Contextual Diagnostic Decision Support for Hyper-Local Infectious Testing
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Solution Overview
Problem
Current diagnostic systems face challenges in accurately diagnosing infectious diseases due to lack of real-time population data analysis, environmental data integration, and geolocation-based insights, leading to false positives and negatives, increased costs, and delayed treatment.
Innovation Solution
A networked system that collects and analyzes real-time population, environmental, and epidemiological data to generate hyper-localized pre-test and post-test probabilities, using nonlinear regression and AI algorithms to guide diagnostic instrumentation and provide contextual insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic systems are used without real-time population data and geolocation analysis, then device complexity and data collection requirements are reduced, but diagnostic accuracy and reliability deteriorate due to false positives and negatives
Solution Approach 1:
The patent introduces a server as an intermediary component that handles complex data collection, normalization, and analysis tasks. The server receives data from multiple diagnostic instruments, normalizes it against population and environmental data, and generates pre-test and post-test probabilities. This mediator approach allows individual diagnostic instruments to remain relatively simple while achieving high diagnostic accuracy through the centralized processing of contextual information.
Solution Approach 2:
The system implements feedback mechanisms where diagnostic results and population data are continuously collected, analyzed, and used to update pre-test and post-test probabilities. The server compares individual diagnostic results with aggregated population data from the same geographic area, creating a feedback loop that improves diagnostic accuracy over time by adjusting probabilities based on real-world outcomes and epidemiological patterns.
2Reliability
If comprehensive population data and environmental data are collected and analyzed in real-time, then diagnostic reliability and contextual insights are improved, but data collection complexity and processing requirements increase
Solution Approach 1:
The system segments data collection and processing into distinct functional modules: diagnostic instruments that collect individual patient data, a server that aggregates and normalizes data from multiple sources, and analysis components that generate probabilities. This segmentation allows each component to handle specific tasks efficiently, reducing overall system complexity while maintaining comprehensive data collection for improved reliability.
Solution Approach 2:
The server acts as an intermediary that manages the complexity of data collection and processing. It receives raw data from multiple diagnostic instruments and external sources, normalizes the data formats, and performs the complex task of comparing individual results with population data. This intermediary layer shields individual instruments from complexity while enabling reliable, context-aware diagnostics.
3Loss of information
If geolocation-based contextual analysis is implemented, then diagnostic insights and treatment guidance are improved, but information processing requirements and computational demands increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing population data, environmental data, and epidemiological information in a normalized format before they are needed for specific diagnostic decisions. The server maintains pre-computed datasets that can be quickly queried and compared with individual diagnostic results, reducing the computational burden during actual diagnostic processing while preserving comprehensive contextual information.
4Productivity
If real-time data analysis and reporting to health organizations is implemented, then public health management and surveillance effectiveness are improved, but data transmission requirements and system operational complexity increase
Solution Approach 1:
The system merges multiple functions into the server: data collection from diagnostic instruments, data normalization, population data integration, diagnostic analysis, and reporting to health organizations. This consolidation creates a single operational entity that handles the entire workflow, improving public health management efficiency by providing centralized real-time surveillance while managing operational complexity through unified control.
Data Source
AI summary
A method for visualizing and supporting a contextual diagnostic decision for contagious diseases is provided. The method includes receiving, from a diagnostic instrument, information regarding a sample cartridge, including a location data and a risk factor, the sample cartridge including multiple test assays for diagnosing multiple infectious diseases. The method also includes selecting a test assay for reporting a diagnostic result, instructing the diagnostic instrument to run the test assay from the sample cartridge, receiving, from the diagnostic instrument, a first data set when the test assay is completed, and assessing a diagnostic result based on the first data set. A system and a non-transitory, computer-readable medium storing instructions to cause the system to perform the above method are also provided.