OBJECTIVES: The concept of whole-disease model has rarely been applied in practice or considered in the published literature. No studies to date have addressed the applicability of this approach in rare diseases. This research aims to demonstrate the applicability, technique, and framework of the model designed to educate US Payers of the estimated patient-level costs and resource utilization, using a rare disease as a case study. METHODS: The lack of specific ICD-9-CM code, prevalence overestimation, poorly documented US epidemiology data, complex diagnostics, and off-label pharmacotherapy were evident in rare diseases. In order to estimate patient-level resource utilization, diagnosis costs, and 12-month treatment cost from the US Payer’s perspective, a clinical guideline-based approach was developed. We reviewed diagnosis and treatment guideline recommendations to develop a resource utilization algorithm which was subsequently validated by clinicians to reflect the real-world clinical practice. Model cost inputs were derived from published resources including the most recent year Medicare Fee Schedules and H-CUP National Inpatient Sample database. Adverse event costs associated with the treatments were not accounted for in the model. RESULTS: An interactive, MS-Excel based economic model was constructed based on guideline recommendations. The model inputs were customizable, allowing flexibility in plan/Payer specific disease burden estimation. Cost of diagnosis, pharmacotherapy, and surgical therapies were calculated for a single patient over a 12-month period. CONCLUSIONS: The study demonstrates that the whole-disease model approach can be applied to rare diseases to provide the disease burden estimation, serving as an information tool for Payers, for diseases with small patient volume and unknown cost burden. The model also provides a platform for manufacturers to: 1) incorporate real-world data as they become available; 2) add/remove interventions as the market evolves and; 3) add various economic elements to further calculate budget impact or cost-effectiveness of an intervention over a product life-cycle.