Full-process quality control management system for neonatal disease screening

Abstract

The invention relates to the field of medical treatment, and discloses a full-process quality control management system for neonatal disease screening, which comprises a data real-time acquisition module for realizing data intercommunication and dynamic acquisition with a laboratory information system, detection equipment and a sample management system through a standardized API (Application Program Interface) and a data transmission protocol; the indoor quality control module is used for performing real-time monitoring and rule management on laboratory internal detection quality by applying a Westgard multi-rule control algorithm and a real-time data monitoring technology; and the interroom quality evaluation module is used for carrying out multi-laboratory detection result comparison and quality evaluation by adopting a data encryption and anonymization processing technology. Through cooperation of a cross-system docking unit and a dynamic data analysis unit of the data real-time acquisition module, data islands of a laboratory information system, detection equipment and a sample management system are broken through, and real-time intercommunication and standardized processing of sample information, detection data and process nodes are realized.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and specifically to a whole-process quality control management system for neonatal disease screening. Background Art

[0002] Neonatal disease screening is a key means of quickly detecting congenital hypothyroidism, phenylketonuria, glucose-6-phosphate dehydrogenase deficiency and other genetic metabolic diseases and congenital defects in newborns, and is an important public health measure to reduce the birth defect rate, disability rate and mortality rate. Since the screening work was carried out in China in the 1980s, the coverage has gradually expanded. However, the screening process involves multiple links such as sample collection, transportation, testing, report issuance, and positive recall. Quality control failures in any link may lead to missed diagnoses and misdiagnoses, directly affecting the intervention effect and prognosis of children. Therefore, establishing a whole-process and traceable quality control system is the core requirement to ensure the accuracy and reliability of screening.

[0003] In the prior art, data in each link is scattered in laboratory information systems, testing equipment, and sample management systems, forming data islands, lacking real-time interconnection and standardized processing mechanisms, lacking closed-loop control over all process nodes such as sample collection, transportation, testing, and reporting, and incomplete recording of key information such as operators and timestamps, resulting in insufficient data integrity and full-chain traceability capabilities and making it difficult to accurately locate abnormal links. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a whole-process quality control management system for neonatal disease screening, which solves the problems that data in each link of the prior art is scattered in laboratory information systems, testing equipment, and sample management systems, lacking real-time interconnection and standardized processing mechanisms, lacking closed-loop control over all process nodes such as sample collection, transportation, testing, and reporting, and incomplete recording of key information such as operators and timestamps, resulting in insufficient data integrity and full-chain traceability capabilities.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0006] A whole-process quality control management system for neonatal disease screening, comprising:

[0007] A data real-time collection module, used to realize data interconnection and dynamic collection with laboratory information systems, testing equipment, and sample management systems through standardized API interfaces and data transmission protocols;

[0008] An in-house quality control module, used to perform real-time monitoring and rule management of the internal testing quality of the laboratory by applying the Westgard multi-rule control algorithm and real-time data monitoring technology;

[0009] External quality assessment module, which is used to carry out comparison and quality evaluation of test results in multiple laboratories by using data encryption and anonymization processing technologies;

[0010] Omnidirectional data analysis module, which is used to complete multi-dimensional data statistics, error assessment and distribution characteristic analysis by using statistical analysis algorithm library and data mining technologies;

[0011] Visualization management module, which is used to realize visual display of quality control data and monitoring of abnormal data by means of data visualization engine and front-end display framework;

[0012] Quality index monitoring module, which is used to cover and monitor the core indexes of the whole process of neonatal disease screening based on the preset hierarchical index system and dynamic threshold algorithm;

[0013] Whole-process quality control management module, which is used to carry out closed-loop control and system integration of the whole process nodes of screening through data integration engine and workflow management system.

