Optimizing QC Strategy for Diagnostic Device Throughput
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Solution Overview
Problem
Diagnostic devices face challenges in ensuring accurate results due to systematic errors, which can lead to incorrect patient sample readings, and current quality control strategies often require excessive testing, increasing costs and reducing the number of patient samples that can be tested.
Innovation Solution
A method is proposed to optimize quality control strategies by generating candidate rules that maximize the number of patient specimens tested between quality control events while keeping the expected number of unacceptable results below a specified maximum, using metrics like quality control utilization rate and statistical calculations to determine the optimal testing intervals and sample retesting protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of QC events is increased to reduce erroneous patient results, then the reliability of patient results is improved, but the cost increases and the number of patient samples that can be tested decreases
Solution Approach 1:
The patent applies parameter changes by optimizing the QC frequency and number of reference samples tested based on calculated performance metrics. Instead of using fixed QC schedules, the system dynamically adjusts QC parameters (frequency, number of samples) to achieve desired reliability targets while maximizing patient sample throughput. This resolves the contradiction by finding the optimal balance point between QC intensity and productivity.
Solution Approach 2:
The patent implements dynamics by making QC strategy adaptive rather than static. The system continuously calculates performance metrics and adjusts QC event frequency and sample numbers based on current system performance. This dynamic approach allows the system to maintain high reliability while optimizing productivity, as QC intensity can be increased when performance degrades and decreased when performance is stable.
2Reliability
If more reference samples are tested at each QC event to reduce erroneous results, then the reliability of patient results is improved, but the cost increases and the number of patient samples that can be tested decreases
Solution Approach 1:
The patent applies parameter changes by optimizing the number of reference samples tested at each QC event based on calculated performance metrics. The system determines the minimum number of reference samples needed to achieve desired reliability targets, avoiding excessive testing. This resolves the contradiction by finding the optimal balance between the number of reference samples tested and the resulting reliability improvement.
3Measurement precision
If QC testing is performed frequently to ensure accurate results, then the measurement precision is improved, but the productivity of the diagnostic device decreases
Solution Approach 1:
The patent applies parameter changes by optimizing QC frequency and intensity based on calculated performance metrics and desired accuracy targets. The system determines the minimum QC frequency needed to maintain measurement precision while maximizing patient sample throughput. This resolves the contradiction by finding the optimal balance between QC intensity and productivity.
Solution Approach 2:
The patent applies partial action by implementing QC testing at optimized frequencies rather than continuous or maximum frequency. The system calculates the minimum necessary QC events to maintain desired accuracy levels, avoiding excessive testing that would reduce productivity. This resolves the contradiction by applying just enough QC action to maintain precision without over-testing.
Data Source
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AI summary
The present invention proposes a method for optimizing a quality control strategy for rapid release results. An embodiment of the invention includes generating a set of candidate quality control rules and for each candidate rule, computing a maximum number of patient specimens that can be tested between quality control events while keeping the expected number of correctible unacceptable results below a predetermined correctible maximum and keeping the expected number of final unacceptable results below a predetermined final maximum. Furthermore a quality control utilization rate can be computed based on the number of patient specimens tested between each quality control event and the number of reference samples tested at each quality control event. The candidate rule for which the best quality control utilization rate may be selected along with the corresponding number of patients to be tested between each quality control as the optimum quality control strategy.