After decades of the advancing evolution of core revenue cycle management (RCM) operations through such methods as business process automation (BPA)—also known as robotic process automation (RPA), which have been successfully incorporated into core RCM processes around managing all claims processes—a new phenomenon is emerging that could prove very helpful: the leveraging of machine learning- and artificial intelligence (AI)-based technologies to achieve what is being called predictive denials management. Essentially, this involves the development of algorithms based on data analytics, that can trigger interventions based on anticipated insurance claims denials.
That evolution was the subject of one of the Ten Transformative Trends that the editors at Healthcare Innovation examined in the publication’s March/April issue. In an article entitled “The New Revenue Cycle Management,” Healthcare Innovation Editor-in-Chief Mark Hagland interviewed a variety of industry leaders regarding the rapid advances taking place now in revenue cycle management, which has become an absolutely critical function for hospitals, medical groups, and health systems, as their leaders attempt to weather the financial storm that has come in the past year with the emergence of the COVID-19 pandemic in the United States, and its impact on patient care organizations nationwide.
One of the industry experts whom Hagland interviewed for that Trend article was James McHugh, managing director of revenue cycle consulting at the Naperville, Ill.-based Impact Advisors consulting firm. Below are excerpts from their full interview, which took place in February.
What’s most important to understand about the broadest trends involving changes in the revenue cycle management area right now?
It’s really about efficiency. Revenue cycle is all about workforce efficiency now; the revenue cycle is an assembly line. Now, you have these great EHRs [electronic health records] in place; but even then, with all that you can do with those systems, it’s still about leveraging the system you have. And COVID has accelerated the workforce management piece, including driving efficiency and the work-by-exception process. And I don’t think these are new things, but there are more and more tools and technology available for us to work with.
The revenue cycle is an assembly line, and most things should work automatically or semi-automatically, and then address exceptions. And what the COVID-19 pandemic has done is to put us into this new environment. We can’t have daily oversight anymore of our staff; so how do we manage them remotely? And are there technologies to support that? And also, some of it might also be sitting on top of AI, or a workstation, with task-based monitoring.
Is artificial intelligence—AI—helping RCM leaders to catch more gaps and issues now?
Let’s get the vernacular correct. AI is actually very, very specific. Business process automation is the broader term, and is most of what’s happened to date. People will tend to confuse AI with RPA, and RPA with analytics. So, first, organizations will begin by introducing business process automation, including work by exception; and that is very important. First, you look to your host system and maximize that. And I’ve seen a lot of vendors that can put business process automation in place. So first, you look to your host EHR system. And then you go to robotic process automation. That’s not AI.
That second level of operations doesn’t really require a lot of thinking; instead, it involves binary questions. It’s that third level that involves truly advanced analytics, or AI. And that’s pointing people in the right direction.
Beyond BPA and RPA, a lot about truly advanced analytics, or AI, is aspirational, but will become reality, and that involves denials management. The best way of handling denials is to prevent them from occurring in the first place. If we know that Health Plan X is always going to deny endoscopy for reason Y, using advanced analytics and AI, we can prevent those denials in advance by identifying what the cause would be; that is predictive denials management. Understanding, based on data and data trends, what would be occurring. Right now, that involves fewer than 1 percent of patient care organizations.
When will the adoption of predictive denials management become commonplace?
Probably within the next three to five years.
What will be critical will be that organizational leaders will be able to flag a potential problem the moment that it’s coded, correct?
Yes, that’s correct.
And what will be the keys to success with AI- and machine learning-based predictive denials management/predictive analytics for revenue cycle management?
There are two. The foundation to this is a really good analytics platform. If you don’t have a good analytics program, you’re going to struggle to do this. And it’s funny, because when RPA was first becoming hot, the IT people would say, that’s operational, you can build that, so RCM would take it on. So because of that, there wasn’t an enterprise-wide approach, and as a result, RPA evolved in silos. It’s really important to have a center of excellence for analytics and a center of excellence for business process automation. Those are two very different, distinct things. IT doesn’t do everything, but they’re a coordinating entity in this. And you need to prevent silos. Also, a lot of health systems are outsourcing too much to their technology vendors around this. You need the control.
And, specifically per RPA, vendors will create these bots that will be pointed solutions, but they’re not really there to police them and turn them off when needed. For example, Epic may come out with new functionality, you put in a new bot, but your vendor won’t be there to tell you when to turn off the bot. So you need a center of excellence in IT, to manage this.
What are the biggest obstacles to creating predictive denials management processes in patient care organizations?
The biggest barrier is analytics. AI is informed through analytics. A lot of people think that AI is this independent brain that just works; and that’s not true—you need to feed the brain information. And so you need a really good analytics department, and IT support.
Will big, richly resourced systems do this faster than smaller and less richly resourced hospitals and other patient care organizations?
I don’t think that the problem is the money, per se; it’s the willingness to do it. Yes, the larger health systems are more advanced, and already have the good analytics departments.
But only the richly resourced hospitals will have big analytics departments, yes?
Yes, that’s correct.
What should the CIOs and CFOs of patient care organizations be thinking about right now?
A CFO will look at IT and say, OK, why am I going to build anything more? Our IT budget has already ballooned recently. But you can defend the ROI. But you have to have a center of excellence for analytics in place first. Otherwise, it will be hard to get there. So, the predicate to having a good AI program is first, an analytics department.
And what is your concluding overall advice in all of this?
The data is coming from your EHR. You have to be a top performer, and continue to make investments in getting everything out of your EHR, because that informs everything you do. So you’ve got to maximize the value of your EHR; you have to. A lot of organizations don’t do that. They implement an EHR, but don’t maximize their operations. And they’ll say, we want to put in AI, but their operations aren’t even aligned to their EHR. I often think that in healthcare, we skip the basics first. And the large systems are there, but a lot of medium-sized hospital organizations haven’t built the foundations for this. So, great analytics platform, maximize your EHR, then move into your advanced analytics system. It’s like telling someone with a high school diploma to get a Ph.D. It doesn’t work.
