Hospital electronic information data comprehensive management method and system based on big data

Abstract

The application discloses a hospital electronic information data comprehensive management method and system based on big data, relates to the technical field of data management, and comprises the following steps: dividing hospital electronic information data into real-time updating data and historical data; determining at least one data updating calling terminal of the real-time updating data; determining a data main management terminal; obtaining an access request of the real-time updating data, and executing based on the access request of the real-time updating data; and for the historical data, allocating a storage node to the historical data based on the attribute of the historical data. The application has the advantages that: by optimizing the management structure of hospital electronic information data, the data access efficiency, medical service quality and data security are significantly improved, hospital management and decision-making are supported, the system operation and maintenance cost is reduced, and finally the patient satisfaction and the overall operation efficiency of the hospital are improved.

Description

Technical Field

[0001] This invention relates to the field of data management technology, specifically to a method and system for comprehensive management of hospital electronic information data based on big data. Background Technology

[0002] With the rapid development of information technology, hospitals generate a large amount of electronic data in their daily operations, including patient medical records, test results, imaging data, drug management information, and financial data. This data is usually distributed across different information systems and platforms, such as Electronic Medical Record (EMR) systems, Hospital Information Systems (HIS), Laboratory Information Systems (LIS), and Picture Archiving and Communication Systems (PACS).

[0003] Due to differences between systems, data silos within hospitals are becoming increasingly severe, leading to low efficiency in data sharing and utilization. This makes it difficult to obtain real-time updates of patient data and to update patient treatment plans in a timely and effective manner, especially during multi-departmental collaborative treatment. Therefore, how to comprehensively manage hospital electronic information data, break down data silos, and achieve efficient data integration, sharing, and utilization has become a critical issue that urgently needs to be addressed in current hospital information technology development. Summary of the Invention

[0004] To address the aforementioned technical issues, this paper provides a comprehensive governance method and system for hospital electronic information data based on big data. This technical solution resolves the increasingly serious problem of data silos within hospitals, which leads to low efficiency in data sharing and utilization. It also addresses the difficulty in timely and effective acquisition of real-time updates of patient data and timely updates of patient treatment plans during multi-departmental collaborative treatment.

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

[0006] A comprehensive governance method for hospital electronic information data based on big data includes:

[0007] Based on the patient status corresponding to the data, the hospital's electronic information data is divided into real-time updated data and historical data;

[0008] For real-time updated data, based on the patient's status, at least one data update calling terminal is determined to access the real-time updated data.

[0009] Based on the attributes of several data update calling terminals, a data master management terminal is determined, and the remaining data update calling terminals are set as data read-only terminals. Real-time update data is stored in the data master management terminal.

[0010] If the access request is a read request, the latest version of the real-time update data is retrieved from the data master management terminal and distributed to the data update calling terminal that issued the read request. If the access request is a write command, the data master management terminal is accessed and the write command request is executed. The real-time update data after the execution of the write command is used as the latest version of the real-time update data, while the real-time update data before the execution of the write command is retained.

[0011] For historical data, storage nodes are allocated based on the attributes of the historical data.

[0012] Preferably, the step of dividing the hospital electronic information data into real-time updated data and historical data according to the patient status corresponding to the data specifically includes:

[0013] Based on the patient's in-hospital status corresponding to the hospital's electronic information data, if the patient is in-hospital, the electronic information data is classified as real-time updated data; if the patient is not in-hospital, the electronic information data is classified as historical data.

[0014] Preferably, the step of determining at least one data update invocation terminal based on the patient's status to update real-time data specifically includes:

[0015] Obtain the patient's status and determine at least one associated department during the patient's hospital stay based on the patient's status;

[0016] Record all data terminals of related departments as data update call terminals for real-time data updates.

[0017] Preferably, the step of determining a primary data governance terminal based on the attributes of several data update calling terminals and setting the remaining data update calling terminals as read-only data terminals specifically includes:

[0018] Based on the patient's status, assign at least one status label to the patient;

[0019] Based on the hospital’s historical work logs, determine the data retrieval frequency of each department corresponding to each status label;

[0020] By summarizing the data call frequency of the corresponding departments for all patient status tags, we can obtain the data call frequency of the associated departments for the patient's real-time updated data.

