Automated Sample Tracking System for Laboratory Workflow
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Solution Overview
Problem
Laboratories face challenges in locating and processing delayed or missing patient test samples within an acceptable time frame, which can lead to patient safety issues and reputational damage due to the absence of timely test results.
Innovation Solution
A computer-implemented method and system that automatically identifies unprocessed test samples, uses historical data to predict their location, and alerts operators to recalculate processing steps or collect new samples, ensuring timely processing and troubleshooting to prevent delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring of test samples is used, then operators can track samples, but time is lost due to manual effort and samples may still be missed
Solution Approach 1:
The system performs self-monitoring by automatically tracking test samples through the laboratory workflow without requiring manual intervention. The automated system detects missing samples and initiates recovery procedures independently, eliminating the need for operators to manually track each sample while maintaining high reliability.
Solution Approach 2:
The system implements continuous feedback loops where the automated monitoring system detects missing samples and provides real-time alerts to operators. This feedback mechanism enables rapid response to missing samples, significantly reducing the time lost compared to manual monitoring while maintaining high tracking reliability.
2Reliability
If comprehensive tracking of all test samples is implemented, then no samples are missed, but system complexity increases
Solution Approach 1:
The automated monitoring system serves multiple functions: it tracks test samples, identifies missing samples, determines their likely locations using historical data, and initiates recovery procedures. This multi-functional approach achieves comprehensive tracking reliability without requiring separate complex systems for each function, thereby reducing overall system complexity.
Solution Approach 2:
The system uses historical data as an intermediary to bridge the gap between sample disappearance and sample recovery. By analyzing patterns from historical data, the system can predict likely locations of missing samples without requiring complex real-time tracking of every sample movement, simplifying the tracking mechanism while maintaining completeness.
3Measurement precision
If historical data analysis is used to locate samples, then location accuracy improves, but data processing time increases
Solution Approach 1:
The system pre-processes and stores historical data in structured formats during normal operations, preparing location patterns in advance. When a sample is missing, the system can quickly query pre-organized historical patterns rather than analyzing raw data from scratch, thereby improving location prediction accuracy while minimizing the time required for data processing during critical recovery situations.
Data Source
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AI summary
A computer-implemented method of automatically locating and processing test sample(s) 200 from a patient within a laboratory setting 100 is disclosed. The method comprises determining that a test sample 200 has not been processed by any device 300, 310, 320 in the laboratory 100 and the test sample 200 is still within a time period of processing, performing inquiry of historical data of previous non-located test samples maintained in a database 120 for possible locations of non-located test sample 200, and notifying laboratory operator of a location with a highest probability of test sample location based on the historical data.