[0014] Preferably, the data real-time acquisition module includes a cross-system docking unit and a dynamic data analysis unit. The cross-system docking unit is used to realize data intercommunication with the laboratory information system, testing equipment and sample management system through standardized API interfaces and data transmission protocols, and synchronize sample information, original test data and process node data. The dynamic data analysis unit is used to perform real-time cleaning, storage and analysis of the collected data by using data cleaning algorithms and storage architectures, and support historical data backtracking query and sample traceability.

[0015] Preferably, the internal quality control module includes a quality control rule management unit, a quality control graph and marking unit, and a target value and batch number management unit. The quality control rule management unit is used to build in the Westgard multi-rule control algorithm, support independent configuration of rule combinations according to test items, and evaluate test data in real time through a rule engine. The quality control graph and marking unit is used to automatically generate Levey-Jennings graphs and Z-score graphs and mark out-of-control points by using drawing algorithms and visualization libraries. The target value and batch number management unit is used to generate target values such as mean, standard deviation and coefficient of variation by using statistical analysis methods based on laboratory historical data, support independent management of different batch numbers of quality control products, and reconstruct the quality control system through a preset process when the batch number is changed.

[0016] Preferably, the inter-laboratory quality assessment module includes a quality assessment plan management unit and a laboratory comparison and analysis unit. The quality assessment plan management unit is used to manage the participating projects, blind sample acceptance, and result reporting by connecting to the interface of the national or provincial inter-laboratory quality assessment plan, or the configuration interface of the customized internal comparison plan. The laboratory comparison and analysis unit is used to compare the laboratory data with the data of other laboratories through data encryption and anonymization processing technology, use the bias analysis algorithm to generate a performance ranking report, and identify systematic errors.

[0017] Preferably, the comprehensive data analysis module includes a basic statistical indicator calculation unit and an error and distribution analysis unit. The basic statistical indicator calculation unit is used to use the statistical analysis algorithm library to automatically calculate the daily average, plate average, standard deviation, coefficient of variation, and standard deviation index, and supports grouping statistics by time, equipment, and personnel dimensions. The error and distribution analysis unit is used to adopt the error calculation model and distribution analysis algorithm to calculate the total error, allowable total error, critical system error, perform percentile analysis and draw a frequency distribution histogram.

[0018] Preferably, the visualization management module includes a data visualization display unit and an abnormal data monitoring unit. The data visualization display unit is used to present the quality control data in the form of dashboards, line charts, and heat maps with the help of a data visualization engine and a front-end display framework, and supports multi-terminal display and custom layout. The abnormal data monitoring unit is used to monitor abnormal situations such as data out-of-control status and indicator exceeding in real time through a real-time data monitoring algorithm and an early warning rule engine, and provide traceability functions through color marking, flashing reminders and early warnings.

[0019] Preferably, the quality indicator monitoring module includes an indicator set management unit, a dynamic threshold engine and an early warning and reporting unit. The indicator set management unit is used to preset a hierarchical indicator system consisting of a process quality indicator group, a detection quality indicator group and a clinical quality indicator group. The dynamic threshold engine is used to build an indicator baseline model based on historical data and dynamically update the indicator threshold using a sliding window algorithm. The early warning and reporting unit is used to configure hierarchical early warning rules, trigger the corresponding early warning level when the indicator is abnormal, and jointly generate rectification tasks and indicator compliance reports.

[0020] Preferably, the process quality indicator group includes the sample transportation temperature compliance rate, blood collection time compliance rate, collection volume qualification rate, blood collection personnel qualification rate, and sample transportation and storage compliance rate.

[0021] Preferably, the detection quality indicator group includes the intra-batch coefficient of variation, the inter-batch coefficient of variation, the calibration pass rate of the detection equipment, the reagent quality pass rate, and the report review timeliness rate; the clinical quality indicator group includes the positive recall rate, the report issuance accuracy rate, the inter-laboratory quality assessment excellent rate, and the out-of-control processing closed-loop rate.

