Unique Solutions for Unique Patients
Running analytics today in healthcare gives you a way to stratify patients into groups that drives resource allocation. But unless the analytics can tell you why the patient has ended up on the list, we will never be able to build the right interventions. Prevalent analytic methods, including the most advanced machine learning, cannot deal directly with the scale of this complexity. For precision medicine to truly work, the data has to be timely and lead to an action.
In 2016, the US wasted $130 billion on patient non-responders. Recent studies estimate that 90% of the conventional and top-selling medications only work for 30-50% of all patients. This results in signiﬁcant higher downstream costs, due to disease progression in non-responding patients. Additionally, side effects and adverse events caused by this “imprecision” results in an increased 20% acute hospital admissions/year.
Predicting treatment response and patient susceptibility accurately
Next generation of high cost utilizers
“One-size-ﬁts-all was the starting point, and we learned from that, we learned what aspects were working and what aspects did not work so well, and the ones that worked well we’ve moved forward with. The market is now moving in that directed area of more personalized care, and more understanding of what is important to the individual.”
David Schweppe, Vice President of Customer Analytics & Reporting at Kaiser Permanente.