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Population Health and the Analytics Opportunity

Scott Mace, for HealthLeaders Media, November 13, 2013
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A variety of tools exist to help stratify risk. Some tools place members of a population on a scatter plot to make the identification of outliers easier. Other tools organize a population into patient registries to track various diseases and treatments. Still other tools use input gathered from patient surveys. A recent study, however, reported that many of those tools had not performed very well.

At St. David's Health System in Austin, which is working with Brookdale on the challenge grant, 60% of readmissions recently were measured as coming from low-risk groups. "To me [this] means either that people hadn't been stratified properly, or that they were being sent home when they probably did need some kind of service or follow-up," O'Neil says.

The biggest hurdle in O'Neil's experience with population health analytics has been engaging with the hospital C-suite to craft the business associate agreements necessary to manage populations. "Once we've developed a relationship with one entity and had success, it's much easier to engage other entities within that system."

In dealing with the two universities, O'Neil says, "We had to resolve some issues related to intellectual property to incorporate INTERACT into electronic information systems," he says. INTERACT is an acronym for Interventions to Reduce Acute Care Transfers, a free quality improvement program for which FAU holds the trademark and copyright. "This has been resolved through a licensing agreement—Loopback [a Dallas-based analytics platform vendor] also has a licensing agreement with FAU to bake INTERACT tools into software programs."

Both Brookdale and its hospital partners are using a common population health analysis dashboard and software provided by Loopback Analytics. "As a geriatrician, this is the most exciting time in my career, because I've always felt that fee-for-service medicine was the bane of good geriatric care because it rewarded volume rather than quality," O'Neil says. "Having that near-real-time data is really going to be extremely helpful to us."

Analytics and meaningful use

Analytics tools produce the patient registries that identify gaps in care, not just to meet ACO objectives, but also to meet the requirements of meaningful use stage 2, which takes effect in 2014, says Gregory Spencer, MD, a practicing general internist and chief medical officer at Crystal Run Healthcare, a multispecialty practice with more than 300 physicians based in Middletown, N.Y.

"There are frequently registry functions within EHRs, but the EHR is set up at the patient level," Spencer says. "It's not optimized for reporting groups of patients, so to kind of get that rollup, you have to have another layer on top of that to gather it up."

Thus, some sort of aggregator function is needed. "Usually that is not something that many EMRs do well," Spencer says. "Registries are mostly condition- or disease-specific lists of patients who satisfy a certain criteria: diabetics, patients with vascular disease, kids with asthma. Care gaps look at all patients who have not had a certain recommended service. There is overlap with the registries, since a list of patients due for their colonoscopy is a kind of registry that needs to be 'worked' to get those patients compliant."

Like numerous other healthcare organizations, Crystal Run's first foray into population health analytics employed Microsoft Excel spreadsheets.

"The basics can be done with available tools," Spencer says. "People shouldn't wait for the killer app that's out there that's fancy and has a slick user interface. You can really do a lot with what you have, probably immediately."

Since 1999, however, Crystal Run has incrementally left Excel behind and built population health analytics reporting tools on top of its NextGen electronic health record software, Spencer says. Crystal Run also adopted the Crimson Population Risk Management service from the Advisory Board Company, which incorporates technology from Milliman Inc. on the back end, he says.

Like other providers, Crystal Run saw the shift coming from fee-for-service to accountable care and took early opportunities to get its hands on claims data and learn how to work with it, Spencer says.

Other resources offering insight to accountable care analytics were the Group Practice Improvement Network and the American Medical Group Association, where Spencer has been able to network with peers who have been pursuing population health analytics longer than Crystal Run has.

The Crystal Run practice, formed in 1996, grew out of a single-specialty oncology practice and today has 1,700 employees. It is designated by the NCQA as a level 3 patient-centered medical home, and in 2012, Crystal Run became one of the first 27 Medicare Shared Savings ACOs.

Analytics have revealed "a lot of surprises at who you think has been getting most of their care from you," he says. Snowbirds—typically someone from the Northeast, Midwest, or Pacific Northwest who spends substantial time in warmer states during the winter—are receiving significant amounts of care that had been outside of Crystal Run's knowledge.

But with Medicare claims data examined through its analytics services, Crystal Run has had its eyes opened to previously unobserved cost centers. For instance, the No. 1 biller of pathology services for a 10,000-patient Crystal Run cohort was discovered to be a local dermatologist.

"What it's all about is improving quality and eliminating waste," Spencer says. "That waste is [in] tests that aren't really required [and even some] visits that are [being required]. It's your habit and custom to see people back at a certain frequency, but when you really start thinking about it, do you really need to see somebody back every three months who has stable blood pressure and has been rock solid? Well, probably not. And so you start doing things like that, and it adds up incrementally."

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