In this episode, we are joined by BESLER’s Wade Wright to discuss how artificial intelligence and automation technologies are poised to benefit the hospital revenue cycle.
Highlights of this episode include:
- Background on artificial intelligence, automation, and machine learning and some everyday examples of these technologies.
- How artificial intelligence and machine learning are well suited for attacking problems with standardized processes in the revenue cycle.
- What should revenue cycle leaders be looking for when evaluating solutions that use these technologies.
- Advice for revenue cycle leaders as they embark on adopting these new advancements.
- And more…
Mike Passanante: Hi, this is Mike Passanante. And welcome back to the Hospital Finance Podcast®.
Artificial intelligence, automation and machine learning are terms we hear talked about more and more in all walks of life. Now, the hospital revenue cycle is poised to benefit more from these techniques in the years ahead.
But what do these terms actually mean to a hospital? And what can health systems expect to derive from implementing these approaches?
To help sort these questions out, I’m joined by Wade Wright, Chief Technology Officer at BESLER.
Wade, welcome to the show.
Wade Wright: Thank you very much. I appreciate it.
Mike: Just to get us level set, why don’t you start off by giving us some every day examples that we might see of artificial intelligence, automation and machine learning?
Wade Wright: You bet! AI, machine learning and automation are all part of the same kind of field of technology. And most of us see it and use it and experience it and don’t even realize that it’s what’s going on. It can be as simple as Amazon suggesting things for you to buy based on what you’ve bought, what other people like you have purchased, the best route when you’re using Uber and Lift and how you’re getting there.
They use it in fraud detection. So if your credit card seems like it’s being used somewhere that it’s not, they detect that using this kind of technology. And even my wife’s personal favorite, her robot vacuum. It goes around our house. It learns the house, where it can go, where it can’t. It cleans up. It parks itself and it charges. It makes her life a lot easier.
Mike: It’s all around us which is kind of cool. And certainly, it is inching its way into the healthcare space probably more rapidly than any of us realize.
So, why don’t you talk to us about healthcare in general and what some of the benefits are that people are seeing or might see using these technologies?
Wade Wright: There’s some amazing work in imaging, in medical imaging, using machine learning and artificial intelligence. We’re beginning to have machines that can detect cancer and images at a higher accuracy rate than physicians. And this of course leads to some great things—detecting it sooner which would mean earlier treatment. It’s better all the way around for everybody. So there’s some amazing stuff in that field for sure.
Mike: So, let’s focus in on the revenue cycle then specifically. Can you apply each of these concepts to some aspect of the revenue cycle, so we could see what these technologies might actually do in specific situations?
Wade Wright: Sure! Artificial intelligence and machine learning are well-suited to attack problems where you have standardized processes where we do things in certain ways (and healthcare is really good about that), and also for having things where there’s large data sets. So it can learn what to expect and outcomes and things like that.
So, of course, everybody wants their denial rates to improve, their days and AR to shrink, their coding to improve, and of course, wanting to get paid accurately faster. All of these have really nice potential to be improved, and in some cases, totally solved using artificial intelligence and machine learning.
So, imagine if a hospital could collect proper payment and reduce how fast that happens and basically reduce their cost for collecting revenue. They could utilize the money they’d normally spend to collect revenue in other ways.
Personally, my sister-in-law recently had breast cancer. And I’d love more money spent on that than say trying to collect payment for it.
Mike: Yeah, no doubt. Certainly, an opportunity cost comes along with trying to run a revenue cycle operation more efficiently. You can do that the better, right?
Wade Wright: Absolutely!
Mike: So Wade, this is an issue that I’m interested whether or not it divides along the lines of the size of organizations. Some larger systems may have enough horsepower to build applications on their own that employ these technologies while others are going to be more dependent on vendor-based solutions.
So, for the revenue cycle leader, what should they be looking for when evaluating potential solutions that use these technologies?
Wade Wright: Of course, the answer is results. It’s totally about what is accomplished and not much about how it’s done. If a vendor provides you the best results you can get, and they’re using machine learning, great. But obviously, choosing that solution just because it’s kind of flashy and have a sexy tech appeal to it is not the best choice in those cases.
Mike: So, what would you say are the top benefits that a revenue cycle leader should expect or should be looking for when they’re evaluating these technologies?
Wade Wright: At a high level, I’d say the best segment you’d care about is cost reduction and of course improved accuracy and reimbursement. In general, if we spend less to collect the money that is due our organizations, we can simply do more with those cost things. We can do a lot more important things with that money. And I think that’s the top benefit that this technology can ultimately provide.
Mike: So, you’re a very knowledgeable technology leader. And I want to dig into what we’re doing at BESLER because, frankly, it fascinates me. Can you talk to us about what you’re doing to guide the use of these technologies in our technical platforms?
Wade Wright: You bet! At BESLER, we pretty much have a two-pronged approached of how we’re beginning to catalyze artificial intelligence, machine learning and automation. You know, automation sometimes is referred to as robotic process automation and things like that. So we actually have several things in development using all of these, and some are already in our existing systems.
Our first approach is to directly impact our clients with these things when it comes down to collecting proper payment, reducing AR, improving denials and prevention—a lot of the things that we have been discussing.
Another very tangible prong of our multi-pronged approach is indirectly improving our clients’ lives by applying these technologies to our internal systems.
So, for example, Revenue Integrity Services is an offering from BESLER. And we have an internal tool that our development team has built. And this tool is taught by our analysts and experts. They teach it how to identify situations in claims and coding to be more accurate. And so they’ve literally taught it thousands of these types of situations that it can find.
And then, we use machine learning and AI to discover what’s the most bang for the buck, if you will, or the best of that, handing a client “Here’s a thousand things you need to do”. Maybe not all of them will return you a lot of value on it. Our machine learning algorithms and things have taken the consideration much more than ‘Hey, this is the dollar amount,’ like how long does it take to make the change, what is the impact to the organization in terms of their effort and what they have to do.”
And so, ours help figure out and tune our engine for that on a facility-by-facility basis.
Mike: So Wade, just to wrap things out here, do you have any advice for hospital revenue cycle leaders as we all move forward towards the adoption of these new advancements?
Wade Wright: I would say experiment and learn and really start the questions that you might want to solve like “What if we could predict the claims prior to us dropping the bill?” or “Hey, what if we could jumpstart our cost report from previous years? Could machine learning help me do those types of things?”
And starting with the questions that can be answered by this technology will lead you to the right choices we can make along that way.
There’s a lot of competing algorithms and different things that most people don’t know or care about, but as a leader, you look at it, and you want to be figuring out the best choice. Start with the questions that you want to solve. And then, you can talk to your vendors and your own internal people if you have that kind of ability in your organization to help figure out how to get there.
Mike: Great insights, Wade. Thanks for joining me today on the show to talk more about these new burgeoning technologies.
Wade Wright: Thank you. I appreciate it.