To this end, Independence Blue Cross Blue Shield in southeastern Pennsylvania, serving two million members in five counties in and around Philadelphia, uses a predictive tool that calculates an individual’s likely future health state based on associated clinical conditions or diagnoses. The risk matrix, from San Mateo, Calif.-based healthcare analytics company Lumiata, helps the payer identify where members might be at risk for or might have certain conditions, and then helps alert their providers, explains Michael Vennera, senior vice president and CIO at Independence Blue Cross Blue Shield.
Vennera says that with the analytics tool, the payer can go to providers in its market and say that there is a chance patient X has a certain condition, even though it’s not diagnosed on his or her claims. All different types of data goes into that risk engine, says Vennera—medical claims data, prescription drug claims, lab results, and also basic demographics such as age, gender, and location. “Then what we get out of it is a prediction around diseases with different confidence levels for different members. And then you can use that to follow up,” he says.
Being right in the thick of the payer-provider relationship, Vennera notes how there are now lots of opportunities to combine the depth of information that a payer has with the depth of information a provider has, and put analytics on top of it. “What you typically see in most markets is that providers will have deep information, such as services rendered in an EHR,” he says. “So if you go to a hospital, all the information about that stay and the procedures done make for a rich and deep data set. But then the challenge is when people go to multiple providers. We know interoperability is long way off. But payers do have a broad set of data which covers most of your healthcare since most of healthcare flows back to your insurers,” he says.
Rose Higgins, president of the West Hartford, Conn.-based analytics solution company SCIO Health Analytics, additionally notes the challenge of payers and providers being as transparent with the data as possible. Higgins says that while it begins with recognizing that the data has intrinsic value to both sides, there has to be a willingness to be open with respect to the information, and share it, for opportunities to be identified and acted on. “It’s challenging to mix different types of payer data sets. Providers have multiple contracts, so there are different approaches with each payer. This means that a payer may not want to share data with a provider when they know another payer’s data may be mingled with their own,” Higgins explains.
Drilling Down with Policy Implications
When CMS’ Bundled Payments for Care Improvement (BPCI) initiative first got off the ground five years ago, there were high expectations for investments in technology—to track performance on bundles, to make more predictions on performance, and to potentially price commercial bundles, notes Matthew Cinque, executive director, product management at The Advisory Board, a Washington, D.C.-based consulting and technology company. “But at that time, the market did not move as quickly as was expected on the analytics side,” Cinque says. “Folks signed up for the bundled payment programs, so there were lots of conversations around bundles in general, but when push came to shove, there was not a lot of movement.”
Cinque explains that one of the reasons for this was the upside in the CMS program was not big enough to justify freestanding investments in new analytics. “Folks would get by with what they had in Excel. On the commercial side, we saw a lot of interest but there was hesitance on the part of payers to try to adjudicate bundled payments,” he says, adding that with a commercial population under the age of 65, the numbers showed that there was not a lot of volume of any one thing.” Even with the [Comprehensive Care for Joint Replacement Model] announced last year, there has not been “a huge move around analytics investment,” Cinque says, noting that he expects that to “get more serious sooner than later.” He says, “I would say it is an immature market, but one that I expect to have more dedicated focus on bundled payment-specific analytics as CMS rolls out more mandatory programs related to this.”
Cinque adds that “getting more serious” involves an investment in integrating different data sources. “One thing that makes bundled payments so challenging, especially if you look at the cardiac care model, is that so much of what you’re trying to manage happens outside of the four walls of the hospital. You need to be able to get data across inpatient metrics and get visibility into what happens in the physician office and skilled nursing facilities. Those are almost always entirely different data sets,” he says. Thus, data aggregation has to be a big point of investment, be it through a data warehouse or something else, he notes. “It’s about accumulating that data and then manipulating it. The data aggregation component of it is really what makes it cost prohibitive today,” he says.
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