Hospital condition data real-time visualization method and platform for smart hospital operation center

Abstract

The application provides a hospital situation data real-time visualization method of a smart hospital operation center, first classifies hospital personnel, finance, material and treatment full-dimension hospital situation data in a standard way; develops a micro-service architecture standardized collection interface, configures an abnormal reconnection mechanism to realize second-level data collection; through a data integration platform, converts the format, maps the fields and cleans the abnormalities of multi-source heterogeneous data, uniformly generates standardized structured data and adds a traceability label; relies on a digital twin to build a hospital physical scene mapping model, sets up a multi-dimension visualization module and a double intelligent early warning mechanism; deploys a visualization platform, completes system joint debugging and optimization, and establishes a second-level automatic update and model iteration mechanism. The problems of existing hospital situation data dispersion, collection lag, integration difficulty, single visualization and untimely early warning are solved, second-level hospital situation data collection, standardized integration, full-dimension real-time visualization and intelligent early warning are realized, and the fine operation control and intelligent decision-making level of the hospital are improved.

Description

Technical Field

[0001] This invention relates to the field of smart hospital operation and management technology, and in particular to a method and platform for real-time visualization of hospital status data in a smart hospital operation center. Background Technology

[0002] The Smart Hospital Operations Center (HOC) is the core hub for hospitals to achieve refined management and intelligent decision-making. Hospital data (personnel, finances, supplies and equipment, medical services, etc.) is the core basis for the HOC's control and decision-making. Currently, smart hospital data is scattered across multiple independent business systems such as HIS, LIS, PACS, human resource management, financial management, supplies and equipment management, and surgical management. These systems are developed by different vendors, resulting in inconsistent data formats, field definitions, and data collection methods. This leads to numerous technical problems in the current management of hospital data:

[0003] Data collection is often delayed. Existing technologies rely heavily on manual statistics and scheduled synchronization to collect data, resulting in update delays typically exceeding 24 hours. This hinders real-time data collection and leaves operational decisions without real-time data support. Furthermore, integrating multi-source data is challenging. Heterogeneous data from various business systems cannot be standardized, leading to issues such as conflicting data formats, inconsistent fields, and duplicate data. This results in low data integration accuracy and prevents the formation of a complete hospital data system. Visualization is also poor. While some hospitals have established simple data visualization platforms, these only visualize data from a single system or dimension, failing to provide integrated visualization of multi-source, multi-dimensional hospital data. Moreover, the visualization format is limited and cannot accurately map to the hospital's physical environment. Finally, abnormal data alerts are untimely. The lack of intelligent abnormal data monitoring and alert mechanisms means that data exceeding normal thresholds cannot be detected in real time. This leads to operational problems such as insufficient consumable inventory, untimely equipment malfunctions, and excessively high / low bed occupancy rates, impacting hospital operational efficiency and the quality of medical services.

[0004] In summary, existing hospital data visualization methods are mostly designed for single business systems and fail to address the core issues of real-time collection, standardized integration, and multi-dimensional visualization of multi-source hospital data. Therefore, they cannot meet the "real-time, refined, and intelligent" management and control requirements of smart hospital operation centers. Thus, there is an urgent need for a method that can achieve second-level collection, standardized integration, real-time multi-dimensional visualization, and intelligent early warning of multi-source hospital data to improve the operational management and control capabilities of smart hospitals. Summary of the Invention

[0005] To address the shortcomings of the existing technologies, this invention provides a method and platform for real-time visualization of hospital status data in a smart hospital operation center, which solves technical problems such as scattered hospital status data, delayed collection, difficulty in integration, incomplete visualization, and untimely anomaly warnings.

[0006] To achieve the above objectives, the present invention provides a method for real-time visualization of hospital status data in a smart hospital operation center, comprising the following steps:

[0007] S1. Comprehensively analyze all hospital data during the operation of the smart hospital, and standardize the classification according to personnel, finance, materials and equipment, and diagnosis and treatment business dimensions, clarifying the source business system, data type, and real-time collection frequency requirements of data for each dimension;

[0008] S2. For the source business systems of the hospital's information data, develop standardized real-time data acquisition interfaces, adopt a microservice architecture to design interface modules, support multiple common communication protocols, realize second-level acquisition of hospital information data from various business systems, and set up an interface abnormal reconnection mechanism.

