Medical detection data management system and management method

Abstract

The invention discloses a medical detection data management system and management method.According to the scheme, on the basis of the data management technology, medical detection data of all medical institutions are processed, a reliable data basis is provided for mutual recognition of the medical detection data of all the institutions, and in the aspect of data standardization, the medical detection data of all the institutions are processed; the real-time detection standard condition of each medical institution can be accurately reflected through guidance suggestions collected by a data center platform with high authority and standardization processing on each data; according to the scheme, on the premise of data summarization of the medical institutions, multi-dimensional and multi-situation summarized report data is formed through big data analysis, so that a provincial data center platform can know the detection conditions of the medical institutions in time and provide a reference basis for subsequent guidance. In the aspect of data mutual recognition, through mutual recognition management and summarization of annual medical detection quality conditions of each medical institution, tedious procedures of reporting mutual recognition qualification by each medical institution are reduced, and electronic office, high efficiency and rapidness are realized.

Description

Technical Field

[0001] This invention relates to information management technology and medical information technology, and in particular to a medical testing data management system and management method. Background Technology

[0002] In the current medical testing environment, data from various medical tests are largely not interoperable. For example, a test result from laboratory A cannot be recognized by laboratory B. If a person being tested consults multiple hospitals within a short period, they need to undergo the same test repeatedly, wasting time, energy, and money for both parties, resulting in a very poor medical experience for the public. This lack of data interoperability between medical testing institutions leads to a certain waste of medical resources.

[0003] To implement the "Guiding Opinions on Further Regulating Medical Practices and Promoting Rational Medical Examinations" issued by the National Health Commission and eight other departments, the "Notice on Accelerating the Mutual Recognition of Examination and Test Results" issued by the General Office of the National Health Commission, and the "Notice on Further Regulating Medical Practices and Promoting Rational Medical Examinations" issued by the Fujian Provincial Health Commission and seven other departments, and to standardize medical service practices, promote the mutual recognition and sharing of examination and test results, facilitate rational examinations, improve the efficiency of medical resource utilization, reduce medical costs, and improve the public's medical experience, relevant departments in Fujian Province require a data collection-based quality control result aggregation and management system. This system will be used to verify whether each medical testing institution meets the mutual recognition qualifications and to issue mutual recognition qualification certificates. Summary of the Invention

[0004] In view of this, the purpose of this invention is to propose a medical testing data management system and management method. By using information technology, the system aggregates the quality control testing data of various medical institutions, combines real-time monitoring and annual summary scoring, and issues mutual recognition certificates to qualified institutions. Institutions that obtain certificates can achieve mutual recognition of test results, thereby improving the reliability of data mutual recognition between different medical testing institutions.

[0005] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is as follows:

[0006] A medical testing data management system, comprising:

[0007] The database, deployed on a server, is used to store medical testing data and operational information;

[0008] A data center platform is connected to the Internet and communicates with a database deployed on a server. The data center platform is used to build or send and receive business project information to achieve data interaction with the database.

[0009] A data exchange platform is connected to the Internet and establishes communication with a data center platform and / or a database deployed on a server. The data exchange platform is used to report, download, and / or query data to the data center platform and / or the database.

[0010] Multiple terminal devices are installed in medical testing laboratories and connected to the Internet. The terminal devices are used to establish communication connections with data center platforms and / or data exchange platforms to upload medical testing data from the laboratory and to obtain information from data center platforms and / or databases.

[0011] The integrated monitoring module is connected to the Internet and establishes communication with the data center platform and / or the database deployed on the server. The integrated monitoring module is used to monitor and analyze the medical testing data and operational information in the database to determine the operational status of the data center platform and / or the database.

[0012] As one possible implementation, the operational information described in this solution further includes the registration information of the laboratory institution corresponding to the terminal device, as well as the docking information for data interaction between the terminal device and the data center platform and / or database;

[0013] The registration information includes one or more of the following: the organizational structure of the laboratory, its location, the person in charge, and the type of department.

[0014] The interface information includes one or more of the following: login authentication information, organization-specific code, interface documentation, test address, and official address;

[0015] The interface document contains the information that the laboratory institution needs to report through the terminal device, including one or more of the following: instrument information, quality control product information, reagent information, daily plan information, quality control parameter information, and daily quality control test results information.

[0016] As one possible implementation, the terminal device described in this solution is a computer or a local server device, both of which are connected to the Internet; the server is also equipped with an operation and maintenance monitoring module and a data monitoring module. The operation and maintenance monitoring module is used to monitor the working status of the database and the activity status of the ports accessing the database; the data monitoring module is used to verify the real-time performance, consistency, accuracy and / or integrity of the data uploaded to the database.

[0017] As one possible implementation, the data center platform described in this solution further includes a provincial data center platform and multiple municipal data center platforms, each configured with different working permissions. Both the provincial data center platform and the multiple municipal data center platforms communicate with a database deployed on a server, and the provincial data center platform also interacts with the multiple municipal data center platforms via data communication.

[0018] As a preferred implementation option, the terminal device described in this solution is further equipped with a data acquisition module and a front-end module. The front-end module includes a standard conversion unit and a front-end library. The data acquisition module uses the standard conversion unit and the front-end library of the front-end module to convert the medical test data to be uploaded in a standardized format.

[0019] As a preferred implementation option, the standard conversion unit described in this solution is connected to the Internet and establishes communication with the data center platform and / or the database deployed on the server, and then subscribes to receive data standard information or data standard change information issued by the data center platform and / or the database.

[0020] Based on the above, this solution also proposes a medical testing data management method, which includes the aforementioned medical testing data management system, wherein the management method includes:

[0021] Laboratory institutions submit a registration application to the data center platform. After the application is approved, the data center platform issues the connection information, which includes one or more of the following: login authentication information, institution's independent code, interface documentation, test address, and official address.

[0022] Laboratory institutions configure their equipment using terminal devices based on docking information to establish communication connections with data center platforms and / or databases;

[0023] Data acquisition and preprocessing modules are deployed on the terminal equipment of the laboratory to collect and preprocess the medical test data obtained by the laboratory, and then the preprocessed data is uploaded to the data center platform.

[0024] After receiving data uploaded by terminal devices, the data center platform verifies the data according to preset conditions, calculates the quality control results for the verified data according to preset conditions, and finally stores the data in the database deployed on the server.

[0025] As a preferred implementation option, this solution, on the terminal device, includes a front-end module comprising a standard conversion unit and a front-end library. The data acquisition module uses the standard conversion unit and the front-end library of the front-end module to perform format conversion on the medical test data to be uploaded, so as to unify its content format.

[0026] As a preferred implementation option, the standard conversion unit described in this solution is connected to the Internet and establishes communication with the data center platform and / or the database deployed on the server, and then subscribes to receive data standard information or data standard change information issued by the data center platform and / or the database;

[0027] The data standard information includes one or more of the following: quality control project standards, evaluation standards, instrument information, and reagent standards.

