People with chronic obstructive pulmonary disease (COPD) will soon have new resources available to them following the partnership of the COPD Foundation, Jvion, Geisinger, and GSK to find new ways to promote patient health, avoid hospitalizations, and prevent readmissions after an inpatient stay.
The venture will use an innovative approach that leverages cognitive machine learning and COPD patient data. The goal is to identify COPD patients at risk of hospitalization and who have the highest probability of benefitting from new medications.
The partnership will build on Jvion’s Cognitive Clinical Success Machine, an advanced artificial intelligence solution that is ultimately able to anticipate the risk of an event, and the clinical actions most likely to improve outcomes and promote patient engagement.
“By combining COPD disease expertise with the exceptional patient care delivered by Geisinger and Jvion’s leading Cognitive Clinical Success Machine, we are positioned to help patients across the nation,” Craig Kephart, chief executive officer of the COPD Foundation, said in a press release.
The project is divided into two phases. First, researchers will identify COPD patients who have had an inpatient stay and are at risk of readmission within 30 days of their initial discharge. Then, researchers will identify patients who are at-risk of an avoidable hospitalization.
“We believe that the use of cognitive machine technology will improve our ability to assess the potential of novel therapies for individuals at risk of COPD hospital admission by selecting the right patients for clinical trials and thereby ultimately improving patient outcomes,” said Ruth Tal-Singer, vice president and medicine development leader at GSK. GSK will provide funding for the initiative.
“This work has the potential to revolutionize how we support COPD patients both inside and outside of the hospital,” added Paul Simonelli, MD, director of thoracic medicine at Geisinger.
Jvion’s Cognitive Clinical Success Machine was built using an approach called Eigenspace, which can solve complex challenges in several fields.
The Eigenspace platform is a tool upon which millions of patients are mapped against tens of thousands of “Eigen Spheres.” Each sphere comprises patients who demonstrate clinical or behavioral similarities.
The machine continuously readjusts the spheres and patients mapped within the Eigenspace with every new piece of information entered into it. Based on other patients with similar profiles, the machine can “see” beyond the patient by anticipating if they are moving toward or away from risk. With this information, a risk event can be changed through adequate medical interventions.
We are sorry that this post was not useful for you!
Let us improve this post!
Tell us how we can improve this post?