Data processing method and device, electronic equipment and computer readable storage medium

Abstract

The invention provides a data processing method and device, electronic equipment and a computer readable storage medium, is applied to a hospital information system, and relates to the technical field of medical information. The method comprises the steps of obtaining an operation signal input by a user, and determining a target analysis object based on the operation signal; acquiring target data corresponding to the target analysis object based on a preset data acquisition scheme; wherein the data acquisition scheme at least comprises one of the following: data acquisition through a preset data interface, a data crawler technology and a real-time acquisition technology based on indexes and dimensions; performing analysis operation on the target data to obtain an analysis result; the data of the target analysis object is acquired through the preset data acquisition scheme, and then the data is analyzed to obtain the analysis result compatible with the original system, so that a new analysis system is effectively compatible with the original system, the data utilization efficiency is improved, the manpower and material resource cost is reduced, and the management efficiency and the service quality are improved.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to a data processing method, device, electronic equipment and computer-readable storage medium. Background Art

[0002] With the surge in the number of patients seeking medical treatment, the amount of data generated by hospital information systems has increased rapidly. However, the valuable data generated by these systems is usually displayed and analyzed in business intelligence applications through only a single interface or form, resulting in the value of the data being buried. At the same time, the existing interface is difficult to meet the personalized scenario needs of hospitals.

[0003] Although some third-party vendors provide multi-dimensional analysis systems that allow users to display graphs by dragging different indicators and dimensions, these systems often cannot be effectively integrated with the hospital's existing business intelligence systems due to differences in processes and technical architectures, resulting in the need for hospitals to re-purchase and reconnect, increasing manpower and material costs. In addition, most analysis systems developed by third-party vendors are difficult to be compatible with the hospital's existing old systems, which not only affects the hospital's data utilization efficiency, but also restricts the improvement of management efficiency and service quality. Summary of the invention

[0004] The purpose of the present invention is to provide a data processing method, device, electronic device and computer-readable storage medium, which acquire the data of the target analysis object through a pre-set data acquisition scheme, and then perform analysis operations on the data to obtain analysis results that are compatible with the original system, thereby achieving effective compatibility between the new analysis system and the original system, improving data utilization efficiency, reducing manpower and material costs, and improving management efficiency and service quality.

[0005] In a first aspect, the present invention provides a data processing method, which is applied to a hospital information system; the method comprises:

[0006] Acquire an operation signal input by a user, and determine a target analysis object based on the operation signal;

[0007] Acquire target data corresponding to the target analysis object based on a pre-set data acquisition scheme; wherein the data acquisition scheme includes at least one of the following: acquiring data through a preset data interface, data crawler technology, and real-time acquisition technology based on indicators and dimensions;

[0008] Perform analysis operations on target data to obtain analysis results; wherein the analysis operations include at least one of the following: graphic transformation, perspective analysis, aggregation analysis, drill-down mining and style adjustment; the analysis results include at least one of the following: table, perspective chart, bar chart, line chart, radar chart, waterfall chart, scatter chart and stacked chart.

[0009] In some preferred embodiments of the present invention, the step of acquiring target data corresponding to the target analysis object based on a preset data acquisition scheme includes:

[0010] Obtain identification parameters of the target analysis object;

[0011] The target data corresponding to the identification parameters is obtained through a preset data interface.

[0012] In some preferred embodiments of the present invention, the first data specification of the data corresponding to the target analysis object is the same as the second data specification corresponding to the analysis result; wherein the first data specification and the second data specification each include at least one of the following: field name, field value, dimension and indicator.

[0013] In some preferred embodiments of the present invention, after the step of acquiring target data corresponding to the target analysis object based on a preset data acquisition scheme, the method further includes:

[0014] The target data is stored in a preset database; and a data source corresponding to the target data is constructed based on a modular approach, and a tree-shaped data source list is constructed according to preset business identifiers; wherein the business identifier includes at least one of the following: hospital area, department, physician and patient.

[0015] In some preferred embodiments of the present invention, after the step of analyzing the target data to obtain the analysis result, the method further includes:

[0016] Determine an analysis template based on the analysis results; wherein the analysis template includes at least one of the following: service efficiency, case-disease analysis, cost analysis, medical technology efficiency, drug analysis, medical record analysis, public health service and health statistics data analysis;

[0017] Mount the analysis template on the hospital information system.

[0018] In some preferred embodiments of the present invention, the method further comprises:

[0019] Get the target user's permissions;

[0020] Perform management operations on the analysis template based on permissions; wherein the management operations include at least one of the following: modifying the analysis template, calling target data, and viewing analysis results.

[0021] In some preferred embodiments of the present invention, the step of performing an analysis operation on the target data to obtain an analysis result includes:

[0022] Get the target identifier in the target data;

[0023] Based on the target identifier, the target data is multi-dimensionally associated to obtain the analysis results.