[0028] The quality control project standards include one or more of the following: project classification, project name, project medical insurance code, project sub-code, project complete code, and sample type;

[0029] The evaluation criteria include one or more of the following: maximum SD offset value, normal range of project test results, and maximum offset value.

[0030] Instrument information includes one or more of the following: instrument name, model, unique serial number, and manufacturer.

[0031] The reagent standard includes one or more of the following: reagent name, batch number, production date, expiration date, manufacturer, and application.

[0032] As a preferred implementation option, the management method described in this plan further includes: the laboratory institution submitting a data mutual recognition application to the data center platform, and the data center platform granting mutual recognition qualifications to the laboratory institution based on the historical verification and analysis of the application information submitted by the laboratory institution and its corresponding medical testing data, according to preset conditions.

[0033] As a preferred implementation option, the data center platform of this solution uses AI preprocessing tools to perform preliminary verification of the declared information; the laboratory institution is one or more of the following: an independent laboratory, a hospital laboratory, or a basic medical and health center.

[0034] As a preferred implementation option, this solution requires that before uploading the preprocessed data to the data center platform, the terminal equipment also verifies the uploaded data and the subject. The verification items include organizational information, reporting qualifications, and whether the data content conforms to data standards.

[0035] As a preferred implementation option, the data center platform of this solution, after receiving data uploaded by terminal devices, verifies the following items according to preset conditions: data content format, whether the corresponding test values ​​of the test data deviate abnormally, whether the testing unit is accurate, whether the specifications and expiration dates of the quality control products are within a reasonable range, and whether the quality control parameters are set or meet the expected time. When there are any verification failures, the medical test data is marked, and the abnormal items are summarized separately to form abnormal summary data, which is then stored in the database for unified management. At the same time, the abnormal items of the medical test data are also pushed to the corresponding uploaded terminal devices to realize the feedback of abnormalities to the laboratory institutions.

[0036] As a preferred implementation option, the data center platform of this solution also monitors the IP addresses that interact with the access platform. When the interaction frequency exceeds a preset threshold, it is defined as abnormal, and the access permissions of the corresponding IP address are restricted.

[0037] As a preferred implementation option, this scheme calculates the quality control results of the verified data according to preset conditions, including:

[0038] Quality control calculations for medical testing data are performed based on the general Westgard rules, specifically one or more of the following rules:

[0039] (1) 12s: A quality control measurement value exceeds the ±2s control limit. This is a warning rule, indicating that there may be random or systematic errors, and further inspection is required.

[0040] (2) 13s: A quality control measurement value exceeds the ±3s control limit, which is out of control and indicates that there is an unacceptable random error.

[0041] (3) 22s: Two consecutive quality control measurements exceed the ±2s control limit at the same time. This is an out-of-control rule, indicating that there is a systematic error.

[0042] (4) R4s: The difference between the highest and lowest quality control values ​​in the same batch exceeds 4s. This is out of control and indicates the presence of random error.

[0043] (5) 41s: Four consecutive quality control measurements exceed the ±1s control limit simultaneously. This is an out-of-control rule, indicating the presence of systematic error.

[0044] (6) 10x: Ten consecutive quality control measurements fall to one side of the mean. This is the out-of-control rule, indicating the presence of systematic error.

[0045] When an R4s event occurs, the system searches for the same test data of different quality control products at the same time based on the preset quality control product information, and calculates the quality control value. At the same time, if the result of the retrieved quality control data violates the rules because there was no comparison data when it was calculated before, or if the comparison data is available, the violation data will be synchronized to ensure the accuracy of the quality control data.

[0046] As a preferred implementation option, the management method described in this solution preferably also includes:

[0047] Based on medical monitoring data in the database, and using single items and quality control products as the basic information, the system analyzes and judges anomalies in data from multiple laboratories. By retrieving test results of the same quality control products from other laboratories on the same date, the system calculates the average value, target value X, and coefficient of variation CV based on the retrieved data. The calculation results are then used as temporary parameters to calculate the deviation coefficient z-score of the data under these parameters. If the z-score exceeds the threshold (±2SD, which is set by the platform and can be dynamically changed), the system automatically triggers anomaly data identification and generates identification results.

[0048] If the identification result indicates that the quality control test result is out of control and no action is taken, such as the Z score result of the quality control item (hematocrit assay) being -3SD on the same day, indicating that the quality control is out of control, the person in charge of the institution should be notified immediately through the contact information provided by the institution.

[0049] If the identification result indicates that the quality control is normal under the preset rules, but the platform's out-of-control conditions are triggered, such as 10 consecutive quality control results deviating from the same side of the target value, the information will also be fed back to the corresponding person in charge.

[0050] As a preferred implementation option, this solution, based on medical monitoring data in the database and using single-item and quality control materials as the basic information, analyzes and judges anomalies in cross-laboratory data, including:

[0051] A dynamic weighted normalized deviation index is established to address the anomaly judgment bias caused by differences in the dimensions of different inspection items. By dynamically adjusting the weights, the sensitivity to high-frequency anomalies is improved. The index is defined as follows:

[0052]

[0053] Where, x i The current test value of the quality control sample, μ group This represents the average daily testing values ​​for the same item using the same quality control material from different institutions; i This is a dynamic weight value, which is automatically adjusted based on the historical anomaly frequency of the project. The higher the anomaly frequency, the greater the weight, defined as follows: AD iThe platform's preset allowable deviation value for projects, σ hist is the standard deviation of the project over the past 30 days, which reflects the baseline of data fluctuation; ∈ is the smoothing constant, which is set to 0.01 to avoid the denominator being 0;

[0054] Anomaly detection is performed based on the calculated outlier index (NDI). Under normal threshold conditions, if NDI > 2.0, it is marked as an anomaly; during dynamic reinforcement verification, if NDI > 1.5 and w i When verifying high-frequency abnormal items with a value >0.3, a manual review process is triggered.

[0055] As a preferred implementation option, the preferred method in this solution, after the data center platform receives the data uploaded by the terminal device, also includes verifying it according to preset conditions as follows:

[0056] A multidimensional anomaly index (MAI) based on quantile dispersion is established to identify group biases (such as reagent failures from the same batch across multiple institutions). It combines allowable deviation values ​​and cross-institutional distribution characteristics, and is defined as follows:

[0057]

[0058] Among them, Q 50 The median of the same test results across the province on that day is given by MAD, which is the absolute deviation of the median. The function is defined as: MAD = median(|x j -Q 50 |);μ cross This refers to the average value across institutions for the same quality control product and the same project (e.g., the average value of all hospitals using the same batch of quality control products). It is used to represent the consistency ratio of batch number data; λ is the attenuation coefficient, which is 0.5 by default. The higher the consistency, the stronger the attenuation of the anomaly index.