[0009] S3. Establish a hospital information data integration center, formulate unified standardized processing rules for hospital information data, and perform format conversion, field mapping, and abnormal data cleaning on multi-source heterogeneous hospital information data obtained through the collection interface, so as to uniformly transform heterogeneous data into standardized structured data.

[0010] S4. Construct a digital twin model of hospital operation based on digital twin technology to accurately restore the physical scene of the hospital and design a multi-dimensional visualization display module. The display module includes an overall operation overview module, a detailed display module for each dimension, and an intelligent early warning module for abnormal data. The intelligent early warning module for abnormal data sets early warning thresholds for the core operation indicators of the hospital. When the indicator exceeds the threshold, a dual early warning mechanism of sound and light warning and platform pop-up warning is triggered.

[0011] S5. Build a real-time visualization platform for hospital data in the smart hospital operation center, deploy the data collection interface, hospital data integration platform, and visualization display model to the platform, complete the connection and joint debugging between the visualization platform and the business systems from which the hospital data comes, and optimize the relevant parameters and models for the problems that arise during the joint debugging.

[0012] S6. Set up an automatic data update mechanism for the visualization platform to achieve real-time updates of hospital data and dynamic, smooth refresh of the visualization interface at a second-level collection frequency. At the same time, iterate the visualization display model according to changes in hospital operational needs.

[0013] In some embodiments, the personnel dimension in step S1 includes: medical staff scheduling, attendance, on-duty status, and professional title distribution data;

[0014] The financial dimension includes: outpatient charges, inpatient settlement, consumable costs, medical insurance settlement, and cash flow data;

[0015] The material and equipment dimension includes: drug inventory, medical consumable inventory, medical equipment operating status, and equipment maintenance records;

[0016] The diagnostic and treatment business dimensions include: outpatient volume, inpatient volume, surgical volume, bed occupancy rate, and laboratory test volume data.

[0017] In some embodiments, the communication protocol in step S2 supports TCP / IP and HTTP / HTTPS; the interface abnormal reconnection mechanism is set with a reconnection interval of ≤3 seconds and a maximum number of reconnections of ≥3 times.

[0018] In some embodiments, the multi-source heterogeneous hospital data in step S3 is uniformly converted into standardized structured data in JSON format; the abnormal data includes null values, wrong values, and values ​​that exceed the reasonable range.

[0019] In some embodiments, step S3 adds three tags—time stamp, data source, and data dimension—to each processed hospital information data, enabling full-process traceability and precise management of hospital information data.

[0020] In some embodiments, the hospital operation digital twin model in step S4 is constructed at a 1:1 scale to accurately reproduce the physical scene of the hospital's layout, department distribution, medical equipment location, and bed distribution.

[0021] In some embodiments, the core operational metrics include: bed occupancy rate, medical consumables inventory, core equipment uptime, and peak outpatient volume. The warning threshold can be customized and adjusted according to the hospital's operational needs.

[0022] This invention also provides a real-time visualization platform for hospital status data in a smart hospital operation center, comprising:

[0023] The data layer is configured with standardized real-time data acquisition interfaces for connecting to various business systems of the hospital. It adopts a microservice architecture, supports multi-protocol communication and interface reconnection mechanisms in case of abnormality, and realizes second-level acquisition of hospital status data.

[0024] The middle platform processing layer builds a hospital data integration middle platform, which is used to perform format conversion, field mapping, and abnormal data cleaning of multi-source heterogeneous data, and uniformly convert it into standardized structured data in JSON format, and attach timestamps, data source, and data dimension traceability tags.

[0025] The model display layer is equipped with a digital twin model and a multi-dimensional visualization display module, which has the functions of overall operation overview, detailed dimension display, and intelligent dual early warning of abnormal data.

[0026] The application terminal layer supports multi-terminal display and interaction, including large screens, computers, tablets, and mobile phones.

[0027] In some embodiments, the accuracy rate of multi-source hospital information data integration in the middleware processing layer is ≥99%, ensuring full-process traceability management of hospital information data.

