The goal of Jvion’s Cognitive Clinical Success Machine is to enable the best action that will improve outcomes. The machine is built using Eigenspace—a technique that is completely different from predictive analytic or machine learning models. We go beyond simply predicting risk to patient-specific, effective interventions and assurance of success.
Eigenspace helps us understand complex, multi-dimensional concepts like quantum physics, consumer behavior, internet search intent, and now patient health. At Jvion, we use this approach to answer hundreds of questions about a patient’s health without having to create, train, and test new models.
For each question (or what we call a vector) a clinician receives a specific patient’s risk propensity, the clinical and non-clinical factors driving that risk, and answers to IF the risk can be mitigated and if so -- HOW.
Because the machine is designed to quickly scale to new questions and adapt to new patient populations, it works as a true Artificial Intelligence (AI) asset. This means that with the Jvion machine, providers can:
Jvion’s Cognitive Machine goes beyond the risk of an event to the interventions that will improve outcomes. For each patient, the clinical, behavioral, and socioeconomic factors contributing to his or her risk are identified. Using the Eigen Sphere technology at the heart of the machine’s engine, the machine extrapolates which interventions will result in the best outcome while also ensuring patient engagement. The output is actionable, highly personalized, patient-specific recommendations with the greatest likelihood of improving health. This capability to truly impact outcomes and drive clinical action is simply not possible with typical predictive or machine learning models.
A model that is only focused on the high-risk bands within any patient population isn’t doing enough to impact patient lives. Treating high-risk patients is an imperative. But AI solution should enhance treatment effectiveness, not just identify the patients that you already know are at risk. Jvion’s cognitive machine goes beyond just the high-risk bands to identify the “hidden-risk” patients who are on a trajectory to becoming high risk. These are the patients whose outcomes you can change and who will benefit the most from interventions.
The structure of the Cognitive Clinical Success Machine means that it can quickly scale to new challenges and patient needs. Unlike predictive or machine learning models that have to be built from the ground up with every new application, the machine leverages the same, tuned Eigen Spheres for each additional clinical vector. You know what kind of performance the machine will deliver because it already understands your patient population. Moreover, the Eigen Sphere foundation means that new clinical vectors can be activated within weeks (not years) and with minimal additional data requirements if any.
The Eigen Spheres that exist within the Cognitive Machine’s engine form a patient topography that allows us to quickly tune the solution to new patient populations and clinical applications. We don’t have to build new models for every new client. Instead, we transpose the patient population onto the topography and tune the existing spheres to any nuances and differences. This approach enhances the speed of the machine. It can start firing risk and intervention outputs in less than twelve weeks versus the typical predictive analytic model build timeline that extends to more than eighteen months.
Emerging technologies are different from enterprise-wide technologies like Electronic Medical Records (EMR) in many ways. Maybe one of the biggest differences is that they do not dictate workflow. Emerging solutions like Jvion’s Cognitive Machine are designed to provide the information that will reduce a clinician’s cognitive load by pointing resources to the right patients and the right time. The outputs of the machine can integrate into any clinical workflow. They have been successfully incorporated into every major EMR system. Moreover, the machine can use the data that is on hand, regardless of its completeness. The Eigen-based engine enables the machine to make sense of dirty, inconsistent, and incomplete data so that you and your IT team don’t have to.
To help our clients realize the highest potential value from the Cognitive Clinical Success Machine, we bring to bear the talent and support of our Success Activation Team. This group—which comprises clinical, technical, cognitive machine learning, and process experts—works across the lifespan of the machine to ensure our clients’ clinical and business success. They help accelerate integration of the cognitive appliance into the organic workflow; reduce demands on IT and clinical resources; measure and report on clinical and business impact; and provide ongoing support for training, communication, and executive reporting and performance measures.
We measure our success by the success of our clients. For every client, we capture the return on investment delivered by the machine including the number of patients impacted, dollars saved, and adverse events avoided. More than 330 hospitals and 1,000 clinics use Jvion’s Cognitive Clinical Success Machine. And they are achieving remarkable results across clinical vectors such as a 20% drop in readmissions, a 30% drop in pressure injuries, and a 40% drop in avoidable ER and inpatient admissions.
The engine is proven, adopted, and producing value for some of the most prominent providers in the nation including Mayo Clinic and Trinity Health. This performance hasn’t gone unnoticed; Jvion’s solution has won numerous external awards including designation as the #1 solution in the market by Black Book Market Research, Top Innovator by Accenture, CB Insights top 80 companies reinventing healthcare, and top 50 disruptive companies in HIT.