[0059] Anomaly assessment is based on the calculated value of the outlier MAI. In the case of a single-point anomaly, if MAI > 3.0, it is considered a significant anomaly. In the case of group risk assessment, if the consistency of the same batch number is < 0.7 and MAI > 2.5, a batch quality control review is triggered.

[0060] Compared with existing technologies, the present invention, based on the above technical solution, has the following beneficial effects: This solution, based on data management technology, collects, verifies, and analyzes medical testing data from various medical institutions, providing a reliable data foundation for mutual recognition of medical testing data among these institutions. Simultaneously, this solution also verifies and analyzes abnormal information and items, providing feedback to improve the reliability and timeliness of data interaction. Regarding data standardization, it utilizes guidance collected from a high-authority data center platform, and through standardization processing of various data, it accurately reflects the real-time testing standards of each medical institution. Furthermore, based on the data aggregation of various medical institutions, this solution uses big data analysis to generate multi-dimensional, multi-faceted summary reports, enabling the provincial data center platform to promptly understand the testing status of each medical institution and provide reference for subsequent guidance. In terms of data mutual recognition, this solution, through mutual recognition management, aggregates the annual medical testing quality of each medical institution, reducing the cumbersome procedures for applying for mutual recognition qualifications and achieving electronic office work that is efficient and fast. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a simplified connection diagram of the management system in this solution;

[0063] Figure 2 This is a schematic diagram of the connection and functions of the management system during implementation;

[0064] Figure 3 This is a schematic diagram of data transfer in the data mutual recognition process of the management system in this solution. Detailed Implementation

[0065] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] Combination Figure 1 or Figure 2 As shown in the figure, this embodiment provides a medical testing data management system, which includes:

[0067] The database, deployed on a server, is used to store medical testing data and operational information;

[0068] A data center platform is connected to the Internet and communicates with a database deployed on a server. The data center platform is used to build or send and receive business project information to achieve data interaction with the database.

[0069] A data exchange platform is connected to the Internet and establishes communication with a data center platform and / or a database deployed on a server. The data exchange platform is used to report, download, and / or query data to the data center platform and / or the database.

[0070] Multiple terminal devices are installed in medical testing laboratories and connected to the Internet. The terminal devices are used to establish communication connections with data center platforms and / or data exchange platforms to upload medical testing data from the laboratory and to obtain information from data center platforms and / or databases.

[0071] The integrated monitoring module is connected to the Internet and establishes communication with the data center platform and / or the database deployed on the server. The integrated monitoring module is used to monitor and analyze the medical testing data and operational information in the database to determine the operational status of the data center platform and / or the database.

[0072] In order to improve the convenience of data collection and query, and to strengthen the information management of laboratory institutions connected to the database, as a possible implementation method, the operational information in this solution further includes the registration information of the laboratory institution corresponding to the terminal device, as well as the docking information of the terminal device and the data center platform and / or database for data interaction.

[0073] As an example, the registration information includes one or more of the following: organizational structure information corresponding to the laboratory institution, its region, person in charge information, and department type. The interface information includes one or more of the following: login authentication information, institution-specific code, interface documentation, test address, and official address.

[0074] The interface document contains the information that the laboratory institution needs to report through the terminal device, including one or more of the following: instrument information, quality control product information, reagent information, daily plan information, quality control parameter information, and daily quality control test results information.

[0075] In this solution, as one possible implementation, the terminal device is a computer or a local server device, both of which are connected to the Internet. The server is also equipped with an operation and maintenance monitoring module and a data monitoring module. The operation and maintenance monitoring module is used to monitor the working status of the database and the activity status of the ports accessing the database. The data monitoring module is used to verify the real-time performance, consistency, accuracy, and / or integrity of the data uploaded to the database.

[0076] As one possible implementation, the data center platform described in this solution further includes a provincial data center platform and multiple municipal data center platforms, each configured with different working permissions. Both the provincial data center platform and the multiple municipal data center platforms communicate with a database deployed on a server, and the provincial data center platform also interacts with the multiple municipal data center platforms via data communication.

[0077] In this solution, the provincial data center platform can be used to establish a standardized framework to address the heterogeneity of data formats, units, and codes among medical institutions, ensuring data comparability across the entire chain.

[0078] As an example, it may include the following functions:

[0079] 1. Construction of a unified standard framework

[0080] 1.1 Definition of Provincial Standardization Documents: Standardization rules specified by the provincial platform covering the four core elements.

[0081] 1.1.1 Quality control items (such as the range of indicators for routine blood tests and biochemical tests);

[0082] 1.1.2 Evaluation criteria (such as allowable error threshold, confidence interval);

[0083] 1.1.3 Instrument Information (Equipment Signals, Calibration Cycle Code);

[0084] 1.1.4. Reagent standards (batch number rules and concentration units are uniformly in the International System of Units).

[0085] 1.2 Dynamic Expansion Mechanism: Supports rapid standard access for newly added detection projects, and achieves standard version control and traceability through metadata management tools.

[0086] 2. Heterogeneous Data Transformation Engine

[0087] 2.1 Utilizing intelligent mapping technology for data transformation: Medical institutions use ETL / ELT tools to transform local data according to standardized rules;

[0088] 2.1.1 Format Conversion: Dates are standardized to ISO 8601 (YYYY-MM-DD), and numerical values ​​are retained to two decimal places;

[0089] 2.12 Semantic Alignment: Ontology techniques are used to map terms such as "HbA1c" and "glycated hemoglobin" to the same encoding;

[0090] 2.2 Real-time verification rules: Embed verification logic (such as threshold range detection) in the data upload interface to intercept illegal data.

[0091] 3. Intelligent cleaning and standardized production line

[0092] 3.1 Outlier Handling: Automatically identify abnormal and unreasonable detection data, mark the data, and trigger manual review.

[0093] 3.1.1 Data standardization as the basis for judgment: instrument calibration threshold range (e.g., biochemical analyzer data ±2SD);

[0094] 3.1.2 Multi-dimensional anomaly detection:

[0095] Static rules: Violation of the preset allowable range of the test item (e.g., blood glucose control product > 6.1 mmol / L); Dynamic rules: Determine the deviation range based on Westgard rules (e.g., data greater than 2 SD or less than -2 SD is abnormal data);

[0096] 3.1.3 Cross-institutional data analysis to identify anomalies: The test results of the same quality control product in different institutions are compared using a preset model (based on the same item, the average value, target value X, and coefficient of variation CV of the non-current data are calculated) to determine the Z score of the current data under the average data. When the value exceeds the threshold (±2SD), the abnormal data identification is automatically triggered.

