Veradigm, which provides clearinghouse services to review and verify claims data accuracy, is at the heart of a data evolution in healthcare. In an interview, Will Barnett, Veradigm Senior Solutions Manager, talked about the health tech vendor’s clearinghouse capabilities and how it is helping to manage the relationship between payers and providers in support of evidence-based care and value-based care strategies.
At Veradigm, what role does claims data play in advancing value-based care strategies, especially when clinical data is fragmented or delayed?
When you really boil it down, the role of value-based care is to get better outcomes for less money. We’re trying to make patients healthier and spend less. It’s a big goal, and when you start to break it down, you need some benchmarks. You need to know what the organization spends per patient and what the hospital readmission rates are. How many people are coming into the emergency department? How many of those visits are avoidable? This is the kind of information that shows up in claims data.
How is claims data being shared with providers to support more coordinated evidence-based care?
It ties back to question one. I think you know when we talk about surfacing care gaps and identifying the correct place for a provider to intervene, that’s in the claims data. Those decisions can be supplemented by the claims data. We’re looking at how well we are meeting high-risk patients’ needs, thinking about chronic condition management. Those are the things that you know that claims data can surface.
What are some of the common challenges payers face when it comes to claims data? How are you helping payers address that?
When I talk to payer organizations, the biggest challenge that they’re dealing with from a claims perspective is there are too many data inputs. You’ve got maybe five vendors that are taking claims in at what we call their front door, and that gets a little messy. The biggest challenges are the cleanliness of the data and secondly, how providers manage the claims denials. You might have one provider that’s in primary care that is filing claims and getting few denials. They’re not necessarily working those denials quite as hard as maybe an orthopedic surgeon might be. In the case of an orthopedic surgeon, he may have fewer claims and more denials where the denials have a bigger impact on the bottom line.
What are some of the most promising trends in payer-provider collaboration that’s enabled by claims data that you’re seeing?
The opportunity to fix the problems I just mentioned. I think when you look at data structure and data format, the cleanliness of data when providers and payers are working together and collaborating, using tools like clearinghouses, there’s a really good opportunity to get good clean data in the front door the first time. I think that’s been a trend where I’ve seen a good, positive impact in the last few years.
Are there any regulatory or policy shifts on the horizon that might affect how claims data is used, reported, or shared?
There are always regulatory and policy shifts on the horizon. The one that is at my front door right now is the prior authorization requirement in 2027, when providers are mandated to be able to perform at least one electronic prior authorization transaction. In our business, where we have a provider lane and a payer lane, we’re seeing both sides of the coin. But that requirement is driving providers to adopt that transaction to be able to meet that deadline, and on the payer side too. They have to be able to accept it when the provider sends it.
Can you share a recent initiative where claims data was pivotal to a positive outcome, either internally or across a payer-provider partnership?
It’s such good historical context both on the payer side and the provider side. What you know when you have that data, when you have adjudicated claims, you’re able to better prioritize your work. You can really drive towards successful outcomes.
With our submissions product, encounter data is rolled up and then given back to a payer on a quarterly basis for reimbursement by government payers. So you’re aggregating and you’re making sure you have all the data to report to the payer and for the payer to report to the government. We have a client that has a pretty specific need in their data. It’s one specific code that they have to have to send back to Medicare for reimbursement for specific types of conditions. Over the course of three quarters, they didn’t have this one code and the data. It caused a big delay in their payments from Medicare. Through working with us, the clearinghouse, we were able to put in place what we call an edit, so if a provider submitted a claim without the piece of data that this client needed, we can bounce it back to the provider before it’s submitted to the payer.
Let’s say a patient has a headache, and the payer says, OK, for all the patients with headaches that the physician sees, they have to note if they were dehydrated. I’m the clearinghouse in the middle. When that claim is sent, I can look at it and see if the claim has the dehydration check on it. If it does not, I can send it back to the provider so they can add it. Previously, that claim was getting all the way through to the payer and would be stalled for weeks, maybe months, before it was reported to CMS. Then CMS would say this isn’t going to work, because the physician didn’t say that she checked for dehydration. The amount of time that it takes for that loop to be closed is the worst case scenario. But if you put a good clearing house between the provider organization and the payer, we’re able to check for those things and make sure that the correct data is being shared.
How are payers using artificial intelligence or predictive analytics tools to enhance insights from claims data?
There are a ton of use cases. We have an AI center of excellence within Veradigm that’s looking at a lot of cool possibilities. Just from a conceptual standpoint, there are coding applications, there are denial trend analysis applications. But there’s uncertainty and potential danger out there too. We’re being cautious. We’re using the data responsibly and making sure that there’s always a human in the loop. I think, from a denial trending standpoint, there’s a ton of opportunity to look at what typically gets through, what typically gets denied and how we can fix that.
How are APIs and FHIR standards changing the way payers exchange and act on claims data across the ecosystem?
It’s making things much more interoperable. It’s making data so much easier and faster to exchange. It’s making it easier for organizations to connect with each other. It has allowed us to format our data better. Really, the challenges are that they’re not widely adopted. If anything, we could be moving faster.
In what ways are organizations using claims data to proactively identify care gaps, high-risk patients and potential fraud?
We’ve got products that identify care gaps at the point of care. We’re using claims data, we’re using clinical data, we’re using risk analytics, all rolled into one product to help doctors visualize who is on their schedule, what they need to talk about and when so we can get the complete picture to the payer and ultimately, supplement that care journey.
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