[0024] In a second aspect, the present invention provides a data processing device, which is applied to a hospital information system; the device comprises:

[0025] The corresponding operation module is used to obtain the operation signal input by the user and determine the target analysis object based on the operation signal;

[0026] A data acquisition module is used to acquire target data corresponding to a target analysis object based on a preset data acquisition scheme; wherein the data acquisition scheme includes at least one of the following: acquiring data through a preset data interface, data crawler technology, and real-time acquisition technology based on indicators and dimensions;

[0027] The data processing module is used to perform analysis operations on the target data to obtain analysis results; wherein the analysis operations include at least one of the following: graphic transformation, perspective analysis, aggregation analysis, drill-down mining and style adjustment; the analysis results include at least one of the following: table, perspective chart, bar chart, line chart, radar chart, waterfall chart, scatter chart and stacked chart.

[0028] In a third aspect, the present invention provides an electronic device including a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the data processing method of the first aspect.

[0029] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the data processing method of the first aspect.

[0030] The present invention brings the following beneficial effects:

[0031] The present invention provides a data processing method, device, electronic device and computer-readable storage medium, which are applied to a hospital information system; the method comprises: obtaining an operation signal input by a user, and determining a target analysis object based on the operation signal; obtaining target data corresponding to the target analysis object based on a pre-set data acquisition scheme; wherein the data acquisition scheme comprises at least one of the following: obtaining data through a preset data interface, data crawler technology and real-time acquisition technology based on indicators and dimensions; performing analysis operations on the target data to obtain analysis results; wherein the analysis operations comprise at least one of the following: graphic transformation, perspective analysis, aggregation analysis, drill-down mining and style adjustment; the analysis results comprise at least one of the following: a table, a perspective chart, a bar chart, a line chart, a radar chart, a waterfall chart, a scatter chart and a stacked chart; obtaining data of the target analysis object through a pre-set data acquisition scheme, and then performing analysis operations on the data to obtain analysis results compatible with the original system, thereby achieving effective compatibility between the new analysis system and the original system, improving data utilization efficiency, reducing manpower and material costs, and improving management efficiency and service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 A flowchart of a data processing method provided by an embodiment of the present invention;

[0034] Figure 2 A data processing principle flow chart provided for an embodiment of the present invention;

[0035] Figure 3 A schematic diagram of the structure of a data processing device provided by an embodiment of the present invention;

[0036] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0037] Icons: 310 - operation corresponding module; 320 - data acquisition module; 330 - data processing module; 400 - memory; 401 - processor; 402 - bus; 403 - communication interface. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0039] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0041] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0042] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0043] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] With the surge in the number of patients seeking medical treatment, the amount of data generated by hospital information systems (HIS, LIS, PACS, RIS, EMR, CIS, etc.) has increased rapidly. However, the valuable data generated by these systems is usually displayed and analyzed in BI applications through only a single interface or form, resulting in the value of the data being buried. At the same time, the existing interface is difficult to meet the personalized scenario needs of hospitals.

[0045] Business Intelligence (BI), also known as business wisdom or BI, refers to the use of modern data warehouse technology, online analysis and processing technology, data mining and data presentation technology to analyze data to realize business value.

[0046] HIS (Hospital Information System) refers to an information system that uses modern means such as computer software and hardware technology and network communication technology to comprehensively manage the flow of personnel, logistics and finance in a hospital and its affiliated departments, collects, stores, processes, extracts, transmits and summarizes the data generated at all stages of medical activities, and processes them into various information, thereby providing comprehensive automated management and various services for the overall operation of the hospital.

[0047] Laboratory Information System (LIS) is a type of software used to process laboratory process information. This system is usually connected to other information systems such as hospital information system (HIS). Laboratory Information System consists of a variety of laboratory process modules, which can be selected and configured according to the actual situation of the customer.

[0048] The Radiology Information System (RIS) is one of the important medical imaging information systems in hospitals. Together with the PACS system, it constitutes the information environment of medical imaging. The Radiology Information System is a computer information system based on the task execution process management of the workflow of the hospital imaging department. It mainly realizes the computer network control and management of the workflow of medical imaging inspection and the sharing of medical graphic information, and realizes telemedicine on this basis.

[0049] PACS (picture archiving and communication system) is a system used in hospital imaging departments. Its main task is to store a large number of medical images (including images produced by MRI, CT, ultrasound, various X-ray machines, various infrared devices, microscopes, etc.) produced daily in a digital way through various interfaces (analog, DICOM, network). When needed, they can be quickly retrieved and used under certain authorizations, while adding some auxiliary diagnosis management functions. It plays an important role in transmitting data between various imaging devices and organizing and storing data.

[0050] Electronic medical records (EMR) refer to digital medical records that are stored, managed, transmitted and reproduced through electronic devices (computers, health cards, etc.) to replace handwritten paper medical records. Its content includes all the information in paper medical records. Some medical research institutes define it as: EMR is an electronic patient record based on a specific system that provides users with access to complete and accurate data, alerts, prompts and clinical decision support systems.

[0051] CIS (Clinical pathway) refers to the establishment of a set of standardized treatment models and treatment procedures for a certain disease. It is a comprehensive model of clinical treatment, which promotes treatment organization and disease management methods guided by evidence-based medicine and guidelines. Ultimately, it plays a role in standardizing medical behavior, reducing variation, reducing costs, and improving quality.

