Sift's Blake Sollenberger and Affinity Strategies' Claire Vincent talk about the impact of AI on healthcare payments. They cover the challenges health systems face as they increasingly act as financial lenders, the importance of a positive patient experience in billing, the need for compassionate collections, and the challenges of moving to AI in a risk-averse environment. It’s an enlightening listen and will give you a new perspective on AI and the revenue cycle.
AI can drive payments and cut waste, but where do you start? Download our roadmap for healthcare executives, AI for the Healthcare Revenue Cycle, a free and unbiased implementation guide.
Automation (RPA) makes the revenue cycle more efficient — saving time and decreasing errors. While this is an improvement for health systems and hospitals RPA efficiencies are one-dimensional. 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.
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.
Machine Learning makes RPA more effective in the revenue cycle. Being able to truly leverage payments data to drive decision-making makes automation efforts meaningful. ML enables RPA efforts to move from automating repetitive human processes to attacking the root causes of inefficiencies and implementing data-driven strategies around revenue cycle work efforts, vendor outsourcing, patient financial engagement and payer reimbursement.
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.
Wonder how Sift impacts healthcare? Watch Sift Healthcare's Founder and CEO, Justin Nicols talk with StartUp Health's Logan Plaster about the impact of Sift's AI and analytics on healthcare payments.
In late 2019, Sift Healthcare and State Collection joined forces to test AI in the revenue cycle. Together, we conducted a rigorous 120-day live, scientific test (with a control group) for Wake Forest Baptist Health, to determine if machine learning impacted patient financial engagement outcomes. The results were impressive — a 6.5% increase in patient collections.
We're at a tipping point where healthcare providers will *have* to implement automation and AI into the revenue cycle. Not only to recover more dollars but also to keep up with the growing (& massive) administrative burden that is persistent in healthcare payments.
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.