Jvion’s Effectiveness Score Index (ESI) approach is helping providers make sense of the complex predictive solution landscape that has erupted across the healthcare industry.
In a landscape in which many healthcare vendors claim to have a predictive solution, cognitive science leader Jvion is working to clear the noise. During a recent presentation, Jvion data science leaders introduced the idea of the Effectiveness Score Index (ESI) to help illustrate and mitigate the accuracy fallacy inherent to many of the predictive solutions on the market.
According to the presentation, the accuracy fallacy implies greater precision than what actually exists. For example, a disease could impact one in one-hundred people or 1% of the population. A predictive solution could predict that 100 out of the 100 people will not get the disease. The accuracy fallacy is that the solution is 99% accurate. In fact, the solution is 100% inaccurate at identifying the at-risk individuals.
ESI is designed to help providers mitigate the accuracy fallacy and provide a framework for better assessing the effectiveness and applicability of a solution. The ESI measures the ability of a solution to pin point patients who are truly at risk of a condition and provide patient-level action recommendations. It serves as a baseline comparison point that clarifies the actual potential impact that a solution can have on a population.
“The ESI is critically needed as the market is flooding with hundreds of solutions that carry the “predictive” label,” explained the presenter. “ESI will help providers differentiate between vendors with real solutions and those who are simply riding the market wave.”