A method and system for constructing a trauma clinical information service platform

Abstract

The present invention relates to the technical field of information service platforms, and in particular to a method and system for constructing a trauma clinical information service platform. The method comprises the following steps: obtaining trauma clinical information service demand data and constructing a trauma clinical information service platform; obtaining clinical record data of trauma patients, clinical imaging data of trauma patients, and bedside equipment monitoring information data and uploading them to the data processing unit of the trauma clinical information service platform; using the data processing unit to perform trauma grade assessment to obtain clinical trauma grade status data of patients; obtaining trauma department information data and performing inter-department coupling analysis and department allocation collaborative operations to obtain trauma patient department collaborative allocation result data; performing abnormal patient condition monitoring and abnormal event prediction processing on bedside equipment monitoring information data, and performing department notification to execute corresponding trauma patient clinical notification service tasks. The present invention can provide more efficient clinical information services for medical staff.

Description

Technical Field

[0001] The present invention relates to the technical field of information service platforms, and in particular to a method and system for constructing a trauma clinical information service platform. Background Art

[0002] In trauma clinical work, efficient information management and coordination are crucial. The information service platform can integrate functions such as clinical data management, remote collaboration and medical knowledge sharing. The traditional method of building a trauma clinical information service platform cannot meet the medical staff's real-time and comprehensive trauma level assessment and departmental collaborative allocation process, thus lacking the response speed for trauma clinical information integration and clinical medical information services. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for constructing a trauma clinical information service platform to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for constructing a trauma clinical information service platform includes the following steps:

[0005] Step S1: Obtain trauma clinical information service demand data, and use cloud computing technology to construct a network model framework for the trauma clinical information service demand data to obtain a trauma clinical information service network model framework; upload the trauma clinical information service network model framework to the cloud platform to construct a trauma clinical information service platform, wherein the trauma clinical information service platform includes a data processing unit; obtain trauma patient clinical record data, trauma patient clinical imaging data, and bedside equipment monitoring information data through the trauma clinical information service platform and upload them to the data processing unit of the trauma clinical information service platform;

[0006] Step S2: Desensitizing the clinical record data of the trauma patient by using the data processing unit to obtain desensitized clinical record data of the trauma patient; evaluating the trauma grade of the desensitized clinical record data of the trauma patient based on the clinical imaging data of the trauma patient and the bedside equipment monitoring information data to obtain the clinical trauma grade status data of the patient;

[0007] Step S3: Obtain trauma department information data, and perform inter-department coupling analysis on the trauma department information data to obtain coupling influencing factors between trauma departments; perform department allocation collaborative operations on patient clinical trauma grade status data based on the coupling influencing factors between trauma departments to obtain trauma patient department collaborative allocation result data;

[0008] Step S4: Based on the trauma patient department collaborative allocation result data, the bedside equipment monitoring information data is monitored for abnormal patient conditions to obtain abnormal information data on the trauma patient conditions; abnormal event prediction and processing is performed on the abnormal information data on the trauma patient conditions to obtain abnormal event alarm notification decisions for trauma patients, and the decisions are uploaded to the trauma clinical information service platform for department notification to perform corresponding trauma patient clinical notification service tasks.

[0009] The present invention first collects demand data on trauma clinical information services, including but not limited to service demands in the trauma clinical field, workflow demands of medical staff (such as medical staff in various trauma departments), and data information demands of patients, etc., so as to fully understand and grasp the service demand situation in the field of trauma clinical information, which is helpful for subsequent platform design and service optimization, and ensures the pertinence and practicality of the service. At the same time, by using cloud computing technology to construct a network model framework for trauma clinical information service demand data, the elasticity, flexibility and scalability of cloud computing can be combined to provide a good foundation for the construction of the network model architecture, so that the trauma clinical information service platform can effectively collect, store and process various trauma-related data. This platform not only provides a data processing unit, but also uses the cloud platform to build a convenient service architecture, which can realize the real-time acquisition and upload of clinical record data, clinical imaging data and bedside monitoring information data of trauma patients. Such a platform architecture can provide a reliable foundation for trauma clinical information services, and can also realize the integration process of trauma clinical information data, thereby providing solid support for subsequent data processing and services. In addition, by uploading the collected data to the data processing unit of the trauma clinical information service platform, the data can be analyzed and processed, thereby providing more accurate and personalized services for medical staff. Secondly, by using the data processing unit to desensitize the clinical record data of trauma patients, the security of patient privacy and data compliance can be ensured. In addition, by combining the patient's clinical imaging data and bedside equipment monitoring information data, the desensitized clinical record data was evaluated for trauma level, thereby obtaining the patient's clinical trauma level status data. This process not only helps medical staff to objectively evaluate the patient's trauma status, but also provides an important basis for subsequent departmental collaborative allocation decisions, thereby achieving a comprehensive and accurate trauma level assessment process. Then, by obtaining the trauma department information data and conducting inter-departmental coupling analysis, the coupling influencing factors between trauma departments can be obtained. Based on these coupling influencing factors, the department allocation collaborative operation of the patient's clinical trauma level status data can be realized, thereby obtaining the department collaborative allocation result data of trauma patients. The key to this step is to optimize the department collaborative allocation process of trauma patients, so that trauma patients can get more balanced and personalized medical information services between different departments. Finally, by obtaining the real-time monitoring data of the physiological indicators of trauma patients in the assigned department from the bedside equipment monitoring information data based on the collaborative allocation result data of trauma patients, and monitoring the abnormal conditions of patients, it is possible to identify potential abnormal conditions through comprehensive analysis of the changing trends of the patients' physiological indicators. The key to this step is that it can provide early warnings of the patient's condition, allowing medical staff to pay attention to the patient in a timely manner and take appropriate medical information service measures, thereby minimizing the patient's health risks.In addition, abnormal event prediction and processing are also performed on abnormal information data of trauma patients to predict more serious medical information service problems that patients may face. By integrating the changing trends of patients' physiological indicators, it can help medical staff to promptly discover patients' clinical abnormalities, and can also achieve timely processing and prevention of abnormal events, providing clinical medical teams with more sufficient preparation time to effectively respond to potential emergencies. At the same time, the abnormal event alarm notification decisions of trauma patients obtained by analysis are uploaded to the trauma clinical information service platform, which can achieve timely notification between departments, which helps to promote collaborative work between different departments and ensure that patients can quickly obtain professional clinical medical information services when abnormal events occur, thereby improving the overall clinical information service quality and service response speed.

