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Failure to follow up after abnormal screening results is a persistent ambulatory safety problem. Because the diagnostic process often spans patient notification, scheduling, primary care review, and reminder outreach, delays may emerge gradually before they become visible in registry reports. Existing care gap reports commonly classify results as closed or open after a defined time window. This retrospective framing limits the ability to detect patients who are currently moving toward delayed diagnostic resolution. The objective of this article is to describe a predictive model that estimates the probability of delayed diagnostic follow-up after an abnormal screening result. The proposed model integrates patient engagement signals, scheduling activity, clinician workload, result severity, and reminder history. The model would use a gradient-boosted classification framework trained on historical abnormal results and longitudinal care-process features. Inputs would be extracted from patient portals, scheduling systems, primary care workload records, laboratory and radiology result metadata, and reminder logs. Conceptually, the model would generate a daily updated risk score for each unreconciled abnormal result. It would also identify the dominant drivers of risk, such as unread portal messages, repeated appointment cancellations, limited primary care availability, high-severity findings, or escalating reminder activity. A predictive approach could shift delayed follow-up management from retrospective audit to proactive prioritization. By identifying patients at greatest risk before the diagnostic window closes, care teams could better allocate outreach and navigation resources.