Presentation (CTI)

OBJECTIVES: Economic evaluations of positron emission tomography (PET)-myocardial perfusion imaging (MPI) for coronary artery disease (CAD) diagnosis that focus on payer perspectives overlook hospital operational challenges, including infrastructure constraints, staffing requirements, downtime, and workflow inefficiencies. Differences in the production of the radiotracers Rubidium (generator‑based) and Flurpiridaz (on-demand) call for a framework to model hospital‑level cost drivers. This research outlines the design of a comprehensive cost calculator intended to support hospitals in evaluating the implementation of different PET-MPI radiotracers (i.e., Rubidium and Flurpiridaz) for CAD diagnosis and associated costs.
METHODS: The calculator is a modular framework integrating financial, operational, and workflow‑related components into a unified decision‑support tool. Inputs include radiotracer acquisition costs; labor time; infrastructure needs; scan volumes; reimbursement assumptions; and operational variables (e.g., repeat‑scan rates). Generator‑related fixed costs (e.g., leasing, calibration, maintenance) and downtime are included specifically for Rubidium PET. Formulas allocate fixed and variable costs across expected volumes, quantify downtime losses, and link workflow efficiencies to throughput potential. Scenario and sensitivity capabilities allow adjustment of key parameters, such as generator costs, radiotracer pricing, patient volumes, procedural times, and reimbursement, to explore opportunities to refine parameters and improve decision‑making. Key outputs include cost‑per‑scan estimates, total annual costs, cost breakdowns across categories, and efficiency metrics.
RESULTS: The model highlights that generator‑dependent protocols require greater fixed infrastructure and labor, whereas on-demand delivery models reduce operational complexity and workflow inefficiencies. Overall, the framework supports objective, data‑driven evaluations of PET-MPI radiotracers to inform utilization. Results using potential inputs will be presented during the session.
CONCLUSIONS: This conceptual model provides a structured, hospital‑focused framework for evaluating PET-MPI agents to diagnose CAD by incorporating financial, operational, and workflow considerations not captured in traditional payer‑based models. By quantifying visible and hidden costs, the calculator can support strategic planning, resource allocation, and technology adoption decisions at the hospital level.