[0052] Although some third-party vendors such as Fanruan, Tableau, Power BI, and the emerging AI-driven Chat2Bi provide multi-dimensional analysis systems that allow users to display graphs by dragging different indicators and dimensions. However, due to differences in processes and technical architectures among systems, these systems often cannot be effectively integrated with the hospital's existing BI system, resulting in the need for hospitals to re-purchase and re-connect, increasing manpower and material costs. In addition, data consistency issues, such as inconsistent statistical calibers, differences in data extraction, and the complexity of data governance links, make compatibility with older systems more difficult, which not only affects the efficiency of data utilization in hospitals, but also restricts the improvement of management efficiency and service quality. Therefore, in order to better serve patients, improve hospital management efficiency, and meet hospital rating requirements, hospitals urgently need to use advanced data analysis and visualization technologies to maximize the value and application potential of data.

[0053] Based on this, the present invention provides a data processing method to solve the problems faced by hospitals in the information construction, such as low efficiency of data display and analysis, inability to meet personalized needs, and inconsistency with the old system data due to differences in docking and data acquisition processes when repurchasing a third-party system, and realize the following functions:

[0054] By jumping on the basis of the original interface to achieve data collection, graphic changes, aggregation analysis, perspective analysis, drill-down mining and style adjustment, the consistency of data sources is ensured.

[0055] It supports downloading and exporting graphics after secondary analysis, saving, opening, sharing and mounting as templates, matching different analysis scenarios, and classifying and displaying analysis content according to predefined dimensions. The query dimensions include service efficiency, cost analysis, medical technology efficiency, drug analysis, medical record analysis, public health services and health statistics data analysis. In the subsequent use process, the template can be adjusted according to the actual usage.

[0056] For a single indicator, it supports displaying data details through different dimensions, cascading dimension drill-down, and calculation and analysis of common functions such as year-on-year and year-on-year growth, average value, and proportion.

[0057] Provides multi-directional, multi-scenario, and multi-dimensional viewing and monitoring functions for multiple indicators.

[0058] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0059] Embodiment 1

[0060] The present invention provides a data processing method for use in a hospital information system; see Figure 1 A flowchart of a data processing method provided by the embodiment of the present invention includes:

[0061] Step S102: acquiring an operation signal input by a user, and determining a target analysis object based on the operation signal.

[0062] Specifically, business intelligence (BI) tools are mainly used for data analysis, visualization, decision support and business monitoring. Among them, data visualization is one of the core functions of BI tools, which can intuitively display complex data in the form of charts, dashboards, etc., to help users quickly understand and analyze data.

[0063] Take the hospital's existing BI patient details module as an example. This module displays information such as patient name, gender, age, province, department, physician, and time of consultation in a table. However, a single table display can no longer meet the hospital's diverse scenario needs, limiting the hospital's ability to obtain and dig deeper into information.

[0064] The user first interacts with the interface of the BI application. The user sends an operation signal by clicking the floating data analysis jump button or icon. This signal is captured and recognized by the system so that the system knows the specific object that the user wants to analyze. For example, the user may be interested in the patient visit data of a specific department, or want to analyze the use of medicines in a certain time period. The system will determine the target analysis object based on these inputs and prepare for the next step of data capture and analysis.

[0065] Step S104, acquiring target data corresponding to the target analysis object based on a preset data acquisition scheme; wherein the data acquisition scheme includes at least one of the following: acquiring data through a preset data interface, data crawler technology, and real-time acquisition technology based on indicators and dimensions.

[0066] Specifically, after determining the analysis object, the system will obtain the corresponding data according to the preset data acquisition plan. These paths may include:

[0067] Preset data interface: The system extracts the required data from the hospital information system through the configured webservice or http interface. These interfaces are usually designed according to specific query parameters so that the data fragments required by the user can be retrieved.

[0068] Web site synchronization: Using crawler technology, all website data under the same domain name is automatically captured through the specified entry website. Users only need to enter the URL and selector, and the system will automatically crawl the information of the visualization module. For compliance and security reasons, the system only supports data crawling of static websites. For some static websites or reports, data crawler technology can be used to automatically crawl information, and this method is usually used in situations where no API interface or direct database access is provided. In addition, the crawler technology used in this application operates within the scope permitted by laws and regulations to prevent legal risks caused by the use of crawler technology.

[0069] Real-time acquisition of data based on indicators and dimensions: In multi-scenario self-service analysis services, the data source does not store the data (field names and field values) of the original BI application module, but only saves the indicator and dimension information. In the visual drag-and-drop interface of the software system corresponding to the output processing method provided in an embodiment of the present invention, users can pull data in real time according to the indicator-dimensional association method of the hospital's existing system to ensure data consistency. Exemplarily, the system does not need to store all the data, but only saves the indicator and dimension information. Before analyzing the data, users can pull data from the original data source in real time according to the indicator-dimensional association method of the hospital's existing system based on the existing indicators and dimensions. This ensures the timeliness and consistency of the data.

[0070] Step S106, performing an analysis operation on the target data to obtain an analysis result; wherein the analysis operation includes at least one of the following: graphic transformation, perspective analysis, aggregation analysis, drill-down mining and style adjustment; the analysis result includes at least one of the following: table, perspective chart, bar chart, line chart, radar chart, waterfall chart, scatter chart and stacked chart.