[0010] Preferably, the present invention further provides a trauma clinical information service platform construction system, which is used to execute the trauma clinical information service platform construction method as described above, and the trauma clinical information service platform construction system comprises:

[0011] The information service platform construction and data processing module is used to obtain trauma clinical information service demand data, and use cloud computing technology to construct a network model framework for the trauma clinical information service demand data to obtain a trauma clinical information service network model framework; upload the trauma clinical information service network model framework to the cloud platform to build a trauma clinical information service platform, wherein the trauma clinical information service platform includes a data processing unit; obtain trauma patient clinical record data, trauma patient clinical imaging data, and bedside equipment monitoring information data through the trauma clinical information service platform and upload them to the data processing unit of the trauma clinical information service platform;

[0012] The trauma grade assessment module for trauma patients is used to perform desensitization processing on the clinical record data of trauma patients by using the data processing unit to obtain desensitized clinical record data of trauma patients; perform trauma grade assessment on the desensitized clinical record data of trauma patients based on the clinical imaging data of trauma patients and the monitoring information data of bedside equipment, thereby obtaining the clinical trauma grade status data of patients;

[0013] The trauma patient department collaborative allocation module is used to obtain trauma department information data and perform inter-department coupling analysis on the trauma department information data to obtain coupling influencing factors between trauma departments; based on the coupling influencing factors between trauma departments, the patient's clinical trauma grade status data is collaboratively allocated to departments to obtain trauma patient department collaborative allocation result data;

[0014] The department patient abnormal condition prediction and notification module is used to monitor the abnormal patient condition of the bedside equipment monitoring information data based on the trauma patient department collaborative allocation result data, and obtain the trauma patient condition abnormal information data; perform abnormal event prediction and processing on the trauma patient condition abnormal information data, obtain the trauma patient abnormal event alarm notification decision, and upload it to the trauma clinical information service platform for department notification, so as to perform the corresponding trauma patient clinical notification service tasks.

[0015] In summary, the present invention provides a trauma clinical information service platform construction system, which consists of an information service platform construction and data processing module, a trauma patient trauma level assessment module, a trauma patient department collaborative allocation module, and a department patient abnormal condition prediction notification module. It can realize any trauma clinical information service platform construction method described in the present invention, and is used to realize a trauma clinical information service platform construction method by combining the operations between the computer programs running on each module. The internal structure of the system cooperates with each other. The information service platform construction and data processing module is a key component in the trauma clinical information service system. The module is responsible for obtaining trauma clinical information service demand data and using cloud computing technology to construct a network model framework to form a trauma clinical information service network model framework. The establishment of this model framework provides theoretical support and technical basis for subsequent information services. In addition, by uploading the network model framework to the cloud platform to construct a trauma clinical information service platform, which contains a data processing unit, the construction of this platform enables the processing and service of trauma clinical information to be centrally managed and efficiently implemented. Through this platform, clinical record data, clinical imaging data, and bedside equipment monitoring information data of trauma patients can be obtained, and uploaded to the data processing unit for further analysis and processing. Secondly, the trauma grade assessment module for trauma patients is designed to assess and grade the clinical conditions of trauma patients. The clinical record data of trauma patients is desensitized by using the data processing unit to ensure the privacy of patients. Based on the clinical imaging data and bedside equipment monitoring information data of trauma patients, the trauma grade of the desensitized clinical record data is assessed to obtain the clinical trauma grade status data of the patients. This assessment result is of great significance for the clinical medical team to formulate information service plans and predict the patient's recovery. Then, the department collaborative allocation module for trauma patients aims to achieve the reasonable allocation of trauma patient resources and the collaborative operation between departments. By obtaining the information data of the trauma department and conducting coupling analysis on it, the coupling influencing factors between departments are obtained. Based on these factors, the clinical trauma grade status data of patients is collaboratively allocated to departments to achieve the optimal allocation of patient department resources, which helps to improve the utilization efficiency of clinical medical resources and the quality of information services.Finally, the department patient abnormal condition prediction and notification module aims to promptly detect abnormal conditions of patient conditions and take corresponding early warning measures. It monitors abnormal patient conditions by combining the result data of the collaborative allocation of trauma patient departments with the bedside equipment monitoring information data. It also predicts and processes abnormal events on these abnormal information data to generate abnormal event alarm notification decisions for trauma patients, and uploads them to the trauma clinical information service platform for department notification to implement the corresponding trauma patient clinical notification service tasks. The establishment of this module helps to improve the effectiveness and safety of clinical information services for trauma patients and ensure the health and safety of patients. This can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient trauma clinical information service platform construction process, thereby simplifying the operating procedures of the trauma clinical information service platform construction system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0017] Figure 1 A schematic diagram of the steps of the method for constructing a trauma clinical information service platform of the present invention;

