The first piece in this blog series examined whether autonomous medical coding or other artificial intelligence (AI) and machine learning tools could replace human medical coders. Both ChatGPT and the Bureau of Labor Statistics agreed that while AI will influence the profession, human involvement will remain essential. ChatGPT noted, however, that the role of medical coders is likely to evolve with the integration of AI.
This evolution raises questions about how AI will reshape the tasks and responsibilities of medical coders. ChatGPT's insights suggest that these changes will not only redefine the coder’s role but also alter the skills required to thrive in a field increasingly shaped by advanced technologies.
In part three of this series, we’ll explore how medical coders can adapt their skill sets to meet these new demands. For now, this piece focuses on how the role of the medical coder might shift as AI becomes an integral part of their daily work.
Question
How will the introduction of autonomous coding systems or other artificial intelligence or machine learning affect the work of human medical coders?
Response
The introduction of autonomous coding systems and other artificial intelligence (AI) or machine learning (ML) technologies will significantly affect the work of human medical coders, but in ways that will likely enhance their roles rather than eliminate them. Here's how AI and ML are expected to impact medical coders:
1. Increased Efficiency and Productivity
AI and ML systems can process large amounts of data quickly, helping coders automate repetitive and time-consuming tasks. For example, AI can assist with the initial coding of common procedures or diagnoses, reducing the amount of manual work required. Coders will be able to focus their attention on more complex or nuanced coding tasks, improving their overall productivity.
Impact on coders: Coders will spend less time on routine coding assignments and more time on high-level review and complex cases, leading to faster claim processing and less manual data entry.
2. Improved Accuracy and Reduced Errors
AI systems trained on large datasets can help identify patterns and ensure that the most accurate codes are applied based on the medical documentation provided. By assisting in this process, AI can reduce human error and inconsistencies that arise from manual coding, such as misinterpretation of medical records or failure to apply the correct modifier or code.
Impact on coders: Human coders will rely more on AI to ensure accuracy, reducing errors and minimizing the chances of audits, denials, or compliance issues. Coders will, however, need to verify AI-generated codes, especially in complex cases, ensuring the final coding reflects the correct interpretation of the medical documentation.
3. Focus on Complex and Specialized Cases
Autonomous systems can handle routine or straightforward coding tasks (e.g., coding common procedures or conditions), but human coders will still be needed for more complex, rare, or nuanced cases that require contextual understanding. For example, cases with unusual diagnoses, comorbidities, or multiple treatments will still require human intervention to ensure proper coding.
Impact on coders: Coders will likely shift their roles towards handling specialized, ambiguous, or complex coding situations. Their work will involve more decision-making and problem-solving, which is where their expertise will remain essential. This shift can help coders become more strategic contributors to healthcare teams.
4. Increased Demand for Coding Audits and Quality Control
While AI can assist with coding, it will still require oversight. AI systems might generate errors, particularly in cases that deviate from standard protocols or when working with poor-quality data. Coders will need to review and validate AI-generated codes, ensuring that all codes are compliant with healthcare regulations and payer requirements.
Impact on coders: Coders will spend more time on quality control, auditing, and ensuring that the automated systems are working correctly. They will need to refine their skills in identifying discrepancies and correcting errors, particularly for complex or non-routine cases.
Conclusion:
The introduction of autonomous coding systems and AI/ML technologies will change the nature of the work for human medical coders, but it will not eliminate their role. Instead, coders will be empowered to focus on more complex tasks, such as quality control. Coders’ expertise in medical coding and understanding of healthcare processes will continue to be essential for the accuracy and integrity of the healthcare system. AI will be a tool to augment human capabilities, making coders' work more efficient, accurate, and impactful.
The most conspicuous adjustment in a coder’s work as suggested by ChatGPT is a shift in responsibility to handling complex rather than routine cases. Handling more complex cases will require coders to have advanced coding expertise. Advanced expertise can take many shapes including:
Stay tuned for Part 3 in this blog series, Artificial Intelligence and Medical Coding Part 3 - AI’s Impact on a Coder’s Skill Set to find out how a coder’s skills will need to grow and adapt to keep up with the changes brought about by the implementation of AI tools.
Since 1992, HIA has been the leading provider of compliance audits, coding support services and clinical documentation audit services for hospitals, ambulatory surgery centers, physician groups and other healthcare entities. HIA offers PRN support as well as total outsource support.
The information contained in this coding advice is valid at the time of posting. Viewers are encouraged to research subsequent official guidance in the areas associated with the topic as they can change rapidly.