Diagnostic Test Sequence Optimizer for Medical Condition Analysis
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Solution Overview
Problem
Diagnostic procedures in various industries are highly dependent on the expertise of technical experts, leading to inefficiencies due to incomplete information about historical outcomes, resulting in unnecessary time and cost in diagnostic testing.
Innovation Solution
A computer-implemented method and apparatus that optimizes diagnostic test sequences by determining relevant tests, conducting probabilistic medical condition analysis, analyzing comparative utility, assigning weights to factors, and ordering tests to minimize time or cost, using a diagnostic tool with components like a selector, analyzer, weighter, and optimizer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diagnostic procedures are developed based on expert knowledge, then diagnostic accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical diagnostic outcome data before executing new diagnostic procedures. This pre-analysis enables the optimization of test sequences based on proven effective patterns, reducing the time required for current diagnostics while maintaining accuracy.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting diagnostic outcome data and using it to refine and optimize future diagnostic sequences. This closed-loop approach ensures that diagnostic accuracy is maintained or improved while progressively reducing time consumption through learned optimizations.
2Reliability
If comprehensive diagnostic testing is performed, then diagnostic completeness is improved, but cost increases
Solution Approach 1:
The system applies partial action by selectively executing only the most relevant diagnostic tests based on historical outcome analysis. Rather than performing all possible tests, it identifies and executes the subset of tests that provide the highest diagnostic value, reducing cost while maintaining completeness.
Solution Approach 2:
The system changes parameters by dynamically adjusting the diagnostic test sequence based on historical data analysis. It optimizes the order, selection, and depth of testing based on learned patterns, achieving comprehensive diagnostics with reduced resource consumption through parameter optimization.
3Reliability
If expert-dependent diagnostic procedures are used, then diagnostic quality is improved, but device complexity increases
Solution Approach 1:
The system implements self-service by automatically analyzing historical diagnostic data and optimizing test sequences without requiring continuous expert intervention. The system serves itself by learning from past outcomes and autonomously improving diagnostic procedures, reducing the complexity burden of expert dependency.
Solution Approach 2:
The system substitutes mechanical expert knowledge with an automated computational system that analyzes historical data and generates optimized diagnostic sequences. This replacement reduces system complexity by transforming subjective expert judgment into objective, reproducible algorithms.
4Productivity
If historical data is collected and analyzed, then diagnostic optimization is improved, but information processing requirements increase
Solution Approach 1:
The system extracts only the essential and relevant features from historical diagnostic data that directly impact test sequence optimization. By filtering and selecting only the critical information needed for optimization, it reduces information processing requirements while maintaining diagnostic efficiency improvements.
Data Source
AI summary
In a computer-implemented method of optimizing a diagnostic test sequence to diagnose a medical condition of a subject. A group of diagnostic tests related to a symptom is determined from a pool of diagnostic tests. A probabilistic failure mode analysis is conducted to determine the efficacy of each of the diagnostic tests based on historical outcomes of actual diagnostic testing. The comparative utility of each diagnostic tests based on a plurality of factors that can affect problem resolution is analyzed. A weight is assigned to each factor involved in the probabilistic failure mode analysis. The diagnostic tests are ordered based upon at least one of: a probability of the diagnostic test identifying a cause of the failure mode in a minimum amount of time; a probability of the diagnostic test identifying the cause of the failure mode at a minimum cost; and a relative weighting of minimizing time versus minimizing cost. A first diagnostic test is selected from the group based at least in part on a probabilistic failure mode analysis and the weighted factors involved therein.