Discover how clinically trained AI has begun transforming risk adjustment. By improving accuracy, increasing efficiency, and enhancing visibility, AI offers incredible potential. However, understanding its limitations is crucial.
Within the last few years, the use of artificial intelligence (AI) within the healthcare industry has gained significant momentum. Within risk adjustment and medical coding, AI technology presents an opportunity to increase the accuracy and efficiency of coding projects, thereby improving population health management, ensuring regulatory compliance and accurate reimbursement.
Unfortunately, natural language processing (NLP), the subset of AI that’s been utilized in medical coding for years, has fallen short of its potential, resulting in a general negative reaction when medical coders hear the words, “NLP” or “AI.”
This leads us to the emergence of clinically trained AI technology, a game-changer in the medical coding landscape. To effectively support medical coders, AI technology must be customized, have domain-specific training, undergo ongoing refinement, and integrate expertise from subject matter experts to ensure accurate and compliant coding practices.
As Medicare Advantage Organizations (MAOs) adopt and integrate clinically trained AI technology into their medical coding processes, it’s crucial to appreciate its capabilities while remaining aware of its limitations.
Understanding what clinically trained AI brings to your MAO’s coding practices
Increase coding production
Clinically trained AI technology can organize a chart and automate the coding processes by extracting and pre-populating relevant information including data such as, the suggested risk adjusted ICD-10-CM code, encounter date, provider name, signature, note type, section, provider type, and the corresponding clinical support. By supplying this information automatically, it reduces the keystrokes and mouse clicks needed to review a chart. Additionally, this information can be presented to the coder only when pertinent, allowing the coder to focus on the diagnosis being reviewed thereby speeding up the coding process and reducing manual efforts.
Improve coding accuracy
Clinically trained AI models use information from standard content (e.g. ICD-10-CM) for any given year, therefore it doesn't allow for typos that happen during manual coding processes. Clinically trained AI aligns with coding guidelines to consistently ensure compliant coding practices and identify all risk adjusted codes in structured and unstructured text enabling complete code capture. Additionally, it can identify and link relevant clinical supporting documentation such as symptoms, procedures, medications, and laboratory information
Increase visibility
Clinically trained AI can help enhance visibility into coding projects in three ways:
- Produce detailed reporting on coding results that can aid in identifying trends for coder and physician education initiatives.
- Assist in coding project management to include detailed production and accuracy metrics, allowing managers to have real time insights into coding activities.
- Assign confidence scoring to each diagnosis prior to a human coder reviewing the chart which will assist in chart prioritization, saving valuable time when determining which charts need to be reviewed.
What clinically trained AI cannot do in your MAO's coding practices
Substitute coders
Medical records are complex and varied, requiring human understanding and reasoning, especially when it comes to interpreting diverse documentation styles and applying compliance guidelines. Each EMR template displays information differently, each physician has a unique documentation style, and each medical record will contain a diverse range of information. Clinically trained AI technology has limitations when it comes to understanding a medical record, particularly in tasks requiring common sense reasoning because variability in documentation is where a human coder is absolutely required. Once a risk adjusted diagnosis has been identified, the coders must apply the Official Guidelines for Coding and Reporting in the ICD-10-CM book to ensure compliance.
Let’s face it, with the recent regulatory changes concerning RADV repayment methodologies, there is simply too much on the line financially to not get it right. Clinically trained AI is intended to complement and empower the coder to do their important work better, faster, and more accurately.
Be perfect
We use AI in our daily lives, and we know AI doesn’t always get it right. How often do the ads or suggestions on Instagram or Amazon get it right? How often does it get it wrong? The answer probably isn’t 100 percent right or 100 percent wrong but somewhere in between. This applies to clinically trained AI as well. However, by collaborating with AI technology, coders can capitalize on both their expertise and the technological intelligence to achieve maximum efficiency and effectiveness in coding processes.
What the future holds for AI and risk adjustment
Clinically trained AI has immense potential for revolutionizing risk adjustment programs by bolstering productivity, accuracy, and visibility. At the same time, it is essential to acknowledge its limitations and the ongoing need for human expertise and iterative refinement. By harnessing the collaborative power of clinically trained AI and human coders, MAO’s can achieve optimal outcomes in their risk adjustment programs.
If you’re interested in learning what advanced technology can do for your risk adjustment program, we’re here to help! Watch our on-demand webinar or reach out today to learn more about how clinically trained AI powers the Health Language Coder Workbench and can help you identify 5-7% net new codes.
