Urolithiasis Recurrence Prediction Using Hölder Exponent Analysis

The patent applies preliminary action by implementing a predictive model that identifies patients at high risk of stone recurrence before actual recurrence occurs. The system analyzes temporal patterns in urinalysis parameters and generates risk scores, enabling early intervention with dietary modifications, pharmacologic therapy, or increased monitoring. This proactive approach aims to prevent recurrence rather than merely treating it, thereby improving the effective stone-free rate and reducing the number of repeated interventions needed.

The patent applies self-service by designing a system that automatically collects urinalysis data from routine patient tests, processes the data through algorithms that calculate temporal patterns and Hölder exponents, and generates risk scores without requiring complex manual analysis. The system leverages existing electronic health record infrastructure and routine laboratory tests, transforming ordinary data into predictive insights through automated computation rather than requiring sophisticated specialized equipment or extensive human expertise.

The patent applies local quality by implementing risk-stratified care where preventive interventions are tailored to individual patient risk levels rather than applying uniform prevention to all patients. The system generates individualized risk scores and recommends specific interventions appropriate to each patient's predicted recurrence probability, such as dietary modifications for moderate-risk patients or more intensive pharmacologic therapy and monitoring for high-risk patients. This targeted approach concentrates healthcare resources on patients who will benefit most, reducing overall resource utilization while maintaining effectiveness in preventing recurrence.