[0021] Obtain the data communication latency between all related departments, and determine the central scheduling index for each related department based on the data call frequency of the related departments corresponding to the patient's real-time updated data and the data communication latency between all related departments.

[0022] Based on the hospital's historical work logs, determine the workload of each related department;

[0023] Based on the central scheduling index of each related department and the workload of each related department, the data master governance index of each related department for real-time updated data is comprehensively evaluated.

[0024] Select the associated department corresponding to the minimum value of the data master governance index, and use the data terminal of the associated department as the data master governance terminal for real-time data updates, and set the other data update call terminals as data read-only terminals.

[0025] Preferably, the determination of the central scheduling index for each associated department based on the patient's real-time updated data, the data retrieval frequency of the associated departments, and the data communication latency between all associated departments specifically includes:

[0026] The related departments whose central scheduling indicators are to be calculated are denoted as the central departments;

[0027] The related departments other than the central department are grouped into a central dispatch department subset;

[0028] Determine the data communication delay between the central department and the associated departments corresponding to each element in the central dispatch department subset, and the data call frequency of the associated departments corresponding to each element in the central dispatch department subset.

[0029] The centralization assessment formula is used to calculate the centralization scheduling index of the centralized departments;

[0030] The specific formula for centering assessment is as follows:

[0031] C=∑ b∈B D b ×f b

[0032] In the formula, C is the central dispatch index of the central department, b is an element in the central dispatch department subset, B is the central dispatch department subset, and D... b f is the data communication delay between the corresponding central department and the associated department corresponding to element b in the central dispatch department subset. b This represents the data retrieval frequency of the associated department corresponding to element b in the centrally scheduled department subset.

[0033] Preferably, determining the workload of each related department based on the hospital's historical work logs specifically includes:

[0034] Collect historical workload data for each related department;

[0035] The workload of related departments can be obtained by averaging the historical workload of related departments or by using linear regression to predict the historical workload of related departments.

[0036] Preferably, the data master governance indicators for real-time data updates by comprehensively evaluating each related department based on the central scheduling index of each related department and the workload of each related department specifically include:

[0037] Based on the patient's condition, set condition weights;

[0038] The main governance indicators for related departments based on real-time updated data are calculated using the governance indicator evaluation formula.

[0039] The specific formula for evaluating the governance indicators is as follows:

[0040]

[0041] In the formula, C represents the data master governance indicator for the i-th related department regarding real-time updated data. i F is the central dispatch indicator for the i-th related department. i Let α represent the workload of the central scheduling indicator for the i-th associated department, n represent the total number of associated departments, and α represent the disease weight.

[0042] Preferably, the request to execute a write instruction specifically includes:

[0043] Get the number of write command requests;

[0044] If the number of write commands is equal to 1, then the write command accesses the data master management terminal. If the number of write commands is greater than 1, then the write command accesses the data master management terminal in the order of the request time of the multiple write commands.

[0045] The system loads real-time updated data based on write commands, generates new versions of real-time updated data based on write commands, and updates the real-time updated data with new versions for subsequent write commands.

[0046] Furthermore, a comprehensive hospital electronic information data management system based on big data is proposed to implement the aforementioned comprehensive hospital electronic information data management method based on big data, including:

[0047] The data partitioning module is used to divide hospital electronic information data into real-time updated data and historical data according to the patient status corresponding to the data.

[0048] A status analysis module, which is electrically connected to the data partitioning module, is used to determine at least one data update calling terminal for real-time updated data based on the patient's status.

[0049] The data governance partitioning module is electrically connected to the status analysis module. The data governance partitioning module is used to determine a data master governance terminal based on the attributes of several data update calling terminals, and set the remaining data update calling terminals as data read-only terminals, and store the real-time update data in the data master governance terminal.