[0022] Preferably, the full-process quality control management module includes a process monitoring and data integration unit and a system report generation unit. The process monitoring and data integration unit is used to monitor the full-process nodes of sample collection, transportation, testing, and report issuance, integrate the data of each module through the data integration engine, record the operator, timestamp, and equipment number operation information, and support full-chain traceability of problem samples. The system report generation unit is used to utilize the report template engine and data aggregation interface to integrate the data of each module, generate a full-process quality control report, and connect to the hospital information system and maternal and child health care platform.

[0023] The present invention provides a full-process quality control management system for neonatal disease screening. It has the following beneficial effects:

[0024] 1. The present invention cooperates with the cross-system docking unit and dynamic data analysis unit of the real-time data acquisition module to connect the data islands of the laboratory information system, detection equipment, and sample management system, realize real-time intercommunication and standardized processing of sample information, detection data, and process nodes, and combines the process monitoring and data integration unit of the full-process quality control management module to perform closed-loop control of all nodes such as collection, transportation, detection, and reporting, record key information such as operators and timestamps, ensure data integrity, accuracy, and full-chain traceability, provide a reliable data basis for subsequent Westgard rule evaluation, and quickly locate abnormal links, significantly improving quality control efficiency and problem response speed.

[0025] 2. The present invention realizes multi-level quality control from a single detection project to the entire process through the Westgard multi-rule algorithm of the indoor quality control module, the dynamic threshold engine and the graded early warning mechanism of the quality indicator monitoring module.

[0026] 3. The present invention integrates the anonymous comparison of the inter-laboratory quality assessment module, the multi-terminal display of the visualization management module, and the external platform docking of the system report generation module to form a full-chain collaboration of "laboratory internal quality control - multi-laboratory comparison - clinical intervention closed loop". This internal and external collaboration mechanism not only meets the requirements of national quality assessment standards, but also promotes the standardization of screening processes and the improvement of public health management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is an architecture diagram of a full-process quality control management system for neonatal disease screening according to the present invention;

[0028] Figure 2 This is a data real-time acquisition module architecture diagram of a full-process quality control management system for neonatal disease screening according to the present invention;

[0029] Figure 3 This is an architecture diagram of an indoor quality control module of a full-process quality control management system for neonatal disease screening according to the present invention;

[0030] Figure 4 This is the architecture diagram of the external quality assessment module of the whole-process quality control management system for neonatal disease screening of the present invention;

[0031] Figure 5 This is the architecture diagram of the all-round data analysis module of the whole-process quality control management system for neonatal disease screening of the present invention;

[0032] Figure 6 This is the architecture diagram of the visualization management module of the whole-process quality control management system for neonatal disease screening of the present invention;

[0033] Figure 7 This is the architecture diagram of the quality index monitoring module of the whole-process quality control management system for neonatal disease screening of the present invention;

[0034] Figure 8 This is the architecture diagram of the whole-process quality control management module of the whole-process quality control management system for neonatal disease screening of the present invention;

[0035] Figure 9 This is the relationship diagram of each module of the whole-process quality control management system for neonatal disease screening of the present invention. Detailed implementation manners

[0036] Next, in combination with the drawings of the present invention, the technical solutions of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0037] Please refer to the attached Figure 1 - attached Figure 9 , the embodiments of the present invention provide a whole-process quality control management system for neonatal disease screening, including:

[0038] A data real-time acquisition module, which is used to realize data intercommunication and dynamic acquisition with the laboratory information system, detection equipment, and sample management system through a standardized API interface and a data transmission protocol;

[0039] An in-house quality control module, which is used to perform real-time monitoring and rule management of the internal detection quality of the laboratory by using the Westgard multi-rule control algorithm and real-time data monitoring technology;

[0040] An external quality assessment module, which is used to carry out comparison and quality evaluation of the detection results of multiple laboratories by using data encryption and anonymization processing technologies;

[0041] The omni-directional data analysis module is used to complete multi-dimensional data statistics, error evaluation, and distribution characteristic analysis by using a statistical analysis algorithm library and data mining technology;

[0042] The visualization management module is used to realize the visual display of quality control data and the monitoring of abnormal data by means of a data visualization engine and a front-end display framework;

[0043] The quality index monitoring module is used to cover and monitor the core indicators of the whole process of neonatal disease screening based on a preset hierarchical index system and a dynamic threshold algorithm;

[0044] The whole-process quality control management module is used to carry out closed-loop control and system integration on the nodes of the whole screening process through a data integration engine and a workflow management system.

