The 21st century has witnessed rapid advancements in the field of Artificial Intelligence (AI), transforming the way we work and live our lives. Since its inception, AI has significantly impacted the employment landscape and has boosted the functioning of industries such as banking, finance, retail, gaming and especially healthcare. It has revolutionised how healthcare is delivered by improving medical diagnosis, increasing accuracy of results and improving patient care outcomes. Generative AI (GenAI) or Large Language Models (LLMs) can create fresh content in audio, video, music and even code. It is especially helpful in the process of medical coding, the process of assigning a dedicated code to medical tests and procedures.
While AI has been a boon for various industries, it has also triggered anxiety in the workforce concerning the fear of being replaced by technological advancements. Within the healthcare industry specifically, the rise of GenAI has prompted several conversations around the ‘AI proof’ nature of medical coding. Medical coding is a challenging field that requires constant human intervention as coders often have to deal with complicated cases and ambiguous information.
A brief introduction to medical coding
Medical coding is the process of assigning medical procedures, diagnosis, services and equipment to a designated code. These alphanumeric codes are universal and ensure standard communication and clinical documentation. This process is critical in ensuring the efficient and smooth functioning of the healthcare industry. Medical coders have to review patient records to assign a particular service or diagnosis to its respective code. This is essential for the purpose of creating bills through which healthcare professionals receive appropriate payment from insurance companies and patients for their services, also known as medical billing.
Moreover, medical coding contributes to medical research, calculating statistics and keeping track of diseases. In addition to being well-versed in medical terminologies and processes, medical coding professionals also need to follow and maintain strict ethical standards, since they work with highly confidential patient data.
The transformative impact of GenerativeAI
AI and machine learning tools have enhanced the medical coding as well as the billing process, which has led to increased accuracy, efficiency as well as improved patient outcomes. Traditionally, medical coding has been a labour-intensive task and medical coders are required to have a vast array of knowledge about the medical industry and its terminologies. However, with recent advancements, many aspects of the healthcare industry, including medical coding, have experienced significant transformation. Coders no longer need to go through the time-consuming process of assigning a standardised code to every medical procedure, as AI can fastrack this process. By automating time-consuming tasks such as the assignment of codes and clinical documentation, Gen AI reduces the time and effort required for medical coding and allows coders to prioritise tasks that require strategic thinking and human judgement. Additionally, Gen AI is trained on vast sets of data and it can easily identify medical diagnosis and procedures from medical documentation. This reduces the possibility of coding errors and ensures that the coding process is being carried out smoothly.
One of the primary objectives of medical coding is to create medical bills so that healthcare providers can be adequately compensated for their services. Faster and more efficient medical coding will speed up medical billing processes as well as ensure that medical professionals are being reimbursed on time. By identifying inconsistencies in the code or errors in clinical documentation, GenAI algorithms provide real-time feedback to medical coders. This ensures they do not have to go through the cumbersome task of verifying the entire code and helps them prioritise other functions.
Since, Medical coding entails dealing with highly confidential and sensitive data such as medical diagnosis, results and patient histories, healthcare organisations must ensure that their AI systems are reliable and medical records are in safe hands. Protecting patient data and ensuring compliance with medical standards and regulations is necessary for organisations to avoid legal as well as financial consequences. In this context, human oversight and intervention are essential as AI alone should not be relied on to handle sensitive information. Organisations should have several measures in place including continuous monitoring of algorithms and training programmes for employees to avoid data breaches and ensure data integrity.
Upskilling in the age of medical coding
With GenAI being such a critical component of healthcare functions, coders also need to keep themselves updated on the latest technology and trends in the industry. This has led medical coders to also develop a more nuanced understanding of the technology and allowed them to stay relevant in the evolving field. The rise of GenAI has also opened up new career opportunities for coders since it allows them to explore additional avenues. Coding professionals can transition into roles such as prompt engineers, coding auditors or data quality analysts among other arenas of work.
To continuously upskill their coders, healthcare firms can invest in corporate training initiatives and organise Learning and development sessions that focus exclusively on AI. Healthcare organisations can also collaborate with other corporate entities to provide comprehensive training to coders. This can include programmes that provide training on understanding AI and its usage as well as how coders can work alongside AI. Coders should also be encouraged to pursue vocational courses or diplomas in subjects related to AI and healthcare. If implemented, these strategies can effectively upskill coders and give them the knowledge and skills to further their career and professional growth.
The future of medical coding: Can AI replace medical coders?
While some aspects of medical coding can be automated and taken care of by AI, human intervention is still necessary in the overall process. Humans can interpret ambiguous medical information, and make situation-specific decisions, something difficult to replicate with AI. Moreover, humans are still required to oversee optimal AI functioning, so rather than replacing humans, AI is more apt to augment the process of medical coding.
Human intervention is also necessary to ensure accuracy and maintain the integrity of the coding process by making sure that any ethical rules and guidelines are not being violated. While AI offers numerous advantages to the industry, the probability of it replacing medical coders in the foreseeable future remains minuscule.