Medical coding ensures all health-related documentation is appropriately marked with the correct alphanumeric codes, which are used in everything from billing to research to compliance with the law. It is an unsung but critical aspect of the healthcare industry, and a complex and ever-growing one. Today, medical coders and billers are turning to artificial intelligence to manage the process.
What is AI for Medical Coding?
Artificial intelligence (AI) aims to duplicate how human beings think into a computer: how we obtain information, process it, and, finally, commit it to memory. In AI for medical coding, medical AI computers run applications such as natural language processing (NLP), machine learning (ML), and predictive analytics to analyze clinical documentation and automatically assign accurate medical codes.
AI for Medical Coding Benefits
Medical coders and billers handle a vast number of documents daily, all of which must be painstakingly reviewed for proper coding. AI, in general, is built to scan, organize, and process vast amounts of data. When applied to medical coding, the potential benefits are enormous. Some include:
Increased Accuracy
AI can go through vast amounts of data and learn from patterns far faster than humans. This includes the ability to red-flag incomplete, missing, contradictory, or incorrect information and documentation. It then assigns the most appropriate codes automatically. If flesh-and-blood billers somehow find errors, the AI learns, reducing the likelihood of future coding errors.
Better Efficiency
Manual coding is time-consuming and labor-intensive. AI can be programmed to handle the repetitive, mundane, and standard coding tasks. Medical coders and billers can then focus on more complex cases while maintaining their daily workload without compromising quality and accuracy.
Lower Costs
Medical coders and billers can process more documents during their shifts thanks to AI. This can help healthcare groups conserve their limited budget. Additionally, AI algorithms such as predictive analytics can reduce the costs associated with getting paid. They do so by determining which claims are most likely to be denied. The billers can focus on them to get accepted, thereby minimizing the costly time and money wasted going back and forth with payers.
Compliance
The healthcare industry is highly regulated, from the medical devices it can use per the government to the privacy of patient data. Through machine learning and automated audits, AI for medical coding can ensure the group's codes are up to date and compliant with guidelines and regulations.
Real-Time Information Identification
Information is the lifeblood for medical groups; the more accurate and in real-time, the better. AI used in medical coding and billing systems can identify trends, patterns, and anomalies with virtually no delay. Groups can then utilize the results in various ways. Incorrect coding practices can be addressed before they become a financial risk. Or trends in certain diseases, such as rabies and tuberculosis, can be identified and reported to the relevant government agency for action before they become a significant threat to communities.
Challenges of Bringing AI to Medical Coding
While the earliest medical coding records can date back to the 17th century, AI is a far more recent innovation, gaining widespread use in the 2000s, as seen with IBM's Watson, which in 2017 determined RNA-binding proteins associated with amyotrophic lateral sclerosis. Healthcare groups looking to integrate AI into their medical coding and billing departments should be aware of the challenges that come with this integration. Here are three of them.
Data Privacy and Security
Medical coding handles highly sensitive patient data, which is regulated by laws such as HIPAA. Yet AI needs access to vast amounts of that data to perform effectively. Healthcare IT will need to ensure all such data is protected with measures ranging from data encryption to secure sign-on (SSO) solutions.
Dependence on Data Quality
AI is only as effective as the quality of its data. Incorrectly used codes, incomplete medical records, and transposition errors in figures can lead to the wrong codes being used or to the AI generating hallucinations while justifying its results. Clinical documentation improvement (CDI) efforts, which aim to ensure that healthcare documentation is complete, precise, accurate, and consistent, can help ensure that data is correct, standardized, and up-to-date. Measures such as continuous monitoring and feedback loops on the AI’s results can also improve quality and accuracy.
Initial Costs and Resource Allocation
Investing in AI for medical coding systems can be substantial. Besides the costs of hardware and software, the staff themselves – the medical coders and billers – must be trained, which takes time and money, something many healthcare groups lack due to limited budgets. Groups should gain buy-in from all involved and take a phased approach to introducing the new systems while keeping costs under control.
Why Medical AI Computers are Right for Medical Coding
One way to maximize the benefits of medical coding while minimizing any challenges is to select the right computers. Generally, the best computers for medical coding and billing are, unsurprisingly, medical PCs. They have been built from the ground up by a true manufacturer to handle the rigors of the job, many of which are unique to the industry. They come with such essential features as:
- IP65 front bezel: The screen of most medical computers is sealed against dust and liquid. This protects the interior and its delicate components from a variety of threats, including accidental coffee spills and harsh disinfectants.
- Low Computer Fail Rate: The last thing any medical coder or biller wants is for their computers to go on the fritz and lose hours of detailed coding. Again, seek a reputable manufacturer, especially one that can ensure its computers have low fail rates.
Medical coders and billers using AI will need to upgrade their medical-grade computers to medical AI computers. It comes with most of the features of the above medical PC, but is optimized for AI:
- GPU: The most important new feature, the Graphics Processing Unit (GPU) performs the intense parallel processing during NLP and ML operations.
- CPU: The Central Processing Unit (CPU) handles most of the computer's non-AI operations, like data storage.
- RAM and HDD/SDD: Both are essential for handling the large, complex computations performed by AI algorithms efficiently and quickly.
Medical Coding and Billing Made Easy with Cybernet Medical AI Computers
An essential part of the healthcare industry, medical coding and billing ensure medical documentation uses the correct code(s) for everything from data collection to billing. Many coders are turning to artificial intelligence as a tool to help manage the increasing number of new codes and more complex documentation.
Contact Cybernet Manufacturing if your medical group is looking for AI solutions for its medical coding and billing department. Our team members will be happy to discuss why our medical computer lineup is the ideal fit to support your coding needs. This includes the ability to customize our products to meet your exact demands and specifications.