Want To Add AI to Your Revenue Cycle? Data Intelligence is the First Step.
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 steps.?
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:
领英推荐
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 step-by-step Implementation Guide for the revenue cycle.?
___________
Justin Nicols is the Founder and CEO of?Sift Healthcare. Sift's AI and machine learning demystify healthcare payments by integrating actionable intelligence into revenue cycle workflows to maximize payments and improve operations.
Connect with Justin on LinkedIn?and learn more about Sift Healthcare at?www.sifthealthcare.com.?
We build custom internal software and AI agents in days, not months. Helped 100+ project managers and founders automate workflows and save 50% on operational costs.
2 年Justin, thanks for sharing!