TY - JOUR
T1 - Artificial Intelligence System for Detecting Preventive Care Delivery Gaps Using Primary Care Records, Screening Eligibility Rules, Reminder Logs, Patient Portal Activity, and Visit Opportunity Windows
AU - Nikolai Ivanov
AU - Sergey Volkov
AU - Elena Morozova
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2025
VL - 5
IS - 2
DO - 10.68159/b775091731
SP - 118
N2 - Preventive care delivery remains inconsistent despite well-established recommendations for cancer screening, immunization, and chronic disease monitoring. Missed preventive services create avoidable downstream morbidity, delayed diagnosis, and inefficient use of primary care resources. Current preventive care gap closure strategies are often reactive, fragmented, and dependent on manual review. Primary care clinicians cannot feasibly reconstruct every patient’s eligibility status, prior reminders, portal behavior, and visit timing during brief encounters. This article proposes an artificial intelligence system that continuously analyzes primary care records, applies screening eligibility rules, integrates reminder logs and patient portal activity, and detects upcoming visit opportunity windows. The system would surface a personalized preventive care gap list before or during clinical contact. The framework includes a rules engine for guideline-based eligibility, an engagement module that interprets reminder history and portal behavior, a visit opportunity detector, and a prioritization engine. Together, these components would rank preventive actions by clinical relevance, timing feasibility, and patient readiness. The system would transform routine primary care encounters into targeted preventive care opportunities by delivering the right recommendation at the right moment. By combining eligibility logic with engagement and visit context, it could support proactive care without relying solely on clinician memory. A proactive, AI-enhanced preventive care system could narrow the gap between evidence-based recommendations and real-world delivery. Its value would depend on transparent rule design, workflow-sensitive implementation, and careful prospective evaluation.
UR - https://cirpublications.com/b775091731
ER -