[0050] The instruction execution module is electrically connected to the data governance and partitioning module. The instruction execution module is used to obtain access requests for real-time updated data. If the access request is a read request, the latest version of the real-time updated data is retrieved from the data master governance terminal and distributed to the data update calling terminal that issued the read request. If the access request is a write instruction, the data master governance terminal is accessed and the write instruction request is executed. The real-time updated data after the write instruction is executed is used as the latest version of the real-time updated data, while the real-time updated data before the write instruction is executed is retained.

[0051] A storage module, electrically connected to the data partitioning module, is used to store historical data.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] The present invention provides a comprehensive management method and system for hospital electronic information data based on big data, which has the following significant beneficial effects:

[0054] By centrally storing real-time updated data on the master data management terminal and setting the data to read-only terminals, the complexity of data retrieval is reduced, significantly improving data access efficiency. Optimizing the processing flow of read and write commands ensures that the latest version of the real-time updated data can be quickly distributed to relevant terminals, reducing data retrieval latency.

[0055] Based on comprehensive indicators such as patient status, departmental data access frequency, data communication latency, and workload, the primary data governance terminal is dynamically determined to ensure optimal data governance configuration. Through centralized management and sharing of real-time updated data, the traditional data silo phenomenon is broken down, achieving efficient data integration and utilization.

[0056] Data sharing among related departments has facilitated collaborative diagnosis and treatment, improved the overall quality of medical services, and enabled medical staff to quickly obtain the latest diagnosis and treatment information for patients, thereby improving the efficiency and accuracy of diagnosis and treatment.

[0057] By retaining real-time updated data before write commands are executed, data version management is achieved, facilitating data traceability and recovery. By setting read-only terminals for data, modification permissions for non-master governance terminals are restricted, enhancing data security and privacy protection.

[0058] By storing and analyzing historical data, comprehensive and real-time data support is provided to hospital management, facilitating optimized resource allocation and improved operational efficiency. The categorized storage and management of historical data also provides rich data resources for medical research, driving its progress.

[0059] By centrally storing and managing real-time updated data, data redundancy is reduced, and storage costs are lowered. Dynamic allocation of data master governance terminals balances system load and extends system lifespan. Attached Figure Description

[0060] Figure 1 This is a flowchart of the comprehensive management method for hospital electronic information data based on big data proposed in this invention;

[0061] Figure 2 This is a flowchart of the method for dividing hospital electronic information data into real-time updated data and historical data in this invention;

[0062] Figure 3 This is a flowchart of a method for determining at least one data update calling terminal for real-time updated data in this invention;

[0063] Figure 4 This is a flowchart of the method for determining a data master governance terminal in this invention;

[0064] Figure 5 This is a flowchart of the method for determining the central scheduling index of each related department in this invention;

[0065] Figure 6 This is a flowchart of the method for determining the workload of each related department in this invention;

[0066] Figure 7 This is a flowchart of the method for comprehensively evaluating the data master governance indicators of each related department for real-time updated data in this invention;

[0067] Figure 8 This is a flowchart of the method for executing a write instruction request in this invention;

[0068] Figure 9 This is a schematic diagram of the computer-readable storage medium structure proposed in this invention. Detailed Implementation

[0069] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0070] Reference Figure 1 As shown, a comprehensive governance method for hospital electronic information data based on big data includes:

[0071] Based on the patient status corresponding to the data, the hospital's electronic information data is divided into real-time updated data and historical data;

[0072] For real-time updated data, based on the patient's status, at least one data update calling terminal is determined to access the real-time updated data.

[0073] Based on the attributes of several data update calling terminals, a data master management terminal is determined, and the remaining data update calling terminals are set as data read-only terminals. Real-time update data is stored in the data master management terminal.

[0074] If the access request is a read request, the latest version of the real-time update data is retrieved from the data master management terminal and distributed to the data update calling terminal that issued the read request. If the access request is a write command, the data master management terminal is accessed and the write command request is executed. The real-time update data after the execution of the write command is used as the latest version of the real-time update data, while the real-time update data before the execution of the write command is retained.

[0075] For historical data, storage nodes are allocated based on the attributes of the historical data.