[0028] In some embodiments, the visualization platform has the ability to automatically update data in seconds, the visualization interface refreshes dynamically without lag, and supports adding display indicators, adjusting display formats and warning thresholds according to the hospital's operational needs, so as to achieve model iterative optimization.

[0029] Compared with the prior art, the present invention has the following advantages:

[0030] The standardized real-time data acquisition interface developed by the smart hospital operation center real-time visualization method provided by this invention adopts a microservice architecture and sets up an abnormal reconnection mechanism, realizing the second-level acquisition of multi-source hospital data with a data acquisition delay of ≤1 second. It completely solves the problem of untimely data acquisition in existing technologies and provides real-time and accurate data support for hospital operation decisions.

[0031] The real-time visualization method for hospital status data in the smart hospital operation center provided by this invention establishes a hospital status data integration platform and formulates unified data standardization rules, which realizes the standardization and integrated integration of heterogeneous multi-source hospital status data. The data integration accuracy rate is ≥99%, which solves the technical problems of data format conflicts and inconsistent fields. At the same time, it adds traceability tags to each data, realizing full-process traceability management of hospital status data.

[0032] This invention is based on a 1:1 digital twin model of hospital operation constructed using digital twin technology. It achieves accurate mapping between hospital status data and the hospital's physical scene. The multi-dimensional visualization display module can comprehensively, intuitively, and clearly display the hospital's operational status in all dimensions, including personnel, finance, materials, and diagnosis and treatment. The efficiency of operational decision-making data support is improved by ≥100%.

[0033] The intelligent early warning module for abnormal data in the real-time visualization platform for hospital operation center provided by this invention adopts a dual early warning mechanism of sound and light + platform pop-up window. It can monitor the core operation indicators of the hospital in real time, and the time for detecting abnormal indicators is shortened by ≥90%. This makes it easier for the hospital to deal with operational problems such as insufficient inventory of consumables, equipment failure, and abnormal bed occupancy rate in a timely manner, thereby improving the hospital's refined operation level.

[0034] The real-time visualization method for hospital status data in the smart hospital operation center provided by this invention features an automatic data update mechanism and an iterative display model mechanism. These mechanisms ensure the real-time performance, flexibility, and applicability of the visualization platform, enabling it to quickly adapt to changes in hospital operation needs and the implementation of new business scenarios. This avoids redundant platform construction and reduces hospital operation and management costs. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the dimensional classification and source system of hospital information data as shown in an embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram illustrating the real-time collection and integration of hospital information data according to an embodiment of the present invention;

[0037] Figure 3 This is a schematic diagram of the architecture of the digital twin model and visualization module for smart hospital operation as shown in an embodiment of the present invention;

[0038] Figure 4 is a schematic diagram of the execution flow of intelligent early warning of abnormal data according to an embodiment of the present invention;

[0039] Figure 5 This is a schematic diagram of the overall process of the real-time visualization method for hospital status data in a smart hospital operation center, as shown in an embodiment of the present invention.

[0040] Figure 6 This is an architecture diagram of the real-time visualization platform for hospital status data in a smart hospital operation center, as shown in an embodiment of the present invention.

[0041] In the attached figures, the following labels are used:

[0042] S1-S6: Steps. Detailed Implementation

[0043] See Figure 1-5 This invention provides a method for real-time visualization of hospital status data in a smart hospital operation center. Figure 1 This is a schematic diagram of the dimensional classification and source system of hospital information data in an embodiment of the present invention, showing the specific content of the four data dimensions and the corresponding business data sources; Figure 2 This is a flowchart illustrating the real-time collection and integration of hospital data according to an embodiment of the present invention, showing the entire process from data collection to standardized integration; Figure 3 Figure 4 is a schematic diagram of the architecture of the digital twin model and visualization display module for smart hospital operation shown in an embodiment of the present invention, illustrating the composition of the digital twin model and the core functions of the three visualization display modules; Figure 5 is a schematic diagram of the execution process of intelligent early warning of abnormal data shown in an embodiment of the present invention, illustrating the complete process of indicator monitoring, threshold judgment and dual early warning. Figure 5This is a schematic diagram of the overall process of the real-time visualization method for hospital status data in a smart hospital operation center, as shown in an embodiment of the present invention.

