AI Will Not Replace Medical Coders (2024)

But it will greatly augment the work they do and create new opportunities.

Computer automation, natural language processing, and artificial intelligence (AI) have greatly streamlined many processes in most job sectors over the years, including medical coding. AI enables computer programs to simulate human intelligence in the form of processing data, formulating conclusions, and learning from experience. This enables computers to perform more human-like tasks such as problem-solving, decision-making, and understanding natural language. But can AI do everything a medical coder can do?

In this article, we’ll look at the obstacles that are preventing AI from fully automating medical coding and how medical coders can position themselves for job security as technology advances.

Medical Records Are Complex

The prospect of automating medical coding is especially daunting because of the complexity of health information management — in particular, medical records, which serve as the foundation for coding and billing healthcare services. A variety of data forms, such as handwritten notes, patient intake information, and narrated lab reports, are contained within electronic health records (EHRs), and this lack of uniformity is problematic for AI systems.

Unlike a well-trained medical coder, current AI systems do not have the contextual awareness required to decipher and extract pertinent information from such diverse and occasionally illegible records. Ambiguous acronyms, local jargon, and specialty medical language and terminology add to the complexity of abstracting documentation. Moreover, much of the information coders are given is subjective and requires a degree of interpretation.

For AI to learn these skills, medical coders will have to teach it and auditors will have to check its accuracy.

Healthcare Regulations Are Fluid

Another roadblock for AI is that code sets, coding guidelines, payer policies, and documentation requirements are regularly updated to account for new medical advancements, changes to insurance coverage, and new state and federal legislation. This is a logistical nightmare for AI.

Medical coders and billers who are well-versed in medical coding and healthcare regulations will continue to be essential to ensure compliance with local and national coverage determinations, to detect claim processing errors, and to initiate appeals.

Security and Privacy Are Paramount

Concerns about security and privacy add another level of complexity to the use of AI for medical coding. Strict laws, such as HIPAA, require strong patient health information (PHI) security measures to effectively regulate the healthcare sector. Although automating medical coding and billing would reduce human contact, AI systems are not impervious to security risks and assaults, particularly AI that is capable of machine learning and deep learning techniques.

As with human coders, automated systems need to adhere to stringent data protection requirements to avoid data breaches and maintain the confidentiality and integrity of PHI. This will necessitate ongoing oversight by compliance officers and information technology professionals.

Implementation Costs Are High

The cost and time involved in implementing automated medical coding systems can be a deterrent. Such systems need many resources for development and maintenance, not just in terms of hardware and software, but also in terms of skilled labor to manage the changeover and maintain the system. It can be especially challenging for smaller healthcare facilities or those with fewer resources to accomplish this change. The cost for a nationwide update could be in the trillions, as it would also impact national Medicare and Medicaid programs and every insurance provider in the country.

AI Benefits in Medical Coding

Although there are many roadblocks to its implementation, AI is making remarkable progress in the healthcare sector. There are many web-based coding tools being used today, and AI has demonstrated the potential to support medical coders in increasing their productivity and precision by processing vast amounts of data rapidly. But the technology has a long way to go before we can entrust it with our lives.

The complexity of medical records, the changing nature of coding guidelines, the subtleties of interpretation and judgment, the significance of quality assurance, implementation challenges and costs, and security and privacy, not to mention the human element, all add up to job security for medical coders. But do not rest on your laurels. Those who do not seek the skills to advance in a changing world risk being replaced by those who do.