I find it hard to imagine that Accountable Care Organizations (ACO) are going to be as wildly successful as projected without an ability to actively use the data they are obligated to collect. The Centers for Medicare & Medicaid Services (CMS) require the average ACO to compile data on their patients and then use that same data to demonstrate that they’ve met the CMS requirements of data use, reporting and improving quality. If those requirements aren’t met… well let’s just say that ACO might not be around for much longer.

To obtain the promised financial rewards, the ACO has to report on 33 quality standards and show improvement in 32 of them within three years, a mere 97% of those CMS ACO standards. If the ACO can prove that the cost of caring for patients is less than what CMS would anticipate under the standard fee-for-services model, the ACO providers get to share in some of the savings. So, if there are no savings, or more correctly, no demonstrable savings, there can be no reward. Here is the take-away message for an ACO: As you develop a plan to demonstrate the CMS required improvements in the standards, you would be wise to find a tool that can combine and analyze the patient (and provider) data you already collect and then not just identify opportunities for savings but actually show the best pathway to realize those savings.

CMS breaks the quality measures into four categories: patient and caregiver experience; care coordination and patient safety; preventive health; and caring for at-risk populations. The last two categories, preventative health and caring for at-risk populations, lend themselves neatly to a well know business intelligence tool that is emerging as the go-to technology to manage risk in healthcare settings – predictive analytics.

Predixion’s predictive analytics technology for healthcare is exactly what an ACO needs, as it is able to accomplish three critical functions of an ACO: analyze data, reveal the exact areas that are most likely to improve care and then quantify that improvement. Predixion’s platform is able to analyze patient-specific data for the many required quality standards and reveal opportunities to reduce cost and improve care related to those standards. Second, it can recommend interventions derived from an analysis of those opportunities actually allowing for a reduction in cost and better care by front line providers, those who actually have the best ability to execute meaningful change. Predixion’s platform also has the ability to evaluate the efficacy of those interventions through built in already built-in analysis tools. Finally, the solution is always learning from your data to make sure you always have the most effective intervention recommendations possible.

An ACO’s success will hinge on its ability to use their patient and provider data wisely and with ever increasing competence. The promise of finally being able to leverage the big data of healthcare to truly improve our healthcare system and drive down costs is within our reach. If ACOs fully utilize this technology to leverage their data, we will finally begin to realize a predictable increase in the health of our population and a resultant decrease in the cost of managing illness – the very goal of the grand ACO experiment.