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.
AI, machine learning and predictive models are abstract terms in the revenue cycle. How do you actually move past the buzzwords and get value out of your healthcare payments data? Here are six ways healthcare providers and RCMs can truly operationalize healthcare payments data to improve patient collections and revenue cycle operations.
Every healthcare CFO and revenue cycle leader should be looking at their insurance payer payments in relation to patient payments -- identifying how they relate and influence one another. This is essential intelligence in an ever-complex revenue cycle.
Normalized and organized data is a gap in healthcare analytics, even in claim denials management. This is why Sift Healthcare is proud to introduce our Denials Dashboards. Sift scrubs and maps your 835 and 837 data to build a single, normalized data set. This data is organized in an intuitive interface that offers you a new level of oversight for the revenue cycle.
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.