Dynamic Quality Control Strategy for Lab Results
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Solution Overview
Problem
Diagnostic devices often produce test results with inherent imprecision and systematic errors, leading to incorrect patient results, which can cause harm if not detected in time, and existing quality control strategies for immediate release of results are impractical due to increased costs and reduced patient specimen testing capacity.
Innovation Solution
A quality control strategy that analyzes patient risk by calculating the probability and severity of harm using reference samples, adjusting the interval between QC events and the number of reference samples tested, to minimize incorrect results and balance resource allocation for quality control and patient specimen testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the interval between QC events is decreased or the number of reference samples is increased, then the reliability of test results is improved, but the productivity and cost efficiency deteriorate
Solution Approach 1:
The QC strategy dynamically adjusts the interval between QC events and the number of reference samples based on real-time assessment of instrument performance and patient risk factors. When instrument stability is high and patient risk is low, QC frequency is reduced to maximize productivity. When instability is detected or patient risk increases, QC frequency automatically increases to maintain reliability, resolving the contradiction between continuous monitoring and operational efficiency.
Solution Approach 2:
The system changes QC parameters (interval between events, number of reference samples) based on assessed conditions. By calculating patient harm probability and instrument reliability metrics, the system selectively intensifies QC only when and where needed, rather than applying uniform frequent QC to all situations. This parameter adaptation maintains reliability while optimizing overall productivity and resource allocation.
2Measurement precision
If more reference samples are tested at each QC event, then the measurement precision of QC assessment is improved, but the loss of time and reduced productivity occur
Solution Approach 1:
The system applies partial QC action by testing only the necessary number of reference samples at each QC event based on assessed needs. Instead of always testing the maximum number of samples, the system calculates the minimum required samples to achieve adequate measurement precision for the current patient risk level and instrument performance, thereby reducing time loss while maintaining sufficient QC assessment precision.
3Reliability
If QC events are performed frequently to detect systematic errors, then the reliability of patient results is improved, but the cost and resource consumption increase
Solution Approach 1:
The system uses feedback from previous QC events, instrument performance data, and patient risk assessments to determine the optimal frequency and intensity of subsequent QC events. This feedback loop allows the system to maintain high reliability for error detection while avoiding unnecessary QC events that would waste resources. The feedback mechanism ensures QC is performed at the minimum frequency needed to maintain safety, optimizing the balance between reliability and cost.
Data Source
AI summary
Methods, apparatuses, and systems are disclosed for analyzing quality control (QC) strategies that are applied to testing processes an analyte in order to meet an acceptable level of probability of patient harm that could result from incorrect test results. The measure of patient harm takes into account severity of patient harm, as well as its occurrence. Methods include calculating, based on the parameters of the QC strategies and the test apparatus, an expected number of incorrect final results E(Nuf) due to a test system failure. The value of E(Nuf) can be used as part of a calculation of a predicted level of probability patient harm. The ratio of the acceptable level of probability of patient harm to the predicted level of probability patient harm can determine the adequacy of the QC strategies.