An AI-guided proactive screening initiative at Geisinger Health System has demonstrated significant clinical benefits, increasing colorectal cancer detection rates and substantially reducing mortality. Published in Manufacturing & Service Operations Management, the research details how predictive analytics, when combined with human-led outreach, can effectively bridge gaps in preventive care. Colorectal cancer remains a leading cause of cancer-related deaths in the United States, despite high preventability through early detection. To address systemic screening gaps, Geisinger launched a targeted program in 2019. The system’s algorithm continuously analyzes clinical and demographic data, including complete blood count results, age, and sex, to identify patients who are overdue for recommended colonoscopies. Individuals flagged by the model are subsequently contacted by nurse coordinators, who provide education on screening benefits and facilitate appointment scheduling. Evaluation of the program reveals measurable improvements in patient engagement and clinical outcomes. Compared to a matched control group, patients receiving AI-flagged outreach were six percent more likely to complete a colonoscopy within three months and nearly seven percent more likely within six months. Most notably, the initiative correlated with a six-point-two percent reduction in two-year mortality, representing a forty-three percent relative decrease. The study, co-authored by researchers from the University of Hong Kong, Columbia Business School, Geisinger, and Children’s Hospital of Philadelphia, underscores a pivotal shift in digital health strategy. Rather than relying solely on risk prediction, the framework emphasizes proactive care delivery. Experts note that the analytical model provides a replicable blueprint for scaling similar interventions across other cancer types and chronic conditions. Successful implementation requires careful consideration of screening capacity, communication protocols, and disease-specific risk factors. Health technology leaders highlight that the program’s success hinges on the synergy between computational identification and clinical empathy. Artificial intelligence efficiently surfaces high-risk individuals who might otherwise fall through traditional care networks, while human coordinators navigate logistical and psychological barriers to appointment completion. This collaborative model challenges the narrative of AI replacing medical professionals, instead positioning machine learning as a critical infrastructure tool that enhances care accessibility and improves population health metrics. As healthcare organizations continue to integrate artificial intelligence into clinical workflows, the Geisinger initiative offers a validated approach to operationalizing predictive technology. By aligning data-driven patient identification with structured outreach, health systems can optimize resource allocation, reduce preventable mortality, and establish sustainable pathways for proactive disease prevention.