Geisinger uses machine learning to speed up diagnosis of potentially fatal internal head bleeding

Wednesday, April 25, 2018 Medical Gadgets
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Time to diagnosis of intracranial hemorrhages reduced by 96 percent

DANVILLE, Pa., April 24, 2018 /PRNewswire-USNewswire/ -- Doctors and researchers at Geisinger have trained computers

to "read" CT scans of patients' heads to detect a life-threatening form of internal bleeding known as intracranial hemorrhage.

By using this innovative approach, Geisinger specialists have reduced the time to diagnosis of intracranial hemorrhages by 96 percent.

This form of internal head bleeding affects approximately 50,000 patients per year in the United States, with 47 percent of patients dying within 30 days. Early and accurate diagnosis is critical for these patients.

Machine learning – using computers to detect patterns in data -- has been so successful, it is now being introduced into the regular clinical workflow at Geisinger.

"This is not about replacing doctors with machines," said Aalpen Patel, M.D, chair, Geisinger System Radiology. "This is about the smart use of machine learning technology to aid medical providers in delivering better and faster care, especially in these areas where time is critical."

As an early adopter of the electronic health record, Geisinger has been able to combine radiographic and other medical imaging data that allows specialists to train computers to accurately pinpoint the worst cases. This flags the most urgent images for priority review by radiologists, leading to earlier diagnosis and life-saving emergency interventions.

In a recent case, an 88-year-old woman presenting with symptoms thought to be related to her medication was rushed to the emergency department after the machine algorithm flagged her CT scan for urgent attention. As it turns out, she was actually suffering from an intracranial hemorrhage which was safely resolved by medical intervention.

"The use of intelligent computer assistance is imperative in order to sustain and improve medical care," said Brandon K. Fornwalt, M.D., Ph.D., associate professor and director, Geisinger Department of Imaging Science & Innovation.

"Geisinger is proud to be at the forefront of clinical applications of these technologies," said Fornwalt, who is applying machine learning in other areas, including patients with congenital heart disease.

About GeisingerGeisinger is an integrated health services organization widely recognized for its innovative use of the electronic health record and the development of innovative care delivery models such as ProvenHealth Navigator®, ProvenCare® and ProvenExperience®. As one of the nation's largest health service organizations, Geisinger serves more than 3 million residents throughout 45 counties in central, south-central and northeast Pennsylvania, and also in southern New Jersey at AtlantiCare, a Malcolm Baldrige National Quality Award recipient. In 2017, the Geisinger Commonwealth School of Medicine became the newest member of the Geisinger Family. The physician-led system is comprised of approximately 30,000 employees, including nearly 1,600 employed physicians, 13 hospital campuses, two research centers, and a 551,000-member health plan, all of which leverage an estimated $10.5 billion positive impact on the Pennsylvania and New Jersey economies. Geisinger has repeatedly garnered national accolades for integration, quality and service. In addition to fulfilling its patient care mission, Geisinger has a long-standing commitment to medical education, research and community service. For more information, visit www.geisinger.org, or connect with us on FacebookInstagram, LinkedIn and Twitter.

CONTACT: David Stellfox, 570-214-6549, dbstellfox@geisinger.edu

Cision View original content:http://www.prnewswire.com/news-releases/geisinger-uses-machine-learning-to-speed-up-diagnosis-of-potentially-fatal-internal-head-bleeding-300634748.html

SOURCE Geisinger Health System



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