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AI’s Insurance Processing Solution

AI has been one of the most disruptive technologies ever released, and has already had far-reaching impacts in many industries. The insurance industry, and specifically the processing of claims, stands much to gain from the introduction of AI. Traditional insurance processing requires a lot of time, money, and accuracy that frequently goes unrealized. There are also no established and efficient insurance data intake methods in place in PM/EHR systems.

Due to the inefficiency of the existing system, health insurance denied claims annually cost an incredible $262 billion. 27% of all denied claims, which is equivalent to $71 billion, is due to errors at patient registration, which is very early in the claims process. This huge waste of money is equivalent to 1/60 of all healthcare spending in the US, and a third of all hospital administrative costs. Clearly, there is a lot of financial gain from reducing this amount of waste.

There are four main reasons that there are so many errors in the insurance processing procedure. Firstly, conventional insurance processing requires human expertise to identify the correct patient information. For example, not only does the insurance payer need to be identified, but also their electronic eligibility payer ID and the accurate claims payer ID where claims must be submitted. This information can be fickle to find because most insurance cards don’t clearly designate this information.

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Secondly, there are a lot of hoops to jump through to correctly identify the correct payer’s location. Insurance companies such as BlueCross BlueShield, Medicare, and Tricare have very specific requirements to select the right locality. Failure to identify it correctly can lead to the claim being denied right off the bat. This is tied to the third error cause, which is human error. The employees that are handling the claims might select the wrong locality, and even the current digital intake systems struggle to decode information from the insurance cards alone. In fact, the error rate is 19.3% among all healthcare insurance information reporting, meaning there is a mistake for every 1 in 5 claims.

Finally, many of the current Optimal Character Recognition (OCR) softwares solutions struggle to identify insurance information that isn’t printed on a card. This proves to be a huge setback for claims processing, because most of the major insurance companies don’t print payer IDs on cards, which leads to cumbersome human intervention. Traditional OCR solutions also can’t process digital insurance cards, even though this is becoming a more popular method with patients. The existing technology is clearly outdated, and without complete reconstruction, there is no way to eliminate misidentified payer and payment information, incorrect data entry from human error, and inaccurate coverage determination.

This is where AI comes in- it is the best way to overcome these limitations in the insurance claims process. Some current AI powered solutions are designed to verify insurance information on a human. They have been trained on 4,000+ insurance payers and 20,000+ insurance plan types in order to quickly validate the information with payers in less than 5 seconds. This information, such as the insurance type, the group number, the claims PayerID, and plan type are all almost instantly identifiable with AI. Traditional insurance processing can take 5-15 minutes to collect the information manually.

These AI solutions can help save a lot of money with their higher accuracy rates. Each reworked claim due to incorrect information input costs $25 per claim, plus net charges incurred by rerouting. Providers can save up to 80% using AI powered insurance card information capturing services. The future is looking very bright for the insurance claims industry if they choose to pursue this technology further.

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