Optimizing immunization in pediatric oncology: Development and evaluation of an automated scheduling tool.

Abstract

Pediatric cancer patients often struggle to adhere to vaccination schedules, requiring personalized catch-up immunization after treatment. Currently, healthcare professionals create these schedules manually, which is time-consuming, error-prone, and limited in accessibility. We developed and validated an automated clinical decision support system to optimize this process through algorithmic schedule generation. Following PRISMA guidelines, we systematically reviewed vaccination recommendations for pediatric cancer patients to develop comprehensive scheduling algorithms. The system implements multi-step optimization modeling incorporating age-dependent dosing, minimum intervals, and treatment-related timing rules for 27 vaccines. We validated the tool through a technical evaluation comparing algorithm-generated schedules against those created by experts for representative pediatric cancer cases and through clinical assessments by healthcare professionals in real-world settings. Technical validation revealed high agreement between algorithm-generated and expert-created schedules, with only minor acceptable variations. Clinical evaluation demonstrated strong user acceptance, with 70% (95% CI: 58.1%-81.9%) of professionals using the system for schedule validation, 61% (95% CI: 48.3%-73.7%) for schedule creation, and 47% (95% CI: 34.0%-60.0%) for vaccination history verification. Users reported median time savings of 20 min per schedule and rated the tool's quality highly (median 4.5/5). Our clinical decision support system demonstrates technical accuracy and clinical usefulness. It offers an effective solution for managing post-cancer treatment immunizations while alleviating healthcare professionals' workloads, particularly in resource-limited settings.