Data Analytics: Big Data Tackles Revenue Cycle Deficiencies
By Matt Seefeld
For The Record
Vol. 31 No. 2 P. 8
Big data is transforming clinical outcomes in multiple ways—from extracting information trapped in unstructured EHR fields to deciphering population health trends—helping providers up their game with qualitative medicine.
But we’re only beginning to unearth the potential for big data, or analytics, in revenue cycle management (RCM). Over the last few years, terms such as artificial intelligence (AI) and machine learning have entered our vocabulary, and we’re continually learning about new capabilities for advanced algorithms to process raw data, turn them into actionable insights, and automate workflows.
Such advances in big data couldn’t have come at a more opportune time, as health systems are bombarded with multiple operational challenges. Two of the larger ones are organizing vast amounts of data within multiple IT systems and isolating root-cause problems in the revenue cycle that have increased collection costs and decreased cash flow.
This is where the right health care data analytics solutions can help by adding context to insurance claims, helping billing staff monitor key performance indicators, and showing provider groups where they’re struggling—oftentimes before they even realize it.
Here, we take a closer look at how analytics has evolved to address RCM needs.
Analytics Meets the Revenue Cycle
As recently as 10 years ago, the idea of relying on predictive analytics for the purpose of getting paid was nearly unheard of, albeit because it wasn’t as critical as it is today. The market for high-level business intelligence in the revenue cycle was not a necessity. Life was good.
Between 2007 and 2010, however, data warehousing and visualization companies began releasing software that could process and analyze data to produce structured patterns or “trends.” The only problem was that the end users—health care organizations—didn’t always know what information to look for once they had it in their grasp.
Then, as the transition to value-based care gained momentum and medical groups were incentivized to adopt costly EHRs, providers started feeling the burn in their pockets from every unpaid claim. Today, health care organizations can’t afford to have dedicated billing staff arbitrarily processing claims. They need real-time intelligence and insights, which they can use to automate and streamline workflows and make informed business decisions quickly.
Thus, the need for modern-day analytics solutions—where highly sophisticated algorithms can decipher data and make recommendations—has come to the forefront.
Deriving Insights and Driving New Behaviors
While analytics technology has advanced in general, not all predictive analytics tools are the same. This makes sense, as clinical insights lend themselves to qualitative patient care while financial insights drive operations.
When it comes to improving the revenue cycle, the ideal big data solutions are equipped with machine-learning algorithms that offer automated workflow management. This capability allows providers to gain insights into their performance metrics such as “average days in A/R” or “first pass resolution rate” upon request while processing multiple variables, or determinants (eg, the average dollar amount paid by an insurer for a particular type of claim, prior authorization, etc), to guide staff workflow.
Analytics allows users access to a variety of important data. For example, health care organizations can learn that 20% of all patients in an elective surgery practice don’t pay balances within 90 days of services rendered. Having that information readily available empowers them to investigate the “why” behind that practice trend. Are patients consistently informed of their payment responsibility at intake? Are they sent bills that they can pay easily in the manner they prefer (eg, electronically through a portal)? Is there a time and day in which sending an electronic message elicits a greater number of responses (and can that information be used to the organization’s advantage)?
Ultimately, if an organization figures out what is triggering the 90-day lag, it’s in a better position to make a change that will positively affect the outcome.
We often hear busy executives and administrators say they don’t have time to filter through analytics dashboards and derive actionable insights. For this reason, a health care consultant or vendor partner that can offer guidance on how to put the data to work can be a valuable asset. Medical groups can look at red lines all day long on a dashboard, but having the expertise to properly “drill down” into the data—and find out what’s causing a negative financial outcome and execute an action plan to remediate it—saves a group time and money.
AI-fueled workflow automation tools can also drive change at the staffing level. Instead of coming into work, printing a bunch of spreadsheets, and trying to figure out what to do, our biller (whom we’ll call Sally), logs into her computer and is told, through a series of prompts, which claim to work first, second, third, and so on.
As a result, Sally’s workflow is more efficient and beneficial to the organization. She might hate this at first—it is, after all, a vastly different workflow than the one she’s accustomed to—but in the long run, the Sallys in provider organizations typically grow to appreciate having the structure in place to help them to do their jobs better.
Simultaneously, administrative leaders benefit from insights into which billing staffers are handling claims efficiently and which ones are struggling. Of course, having such visibility can raise difficult questions as to whether a health care group’s in-house billing staff delivers quality, cost-effective service or whether outsourcing is a better option.
Many organizations have already opted for the latter path. According to one forecast, the RCM outsourcing market is poised to skyrocket, with its valuation rising from $11.7 billion in 2017 to $23 billion by the end of 2023.
Business intelligence is no longer a luxury. In fact, it’s become a necessity. The sooner the health care industry can leverage tools to dig into its droves of data and use the information to streamline operations, the more sustainable organizations will be in the years to come.
As the industry moves deeper into value-based care, it’s become clear that health care organizations must do more to improve efficiencies. Patient balances after insurance, which rose from 8% of the total bill responsibility in the first quarter of 2012 to 12.2% in the first quarter of 2017 among hospitals, show no sign of slowing down at medical practices.
Big data and AI will undoubtedly continue to evolve and offer even more granular insights into the revenue cycle. But the extent to which that will drive adoption among medical practices will depend on whether analytics solutions in the marketplace can truly prove their value.
In 2019 and 2020, optimization will be key. Insights must be able to drive real, meaningful change and help organizations capture and protect as many dollars as possible.
— Matt Seefeld is the executive vice president of MedEvolve.