automatic medical coding could add to those worries. But people will still be a part of this AI-driven evolution of medical coding; the link between humans and technology will be more like working together than technology taking over for humans.
Getting Ready for The Next Step in Medical Automation
There is no doubt that AI can improve automated medical coding, and AI automation could be the next step in the process. However, some problems must be solved before it can be used effectively. Because the AI tool’s performance depends a lot on the data it is taught, it needs to be given good data. But this isn’t a given based on medical facts from the past.
Dr. Wilke of TECHealth says,
“If you don’t give the [AI] the most accurate information upfront, then you don’t always get the most accurate information later.” “If I could go back and redo implementation, one thing I would have done differently is check that our coding team was using the right codes. Anyone using an AI engine must use historical data to build the AI’s prediction model.”
So, for healthcare organizations to prepare for using technology in medical coding, they will need to ensure that their medical data is correct and of good quality. Then, this can be used to better teach AI tools. On the other hand, medical writers need to be taught how to use AI medical coding automation tools. By adding AI to the work of human coders, problems in the current medical coding process can be fixed.
Challenges In Medical Coding
Inefficient medical billing can make it hard to get reimbursed on time. Payers may not pay claims if there are coding flaws or problems with how the claims were coded. A study done in 2021 found that more than a third of hospital executives said that claims were denied almost 10% of the time.
As the number of denials increases, improving how you code medical records can prevent your practice from having a bad result. To ensure you don’t lose money because of wrong coding or modifier use, your healthcare organization should figure out the right codes for clinical visits while still following payer rules.
5 Advantages That AI Will Help Shape the Future of Medical Coding
People are worried that adding AI to medical coding will put jobs in medical billing and coding at risk because AI is so popular. Artificial intelligence (AI) applications can speed up bills and make coding more efficient, but AI will not likely make human coders and billers obsolete.
Instead of choosing one over the other, the key to the future of medical coding is to use powerful AI technology and human expertise together to achieve the best results. AI can help your billing and coding team identify trends and determine what went wrong, lowering the risk of billing fraud and code duplication.
These are some more benefits of using AI in your medical coding and billing:
1. Adapting To Changes in How Hard the Code Is to Understand
The most recent change to the International Classification of Diseases (ICD) shows that the field of medical coding is always changing. There are now hundreds or even thousands of more codes and description standards than there were before the change from ICD-10 to ICD-11. With this growth, AI has become very important for assigning codes correctly.
Medical coding systems that are built on AI and automated make it easier to check and identify codes. So, AI input in real time will help coders work faster and make the documentation less error-prone.
2. Improves the Way You Code
The old way of medical bills and coding requires a lot of paperwork and handwritten notes, which takes time and makes turnaround times longer. Paper-based methods are less efficient and more likely to result in mistakes and data gaps.
Now that artificial intelligence is being used in medicine, many businesses and healthcare groups are using this new technology to improve how they code medical records. With AI-based medical coding, you can put unstructured data in its proper context, match data from different sources, and put it in a way that makes sense.
Web-based systems and code methods that use machine learning (ML) are the new AI tools in the medical field. These systems, called Computer Assisted Coding (CAC) systems, automatically pull data from different documents and identify them. This makes it easy to fill in the necessary information. The processes of treatment and diagnosis are sped up when you use web-based tools. The AI looks at the paperwork and finds the appropriate medical codes, which helps you give better care to your patients more quickly.
3. Makes Imaging and Disease Management Better
AI is about to change the medical imaging business, which makes you happy. This technology helps your medical practice be more productive, diagnose better, and personalize treatment plans.
With the help of AI, radiology departments can do more diagnostic imaging processes, even in places where there aren’t enough radiologists. Additionally, cloud computing and deep learning are speeding up the creation of medical picture analysis tools that use AI. This new technology makes medical imaging faster and better at giving each patient individualized care.
4. Makes Work Easier
Using artificial intelligence (AI) and medical coding together makes data handling more accurate and faster. Electronic medical records (EMRs) store a lot of data. AI systems can quickly review and extract relevant data, giving you useful information to help you make better decisions. Patients can be more involved in their care when your medical office uses this improved method.
5. Make sure That Workers Are Trained Well
As a healthcare worker, you know how important it is for medical coders to ensure that medical codes and billing are correct. AI-based systems learn by watching how coders deal with users and recording their actions. So, for systems to work better and make fewer mistakes, coders and AI systems need to get along.
However, as code becomes more complicated, the need for on-the-job training becomes even more important. To advance in your career, you need to keep up with changes in your field by following current trends.
When humans and AI work together, medical coding can be more accurate and faster, which can improve patient care and help the company succeed.