[0071] Specifically, once the target data is successfully captured and organized, the next step is to perform analytical operations on the data. These analytical operations may include but are not limited to the following types:

[0072] Graphics transformation allows users to display data in different graphics, such as a donut chart showing the gender distribution of patients, a horizontal bar chart showing the age structure, and a map showing the source of patients' treatment areas, etc. This helps to quickly identify patterns or trends.

[0073] Aggregate analysis is the combination or aggregation of certain data in a data set to gain a macro-level understanding. In database terminology, this operation is often called "group by". For example, in medical data analysis, aggregate analysis can be used to calculate the number of patient visits to a department within a specific time period, or to calculate the average cost of treatment for a certain disease. In this way, users can understand the overall trends and patterns of the data, not just the information of a single data point. Count the number of patient visits by department and physician, calculate month-on-month and year-on-year data, and evaluate changes and trends in service demand.

[0074] Pivot analysis is a special data aggregation and analysis method that allows users to dynamically view, aggregate, and analyze data from multiple dimensions. Traditionally in Excel, this function is implemented through pivot tables, while in more complex BI tools, pivot analysis may involve more complex operations and visualization components. For example, hospital managers may want to analyze patient visits from three dimensions: department, physician, and time. Pivot analysis can quickly provide views for these different dimensions and allow users to adjust views as needed to view different aspects of the data, helping management understand business performance under different dimensional combinations.

[0075] Drill-down mining, also known as "data drilling" or "data drilling down", is a data analysis technique that allows users to start with general or summary data and then drill down to more specific details. This analysis method can help users better understand the specific content and context behind the data. For example, in hospital data analysis, users may first see the total number of visits to a department, and then through drill-down mining, they can further view the specific number of visits to each physician in the department, and even continue to drill down to the medical records of each patient. This gradual deepening process helps to discover the deep information of the data, so as to make more accurate decisions.

[0076] Style adjustment is to make the chart more intuitive and easy to understand. Users can adjust the table header and body style, modify the bar chart color and legend settings as needed to meet personalized visual needs.

[0077] After completing these analysis operations, the system will generate analysis results, which can be dynamic or static, and may be presented in the form of tables, perspective charts, bar charts, line charts, radar charts, waterfall charts, scatter charts, and stacked charts. Users can create a variety of visual analyses through drag-and-drop operations to gain in-depth insights into the data. In addition, the analysis results can be downloaded and exported in graphical form, and saved, shared, and integrated into third-party systems in templates according to business areas. In some preferred embodiments of the present invention, the system is also connected to a permission control module, and the template list also supports functions such as permission allocation.

[0078] On the basis that the existing BI applications cannot meet the needs, hospitals can add floating data analysis jump buttons or icons in the patient detail module, so that users can access deeper analysis functions by clicking buttons or icons when browsing data. This improvement will enhance the user experience and improve data interactivity, allowing users to quickly perform graphic changes, aggregate analysis, perspective analysis, drill-down mining, and style adjustment, so as to understand and use data more efficiently.

[0079] The embodiment of the present invention provides a data processing method, which can realize the effective linkage between multi-scenario self-service analysis services and the hospital's existing BI applications, perform in-depth data analysis based on the original data source, and avoid data docking and inconsistency problems caused by re-purchasing third-party systems.

[0080] A data processing method provided by an embodiment of the present invention is applied to a hospital information system; the method includes: obtaining an operation signal input by a user, and determining a target analysis object based on the operation signal; obtaining target data corresponding to the target analysis object based on a pre-set data acquisition scheme; wherein the data acquisition scheme includes at least one of the following: obtaining data through a preset data interface, data crawler technology, and real-time acquisition technology based on indicators and dimensions; performing analysis operations on the target data to obtain analysis results; wherein the analysis operations include at least one of the following: graphic changes, perspective analysis, aggregation analysis, drill-down mining, and style adjustment; the analysis results include at least one of the following: tables, perspective charts, bar charts, line charts, radar charts, waterfall charts, scatter charts, and stacked charts; obtaining data of the target analysis object through a pre-set data acquisition scheme, and then performing analysis operations on the data to obtain analysis results compatible with the original system, thereby achieving effective compatibility between the new analysis system and the original system, improving data utilization efficiency, reducing manpower and material costs, and improving management efficiency and service quality.

[0081] Embodiment 2

[0082] Based on the above embodiment, the present invention provides another data processing method, which is further described for multi-scenario applications and analysis results. Figure 2 The embodiment of the present invention provides a data processing principle flow chart.

[0083] On the basis of the above principles, in some preferred embodiments of the present invention, the step of acquiring target data corresponding to the target analysis object based on a preset data acquisition scheme includes: acquiring identification parameters of the target analysis object; and acquiring the target data corresponding to the identification parameters through a preset data interface.

[0084] Specifically, identification parameters are unique or a set of attributes used to identify and locate the target analysis object. In hospital information systems, this may include patient ID, physician ID, department code, etc., or it may be a label automatically encoded by the system. These parameters are key to subsequent data capture because they ensure the accurate extraction of required information from large amounts of data. Users can enter these parameters through the user interface, or the system can automatically determine these parameters based on user selections (such as clicking on a department icon).

