A Big Data ACO Effort

Health care Big Data might be the talk of the town, but many efforts are still in that “talk” stage of implementation. HDM this month is profiling real-world efforts to harness Big Data for some of the pressing challenges facing providers

PROFILE: Crystal Run Healthcare, Middletown, N.Y. 

Big Data at Crystal Run Healthcare, a rapidly growing group practice and accountable care organization in New York’s Hudson Valley, is actually only a couple of terabytes—small enough to fit on a desktop back-up drive. But it still chokes many standard analytical tools, says Chief Medical Officer Gregory Spencer, M.D.

It’s enough data to warrant a data warehouse, which Crystal Run has built with Health Catalyst. The warehouse includes not only those 2 TB of internal data—EHR, lab, radiology, general ledger, and practice management—but also claims data from payers. That’s particularly vital for the Medicare Shared Savings ACO patients—about 11,000 out of the practice’s total panel of 200,000. “Medicare recipients can go to whoever takes Medicare, so you can’t lock them down [into using the ACO’s providers], even though you’re going to be responsible for their care,” Spencer says. Having access to their claims data, regardless of where they’re getting their care, can reveal patterns that could otherwise do major damage to the practice’s cost structure.

Analysis showed a number of cost anomalies. For example, a single dermatologist had markedly higher pathology spending than any other provider taking care of the ACO patients. “It was a solo practice that seemed to biopsy everything that walked through the door,” Spencer says. “Maybe it’s not the wrong thing to do, but maybe there’s a better or different way to do it where there’s less waste. It’s potentially actionable, though it’s going to be a weird discussion.”

Crystal Run is now looking at bringing in socioeconomic data, based on ZIP codes and other factors, to help predict which patients will do better with more attention, and weather data, so that it can correlate which patients are likely to be no-shows during bad weather. Staff can reach out to those patients several days in advance and reschedule them.

Spencer sees that his datasets are going to balloon over the next few years, particularly once genomic data gets into the mix. He advocates that every organization consider a data warehouse—“or at least a data parking lot”—so that it’s prepared for the sophisticated analysis that value-based care is going to require.

“You don’t have to trend every single thing, but you should start gathering vitals and labs and other interesting pieces sooner than you think you have to.” While EHR vendors are beginning to build more sophisticated analytics into their product offerings, Spencer says data warehousing goes beyond the expertise that most vendors have. “It’s a fairly separate skill during the setup, but once the warehouse is up and humming, you can do queries and reporting over many different kinds of data and systems.”