[0076] Reference Figure 2 As shown, based on the patient status corresponding to the data, hospital electronic information data is divided into real-time updated data and historical data, specifically including:

[0077] Based on the patient's in-hospital status corresponding to the hospital's electronic information data, if the patient is in-hospital, the electronic information data is classified as real-time updated data; if the patient is not in-hospital, the electronic information data is classified as historical data.

[0078] Real-time updated data is used to support data interaction in the current diagnosis and treatment process, while historical data is used for archiving and subsequent analysis. Historical data is classified and stored according to attributes such as time, department, and patient for easy subsequent querying and analysis.

[0079] Reference Figure 3 As shown, based on the patient's status, at least one data update calling terminal specifically includes:

[0080] Obtain the patient's status and determine at least one associated department during the patient's hospital stay based on the patient's status;

[0081] Record all data terminals of related departments as data update call terminals for real-time data updates.

[0082] Based on the patient's condition, the system analyzes the departments the patient may be involved in during the diagnosis and treatment process, and facilitates data exchange between these departments. This not only increases the ability of each department to obtain the latest diagnosis and treatment data in a timely manner, but also enhances data security and privacy protection.

[0083] Reference Figure 4 As shown, based on the attributes of several data update calling terminals, a primary data governance terminal is determined, and the remaining data update calling terminals are set as read-only data terminals. Specifically, this includes:

[0084] Based on the patient's status, assign at least one status label to the patient;

[0085] Based on the hospital’s historical work logs, determine the data retrieval frequency of each department corresponding to each status label;

[0086] By summarizing the data call frequency of the corresponding departments for all patient status tags, we can obtain the data call frequency of the associated departments for the patient's real-time updated data.

[0087] Obtain the data communication latency between all related departments, and determine the central scheduling index for each related department based on the data call frequency of the related departments corresponding to the patient's real-time updated data and the data communication latency between all related departments.

[0088] Based on the hospital's historical work logs, determine the workload of each related department;

[0089] Based on the central scheduling index of each related department and the workload of each related department, the data master governance index of each related department for real-time updated data is comprehensively evaluated.

[0090] Select the associated department corresponding to the minimum value of the data master governance index, and use the data terminal of the associated department as the data master governance terminal for real-time data updates, and set the other data update call terminals as data read-only terminals.

[0091] Reference Figure 5 As shown, based on the data retrieval frequency of related departments according to the real-time updated data of patients and the data communication latency between all related departments, the central scheduling indicators for each related department are determined as follows:

[0092] The related departments whose central scheduling indicators are to be calculated are denoted as the central departments;

[0093] The related departments other than the central department are grouped into a central dispatch department subset;

[0094] Determine the data communication delay between the central department and the associated departments corresponding to each element in the central dispatch department subset, and the data call frequency of the associated departments corresponding to each element in the central dispatch department subset.

[0095] The centralization assessment formula is used to calculate the centralization scheduling index of the centralized departments;

[0096] The specific formula for centering assessment is as follows:

[0097] C=∑ b∈B D b ×f b

[0098] In the formula, C is the central dispatch index of the central department, b is an element in the central dispatch subset of departments, B is the central dispatch subset of departments, and D... b f is the data communication delay between the corresponding central department and the associated department corresponding to element b in the central dispatch department subset. b This represents the data retrieval frequency of the associated department corresponding to element b in the centrally scheduled department subset.

[0099] The performance of each department during central scheduling is comprehensively evaluated by using the data call frequency and communication latency of related departments. Departments with high call frequency are more closely related to the patient's condition and need to obtain the patient's status more frequently to make more accurate diagnosis and treatment decisions. Therefore, they require lower communication latency. Based on this, this solution uses a weighted summation of data call frequency and communication latency to obtain the performance of central scheduling of departments.

[0100] Reference Figure 6 As shown, based on the hospital's historical work logs, the workload of each related department is specifically determined to include:

[0101] Collect historical workload data for each related department;

[0102] The workload of related departments can be obtained by averaging the historical workload of related departments or by using linear regression to predict the historical workload of related departments.

