Healthcare organizations are dealing with an overload of payments data. But with this mountain of data, the revenue cycle is primed For Machine Learning.
In the revenue cycle, healthcare providers are flooded with data from EHR(s), patient engagement platform(s), BI teams, technology vendors, and third-party collections partners. However, this vast amount of healthcare payments data is rarely unified, organized, sanity-checked or accessible.
Translating such an abundant amount of disparate data into intelligence and action requires advanced data analytics and AI tools (like machine learning).
In a 2020 KLAS survey, health system leaders named revenue cycle management as the area with the greatest need for AI-focused innovation and disruption. Additionally, a report by Black Book Research found that only 44% of healthcare organizations had adopted some form of AI, but 88% were looking to adopt AI technologies.
The time is now...
The revenue cycle urgently needs data unification, machine learning, and predictive analytics. Harnessing the intelligence within payments data enables healthcare organizations to accelerate cash flow, increase revenue, decrease cost-to-collect and forge better relationships with patients.
Where do you start?
While there is recognition that AI has a meaningful place in the revenue cycle, knowing where to start and how to implement is a hurdle for most revenue cycle leaders.
Rather than jumping in with a vendor or committing to the shiny appeal of RPA, healthcare leaders need to construct a strategic, phased approach to undertaking AI. Download Sift's step-by-step guide to implementing AI in the revenue cycle. This guide covers the considerations, planning, and steps to integrating meaningful AI optimizations within the revenue cycle.
