Developments in artificial intelligence have made big waves in the medical world during the last decade. Supercomputers like IBM's Watson can analyze thousands of clinical studies at a time to suggest treatment options for patients facing difficult conditions, along with much more. This type of AI could have implications far beyond clinical. With healthcare providers struggling to improve the revenue cycle, advanced machine learning could provide the tools needed to increase patient satisfaction while reducing costs and raising cash flow.Patterns present in millions of billing records that are otherwise indecipherable by humans could show up
The Limits of Human Performance
Despite strict policies and procedures as well as intensive training, healthcare providers experience high denial rates for their insurance claims. The biggest cause of this problem is human error. Humans are prone to making errors throughout the claims process, and this problem only compounds over thousands of transactions per year. Artificial intelligence could help solve this problem by predicting the most common types of human errors and preventing them from happening in the first place. As a person fills out a claim, AI could analyze the data and immediately tell the user whether or not the data will result in a denial. This saves the provider the hassle of sending the claim in and then having to resubmit it.
Traditionally, hospitals and healthcare providers use boiler plate billing methods for their patients. Unfortunately, this way of doing things can lead to significant patient dissatisfaction as most bills lack detailed information about specific costs and treatments provided. Artificial intelligence could help solve this problem by personalizing each invoice for every patient. Preferences and previous behaviors could be analyzed to figure out a billing method that the patient is most likely to respond to in a timely manner. By utilizing online advertising technologies already in place through search engines like Google, healthcare providers could improve their own revenue cycle.
Challenges to Implementing AI
Large healthcare organizations are most poised to take advantage of more powerful artificial intelligence while smaller organizations will find more challenges. This is because smaller organizations often lack the data required to take advantage of computer analysis, but they may be able to leverage the support of third-party vendors. For example, a claims management company may have access to billing data from several large organizations that they can use to benefit smaller ones. Providers should not consider new artificial intelligence technology as a do-it-yourself project unless they have expert staff on hand to handle the implementation.