[0103] When the historical workload of related departments is relatively stable, the average value is used to obtain the workload of related departments. When the historical workload of related departments shows a clear upward or downward trend, linear regression prediction is required to obtain the workload of related departments.

[0104] Reference Figure 7 As shown, based on the central scheduling index of each related department and the workload of each related department, the data master governance index for each related department to comprehensively evaluate the real-time data updates specifically includes:

[0105] Based on the patient's condition, set condition weights;

[0106] The main governance indicators for related departments based on real-time updated data are calculated using the governance indicator evaluation formula.

[0107] The specific formula for evaluating governance indicators is as follows:

[0108]

[0109] In the formula, C represents the data master governance indicator for the i-th related department regarding real-time updated data. i F is the central dispatch indicator for the i-th related department. i Let α represent the workload of the central scheduling indicator for the i-th associated department, n represent the total number of associated departments, and α represent the disease weight.

[0110] This solution comprehensively evaluates the data communication efficiency between patient care departments and the workload of each department to assess the data master governance indicators of related departments for real-time data updates. When a patient's condition is serious, more timely communication between departments is required, and the weight of the condition needs to be increased to ensure the efficiency of data communication between patient care departments. At the same time, the workload of each department is introduced as an auxiliary reference indicator to avoid a single department terminal storing too much data as the main data governance terminal, thus balancing the system load of each department.

[0111] Reference Figure 8 As shown, executing a write command request specifically includes:

[0112] Get the number of write command requests;

[0113] If the number of write commands is equal to 1, then the write command accesses the data master management terminal. If the number of write commands is greater than 1, then the write command accesses the data master management terminal in the order of the request time of the multiple write commands.

[0114] The system loads real-time updated data based on write commands, generates new versions of real-time updated data based on write commands, and updates the real-time updated data with new versions for subsequent write commands.

[0115] When multiple terminals simultaneously issue write commands, the system will respond to the first terminal's write command and simultaneously send a message to other terminals indicating that the department is updating patient treatment data. This allows other departments to see a summary of the treatment data, facilitating the dynamic and real-time acquisition of the latest patient treatment information by multiple departments.

[0116] Furthermore, based on the same inventive concept as the aforementioned big data-based comprehensive management method for hospital electronic information data, this solution proposes a big data-based comprehensive management system for hospital electronic information data, comprising:

[0117] The data segmentation module is used to divide hospital electronic information data into real-time updated data and historical data based on the corresponding patient status. The specific implementation steps are as follows:

[0118] The data is categorized based on the patient's in-hospital status corresponding to the hospital's electronic information data. If the patient is in-hospital, the data is classified as real-time updated data; if the patient is not in-hospital, the data is classified as historical data.

[0119] Real-time data updates are used to support data interaction during the current diagnosis and treatment process, while historical data is used for archiving and subsequent analysis.

[0120] The status analysis module, electrically connected to the data partitioning module, is used to determine at least one data update invocation terminal for real-time data updates. The specific implementation steps are as follows:

[0121] Obtain the patient's status and determine at least one associated department during the patient's hospital stay based on the patient's status.

[0122] Record all data terminals of related departments as data update call terminals that update data in real time.

[0123] The data governance partitioning module, electrically connected to the status analysis module, is used to determine a primary data governance terminal and set all other data update and access terminals as read-only terminals. The specific implementation steps are as follows:

[0124] Based on the patient's status, assign at least one status label to the patient.

[0125] Based on the hospital's historical work logs, the data retrieval frequency for each department corresponding to each status label is determined.

[0126] By summarizing the departmental data call frequencies corresponding to all patient status tags, we can obtain the associated departmental data call frequencies for the patient's real-time updated data.

[0127] The system obtains the data communication latency between all related departments, and determines the central scheduling index for each related department based on the data call frequency of the related departments corresponding to the patient's real-time updated data and the data communication latency between all related departments.

[0128] Based on the hospital's historical work logs, the workload of each related department is determined.

[0129] Based on the central scheduling indicators and workload of each related department, the data master governance indicators of each related department for real-time data updates are comprehensively evaluated.

