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University of Tennessee Medical Center

University of Tennessee Medical Center Saves More Than $4M in Just Months Using Jvion's Cognitive Machine

The University of Tennessee Medical Center, a Level I trauma center and the Knoxville region's only academic medical center, applied Jvion's advanced Cognitive Clinical Success Machine to lower readmission rates and improve the health outcomes of the at-risk patient population.

The University of Tennessee Medical Center reduced avoidable readmissions with Jvion’s leading Cognitive Clinical Success Machine. The medical center integrated cognitive machine outputs directly into the clinical workflow. These insights empowered the more than 30 case managers responsible for reducing readmissions and improving the health of patients post-discharge. These efforts led to an average drop of approximately 70 readmissions per month, which, in accordance with the Medicare average for cost of admission, could save health systems millions annually in potential losses.

UT Medical Center is a 609-bed academic medical center that delivers care to some of the state's most complex and underserved communities.

Dr. Trey La Charité, a UT Medical Center hospitalist, explained, "what everyone knows is that 5% of the population uses 50% of the available resources we have in healthcare and that 20% of the population uses 80% of those same healthcare resources. The problem is that the 5 or 20% you identify today is not going to be the same 5 or 20% next month, next quarter, or next year."

Over 2014 and 2015, the medical center saw an increase in monthly readmissions. UT Medical Center's Chief Quality Officer, Dr. Inga Himelright (Q1, 2017), sought to stop the rise in readmissions and reduce overall rates through the application of cognitive machine technology.

"We really wanted to find a solution that would take us from the static statistical methods that we are all familiar with like LACE to the next level in clinical risk and intervention insights. Jvion's solution combines advanced artificial intelligence and machine learning with a totally innovative approach that uses Eigen Spheres. Because of the capabilities inherent to Jvion's solution, we know with astonishing effectiveness if someone is going to have an avoidable readmission event. And we can do this using our existing data while leveraging standard integration into our clinical applications. The results after just a few months have proven to be quite effective.

Dr. Trey La Charité, Hospitalist for UT Medical Center

UT Medical Center integrated the solution's outputs directly into the case manager workflow. These risk propensities delivered patient level insights into the factors driving the likelihood of a readmission and the best possible interventions. Based on this information, case managers developed the custom and targeted plans that led to reduced readmission rates for the medical center.

"Our plan beyond the summer of 2017 is to expand the adoption of Jvion's solution outputs to more groups within the medical center who are responsible for care," said Dr. La Charité. "The potential long-term impact to our readmission rates is huge. We could see a drop of 50-70% as we drive further use and application of the solution. And we are looking at other areas for cognitive machine application including hospital acquired conditions and infections where we could really improve patient health outcomes and the allocation of care resources within UT Medical Center."

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