[0097] 3.1.4 Automated detection and judgment of instruments, quality control products and reagents: Based on the manufacturer's synchronous interface for quality control product data and the materials reported by various institutions, the system judges the expiration status of quality control product data in real time, marks data that is not within the compliance range, and excludes it from the quality control summary;

[0098] 3.1.5 Manual review: Provide a dedicated workbench for laboratory personnel, displaying quality control data in the form of a comparison table and LJ chart, and provide methods for handling abnormal data.

[0099] 3.2 Dynamic target value collaboration: Supports institutions to define their own target values, while allowing provincial platforms to be forced to synchronize with a standard (such as infectious disease detection thresholds).

[0100] 3.2.1. Institutional Custom Target Values: The platform provides a standard interface to read the institution's preset target value data in real time, or the platform provides a standard operation page to modify the data in real time.

[0101] 3.2.2 Standardized processing of target values ​​on provincial platforms

[0102] 3.2.2.1 Provincial-level authorities issue a lockout order (e.g., HIV testing thresholds must not be modified);

[0103] 3.2.2.2 The system automatically overrides the custom values ​​defined by the organization (subject to the provincial platform).

[0104] 3.2.2.3 The platform's visual interface prominently displays a "Provincial-level locked status".

[0105] To improve the convenience and reliability of data collection, as a preferred implementation option, the terminal device described in this solution is preferably equipped with a data collection module and a front-end module. The front-end module includes a standard conversion unit and a front-end library. The data collection module uses the standard conversion unit and the front-end library of the front-end module to convert the medical test data to be uploaded in a standardized format.

[0106] As a preferred implementation option, the standard conversion unit described in this solution is connected to the Internet and establishes communication with the data center platform and / or the database deployed on the server, and then subscribes to receive data standard information or data standard change information issued by the data center platform and / or the database.

[0107] Based on the above, this solution also proposes a medical testing data management method, which includes the aforementioned medical testing data management system, wherein the management method includes:

[0108] Laboratory institutions submit a registration application to the data center platform. After the application is approved, the data center platform issues the connection information, which includes one or more of the following: login authentication information, institution's independent code, interface documentation, test address, and official address.

[0109] Laboratory institutions configure their equipment using terminal devices based on docking information to establish communication connections with data center platforms and / or databases;

[0110] Data acquisition and preprocessing modules are deployed on the terminal equipment of the laboratory to collect and preprocess the medical test data obtained by the laboratory, and then the preprocessed data is uploaded to the data center platform.

[0111] After receiving data uploaded by terminal devices, the data center platform verifies the data according to preset conditions, calculates the quality control results for the verified data according to preset conditions, and finally stores the data in the database deployed on the server.

[0112] As a preferred implementation option, this solution, on the terminal device, includes a front-end module comprising a standard conversion unit and a front-end library. The data acquisition module uses the standard conversion unit and the front-end library of the front-end module to perform format conversion on the medical test data to be uploaded, so as to unify its content format.

[0113] As a preferred implementation option, the standard conversion unit described in this solution is connected to the Internet and establishes communication with the data center platform and / or the database deployed on the server, and then subscribes to receive data standard information or data standard change information issued by the data center platform and / or the database;

[0114] The data standard information includes one or more of the following: quality control project standards, evaluation standards, instrument information, and reagent standards.

[0115] The quality control project standards include one or more of the following: project classification, project name, project medical insurance code, project sub-code, project complete code, and sample type;

[0116] The evaluation criteria include one or more of the following: maximum SD offset value, normal range of project test results, and maximum offset value.

[0117] Instrument information includes one or more of the following: instrument name, model, unique serial number, and manufacturer.

[0118] The reagent standard includes one or more of the following: reagent name, batch number, production date, expiration date, manufacturer, and application.

[0119] As a preferred implementation option, the management method described in this plan further includes: the laboratory institution submitting a data mutual recognition application to the data center platform, and the data center platform granting mutual recognition qualifications to the laboratory institution based on the historical verification and analysis of the application information submitted by the laboratory institution and its corresponding medical testing data, according to preset conditions.

[0120] As a preferred implementation option, the data center platform of this solution uses AI preprocessing tools to perform preliminary verification of the declared information; the laboratory institution is one or more of the following: an independent laboratory, a hospital laboratory, or a basic medical and health center.

[0121] As a preferred implementation option, this solution requires that before uploading the preprocessed data to the data center platform, the terminal equipment also verifies the uploaded data and the subject. The verification items include organizational information, reporting qualifications, and whether the data content conforms to data standards.

[0122] As a preferred implementation option, the data center platform of this solution, after receiving data uploaded by terminal devices, verifies the following items according to preset conditions: data content format, whether the corresponding test values ​​of the test data deviate abnormally, whether the testing unit is accurate, whether the specifications and expiration dates of the quality control products are within a reasonable range, and whether the quality control parameters are set or meet the expected time. When there are any verification failures, the medical test data is marked, and the abnormal items are summarized separately to form abnormal summary data, which is then stored in the database for unified management. At the same time, the abnormal items of the medical test data are also pushed to the corresponding uploaded terminal devices to realize the feedback of abnormalities to the laboratory institutions.

[0123] As a preferred implementation option, the data center platform of this solution also monitors the IP addresses that interact with the access platform. When the interaction frequency exceeds a preset threshold, it is defined as abnormal, and the access permissions of the corresponding IP address are restricted.

[0124] As a preferred implementation option, this scheme calculates the quality control results of the verified data according to preset conditions, including:

[0125] Quality control calculations for medical testing data are performed based on the general Westgard rules, specifically one or more of the following rules:

[0126] (1) 12s: A quality control measurement value exceeds the ±2s control limit. This is a warning rule, indicating that there may be random or systematic errors, and further inspection is required.

[0127] (2) 13s: A quality control measurement value exceeds the ±3s control limit, which is out of control and indicates that there is an unacceptable random error.

[0128] (3) 22s: Two consecutive quality control measurements exceed the ±2s control limit at the same time. This is an out-of-control rule, indicating that there is a systematic error.

[0129] (4) R4s: The difference between the highest and lowest quality control values ​​in the same batch exceeds 4s. This is out of control and indicates the presence of random error.

[0130] (5) 41s: Four consecutive quality control measurements exceed the ±1s control limit simultaneously. This is an out-of-control rule, indicating the presence of systematic error.

[0131] (6) 10x: Ten consecutive quality control measurements fall to one side of the mean. This is the out-of-control rule, indicating the presence of systematic error.

[0132] When an R4s event occurs, the system searches for the same test data of different quality control products at the same time based on the preset quality control product information, and calculates the quality control value. At the same time, if the result of the retrieved quality control data violates the rules because there was no comparison data when it was calculated before, or if the comparison data is available, the violation data will be synchronized to ensure the accuracy of the quality control data.