[0044] The real-time visualization method for hospital status data in a smart hospital operation center provided in this embodiment includes the following steps:

[0045] S1. Comprehensively analyze all hospital data during the operation of the smart hospital, and standardize the classification according to personnel, finance, materials and equipment, and diagnosis and treatment business dimensions, clarifying the source business system, data type, and real-time collection frequency requirements of data for each dimension;

[0046] S2. For the source business systems of the hospital's information data, develop standardized real-time data acquisition interfaces, adopt a microservice architecture to design interface modules, support multiple common communication protocols, realize second-level acquisition of hospital information data from various business systems, and set up an interface abnormal reconnection mechanism.

[0047] S3. Establish a hospital information data integration center, formulate unified standardized processing rules for hospital information data, and perform format conversion, field mapping, and abnormal data cleaning on multi-source heterogeneous hospital information data obtained through the collection interface, so as to uniformly transform heterogeneous data into standardized structured data.

[0048] S4. Construct a digital twin model of hospital operation based on digital twin technology to accurately restore the physical scene of the hospital and design a multi-dimensional visualization display module. The display module includes an overall operation overview module, a detailed display module for each dimension, and an intelligent early warning module for abnormal data. The intelligent early warning module for abnormal data sets early warning thresholds for the core operation indicators of the hospital. When the indicator exceeds the threshold, a dual early warning mechanism of sound and light warning and platform pop-up warning is triggered.

[0049] S5. Build a real-time visualization platform for hospital data in the smart hospital operation center, deploy the data collection interface, hospital data integration platform, and visualization display model to the platform, complete the connection and joint debugging between the visualization platform and the business systems from which the hospital data comes, and optimize the relevant parameters and models for the problems that arise during the joint debugging.

[0050] S6. Set up an automatic data update mechanism for the visualization platform to achieve real-time updates of hospital data and dynamic, smooth refresh of the visualization interface at a second-level collection frequency. At the same time, iterate the visualization display model according to changes in hospital operational needs.

[0051] In this embodiment, the personnel dimension in step S1 includes: medical staff scheduling, attendance, on-duty status, and professional title distribution data; the financial dimension includes: outpatient charges, inpatient settlement, consumable costs, medical insurance settlement, and cash flow data; the material and equipment dimension includes: drug inventory, medical consumable inventory, medical equipment operating status, and equipment maintenance records data; the diagnosis and treatment business dimension includes: outpatient volume, inpatient volume, surgical volume, bed occupancy rate, and laboratory test volume data; the data collection frequency for each dimension is 1 second / time.

[0052] In step S2, the communication protocol supports TCP / IP and HTTP / HTTPS, enabling second-level data acquisition. The interface reconnection mechanism for abnormal connections sets a reconnection interval of ≤3 seconds and a maximum number of reconnections of ≥3. Specifically, the data acquisition interface has a acquisition delay of ≤1 second, and the interface reconnection mechanism for abnormal connections can complete reconnection within 5 seconds after the interface is disconnected, ensuring the continuity of data acquisition.

[0053] In this embodiment, step S3 involves uniformly converting multi-source heterogeneous hospital information data into standardized structured data in JSON format; the abnormal data includes null values, incorrect values, and values ​​exceeding a reasonable range, with a data cleaning accuracy rate of ≥99%; in step S3, three tags are added to each processed hospital information data: timestamp, data source, and data dimension, to achieve full-process traceability and precise management of hospital information data.

[0054] In this embodiment, the hospital operation digital twin model in step S4 is constructed at a 1:1 scale, accurately recreating the physical scene of the hospital's layout, department distribution, medical equipment location, and bed distribution. The core operation indicators include: bed occupancy rate, medical consumable inventory, core equipment operating rate, and peak outpatient volume. The early warning threshold can be customized and adjusted according to the hospital's operational needs.

