Subsequent screenings showed significant findings in 70% of high-risk group
DANVILLE, Pa. – A machine-learning algorithm detected potential signs of colorectal cancer (CRC) in patients identified as high-risk who had missed a routine colonoscopy, according to a new study led by Geisinger and Medial EarlySign.
The findings, published this month in NEJM Catalyst Innovations in Care Delivery, present a noninvasive method to increase screening among those who may have CRC.
Despite evidence of the benefits of regular CRC screening and significant efforts among providers and healthcare systems to increase screenings, approximately 32% of age-eligible adults in the United States do not follow current CRC screening guidelines, according to the National Cancer Institute. Serious illness and death from CRC can be prevented if asymptomatic polyps and other early-stage cancers are detected and treated early.
In the study, Geisinger identified a group of 25,610 patients who were overdue for CRC screening, and used a machine-learning algorithm to flag those at highest risk for developing cancer. The algorithm, developed by EarlySign, identified patients as high-risk by analyzing age, gender, and a recent outpatient complete blood count (CBC). A nurse then called the patients to inform them of their risk and offer to schedule a colonoscopy.
Of the patients flagged as high-risk, 68% were scheduled for a colonoscopy, and of those, approximately 70% had a significant finding.
“When carefully implemented and supported by healthcare providers, machine learning can be a low-cost, noninvasive supplement to other colorectal cancer screening efforts,” said Keith Boell, D.O., chief quality officer for population initiatives at Geisinger and a co-author of the study. “This technology can act as a safety net, potentially preventing missed or delayed diagnosis among some patients who may already have undiagnosed signs of disease.”
“Our partnership with Geisinger has focused on addressing the devastating impact of CRC with predictive algorithms that can impact early detection, coupled with integration into clinical workflows that lead to a personalized approach to care that engages patients in prevention and treatment,” said Ori Geva, EarlySign co-founder and CEO. “Inclusion of our joint study with Geisinger in NEJM Catalyst Innovations in Care Delivery is a great honor for our team, and we are grateful to all the co-authors and project teams from EarlySign and Geisinger for their achievements in quality research and outcomes.”
Medial EarlySign helps healthcare stakeholders keep patients healthier longer with software solutions that derive actionable and personalized clinical insights from real-world health data. With a focus on improved outcomes and reduced cost, EarlySign's AlgoMarkers and predictive solutions allow for early detection of complications from serious disease and help clients more accurately identify and prioritize patients for multiple conditions for interventions to halt or prevent the serious complications from the onset of disease. The company's machine learning purpose-built platform and development environment enables fast and high-quality development of both custom models and pre-built models supported by peer-reviewed research published by internationally recognized health organizations and hospitals. Founded in 2013, Medial EarlySign is headquartered in Tel Aviv, Israel. For more information, please visit: https://earlysign.com/.
Geisinger is committed to making better health easier for the more than 1 million people it serves. Founded more than 100 years ago by Abigail Geisinger, the system now includes 10 hospital campuses, a health plan with more than half a million members, a research institute and the Geisinger College of Health Sciences, which includes schools of medicine, nursing and graduate education. With more than 25,000 employees and 1,700+ employed physicians, Geisinger boosts its hometown economies in Pennsylvania by billions of dollars annually. Learn more at geisinger.org or connect with us on Facebook, Instagram, LinkedIn and Twitter.
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