[0133] As a preferred implementation option, the management method described in this solution preferably also includes:

[0134] Based on medical monitoring data in the database, and using single items and quality control products as the basic information, the system analyzes and judges anomalies in data from multiple laboratories. By retrieving test results of the same quality control products from other laboratories on the same date, the system calculates the average value, target value X, and coefficient of variation CV based on the retrieved data. The calculation results are then used as temporary parameters to calculate the deviation coefficient z-score of the data under these parameters. If the z-score exceeds the threshold (±2SD, which is set by the platform and can be dynamically changed), the system automatically triggers anomaly data identification and generates identification results.

[0135] If the identification result indicates that the quality control test result is out of control and no action is taken, such as the Z score result of the quality control item (hematocrit assay) being -3SD on the same day, indicating that the quality control is out of control, the person in charge of the institution should be notified immediately through the contact information provided by the institution.

[0136] If the identification result indicates that the quality control is normal under the preset rules, but the platform's out-of-control conditions are triggered, such as 10 consecutive quality control results deviating from the same side of the target value, the information will also be fed back to the corresponding person in charge.

[0137] As a preferred implementation option, this solution, based on medical monitoring data in the database and using single-item and quality control materials as the basic information, analyzes and judges anomalies in cross-laboratory data, including:

[0138] A dynamic weighted normalized deviation index is established to address the anomaly judgment bias caused by differences in the dimensions of different inspection items. By dynamically adjusting the weights, the sensitivity to high-frequency anomalies is improved. The index is defined as follows:

[0139]

[0140] Where, x i The current test value of the quality control sample, μ group This represents the average daily testing values ​​for the same item using the same quality control material from different institutions; i This is a dynamic weight value, which is automatically adjusted based on the historical anomaly frequency of the project. The higher the anomaly frequency, the greater the weight, defined as follows: AD iThe platform's preset allowable deviation value for projects, σ hist is the standard deviation of the project over the past 30 days, which reflects the baseline of data fluctuation; ∈ is the smoothing constant, which is set to 0.01 to avoid the denominator being 0;

[0141] Anomaly detection is performed based on the calculated outlier index (NDI). Under normal threshold conditions, if NDI > 2.0, it is marked as an anomaly; during dynamic reinforcement verification, if NDI > 1.5 and w i When verifying high-frequency abnormal items with a value >0.3, a manual review process is triggered.

[0142] As a preferred implementation option, the preferred method in this solution, after the data center platform receives the data uploaded by the terminal device, also includes verifying it according to preset conditions as follows:

[0143] A multidimensional anomaly index (MAI) based on quantile dispersion is established to identify group biases (such as reagent failures from the same batch across multiple institutions). It combines allowable deviation values ​​and cross-institutional distribution characteristics, and is defined as follows:

[0144]

[0145] Among them, Q 50 The median of the same test results across the province on that day is given by MAD, which is the absolute deviation of the median. The function is defined as: MAD = median(|x j -Q 50 |);μ cross This refers to the average value across institutions for the same quality control product and the same project (e.g., the average value of all hospitals using the same batch of quality control products). It is used to represent the consistency ratio of batch number data; λ is the attenuation coefficient, which is 0.5 by default. The higher the consistency, the stronger the attenuation of the anomaly index.

[0146] Anomaly assessment is based on the calculated value of the outlier MAI. In the case of a single-point anomaly, if MAI > 3.0, it is considered a significant anomaly. In the case of group risk assessment, if the consistency of the same batch number is < 0.7 and MAI > 2.5, a batch quality control review is triggered.

[0147] As an example of one management and operation method of the management system in this solution, it may include the following:

[0148] Taking the transmission of quality control testing data from different testing departments within the same medical institution from different terminal devices on the hospital side to the data acquisition platform (data center platform) as an example, the medical data acquisition process and standard conversion scheme are explained as follows:

[0149] For a large medical institution, there may be different campuses or different testing centers in the same campus, and all of them need to report daily quality control test data through terminal equipment.

[0150] In the above situation, the medical institution logs into the provincial platform (provincial data center platform) registration and review center to register, fills in the medical institution information, and registers different testing center sub-institution information separately, requiring information such as institution name, region, person in charge information, and department type. After completion, platform staff will conduct manual review to confirm the submitted qualifications. After the review is completed, the platform will issue login authentication information and an independent institution code, which is based on each testing center as the basic unit.

[0151] Once the institution's application is approved, the platform will issue the connection materials (connection information), which includes interface documents (reporting required information: instrument information, quality control product information, reagent information, daily plan information, quality control parameter information, and daily quality control test result information), testing address, and official address.

[0152] Based on the feedback of the docking information, a terminal device is set up on the hospital side to dock with the medical institution's business database. The terminal device is equipped with a front-end machine, which includes a standard conversion unit and a front-end database. The standard conversion unit automatically subscribes to the data standards and data standard change messages on the platform side.

[0153] A data acquisition program is deployed on the front-end machine. Through the standard conversion unit and front-end library on the front-end machine, the data that needs to be standardized is automatically replaced. For example, the medical test code for "hematocrit assay" is automatically converted into the platform's standardized data code "25010101500401". Dates are automatically processed and unified to ISO 8601 (YYYY-MM-DD) format.

[0154] The provincial platform center management platform (provincial data center platform) standardizes the data required at each level, including the following items:

[0155] 1. Standardization of quality control items: item classification, item name, item medical insurance code, item sub-code, complete item code, sample type, etc.

[0156] 2. Evaluation criteria: SD maximum offset value, normal range of project test results and maximum offset value.

[0157] 3. Instrument Information: Instrument Name, Model, Unique Serial Number, Manufacturer

[0158] 4. Reagent Standards: Reagent name, batch number, production date, expiration date, manufacturer, application, etc.

[0159] Medical institutions use automated data collection tools to upload quality control data in real time. For institutions that develop their own collection tools, data must first pass the platform's data verification test before it can be officially uploaded. The platform provides a testing portal and testing account. If no institution has its own collection tool, it can use the platform's standardized data collection tool. This tool compares the data with standard library data and uses its own verification logic to complete the data testing and verification before automatically uploading it to the platform's data center.

[0160] When medical institutions collect and upload data, an initial data verification is performed on the terminal device. This primarily involves checking the reporting institution information, qualifications, data format, and identifying any abnormal deviations. After accurate data verification, institution-specific quality control data is generated based on each institution's pre-set quality control information, and platform-standard quality control data is generated based on the unified quality control information from the platform (provincial or municipal data center platform). The two sets of data are then cross-referenced and compared. IP addresses with frequent abnormal requests will be blocked. Reactivation of such addresses requires platform review and unblocking.

[0161] After receiving data uploaded by the data acquisition tool, the data center platform performs secondary verification, including data format (automatically determining the data format based on the project type; qualitative descriptions cannot be uploaded for quantitative projects), whether the test values ​​deviate abnormally, whether the testing units are accurate, whether the specifications and expiration dates of quality control materials are within a reasonable range, and whether quality control parameters are set or meet expected timeframes. Data that fails verification is marked, and abnormal data is aggregated in an anomaly database for unified management. Simultaneously, abnormal data is pushed to relevant medical institutions through the platform.

