Harm Prevention

Getting Beyond the Stigma — The Prescriptive Analytic Breakthrough for Depression

The face of depression can deceive us.

John Moe, comedian and former host of the NPR show “Wits,” explores this contradiction in his popular podcast “The Hilarious World of Depression.”

The episodes feature interviews with well-known comedians, including Dick Cavett and Andy Richter, sharing their stories of struggles with depression and how sometimes the person laughing hardest is hiding the most pain.

Now in its second year, the show brings levity to a serious subject, making the topic more accessible and somehow less frightening and stigmatized.
That stigma is a recurring theme on the podcast and reflects the challenges the medical community faces in identifying patients at risk of depression. When people fear opening up to doctors or loved ones, depression can hide in plain sight.
And that’s a big problem. An estimated 6.7 percent of the U.S. population suffers from at least one major depressive episode each year. Unfortunately, only 35 percent of patients with severe symptoms see a mental health provider. Only about 20 percent receive care consistent with current guidelines.
Here’s where the Jvion Machine makes a major, unique difference. The AI-enabled prescriptive analytic solution works beyond the stigma, assessing and understanding comprehensive clinical and exogenous data to identify depression risk. Unbiased, unemotional data can speak when patients themselves are reluctant to.
The Jvion Major Depression Vector improves the diagnosis rate for major depression and identifies the most appropriate treatment from the best resources. The vector identifies the risk of a patient experiencing a major depressive episode within the next six months. It considers both the medical and socioeconomic factors driving the individual’s risk and makes intelligent recommendations for screening, referrals, and overcoming individual barriers to treatment.

The results of the Jvion Machine for identifying depression risk have been remarkable.
  • Statistically outperforms published prediction models using patient questionnaires
  • High risk group has 10 times the risk of developing an episode of major depression
  • Almost 7 percent of episodes could be prevented by acting on the 3 percent of patients at highest risk

That specific understanding of risk targets at various stages (not just the highest risk patients) and ability to identify depression risk well in advance set the Jvion Machine apart. As noted, the depression vector outperformed prediction models that rely on patient questionnaires—a particularly challenging approach when dealing with depression and its associated stigma.
With the vector approach, an organization can get this tremendous insight into depression risk among a patient population in just a couple of weeks. The Jvion Machine serves as an artificial intelligence asset for your healthcare organization, providing the ability to turn on vectors for additional conditions with ease—be it depression, sepsis, readmissions, chronic conditions, and anything else you can imagine. The opportunities for recognizing risk in patient communities are literally without bounds.
And the Jvion Machine enables intervention. It finds the people within the community on a trajectory toward a potentially avoidable depression event and directs clinicians to take the best next steps for patient outcomes. The machine offers the most comprehensive advance in healthcare to address the unique challenges in diagnosing depression and deliver consistently better outcomes.
People like John Moe are helping shine a spotlight on depression and ease the stigma attached.
“’Well, if I own up to having a mental illness, am I going to be committed? Are people going to see me as unstable?’” Moe said in an interview with Mother Jones magazine about the podcast. “What that stigma fails to own up to is that people with mental illness are your friends and neighbors and co-workers, living regular lives.”
At the same time, the Jvion Machine and the Major Depression Vector are shining an unprecedented light on entire patient populations, identifying depression risk and intervention for those who previously might have quietly slipped through the cracks.
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