Artificial intelligence (AI) has become a disruptive force in a lot of industries. It might quickly begin to overtake the human-powered jobs for a lot of companies, due to its ability to mimic human thought and learning processes. It has the potential to begin this transformative process through its ability to analyze data, see patterns, and make predictions based on information at hand. This is especially true in roles that are characterized by simple and repetitive work. Surprisingly, the healthcare insurance field is one that stands to be changed a great deal by AI, especially when it comes to the area of claims processing procedures.
The current traditional claims submission method is costly and time-consuming, and a significant percentage of these claims are actually denied. The astounding annual expense of rejected claims approaches $262 billion. Mistakes made during the initial patient registration stage account for 27% of this total, or $71 billion. This suggests that even for legitimate claims, small mistakes like choosing the incorrect payerID or providing inaccurate patient information may result in complete claim denials. This is a substantial amount that is equal to one-third of yearly hospital administration expenditures and one-fifth of total healthcare spending in the US.
This high error rate is caused by a number of variables, the main one being the insurance processing industry’s reliance on human expertise. Workers are responsible for finding and recognizing important data, such as insurance payer information and electronic eligibility payer IDs. Employees must work hard to interpret insurance cards as most of them don’t display this information clearly. If they enter the wrong information, they run the danger of having the claims denied. In the United States, the average percentage of human error in health insurance data is 19.3%.
The challenge of providing accurate information adds even more complexity to the existing system. Claims submitters must choose the right location carefully in order for their claims to be approved. Blue Cross Blue Shield, Medicare, and Tricare all have stringent guidelines that must be followed for every state, city, and ZIP code. Due to this intricacy, workers must navigate 34 multi-state locales across the US, two Tricare regions, twelve medical and four equipment claim jurisdictions for Medicare.
There are also a lot of inefficiencies in the claims submission process caused by the shortcomings of the existing digital intake systems, especially optical character recognition (OCR) systems. Although the goal of OCRs is to turn scanned documents into data, the majority of insurance cards lack the necessary data. This forces the process of gathering information to rely more on human intervention, which is much slower. Moreover, OCRs find it difficult to process the increasingly common digital insurance cards that patients are using.
An encouraging answer to these problems is the development of AI technologies. AI software has proven its ability to quickly validate data after being trained on large datasets that include more than 4,000 insurance payers and 20,000 distinct types of insurance plans. In as little as five seconds, many AI solutions can recognize and validate data, including payer name, insurance type, and claims payerID. In comparison to traditional manual processing, which takes 5–15 minutes, this is a huge improvement.
