A Governance-Driven, Real-World Data-Calibrated Health Informatics Framework for Longitudinal Utilization Forecasting in Oncology and Complex Chronic Conditions

BackgroundHealthcare utilization forecasting systems are often derived from static, annualized market share assumptions that fail to represent real-world treatment dynamics. Such approaches systematically misestimate future utilization by ignoring longitudinal treatment sequencing, discontinuation with surveillance, recurrence-driven re-entry, and provider adoption dynamics. ObjectiveThis study proposes a reusable, governance-driven health informatics forecasting framework designed to generate realistic utilization forecasts using real-world data by integrating longitudinal patient-flow modeling, persistence-based exposure estimation, provider behavioral adoption, and multi-source calibration into a single architecture. MethodsLongitudinal U.S. administrative claims data representing oncology treatment populations (approximately 80,000 treated patients annually across therapy lines) were curated through a governance layer that refines diagnosis and treatment pools, infers clinically valid lines of therapy, and corrects for lookback-limited recurrence bias. Patients were modeled as transitioning across explicit clinical states, including treatment initiation, sequential therapy lines, discontinuation, surveillance, and recurrence-driven re-entry. Forecast outputs were calibrated using volume-weighted and behaviorally dampened provider adoption dynamics derived from primary research and claims-revealed utilization and evaluated against static share-based forecasts under identical peak-share assumptions. ResultsAcross multiple oncology contexts, longitudinal patient-flow-based forecasting recovered approximately 50-70% more cumulative treated months than static approaches. Underestimation in traditional models was driven primarily by failure to capture later-line persistence, surveillance exit, and re-treatment dynamics. Setting-specific calibration revealed earlier adoption in academic centers and slower, payer-constrained uptake in community practices. ConclusionsThe proposed framework demonstrates a forecast-oriented health informatics architecture that improves utilization estimation and decision support in complex, longitudinal care ecosystems. The methodology generalizes across tumor types and chronic conditions characterized by treatment sequencing, persistence variability, and relapse-driven re-entry.