[0045] Please refer to the appendix Figure 2 , the data real-time acquisition module includes a cross-system docking unit and a dynamic data analysis unit. The cross-system docking unit is used to realize data interconnection with the laboratory information system, detection equipment, and sample management system through standardized API interfaces and data transmission protocols, and synchronize sample information, raw detection data, and process node data. The dynamic data analysis unit is used to use data cleaning algorithms and storage architectures to perform real-time cleaning, storage, and analysis of the acquired data, and support historical data backtracking queries and sample traceability.

[0046] Specifically, through the cooperation of the cross-system docking unit and the dynamic data analysis unit, the real-time interconnection and standardized processing of multi-source heterogeneous data, namely the data of the laboratory information system, detection equipment, and sample management system, are realized, ensuring the integrity, accuracy, and traceability of the raw data. The cross-system docking unit solves the data island problem between different systems. The dynamic data analysis unit provides a reliable data source for subsequent quality control analysis through cleaning, storage, and backtracking technologies, supports the whole-process data traceability and abnormal problem positioning, and effectively improves the data processing efficiency and the basic guarantee ability of quality control.

[0047] Please refer to the appendix Figure 3 , the in-house quality control module includes a quality control rule management unit, a quality control graph and marking unit, and a target value and batch number management unit. The quality control rule management unit is used to build in the Westgard multi-rule control algorithm, support independent configuration of rule combinations according to detection items, and evaluate detection data in real time through a rule engine. The quality control graph and marking unit is used to automatically generate Levey-Jennings charts and Z-score charts and mark out-of-control points by using drawing algorithms and visualization libraries. The target value and batch number management unit is used to generate target values such as mean, standard deviation, and coefficient of variation based on laboratory historical data by using statistical analysis methods, support independent management of different batches of quality control products, and reconstruct the quality control system through a preset process when the batch number is changed.

[0048] Specifically, through the refined design of the quality control rule management unit, quality control diagram and marking unit, and target value and batch number management unit, the refined control of the internal detection quality of the laboratory is achieved. The rule management unit supports the configuration of personalized quality control strategies. The diagram marking unit visualizes the detection status in real time. The target value and batch number management unit ensures the result consistency under different detection conditions. The three work together to promptly identify out-of-control states during the detection process, such as out-of-control points triggered by Westgard rules, dynamically adjust detection parameters, reduce the detection coefficient of variation, and ensure the accuracy and reliability of the single laboratory's detection results.

[0049] Please refer to the appendix Figure 4 , the external quality assessment module includes a quality assessment plan management unit and a laboratory comparison and analysis unit. The quality assessment plan management unit is used to manage participating projects, blind sample reception, and result reporting by docking with the interfaces of national or provincial external quality assessment plans or customizing the configuration interfaces of internal comparison plans. The laboratory comparison and analysis unit is used to compare the data of this laboratory with the data of other laboratories through data encryption and anonymization processing technologies, generate a performance ranking report using a bias analysis algorithm, and identify systematic errors.

[0050] Specifically, the design of the quality assessment plan management unit and the laboratory comparison and analysis unit enables the efficient docking of external quality assessment plans and the anonymization comparison of multi-laboratory data. The former ensures the standardization of the participating process, and the latter, through bias analysis and performance ranking, identifies systematic errors between laboratories, such as reagent differences and equipment calibration deviations, provides an objective basis for quality evaluation for the laboratory, promotes the coordinated improvement of the detection levels between laboratories, and meets the requirements of national or provincial quality assessment standards.

