Automated Sampling Identification System for Error Prevention
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Solution Overview
Problem
Existing sample-taking methods in healthcare settings are prone to errors, particularly in large throughput environments, leading to mix-ups and incorrect biological sample values due to manual identification processes, which can result in contaminated samples and invalid analyses.
Innovation Solution
A data processing system with a user interface and data storage that records and links sample-related and analysis-related data records, using unique identifiers for patients and sample containers, ensuring unambiguous identification and traceability through contactless or barcode-based identification features, and automated error detection for correct sample container usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual application of identification labels is used, then flexibility in sample identification is improved, but error rate increases and reliability deteriorates
Solution Approach 1:
The patent replaces the manual mechanical process of applying identification labels with an automated optical/electronic reading system. The reading device automatically captures identification features (barcodes, RFID tags, or other machine-readable formats) from sample containers, eliminating manual label application while maintaining flexibility through programmable identification formats. This substitution directly reduces human error and improves reliability.
Solution Approach 2:
The system enables self-service through automated identification feature reading. The reading device automatically detects and processes identification information from sample containers without requiring manual intervention for label application or data entry. The system self-validates the identification against the collection sequence, automatically detecting errors and preventing incorrect sample processing.
2Adaptability or versatility
If adhesive labels are applied manually, then adaptability in identification formats is improved, but measurement precision deteriorates due to misalignment
Solution Approach 1:
The patent replaces manual label application with automated reading devices that can scan identification features from various formats (barcodes, RFID tags, data matrices) without requiring precise manual alignment. The reading device uses optical or electromagnetic fields to automatically detect and read the identification information, eliminating alignment errors while maintaining adaptability to different identification formats through programmable reading capabilities.
Solution Approach 2:
The system performs preliminary validation by reading and verifying the identification feature before the sample processing begins. The reading device captures the identification information in advance, validates it against the collection sequence, and only then proceeds with sample processing. This preliminary action prevents misalignment errors from affecting the analysis.
3Productivity
If automated reading of identification features is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The reading device is designed with multi-functionality to handle various identification formats (barcodes, RFID tags, data matrices, optical codes) using a single unified system. This universal approach increases productivity by accommodating diverse sample container types while managing device complexity through integrated multi-format reading capabilities rather than separate specialized devices for each format.
Solution Approach 2:
The system implements automated feedback loops where the reading device continuously validates identification features against the collection sequence and provides immediate feedback on correctness. If an error is detected (wrong sample, wrong sequence), the system automatically alerts the operator and prevents further processing. This feedback mechanism increases productivity by preventing rework while managing complexity through automated error detection and correction protocols.
4Ease of operation
If sample containers with pre-applied unique identifiers are used, then ease of operation is improved, but traceability deteriorates due to lack of sequence verification
Solution Approach 1:
The system implements automated feedback by reading the unique identifier from the sample container, comparing it against the expected collection sequence, and providing immediate validation feedback. The reading device verifies that the correct sample is being processed at the correct step in the sequence, maintaining complete traceability while keeping the operation simple through automated verification rather than manual tracking.
Solution Approach 2:
The patent replaces manual traceability tracking with automated electronic verification. The reading device automatically captures the unique identifier, the system electronically validates it against the collection sequence in the data processing system, and maintains a digital trail of verification. This substitution preserves complete traceability information while simplifying the operator's task to merely presenting the sample for reading.
Data Source
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AI summary
The invention relates to a sampling method for performing sampling in an unmistakeable manner with execution on a data processing system (1) having a data memory (2), wherein the data memory (2) stores a plurality of sample-related (3) and analysis-related (4) data records and wherein the analysis-related data records (4) are relationally linked to the sample-related data records (3), and wherein a user interface (5) having an input/output apparatus is present which is communicatively connected to the data processing system (1). In this case, an order data record (10) is produced from a captured first identification feature (7), particularly an identifier (11), from a first person and the subset of analysis-related data records (4) and is stored in the data memory (2). Following performance of the sampling, an identification feature (15) from a sampling container (14) and a second identification feature (18) from a second person (13) are captured and an identifier (17, 19) which is read from the respective identification feature is stored in the order data record (10). In addition, a piece of type information is read from the stored explicit identifier (17) of the sampling container (14) and is compared with a piece of type information which is stored in the analysis-related data record (4) of the subset, and a faulty match prompts the output of a first error message on the display means.