[0055] The following is a more detailed and specific description of each step in the real-time visualization method for hospital status data in the smart hospital operation center provided in this embodiment:

[0056] Hospital Data Analysis and Classification: A comprehensive analysis of all hospital data generated during the operation of the smart hospital is conducted, with standardized classifications based on personnel, finance, supplies and equipment, and medical services. The data sources, data types (numerical, character, time, etc.), and real-time collection frequency requirements for each dimension are clearly defined. Specifically, the personnel dimension includes data on staff scheduling, attendance, on-duty status, and professional title distribution; the financial dimension includes outpatient billing, inpatient settlement, consumable costs, medical insurance settlement, and cash flow; the supplies and equipment dimension includes drug inventory, medical consumable inventory, medical equipment operating status, and equipment maintenance records; and the medical services dimension includes outpatient volume, inpatient volume, surgical volume, bed occupancy rate, and laboratory / examination volume.

[0057] Real-time data acquisition interface development: For the various business systems that provide hospital data (HIS, LIS, PACS, human resource management system, financial management system, etc.), standardized real-time data acquisition interfaces are developed. The interface modules are designed using a microservice architecture, supporting multiple common communication protocols such as TCP / IP and HTTP / HTTPS, to achieve second-level acquisition of hospital data from various business systems. At the same time, an interface abnormal reconnection mechanism is set up, with a reconnection interval of ≤3 seconds and a maximum number of reconnections of ≥3 times. When the interface is disconnected, reconnection is automatically triggered to ensure the continuity and stability of data acquisition.

[0058] Standardized integration of multi-source data: A data integration platform for hospital information was built, and unified standardized processing rules for hospital information data were formulated. The heterogeneous hospital information data from multiple sources obtained through the collection interface was converted into formats, mapped into fields, and cleaned of abnormal data. Heterogeneous data with different formats and fields were uniformly converted into standardized structured data in JSON format. At the same time, three tags were added to each processed hospital information data: timestamp, data source, and data dimension, to achieve full-process traceability and precise management of hospital information data.

[0059] Visualization Model Design: A 1:1 scale digital twin model of hospital operations is constructed based on digital twin technology to accurately reproduce the physical scene of the hospital's layout, department distribution, medical equipment location, and bed distribution. A multi-dimensional visualization module is designed, which includes three core modules: an overall operation overview module, a detailed display module for each dimension, and an intelligent early warning module for abnormal data. Among them, the intelligent early warning module for abnormal data sets early warning thresholds for the hospital's core operational indicators. When the indicator data exceeds the threshold, a dual early warning mechanism of sound and light warning + platform pop-up warning is triggered.

[0060] Visualization Platform Deployment and Optimization: A real-time visualization platform for hospital data was built in the Smart Hospital Operation Center (HOC), and all the aforementioned data acquisition interfaces, hospital data integration platform, and visualization display models were deployed to this platform. The visualization platform was then integrated and tested with the business systems that provide the hospital data, including testing the real-time performance of data acquisition, the accuracy of data integration, the smoothness of visualization display, and the timeliness of anomaly alerts. For issues encountered during the integration process, such as acquisition delays, data errors, display lag, and untimely alerts, the interface parameters, data processing rules, and display model parameters were optimized.

[0061] Automatic data updates and model iteration optimization: An automatic data update mechanism is set up for the visualization platform to achieve real-time updates of hospital data and dynamic, smooth refresh of the visualization interface at a second-level collection frequency; at the same time, based on changes in hospital operational needs, the implementation of new business scenarios, and the addition of new operational indicators, the visualization display model is iterated and optimized in real time, adding / adjusting display indicators, display formats, and early warning thresholds to ensure the applicability and flexibility of the visualization platform.

[0062] Figure 6 This is an architecture diagram of the real-time visualization platform for hospital status data in the smart hospital operation center, as shown in this embodiment of the invention; see also... Figure 6 Another embodiment of the present invention provides a real-time visualization platform for hospital status data in a smart hospital operation center, comprising:

[0063] The data layer is configured with standardized real-time data acquisition interfaces for connecting to various business systems of the hospital. It adopts a microservice architecture, supports multi-protocol communication and interface reconnection mechanisms in case of abnormality, and realizes second-level acquisition of hospital status data.

[0064] The middle platform processing layer builds a hospital data integration middle platform, which is used to perform format conversion, field mapping, and abnormal data cleaning of multi-source heterogeneous data, and uniformly convert it into standardized structured data in JSON format, and attach timestamps, data source, and data dimension traceability tags.