[0051] Please refer to the appendix Figure 5 , the all-round data analysis module includes a basic statistical index calculation unit and an error and distribution analysis unit. The basic statistical index calculation unit is used to automatically calculate the daily average value, plate average value, standard deviation, coefficient of variation, and standard deviation index by using a statistical analysis algorithm library, and supports grouped statistics according to time, equipment, and personnel dimensions. The error and distribution analysis unit is used to calculate the total error, allowable total error, and critical systematic error by using an error calculation model and a distribution analysis algorithm, perform percentile analysis, and draw a frequency distribution histogram.

[0052] Specifically, the combination of the basic statistical index calculation unit and the error distribution analysis unit enables the multi-dimensional quantitative analysis of detection data. The former provides basic indicators such as the daily average value and coefficient of variation, and supports grouped statistics according to dimensions such as personnel and equipment; the latter, through error models and distribution analysis, identifies potential risks such as trend offsets. The two together provide data support for quality improvement, help the laboratory accurately locate problem links, such as abnormal batch-to-batch coefficient of variation of a certain equipment, and optimize the detection process and quality control rules.

[0053] Please refer to the appendixFigure 6 The visualization management module includes a data visualization display unit and an abnormal data monitoring unit. The data visualization display unit is used to present quality control data in the form of dashboards, line charts, and heat maps with the help of a data visualization engine and a front-end display framework. It supports multi-terminal display and custom layouts. The abnormal data monitoring unit is used to monitor abnormal situations such as data out of control and indicator exceeding in real time through real-time data monitoring algorithms and early warning rule engines, and provides color marking, flashing reminders and early warnings and traceability functions.

[0054] Specifically, the design of the data visualization display unit and the abnormal data monitoring unit enables intuitive presentation of quality control data and real-time warning of abnormal conditions. The former improves data transparency through dashboards, heat maps, etc., and supports multi-terminal access; the latter uses real-time monitoring algorithms and warning rules to provide graded warnings for abnormal conditions such as out-of-control data and indicators exceeding the standard, such as color marking and flashing reminders, and provides traceability functions such as full-process operation records of associated out-of-control samples. The combination of the two significantly improves the efficiency of problem response and assists managers in making quick decisions and interventions.

[0055] Please refer to the attached Figure 7 The quality indicator monitoring module includes an indicator set management unit, a dynamic threshold engine and an early warning and reporting unit. The indicator set management unit is used to preset a hierarchical indicator system consisting of a process quality indicator group, a detection quality indicator group and a clinical quality indicator group. The dynamic threshold engine is used to build an indicator baseline model based on historical data and dynamically update the indicator threshold using a sliding window algorithm. The early warning and reporting unit is used to configure hierarchical early warning rules. When an indicator is abnormal, the corresponding early warning level is triggered, and rectification tasks and indicator compliance reports are generated in conjunction; the process quality indicator group includes the sample transportation temperature compliance rate, blood collection time compliance rate, collection volume qualification rate, blood collection personnel qualification rate, and sample transportation and storage compliance rate; the detection quality indicator group includes the intra-batch coefficient of variation, the inter-batch coefficient of variation, the detection equipment calibration qualification rate, the reagent quality qualification rate, and the report review timeliness rate; the clinical quality indicator group includes the positive recall rate, the report issuance accuracy rate, the inter-laboratory quality assessment excellent rate, and the out-of-control processing closed-loop rate.

[0056] Specifically, the hierarchical design of the indicator set management unit, the dynamic threshold engine, and the early warning report unit realizes the coverage and dynamic monitoring of 16 core indicators in the whole process of neonatal disease screening. The indicator set management unit ensures that quality control covers the entire chain from sample collection to report issuance through a hierarchical indicator system composed of process quality indicator groups, detection quality indicator groups, and clinical quality indicator groups; the dynamic threshold engine adaptively adjusts the monitoring standard based on historical data to avoid the limitations of static thresholds; the early warning report unit transforms indicator anomalies into executable quality improvement tasks such as triggering equipment calibration and personnel training through hierarchical early warning and rectification linkage. Overall, it realizes the indexation, quantification, and closed-loop management of quality control, and improves the standardization and reliability of the screening process.

