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Written by: Carrie Ayala | HIM Practice Director | Oxford Global Resources
Artificial intelligence (AI) is changing the way we approach healthcare information management (HIM) coding. AI is playing a pivotal role in enhancing HIM coding accuracy with improved documentation and human validation. The synergy between technology and human expertise promises to streamline processes, reduce errors, and improve patient care.
AI implementation within HIM coding is currently one of the leading discussions in healthcare. While some people believe AI will completely take over coding tasks, they fail to account for important elements like the quality of documentation and essential human supervision.
HIM coding involves the classification and documentation of medical diagnoses, procedures, and treatments. Accurate coding is critical for billing, regulatory compliance, and statistical analysis. However, the complexity and volume of medical data pose significant challenges. Traditional manual coding methods are time-consuming and prone to errors, leading to potential discrepancies in patient records and accompanying financial losses. Accordingly, AI can simultaneously boost efficiency while minimizing human mistakes.
The potential of AI in medical record coding remains limited, because inadequate clinical documentation prevents accurate record coding. The accuracy of AI coding systems suffers when processing structured data that contains documentation discrepancies or cloning. The current system demonstrates the indispensable role human coders serve.
The Role of AI in HIM Coding
So, what exactly is the role of AI in HIM coding? AI technologies, such as machine learning (ML) and natural language processing (NLP), have the potential to transform HIM coding. By automating data extraction and classification processes, AI can significantly reduce the burden on human coders and improve accuracy. AI algorithms can analyze vast amounts of medical data, identify patterns, and classify codes with unprecedented precision.
AI can enhance documentation by ensuring that medical records are complete, accurate, and standardized. NLP algorithms can extract relevant information from clinical notes, lab reports, and imaging studies, transforming unstructured data into structured formats. This saves time and minimizes the risk of missing critical information, leading to more reliable patient records, essential for effective treatment planning and continuity of care.
Additionally, AI-driven automation streamlines coding processes, reducing the time required to classify and document medical data. Human coders can work more efficiently, focusing on complex cases that require clinical judgment. This increased productivity translates into faster billing cycles and improved revenue management for healthcare organizations.
Finally, AI can contribute to substantial cost savings by minimizing errors and optimizing workflows. Accurate coding reduces the likelihood of claim denials and audits, leading to higher reimbursement rates.
One advantage of AI in documentation is its ability to learn and adapt. Machine learning models can be trained on large datasets to recognize clinical language and terminology, enabling them to accurately interpret complex medical information. As these models evolve, they become increasingly proficient in handling diverse data sources and specialties.
The Need for Human Oversight
AI in HIM coding is not without its drawbacks. Human validation remains essential to ensure the highest level of accuracy. Human validators can review and validate codes generated by AI systems, providing an additional layer of quality control, meaning human coders are not obsolete. They bring clinical expertise and contextual understanding that AI algorithms may lack. By working in tandem with AI, human coders‘ functions continue to change and develop, not cease to exist.
AI-generated codes need validation thorough documentation reviews to maintain both accuracy and compliance standards. Coders can help fill the gap by collaborating with healthcare providers to enhance documentation quality, which subsequently improves AI performance.
A recent study found that AI had success rates below 50% when performing medical coding tasks on its own, demonstrating the need for continued human oversight. Large language model (LLM) GPT-4 performed the highest among the models in producing codes „that technically conveyed the correct meaning,“ meaning it was able to grasp certain medical terminology nuances. However, the study found that it still exhibited „a significant number of errors.“ Other models tested included GPT-3.5, Gemini-pro, and Llama-2-70b.
AI is the Future of HIM Coding and Healthcare Billing
It’s undeniable that AI integration is growing, and the need to work alongside AI in some capacity in all industries is inevitable. In September 2024, St. John’s University Associate Professor and Director of Research Syed Ahmad Chan Bukhari, Ph.D., was awarded a grant totaling over half a million ($550,000) „to develop an AI remedy to the time-consuming process of medical coding and healthcare billing.“ The grant given by the U.S. National Science Foundation seeks to „use AI’s machine learning capabilities to read doctor’s notes, identify symptoms that could lead to diagnosis, and, later, code the conditions for more efficient insurance filing.“
The one-time grant funding two years‘ worth of research shows the importance of integrating AI into healthcare and, more specifically, medical coding and billing; but it does not diminish the role of human coders. Instead, it’s imperative for human coders to learn how to leverage AI in their position.
The collaboration between AI and human validators should foster a symbiotic relationship where each complements the other’s strengths, with the idea being to empower rather than replace. Accordingly, AI handles routine and repetitive tasks, freeing up human coders to focus on more complex cases and decision-making. This enhances efficiency and productivity and ensures nuanced medical information is accurately captured.
In short, the use of AI in HIM coding represents transformation instead of replacement. The expert knowledge and meticulous attention to detail of coders make them essential for producing precise and productive AI coding results. Healthcare providers who concentrate on documentation enhancements will find that human coders maintain their critical role in validating and optimizing the performance of AI systems.
Oxford Can Help
We deliver customized staffing solutions that support healthcare organizations when incorporating AI into HIM coding operations to achieve strategic targets.
Despite its promising benefits, integrating AI in HIM coding is not without challenges. Ethical considerations, data privacy, and the need for continuous training and validation are critical factors that must be addressed. Healthcare organizations must ensure that AI systems are transparent, accountable, and compliant with regulatory standards. Oxford can help.
When you partner with us, we can help ensure that AI works for you and not against you. The future of AI in HIM coding is bright. Let Oxford help move you into the future, ensuring that your healthcare information management evolves to meet the demands of a rapidly changing healthcare environment.