[0065] The model display layer is equipped with a digital twin model and a multi-dimensional visualization display module, which has the functions of overall operation overview, detailed dimension display, and intelligent dual early warning of abnormal data.

[0066] The application terminal layer supports multi-terminal display and interaction, including large screens, computers, tablets, and mobile phones.

[0067] In this embodiment, the accuracy rate of multi-source hospital information data integration in the middleware processing layer is ≥99%, ensuring full-process traceability management of hospital information data. The visualization platform described in this embodiment has the ability to automatically update data at the second level, the visualization interface refreshes dynamically without lag, and supports adding display indicators, adjusting display formats and warning thresholds according to the hospital's operational needs, thereby achieving iterative optimization of the model.

[0068] The following section provides a detailed and complete description of the specific implementation methods of this invention, using a real-world case study of a medium-sized comprehensive smart hospital as an example.

[0069] (I) Implementation Background

[0070] This medium-sized general hospital has 800 beds and eight independent business systems, including HIS, LIS, PACS, human resources management, financial management, materials and equipment management, and surgical management. Hospital data is scattered across these systems, requiring manual cross-system data collection. Data is updated every 24 hours. The operations center can only display basic outpatient and inpatient data, failing to provide multi-dimensional and detailed visualizations. Due to untimely data collection and a lack of abnormal early warnings, the hospital has repeatedly experienced operational problems such as insufficient medical consumables inventory, untimely handling of core medical equipment malfunctions, and excessively high bed occupancy rates (≥95%). There is an urgent need to build a real-time visualization platform for hospital data to improve the hospital's operational management capabilities.

[0071] (II) Specific Implementation Steps

[0072] Hospital data analysis and classification: A comprehensive analysis of all hospital data was conducted, categorized into four dimensions: personnel, finance, supplies and equipment, and medical services. The source systems, data types, and collection frequency requirements for each dimension were clearly defined: personnel data came from the Human Resources Management System, collected at a frequency of 1 second per instance; finance data came from the Financial Management System and the HIS billing module, collected at a frequency of 1 second per instance; supplies and equipment data came from the Supplies and Equipment Management System and the Equipment Management System, collected at a frequency of 1 second per instance; and medical services data came from the HIS, LIS, PACS, and Surgical Management Systems, collected at a frequency of 1 second per instance.

[0073] Real-time data acquisition interface development: Based on a microservice architecture, eight standardized real-time data acquisition interfaces were developed, each connecting to one of the hospital's eight business systems. The interfaces support the HTTP / HTTPS common communication protocol. An interface reconnection mechanism was set up with an interval of 3 seconds and a maximum of 5 reconnections. After the interface development was completed, testing showed that the acquisition delay of hospital data by each interface was ≤1 second, and the interface could be reconnected within 5 seconds after being disconnected, with 100% data acquisition continuity.

[0074] Multi-source data standardization and integration: A hospital data integration platform was built based on cloud computing technology, and unified standardized processing rules for hospital data were formulated. Heterogeneous data of different formats such as strings, values, and time from various systems were uniformly converted into standardized structured data in JSON format. Precise field mapping was performed on the same type of data with different field names such as "outpatient volume" and "inpatient volume". Abnormal data such as null values, wrong values, and values ​​outside the reasonable range were automatically cleaned, with a data cleaning accuracy rate of 99.5%. At the same time, timestamps, data source, and data dimension tags were added to each processed hospital data, realizing full-process data traceability.

[0075] Visualization Model Design: Utilizing digital twin technology, a 1:1 scale digital twin model of hospital operations is constructed, accurately reproducing the physical layout and location of one main hospital campus, 28 clinical departments, 800 beds, and over 150 core medical devices. Multi-dimensional visualization modules are designed: ① Overall Operation Overview Module: Displaying five core hospital operation indicators: outpatient volume, inpatient volume, bed occupancy rate, core equipment operation rate, and remaining inventory of medical consumables; ② Detailed Display Modules for Each Dimension: Viewing detailed data such as the on-duty status of medical staff in each department, hourly financial income and expenditure details, real-time inventory of various types of medical consumables, and surgical progress in each operating room; ③ Intelligent Early Warning Module for Abnormal Data: Setting early warning thresholds for core indicators: bed occupancy rate ≥ 95%, medical consumable inventory ≤ safety stock, core equipment operation rate ≤ 90%, peak outpatient volume ≥ hospital capacity. When the indicator data exceeds the threshold, a dual early warning mechanism of audible and visual warning + platform pop-up warning is triggered.