[0057] Please refer to the appendix Figure 8 , the whole-process quality control management module includes a process monitoring and data integration unit and a system report generation unit. The process monitoring and data integration unit is used to monitor all process nodes of sample collection, transportation, detection, and report issuance, integrate the data of each module through a data integration engine, record operation information such as the operator, timestamp, and equipment number, and support the full-chain traceability of problem samples. The system report generation unit is used to integrate the data of each module, generate a whole-process quality control report by using a report template engine and a data aggregation interface, and connect to the hospital information system and the maternal and child health care platform.

[0058] Specifically, the design of the process monitoring and data integration unit and the system report generation unit realizes the node control and system integration of the whole screening process. The process monitoring unit supports the full-chain traceability of problem samples such as hemolyzed samples that can be traced back to abnormal transportation temperature in the collection link by recording operation information including the operator, timestamp, and equipment number; the system report generation unit integrates the data of each module, generates a standardized quality control report and connects to external platforms such as the hospital information system and the maternal and child health care platform to promote the closed-loop management of screening diagnosis and intervention. The combination of the two ensures that the whole-process operation is traceable, problems can be located, and results can be analyzed, improving management efficiency and system coordination ability to meet the clinical and public health management needs of neonatal disease screening.

[0059] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A full-process quality control management system for neonatal disease screening, characterized in that: include: The real-time data collection module is used to realize data intercommunication and dynamic collection with laboratory information systems, testing equipment, and sample management systems through standardized API interfaces and data transmission protocols; Indoor quality control module, which uses Westgard multi-rule control algorithm and real-time data monitoring technology to conduct real-time monitoring and rule management of laboratory internal testing quality; The inter-laboratory quality assessment module is used to conduct multi-laboratory test result comparison and quality assessment using data encryption and anonymization processing technology; Comprehensive data analysis module, which uses statistical analysis algorithm library and data mining technology to complete multi-dimensional data statistics, error assessment and distribution feature analysis; Visual management module, which is used to realize the visual display of quality control data and abnormal data monitoring with the help of data visualization engine and front-end display framework; The quality indicator monitoring module is used to cover and monitor the core indicators of the entire process of neonatal disease screening based on a preset hierarchical indicator system and dynamic threshold algorithm; The full-process quality control management module is used to perform closed-loop control and system integration of the entire screening process nodes through the data integration engine and workflow management system.

2. The full-process quality control management system for neonatal disease screening according to claim 1 is characterized in that: The real-time data acquisition module includes a cross-system docking unit and a dynamic data analysis unit. The cross-system docking unit is used to achieve data intercommunication with the laboratory information system, detection equipment, and sample management system through a standardized API interface and data transmission protocol, and synchronize sample information, detection raw data, and process node data. The dynamic data analysis unit is used to use data cleaning algorithms and storage architectures to perform real-time cleaning, storage, and analysis of collected data, and support historical data backtracking queries and sample traceability.

3. The full-process quality control management system for neonatal disease screening according to claim 1 is characterized in that: The indoor quality control module includes a quality control rule management unit, a quality control diagram and marking unit, and a target value and batch number management unit. The quality control rule management unit is used to have a built-in Westgard multi-rule control algorithm, supports independent configuration of rule combinations according to test items, and evaluates test data in real time through a rule engine. The quality control diagram and marking unit is used to automatically generate Levey-Jennings diagrams and Z-score diagrams and mark out-of-control points using drawing algorithms and visualization libraries. The target value and batch number management unit is used to generate target values ​​such as mean, standard deviation, and coefficient of variation based on laboratory historical data using statistical analysis methods, supports independent management of quality control products of different batches, and rebuilds the quality control system through a preset process when the batch number is changed.

