Automated Clinical Diagnostic System with Dynamic Quality Control
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Solution Overview
Problem
Current clinical diagnostic systems using liquid chromatography coupled with mass spectrometry face challenges in achieving high throughput and reliability due to manual sample preparation, regulatory issues, and the need for frequent quality control procedures, which compromise sample processing efficiency and fail to detect malfunctions in real time.
Innovation Solution
A clinical diagnostic system comprising automated sample preparation, liquid chromatography, and mass spectrometry modules, along with a controller that monitors operational parameters to trigger quality control and maintenance procedures only when necessary, ensuring continuous analytical performance and predicting potential failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent quality control procedures are executed to ensure system reliability, then analytical performance is maintained, but sample processing throughput is compromised
Solution Approach 1:
The system continuously monitors operational parameters and uses feedback to dynamically adjust quality control frequency. When parameters indicate stable system performance, quality control procedures are minimized or skipped. When parameters deviate from specifications, the system automatically triggers appropriate quality control procedures, ensuring reliability without unnecessary throughput loss.
Solution Approach 2:
The quality control schedule is made dynamic rather than static. The system adapts the frequency and type of quality control procedures based on real-time system state, transitioning between different operational modes (normal operation vs. quality control mode) based on monitored parameters, thereby optimizing both reliability and throughput.
2Reliability
If quality control procedures are performed at scheduled intervals to detect malfunctions, then system reliability is maintained, but unnecessary procedures are executed reducing operational efficiency
Solution Approach 1:
Real-time monitoring of operational parameters provides continuous feedback on system health. This feedback mechanism allows the system to distinguish between systems that are actually deteriorating and those that remain stable, triggering quality control procedures only when necessary and avoiding unnecessary scheduled procedures.
Solution Approach 2:
The system performs preliminary monitoring and assessment of operational parameters before executing quality control procedures. By evaluating parameters in advance, the system can predict potential issues and schedule quality control procedures proactively only when needed, rather than reacting to failures after they occur or executing routine procedures regardless of system state.
3Adaptability or versatility
If manual sample preparation is used to handle diverse analytes, then flexibility for different samples is achieved, but labor intensity and processing time increase
Solution Approach 1:
The system employs universal sample preparation modules that can handle multiple analyte types through programmable workflows. Instead of dedicated manual protocols for each analyte, the system uses a single automated platform that adapts its behavior through software control, achieving both versatility and automation.
Solution Approach 2:
The automated system changes operational parameters (reagent volumes, incubation times, temperatures, flow rates) based on the specific analyte being analyzed. This parameter adaptation allows the same hardware to efficiently process different analytes without manual intervention, maintaining flexibility while reducing processing time through optimization.
4Ease of operation
If batch processing is used to process multiple samples under same conditions, then operational simplicity is maintained, but flexibility for emergency samples and re-scheduling is lost
Solution Approach 1:
The system transitions from static batch processing to dynamic random access processing. The workflow scheduler continuously adapts to incoming sample types, priorities, and system availability, allowing emergency samples to be inserted at appropriate positions in the processing sequence without disrupting overall operational simplicity.
Solution Approach 2:
The processing system is segmented into independent modules (sample preparation, separation, detection) that can operate semi-autonomously. This segmentation allows flexible scheduling where emergency samples can be routed through specific modules independently, maintaining operational simplicity at the module level while achieving system-level adaptability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system ensures analytical performance without compromising throughput, identifies and predicts malfunctions in real time, and minimizes unnecessary quality control procedures, enhancing operational efficiency and reliability.
Implementation Method 1
liquid chromatography coupled with mass spectrometry
Implementation Method 2
mass spectrometry module for detecting and quantifying analytes
Implementation Method 3
Protein precipitation with subsequent centrifugation is the most popular method to remove unwanted and potentially disturbing sample matrix
Implementation Method 4
Protein precipitation with subsequent centrifugation
Data Source
AI summary
A diagnostic system and method and an interconnected laboratory system comprising clinical diagnostic systems are presented. The diagnostic system comprises a sample preparation module, a liquid chromatography (LC) separation module coupled to the sample preparation module via a sample preparation/LC interface, a mass spectrometer (MS) module coupled to the LC separation module via an LC/MS interface, and a result calculation module for identifying and/or quantifying analytes or substances of interest contained in the samples and passed through the LC separation and MS modules. The diagnostic system comprises a controller programmed to monitor operational parameters (1-n) indicative of a performance status of the diagnostic system, to trigger a quality control procedure and/or a maintenance procedure whenever one or more parameters (1-n) of the operational parameters (1-n) is out of specification, and to minimize the quality control and/or maintenance procedures as long as the operational parameters (1-n) remains within specification.