TEN TRANSFORMATIVE TRENDS 2021 SDOH continued from page 26
submissions, we create a representative set that we adjudicate to the subject matter experts,” DeSilvey says. It’s the experts who are there to clarify if something truly is
or isn’t housing instability, for example. This comprehensive process, says DeSilvey, is “necessary in the social determinants space because it’s kind of like the Wild Wild West. There’s lots of hope and goodwill, but it’s not yet sorted. That all happens within Gravity.” Since it’s a little bit of the Wild Wild West, the Gravity Project is actually responsible for defining the domains themselves as well, DeSilvey notes. “So, for food insecurity, hous- ing instability, and homelessness, we have to create our own definitions, separate from the public health literature, for clinical and community applications in order to ground the terminology that we’re creating.” Everything that’s done at the Gravity Project is open, public
and consensus-based. To become a member, that individual or entity just has to essentially say, “I want to become a member,” DeSilvey says. Current participants include patients, payers, community-based organizations, clinicians, federal partners, and health IT vendors. One recently joined member is the Pittsburgh- based integrated healthcare company Highmark Health, which operates a health system as well as a health plan. Speaking to the motivation for joining the initiative, Deborah Donovan, Highmark Health’s vice president, SDOH strategy and operations, says, “For us to be able to truly start to look at the social determinants of our populations, both at the population level as well as at the patient level, the work that Gravity is leading is critical to actually creating the national infrastructure to do that.” What’s more, Donovan affirms what CMS’ report uncovered
about the ICD-10 Z codes: while the ones available are being used sparingly by some, they don’t reflect the full breadth of social issues that are impacting populations. For example, she offers, Highmark Health’s clinicians will often say that transportation services and resources is the one social determinant they would most want to fix, as that’s what their patients need most. “But we don’t have an ICD-10 Z code to capture that. So, without it being built through the work that Gravity is doing, we really have a gap in our ability to truly understand the social risks of our populations,” Donovan asserts. As leaders of the Gravity Project and others surge forward
in their work around SDOH data standardization, they’re also keeping their eyes on the eventual goal of implementing this data into organizations’ respective electronic health records (EHRs). When could that become a reality? “That’s the million-dollar question,” DeSilvey acknowledges, again noting the timelines for building codes. In the interim, providers can ramp-up efforts around developing
SDOH screening assessments and getting those into EHR systems. Donovan notes that one of Highmark Health’s plans, Highmark Blue Cross Blue Shield, and its partners have come to consensus on a social determinants screening assessment that’s now live at the Pittsburgh-based Allegheny Health Network and is part of the organization’s Epic EHR platform. Across the nation, however, most healthcare professionals still aren’t screening for SDOH at all; a 2019 JAMA Open Network study found that just one-quarter of U.S. hospitals and 16 percent of physician practices self-report screening patients for social determinants of health such as food, housing, transportation, utilities, and interpersonal violence needs. Ultimately, both Donovan and DeSilvey emphasize that the
work they and others connected to the Gravity Project are doing will create momentum in the healthcare sector through value- based program design that “considers social risk as well as clinical risk when we model care coordination payments to providers,” Donovan says. She adds, “There are many ways we can tackle this, but we need the infrastructure first, and Gravity is really being looked at as the source that we need to funnel all this through.” HI
Revenue Cycle continued from page 27
these are new things, but there are more and more tools and technology available for us to work with.” Beyond BPA/RPA, McHugh says, “A lot about truly advanced analytics, or AI, is aspirational, but will become reality, and involves denials management. The best way of handling denials is to prevent them from occurring in the first place. If we know that Health Plan X is always going to deny endoscopy for reason Y, using advanced analytics and AI, we can prevent those denials in advance by identifying what the cause would be; that is predictive denials management.” But, he says, fewer than 1 percent of U.S. patient care organizations are yet using predictive analytics or AI/machine learning. Meanwhile, what are the keys to success with AI- and
machine learning-based predictive denials management/ predictive analytics for revenue cycle management? Industry leaders see two. “The foundation to this is a really good analyt- ics platform,” says Impact Advisors’ McHugh. “If you don’t have a good analytics program, you’re going to struggle to do this. And it’s funny, because when RPA was first becoming hot, the IT people would say, that’s operational, you can build that, so RCM would take it on. So because of that, there wasn’t an enterprise-wide approach, and as a result, RPA evolved in silos. It’s really important to have a center of excellence for analytics and a center of excellence for business process automation. Those are two very different, distinct things. IT doesn’t do everything, but they’re a coordinating entity in this.
“First, you have to invest in the people—you need actual data scientists, who have a high technical capability, but those people are difficult to get, and are expensive.” —James McHugh
And you need to prevent silos. Also, a lot of health systems are outsourcing too much to their technology vendors around this. You need the control.” UPMC’s Porter says very firmly, “First, you have to invest in the people—you need actual data scientists, who have a high technical capability,” he emphasizes. “But those people are difficult to get, and are expensive.” So, clearly, there are prerequisites involved: a strong data analytics foundation, and actual data scientists. And, everyone agrees, there’s no question that it will be a major challenge to acquire the data scientists with the expertise to make AI- and machine learning-based analytics work. Once the AI- and machine learning-based tools are imple-
mented, Porter says, he and his colleagues will be engaged not only in predictive denials management, but also in developing models to predict patient payment behavior. “We’re looking at our patient segmentation, to understand patient payment patterns and behaviors—who pays and how, for example, on the first bill, second bill, etc. We want to create a better patient experience around this.” Clearly, there are challenges involved here; but, everyone
agrees, this is one area in which the potential of technology to fuel a new kind of revenue cycle management is very real; and all agree that a significant number of patient care organizations will be engaged in the most advanced forms of RCM within the next two to three years. HI
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