Quality Control Rule Recommendation Using Historical Lab Data
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Solution Overview
Problem
Conventional internal quality control rules in medical testing require manual calculations, which are cumbersome and prone to user experience-based mismatches, affecting the accuracy and reliability of test results.
Innovation Solution
A method and device for recommending quality control rules that provide a recommendation interface displaying quality control materials and rules, eliminating the need for manual calculations by using a cloud-based system to analyze quality control data and automatically suggest rules based on historical data and preset algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calculations are used for quality control rules, then flexibility in rule selection is maintained, but the accuracy and efficiency of rule configuration deteriorates
Solution Approach 1:
The system automatically calculates and recommends quality control rules by analyzing historical quality control data without requiring manual user intervention. The server端 system performs sigma value calculations, rule recommendations, and parameter optimizations autonomously based on stored quality control records, eliminating the need for users to perform complex manual calculations while maintaining high accuracy.
2Productivity
If automated recommendation system is implemented, then efficiency of quality control rule configuration is improved, but system complexity increases
Solution Approach 1:
A server端 recommendation system acts as an intermediary between quality control data storage and user interface. The server performs automated calculations, analyzes historical data, generates rule recommendations, and communicates results to users through a simplified interface. This intermediary handles the computational complexity internally while presenting a user-friendly experience, thereby improving efficiency without burdening users with system complexity.
3Measurement precision
If automated analysis of historical quality control data is performed, then accuracy of rule recommendations is improved, but time required for data processing increases
Solution Approach 1:
The system pre-processes and stores quality control data in structured formats with calculated parameters (such as sigma values, control limits, and statistical metrics) already computed and archived. When recommendation requests are made, the system retrieves pre-processed data rather than performing complete re-analysis, significantly reducing processing time while maintaining recommendation accuracy based on the pre-calculated historical statistics.
Data Source
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AI summary
The present application relates to a method, a device for recommending quality control rules and a computer equipment. The method includes: displaying a recommendation interface, and the recommendation interface comprising an information display area and a rule recommendation area; displaying a target quality control material in the information display area; obtaining at least one quality control recommendation rule recommended for the target quality control material; displaying the at least one quality control recommendation rule for the target quality control material in the rule recommendation area of the recommendation interface. By providing a recommendation interface and displaying at least one quality control recommendation rule recommended for the target quality control material on the recommendation interface, the method eliminates manual calculation of internal quality control rules by users, significantly enhancing both the accuracy and efficiency of quality control rule configuration.