It’s time to rethink revenue cycle tech from the outside in.

Keeping your revenue cycle technology “caught up” with customer requirements — a process we call platform modernization — doesn’t just let you grow your business, it enables your organization to react to market changes from any direction. This includes everything from labor to scalability issues, backup, disaster recovery, and the continuous evolution of HIPAA and other security regulations.

While platform modernization sounds like it involves a complex laundry list of new technologies, it’s actually highly accessible through artificial intelligence (AI). Let’s take a look at the key considerations to modernizing a revenue cycle platform.

Fewer Errors with Language Interpretation

You’ve probably used natural language processing (NLP) when you asked Google, Alexa, or Siri a question. But in the electronification world, the potential goes a lot further than ordering another box of laundry detergent or asking what the weather will be.

NLP is already used by clinicians to document care, but it’s also being developed to log natural free-flowing conversations between patients and providers. What does this mean for the revenue cycle? It means AI could turn the billing and coding process on its head.

According to CMS, coding errors resulted in $31.62 billion in improper payments in FY2017 alone. NLP, in conjunction with machine learning, can reduce and potentially eliminate the highly manual and error-prone coding process and bring healthcare organizations one step closer to straight-through electronification of their revenue cycle workflows.

Electronifying Remits and Paper with AI Automation

Would you consider OCR (optical character recognition), to be a part of AI? Well traditionally, it isn’t. OCR was purely a method of deploying algorithms to match shapes and formats. However, as time has evolved, different flavors of OCR are using AI technologies.

When it comes to reading healthcare remittances, correspondence letters and other traditional paper-originated transaction documents, traditional OCR just isn’t good enough. So RCM companies had to incorporate heavy data entry, validation and rekeying processes to overcome subpar results to mitigate posting errors and customer/patient satisfaction issues.

When used in conjunction with the layered neural networks that make up deep learning technology, OCR becomes complementary to the process, rather than the only data source. This transformation enables a “machine processing” environment with 99.9% accuracy. The result is an electronification of the workflow!

AI automation brings other benefits; it is not susceptible to attrition, illness, weather changes, or labor issues like other systems. Self-learning systems also improve over time as they process higher volumes of remittances. Much better than human processes!

Keeping Technology Relevant with Predictive Analytics

Patients play a major role in the revenue cycle, so getting them on the electronification wagon matters too. Patient communication and transactions are heavy drivers in keeping the revenue cycle stuck in the age of paper — from using paper statements to checks.

Too many providers default to sending paper statements on a set cycle in 30-day increments. Many organizations, though, are using AI (in the form of predictive analytics powered by deep learning) to determine the most effective billing methods that align with individual patient behaviors, and send paper statements only when necessary. Beyond that, AI is also being used to identify which patients are more likely to pay via electronic methods and encourage this payment whenever possible.

Want to learn more about AI, self-learning, and deep learning technologies that apply to today’s healthcare challenges?