In a 2012 pilot, which is now live, the firm worked with Independence Blue Cross (IBC) to help the Philadelphia-area insurer identify patients who were likely to experience customer satisfaction issues and provide outreach to nip those problems in the bud-sometimes three months before they'd otherwise arise, Dutcher says. (The company also helped IBC identify potential new customers as well as existing members who may benefit from services they weren't using.)
To do this, IBC looks at data from its call center, to see which patients make frequent inquiries and therefore may need some extra attention. It also looks at data from member healthcare organizations, to see which medical procedures prompt the most follow-up inquiries from patients and also to see why an individual patient needed treatment. This analysis can alert IBC that a particular patient has "a high probability of a negative outcome," which may trigger the insurer to send information about preventive care (to avoid repeat hospitalizations for the same condition) or long-term or home health services (if an upcoming procedure will have a long recovery period).
Such a proactive approach improves the overall patient experience, Dutcher says, while potentially saving healthcare providers money on unnecessary or repeat procedures. InsightOne calls this sort of analytics " predictive intelligence," he says, and it lets analytics get specific enough to identify a "pattern of one" for a single patient.
Analytics needs talent, data warehouses; both in short supply
Getting insurers to spend money on advanced analytics, as stated, is easier than getting healthcare providers to invest. But there are two key reasons providers can't stay on the sidelines for long, says Cynthia Burghard, research director for accountable care IT strategies with IDC Health Insights.
One is the argument that a patient is more likely to participate in a wellness program (that back-end analytics has identified her as a good candidate for) if the recommendation comes from her physician as opposed to her health plan.
The other is that healthcare reform efforts of the 1990s failed largely because of a lack of data. "Not only was the available information limited to claims but it was retrospective and not in a format that was useful to physicians in understanding their current performance compared with targets," Burghard notes in a recent report,Business Strategy: Analytics Leads Accountable Care Investment Priority. "Most discussions between payers and providers resulted in arguments about the accuracy and timeliness of the data."
The emerging ACO model, introduced in healthcare reform as a way to shift the industry from a fee-for-service model to one centered instead on coordinated care, placed added emphasis on analytics and data warehousing technology. The need here is identifying patients who will benefit from a particular care program, engaging those patients in order to manage and improve their care and to incorporate such care interventions into a physician or clinician workflow, Burghard says.
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