Collaboration to Improve Colorectal Cancer Screening Using Machine Learning

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Daniel Underberger

Keith Boell

Jeremy Orr

Cory Siegrist

Sara Hunt


Collaboration to improve Colorectal Cancer Screening using Machine Learning

Background & Aims

Despite significant efforts and evidence to suggest the benefits of being screened for colorectal cancer (CRC), many eligible patients are not being screened for it.


To help, Geisinger Health System and Medial EarlySign identified patients overdue for CRC screening and used a machine-learning algorithm to flag those at the highest risk. Patients were then called by a nurse who informed them of their risk and offered to schedule a colonoscopy to complete their screening.


Geisinger and Medial EarlySign were able to schedule colonoscopies for 68.1% of the patients flagged. Of these flagged patients, approximately 70% had a significant finding.


The authors feel this is an evidenced-based way to identify patients overdue for CRC screening who are at the highest risk for abnormal results and reach out to get them scheduled for a colonoscopy.