[0162] For verified data, the data center platform will input and store it in a database on the server. An automated quality control data processing tool can be deployed on the server, which will then calculate the quality control results based on preset quality control parameters and rules. Unless otherwise specified, quality control calculations will be performed based on the general Westgard rules, specifically:

[0163] 1.12s: A quality control measurement value exceeding the ±2s control limit is a warning rule, indicating that there may be random or systematic errors, and further investigation is required.

[0164] 2.13s: A quality control measurement value exceeding the ±3s control limit is out of control and indicates the presence of unacceptable random error.

[0165] 3.22s: Two consecutive quality control measurements simultaneously exceed the ±2s control limit. This is an out-of-control rule, indicating the presence of systematic error.

[0166] 4. R4s: The difference between the highest and lowest quality control values ​​within the same batch exceeds 4s. This is out-of-control and indicates the presence of random error.

[0167] 5.41s: Four consecutive quality control measurements simultaneously exceed the ±1s control limit. This is an out-of-control rule, indicating the presence of systematic error.

[0168] 6.10x: Ten consecutive quality control measurements falling to one side of the mean is out-of-control and indicates the presence of systematic error.

[0169] In the case of R4S, the system will search for the same test data of different quality control products at the same time based on the preset quality control product information, and calculate the quality control value. At the same time, if the result of the retrieved quality control data violates the rules because there was no comparison data in the previous calculation, or if the comparison data is available but no rules are found, the violation data will be synchronized to ensure the accuracy of the quality control data.

[0170] For the (2OF 3)2S rule (where two out of three consecutive quality control results exceed the X+2S or X-2S control limits, denoted as (2of 3)2S), retrieve the corresponding quality control rule data preceding this quality control data for data calculation.

[0171] After the quality control data calculation is completed, it is aggregated to the provincial data center platform client. The comprehensive supervision module acts as a real-time data analysis module to perform data supervision and analysis. The provincial data platform center or server can also deploy other modules (such as operation and maintenance monitoring modules, data monitoring modules, etc.), or utilize the comprehensive supervision module to establish sub-functional modules to perform specific functions. The mentioned modules or module functions can include: data upload status module, total data volume module, data out-of-control / under-control module, real-time data monitoring module, etc. Their functional mechanisms include the following:

[0172] (1) Data Upload Status Module: The data upload status of each institution on the day includes the total amount of data uploaded, the total amount of data already uploaded, the total amount of data not uploaded, the amount of duplicate data uploaded, the total amount of abnormal data, and the percentage.

[0173] (2) Data Total Module: The summary of data reported by each institution includes the total uploaded data, the total uploaded data, the total unuploaded data, the amount of duplicate uploaded data, the total amount of abnormal data, and the percentage.

[0174] (3) Data Out of Control Module: Based on the results calculated by the real-time data analysis module, summarize the proportion of controlled data and abnormal data of each institution on the same day, and provide an intuitive pie chart of the detection status.

[0175] (4) Real-time Data Monitoring Module: Based on preset information, the module summarizes the daily quality control status of each quality control item by organization, including whether it has been tested, the test results, whether the test results are under control, whether they are out of control, and the handling status of out-of-control items, etc., to achieve a most intuitive summary and preview of the quality control status.

[0176] (5) Real-time data monitoring module: Data is refreshed in real time, and the latest raw data of quality control and the calculation of quality control results are displayed in real time.

[0177] In terms of anomaly detection through cross-institutional data analysis, this solution leverages the provincial platform's large database. Using a single project and quality control product as the basic information, it retrieves test results from other institutions for the same quality control product on the same date. Based on the retrieved data, it calculates the average value, target value (X), and coefficient of variation (CV). The calculated results are then used as temporary parameters to calculate the offset coefficient (z-score) of the data under this parameter. If the z-score exceeds a threshold (±2SD, set by the platform and dynamically adjustable), anomaly data identification is automatically triggered.

[0178] If the quality control test results are out of control and not addressed, such as a Z-score of -3SD for the quality control item (hematocrit) on the same day, indicating a quality control failure, the institution's responsible person should be notified immediately through the contact information provided by the institution. If the quality control is normal under the preset rules, but the platform's out-of-control conditions are triggered, such as 10 consecutive quality control results deviating from the target value by the same side, the information will also be fed back to the corresponding responsible person.

[0179] When the person in charge of the organization receives a notification from the provincial platform, they can go to the platform's client to view the data and submit the reasons for the abnormal data and the implementation plan for correction.

[0180] As an example, this solution, based on medical monitoring data in the database and using single items and quality control products as the basic information, analyzes and identifies anomalies in cross-laboratory data. Other anomalies include:

[0181] A dynamic weighted normalized deviation index is established to address the anomaly judgment bias caused by differences in the dimensions of different inspection items. By dynamically adjusting the weights, the sensitivity to high-frequency anomalies is improved. The index is defined as follows:

[0182]

[0183] Where, x i The current test value of the quality control sample, μ group This represents the average daily testing values ​​for the same item using the same quality control material from different institutions; i This is a dynamic weight value, which is automatically adjusted based on the historical anomaly frequency of the project. The higher the anomaly frequency, the greater the weight, defined as follows: ADi The platform's preset allowable deviation value for projects, σ hist is the standard deviation of the project over the past 30 days, which reflects the baseline of data fluctuation; ∈ is the smoothing constant, which is set to 0.01 to avoid the denominator being 0;

[0184] Anomaly detection is performed based on the calculated outlier index (NDI). Under normal threshold conditions, if NDI > 2.0, it is marked as an anomaly; during dynamic reinforcement verification, if NDI > 1.5 and w i When verifying high-frequency abnormal items with a value >0.3, a manual review process is triggered.

[0185] As a preferred implementation option, the preferred method in this solution, after the data center platform receives the data uploaded by the terminal device, also includes verifying it according to preset conditions as follows:

[0186] A multidimensional anomaly index (MAI) based on quantile dispersion is established to identify group biases (such as reagent failures from the same batch across multiple institutions). It combines allowable deviation values ​​and cross-institutional distribution characteristics, and is defined as follows:

[0187]

[0188] Among them, Q 50 The median of the same test results across the province on that day is given by MAD, which is the absolute deviation of the median. The function is defined as: MAD = median(|x j -Q 50 |);μ cross This refers to the average value across institutions for the same quality control product and the same project (e.g., the average value of all hospitals using the same batch of quality control products). It is used to represent the consistency ratio of batch number data; λ is the attenuation coefficient, which is 0.5 by default. The higher the consistency, the stronger the attenuation of the anomaly index.

[0189] Anomaly assessment is based on the calculated value of the outlier MAI. In the case of a single-point anomaly, if MAI > 3.0, it is considered a significant anomaly. In the case of group risk assessment, if the consistency of the same batch number is < 0.7 and MAI > 2.5, a batch quality control review is triggered.