[0076] Visualization Platform Deployment and Optimization: A real-time visualization platform for hospital data was built in the Hospital Operations Center (HOC), completing the deployment of the data acquisition interface, hospital data integration platform, and visualization display model. Full-process integration testing was conducted with eight business systems. Test results showed that the accuracy rate of multi-source hospital data integration was 99.5%, and the visualization display interface dynamically refreshed at a frequency of 1 second, with no lag or delay. Simulating abnormal scenarios such as insufficient medical consumables inventory, core equipment failure, and bed occupancy rate ≥95%, the platform could trigger dual early warnings within 1 second, with warning information including abnormal indicators, abnormal locations, and handling suggestions.

[0077] Automatic data updates and model iteration optimization: An automatic data update mechanism is set up for the visualization platform, enabling real-time updates of hospital data and dynamic, smooth refresh of the display interface at a collection frequency of 1 second / time; based on the hospital's operational needs, "medical insurance settlement data" and "patient satisfaction data" display modules are added to the visualization platform, the display format of departmental-level diagnosis and treatment data is optimized, the bed occupancy rate warning threshold is adjusted to 90%, and the visualization display model is rapidly iterated and optimized to meet the hospital's refined operation and management needs.

[0078] (III) Implementation Results

[0079] After implementing the method of this invention to achieve real-time visualization of hospital data, this medium-sized general hospital completely solved the technical problems of scattered hospital data, delayed collection, difficulty in integration, and incomplete visualization: the hospital data collection was improved from 24-hour updates to second-level updates, allowing hospital leaders to have a real-time and comprehensive grasp of the hospital's operational status across all dimensions of personnel, finance, materials, and treatment from the operations center; the time to detect abnormal operational indicators was shortened from several hours to within 1 second, and the efficiency of handling operational problems such as insufficient medical consumables inventory and equipment failures was improved by 90%; the bed occupancy rate was optimized to a reasonable range of 85%-90%, and the hospital's patient reception efficiency was improved by 20%; the efficiency of operational decision-making data support was improved from daily calculations to second-level, and the hospital's level of refined and intelligent operation and management was significantly improved.

[0080] In summary, the real-time visualization method and platform for hospital situation data in smart hospital operation centers provided by this invention are applicable to the collection, integration, visualization, and intelligent early warning of comprehensive data on personnel, finances, materials, and medical treatment in smart hospital operation centers (HOCs) at all levels. It is particularly suitable for the refined operation and management of medium and large-sized general hospitals and specialized hospitals. It achieves second-level collection, standardized integration, and multi-dimensional real-time visualization of comprehensive hospital situation data on personnel, finances, materials, and medical treatment, while also providing intelligent real-time early warning of abnormal data. This provides timely, comprehensive, accurate, and efficient data support for smart hospital operation decisions, thereby improving the level of refined operation and management in hospitals.

[0081] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for real-time visualization of hospital status data in a smart hospital operation center, characterized in that: Includes the following steps: S1. Comprehensively analyze all hospital data during the operation of the smart hospital, and standardize the classification according to personnel, finance, materials and equipment, and diagnosis and treatment business dimensions, clarifying the source business system, data type, and real-time collection frequency requirements of data for each dimension; S2. For the source business systems of the hospital's information data, develop standardized real-time data acquisition interfaces, adopt a microservice architecture to design interface modules, support multiple common communication protocols, realize second-level acquisition of hospital information data from various business systems, and set up an interface abnormal reconnection mechanism. S3. Establish a hospital information data integration center, formulate unified standardized processing rules for hospital information data, and perform format conversion, field mapping, and abnormal data cleaning on multi-source heterogeneous hospital information data obtained through the collection interface, so as to uniformly transform heterogeneous data into standardized structured data. S4. Construct a digital twin model of hospital operation based on digital twin technology to accurately restore the physical scene of the hospital and design a multi-dimensional visualization display module. The display module includes an overall operation overview module, a detailed display module for each dimension, and an intelligent early warning module for abnormal data. The intelligent early warning module for abnormal data sets early warning thresholds for the core operation indicators of the hospital. When the indicator exceeds the threshold, a dual early warning mechanism of sound and light warning and platform pop-up warning is triggered. S5. Build a real-time visualization platform for hospital data in the smart hospital operation center, deploy the data collection interface, hospital data integration platform, and visualization display model to the platform, complete the connection and joint debugging between the visualization platform and the business systems from which the hospital data comes, and optimize the relevant parameters and models for the problems that arise during the joint debugging. S6. Set up an automatic data update mechanism for the visualization platform to achieve real-time updates of hospital data and dynamic, smooth refresh of the visualization interface at a second-level collection frequency. At the same time, iterate the visualization display model according to changes in hospital operational needs.