4. The full-process quality control management system for neonatal disease screening according to claim 1 is characterized in that: The inter-laboratory quality assessment module includes a quality assessment plan management unit and a laboratory comparison and analysis unit. The quality assessment plan management unit is used to manage the participating projects, blind sample acceptance, and result reporting by connecting to the interface of the national or provincial inter-laboratory quality assessment plan, or the configuration interface of a customized internal comparison plan. The laboratory comparison and analysis unit is used to compare the laboratory data with the data of other laboratories through data encryption and anonymization processing technology, use the bias analysis algorithm to generate a performance ranking report, and identify systematic errors.

5. The full-process quality control management system for neonatal disease screening according to claim 1 is characterized in that: The comprehensive data analysis module includes a basic statistical indicator calculation unit and an error and distribution analysis unit. The basic statistical indicator calculation unit is used to use the statistical analysis algorithm library to automatically calculate the daily average, plate average, standard deviation, coefficient of variation, and standard deviation index, and supports grouping statistics by time, equipment, and personnel dimensions. The error and distribution analysis unit is used to adopt the error calculation model and distribution analysis algorithm to calculate the total error, allowable total error, critical system error, perform percentile analysis, and draw a frequency distribution histogram.

6. The full-process quality control management system for neonatal disease screening according to claim 1 is characterized in that: The visualization management module includes a data visualization display unit and an abnormal data monitoring unit. The data visualization display unit is used to present quality control data in the form of dashboards, line charts, and heat maps with the help of a data visualization engine and a front-end display framework, and supports multi-terminal display and custom layouts. The abnormal data monitoring unit is used to monitor abnormal situations such as data out-of-control status and indicator exceeding in real time through a real-time data monitoring algorithm and an early warning rule engine, and provide a traceability function through color marking and flashing reminders and early warnings.

7. The full-process quality control management system for neonatal disease screening according to claim 1 is characterized in that: The quality indicator monitoring module includes an indicator set management unit, a dynamic threshold engine and an early warning and reporting unit. The indicator set management unit is used to preset a hierarchical indicator system consisting of a process quality indicator group, a detection quality indicator group, and a clinical quality indicator group. The dynamic threshold engine is used to build an indicator baseline model based on historical data and dynamically update the indicator threshold using a sliding window algorithm. The early warning and reporting unit is used to configure hierarchical early warning rules. When an indicator is abnormal, the corresponding early warning level is triggered, and rectification tasks and indicator compliance reports are generated in a linked manner.

8. A full-process quality control management system for neonatal disease screening according to claim 7, characterized in that: The process quality indicator group includes the sample transportation temperature compliance rate, blood collection time compliance rate, collection volume qualification rate, blood collection personnel qualification rate, and sample transportation and storage compliance rate.

9. The full-process quality control management system for neonatal disease screening according to claim 7, characterized in that: The detection quality indicator group includes the intra-batch coefficient of variation, the inter-batch coefficient of variation, the calibration pass rate of the detection equipment, the reagent quality pass rate, and the report review timeliness rate; the clinical quality indicator group includes the positive recall rate, the report issuance accuracy rate, the inter-laboratory quality assessment excellent rate, and the out-of-control processing closed-loop rate.

10. The full-process quality control management system for neonatal disease screening according to claim 1, characterized in that: The full-process quality control management module includes a process monitoring and data integration unit and a system report generation unit. The process monitoring and data integration unit is used to monitor the full-process nodes of sample collection, transportation, testing, and report issuance, integrate the data of each module through the data integration engine, record the operator, timestamp, and equipment number operation information, and support full-chain traceability of problem samples. The system report generation unit is used to use the report template engine and data aggregation interface to integrate the data of each module, generate a full-process quality control report, and connect to the hospital information system and maternal and child health care platform.

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