With the increasing complexity in healthcare processes, medical coding has had to keep pace and has done so with the inclusion of Artificial Intelligence and Machine Learning (ML) technologies. These technologies are helping to improve the accuracy and efficiency of medical coding, enabling healthcare providers to more accurately and efficiently diagnose and treat their patients. By automating many of the processes involved in medical coding, AI and ML help to reduce the number of errors and improve the overall quality of medical coding.
One of the current key trends in the use of AI and ML in medical coding is the increasing use of natural language processing (NLP) technology. This technology allows AI and ML systems to understand and interpret human language, making it possible for them to automatically analyze large amounts of text data and identify patterns and trends. This can be useful for a variety of purposes, such as identifying common medical conditions or identifying trends in patient diagnoses.
Medical codes are so complex that they can be mistaken for a different language by a layperson. In fact, ICD-11, the latest international standard for medical coding, has over 55,000 diagnostic codes. This is four times the number of codes that were in WHO’s ICD-10. The complexity is further increased by the ever increasing quantity of incoming data. When you consider an aging population where 40 percent of adults have more than one chronic disease, it is a wonder how a medical coder can decipher the right code efficiently. This is perhaps one of the reasons why medical coding is one of the fastest growing professions in the US. The US Bureau of Labor Statistics projects an 8% growth for the profession between 2019 and 2029. This is faster than many other professions.
ML algorithms can be trained to analyze large amounts of data and identify patterns and trends that may not be immediately obvious to humans. This can be particularly useful for identifying trends in patient diagnoses or identifying common medical conditions that may be missed by human coders. The technology available currently does not mean that we can completely automate medical coding with AI-driven bots. Medical coding is so complex and nuanced that while most medical coding companies use AI and ML algorithms to narrow down the search of the right code, it still takes an expert medical coder to finally decide on the right code from the choices given by the algorithm.
Machine learning algorithms can be embedded into a medical coder’s workflow so that the process is made more efficient and accurate. The intent of these interventions are to improve outcomes that would shorten payment cycles and mitigate audit risks.