[0018] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0019] Figure 3 for Figure 2 Detailed step flow chart of step S15 in FIG. DETAILED DESCRIPTION

[0020] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0022] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0023] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for constructing a trauma clinical information service platform, the method comprising the following steps:

[0024] Step S1: Obtain trauma clinical information service demand data, and use cloud computing technology to construct a network model framework for the trauma clinical information service demand data to obtain a trauma clinical information service network model framework; upload the trauma clinical information service network model framework to the cloud platform to construct a trauma clinical information service platform, wherein the trauma clinical information service platform includes a data processing unit; obtain trauma patient clinical record data, trauma patient clinical imaging data, and bedside equipment monitoring information data through the trauma clinical information service platform and upload them to the data processing unit of the trauma clinical information service platform;

[0025] Step S2: Desensitizing the clinical record data of the trauma patient by using the data processing unit to obtain desensitized clinical record data of the trauma patient; evaluating the trauma grade of the desensitized clinical record data of the trauma patient based on the clinical imaging data of the trauma patient and the bedside equipment monitoring information data to obtain the clinical trauma grade status data of the patient;

[0026] Step S3: Obtain trauma department information data, and perform inter-department coupling analysis on the trauma department information data to obtain coupling influencing factors between trauma departments; perform department allocation collaborative operations on patient clinical trauma grade status data based on the coupling influencing factors between trauma departments to obtain trauma patient department collaborative allocation result data;

[0027] Step S4: Based on the trauma patient department collaborative allocation result data, the bedside equipment monitoring information data is monitored for abnormal patient conditions to obtain abnormal information data on the trauma patient conditions; abnormal event prediction and processing is performed on the abnormal information data on the trauma patient conditions to obtain abnormal event alarm notification decisions for trauma patients, and the decisions are uploaded to the trauma clinical information service platform for department notification to perform corresponding trauma patient clinical notification service tasks.

[0028] In the embodiment of the present invention, please refer to Figure 1FIG. 1 is a flow chart of the steps of the method for constructing a trauma clinical information service platform of the present invention. In this example, the steps of the method for constructing a trauma clinical information service platform include:

[0029] Step S1: Obtain trauma clinical information service demand data, and use cloud computing technology to construct a network model framework for the trauma clinical information service demand data to obtain a trauma clinical information service network model framework; upload the trauma clinical information service network model framework to the cloud platform to construct a trauma clinical information service platform, wherein the trauma clinical information service platform includes a data processing unit; obtain trauma patient clinical record data, trauma patient clinical imaging data, and bedside equipment monitoring information data through the trauma clinical information service platform and upload them to the data processing unit of the trauma clinical information service platform;

[0030] The embodiment of the present invention communicates with relevant stakeholders such as medical institutions, clinical experts, and patients to understand their needs and expectations, and collects demand data for trauma clinical information services, including but not limited to service needs in the trauma clinical field, workflow needs of medical staff (such as medical staff in various trauma departments), and data information needs of patients, etc., thereby obtaining trauma clinical information service demand data. At the same time, by using the collected trauma clinical information service demand data, a network model architecture for trauma clinical information services is designed, including system composition, module division, data flow, etc., and the functions and relationships of each component are determined. Secondly, by combining the elasticity, flexibility, and scalability of cloud computing technology, the designed network model architecture is constructed using corresponding tools or programming languages ​​to build a model framework, so as to establish a network model framework for trauma clinical information services, so that it can cope with the ever-changing service needs and data scale, thereby obtaining a network model framework for trauma clinical information services. Then, by developing an API service interface corresponding to the trauma clinical information service network model framework, including an authentication interface, a data transmission interface, etc., and integrating the trauma clinical information service network model framework and uploading it to the cloud platform (where the cloud platform includes a data processing unit) through the developed service interface, a complete platform architecture is constructed, thereby constructing a trauma clinical information service platform. Finally, by using the trauma clinical information service platform to provide relevant data of the clinical information system, imaging system, and bedside monitoring equipment, clinical record data of trauma patients, clinical imaging data of trauma patients, and bedside equipment monitoring information data are obtained, and the acquired data is uploaded to the data processing unit of the trauma clinical information service platform.

