AI is on track to transform every industry, and healthcare is no exception. And while medical care will certainly see the impacts of AI with enhanced diagnostics, personalized treatments, predictive analytics and more, so too will the management side of healthcare businesses – especially when it comes to the medical revenue cycle. AI, in the very near future, will support and upskill medical coders by reducing human error and assisting in audits and reviews through machine learning and Natural Language Processing (NLP). Below I explain how, plus share the pros and cons to consider as AI is further integrated into the healthcare system.
Reducing Human Error
AI’s ability to reduce human error is a key factor in improving medical coders’ day-to-day lives. It will help analyze medical data and assign the most accurate codes. This process guarantees efficiency and accelerates the billing process, successfully resulting in fewer claim denials and faster payments for the practice. As AAPC explains in their AI in Medical Coding and Billing Course, “AI algorithms can analyze vast amounts of medical data, including patient records, clinical documentation, and coding guidelines. By leveraging AI, human coders can receive real-time suggestions and recommendations during the coding process, reducing the likelihood of errors and ensuring compliance with coding guidelines.” AI’s ability to speed up the coding process allows coders to focus on more complex cases, ensuring higher productivity rates without compromised accuracy. With the help of the right AI software and programming, health practices have a lot to look forward to. Being able to submit clean claims each time is extremely important and benefits everyone in the practice, including patients.
Auditing Medical Data
Conducting regular audits and reviews of providers’ data is a necessary task for healthcare management. With the help of machine learning and NLP, AI will be able to examine patterns and trends in providers’ documentation to identify possible errors or inconsistencies that may be missed by the human eye. AI technologies provide instant feedback regarding coding corrections that need to be changed and any compliance issues that may be present. This will benefit coders during the audit and review process by enhancing effectiveness and quality, reducing cost, and improving analytics.
AI Tools for Medical Coders
The main AI tools that will assist medical coders are machine learning and Natural Language Processing (NLP). The three types of machine learning are supervised, unsupervised, and reinforcement. A supervised AI algorithm will be able to predict the disease of a patient based on their symptoms. An unsupervised AI algorithm is used to cluster patients together based on similarities. This knowledge can be used for research of new drugs and diseases. Lastly, reinforcement learning AI algorithms can be used to develop treatment strategies. This involves finding the best order of treatments for a patient with a chronic disease based on the patient’s response to previous treatments. Each machine learning tool has its own strengths and is suitable for different use cases.
The Pros and Cons of NLP
NLP combines computer science and artificial intelligence (AI) into a machine learning device that enables computers to understand and communicate with human language. As with just about everything in life, there are pros and cons to NLP. Pros include faster insight discovery, greater budget savings, and quicker access to corporate data. Organizations can find patterns, trends, and similarities between different pieces of content, allowing for more precise decision making. By automating the gathering and processing of information, organizations will have less manual effort. Some cons to NLP revolve around misinterpretation, new vocabulary, and tone of voice. If providers speak into the software incorrectly, it could download false information into the patient’s records, resulting in more work for the practice and possible fraud. “Human language is filled with many ambiguities that make it difficult for programmers to write software that accurately determines the intended meaning of text or voice data,” shares Jim Holdsworth of IBM. “Human language might take years for humans to learn – and many never stop learning. But then programmers must teach natural language-driven applications to recognize and understand irregularities so their applications can be accurate and useful.” For this reason, it will be vital that medical coders help set up the NLP flow.
In conclusion, AI is not only beneficial for medical coders, but it is in fact helpful for the entire medical practice. The benefits go far beyond what I’ve detailed in this article. Some medical coders may be worried that AI will take over their career, but luckily that is not the case. Medical coders will remain responsible for the final review of providers’ documentation to determine the most accurate codes. Medical coders will also be in control of updating AI’s algorithms to provide guideline changes for ICD-10-CM, ICD-10-PCS, CPT, and HCPCS Level II. As an advanced tool for coders, AI will help drive organizations’ overall development and progression – if teams are willing to embrace it.
References:
AAPC – AI in Medical Billing and Coding – Student Course
What is NLP (natural language processing)? Jim Holdsworth https://www.ibm.com/topics/natural-language-processing
Medical Auditing in the Age of AI: Revolutionizing Healthcare Efficiency and Accuracy https://gebbs.com/blog/medical-auditing-in-the-age-of-ai-revolutionizing-healthcare-efficiency-and-accuracy/
Michelle Hornsby is a medical coder at Medic Management Group and is responsible for working with external clients and MMG revenue cycle management staff to ensure that provider claims are accurately submitted and appropriately reimbursed. chornsby@medicmgmt.com