June 18, 2019
We’ve taken a look at the evolution of big data in the hospital revenue cycle before, but things are evolving quickly, so we decided that it was time to take another pass. This is mostly because artificial intelligence (AI) is emerging as the tech concept that defines how revenue cycle professionals interact with the mountains of data we process.
Here’s a look at some of the trends and changes we expect in the future of big data and AI.
First, let’s take a deeper look at what big data means right now on a practical level.
“Small data” has been bubbling up as the more realistic expression of big data for a while now, particularly because it enables healthcare companies to analyze and leverage real-world, facility-specific evidence across an organization.
As revenue cycle tech demands grow, small data is proving even more useful, especially in cutting through any lingering hype. Small data is enabling healthcare organizations to make increasingly important pivots toward lean processes, practical use cases, and incremental infrastructure developments.
In the future, expect to see advanced machine learning applied to these scenarios to bring even more clarity and customized results.
Revenue cycle analytics technology has been making huge progress over the last few years, largely thanks to big data. But the future lies in machine-learning algorithms that open the door to automated workflow management.
Performance metrics like average days in A/R are still important, but deeper insight into variables like average dollar amount paid per insurer for specific claim types will drive the future application of technology. Think of guiding staff workflows and improving efficiency of the manual tasks that remain.
Ultimately, this level of analytics enables a deeper look at root causes. For example, knowing that 30 percent of laparoscopic surgery patients are cracking the 90-day mark enables a much more precise and laser-focused response — it allows for more refined measures like examining how patients are billed, updating point-of-service conversations, and leveraging targeted surveys.
According to Morgan Stanley, about 18 percent of U.S. hospitals are at risk of closure, and it’s not just rural facilities.
Simply having a higher-capacity hospital within 10 miles is a key risk factor for closure. Hospitals are facing increasing pressure to clean up revenue cycle performance; and as patients demand more from providers, even revenue cycle leadership will be tapped in efforts to stand out and attract and retain patients.
Never forget that the patient financial experience drives satisfaction and loyalty. Patients are consumers and expect to have an experience in healthcare that mirrors other industries. Consider the fact that 47 percent of patients want the ability to understand cost upon scheduling, and seven percent have switched providers because of a healthcare experience that didn’t meet their expectations.
Leveraging big data and AI will become key tactics on the patient-facing side revenue cycle continues to evolve as a key player in the healthcare consumer experience. This is especially true as the industry wakes up to concepts like financial toxicity that highlight the role revenue cycle plays in health outcomes. Expect to see AI, in particular, become a key tool and core differentiator in creating a pleasant financial experience for healthcare consumers.
As a new level of big data and AI become standard requirements for your organization, make extra effort to move past the hype to understand your patients, and guide them toward optimal outcomes.
Integrated payment communication is part of RevSpring’s DNA. We tailor the payment conversation to influence behavior and inspire action. Our segmentation rules and workflows help you become hyper-focused on the patient, understanding their ability to pay, and mapping their financial obligations to repayment pathways.
If you’d like to learn more about our comprehensive patient engagement and billing solutions, we’d love to help you. Request a demo to see how we can help your organization meet its goals.