Healthcare's Big Data Problem
The program is the backbone of the Seton Total Health Partners program, which, as Leslie explains, is an "extensivist" program, under which a physician outside the hospital works with a team of social workers, nurses, and others to visit patient homes and figure out what's keeping a patient from effectively following treatment protocols that will likely keep them out of the hospital. The problem is, as Leslie says, "you'll never have the resources to do that with every patient."
The software helps determine based on a host of combined data which patients are most likely to be rehospitalized within 30 days. Targeting the patients is like looking into a crystal ball. "If you target the wrong patient, you get all the cost and accrue none of the benefits. That's where we're taking this sea of information and filtering it to make it relevant, predictive, and actionable."
It's a task that's easier described than done. Much of the magic comes from natural language processing technology that integrates clinician notes from the patient's EMR—which are too often ignored by other clinicians because they are difficult to review in a timely manner—to be used to determine social or other difficulties that might result in a readmission. Combined with statistical analysis and data mining, such tools can provide a powerful picture of the patient's needs outside the hospital.
Seton chose to focus on CHF patients first because it already had clinical programs to address such patients. The disease is prevalent among the large number of uninsured patients it treats in Central Texas.
"Untreated, it steadily gets worse, but it's very treatable," says Leslie. "But like a ratchet, when it gets worse, you can't get people back to where they were before. We're trying to prevent people from getting sicker."
Next, Seton might look at using the technology to take better care of patients with diabetes or other chronic maladies.
"Now we have a number of these modular programs set up, and we're refining the process of using the predictive information," he says. "This particular work with IBM was a proof of concept of the value of our unstructured data and the value of the technology."
As one of a handful of organizations that are part of CMS' Pioneer ACO program, the work holds big promise for cutting treatment costs, he says.
"Pioneer is really putting a lot of this stuff on the line for us," he says. "We have to do this to bend the cost curve."
This article appears in the July 2012 issue of HealthLeaders magazine.
Philip Betbeze is senior leadership editor with HealthLeaders Media.
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