2. The method for real-time visualization of hospital status data in a smart hospital operation center according to claim 1, characterized in that: The personnel dimension in step S1 includes: medical staff's shift schedule, attendance, on-duty status, and professional title distribution data; The financial dimension includes: outpatient charges, inpatient settlement, consumable costs, medical insurance settlement, and cash flow data; The material and equipment dimension includes: drug inventory, medical consumable inventory, medical equipment operating status, and equipment maintenance records; The diagnostic and treatment business dimensions include: outpatient volume, inpatient volume, surgical volume, bed occupancy rate, and laboratory test volume data.

3. The method for real-time visualization of hospital status data in a smart hospital operation center according to claim 1, characterized in that: In step S2, the communication protocol supports TCP / IP and HTTP / HTTPS; the interface abnormal reconnection mechanism is set with a reconnection interval of ≤3 seconds and a maximum number of reconnections of ≥3 times.

4. The method for real-time visualization of hospital status data in a smart hospital operation center according to claim 1, characterized in that: In step S3, the multi-source heterogeneous hospital information data is uniformly converted into standardized structured data in JSON format; the abnormal data includes null values, wrong values, and values ​​that exceed the reasonable range.

5. The method for real-time visualization of hospital status data in a smart hospital operation center according to claim 4, characterized in that: In step S3, three tags are added to each processed hospital information data: timestamp, data source, and data dimension, to achieve full-process traceability and precise management of hospital information data.

6. The method for real-time visualization of hospital status data in a smart hospital operation center according to claim 1, characterized in that: The hospital operation digital twin model in step S4 is constructed at a 1:1 scale, accurately restoring the physical scene of the hospital's layout, department distribution, medical equipment location, and bed distribution.

7. The method for real-time visualization of hospital status data in a smart hospital operation center according to claim 6, characterized in that: The core operational metrics include: bed occupancy rate, medical consumables inventory, core equipment operating rate, and peak outpatient volume. The warning thresholds can be customized and adjusted according to the hospital's operational needs.

8. A real-time visualization platform for hospital status data in a smart hospital operation center, characterized in that: include: The data layer is configured with standardized real-time data acquisition interfaces for connecting to various business systems of the hospital. It adopts a microservice architecture, supports multi-protocol communication and interface reconnection mechanisms in case of abnormality, and realizes second-level acquisition of hospital status data. The middle platform processing layer builds a hospital data integration middle platform, which is used to perform format conversion, field mapping, and abnormal data cleaning of multi-source heterogeneous data, and uniformly convert it into standardized structured data in JSON format, and attach timestamps, data source, and data dimension traceability tags. The model display layer is equipped with a digital twin model and a multi-dimensional visualization display module, which has the functions of overall operation overview, detailed dimension display, and intelligent dual early warning of abnormal data. The application terminal layer supports multi-terminal display and interaction, including large screens, computers, tablets, and mobile phones.

9. The real-time visualization platform for hospital status data of the smart hospital operation center according to claim 8, characterized in that: The accuracy rate of multi-source hospital information data integration in the middle platform processing layer is ≥99%, ensuring full-process traceability management of hospital information data.

10. The real-time visualization platform for hospital status data of the smart hospital operation center according to claim 8, characterized in that: The visualization platform has the ability to automatically update data in seconds, the visualization interface refreshes dynamically without lag, and supports adding display indicators, adjusting display formats and warning thresholds according to the hospital's operational needs, so as to achieve model iteration and optimization.

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