Healthcare coding is a challenging enterprise. Conditions are often ambiguous, and treatments may vary to account for specific circumstances. As such, medical codes frequently fail to match real-world complexities.
The most recently adopted U.S. coding system, ICD-10-CM, is far more detailed than previous models. While this system has increased the nuance coders can describe, its depth is daunting. So, coders are increasingly relying on automation-enabled RCM services to accurately categorize conditions and treatments.
Here’s how automation will impact the future of medical coding services.
AI specializes in pattern recognition. It can sort through thousands of codes and identify keywords that match a physician’s description. These systems are flexible enough to incorporate a patient’s age, previous health conditions, and the procedure’s unique details
Such details are integral to handling ICD-10-CM’s complexity.
In 2020, the denial rate of claims rose to roughly 11%. Coding errors only accounted for approximately 5% of these denials. However, insurers rejecting 5% of claims still costs practices billions of dollars annually.
Accurate coding is highly demanding because staff are overworked and face increasingly complex codes. Automated programs help this problem by delivering pinpoint matches most of the time. When paired with an experienced coder, these systems are far more accurate and faster than human-only teams.
Some systems are nuanced enough to handle highly complex circumstances. However, these circumstances are where most mistakes occur. Even advanced AI can struggle to differentiate between previous ailments and new conditions undergoing treatment.
Services and conditions frequently have multiple codes that are technically appropriate matches. Unfortunately, most programs are too underdeveloped to recognize which option is most beneficial.
These limitations often result in significant errors. Coders overestimate AI’s ability and fail to routinely review patient charts and physician notes. To fully benefit from technology, practices must learn its restrictions. AI is a tool to enhance coders’ knowledge, not a replacement.
Modern healthcare artificial intelligence can code simple conditions and services. Previously, inexperienced billers handled these cases. With AI taking over this role, green coders are far less valuable.
Instead, practices must hire proven billers who understand medical codes and automatic coding systems. Unfortunately, this demographic is rare, and many providers struggle to find appropriately credentialed staff.
Increasingly complex medical codes and AI is turning healthcare coding into a specialized task. Highly trained, experienced workers are necessary to gain significant value from automatic systems. As such, many practices are turning to professional coding and billing services.
Plutus Health understands how to use healthcare AI to boost efficiency. We’ve submerged our team into a detailed study of how to use these systems effectively. Our clients have seen the benefits of this research in the form of fast and high reimbursements. Visit our website to discover our improvement options for your practice.
1. Coding AI can sort through thousands of codes to find the appropriate match.
2. Programs are faster and more accurate than human coders.
3. Automatic systems still still require experienced oversight to accurately code complex cases
4. Practices struggle to find highly trained workers to oversee AI coding.
5. Professional coding and billing companies invest in the training and tools to create a powerful staff/AI team.
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