Scientists from Geisinger and Medial EarlySign found a machine-learning algorithm that detected potential signs of colon cancer in patients identified as high-risk and who had missed a routine colonoscopy.
The findings offer a noninvasive method to increase screening among those who may have colon cancer.
The research is published in NEJM Catalyst Innovations in Care Delivery and was conducted by Keith Boell et al.
Despite evidence of the benefits of regular colon cancer 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 colon cancer screening guidelines.
Serious illness and death from colon cancer can be prevented if asymptomatic polyps and other early-stage cancers are detected and treated early.
In the study, the team identified a group of 25,610 patients who were overdue for colon cancer screening and used a machine-learning algorithm to flag those at the highest risk for developing cancer.
The algorithm 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.
The team says that when carefully implemented and supported by healthcare providers, machine learning can be a low-cost, noninvasive supplement to other colorectal cancer screening efforts.
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.
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