Health insurers can use big data to predict which individuals are more likely to develop metabolic syndrome and create personalized programs to help prevent the syndrome from developing, according to new research published in the American Journal of Managed Care.
The study analyzed 37,000 Aetna members with employer-based coverage, finding that predictive modeling tools can forecast the risk of metabolic syndrome down to the specific risk factor.
Targeting metabolic syndrome's primary risk factors--large waist size, high blood pressure, high triglycerides, low HDL cholesterol and high blood sugar--can prevent individuals from becoming diabetic. And since metabolic syndrome affects one in four adults in the United States, preventing the health condition can save insurers substantial costs.
For example, covering a member without a chronic disease costs UnitedHealthcare about $4,400 annually, while members with diabetes who experience complications cost $20,700, FierceHealthPayer previously reported.
"Using data to help predict when people might have health problems means we can try to help them avoid those problems," Greg Steinberg, Aetna's head of clinical innovation, wrote for the insurer. "This is another great example of how technology and information can create population health tools that help empower people to lead healthier lives, reduce healthcare costs and improve the healthcare system."
What's more, the study found predictive modeling tools can help develop targeted, data-driven programs--including exercise, weight management and care management initiatives--to help at-risk individuals.
The study results show predictive analytics can help health researchers generate insights faster; improve intervention program design, impact and returns; and boost adherence to scheduled preventive visits.