[0085] Once the identification parameters are determined, the system will use these parameters to obtain the corresponding data through the pre-set data interface. These interfaces may be webservices, APIs, or query interfaces that directly access the database. For example:

[0086] Webservice or HTTP interface: The system will pass the identification parameters as request parameters according to the defined webservice interface or HTTP request. After receiving the request, the server will retrieve the corresponding data from the backend database according to the provided parameters and return the data to the requester in XML, JSON or other formats.

[0087] Direct database query: In some existing BI applications, BI applications have direct access to hospital databases. They can directly construct SQL query statements and use identification parameters as query conditions to obtain data.

[0088] After acquiring the data, the BI application will load the data into its analysis engine so that users can perform subsequent operations such as graphic transformation, aggregation analysis, perspective analysis, drill-down mining, and style adjustment. In this way, users can deeply explore and visualize the data in an integrated environment to make more informed decisions.

[0089] Furthermore, in some preferred embodiments of the present invention, the first data specification of the data corresponding to the target analysis object is the same as the second data specification corresponding to the analysis result; wherein the first data specification and the second data specification each include at least one of the following: field name, field value, dimension and indicator.

[0090] Specifically, the raw data specification defines how to collect, store, and access the initial data of the target analysis object. Field name: used to identify the name of each column in the data table. Field value: the specific data instance of each row in the data table. Dimension: used to describe different classifications or organizational methods of data, such as time (year, month, day), location (region, city), etc. Indicator: a quantitative measure used to measure or evaluate business performance, such as sales, number of visits, customer satisfaction, etc.

[0091] Keeping the first data specification and the second data specification consistent has multiple benefits:

[0092] Data consistency: Ensure data consistency during the analysis process to avoid data misunderstanding or errors caused by different specifications. Easy to trace: When problems arise or further analysis is needed, it is easier to trace the analysis results back to the original data. Improve efficiency: Standardized data specifications can reduce the complexity of data processing and improve the efficiency and speed of analysis. Enhance credibility: When data specifications are consistent, the analysis results are more reliable, which enhances the end user's trust in the analysis results. In short, whether in the collection stage of raw data or in the presentation stage of analysis results, maintaining consistent data specifications is the key to achieving efficient, accurate and reliable data analysis.

[0093] Furthermore, in some preferred embodiments of the present invention, after the step of acquiring target data corresponding to the target analysis object based on a preset data acquisition scheme, the method also includes: storing the target data in a preset database; and constructing a data source corresponding to the target data based on a modular method, and constructing a tree-shaped data source list according to preset business identifiers; wherein the business identifier includes at least one of the following: hospital area, department, physician and patient.

[0094] Specifically, once the target data is acquired through the pre-set data acquisition solution, the data needs to be stored in a central database. This database may be a relational database, a data warehouse, or any other type of database system that is used to efficiently store, retrieve, and manage large amounts of data. The storage process may include: Data cleaning: ensuring that the data is correct and removing duplicate or irrelevant data. Data conversion: converting the data into a format that the database can recognize and store. Data loading: loading the cleaned and transformed data into the database, which is usually done through ETL (extraction, transformation, loading) tools.

[0095] Building data sources based on a modular approach means that data is not processed as a single large dataset, but is divided into multiple modules or datasets based on its source, type or other relevant characteristics, which helps to manage and analyze data more efficiently.

[0096] To further optimize the accessibility and manageability of data, data sources are organized according to business identifiers to form a tree-structured data source list. This structure enables users to easily navigate and select data sources based on different business logics. Business identifiers may include, but are not limited to: Campus: A hospital may have multiple campuses, and the data for each campus may be different. Department: The data generated by different departments (such as internal medicine, surgery, pediatrics, etc.) is also different. Physician: The data of individual physicians may be useful for specific analysis. Patient: Patient-related data is very important, especially for long-term disease management or specific case studies.

[0097] In this way, BI applications provide an organized list of data sources, allowing users to select the most relevant data sources for analysis based on specific business needs. This tree structure also makes it easier for administrators to manage and maintain data sources to ensure data quality and security.

[0098] It should be emphasized that there is no particular order for the above data storage and data source construction.

[0099] Furthermore, in some preferred embodiments of the present invention, after performing an analysis operation on the target data to obtain the analysis results, the method also includes: determining an analysis template based on the analysis results; wherein the analysis template includes at least one of the following: service efficiency, case-disease analysis, cost analysis, medical technology efficiency, drug analysis, medical record analysis, public health services and health data analysis; and mounting the analysis template on the hospital information system.

[0100] Specifically, after completing the data analysis operation, the system will generate a series of specific analysis results. Based on these results, users can select one or more appropriate analysis templates to further explore and display the data. These templates usually cover a variety of common business areas and analysis needs, such as: Service efficiency: evaluate the efficiency of medical services, such as patient waiting time, consultation cycle, etc. Medical record analysis: in-depth analysis of medical record data to understand disease distribution, treatment plans, etc. Cost analysis: calculate medical expenses, evaluate cost control and optimization space. Medical technology efficiency: evaluate the efficiency of medical technology use, such as equipment utilization, technician workload, etc. Drug analysis: analyze drug use, including drug consumption, inventory management, etc. Public health services: evaluate the implementation and effectiveness of public health projects. Health statistics data analysis: comprehensive statistics and analysis of health statistics data to provide macro decision-making support.

