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 cognitive 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 cognitive 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 cognitive 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 cognitive 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 cognitive machine and the Jvion 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.