[0130] Select the associated department corresponding to the minimum value of the data master governance index, and use the data terminal of the associated department as the data master governance terminal for real-time data updates, and set the other data update call terminals as data read-only terminals.

[0131] The instruction execution module, electrically connected to the data governance and partitioning module, is used to handle access requests for real-time updated data. The specific implementation steps are as follows:

[0132] Retrieve access requests for real-time updated data. If the access request is a read request, retrieve the latest version of the real-time updated data from the data master governance terminal and distribute it to the data update calling terminal that issued the read request.

[0133] If the access request is a write command, then access the data master management terminal and execute the write command request. The real-time update data after the write command is executed is used as the latest version of the real-time update data, while the real-time update data before the write command was executed is retained.

[0134] If the number of write commands is greater than 1, the data master management terminal will be accessed sequentially for each write command according to the order of their request times.

[0135] The storage module, electrically connected to the data partitioning module, is used to store historical data. The specific implementation steps are as follows:

[0136] Based on the attributes of historical data, storage nodes are allocated to historical data.

[0137] Historical data is categorized and stored according to attributes such as time, department, and patient for easy retrieval and analysis later.

[0138] Figure 9 This is a schematic diagram of a computer-readable storage medium structure provided in one embodiment of this application. Figure 9 The diagram illustrates a computer-readable storage medium 600 according to one embodiment of this application. The computer-readable storage medium 600 stores a computer-readable program. When the computer-readable program is executed by a processor, it can perform a comprehensive management method for hospital electronic information data based on big data, as described in the above-described embodiments of this application. The storage medium 600 includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0139] In summary, the advantages of this invention are: by optimizing the governance structure of hospital electronic information data, it significantly improves data access efficiency, medical service quality, and data security, while supporting hospital management and decision-making, reducing system operation and maintenance costs, and ultimately improving patient satisfaction and overall hospital operational efficiency.

