Let's Keep the Conversation Going

Preventing Sepsis with Cognitive Machines

  • JVION Health

Sepsis confounds care providers because we should be able to avoid it. Nonetheless, it is the most preventable cause of death worldwide.

Sepsis is especially cruel because our own bodies cause it through the natural response to infection, working so hard to battle that they break down tissues and organs. And it is stealth, often not diagnosed until it is too late.

A Better Way to Prevent Inpatient Falls

  • JVION Health

No one likes falling down. For hospital patients, it can be life threatening and represents one of the most prevalent preventable harm conditions. Falls exacerbate patient pain and suffering while extending hospital stays. CMS regulations in place since 2008 have amplified the cost of falls for hospitals, with no reimbursements for treatment of traumas and other fall-related conditions acquired after admission.  Facilities risk further CMS financial penalties for not providing safe environments for patients and residents. The Joint Commission calculates the cost of hospital falls at $14,000 per incident.

Data feeds the Cognitive Clinical Success Machine. Anyone working in healthcare probably has a rough memory about adopting a new technology and struggling to connect new systems with data from existing ones. Not so with Jvion—swift, painless data integration means the machine is giving smart clinical guidance for patients within weeks. Customers share more in this video.

Jvion Chief Product Officer John Showalter

Predictive analytic companies have been latching on to the idea of “impactability” and it is masking the intent of the term and overstating the real capabilities of predictive analytic solutions. The idea behind “impactability” is this: there are patients whose outcomes can be changed with the right intervention and there are patients whose outcomes can’t be changed no matter what you do. This isn’t about caregiving. Clinicians should provide the best care to all patients regardless of the outcome. This is about focusing the right actions to the exact patients who can benefit.


Most technology advances in iterations. New healthcare devices and technologies often improve on current ways we diagnose and treat illnesses and injuries.

In the 1960s, Holter monitors provided a way to record a full day of heart activity. In the 1980s, cardiac event monitors enabled us to capture specific heart behaviors over a longer period. More recently, wireless technology is promising improved flexibility and ease of data sharing for both types of cardiac monitors.

And then there are technologies that leap ahead to transform what healthcare can do and what we can imagine.


The MRI. Antibiotics. Vaccines. X-Rays. These scientific and technological advances changed medicine and healed millions.

Our present and future hold many more similarly profound advances in the health and well-being of people and communities. Cognitive machine technology—think of a hypersmart virtual “brain” that guides nurses and doctors to the right, best clinical decisions for individual patients and entire populations—may well be the most important one.


This is the part we love most at JVION—sharing the remarkable results our provider partners and their patients are achieving with the Cognitive Clinical Success Machine. And wow, how it’s working. Across all pathologies. Addressing every key area of quality. Improving every efficiency and performance metric that matters to healthcare providers and systems.

Let’s start with a quick study in what makes a technology fail.

The CueCat was a much-hyped, well-funded and frankly terrible idea from the Internet boom days. The founding company raised about $250 million in big chunks from NBC, Forbes, Radio Shack and others. They bet that consumers would want to use a whiskered, mouse-like device to scan barcodes in newspapers and on Coke cans that linked to Web pages. Really. The whole thing folded up within a year of its September 2000 launch. Time later named it one of the 50 worst inventions.