[0031] Step S2: Desensitizing the clinical record data of the trauma patient by using the data processing unit to obtain desensitized clinical record data of the trauma patient; evaluating the trauma grade of the desensitized clinical record data of the trauma patient based on the clinical imaging data of the trauma patient and the bedside equipment monitoring information data to obtain the clinical trauma grade status data of the patient;

[0032] In the embodiment of the present invention, the data processing unit of the trauma clinical information service platform uses encryption technology, de-identification, desensitization and other means to process the sensitive information in the clinical record data of trauma patients, so as to ensure the privacy of patients while retaining useful medical information, and effectively prevent the leakage of personal privacy of patients, thereby obtaining the desensitized data of clinical records of trauma patients. Secondly, by using a computer vision algorithm to analyze the clinical image data of trauma patients, the area of ​​the trauma site is extracted from the corresponding clinical image, so as to more intuitively understand the location and range of the trauma of the trauma patients, and by using a corresponding image analysis method or machine learning algorithm to analyze the extracted patient trauma site area image, the degree and probability of trauma lesions in the patient's trauma site area image are statistically quantified for evaluation and analysis, so as to analyze and find the influencing factors of trauma lesions in trauma patients, and then, by combining the abnormal physiological indicators of patients in the bedside equipment monitoring information data, the desensitized data of clinical records of trauma patients are analyzed, so as to more comprehensively understand the physiological condition of patients, to evaluate the loss of clinical physiological indicators of patients, and to quantify the grade score of trauma patients by comprehensively considering the influencing factors of patients and the loss of clinical physiological indicators of patients, thereby obtaining the grade score of patients. Finally, the calculated patient trauma grade score was analyzed by using the corresponding trauma grade assessment standard. When the patient trauma grade score was less than or equal to 20, it was defined as the first trauma grade; when it was greater than 20 and less than or equal to 40, it was defined as the second trauma grade; when it was greater than 40 and less than or equal to 60, it was defined as the third trauma grade; when it was greater than 60 and less than or equal to 80, it was defined as the fourth trauma grade; and when it was greater than 80, it was defined as the fifth trauma grade. In this way, the patient trauma grade score was matched with the corresponding trauma grade, and finally the patient's clinical trauma grade status data was obtained.

[0033] Step S3: Obtain trauma department information data, and perform inter-department coupling analysis on the trauma department information data to obtain coupling influencing factors between trauma departments; perform department allocation collaborative operations on patient clinical trauma grade status data based on the coupling influencing factors between trauma departments to obtain trauma patient department collaborative allocation result data;

[0034] The embodiment of the present invention collects information related to the trauma department by using the trauma clinical information service platform to establish a comprehensive understanding of the operating status of the trauma department, including information data on the number of patients in the department, patient care information, equipment resources, medical staff configuration, etc., thereby obtaining information data of the trauma department. Secondly, the information data of trauma departments were analyzed by using mathematical statistical methods to obtain the patient flow information of each trauma department, that is, the number of trauma patients received by each department. At the same time, the frequency of joint care for trauma patients by medical staff in each trauma department was obtained, that is, the frequency information of multiple departments jointly performing clinical information services for trauma patients. By combining the above data, the probability of work loss between different departments was calculated and quantified to identify potential cooperation bottlenecks and improve the efficiency of collaborative work between departments, and a network showing the association relationship between trauma departments was constructed. Then, by comprehensively considering the probability of work loss between different departments, a suitable department coupling strength calculation formula was constructed to calculate the coupling strength of the constructed association network to quantify the coupling strength between different trauma departments, and by using the calculated coupling strength between trauma departments, the relevant trauma departments in the trauma department information data were evaluated and analyzed to evaluate the collaborative work relationship between different departments and identify potential factors affecting collaborative obstacles, thereby obtaining the coupling influencing factors between trauma departments. Finally, by assigning the corresponding department for service inspection according to the patient's trauma level based on the patient's clinical trauma level status data, trauma patients with different trauma levels can be assigned to the corresponding department, and the initial department allocation results are reallocated by using the coupling influencing factors between trauma departments obtained by analysis to further optimize the patient's department classification matching and ensure that trauma patients can receive better medical information services in departments with synergistic effects, and finally obtain the result data of department collaborative allocation of trauma patients.

[0035] Step S4: Based on the trauma patient department collaborative allocation result data, the bedside equipment monitoring information data is monitored for abnormal patient conditions to obtain abnormal information data on the trauma patient conditions; abnormal event prediction and processing is performed on the abnormal information data on the trauma patient conditions to obtain abnormal event alarm notification decisions for trauma patients, and the decisions are uploaded to the trauma clinical information service platform for department notification to perform corresponding trauma patient clinical notification service tasks.

