AI-guided outreach increased cancer screenings and reduced mortality, new study finds

by Institute for Operations Research and the Management Sciences

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A machine learning program that identified patients overdue for colorectal cancer screening helped increase screening rates and was associated with significantly lower mortality, according to new research published in the journal Manufacturing & Service Operations Management.

The study examined a real-world outreach initiative at Geisinger Health System that used predictive analytics to identify patients at elevated risk for colorectal cancer and encourage them to schedule potentially life-saving screenings.

Researchers found that patients targeted through the program were 6% more likely to complete a colonoscopy within three months and 6.9% more likely within six months compared with similar patients who did not receive the outreach. Most notably, the program was associated with a 6.2% reduction in two-year mortality, representing a 43% decrease relative to the study's control group.

The study, "Cancer Screening Outreach Guided by Machine Learning: The Benefits of Proactive Care," highlights how artificial intelligence can help health care providers move beyond predicting risk and toward proactively connecting patients with care. The authors of the study are Minje Park of the University of Hong Kong; Carri Chan of Columbia Business School; Keith Boell, Elliot Mitchell and David Vawdrey of Geisinger; and Abdul Tariq of Children's Hospital of Philadelphia.

"Our results showed that a proactive cancer screening outreach program guided by machine learning can significantly improve patient outcomes in addition to achieving higher disease detection rates," said Park. "The program not only boosts screening participation but also meaningfully reduces mortality."

Colorectal cancer is the second-leading cause of cancer deaths in the United States, despite the fact that early detection can dramatically improve survival. Yet many eligible patients remain overdue for recommended screenings.

The Geisinger program sought to address that challenge by combining machine learning with personalized human outreach. The health system's algorithm analyzed information including complete blood count results, age and sex to identify patients at elevated risk among those who had not completed recommended screenings.

Patients flagged by the system received outreach from nurse coordinators who educated them about the benefits of colonoscopy and helped schedule appointments. The research suggests that one of the most effective uses of artificial intelligence in health care may be identifying patients who are likely to benefit from intervention and helping providers reach them before disease progresses.

"This work demonstrates an analytical framework for rigorously evaluating machine learning-aided outreach programs for other cancers and diseases," said Chan. "Establishing unbiased estimates of the impact is critical for capacity planning of screening resources such as colonoscopies."

"Colorectal cancer is one of the most preventable causes of cancer death if we can reach patients in time. This study shows that thoughtfully applied AI can do exactly that—identify individuals who might otherwise fall through the cracks and connect them to life-saving screening," added Vawdrey.

Researchers analyzed outcomes from the program, which has been operating since 2019. Their findings suggest that health care systems can use predictive analytics not only to identify risk but also to improve care delivery and patient engagement.

The study also provides a roadmap for hospitals and health systems seeking to scale similar programs. The authors note that factors such as screening capacity, communication strategies and disease severity should be considered when expanding proactive outreach efforts.

As health care organizations continue to invest heavily in artificial intelligence, the findings point to an important lesson: Some of the biggest benefits may come not from replacing clinicians, but from helping patients receive care they might otherwise miss.

More information

Minje Park et al, Cancer Screening Outreach Guided by Machine Learning: The Benefits of Proactive Care, Manufacturing & Service Operations Management (2026). DOI: 10.1287/msom.2024.1353

Key medical concepts

Screening for Colorectal CancerColorectal CancerColonoscopyBlood Count Tests

Clinical categories

OncologyCommon illnesses & PreventionPreventive medicineGastroenterology Provided by Institute for Operations Research and the Management Sciences Who's behind this story?

Gaby Clark

MA in English, copy editor since 2021 with experience in higher education and health content. Dedicated to trustworthy science news. Full profile →

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Citation: AI-guided outreach increased cancer screenings and reduced mortality, new study finds (2026, July 7) retrieved 7 July 2026 from https://medicalxpress.com/news/2026-07-ai-outreach-cancer-screenings-mortality.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.