Artificial intelligence (AI) is a term we have been hearing a lot lately. Most of the time it concerns the variety of problems AI is solving along with future solutions to those problems. In recent years, health care has adopted AI technology in a number of areas including disease diagnosis, imaging analytics, virtual telehealth services, and most recently, EMR integration. The possibilities for the application of AI are endless, especially when it comes to optimizing common processes in the health care industry, such as medical billing and coding.
What exactly is AI?
Robots and flashbacks to Terminator might ring a bell for some, but AI is actually a very broad term with many branches of application. According to the National Institute of Standards and Technology, or NIST, “Artificial Intelligence (AI) refers to computer systems that think and act like humans, and think and act rationally.” Simply, AI is the capability to learn and problem solve by machines, in contrast to how humans display these behaviors naturally. For machines, the ability to learn and make decisions require lots and lots of data. An algorithm is needed to then test this data, until eventually the machine learns how to tell a bee from a flower, or better yet, identify enlarged lymph nodes in a CT scan of the chest while simultaneously scanning the patient’s medical record to determine if they have lung cancer. These current applications work to make physicians’ workflow more efficient. What if there were a way for AI to similarly improve the efficiency of medical billing?
What problems are the medical billing and coding space facing today?
It depends on who you ask, but the error rates in medical billing are reportedly pretty high. According to a study conducted by the American Medical Association in 2013, the error rate for medical billing was at 7.1 percent, while a study by Nerdwallet in 2014 put the error rate for Medicare claims at a whopping 49 percent. This can be due to many factors such as upcoding, duplicate billing, and even the digit transposing of ICD and charge codes. Most of this is at the hands of human error, but certainly not without reason. With the transition change from ICD-9 to ICD-10 codes in 2015, the amount of codes increased by five times the original amount. Now having roughly 68,000 ICD and 8,000 charge codes to work with, medical billers and coders have had to learn and process tons of new information. In this period of getting used to a new system, there was a 50% loss in productivity at first, but the industry has since adapted. The system will surely change again, and I can’t help but wonder if machine learning might be able to work a little bit of its magic in the medical billing and coding space.
Using AI to assist with medical billing and coding
By using machine learning techniques to process data, it may be possible to deploy an application that can process medical codes and billing codes, identifying errors and making appropriate corrections. AI could be integrated as an assistant and allow for medical billers and coders to work more efficiently and accurately by flagging mistakes and suggesting possible fixes. Having an automated process where computers take care of the more tedious and common clerical tasks, will allow for medical billers and coders to spend their time on the more difficult and comprehensive issues, such as insurance appeals and the claims process. The end result of introducing AI to medical billing and coding: less errors, more money, and more time!
What’s on the horizon for AI in health care?
It took nearly a decade to develop ICD-10 codes and another decade to implement. Innovations in health care can sometimes be referred to as “slow moving giants”, so it’s possible we won’t see AI completely taking over right away, but we can expect to see the adoption of machine learning more frequently. According to Healthcare IT News’ reporting on HIMSS18 “Allscripts, Athenahealth, Cerner, eClinicalWorks and Epic revealed big plans for adding AI into the workflow in forthcoming iterations of their electronic health records platforms.” As you can see, AI is no longer this untouchable, sci-fi enigma, but now is something that is actually being utilized in our day-to-day. There’s much more on the horizon for AI’s role in health care, so we'll just have to stay tuned!
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