[0036] The embodiment of the present invention first determines the assigned department of the trauma patient by using the trauma patient department collaborative assignment result data, and at the same time, obtains the physiological index monitoring information data of the trauma patient in the assigned department in real time from the bedside equipment monitoring information data, and analyzes the patient physiological index monitoring information data of the trauma patient in the assigned department by using the change trend analysis method to understand the changes in the physiological condition of the trauma patient, and also monitors the changes in the physiological condition of the trauma patient by using a suitable abnormal monitoring algorithm and model to monitor whether there are abnormal conditions in the physiological index information of the trauma patient's physiological condition changes, and identifies potential abnormal conditions, thereby obtaining abnormal information data of the trauma patient's condition. Then, the abnormal information data of the trauma patient's condition is analyzed by using an event prediction method to predict more serious medical information service problems that the trauma patient may face, and combined with the change trend of the physiological index of the trauma patient, the abnormal events occurring in the trauma patient are identified, including abnormal changes in various physiological indicators of the trauma clinical patient, etc., thereby obtaining the abnormal event information data of the trauma patient. Next, by alarming the abnormal changes of the patient in the abnormal event information data of the trauma patient, a corresponding notification strategy for the clinical abnormal event alarm is formulated, and specific decision suggestions are provided to the clinical medical service team, thereby obtaining the abnormal event alarm notification decision of the trauma patient. Finally, the obtained trauma patient abnormal event alarm notification decision is uploaded to the trauma clinical information service platform for department notification, so as to achieve timely notification between different trauma departments and ensure that trauma patients can quickly obtain professional clinical medical information services when abnormal events occur, and finally trigger the execution of corresponding trauma patient clinical notification service tasks, such as notifying the relevant departments that handle abnormal changes in patients to make necessary notifications to perform corresponding clinical treatment and intervention measures.

[0037] The present invention first collects demand data on trauma clinical information services, including but not limited to service demands in the trauma clinical field, workflow demands of medical staff (such as medical staff in various trauma departments), and data information demands of patients, etc., so as to fully understand and grasp the service demand situation in the field of trauma clinical information, which is helpful for subsequent platform design and service optimization, and ensures the pertinence and practicality of the service. At the same time, by using cloud computing technology to construct a network model framework for trauma clinical information service demand data, the elasticity, flexibility and scalability of cloud computing can be combined to provide a good foundation for the construction of the network model architecture, so that the trauma clinical information service platform can effectively collect, store and process various trauma-related data. This platform not only provides a data processing unit, but also uses the cloud platform to build a convenient service architecture, which can realize the real-time acquisition and upload of clinical record data, clinical imaging data and bedside monitoring information data of trauma patients. Such a platform architecture can provide a reliable foundation for trauma clinical information services, and can also realize the integration process of trauma clinical information data, thereby providing solid support for subsequent data processing and services. In addition, by uploading the collected data to the data processing unit of the trauma clinical information service platform, the data can be analyzed and processed, thereby providing more accurate and personalized services for medical staff. Secondly, by using the data processing unit to desensitize the clinical record data of trauma patients, the security of patient privacy and data compliance can be ensured. In addition, by combining the patient's clinical imaging data and bedside equipment monitoring information data, the desensitized clinical record data was evaluated for trauma level, thereby obtaining the patient's clinical trauma level status data. This process not only helps medical staff to objectively evaluate the patient's trauma status, but also provides an important basis for subsequent departmental collaborative allocation decisions, thereby achieving a comprehensive and accurate trauma level assessment process. Then, by obtaining the trauma department information data and conducting inter-departmental coupling analysis, the coupling influencing factors between trauma departments can be obtained. Based on these coupling influencing factors, the department allocation collaborative operation of the patient's clinical trauma level status data can be realized, thereby obtaining the department collaborative allocation result data of trauma patients. The key to this step is to optimize the department collaborative allocation process of trauma patients, so that trauma patients can get more balanced and personalized medical information services between different departments. Finally, by obtaining the real-time monitoring data of the physiological indicators of trauma patients in the assigned department from the bedside equipment monitoring information data based on the collaborative allocation result data of trauma patients, and monitoring the abnormal conditions of patients, it is possible to identify potential abnormal conditions through comprehensive analysis of the changing trends of the patients' physiological indicators. The key to this step is that it can provide early warnings of the patient's condition, allowing medical staff to pay attention to the patient in a timely manner and take appropriate medical information service measures, thereby minimizing the patient's health risks.In addition, abnormal event prediction and processing are also performed on abnormal information data of trauma patients to predict more serious medical information service problems that patients may face. By integrating the changing trends of patients' physiological indicators, it can help medical staff to promptly discover patients' clinical abnormalities, and can also achieve timely processing and prevention of abnormal events, providing clinical medical teams with more sufficient preparation time to effectively respond to potential emergencies. At the same time, the abnormal event alarm notification decisions of trauma patients obtained by analysis are uploaded to the trauma clinical information service platform, which can achieve timely notification between departments, which helps to promote collaborative work between different departments and ensure that patients can quickly obtain professional clinical medical information services when abnormal events occur, thereby improving the overall clinical information service quality and service response speed.

[0038] Preferably, step S1 comprises the following steps:

[0039] Step S11: Obtaining trauma clinical information service demand data;

[0040] Step S12: Designing a service network model architecture according to the trauma clinical information service demand data to generate trauma clinical information service network model architecture data;

[0041] Step S13: constructing a network model framework for the trauma clinical information service network model architecture data using cloud computing technology to obtain a trauma clinical information service network model framework;

[0042] Step S14: performing API service interface development processing on the trauma clinical information service network model framework to obtain the platform API service verification interface; uploading the trauma clinical information service network model framework to the cloud platform based on the platform API service verification interface to construct a trauma clinical information service platform, wherein the trauma clinical information service platform includes a data processing unit;

[0043] Step S15: Obtain clinical record data of trauma patients, clinical imaging data of trauma patients, and bedside equipment monitoring information data through the trauma clinical information service platform and upload them to the data processing unit of the trauma clinical information service platform.

