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Background <p>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.</p> Methods <p>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., <i>Quality Adjusted Life Years</i>). Parameters such as disease prevalence, test costs, test performance metrics, and turnaround time are incorporated to enable individualized assessments.</p> Results <p>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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • Mengshu Nie,
  • Yujing Yao,
  • Junyoung Kim,
  • Cong Liu

摘要

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.