The role of claim data management in supporting evidence-based health care
Veradigm provides clearing-house services to review and verify the accuracy of claims data, and is at the heart of the evolution of healthcare data. Will Barnett, senior solutions manager at Veradigm, talked about the ability of health technology providers’ clearinghouses and how it can help manage the relationship between payers and providers to support evidence-based care and value-based care strategies.
What role does the role play in advancing value-based care strategies at Veradigm, especially when clinical data are dispersed or delayed?
When you are really boiling, the role of value-based care is to get better results with less money. We are working to make patients healthier and spend less. This is a big goal and when you start breaking it down, you need some benchmarks. You need to know what the organization spends per patient and what the hospital readmission rate is. How many people are entering the emergency room? How many times of these visits can be avoided? This is the information displayed in the claim data.
How do claims data be shared with providers to support more coordinated evidence-based care?
It can go back to question one. I think you know that when we talk about surfaced gaps and identifying the right place for providers to intervene, that's the claim data. These decisions can be supplemented by claim data. We are investigating the extent to which we meet the needs of high-risk patients and consider chronic disease management. These are the things you know that claiming data can surface.
What are the common challenges the payer faces when it comes to claim data? How do you help payers solve this problem?
When I talk to the payer organization, the biggest challenge they face from a claim standpoint is that they have too much data input. You may have five vendors filed claims in what we call the front door, which is a bit confusing. The biggest challenge is the cleanliness of data, and secondly, how providers manage claims denials. You may have a provider in primary care that is filing a claim with little refusal. They don't necessarily work as hard as orthopedics. As far as orthopedics are concerned, he may have fewer claims, and rejects denials that have a greater impact on the bottom line.
What are the most promising trends in collaboration provided by payers through the claims data you see?
An opportunity to solve the problem I just mentioned. I think the cleanliness of the data (using tools like Clearinghouses) is a very good opportunity to get good clean data at the front door for the first time when you look at the data structure and the data format. I think this is a trend and over the past few years I have seen good positive impacts.
Are there any regulatory or policy shifts on the horizon that could affect the way claims data are used, reported or shared?
There are always regulatory and policy changes. One of my front doors is a previous authorization requirement for 2027 when the provider was asked to perform at least one electronic prior authorization transaction. In our business, we have a Provider Lane and a Payer Lane and we see both sides of the coin. However, this requirement is to prompt the provider to adopt the transaction to meet the deadline, and the same is true with regard to the payer. They must be able to accept it when the provider sends it.
Can you share a recent initiative where positive outcomes of claim data internally or across payer partnerships are crucial?
This is such a good historical background in terms of payers and providers. When you have this data, what you know is that when you decide on a claim, you can better prioritize your work. You can really move towards success.
With our submissions, encounter data is rolled up and then returned to the payer quarterly for reimbursement through the government payer. So you are summarizing and making sure you have all the data that can be reported to the payer and ask the payer to report to the government. We have a customer who has very specific needs in their data. This is a specific code they must return to Medicare for a specific type of condition. During three quarters of the process, they don't have this code and data. This has resulted in a huge delay in their payments from Medicare. By working with our clearinghouse, we are able to place what is called editing in place, so if the provider submits a claim without the data required by that customer, we can bounce it back to the provider before submitting it to the payer.
Assuming a patient has a headache, the payer says that for all the patients with headaches seen by doctors, they must be careful if they are dehydrated. I'm the clearing house in the middle. When the claim is sent, I can check it out and see if the claim has been dehydrated. If not, I can send it back to the provider so they can add it. Previously, this statement stretched to the payer and would stall for weeks, or even months, before reporting it to the CMS. Then, CMS would say it wouldn't work because the doctor didn't say she checked for dehydration. The worst case is the worst case. However, if you have a good clean-up home placed between the provider organization and the payer, we can check this content and make sure the correct data is shared.
How can payers use artificial intelligence or predictive analytics tools to enhance their insights into claim data?
There are many use cases. We have an AI center of excellence in Veradigm, which is looking at many cool possibilities. From a conceptual perspective, there are coding applications and there are negative trend analysis applications. However, there are uncertainties and potential dangers there too. We are cautious. We are using data responsibly and making sure there is always one person in the loop. I think there are many opportunities to see what is usually passed, what is often rejected and how we can solve it from a trend perspective of denial.
How do API and FHIR standards change how payers exchange and take action on claims data across the ecosystem?
It makes things more interoperable. It makes data easier and faster to exchange. This makes it easier for organizations to connect with each other. It allows us to format data better. Indeed, the challenge is that they are not widely adopted. If anything, we might move faster.
In what ways does organizations use claim data to actively identify care gaps, high-risk patients and potential fraud?
We have products that can identify nursing gaps at the care point. We are using claim data, we are using clinical data, we are using risk analysis, all of which are stuck in a product to help doctors visualize who is on the plan, what they need to talk about and when, so we can get the full photo to the payer and ultimately complement this nursing journey.
photo: Krisanapong Debraphit, Getty Images
