Machine Learning

Rules Are Too Rigid For The Revenue Cycle

Post by
Justin Nicols

Automating processes (as in RPA) makes the revenue cycle more efficient — saving time and decreasing errors. These are essential strides for health systems and hospitals, but these efficiencies are one-dimensional.

RPA is rules-based, using basic if/then logic — this automation lacks the flexibility and advanced reasoning to solve complex revenue cycle problems. You gain speed, but your underlying processes may still be riddled with problems (or full of untapped revenue potential).

Going Beyond: A Complete and Holistic Understanding of Payments

Before jumping in with RPA, healthcare organizations need to construct an accurate assessment — a full longitudinal view — of their payments. Most healthcare providers only have a portion of the picture. They’re missing reimbursements from insurance payers, or they can’t map a consistent trend around overturns, or they’re behind on tracking the workflows that are driving denials.

Fully optimizing the revenue cycle and getting the most out of AI (including RPA) requires a clear and holistic view of payments, an understanding of the full lifecycle flow of claims. This knowledge delivers powerful insights that drive better operational decision-making and is the foundation for building RPA bots, predictive models, and machine learning integrations that improve both work efforts and payment outcomes.

Sift's Answer: Payments Intelligence

Sift’s Payments Intelligence Platform normalizes and organizes providers' historical payments data and surfaces actionable insights around claims, denials, payers, team performance and workflows. Sift leverages this intelligence to build machine learning models around workflow efficiency, denials prevention, denials prioritization, payments forecasting, patient payment plan provisioning and patient contact strategy (timing, frequency, message, medium).

This approach -- starting with a foundation of intelligence, enables Sift to equip providers with unprecedented access to their payments data along with actionable insights and machine learning integrations that optimize payment outcomes, have a meaningful impact on net revenue and deliver measurable ROI.

To learn more about Sift's Payments Intelligence Platform or our no-risk Data Assessment program, you can talk with our data science experts here. Or, download our free, step-by-step, AI Implementation Guide to learn more about how to guide the AI journey at your organization.

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