[0190] As an example, the anomaly detection of medical testing data and related data in this solution can be integrated into the process or scenario shown in the table below:

[0191]

[0192] Combination Figure 3 or Figure 4As shown, regarding the review and granting of mutual recognition qualifications, the laboratory institutions in this scheme submit a data mutual recognition application to the data center platform. The data center platform verifies and analyzes the application information submitted by the laboratory institutions and the historical verification of their corresponding medical testing data, and grants mutual recognition qualifications to the laboratory institutions according to preset conditions.

[0193] As an example, it may include the following:

[0194] Building a mutually trusted and recognized medical testing system

[0195] By assessing the reliability, consistency, and institutional compliance of medical data from multiple dimensions, quantitative evidence is provided for mutual recognition qualification review.

[0196] 1. Dynamic Management of Application Window

[0197] 1.1 Dual-channel application mechanism

[0198]

[0199] 2. Project application linked with EQA intelligent system

[0200] 2.1 Automated tagging of application projects

[0201] 2.1.1. Submission of project applications by the organization

[0202] 2.1.2 Provincial Platform EQA Database Retrieval

[0203] 2.1.3. If a qualified record exists, it will be automatically marked as EQA certified, exempting the submission of certain materials.

[0204] 2.1.4. No qualified records exist; mark as pending review and proceed with the automatic-manual review process.

[0205] 2.2 EQA Document Functional Processing Flow

[0206] step Technical solution Output Document Standardization Standard EQA documents from the provincial clinical laboratory center Structured EQA database tables Automatic matching of application projects Precise association between organization code and project code Generate the "EQA-Application Project Mapping Table" Exception handling A warning about unmatched projects will be sent to the provincial administrator. Triggering institutional correction or expert review

[0207] 3. Expanding the depth of institutional application materials

[0208] 3.1 Requirements for Structured Quality Documents

[0209]

[0210] 3.2 Information System Interface Operability Verification

[0211] 3.2.1 Verify the quality control reporting interface process log file and check the organization's interface automation processing capabilities. Organizations that have not reported quality control data must complete interface integration before they can submit applications.

[0212] 3.2.2. Evidence of LIS-HIS integration: Provide interaction logs in HL7 or FHIR format (including anonymized patient IDs).

[0213] 3.3 Supplementary Standards for Interlaboratory Quality Assessment Materials

[0214]

[0215] 4. Platform Intelligent Review Process

[0216] 4.1 Automated Material Planning and Verification Engine

[0217] Combination Figure 3 As shown, the received materials are preprocessed using trained AI tools. For example, the DeepSeek R1 large model is used here as the AI ​​processing unit. The language model is used to analyze the content and compliance of the documents, reducing the workload of staff and improving work efficiency.

[0218] 4.2 Rules for Certificate Issuance and Management

[0219]

[0220] Among them, qualified items can be dynamically synchronized to the front-end database of each medical institution, and used by terminal devices to retrieve and mark the mutually recognized items in the reports.

[0221] The above scheme has the following advantages:

[0222] 1. The dual-window mechanism addresses the pain point of time lag in the access of new institutions / projects (expected to shorten the access cycle by 60%).

[0223] 2. EQA-Declaration Intelligent Linkage reduces the workload of manual verification (mapping accuracy ≥ 98%).

[0224] 3. Structured review of materials enhances the credibility of qualifications.

[0225] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A medical testing data management system, characterized in that: It includes: The database, deployed on a server, is used to store medical testing data and operational information; A data center platform is connected to the Internet and establishes communication with a database deployed on a server. The data center platform is used to build or send and receive business project information in order to realize data interaction with the database. A data exchange platform is connected to the Internet and establishes communication with a data center platform and / or a database deployed on a server. The data exchange platform is used to report, download, and / or query data to the data center platform and / or the database. Multiple terminal devices are installed in medical testing laboratories and connected to the Internet. The terminal devices are used to establish communication connections with data center platforms and / or data exchange platforms to upload medical testing data from the laboratory and to obtain information from data center platforms and / or databases. The integrated monitoring module is connected to the Internet and establishes communication with the data center platform and / or the database deployed on the server. The integrated monitoring module is used to monitor and analyze the medical testing data and operational information in the database to determine the operational status of the data center platform and / or the database.

2. The medical testing data management system as described in claim 1, characterized in that: The operational information includes the registration information of the laboratory institution corresponding to the terminal device, as well as the docking information for data interaction between the terminal device and the data center platform and / or database; The registration information includes one or more of the following: the organizational structure of the laboratory, its location, the person in charge, and the type of department. The interface information includes one or more of the following: login authentication information, organization-specific code, interface documentation, test address, and official address; The interface document contains the information that the laboratory institution needs to report through the terminal device, including one or more of the following: instrument information, quality control product information, reagent information, daily plan information, quality control parameter information, and daily quality control test results information.

3. The medical testing data management system as described in claim 1 or 2, characterized in that: The terminal device is a computer or a local server device, both of which are connected to the Internet; the server is also equipped with an operation and maintenance monitoring module and a data monitoring module. The operation and maintenance monitoring module is used to monitor the working status of the database and the activity status of the ports connected to the database; the data monitoring module is used to verify the real-time performance, consistency, accuracy and / or integrity of the data uploaded to the database. The data center platform includes a provincial data center platform and multiple municipal data center platforms, each configured with different working permissions. Both the provincial data center platform and the multiple municipal data center platforms communicate with a database deployed on a server. The provincial data center platform also interacts with the multiple municipal data center platforms via data communication.

4. The medical testing data management system as described in claim 3, characterized in that: The terminal device is also equipped with a data acquisition module and a front-end module. The front-end module includes a standard conversion unit and a front-end library. The data acquisition module uses the standard conversion unit and the front-end library of the front-end module to convert the medical test data that needs to be uploaded in a standardized format. The standard conversion unit connects to the Internet and establishes communication with the data center platform and / or the database deployed on the server, and then subscribes to receive data standard information or data standard change information issued by the data center platform and / or the database.

5. A method for managing medical testing data, characterized in that: It includes the medical testing data management system as described in any one of claims 1 to 4, wherein the management method includes: Laboratory institutions submit a registration application to the data center platform. After the application is approved, the data center platform issues the connection information, which includes one or more of the following: login authentication information, institution's independent code, interface documentation, test address, and official address. Laboratory institutions configure their equipment using terminal devices based on docking information to establish communication connections with data center platforms and / or databases; Data acquisition and preprocessing modules are deployed on the terminal equipment of the laboratory to collect and preprocess the medical test data obtained by the laboratory, and then the preprocessed data is uploaded to the data center platform. After receiving data uploaded by terminal devices, the data center platform verifies the data according to preset conditions, calculates the quality control results for the verified data according to preset conditions, and finally stores the data in the database deployed on the server.