[0044] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0045] Step S11: Obtaining trauma clinical information service demand data;

[0046] The embodiments of the present invention communicate with relevant stakeholders such as medical institutions, clinical experts, and patients to understand their needs and expectations, and collect demand data on trauma clinical information services, including but not limited to service needs in the trauma clinical field, workflow needs of medical staff (such as medical staff in various trauma departments), and data information needs of patients, so as to fully understand and grasp the service demand situation in the trauma clinical information field, and finally obtain trauma clinical information service demand data.

[0047] Step S12: Designing a service network model architecture according to the trauma clinical information service demand data to generate trauma clinical information service network model architecture data;

[0048] The embodiment of the present invention designs a network model architecture of a trauma clinical information service by using the collected trauma clinical information service demand data, including system composition, module division, data flow, etc., records and saves the designed network model architecture data, determines the functions and relationships of each component, and finally generates trauma clinical information service network model architecture data.

[0049] Step S13: constructing a network model framework for the trauma clinical information service network model architecture data using cloud computing technology to obtain a trauma clinical information service network model framework;

[0050] The embodiment of the present invention combines the elasticity, flexibility and scalability of cloud computing technology and uses corresponding tools or programming languages ​​to construct a model framework for the designed trauma clinical information service network model architecture data, so as to establish a network model framework for trauma clinical information services, so that it can cope with the ever-changing service needs and data scale, and finally obtain a trauma clinical information service network model framework.

[0051] Step S14: performing API service interface development processing on the trauma clinical information service network model framework to obtain the platform API service verification interface; uploading the trauma clinical information service network model framework to the cloud platform based on the platform API service verification interface to construct a trauma clinical information service platform, wherein the trauma clinical information service platform includes a data processing unit;

[0052] The embodiment of the present invention develops an API service interface corresponding to the trauma clinical information service network model framework by using a service interface development method, including an identity authentication interface, a data transmission interface, etc., thereby obtaining a platform API service verification interface. Then, by using the developed platform API service verification interface, the trauma clinical information service network model framework is integrated and uploaded to the cloud platform (wherein the cloud platform includes a data processing unit) to build a complete platform architecture for subsequent deployment and management, and finally a trauma clinical information service platform is constructed. Similarly, the trauma clinical information service platform includes a data processing unit.

[0053] Step S15: Obtain clinical record data of trauma patients, clinical imaging data of trauma patients, and bedside equipment monitoring information data through the trauma clinical information service platform and upload them to the data processing unit of the trauma clinical information service platform.

[0054] The embodiment of the present invention generates a corresponding API access key by using the API service verification interface provided by the trauma clinical information service platform, and then uses the obtained API access key to establish an API service that connects the trauma clinical information service platform with the clinical information system, imaging system and bedside monitoring equipment. The key is used to access the relevant data of the clinical information system, imaging system and bedside monitoring equipment through the API service, thereby obtaining the clinical record data of trauma patients, the clinical imaging data of trauma patients and the monitoring information data of the bedside equipment, and at the same time, the acquired data is uploaded to the data processing unit of the trauma clinical information service platform.

[0055] The present invention first collects demand data on trauma clinical information services, including but not limited to service demands in the trauma clinical field, workflow demands of medical staff (such as medical staff in various trauma departments), and data information demands of patients, etc., so as to fully understand and grasp the service demand situation in the field of trauma clinical information, which is helpful for subsequent platform design and service optimization, and ensures the pertinence and practicality of the service. Secondly, by designing the service network model architecture according to the trauma clinical information service demand data obtained in the early stage, a reasonable service network model can be designed, so as to effectively integrate various service resources, optimize service processes, and improve service efficiency and quality, which is helpful to build a service platform that meets the actual needs of trauma clinical practice. Then, by using cloud computing technology to construct a network model framework for the trauma clinical information service network model architecture data, the elasticity, flexibility and scalability of cloud computing can be combined to provide a good foundation for the construction of the network model architecture, so that it can cope with the ever-changing service demands and data scale, which is helpful to build a stable and reliable service platform infrastructure. Next, by developing an API service interface for the trauma clinical information service network model framework, so that other systems or applications can interact and transfer data with the platform architecture, and through the API service verification interface, the security and accuracy of data transmission can be ensured. At the same time, by using the API service verification interface to upload the trauma clinical information service network model framework to the cloud platform, a trauma clinical information service platform is constructed to provide a unified service platform for medical staff and patients, so that they can easily obtain the information and services they need. Finally, by using the trauma clinical information service platform, it is possible to easily obtain the clinical record data, clinical imaging data, and bedside equipment monitoring information data of trauma patients. These data are of great significance for clinical monitoring and can provide medical staff with necessary information support to help them make more accurate patient abnormality notification service tasks. In addition, by uploading to the data processing unit of the trauma clinical information service platform, the data can be analyzed and processed to provide more accurate and personalized services for medical staff.

