Artificial intelligence (AI) has already been a game-changer for many industries, and the insurance industry is no exception. Its effects are especially seen in the claims processing domain. Insurance claims currently are prone to a lot of costly human errors when entering information. In fact, $71 billion is the result of mistakes made during patient registration, which is a crucial initial step in the claims process. This amount of money is equivalent to a third of all hospital administrative costs. This enormous financial waste highlights the pressing need for the help of AI. 

Why Insurance Processing is So Error Prone

There are four main variables that cause the high error rate in traditional insurance processing. First of all, there is a chance of inaccuracy when patient information identification is not easily identifiable. The lack of precise information on insurance cards can make it more difficult to identify the correct insurance payer, electronic eligibility payer ID, and claims payer ID. Secondly, another layer of complication is added by the requirement to accurately determine the payer’s location while adhering to insurance companies’ specific regulations. Errors in this phase may lead to the instant denial of claims. It can be really expensive to rework these claims, with $25 being required to resubmit each inaccurate claim, in addition to other net charges. 

The third main reason for problems in the insurance processing process is human error. Errors continue to occur even with digital input systems, which adds to the alarming 19.3% mistake rate in healthcare insurance information reporting. This indicates that, on average, there is a mistake in one out of every five claims. Lastly, the inefficiency of processing claims is caused by the inability of current Optimal Character Recognition (OCR) software solutions. This software is designed to quickly pull information but it struggles to process insurance information that is not printed on cards. The incapacity to process digital insurance cards—a growing patient trend—highlights the antiquated state of this current optical character recognition technology.

Introducing AI

AI’s introduction presents a viable way to overcome these restrictions in the insurance claims procedure. AI-powered solutions use training on a large dataset of over 20,000 plan types and 4,000 insurance payers to be able to quickly check insurance information. This helps the AI become competent at confirming information in payers in less than five seconds, which is an incredible improvement from the five to fifteen minutes needed for manual collection in typical processing. AI can almost instantaneously identify the important pieces of data, such as insurance type, group number, claims PayerID, and plan type, which streamlines the entire claims process.

Conclusion

In summary, the use of AI in insurance capture is a revolutionary step forward that offers significant cost savings along with faster and more accurate information collection. By utilizing AI solutions, the problems now caused by manual procedures, human error, and outdated OCR technology can be resolved. The future looks bright for the insurance sector as it becomes more reliant on these innovations, especially within the realm of reducing financial losses and increasing operational effectiveness.