Jvion, the developer of the Cognitive Clinical Success Machine, announced the release of the company’s latest clinical vector to target and treat patients at risk of opioid abuse. The machine’s outputs will be used to identify at-risk patients and the actions that will most effectively reduce risk while ensuring engagement.
“By marrying the latest in proven Artificial Intelligence (AI) with the clinical excellence delivered by the nation’s providers, the healthcare community will be better able to target and treat those individuals who are suffering from opioid addiction and other behavioral health epidemics,” said Dr. John Showalter, Jvion’s Chief Product Officer.
According to the American Society of Addiction Medicine, almost 2.6 million Americans had a substance abuse disorder involving prescription pain killers or heroin; over 75% percent involve misuse of prescription pain killers. And the impact of the epidemic is placing a major burden on an already overtaxed healthcare system.
Using Jvion’s Opioid Abuse Vector, providers will be able to more effectively and efficiently identify those individuals with an increased likelihood of abusing opioids within the next year regardless of current prescribed opioid use. The machine accounts for all patients within a population to ensure that risk is captured regardless of where an individual’s addiction starts—with heroin or prescription pain killers. Providers receive patient-level risk outputs that include the contributing clinical and socioeconomic factors along with the personalized clinical action recommendations that will have the greatest impact on risk reduction.
“We are looking forward to the next steps in this journey as we tackle some of the most critical health challenges that we face across the nation,” said Dr. Showalter. “And we look forward to making great strides in helping providers treat and prevent opioid addiction as well as suicide/self-harm and depression.”
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.