Capacity Planning for Population Oral Health Services

Abstract

This scholarly review examines population risk in hospital demand forecasting and specialty-care operations. It connects evidence on gradient-boosted hospital resource-demand forecasting, low birth weight and dental caries in school-age children through a layered account of observation, representation, decision, and deployment. The cited studies are not pooled, and the article introduces no new experiments, datasets, clinical findings, or performance estimates. Instead, it asks which assumptions must remain visible as information moves from a source study into an operational model. The analysis distinguishes semantic validity from predictive accuracy, identifies interfaces at which provenance can be lost, and proposes review gates for evaluation under distribution shift. The synthesis suggests that robust systems require traceable evidence objects, domain-specific error taxonomies, calibrated human interpretation, and explicit escalation rules. These principles support method transfer without collapsing distinct physical, biological, clinical, or computational settings into a single empirical claim.