Jvion Releases Predictive Solution to Stop Avoidable ER Visits

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Jvion, the leader in predictive analytic solutions, released their latest use case aimed at predicting and preventing avoidable emergency room visits.

Atlanta-based Jvion, one of the region’s top pacesetter companies, recently released their latest predictive use case aimed at preventing avoidable Emergency Room (ER) visits. Jvion’s RevEgis patient phenotype platform is being applied to:

  •     Predict patient level risk of an ER visit within the next 30 days
  •     Predict high-utilizers who would be better served in another healthcare setting
  •     Predict unreimbursed ER care episodes

These insights round out an already impressive list of pre-seeded use cases that span from individual patient illness to population level predictions.

Ritesh Sharma, Jvion COO, said that, “with the release of the ER use case, RevEgis will help hospitals prevent avoidable ER visits while reducing waste and the overall cost of care. It is specifically designed to enable population and individual level actions that will ultimately result in better health outcomes and higher overall quality and performance measures for the hospital.”

As one of the few truly predictive analytic solutions on the market, Jvion’s RevEgis has gained significant market traction and recognition. These accomplishments stem from the company’s innovative approach to predictive analytics, which relies on an advanced machine learning backbone and extensive clinical intelligence. The RevEgis solution is being applied across provider types and populations to help drive interventions and improve the lives of individual patients.

For more information on Jvion’s suite of predictive solutions, please visit http://www.jvion.com.

About Jvion
Jvion is a healthcare technology company that develops software designed to predict and prevent patient-level disease and financial losses leading to increased waste. The company offers a suite of big-data enabled solutions that combine clinical intelligence with deep machine learning to help providers protect their revenues while improving patient health outcomes. Their objective is simple—stop the waste of resources and lives by predicting and stopping losses before they ever happen.

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Allison Alavi
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