6. The medical testing data management method as described in claim 5, characterized in that: On the terminal device, the front-end module includes a standard conversion unit and a front-end library. The data acquisition module uses the standard conversion unit and the front-end library of the front-end module to convert the medical test data that needs to be uploaded in a standardized format. The standard conversion unit is connected to the Internet and establishes communication with the data center platform and / or the database deployed on the server, and then subscribes to receive data standard information or data standard change information issued by the data center platform and / or the database; The data standard information includes one or more of the following: quality control project standards, evaluation standards, instrument information, and reagent standards. The quality control project standards include one or more of the following: project classification, project name, project medical insurance code, project sub-code, project complete code, and sample type; The evaluation criteria include one or more of the following: maximum SD offset value, normal range of project test results, and maximum offset value. Instrument information includes one or more of the following: instrument name, model, unique serial number, and manufacturer. The reagent standard includes one or more of the following: reagent name, batch number, production date, expiration date, manufacturer, and application.

7. A medical testing data management method as described in claim 5, characterized in that: The management method also includes: the laboratory institution submitting a data mutual recognition application to the data center platform; the data center platform verifies and analyzes the application information submitted by the laboratory institution and the historical verification of its corresponding medical testing data, and grants the laboratory institution mutual recognition qualification according to preset conditions. The data center platform uses AI preprocessing tools to conduct preliminary verification of the declared information; the laboratory institution is one or more of the following: independent laboratory, hospital laboratory, and basic medical and health center. Before uploading the pre-processed data to the data center platform, the terminal device also verifies the uploaded data and the subject. The verification items include organizational information, reporting qualifications, and whether the data content conforms to data standards. After receiving data uploaded by terminal devices, the data center platform verifies the data according to preset conditions. This includes checking the data content format, whether the corresponding test values ​​deviate abnormally, whether the testing unit is accurate, whether the quality control product specifications and expiration dates are within a reasonable range, and whether the quality control parameters are set or meet the expected timeframe. If any verification fails, the medical test data is marked, and the abnormal items are summarized separately to form an anomaly summary, which is then stored in the database for unified management. Simultaneously, the platform pushes the abnormal items of the medical test data to the corresponding uploading terminal devices to provide feedback to the laboratory institutions. The data center platform also monitors the IP addresses that interact with the access platform. When the interaction frequency exceeds a preset threshold, it is defined as abnormal, and the access permissions of the corresponding IP address are restricted.

8. A medical testing data management method as described in claim 5, characterized in that: The quality control results, calculated based on preset conditions for the verified data, include: Quality control calculations for medical testing data are performed based on the general Westgard rules, specifically one or more of the following rules: (1) 12s: A quality control measurement value exceeds the ±2s control limit. This is a warning rule, indicating that there may be random or systematic errors, and further inspection is required. (2) 13s: A quality control measurement value exceeds the ±3s control limit, which is out of control and indicates that there is an unacceptable random error. (3) 22s: Two consecutive quality control measurements exceed the ±2s control limit at the same time. This is an out-of-control rule, indicating that there is a systematic error. (4) R4s: The difference between the highest and lowest quality control values ​​in the same batch exceeds 4s. This is out of control and indicates the presence of random error. (5) 41s: Four consecutive quality control measurements exceed the ±1s control limit simultaneously. This is an out-of-control rule, indicating the presence of systematic error. (6) 10x: Ten consecutive quality control measurements fall to one side of the mean. This is the out-of-control rule, indicating the presence of systematic error. When an R4s event occurs, the system searches for the same test data of different quality control products at the same time based on the preset quality control product information, and calculates the quality control value. At the same time, if the result of the retrieved quality control data violates the rules because there was no comparison data when it was calculated before, or if the comparison data is available, the violation data will be synchronized to ensure the accuracy of the quality control data.

9. A medical testing data management method as described in claim 5, characterized in that: The management method also includes: Based on medical monitoring data in the database, and using single items and quality control products as the basic information, the system analyzes and judges anomalies in data across laboratories. By retrieving test results of the same quality control products from other laboratories on the same date, the system calculates the average value, target value X, and coefficient of variation CV based on the retrieved data. The calculation results are then used as temporary parameters to calculate the offset coefficient z-score of the data under these parameters. If the z-score exceeds the threshold, the system automatically triggers anomaly data identification and generates identification results. If the identification result indicates that the quality control test result is out of control and no action is taken, such as the Z score result of the quality control item on that day being -3SD, indicating that the quality control is out of control, the person in charge of the institution should be notified immediately through the contact information reserved by the institution. If the identification result indicates that the quality control is normal under the preset rules, but the platform's out-of-control conditions are triggered, such as 10 consecutive quality control results deviating from the same side of the target value, the information will also be fed back to the corresponding person in charge.

10. A medical testing data management method as described in claim 9, characterized in that: Based on medical monitoring data in the database, and using individual items and quality control products as the basic information, the analysis and judgment of anomalies in cross-laboratory data also includes: A dynamic weighted normalized deviation index is established to address the anomaly judgment bias caused by differences in the dimensions of different inspection items. By dynamically adjusting the weights, the sensitivity to high-frequency anomalies is improved. The index is defined as follows: Where, x i The current test value of the quality control sample, μ group This represents the average daily testing values ​​for the same item using the same quality control material from different institutions; i This is a dynamic weight value, which is automatically adjusted based on the historical anomaly frequency of the project. The higher the anomaly frequency, the greater the weight, defined as follows: AD i The platform's preset allowable deviation value for projects, σ hist represents the standard deviation of the project over the past 30 days, which reflects the baseline of data fluctuation; ∈ is the smoothing constant; Anomaly detection is performed based on the calculated outlier index (NDI). Under normal threshold conditions, if NDI > 2.0, it is marked as an anomaly; during dynamic reinforcement verification, if NDI > 1.5 and w i When a high-frequency anomaly item with a value >0.3 is checked, a manual review process is triggered. After receiving data uploaded by the terminal device, the data center platform performs verification according to preset conditions, including: A multidimensional anomaly index (MAI) based on quantile dispersion is established to identify group shifts. It combines the allowable deviation value and cross-institutional distribution characteristics, and is defined as follows: Among them, Q 50 The median of the same test results across the province on that day is given by the median, and MAD is the absolute deviation of the median. The function is defined as: MAD = median(|x j -Q 50 |);μ cross This represents the average value across institutions for the same quality control products and projects. It is used to represent the consistency ratio of batch number data; λ is the attenuation coefficient; Anomaly assessment is based on the calculated value of the outlier MAI. In the case of a single-point anomaly, if MAI > 3.0, it is considered a significant anomaly. In the case of group risk assessment, if the consistency of the same batch number is < 0.7 and MAI > 2.5, a batch quality control review is triggered.

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