Personalized Reference Interval Calculation for Clinical Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for interpreting clinical test results rely on standard reference intervals defined by healthy subjects, which may not accurately represent individual patient data, especially for patients with complex medical histories or demographics, leading to potential misinterpretation of test results.
Innovation Solution
A computer-implemented method for creating a personalized reference interval (PRI) by correlating a patient's demographic and medical history data with a dataset of other patients to dynamically calculate a tailored reference range for clinical test results, accounting for contradictory and additive effects of demographics and medical history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard reference intervals based on healthy subjects are used, then the method is simple and universally applicable, but the accuracy of interpreting clinical test results for individual patients with complex medical histories deteriorates
Solution Approach 1:
The patent segments the general healthy population into multiple subgroups based on demographic parameters (age, gender, ethnicity) and medical history characteristics. Each segment receives a customized reference interval calculated from subjects with similar profiles, thereby improving measurement precision for individual patients while managing complexity through systematic categorization
Solution Approach 2:
The patent applies local quality by providing different reference intervals tailored to specific patient subgroups rather than using a single universal reference interval. The reference interval characteristics are localized to match the specific demographic and medical history profile of each patient group, enhancing interpretative accuracy for diverse populations
2Measurement precision
If a personalized reference interval is calculated using demographic and medical history data, then the accuracy of clinical test result comparison improves, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing demographic and medical history data into structured formats before actual reference interval calculation. Patient profiles are pre-categorized based on key characteristics, and relevant data subsets are pre-identified from the database, reducing the computational complexity during real-time reference interval generation
Solution Approach 2:
The patent introduces an intermediary layer of demographic and medical history parameters that mediate between the raw patient data and the reference interval calculation. This intermediary structure facilitates systematic data correlation, allowing the system to manage complexity by working through standardized parameter mappings rather than direct complex calculations
3Reliability
If reference intervals are dynamically updated with new patient data, then the reliability and accuracy of the intervals improve, but the time and computational resources required increase
Solution Approach 1:
The patent implements periodic action by updating reference intervals at scheduled intervals or based on trigger events rather than continuously recalculating with every new data point. This approach maintains reliability by incorporating new patient data into the reference intervals through periodic batch updates, reducing the time and computational burden compared to real-time continuous updates
Data Source
AI summary
There is provided a method for creating a personalized reference interval (PRI), comprising: receiving a digital profile of a patient including demographic parameters and associated values, and medical history data parameters and associated values; receiving a clinical test result including a measured value of at least one analyte; accessing a dataset storing digital profiles of other patients; identifying a subset of the dataset based a correlation according to a similarity requirement between demographic parameter(s) of the patient and of other patients, and between the medical history of the patient and the other patients; calculating a PRI for each respective analyte of the clinical test result of the patient according to an analysis of corresponding values of the analyte of the subset, wherein the PRI is dynamically calculated using the most updated version of the dataset.