The healthcare industry is no stranger to technological advancements, and Artificial Intelligence (AI) is rapidly becoming a game-changer in multiple areas. From diagnosing medical conditions to personalizing treatment plans, AI is making healthcare more efficient and effective.
One major field benefitting from AI is medical coding. The global medical coding market is expanding at a rapid pace; in fact, as per a report by MarketsandMarkets, it is expected to reach an impressive $49.87 billion by 2030. Undoubtedly, a critical driver of this growth is AI.
AI is transforming medical coding by making it easier and faster to process information. These intelligent programs can analyze vast amounts of data quickly, abstracting documentation that might otherwise be overlooked by human eyes.
What started as a manual, paper-based process involving massive ICD-9-CM diagnostic and procedural code manuals has now evolved into a highly specialized digital cloud-based process bolstered by predictive analytics, artificial intelligence (AI), and natural language processing (NLP).
In this blog, we will explore the origins of medical coding, its progress throughout the years, and what the future looks like through the AI lens.
What is Medical Coding?
Medical coding is the backbone of the healthcare revenue cycle. It is the process of translating complex medical procedures and diagnosis into universal codes for the purposes of billing and reimbursement. Accurate medical coding is an absolute necessity for healthcare providers to receive proper reimbursements for the range of services rendered.
Incorrect or ambiguous coding can result in underpayment, overpayment, or even denied claims. Thus negatively impacting your revenue cycle.
The Origins of Medical Coding
Medical coding’s origins can be traced back to the early 17th century. One of the first examples of systematic classification in healthcare is the “London Bills of Mortality.” These bills were developed to document the causes of death and such efforts laid the groundwork for more structured approaches to health data.
In the late 19th Century, Jacques Bertillon, a French statistician, introduced a system to unify the classification of diseases. Named “Bertillon Classification of Causes of Death,” this system was adopted at the first International Statistical Congress in 1893 and served as the precursor to modern coding systems.
The Birth of ICD Codes
In recent years, the International Classification of Disease (ICD) has been dubbed as the most pivotal advancement in the history of medical coding. First adopted in 1948 by the World Health Organization (WHO), the ICD has gone through numerous revisions. ICD–11 is the current book of codes utilized across the vast world of healthcare. Its contents are vital not only for maintaining records but also for data to be kept on diseases both domestically and worldwide.
The Evolution of Medical Coding Systems
CPT Codes
While the ICD focused on diagnosing diseases, there was a glaring need for a system to classify medical procedures and services. This ultimately led the American Medical Association (AMA) to come up with the Current Procedural Terminology (CPT) in the 1960s. CPT codes are utilized in the United States to document medical, surgical, and diagnostic services.
HCPCS
The Healthcare Common Procedural Coding System (HCPCS) was established in 1978 to standardize the reporting of medical services to the federal government for reimbursement. It includes codes for procedures, equipment, and supplies – beyond what CPT codes define, highlighting the ever-expanding scope of medical coding.
The Technological Revolution: EHRs and AI-driven Coding
Electronic Health Records (EHRs)
The advent of technology has brought about one of the major shifts in medical coding, i.e., the transition from paper-based records to EHRs. Long gone are the days when most documentation was either handwritten or transcribed and sometimes typed on an electronic typewriter.
EHRs allow healthcare providers to document patient data effectively, eliminating the chances of handwritten errors.
By providing a digital version of a patient’s history, EHRs laid the foundation for the development and refinement of medical coding software. Thus automating the coding process and significantly reducing the time and effort required by medical coders to code patient records.
The Rrise of AI in Medical Coding
Another technology making waves in healthcare revenue management is Artificial Intelligence (AI). It uses sophisticated algorithms to analyze vast amounts of medical data to understand and interpret medical information accurately.
These algorithms continuously learn from patterns and improve themselves, adapting to new information and feedback fed to them by medical coders. This constant refinement enhances accuracy and efficiency in the coding process.
AI in medical coding automates the extraction and assignment of medical codes from clinical documents. By processing unstructured data, AI-powered systems identify relevant diagnostic and procedural information. And then map it to appropriate codes such as ICD-10-CM, CPT, HCPCS, and more.
One of the most notable benefits of using AI-driven solutions is the reduced burden on medical coders. AI automates repetitive tasks, such as verifying patient eligibility and submitting insurance claims, allowing coders to focus on more complex cases. Another robust feature of AI in medical coding is that it can detect errors and inconsistencies in real-time.
The Role of Medical Coders
Despite technological advancement, the role of medical coders remains indispensable. With their expertise in converting complex medical data into standardized codes, they ensure that healthcare providers are accurately compensated and patients get the care they need. Medical coders also have the mammoth task of making sure that they follow stringent compliance laws like the Health Insurance Portability and Accountability Act (HIPAA).
However, with the advent of technologies like autonomous coding, the role of medical coders too has seen a significant transformation. Although, there’s a common misconception that autonomous coding will replace human coders entirely which can’t be further from the truth. Actually, autonomous coding technology is designed to complement human coders, not replace them. It serves as a powerful tool that assists coders in their work, saving time and improving efficiency.
Leveraging the Power of NLP and ML
Natural language processing (NLP) and machine learning (ML) are two key technologies featured within autonomous software solutions.
NLP: It enables computers to understand and analyze human language. In medical coding, NLP is crucial in extracting relevant information from physician notes, patient records, and more. By employing information extraction and entity recognition techniques, NLP algorithms can identify and capture medical concepts, allowing for faster and more accurate coding.
ML: Machine learning algorithms allow computers to learn and enhance performance over time, and that too without explicit programming. These models are trained in medical coding on huge datasets containing labeled medical records and corresponding codes. ML models can then predict the appropriate codes for new, unseen cases solely based on patterns and associations learned during the training.
Typically, most medical coders use manual methods to code around 20 to 30 records per hour. This comes down to 100-150 records a day within an 8-hour shift. Such a pace is influenced by the complexity of medical cases, the need to cross-reference multiple code sets, and the accuracy required to reduce denials and improve revenue.
Thanks to the integration of AI in medical coding, a medical coder can now process about 80 to 100 records per hour. The number might increase depending on the complexity of the cases and the capabilities of the AI system.
So, it’s safe to say that AI integration in medical coding acts as a powerful assistance for medical coders. With repetitive tasks automated, coders can focus on higher-value cases, reducing burnout and significantly improving overall healthcare operations.
Aideo addresses the future of medical coding
According to a recent report, artificial intelligence in the medical coding market size is expected to witness massive growth. It will grow to $5.29 billion in 2029 at a compound annual growth rate (CAGR) of 14.0%.
As AI-autonomous coding continues to evolve, the future of medical coding holds endless possibilities. One key aspect is the increased collaboration between healthcare providers and coding software vendors.
Looking to keep up with the advancements? Aideo could be your ideal partner.
Aideo’s Gemini Solution Suite™empowers medical coders through the combination of Gemini Coder Portal™ and Gemini AutoCode™ solutions. Aideo’s portal, in tandem with AI-driven coding, allows medical coders to concentrate on higher-value, complex coding as the AI learns and processes routine coding tasks.
One of Aideo’s many useful features is a personalized robust analysis dashboard that offers complete audit trailing, providing detailed insight into the auto-coding process. So, no need to worry about transparency when you can track the system’s performance every step of the way.
Request a demo to unlock efficiency!