SAS Analytics is well known in the clinical space for its tools to manage clinical trials data; less well-known are applications in commercial or healthcare-provider applications. But one of its clients, Express Scripts, is touting the Cary, NC, company’s technology for addressing an ongoing problem in life sciences: getting, and keeping, patients on therapy.
Express Scripts uses the predictive models created with SAS Analytics to continually improve its services and become a proactive partner in patients’ health. “We’re talking about treating patients proactively. We can predict who will comply with their medication or not,” says David Tomala, Express Scripts Director of Advanced Analytics. “Even if medication costs increase marginally, overall medical costs are minimized through better health outcomes.”

Tomala’s team worked with 400 variables that are accessible in the Express Scripts’ patient databases, building a predictive tool that helps them analyze patients’ care and adherence. Depending on what results surface from the statistical analysis, the company can tailor an adherence program to the patient’s needs. “Our outreach programs address an individual’s risk factors. People too busy to order a refill can be moved into an automated refill program before a lapse occurs. Patients concerned about side effects might benefit from talking to a pharmacist. We analyze each patient to offer the most effective programs to keep them healthy.”

Medication adherence is but one application that SAS hopes to address with a newly opened Center for Health Analytics, which brings a team of experienced life sciences and healthcare project managers together to identify new application areas for SAS tools.
There are three broad buckets where we see potential for SAS services,” says Jason Burke, managing director at the Center, “improving patient outcomes; providing financial analytics; and providing customer intelligence.” Besides having powerful analytical capabilities built into the SAS platform, Burke says that the company has invested heavily in visualization tools that help users grapple with large datasets. “The adherence type of problem is a good example,” he says. “Large datasets must be brought together from disparate sources; and many variables are present to be factored into the analysis.” At the same time, while Tomala notes that he was able to tap into a pool of experienced SAS analysts to build up his analytics team, Burke emphasizes that “you don’t need to be a PhD mathematician” to use the visually-oriented SAS tools. PC