So you want to add AI to your revenue cycle? You have to start by establishing a solid foundation of data intelligence. This comes from normalizing and organizing payments data in a way that provides actionable insights. This works will establish the where/why/how of your AI goals.
RPA Follows Rules. Machine Learning Generates Intelligence. Your revenue cycle will benefit from both. Learn about the limitations of RPA and how machine learning provides can have a more meaningful impact on the healthcare revenue cycle.
The term “automation” can refer to any number of automatic processes within the revenue cycle workflows. But, it doesn’t necessarily refer to the use of data science. Just because a process is “automated” doesn’t mean predictive analytics or any data science is applied. However, when you do truly use data science to automate your workflow (which you absolutely should do), you pick up a host of efficiencies and improvements. Our breakdown on automation vs rule-based segmentation vs true predictive analytics.
Improving the revenue cycle means minimizing costs and increasing collections, from both patients and payers. Even on a small scale, artificial intelligence has a meaningful impact on healthcare payments and operations.
In healthcare payments, where data flows from multiple systems and standards are a moving target, data can be pretty filthy. This means mismatched formats, errors and inconsistencies. Having clean data is often the biggest roadblock to being able to reap the benefits of data science.