Cardiovascular events remain a leading cause of death in America, yet most population health programs consider risk reduction a secondary concern, preferring to focus on utilization reduction in the very seriously chronically ill out of a belief that this will yield the most short term clinical and economic advantage. A part of the reason CV efforts haven’t been as prominent is that it has, to date, been difficult to quantify clinical and economic benefit of cardiovascular risk reduction programs, which tend to measure their effect in years rather than months.
This presentation describes the ongoing clinical implementation of a set of cardiovascular risk models that allow the quantification of cardiovascular event risk. This implementation is enabling both accurate quantification of risk in high risk individuals, but also the modeling of benefit, at the point of care, in patients who have taken steps to mitigate risk factors. The point-of-care aspect of this tool represents a great example of analytics-driven clinical decision support. Perhaps most importantly, the model allows programs to rigorously quantify the clinical impact of their risk reduction efforts and credibly tie dollars to that effort, proving their value and getting the support they deserve. We will discuss how these models – available in the public domain – are being implemented into the Optum Performance Analytics platform for use by providers delivering value-based care.
