How AI Could Make Medical Coding Better in the Future

by  Find-A-Code™
Sep 5th, 2023

In recent months we have taken advantage of our blog to delve into some of the possibilities of integrating artificial intelligence (AI) into medical coding. We have discussed why AI deserves a place at the table, how it will change medical coding, and why coders don't need to be afraid of it. This post will continue that line of thinking by discussing how AI could make medical coding better.

AI vs. Machine Learning

Before we get started, some definitions are in order. AI and machine learning (ML) are often used interchangeably. They are often confused as well, so it helps to know the difference.

Artificial intelligence is actually a set of tools and technologies developed to give computerized systems the ability to mimic human cognition. A working AI tool can mimic conversational language, data analysis, problem-solving, etc.

Machine learning is just one of many AI tools. It is a tool and technology that gives computers the ability to gather and analyze vast amounts of data, then artificially make decisions based on comparing that data to other data sets. When we talk about integrating AI into medical coding, ML is included in the equation.

3 Ways AI Could Make Things Better

Moving on to AI's integration into medical coding, the potential it offers is quite impressive. Whether or not it will actually improve medical coding depends heavily on how it is implemented. We have a few ideas based on our own research. Here are three ways we believe AI could make medical coding better down the road:

1. Real-Time Assistance

One of the biggest challenges in medical coding is keeping up with all the updates, rule changes, policy implementations, etc. With tens of thousands of medical codes to work with and a mountain of rules to follow, getting lost in especially difficult cases is not hard. AI could make a coder's life easier by way of real-time assistance.

Imagine an AI system continuously fed updated information. Through ML, the system could analyze the new data and compare it against what a coder happens to be doing in real time. If the system suspects the coder might be going down the wrong road in the hunt for an obscure code, it could suggest what its data implies the correct code to be.

2. Improving Coding Accuracy

In addition to real-time assistance, a well-developed AI model could and should improve coding accuracy across the board. AI can spot errors in an instant. It doesn't have the ability to gloss over potentially inaccurate data after a long, hard day. It simply sees data and compares it to establish data sets. Therefore, AI is more capable of spotting errors.

3. Gradual System Improvement

Because ML gives AI systems the ability to constantly take new data and artificially learn from it, there is the potential of gradual system improvement through interaction between coders and AI systems. Coders provide feedback whenever they feel it necessary. The system takes that feedback and integrates it with current data sets. An ongoing combination of feedback and data analysis should gradually improve the system.

The possibilities discussed in this post only scratch the surface. We really don't know the limits of AI in medical coding because its implementation is still emerging. But imagine the possibilities. From optimal resource allocation to greater coding efficiency, AI's potential to finally accomplish medical coding's original goal is actually doable.

As a medical coder, there is no need to fear AI and ML. They are not going to replace you. But they are going to improve the way you do your job.