PRICE: a personalized recursive intelligent cost effectiveness analysis framework for rare disease diagnosis.

BACKGROUND: Rare disease diagnosis often involves complex, lengthy, and costly procedures. Traditional cost-effectiveness analyses typically rely on static diagnostic workflow models that apply uniform diagnostic strategies across heterogeneous patient populations. With recent advancements in artificial intelligence (AI) and a growing emphasis on personalized medicine, there is a pressing need for dynamic frameworks that assess diagnostic cost-effectiveness at the individual patient level. METHODS: We introduce the PRICE analysis framework, a novel, tree-based model designed to evaluate the cost-effectiveness of diagnostic strategies, accommodating both expert-alone and AI-delegated decision-making modes. The model computes the expected cost of a diagnostic process via a back-propagation algorithm and quantifies effectiveness through a utility-based approach (i.e., Quality Adjusted Life Years). Parameters such as disease prevalence, test costs, test performance metrics, and turnaround time are incorporated to enable individualized assessments. RESULTS: We demonstrat the utility of this novel framework in a proof-of-concept study by evaluating four diagnostic strategies for developmental delay (DD) and multiple congenital anomalies (MCA). The results highlight how PRICE can support personalized decision-making by modeling outcomes under varying parameters such as cost, prevalence, yield, and AI accuracy. To better visualize and interpret this framework, we developed an interactive web-based tool to demonstrate how to build PRICE pathways and conduct cost-effectiveness analysis in real time. CONCLUSION: PRICE is a novel cost-effective analysis framework that captures the sequential and recursive nature of real-world diagnostic workflows, with the ability to be extended to future AI-integrated clinical practice. It enables personalized evaluations of diagnostic strategies from both economic and clinical perspectives, promoting more informed and individualized decision-making for rare disease diagnosis.