Data standards are essential for the population health initiatives that serve as the backbone of value-based care, according to a panel of experts presenting at this year's Payer + Provider Summit, hosted virtually by Xtelligent Healthcare.
"The reality today is we have data that is coming from multiple sources. The collection of data is happening as we speak across so many entities," said Sheila Sudhakar, M.D., the vice president of enterprise population health/value-based care for Cigna and Evernorth and one of the session's panelists.
Players from across the healthcare continuum have access to an astronomical amount of data, all of which is geared toward helping payers, providers and patients achieve better outcomes. On the payer and provider side, in particular, data helps stakeholders better understand their populations' health needs and design more tailored interventions. This should boost outcomes and achieve value-based care success.
But as Sudhakar, as well as the other panelists from the National Committee for Quality Assurance (NCQA) and CAQH, went on to say, bringing that data together can be challenging.
Data standards serve as one solution to healthcare's big data problem, but despite widespread agreement on that, industry professionals face continued challenges in putting data standards into practice.
Many of these problems aren't fixable overnight, and, in many ways, they speak to the overall fragmentation that bogs down most healthcare industry efforts. But by enabling collaboration across industry stakeholders, best practices could be better achieved.
Fueling population health programs with standardized data
Having plenty of data is not enough to support an effective population health program; being able to use that data is also important. But with data coming in from numerous places, it can be hard for that information to move from entity to entity or platform to platform.
"When we think about standardization, we think about establishing consistent formats, definitions and structures, and it includes establishing those formats, definitions and structures for different systems, platforms, stakeholders, et cetera," said Kristina Rollings, chief growth officer at CAQH.
By adhering to data standards, healthcare payers and providers can focus on quality standards, added Rachel Harrington, Ph.D., the assistant vice president of health equity sciences at NCQA.
"Data standardization is really key for two things," she said during the panel discussion. "One, for building trust in the data that we're measuring. If we can't trust the consistency of that data, our ability to use it to hold our healthcare system accountable to compare performance between entities is really going to be limited. And the other one is efficiency. We're really looking at how quality as an institution can evolve over time."
Exploring the Challenges and Benefits of Using Data Standards for Population Health
In a time where value-based care success hinges on effective population health management, data becomes paramount, Sudhakar added.
Data standards don't just enable risk management (although that is one important function, she noted); data standards also help payers and their provider partners work together to deliver the right type of care to the right patient at the right time.
"When you have the right information to be able to make the right decisions clinically, you will be saving costs and improving revenue in time, as well, because you are making holistic informed decisions at the point of time that it matters," Sudhakar said.
That type of risk stratification and care management can extend to health-related social needs, too. Without social determinants of health data capture, payers and providers cannot determine what types of community health partnerships they need to forge.
"When I think about data standardization efforts and population health, you have to ensure that the health data that's collected is consistent across diverse populations and that we can better identify disparities in care delivery, health outcomes and access to resources," Rollings asserted. "If you don't have that, then the data isn't going to be that helpful."
You have to ensure that the health data that's collected is consistent across diverse populations and that we can better identify disparities in care delivery, health outcomes and access to resources.
Kristina RollingsChief growth officer, CAQH
But despite the promise of standardizing data, putting those ideals into practice remains an uphill battle.
Evolving data standards create challenges
While it might seem clear that data standards are essential to streamlining quality measurement and population health, getting on the same page might be easier said than done. According to Harrington, it's a lot of work to agree upon data standards, especially as data sources and various standards are constantly evolving.
"Data standards aren't always static. They can change," Harrington pointed out. "You can standardize, but you have to plan for changing code sets, standardized terminology changes. Even when you make that initial investment, if you're not building your systems to be able to be flexible for evolving down the line, that could be a little bit tricky."
There is also the question of data quality, Rollings added. Right now, CAQH is looking at ways it can cleanse data, including through vendor partnerships.
"There are a lot of authoritative sources out there that can validate data and can supplement data that we're getting from the provider," Rollings noted. "How do we take our provider-attested data and bump up the quality even further through augmentation, through scoring?"
When beginning a standardized data approach, Sudhakar encouraged organizations to consider the end user, but that in itself can be challenging. What one user needs out of the data might be different from what another needs.
"Just the level of detail and intention that it takes sometimes thwarts efforts to standardize," Sudhakar explained. "The importance of it cannot be overemphasized, but we sometimes approach the challenge in micro spurts that then ultimately don't yield the macro results that you could ultimately achieve."
She cautioned against allowing the enormity of the task to shortchange efforts to address data standards.
"We have to kind of flip the switch on this to be able to look at it from a big picture," Sudhakar continued. "We're creating our own waste within the healthcare system because we're having to overcompensate for the fact that we don't have data standardization. We're having workarounds and other workflows in place because we have siloed data as opposed to being able to look at data longitudinally."
In order for AI to really truly function as optimally as it can, this data standardization becomes key because it is going to leverage what exists in the system to generate the outputs.
Sheila Sudhakar, M.D.Vice president of enterprise population health/value-based care, Cigna and Evernorth
"In order for AI to really truly function as optimally as it can, this data standardization becomes key because it is going to leverage what exists in the system to generate the outputs," Sudhakar pointed out. "And if what is in the system isn't consistent, the outputs will be inconsistent."
With this new age in healthcare defined by the promise of AI, industry leaders need to come together to solve their biggest data problems.
Coming together to standardize data
The good news is that industry leaders are already collaborating, according to Rollings. At CAQH, she's helped convene experts from payers and providers nationwide to discuss the best way to standardize data at different points of care. This will set the industry up for success as it works toward standardized data by design, she said.
"It's not just taking the data that's already there and standardizing it, it's how do you think about it differently?" Rollings posited. "What are today's use cases, and how can we solve them together in a collaborative way?"
To that end, Rollings added, how can different industry stakeholders get on the same page in terms of incentives for better data standards? Again, this is where open industry collaboration pays dividends.
Sudhakar agreed.
"A good down payment is just starting these conversations, bringing thought leadership together within your organization and external to your organization so that you can start aligning on what are those standards that we should have consistently across," she explained. "And then data governance becomes key."
Organizations need to ensure they focus on those internal conversations, in particular, Harrington added.
"A little education is, and not just education in the technical sense," she said. Education in the sense of, what does this mean for me in my role as a case manager, a customer service rep, a registrar, a nurse, a clinician?"
"Organizations tend to have a handful of people who really get it, who can speak the technical talk, who can do this translation," she concluded. "Then they have a lot of people who are being told, 'Hey, this is what you have to do.' And it's expensive not just in money, but in people's time, energy and effort to make this transition."
Sara Heath has covered news related to patient engagement and health equity since 2015.