[0056] Preferably, step S15 comprises the following steps:

[0057] Step S151: registering an account on the trauma clinical information service platform to obtain account data of the trauma clinical information service platform;

[0058] Step S152: authenticating the account data of the trauma clinical information service platform based on the platform API service verification interface of the trauma clinical information service platform to obtain a platform API access key;

[0059] Step S153: Based on the platform API access key, the trauma clinical information service platform is connected to the clinical information system, the imaging system, and the bedside monitoring device through API access and data structure conversion to obtain the clinical record data of trauma patients, the clinical imaging data of trauma patients, and the monitoring information data of bedside equipment;

[0060] Step S154: Upload the trauma patient's clinical record data, trauma patient's clinical imaging data, and bedside equipment monitoring information data to the data processing unit of the trauma clinical information service platform using wireless transmission technology.

[0061] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S15 in FIG. 1 , in this embodiment, step S15 includes the following steps:

[0062] Step S151: registering an account on the trauma clinical information service platform to obtain account data of the trauma clinical information service platform;

[0063] The embodiment of the present invention first opens the registration page of the trauma clinical information service platform and enters the necessary personal or institutional information, including user name, password, contact information, etc. After confirmation, submits the registration application and waits for the account confirmation or activation of the trauma clinical information service platform, so that medical staff can obtain access rights to the trauma clinical information service platform and finally obtain the account data of the trauma clinical information service platform.

[0064] Step S152: authenticating the account data of the trauma clinical information service platform based on the platform API service verification interface of the trauma clinical information service platform to obtain a platform API access key;

[0065] The embodiment of the present invention uses a registered account to log in to the constructed trauma clinical information service platform, and uses the API service verification interface provided by the trauma clinical information service platform to authenticate the account data of the trauma clinical information service platform to generate and provide an API access key for subsequent API access identity authentication, and ensure that the medical staff account has legal access rights, and finally obtain the platform API access key.

[0066] Step S153: Based on the platform API access key, the trauma clinical information service platform is connected to the clinical information system, the imaging system, and the bedside monitoring device through API access and data structure conversion to obtain the clinical record data of trauma patients, the clinical imaging data of trauma patients, and the monitoring information data of bedside equipment;

[0067] The embodiment of the present invention establishes an API service that interconnects the trauma clinical information service platform with the clinical information system, imaging system and bedside monitoring equipment by using the obtained platform API access key, and accesses relevant data of the clinical information system, imaging system and bedside monitoring equipment through the API service by using the key. At the same time, the data obtained from different systems is structurally converted to convert the data into structural requirements that meet the structural requirements of the trauma clinical information service platform, and is uniformly converted into a standardized data format, ultimately obtaining clinical record data of trauma patients, clinical imaging data of trauma patients and bedside equipment monitoring information data.

[0068] Step S154: Upload the trauma patient's clinical record data, trauma patient's clinical imaging data, and bedside equipment monitoring information data to the data processing unit of the trauma clinical information service platform using wireless transmission technology.

[0069] The embodiment of the present invention first converts the clinical record data of trauma patients, clinical imaging data of trauma patients and bedside equipment monitoring information data into a transmittable data format, and selects appropriate wireless transmission technology, such as Wi-Fi, Bluetooth, etc., to upload the clinical record data of trauma patients, clinical imaging data of trauma patients and bedside equipment monitoring information data to the data processing unit of the trauma clinical information service platform.

[0070] The present invention firstly registers an account on the trauma clinical information service platform so that medical staff can obtain access rights to the trauma clinical information service platform. By registering an account, medical staff can easily log in to the platform and obtain clinical information and imaging data of trauma patients, thereby providing patients with better information services. In addition, registering an account can also help platform managers better manage user information and permissions to ensure data security and privacy protection. Secondly, by using the platform API service verification interface of the trauma clinical information service platform to authenticate the account data of the trauma clinical information service platform, it is ensured that the medical staff account has legal access rights. After obtaining the access key, the medical staff can use the key to access various services of the platform through the API, including obtaining the patient's clinical record data, imaging data and monitoring information data. This step helps to ensure the security and controllability of the platform and prevent unauthorized access and data leakage. Then, by using the authorized platform API access key to connect the trauma clinical information service platform with the clinical information system, imaging system and bedside monitoring equipment through API access, seamless docking with the clinical information system, imaging system and bedside monitoring equipment can be achieved, and the patient's clinical record data, imaging data and monitoring information data can be obtained. At the same time, the acquired data is converted into a data structure to adapt to the structural requirements of the trauma clinical information service platform, and uniformly converted into a standardized data format for subsequent processing and analysis. The implementation of this step helps to improve the availability and consistency of data and provide more comprehensive and accurate information support for medical staff. Finally, by using wireless transmission technology to upload the clinical record data, clinical imaging data and bedside equipment monitoring information data of trauma patients to the data processing unit of the trauma clinical information service platform, the real-time upload of the clinical record data, imaging data and monitoring information data of trauma patients can be achieved, which enables medical staff to obtain the latest information of patients in a timely manner and provide them with timely monitoring information services. In addition, real-time uploading of data also helps the platform data processing unit to analyze and process the data in real time, providing medical staff with more accurate and personalized services. The implementation of this step will help improve the efficiency and quality of clinical information services, thereby improving patients' trauma clinical information service experience.

