Dive Brief:
- In hospitals, population health management uses predictive analytics to separate patients into cohorts by risk level and assign supports accordingly, and some colleges are starting to look to the strategy as a model for addressing retention.
- Inside Higher Ed reports new research from EAB shows promising results for colleges that have organized their advising services based on the population health management model, in which about 70% of people are categorized as low risk, 25% as medium or rising risk, and 5% as high risk.
- Middle Tennessee State University implemented a new model based on the healthcare strategy in fall 2014, hiring more advisors and focusing their efforts on high-risk students, who were identified through analytics as those with low GPAs, and it saw a 3.4% increase in retention in one year.
Dive Insight:
While Middle Tennessee State University focused on student GPAs to launch its program, colleges considering the model must look at their own populations and tailor the assignment of risk based on their individual student populations. That is one of the most promising elements of data analytics. The analysis gives higher education institutions the opportunity to develop programs that respond to their own students’ needs, rather than replicating programs exactly as they worked on another campus. Some schools might find students traditionally thought to be at higher risk of dropping out — commuter students, for example — are actually high performers. Schools are already tailoring support services based on early warning systems. Organizing students by risk level and organizing supports from there is another step on the same continuum.