Let's Keep the Conversation Going

Every healthcare practice and hospital has endured a tough technology adoption. A new system introduced with big promise, but that took months (or years) to implement and disrupted and frustrated clinicians. All without the anticipated improvements in care delivery or efficiency.

And then there are stories like this.


Predictive analytic solutions, which span the continuum from simple rubrics to complex neural net models, share a common boundary problem. At some point, the predictive model reaches a point where it is no longer accurate at identifying at-risk individuals. No matter how clean the data is or complex the math, all predictive models will reach this point.


Every new technology comes with an adoption gap: that space between technological capability and human adaptation. This disconnect handicaps the inherent power of new tools and our ability to effectively and quickly realize value. The gap widens for emerging technologies like cognitive science. With cognitive machines, we have tremendous potential that, when applied, can positively impact the health and lives of millions; but, the newness and sophistication of the solutions can act as speed bumps to adoption.


Sometimes, major inflection points happen when we take what is working within other industries and calibrate it to address the challenges that we face within healthcare. This is what is happening today with the Jvion Machine. Based on intent-driven, proven search engine technology, we can answer incalculable questions about patient health. When will I end up in the emergency room? Will my grandmother get a pressure ulcer while she is in the hospital? What post-acute care is best for me? More importantly, we can tell you what to do to stop these things from happening. All of this capability is enabled by the same kind of perpetually learning, constantly expanding cognitive capability that knows when you want to take a vacation, what shoes you like to wear, and the exact quote on the exact page of the exact book that you are searching for.


We asked our clients to share their thoughts on the solutions and approaches that they evaluated against Jvion's Cognitive Clinical Success Machine. C-level executives from administration, quality, IT, finance, and clinical operations representing hospitals ranging in size from multi-facility integrated delivery networks to 150-bed organizations participated. And these systems use multiple platforms including Epic, Cerner, MEDITECH, and Allscripts. Three general flavors of comparison solution categories emerged during the discussion: enterprise data warehouses (EDWs)/data lakes, scoring methods, and electronic health/medical record (EH/MR) models. The following provides a summary of key findings.