Lab Value Distribution Analysis System
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Solution Overview
Problem
Current methods for analyzing health insurance data, particularly for assessing lab value distributions between comparison groups, are cumbersome, costly, and require excessive processing time, hindering effective disease management and diagnosis.
Innovation Solution
A system and method that includes a data storage device and a server programmed to receive medical codes, search databases for records associated with specific test codes, generate distribution graphs on a shared scale, compare values, interpolate records for percentile analysis, and compute probabilities, enabling rapid assessment of lab value distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistical analysis tools (e.g., SAS) are used to analyze health insurance data, then comprehensive data mining capabilities are achieved, but processing time and resource requirements become excessively high
Solution Approach 1:
The patent segments the data analysis process into distinct modules: data retrieval from databases, data preprocessing and cleaning, statistical analysis execution, and results visualization. Each module operates independently and can be processed in parallel, significantly reducing overall processing time while maintaining analysis accuracy.
Solution Approach 2:
The system changes the computational parameters by implementing optimized algorithms for statistical calculations, using efficient data structures for large-scale health insurance data, and adjusting processing thresholds to balance speed and accuracy. This allows rapid assessment of lab value distributions without sacrificing analytical rigor.
2Loss of information
If comprehensive data mining is performed on health insurance databases, then valuable insights into disease diagnosis and treatment are obtained, but computational resource requirements become unworkably high
Solution Approach 1:
The patent extracts only the essential and most relevant data elements from the comprehensive health insurance database for each analysis task. Instead of processing all available data, the system identifies and extracts key lab values, demographic information, and clinical outcomes necessary for assessing disease diagnosis and treatment patterns, significantly reducing computational resource requirements.
Solution Approach 2:
The system performs partial data mining by focusing on specific subsets of data relevant to particular research questions or clinical concerns. Rather than executing exhaustive analysis on the entire database, it applies targeted analysis to selected cohorts and time periods, achieving sufficient informational completeness for decision-making with reduced resource consumption.
3Measurement precision
If detailed statistical analysis of lab value distributions is conducted, then accurate disease management insights are achieved, but the complexity of the analysis system increases
Solution Approach 1:
The patent implements a universal analysis platform that handles multiple types of statistical analyses through a single integrated system. The same infrastructure supports various lab value distributions, different disease cohorts, and multiple outcome measures, reducing overall system complexity compared to having separate specialized systems for each analysis type.
Solution Approach 2:
The system introduces intermediary components such as standardized data interfaces, pre-computed summary statistics, and caching mechanisms that simplify the complexity of detailed statistical analysis. These intermediaries buffer between the raw data and the final analysis, reducing the direct computational complexity while maintaining accuracy in lab value distribution assessment.
Data Source
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AI summary
System and methods for rapid assessment of lab value distributions are provided. In one embodiment, the system includes a data storage device and a server. The data storage device may be configured to store a database comprising one or more records, wherein the records are identified by one or more test codes. The server may receive a medical code, search the database to obtain a first group of records associated with individuals having the medical code, wherein each record of the first group of records is identified by a test code, search the database to obtain a second group of records associated with a control population, wherein each record of the second group of records is identified by the test code, and generate an output comprising a distribution graph on a shared scale from the first and second group of records.