Making an insurance claim, especially within the healthcare industry, can be extremely difficult. There is a fairly high percentage of claims that get denied for a multitude of reasons, but each denial is expensive. In fact, annually, all of these health insurance denied claims equal more than $262 billion, due to the expenses that come with resubmission and re-reviewal. 27% of these denied claims, which is $71 billion, is actually due to errors that are completed at patient registration, which is the very initial step of the claims submission process. This means that billions are wasted on simple mistakes that come from entering information incorrectly. Let’s learn more about how to fix health insurance card capture below.
Fixing the Health Insurance Claims Process
So why are so many mistakes committed early on in the healthcare insurance claims process? There are a few main reasons that have been identified. Firstly, traditional insurance processes usually require some form of human expertise. These people have been trained to identify information such as the insurance payer, the electronic eligibility payerID, and where the claims must be submitted. This requires an extensive amount of domain expertise because most insurance cards don’t indicate this information blatantly.
Another hoop to jump through in the claims submission process is how difficult it is to select and submit the correct location of the payer. Insurance companies such as BlueCross Blue Shield, Medicare and Tricare have confusing layers of state, local, and ZIP claim processes that all require selecting the right locality, otherwise leading to an outright denial of the claim. There are important nuances to understand, including the fact that there are 12 medical claims and 4 equipment claims for Medicare, 2 Tricare regions, and 34 multi-state localities.
Sometimes, human error is unavoidable when retrieving and then recording this information. In fact, there is an average 19.3% error rate among all health insurance information records in the United States. Even current digital intake systems are not capable of capturing and decoding information from the insurance cards alone. This means that human error is almost unavoidable as long as there is a manual part of the information gathering process. Manual errors can lead to employee dissatisfaction and burnout due to the repetitive nature of this part of the process. It is actually the second largest cause of medical turnover.
A form of a digital intake system that is currently used is an OCR solution, otherwise known as optical character recognition technology. It is helpful for being able to convert documents into editable and searchable data. However, it struggles to perform this function on insurance information that isn’t printed on a card. This is a huge inconvenience for providers. Most major insurance companies don’t usually print the required information on the cards. This means that traditional OCR systems usually need a lot of manual intervention, which slows down the processing time. Also, OCRs can’t even process digital insurance cards, even though more patients are using these more than ever.
AI solutions for health insurance card capture have been identified by experts as the saving grace of the insurance claims industry. They are perceived to be the best way to overcome the human limitations in insurance processing. Also, they can reduce the risk of misidentifying patient and payer information, incorrect data entry, and inaccurate coverage determination. Hopefully, AI-power platforms will be able to capture, verify, and process insurance information without any hiccups soon enough.
Some of the AI solutions entering the market have been trained from datasets. They are made up of thousands of insurance card data points. This includes insurance payers and insurance plan types. They have gotten so fast at validating information. Such information includes insurance type, payer name, and plan type that it can be completed in five seconds. This is a huge improvement from the five to fifteen minutes that it takes to process insurance information manually. It also presents massive cost savings; each reworked claim costs an average of $25 to recomplete. These AI solutions can save providers 80% just by getting the information right on the first try.