AI Precision Medicine Diagnostic Testing Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic testing methods rely on fixed reference values, failing to account for individual variability in health data, leading to incomplete and inaccurate assessments of disease risk and treatment outcomes, as they are not personalized and do not integrate phenotypic, molecular, or therapeutic data.
Innovation Solution
A system and method that utilize clinomic profiles, combining medical history, genomic, metabolomic, and other health data to adjust raw test results, providing personalized diagnostic thresholds and treatment insights by comparing individual data to similar cohorts, leveraging machine learning and AI to contextualize test results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed reference values are used for diagnostic testing, then the testing process is simple and standardized, but the diagnostic accuracy and personalization are insufficient
Solution Approach 1:
The patent transforms static fixed reference values into dynamic personalized reference ranges by continuously adapting to individual subject characteristics. The system adjusts diagnostic thresholds based on each subject's unique clinomic profile, making the reference values dynamic rather than fixed, thereby improving diagnostic accuracy while managing complexity through automation.
Solution Approach 2:
The system changes the parameter of reference values from universal fixed values to personalized variable ranges. By modifying how reference values are determined (from population-based to individual-based), the system achieves higher diagnostic precision without proportionally increasing complexity, as the complexity is managed through algorithmic processing.
2Loss of information
If multiple data sets (phenotypic, molecular, therapeutic) are integrated, then personalized diagnostic accuracy improves, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent merges multiple previously separate data sets (phenotypic, molecular, therapeutic, and clinomic data) into a unified personalized reference framework. By combining these data sources into an integrated system that generates comprehensive personalized reference ranges, the system reduces information loss and provides complete diagnostic context without proportionally increasing operational complexity.
Solution Approach 2:
The system creates a composite clinomic profile that integrates multiple types of biological and clinical data. This composite data structure combines heterogeneous information sources (genomic, metabolomic, phenotypic, therapeutic) into a unified framework that enables comprehensive personalized diagnostics while managing complexity through standardized integration protocols.
3Loss of information
If static test results are provided without contextual data, then the testing process is quick and straightforward, but the clinical utility and actionable insights are limited
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing personalized reference ranges before actual diagnostic testing occurs. By preparing subject-specific reference frameworks in advance based on their clinomic profiles, the system eliminates the need for time-consuming manual interpretation during clinical decision-making, thus reducing information loss without increasing perceived time loss.
Solution Approach 2:
The system implements feedback by continuously comparing new test results against personalized historical data and adjusting future reference ranges based on observed patterns. This feedback mechanism provides ongoing clinical context automatically, enriching test results with actionable insights while the system learns and adapts, thereby reducing both information loss and interpretation time over time.
Data Source
AI summary
A system and method, the method comprising receiving a laboratory diagnostic testing result associated with a specimen of a subject, the steps of receiving a clinomic profile of the subject, identifying a cohort of similar subjects based at least in part on the clinomic profile of the subject, providing the diagnostic testing results, clinomic profile, and the cohort of similar subjects to a smart output module to generate a personalized, precision medicine based laboratory diagnostic testing result as a smart output and displaying the smart output to a user.