[0101] After determining the applicable analysis templates, the next step is to mount them into the hospital's information system. This means that these templates will be integrated into the existing hospital information system and become part of the system so that users can easily access and use these templates for daily data analysis. The mounting process may include: System integration: Integrate the analysis templates with the existing hospital information system to ensure that they can interact and share data seamlessly. User interface update: Add new options or buttons to the user interface so that users can easily select and apply different analysis templates. Permission management: Set up an appropriate permission management mechanism to ensure that only authorized users can access and use specific analysis templates. In this way, the BI application not only provides rich data analysis functions, but also simplifies the user's usage process through predefined analysis templates, thereby improving the efficiency and accuracy of data analysis. This integration method helps hospitals make better use of their data resources and improve overall management efficiency and service quality.

[0102] Furthermore, in some preferred embodiments of the present invention, the method also includes: obtaining permissions of the target user; performing management operations on the analysis template based on the permissions; wherein the management operations include at least one of the following: modifying the analysis template, calling target data, and viewing analysis results.

[0103] Specifically, before a user starts using the analysis function, the system needs to verify the user's identity and grant corresponding permissions. This step ensures data security and compliance. Specific operations may include:

[0104] Authentication: Verify the user's identity through username and password, multi-factor authentication (MFA) or other security measures. Permission assignment: Assign different permission levels based on the user's role and responsibilities. For example, a doctor may have the right to view and analyze the patient data for which he is responsible, while an administrator can manage all user permissions and analysis templates.

[0105] Once users have been granted the appropriate permissions, they can perform various management operations that help better utilize and control analysis templates. Specific management operations include:

[0106] Modify analysis templates: Users can modify existing analysis templates according to actual conditions to adapt to different business needs or data analysis goals, which may involve adjusting data sources, changing chart types, or adding new calculated fields, etc. Call target data: Users can call specific target data for analysis as needed, which can be achieved by selecting specific identification parameters or retrieving data directly from the database. View analysis results: Users can view their analysis results, whether they are visualizations in the form of charts or detailed data reports, which helps users understand and interpret the results of data analysis and make more informed decisions. In this way, BI applications not only provide rich data analysis functions, but also simplify the user's usage process through predefined analysis templates, thereby improving the efficiency and accuracy of data analysis. This integration method helps hospitals make better use of their data resources and improve overall management efficiency and service quality.

[0107] Furthermore, in some preferred embodiments of the present invention, the step of performing an analysis operation on the target data to obtain an analysis result includes: obtaining a target identifier in the target data; and performing multi-dimensional association on the target data based on the target identifier to obtain an analysis result.

[0108] Specifically, in the multi-scenario self-service analysis service of BI applications, drill-down mining is an important data analysis method. It allows users to start from general or summary data and gradually drill down to more specific details. This method helps users better understand the specific content and context behind the data.

[0109] Before drilling down, you first need to identify the key identifiers in the target data. These identifiers are attributes used to identify and locate a specific data set. For example, in a hospital information system, they may include patient ID, physician ID, department code, etc. These identification parameters are key to subsequent data capture because they ensure accurate extraction of required information from large amounts of data. Users can enter these parameters through the user interface, or the system automatically determines these parameters based on user selections (such as clicking on a department icon).

[0110] Based on these identifiers, the target data is associated with multiple dimensions. This means that the data is not only displayed according to a single dimension, but is analyzed comprehensively in combination with multiple dimensions. For example:

[0111] Time dimension: It can be aggregated and analyzed by time units such as year, quarter, month, etc. Department dimension: Patient data of different departments may be different. The department dimension can be used to understand the business performance of each department. Physician dimension: The data of individual physicians may be useful for specific analysis. The physician dimension can be used to view the workload and service quality of each physician. Patient dimension: Analysis of specific patient groups can help hospitals understand their needs and satisfaction, so as to formulate targeted service improvement measures. In this way, users can gradually drill down to the underlying details of the data, gradually transitioning from the macro level to the micro level. This multi-dimensional correlation analysis helps to reveal the complex relationships and patterns hidden behind the data, so as to make more accurate decisions.

[0112] Through the analysis operations in the above steps, the system will generate specific analysis results. These results can be dynamic chart displays or static data reports. Users can create a variety of visual analyses through drag-and-drop operations to gain in-depth insights into the data. In addition, the analysis results can be downloaded and exported in graphical form, and saved, shared, and integrated into third-party systems in templates according to business areas. In some preferred embodiments of the present invention, the system is also connected to a permission control module, and the template list also supports functions such as permission allocation.

[0113] Hospitals can add a click event based on the jump button or icon (patient details module). When the user clicks the button or icon, it jumps to the specified link address, that is, the multi-scenario self-service analysis service interface. During the jump process, the visualization (patient details) module pre-display data will interact and transfer data with the multi-scenario self-service analysis service in the form of a data source, thereby realizing data collection. The specific process is as follows: Steps A1 to A2:

[0114] Step A1, passing identification parameters: During the interaction process, the identification parameters of the patient details module are passed. The identification parameters include at least one of the following: name, gender, age, province, treatment department, doctor and treatment time, etc. A table is created to store the identification parameters. The table header is stored as the database table field name, and the patient details data is stored as the table field value.

