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Through Collaboration between Jvion and Cardinal Health, Northwest Medical Specialties Applies Cognitive Machine to Achieve the Quadruple Aim in Oncology

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According to the Institute of Medicine, there will be 18 million cancer survivors by 2022 and the yearly rate of new cancer diagnoses will reach 2.3 million by 2023. Amplifying the difficulties created by rising rates are key trends that add to the challenges of cancer care including an aging population, a shrinking pool of cancer professionals, and rising costs. Growing demand compounded by the complexity of cancer care and shrinking resources is creating what is being labeled a “crisis in cancer care delivery.”

Through a unique collaboration between Cardinal Health, Inc. and Jvion, Inc., powerful and proven Artificial Intelligence (AI) technology is being made available to oncology practices across the United States. Northwest Medical Specialties (NWMS) is one of the initial sites implementing the solution to drive better care, smarter spending, and improved outcomes.

Applied across NWMS’s six locations and more than 1,700 new oncology cases per year, Jvion’s machine identifies patients at risk of a poor outcome, the clinical and non-clinical factors driving that risk, and the intervening actions that will most likely improve quality and the patient experience. Current efforts are driving Cognitive Machine application to lower rates of mortality, improve pain management, prevent avoidable deterioration, better identify and treat depression, prevent avoidable readmissions, and lower rates of avoidable inpatient admissions and ED visits.

“Not only has NWMS realized impressive results from the Cognitive Machine, but we have also set the stage to achieve healthcare’s Quadruple Aim,” said Dr. Sibel Blau, Physician Champion and Lead Oncologist for NWMS. “With Jvion’s AI, we are driving high-quality cancer care that is patient-centered, outcomes focused, and cost effective while reducing the cognitive burden placed on our caregivers”

To date, NWMS has improved the use of pain agents, improved palliative and hospice referrals by as much as 81%, driven a 68% increase in depression screenings, and realized an 80% increase in case management evaluations.

“The machine is helping us to drive the better utilization of resources through a more targeted focus on high-risk patients, which has also helped improve morale and improve outcomes,” said Blau.

Jvion’s Cognitive Machine is being brought to oncology clinics through Cardinal Health Specialty Solutions, which delivers distribution and practice management solutions to a broad network of oncology practices nationwide.

“The transition to value-based care is an ongoing challenge for our oncology customers. We collaborated with Jvion to customize this AI solution for oncology because we saw the potential for it to help practices make more informed care decisions that could lead to better outcomes and lower costs,” said Dr. Chadi Nabhan, Vice President and Chief Medical Officer, Cardinal Health Specialty Solutions.

Jvion’s Cognitive Machine oncology bundle combines seven vectors all of which are designed to improve outcomes for patients across multiple clinical dimensions. The machine delivers patient-level risk propensities, the clinical and non-clinical factors driving an individual’s risk, and the recommended clinical actions that will best improve a patient’s health trajectory across the following:

  • 30-day mortality risk
  • 30-day readmission risk
  • 6-month deterioration risk
  • Avoidable admission
  • Patient experience and pain management
  • Depression risk

 “This work is going help oncology providers better care for their patients and improve the patient experience,” said John Showalter, Chief Product Officer for Jvion. “The ability to bring AI to support every phase of cancer care is a game changer for patients and for the providers who care for them.”

About Jvion

Jvion delivers healthcare’s only Cognitive Clinical Success Machine. Using Eigen-based technology, the machine does what simple predictive analytics or machine learning models cannot. It goes beyond high-risk patient populations to identify those on a trajectory to becoming high risk. It determines the interventions that will more effectively reduce risk and enable clinical action. And it accelerates time to value by leveraging established Eigen Spheres to drive intelligence across hospitals, populations, and patients. Stop being predictive. Start being cognitive.