Data Science

Want To Add AI to Your Revenue Cycle? Data Intelligence is the First Step.

Post by
Justin Nicols

Revenue Cycle Automation Starts With Data Intelligence

When it comes to revenue cycle technology, data science terms like “AI”, “Machine Learning”, “Analytics” and “Automation” get thrown around everywhere. Over-use has diminished the meaning of these terms and has bred unrealistic expectations of their impact. Health systems and hospitals that have explored these technologies are often disappointed when they learn that they are not equipped for data science or that the road to revenue cycle automation has many necessary stops.

Revenue cycle automation (specifically, RPA  is sold as a one-size-fits-all, solve-all-your-problems technology. There is immense value and opportunity in automation, but it must be added in a way that tackles health systems’ unique revenue cycle challenges, driving towards individualized goals. As such, automation represents the final vision of AI in the revenue cycle. The path of AI in the revenue cycle must begin with data intelligence. Only then can you delve into predictive modeling and machine learning or enter the realm of (effective) automation.

You Can’t Skip Steps In AI

Healthcare providers have mountains of data. Unifying, normalizing and organizing this data is the first step towards any application of AI. This step is the heavy lifting, involving complex and tedious work that is often too time-consuming, expensive and complex for health systems to undertake independently. Many revenue cycle technology vendors gloss over the value of this step. This creates a gap — revenue cycle leaders are unaware that in initiating AI implementations, the upfront work likely will not involve modeling or automation. While there aren’t the buzzword-laden deliverables, establishing data intelligence equips healthcare organizations to strategically and more effectively add AI to the revenue cycle. This data driven foundation paves the way for long-term and impactful ROI. When the value and work effort of data intelligence are overlooked, AI integrations can easily get derailed or devalued.

To effectively integrate AI in the revenue cycle, be it predictive models, machine learning or automation, you need a solid foundation of data intelligence. Your payments data must be organized in a way that offers a holistic picture of how your claims behave across a multitude of variables. This is more than an SQL dump or a retroactive pivot table view of claims. It is cleansing, parsing and matching of 835 to 837 data, tying that data to Epic/EHR data, organizing data into an advanced analytics platform and interpreting that data into a clear flow of every claim and its drivers, over time. This work provides immense value to healthcare administrators in several ways:

  • Data normalization and organization constructs a holistic picture of a health system or hospital’s payment trends, problems and opportunities.
  • Accessible data and detailed analytics drive improvements in revenue cycle operations, helping mitigate bad payment outcomes.
  • Data intelligence helps identify where and how to implement predictive modeling and machine learning.
  • Foundational data work sets realistic expectations around the impact of AI-driven optimizations on payment outcomes, workflows and automation strategy.

Sift’s Payments Intelligence Platform does this heavy lifting of data normalization, providing a foundation for driving upstream revenue cycle interventions, deploying more efficient workflow prioritization and delivering actionable payments insights to healthcare leaders. Integrating AI into the revenue cycle is a journey; data intelligence is the first step.

If you would like 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  And, to learn more about how to guide the AI journey at your organization, download our free AI Implementation Guide.

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