[0140] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A big data-based hospital electronic information data comprehensive management method, characterized in that, The application comprises the following steps: According to the patient state corresponding to the data, the hospital electronic information data is divided into real-time updating data and historical data; For real-time updating data, at least one data updating calling terminal of the real-time updating data is determined based on the patient state; Based on the attributes of the data updating calling terminals, a data master management terminal is determined, and the remaining data updating calling terminals are set as data read-only terminals. The real-time updating data is stored in the data master management terminal; An access request for the real-time updating data is obtained. If the access request is a read request, the latest version of the real-time updating data is called from the data master management terminal and distributed to the data updating calling terminal from which the read request is sent. If the access request is a write instruction, the data master management terminal is accessed, the write instruction request is executed, and the real-time updating data after the execution of the write instruction is taken as the latest version of the real-time updating data. Meanwhile, the real-time updating data before the execution of the write instruction is retained; For historical data, a storage node is allocated for the historical data based on the attributes of the historical data; The method comprises the following steps: Based on the patient state, at least one state label is set for the patient; Based on the historical work log of the hospital, the data calling frequency of each department corresponding to each state label is determined; The data calling frequency of each department corresponding to all state labels of the patient is summarized to obtain the associated department data calling frequency of the real-time updating data corresponding to the patient; The data communication delay between all associated departments is obtained. Based on the associated department data calling frequency of the real-time updating data corresponding to the patient and the data communication delay between all associated departments, the central scheduling index of each associated department is determined; Based on the historical work log of the hospital, the workload of each associated department is determined; Based on the central scheduling index of each associated department and the workload of each associated department, the data master management index of each associated department for the real-time updating data is comprehensively evaluated; The associated department corresponding to the minimum value of the data master management index is screened out, the data terminal of the associated department is taken as the data master management terminal of the real-time updating data, and the remaining data updating calling terminals are set as data read-only terminals. 2.The hospital electronic information data comprehensive management method based on big data according to claim 1, wherein, The method comprises the following steps: Based on the in-hospital state of the patient corresponding to the hospital electronic information data, if the patient is in the in-hospital state, the hospital electronic information data is divided into real-time updating data. If the patient is not in the in-hospital state, the hospital electronic information data is divided into historical data. 3.The hospital electronic information data comprehensive management method based on big data according to claim 2, characterized in that, The method comprises the following steps: The patient state is obtained, and at least one associated department during the in-hospital period of the patient is determined based on the patient state; The data terminals of all associated departments are recorded as data updating calling terminals of the real-time updating data. 4.The hospital electronic information data comprehensive management method based on big data according to claim 3, characterized in that, The method comprises the following steps: The related departments whose central scheduling indicators are to be calculated are denoted as the central departments; The related departments other than the central department are grouped into a central dispatch department subset; Determine the data communication delay between the central department and the associated departments corresponding to each element in the central dispatch department subset, and the data call frequency of the associated departments corresponding to each element in the central dispatch department subset. The centralization assessment formula is used to calculate the centralization scheduling index of the centralized departments; The specific formula for centering assessment is as follows: wherein, is a central dispatching index of a central department, is an element in a central dispatching department subset, is a central dispatching department subset, is a corresponding central department and an element in a central dispatching department subset a data communication delay between corresponding associated departments, is an element in a central dispatching department subset a corresponding associated department data call frequency. 5.The hospital electronic information data comprehensive management method based on big data according to claim 4, characterized in that, The determination of the workload of each related department based on the hospital's historical work logs specifically includes: Collect historical workload data for each related department; The workload of related departments can be obtained by averaging the historical workload of related departments or by using linear regression to predict the historical workload of related departments. 6.The hospital electronic information data comprehensive management method based on big data according to claim 5, characterized in that, The data master governance indicators for each related department, which are based on the central scheduling index and the workload of each related department, and comprehensively evaluate the data for real-time data updates, specifically include: Based on the patient's condition, set condition weights; The main governance indicators for related departments based on real-time updated data are calculated using the governance indicator evaluation formula. The specific formula for evaluating the governance indicators is as follows: In the formula, is the data master index of the real-time update data for the i th associated department, is the centralized scheduling index of the i th associated department, is the workload of the centralized scheduling index of the i th associated department, is the total number of associated departments, is the illness weight. 7.The hospital electronic information data comprehensive management method based on big data according to claim 6, characterized in that, The specific components of the write instruction request include: Get the number of write command requests; If the number of write commands is equal to 1, then the write command accesses the data master management terminal. If the number of write commands is greater than 1, then the write command accesses the data master management terminal in the order of the request time of the multiple write commands. The system loads real-time updated data based on write commands, generates new versions of real-time updated data based on write commands, and updates the real-time updated data with new versions for subsequent write commands.

8. A hospital electronic information data comprehensive management system based on big data, characterized in that, The method for comprehensive management of hospital electronic information data based on big data as described in any one of claims 1-7 includes: The data partitioning module is used to divide hospital electronic information data into real-time updated data and historical data according to the patient status corresponding to the data. A status analysis module, which is electrically connected to the data partitioning module, is used to determine at least one data update calling terminal for real-time updated data based on the patient's status. The data governance partitioning module is electrically connected to the status analysis module. The data governance partitioning module is used to determine a data master governance terminal based on the attributes of several data update calling terminals, and set the remaining data update calling terminals as data read-only terminals, and store the real-time update data in the data master governance terminal. The instruction execution module is electrically connected to the data governance and partitioning module. The instruction execution module is used to obtain access requests for real-time updated data. If the access request is a read request, the latest version of the real-time updated data is retrieved from the data master governance terminal and distributed to the data update calling terminal that issued the read request. If the access request is a write instruction, the data master governance terminal is accessed and the write instruction request is executed. The real-time updated data after the write instruction is executed is used as the latest version of the real-time updated data, while the real-time updated data before the write instruction is executed is retained. A storage module, electrically connected to the data partitioning module, is used to store historical data.

9. A computer-readable storage medium storing a computer-readable program, characterized in that, When the computer-readable program is executed by a processor, it implements the comprehensive governance method for hospital electronic information data based on big data as described in any one of claims 1-7.

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