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Completing the Picture—Why Exogenous Data Means So Much for Quality Patient Outcomes

  • JVION Health

A patient—a person—is not defined by his or her clinical condition. Life is far more wonderful and complex than that.

Great clinicians know that cultural and environmental factors influence the health and outcomes of all patients. The things that determine health and well-being run much deeper than blood tests and scans.

This excellent recent article in Harvard Business Review captures why the non-clinical patient profile means so much to quality patient care:

‘“Janice,’” a hypothetical patient, is female, 46 years old, African-American, and a convenience store clerk living below the poverty level. These traits, particularly her gender, race, and socioeconomic status, immediately elevate her risk of cardiovascular disease.”

“These are important indicators her doctor…needs to keep in mind as he treats her. Several studies have shown that a patient such as Janice might be less likely to have insurance, less likely to have a regular physician, less likely to report symptoms, less likely to seek preventive care, and less informed about the lifestyle changes she should make to improve her health. These combined factors mean Janice is both more likely to have cardiovascular disease and more likely to die from it.”

Clinicians are already overwhelmed with patient loads, clinical information and systems, and time demands. How can they possibly have the capacity to know the full profiles—clinical, social, and cultural—of every patient?

With the power of the Jvion Machine.

Powerful Eigen-based science considers all things related to a patient’s condition, risk trajectory, and outcomes, including the socioeconomic, behavioral, and lifestyle factors. Most importantly, and uniquely, the machine understands and makes meaning out of the data, and applies that understanding into effective clinical action. It tells those busy clinicians what best next action to take in real time.

“We all can see what’s in the medical record, but what doesn’t get translated is those other components about the patient that are causing them to be at risk,” said Edye Cleary, Chief Quality Officer at Health First in central Florida. “[The Jvion Machine] really gave us a way to centralize that data so that everyone has access to it and that it became more meaningful.  We have lot of data, but by utilizing this tool it became information for us about the patient to integrate into our care planning across the continuum.”

Because the Jvion Machine “thinks” about patients in the same way as a clinician (complex, ever-changing), it drives value in two ways. It reduces a clinician’s cognitive load by pointing the focus to the right patients and recommendations at the right time. And it precisely identifies at-risk patients and the actions that will lead to improved outcomes.

The Jvion Machine delivers a full portrait of the patient, including exogenous factors that predictive and clinical systems don’t consider, to establish the most accurate possible future state of a patient's health.

It does so with elegantly complex things called Eigenspace and Eigen Spheres, artificial intelligence components that interpret this data and give meaningful direction. The machine makes more than a quadrillion clinical and non-clinical considerations for each patient. The machine then applies hundreds of thousands of self-learning Eigen Spheres to this data for each patient in real time. (We’ll share more about how these unique technologies make accurate decisions for healing in future posts.)

 “It’s important to bring external factors into the determination because every community is different,” said Penney Burlingame Deal, CEO of Onslow Memorial Hospital in North Carolina. “We have a unique community so the socioeconomic status of our patients, their smoking status, their household income, of that determines --it’s a healthcare determinant.  That’s what makes it so important for a specific patient because their response to a certain illness or a certain disease process is going to be different depending on where they live.”