While there are a variety of tools and solutions available to combat fraud, abuse, and waste, lately pre-payment prevention has come to the forefront. Today we’re going to explore why.
Payment policies and your claims adjudication systems are intended to curb unnecessary/flawed payments. However, there is no perfect payment policy, no adjudication system is free from errors, and bad guys are always there to note any weaknesses in your processes. Because of this, it’s imperative to note the issues with relying upon a post-payment approach. With this approach, once you’ve paid out some money to a person that was not owed the money, you may only recover 70% of the loss.
The common approach to these losses is a pre-payment provider review that is meant to identify providers who are known to be problematic, then review their claims prior to paying out. However, while this can be effective, it is also quite labor-intensive and only addresses issues that you were already aware of. Oftentimes, these processes only target issues that are already known, and additional edits and queries based on changes in patient/provider behavior take up time that you really cannot afford to waste.
Efficiently Preventing Losses Prior to Payment
AI and ML can be quite useful in this department, given their ability to evolve and identify emerging patterns of abuse. Additionally, risk scoring your claims prior to payment with software that employs AI and ML can allow for stopping payment on improper claims. Be prepared for the changes that will come with the implementation of these processes, and educate your staff on what is happening so that everyone is on the same page and can get the job done right.