[0115] Step A2, call the data interface to find the data corresponding to the identification parameters: the multi-scenario analysis service calls the hospital's pre-made webservice or http interface through the identification parameters to obtain the patient details module data, and follows the unified data specifications, including (field name and field value) or (dimension and indicator). For example, in the table display, the table header should be used as the database field name, and the table body data is the field value; in the graphic display (such as a multi-line bar chart), the horizontal axis dimension value is the field name, the legend indicator name is the auxiliary column, and the vertical axis is the field value. Based on this rule, data is obtained for the identification parameters of the patient details module and the data is stored. After receiving the data, the multi-scenario analysis service stores it in the system's built-in database.

[0116] In some preferred embodiments of the present invention, a modular approach is adopted to construct data sources, that is, different data sources are constructed for different BI modules, and a tree-type data source list is formed for easy calling in subsequent multi-scenario analysis services.

[0117] In the multi-scenario self-service analysis service, users can use the rich chart controls provided by the system through intuitive drag-and-drop methods to create a variety of visual analyses and gain in-depth insights into the data. The specific process is as follows: Steps B1 to B4:

[0118] Step B1, loading data source: according to the identification parameters of the visualization (patient details) module, load the data source saved in the multi-scenario self-service analysis service.

[0119] Step B2, select chart controls: Users can select various chart controls (such as tables, perspective charts, bar charts, line charts, radar charts, waterfall charts, scatter charts, stacked charts, etc.), and analyze the field names and field values ​​(dimensions and indicators) in the data source by dragging and dropping. The system supports functions such as graphic transformation, perspective analysis, aggregation analysis, drill-down mining, and style adjustment.

[0120] Step B3, export and share analysis results: The hospital can download and export the analysis results in graphical form, and save, share and integrate them into third-party systems in templates according to business areas. In some preferred embodiments of the present invention, the system is also connected to a permission control module, and the template list also supports functions such as permission allocation.

[0121] In some preferred embodiments of the present invention, the original BI application module mounted by the software system compiled based on the data processing method provided in the embodiments of the present invention can support mounting third-party graphics in the form of links. After step B3, the multi-scenario self-service analysis service further includes the following steps:

[0122] Module echo: Replace the original BI application module with the saved template to achieve deep integration of the original BI system and multi-scenario self-service analysis services.

[0123] The embodiment of the present invention provides a data processing method, which realizes the collection and preservation of data sources based on the existing BI application visualization module for secondary use by a third-party system, and solves the problems of low efficiency of data display and analysis, inability to meet personalized needs, and inconsistency with the old system data due to differences in docking and data acquisition processes when repurchasing a third-party system in the information construction of the hospital based on the existing BI application. Follow the unified data return specification, including (field name and field value) or (dimension and indicator), realize data acquisition and overwriting preservation, ensure data consistency, enhance the analysis function on the basis of the original BI application, including graphic change, aggregation analysis, perspective analysis, drill-down mining and style adjustment, etc., which can be used for in-depth analysis; download and export the analysis results in the form of graphics, and save, share and integrate them into the third-party system in the form of templates according to the business field, save the templates and set permissions for the secondary analysis results according to the business field, support sharing, mounting, and echoing in the third-party system, create diversified visual analysis, and realize template preservation and permission management; through drag-and-drop operations, create diversified visual analysis, realize the optimization of the whole process of hospital viewing, obtaining and using data, thereby reducing procurement costs.

[0124] Embodiment 3

[0125] Based on the above embodiments, the present invention provides a data processing device, which is applied to a hospital information system; Figure 3 The structure diagram of a data processing device provided by an embodiment of the present invention is shown, and the device includes:

[0126] The operation response module 310 is used to obtain an operation signal input by a user and determine a target analysis object based on the operation signal.

[0127] The data acquisition module 320 is used to acquire the target data corresponding to the target analysis object based on a preset data acquisition scheme; wherein the data acquisition scheme includes at least one of the following: acquiring data through a preset data interface, data crawler technology, and real-time acquisition technology based on indicators and dimensions.

[0128] The data processing module 330 is used to perform analysis operations on the target data to obtain analysis results; wherein the analysis operations include at least one of the following: graphic transformation, perspective analysis, aggregation analysis, drill-down mining and style adjustment; the analysis results include at least one of the following: table, perspective chart, bar chart, line chart, radar chart, waterfall chart, scatter chart and stacked chart.

[0129] Furthermore, in some preferred embodiments of the present invention, the data acquisition module 320 is used to acquire identification parameters of the target analysis object; and acquire target data corresponding to the identification parameters through a preset data interface.

[0130] Furthermore, in some preferred embodiments of the present invention, the first data specification of the data corresponding to the target analysis object is the same as the second data specification corresponding to the analysis result; wherein the first data specification and the second data specification each include at least one of the following: field name, field value, dimension and indicator.

[0131] Furthermore, in some preferred embodiments of the present invention, the device also includes: a data management module, used to store the target data in a preset database; and, based on a modular method, construct a data source corresponding to the target data, and construct a tree-shaped data source list according to a preset business identifier; wherein the business identifier includes at least one of the following: hospital area, department, physician and patient.

