Part Two - How Powerful Analytics Drives Revenue and Collaboration Across the Revenue Cycle
Previous: One - Ensuring Accuracy in your Analytics
Transcribed Video Content Below
So we talked about accuracy, we talked about importance of visualization within your analytics, now let's talk about how we use them to drive revenue. One of the biggest problems health systems face today is the revenue silos within their own organizations. I'm sure each organization on the call, address each of the core functional areas, from contract governance to negotiations, chargemaster rate setting, patient estimates, value-based care. Each organization on the call certainly addresses it one way or another. But from what we've seen, most organizations are taking a very siloed approach to this.
Revenue cycle handles contract governance, managed care is doing the negotiations, finance is setting the charges, patient access and marketing are handling any pricing transparency initiatives, and then population health falls somewhere in between the clinical teams and the finance teams, but it's not really clearly defined.
The problem with that is none of these departments are sharing the data between them or talking to each other, and that's very minimal if they are. In order to drive revenue across your organization, we really need to start breaking down these silos and taking a more integrated approach to our revenue.
At PMMC, we view this more as an overall revenue strategy, and it's really building blocks with each function leveraging the data from the others to increase their effectiveness. So, and talking about revenue, we always start with contract governance. That's the core foundational building blocks to revenue these days.
So when we talk about contract governance, we're really talking about, "Am I getting paid what I should be? Where am I being underpaid? Where am I being denied? How does Blue Cross compare to UHC or Aetna? What are my overturn rates or success rates?" And really understanding this information is critical to moving up on the maturity measures.
Once I understand all of this information and data, I can then leverage that in my contract negotiations and rate-setting process and truly understand the real value of my contracts and charges. I can start trying to shift some of my contract value away from frequently denied services to services that are paid more regularly, and I could also do the same with my charges.
Once I understand my contracts and my position in the market, I can then begin to address pricing transparency. If I were to simply post my rates to my website without truly understanding my market position, I'm really taking a big gamble and I could lose big or win big pretty quickly, but I don't think any of us really want to take that risk.
Lastly, population health and outcome-based reimbursement. It's still debatable about what that's really going to look like, but most models that are out there today are still rooted in fee-for-service contracts. So contract governance is still going to be very important, especially as we begin to change our practice patterns. Think about it as, if we change our practice patterns for the Medicare bundles that are coming out, that's also going to change our practice patterns for our commercial patients. We're not going to do one without the other. So what is that real impact going to be on the commercial contracts? What's my real net revenue impact going to be? We have to start understanding that a little better.
The final point I'll make about population health is that it's largely cost-driven. So, at some point, we're going to have to integrate costs into each of these functional areas to assess the profitability across my payers, my contracts, my service lines, as well as every other area. So if I know my labs is a more profitable area than, say, radiology, I can start to do something about that in my negotiation process or my rate-setting process.
So what we're going to do for the rest of the webinar is dive into each one of these functional areas and talk about how we can leverage analytics in them across each one of them to maximize our revenue.
Brad: And Robby, I'm going to pause you here. We actually had a question come in, if you don't mind answering that for us.
Robby: Yeah, no problem.
Q: Brad: Yeah, the question was, "What if we're using different systems today four various functions that you just described? And is that a problem?"
A: Robby: Yeah. So, what we're really talking about here is an integrated approach, and is really talking about, not necessarily one system, but sharing systems or bringing all the data together. The problem with using different systems for each area is, one, you have to maintain multiple systems, but the biggest one is ensuring the accuracy across it. So you're really going to have to pay attention to the accuracy metrics of each one if managed care's using one tool for their modeling and setting up contracts, and rev cycle's using another tool to run the contract governance piece, making sure both are accurate and both are feeding accurate data into your analytics tool is going to be critical. So you can certainly do it with multiple systems or leverage each system that you have in place, but bringing it all together and making sure it's accurate is the real key to it.
Brad: Okay, great. Thanks, Robby.
Robby: So as we dive into contract governance, what we're really talking about with payer performance are underpays, overpays, and denials. But we really need to start going deeper into it. We need to start grading our payers against each other. We need to look at trends, look at our overturn rates or success rates, re-denial rates, our dispute resolution timeframes. We need to really start digging into each core piece of our relationship with our payers, and understanding all of that is going to be essential to driving our negotiations in the future.
So if I know Aetna is denying this code more frequently than Blue Cross, I can start leveraging that at the negotiation table to drive some reimbursement. So we're going to dive into a few dashboards here, which by no means encompasses all the metrics you want to look at, but it's just a few examples. This is a payer dashboard. It shows a visual depiction of the overall payer performance by payer over time. And it can show it either at the system or macro level or you can drill down into a facility or region level, depending on your organization.
Specifically, you can accurately assess, engage the health of multiple payers by overall revenue stream. For example, average days pay, cost centers, charges, denials, net AR, expected payments, collections, and yield. These really allow you to start assessing the overall health or performance of each payer.
So these are also allowing us to quickly identify areas of concern. As an example, we can see we have a large AR bucket out here with this one payer. We could drill into this and understand, is it one service line that's causing the problem? Or if it's not one service line, when did it start occurring? Is it now? Was it last year? Or has it been just adding up over time and gone unaddressed? So this really allows us to start getting a full sense for how our payers are performing.
Another dashboard that we have is the denials dashboard, and this shows the overall historical denials by payers by discharge date over time. It gives you, again, the macro level view, so you can look at it by facility or region. It shows the relevant metrics such as denied claims, denied reimbursement, denied charges with corresponding subcategories.
The point I really want to emphasize on this slide is the metrics that are important to your needs need to be measured and monitored consistently. This will allow you to quickly identify those underperforming payers and address issues quickly. So we see this bump here, in November, we'll be able to catch that in November before we get to January and fix the problem.
The next dashboard is very similar. It is Denials by Category rather than payer. Again, we have the same bump, but we can really see what is causing the issue and whether or not we're addressing it. So this is showing we're not addressing a lot of the medical necessity denials, we're addressing all of the pre-cert denials. Pre-cert denials, we're working 100% of them. We're only working 5% of our medical necessity and claim level denials. That might be a problem we'll want to drill into and identify.