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.
Like the MRI, which changed healthcare completely by creating a way to see the condition of the human body with precise clarity that never existed before. The leap from the X-Ray and CT scan to the MRI was revolutionary. For the first time, healthcare providers could produce accurate, detailed pictures of organs, tissues and bones without radiation exposure, while more accurately identifying abnormal tissue. X-Rays and CT scans still serve valuable diagnosis functions, but the MRI provides precise insight in a way we had never considered before.
The healthcare industry and indeed the world give special recognition to this level of transformation. Paul Lauterbur and Peter Mansfield won the Nobel Prize in 2003 for their breakthrough discoveries and advances in magnetic resonance that led to widespread use of the MRI in medicine. Yet now we almost take for granted the power the MRI has given clinicians.
The Next Transformative Healthcare Advance
The Jvion Machine forges a transformation story that parallels the MRI.
For decades healthcare has looked to big data and predictive analytics to enhance our ability to identify illness risks for individuals and populations. Technologies such as machine learning models have emerged that can identify a patients in a population at high risk for certain conditions—a meaningful but limited clinical application.
Think of it as the X-Ray.
Now consider Eigen-based technology—a powerful clinical brain that not only identifies risk at every level across all patient populations, but also tells you what intervention or next action will promote the best patient outcomes.
That’s the MRI.
Dr. John Frownfelter, the former CMIO of Midwestern health system UnityPoint explains how much this means to doctors and patients:
“The Jvion machine fits in the clinical workflow by being transformative. Clinicians know a patient’s at risk for readmission or a complication. They [think of] everything possible but they don’t really know what the risk is and they don’t what’s going to work. Transformative technology, think of it like an MRI compared to a chest x-ray. The additional level of detail that you see and insights into what you should do next based upon the clarity of the images is pretty telling. In the same way, when you’ve got the answer both of what the risk is and also what the next step is, it’s transformative.”
The difference between iterative and transformative? The Jvion Machine delivers unprecedented power to improve quality care and outcomes. It’s an artificial intelligence (AI) asset that provides a crystal clear view of risk at all stages and the most effective patient interventions. And you can tune it to consider any condition for any population with swift ease, a significant advantage over predictive analytics and other machine thinking. The result (like the MRI) is the clearest, most precise, swift, and specific patient view and course of action.
Imagine the future—and it’s now—with the absolute minimum medical errors, infections, readmissions, hospital acquired infections, and complications. That’s transformative, just like the MRI.