[0132] Furthermore, in some preferred embodiments of the present invention, the device also includes: a template processing module, used to determine the analysis template based on the analysis results; wherein the analysis template includes at least one of the following: service efficiency, case-disease analysis, cost analysis, medical technology efficiency, drug analysis, medical record analysis, public health services and health data analysis; the analysis template is mounted on the hospital information system.

[0133] Furthermore, in some preferred embodiments of the present invention, the device also includes: a permission management module, used to obtain the permissions of the target user; perform management operations on the analysis template based on the permissions; wherein the management operations include at least one of the following: modifying the analysis template, calling target data and viewing the analysis results.

[0134] Furthermore, in some preferred embodiments of the present invention, the data processing module 330 is used to obtain the target identifier in the target data; and to perform multi-dimensional association on the target data based on the target identifier to obtain an analysis result.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the data processing device described above can refer to the corresponding process in the aforementioned embodiment of the data processing method, and will not be repeated here.

[0136] Embodiment 4

[0137] The embodiment of the present invention also provides an electronic device for executing the data processing method; see Figure 4 The structural diagram of an electronic device provided by an embodiment of the present invention is shown, and the electronic device includes a memory 400 and a processor 401, wherein the memory 400 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 401 to implement the above-mentioned data processing method.

[0138] Further, Figure 4 The electronic device shown further includes a bus 402 and a communication interface 403 , and the processor 401 , the communication interface 403 and the memory 400 are connected via the bus 402 .

[0139] The memory 400 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 403 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 402 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0140] The processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 401. The above processor 401 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 400, and the processor 401 reads the information in the memory 400 and completes the steps of the method of the above embodiment in combination with its hardware.

[0141] An embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement a data processing method. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0142] The computer program products of the data processing methods, devices, and electronic devices provided in the embodiments of the present invention include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods in the previous method embodiments. The specific implementation can be found in the method embodiments, which will not be repeated here.

[0143] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and / or device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0144] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0145] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data processing method, characterized in that: Applied to hospital information system; the method comprises: Acquire an operation signal input by a user, and determine a target analysis object based on the operation signal; Acquire the target data corresponding to the target analysis object based on a preset data acquisition scheme; wherein the data acquisition scheme includes at least one of the following: acquiring data through a preset data interface, data crawler technology, and real-time acquisition technology based on indicators and dimensions; An analysis operation is performed on the target data to obtain an analysis result; wherein the analysis operation includes at least one of the following: graphic transformation, perspective analysis, aggregation analysis, drill-down mining and style adjustment; the analysis result includes at least one of the following: table, perspective chart, bar chart, line chart, radar chart, waterfall chart, scatter chart and stacked chart.

2. The data processing method according to claim 1, characterized in that: The step of acquiring target data corresponding to the target analysis object based on a preset data acquisition scheme includes: Obtaining identification parameters of the target analysis object; The target data corresponding to the identification parameter is obtained through a preset data interface.

3. The data processing method according to claim 2, characterized in that: The first data specification of the data corresponding to the target analysis object is the same as the second data specification corresponding to the analysis result; wherein the first data specification and the second data specification each include at least one of the following: field name, field value, dimension and indicator.

4. The data processing method according to claim 1, characterized in that: After the step of acquiring target data corresponding to the target analysis object based on a preset data acquisition scheme, the method further includes: The target data is stored in a preset database; and a data source corresponding to the target data is constructed based on a modular method, and a tree-shaped data source list is constructed for the data source according to a preset business identifier; wherein the business identifier includes at least one of the following: hospital area, department, physician and patient.

5. The data processing method according to claim 1, characterized in that: After the step of analyzing the target data to obtain the analysis result, the method further includes: Determine an analysis template based on the analysis results; wherein the analysis template includes at least one of the following: service efficiency, case-disease analysis, cost analysis, medical technology efficiency, drug analysis, medical record analysis, public health service and health statistics data analysis; The analysis template is mounted on the hospital information system.

6. The data processing method according to claim 5, characterized in that: The method further comprises: Obtain the target user's permissions; Perform management operations on the analysis template based on the authority; wherein the management operations include at least one of the following: modifying the analysis template, calling target data, and viewing analysis results.

7. The data processing method according to claim 1, characterized in that: The step of performing an analysis operation on the target data to obtain an analysis result includes: Obtaining a target identifier in the target data; The target data is multi-dimensionally associated based on the target identifier to obtain the analysis result.

8. A data processing device, characterized in that: Applied to hospital information system; the device comprises: An operation corresponding module is used to obtain an operation signal input by a user and determine a target analysis object based on the operation signal; A data acquisition module, used to acquire target data corresponding to the target analysis object based on a preset data acquisition scheme; wherein the data acquisition scheme includes at least one of the following: acquiring data through a preset data interface, data crawler technology, and real-time acquisition technology based on indicators and dimensions; A data processing module is used to perform analysis operations on the target data to obtain analysis results; wherein the analysis operations include at least one of the following: graphic transformation, perspective analysis, aggregation analysis, drill-down mining and style adjustment; the analysis results include at least one of the following: table, perspective chart, bar chart, line chart, radar chart, waterfall chart, scatter chart and stacked chart.

9. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the data processing method according to any one of claims 1 to 7.

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