[0071] Preferably, step S2 comprises the following steps:

[0072] Step S21: Desensitizing the clinical record data of the trauma patient by using the data processing unit to obtain desensitized clinical record data of the trauma patient;

[0073] The embodiment of the present invention uses encryption technology, de-identification, desensitization and other means to process sensitive information in the clinical record data of trauma patients through the data processing unit of the trauma clinical information service platform, so as to ensure the privacy of patients while retaining useful medical information, and effectively prevent the leakage of patients' personal privacy, and finally obtain desensitized data of clinical records of trauma patients.

[0074] Step S22: extracting the trauma site from the clinical image data of the trauma patient to generate regional image data of the patient's trauma site;

[0075] The embodiment of the present invention uses a computer vision algorithm to analyze the clinical imaging data of trauma patients, extracts the area of ​​the trauma site from the corresponding clinical images, so as to more intuitively understand the location and range of the trauma of the trauma patient, and finally generates the regional imaging data of the patient's trauma site.

[0076] Step S23: performing a lesion probability statistical analysis on the patient's trauma site regional image data to obtain the patient's trauma site lesion probability data; performing a trauma lesion assessment analysis on the trauma patient's clinical image data based on the patient's trauma site lesion probability data to obtain the trauma patient's lesion influencing factors;

[0077] The embodiment of the present invention uses a corresponding image analysis method or machine learning algorithm to analyze the extracted patient trauma area image data to statistically quantify the degree and probability of traumatic lesions in the patient's trauma area image, thereby obtaining the patient's trauma area lesion probability data. Then, by combining the degree and probability of the traumatic lesions obtained by the analysis, the clinical image data of the trauma patient is evaluated and analyzed to analyze and discover the influencing factors of the traumatic lesions of the trauma patient, and finally obtain the influencing factors of the traumatic lesions of the trauma patient.

[0078] Step S24: performing physiological index loss analysis on the desensitized clinical record data of the trauma patient according to the bedside equipment monitoring information data, and obtaining the patient's clinical physiological index loss status data;

[0079] The embodiment of the present invention analyzes the desensitized data of clinical records of trauma patients in combination with the abnormal conditions of the patient's physiological indicators in the bedside equipment monitoring information data to more comprehensively understand the patient's physiological condition, evaluate the loss status of the patient's clinical physiological indicators, and ultimately obtain the patient's clinical physiological indicator loss status data.

[0080] Step S25: scoring and calculating the desensitized data of the clinical records of trauma patients using a trauma grade scoring calculation formula based on the trauma patient's lesion influencing factors and the patient's clinical physiological index loss status data to obtain the patient's trauma grade score value;

[0081] The embodiment of the present invention combines the lower limit and upper limit of the clinical observation time range of trauma patients, the time variable parameter of the clinical observation of trauma patients, the overall adjustment coefficient of the trauma grade, the patient's blood pressure value, the daily average blood pressure value of trauma patients, the patient's blood pressure adjustment coefficient, the patient's serum lactate concentration adjustment coefficient, the trauma patient's lesion impact factor, the trauma lesion impact measurement value, the patient's clinical physiological index loss status data, the trauma grade impact weight parameter and related parameters to form a suitable trauma grade scoring calculation formula to score and calculate the desensitized data of the trauma patient's clinical records, so as to comprehensively consider the patient's lesion condition and the probability of loss of physiological indicators to quantify the grade score of the trauma patient, and finally obtain the patient's trauma grade score value. In addition, the trauma grade scoring calculation formula can also use any trauma grade detection algorithm in the field to replace the scoring calculation process, and is not limited to the trauma grade scoring calculation formula.

[0082] Step S26: Perform trauma grade assessment on the desensitized clinical record data of trauma patients according to the trauma grade score of the patients to obtain clinical trauma grade status data of the patients.

[0083] The embodiment of the present invention analyzes the calculated patient trauma grade score value by using the corresponding trauma grade assessment standard to match the patient trauma grade score value with the corresponding trauma grade. For example, when the patient trauma grade score value is less than or equal to 20, it is determined as the first trauma grade; when it is greater than 20 and less than or equal to 40, it is determined as the second trauma grade; when it is greater than 40 and less than or equal to 60, it is determined as the third trauma grade; when it is greater than 60 and less than or equal to 80, it is determined as the fourth trauma grade; and when it is greater than 80 or above, it is determined as the fifth trauma grade, thereby providing detailed grade information on the severity of the patient's trauma and ultimately obtaining the patient's clinical trauma grade status data.

[0084] The present invention firstly performs desensitization processing on the clinical record data of trauma patients by using a data processing unit to remove or replace sensitive information of patients, such as name, ID number, etc., to protect the privacy of patients, which can effectively prevent the leakage of personal privacy of patients and meet the regulatory requirements of medical information security and privacy protection. At the same time, desensitization processing helps to make data more anonymous, promotes the safe transmission and storage of data, and provides a reliable data basis for subsequent analysis and research. By extracting the trauma site of the clinical imaging data of trauma patients, it is possible to accurately parse and analyze the clinical images of patients, and extract the trauma site information, which enables medical staff to understand the location and range of the patient's trauma more intuitively, and provides visual support for clinical services. The generation of trauma site regional imaging data lays the foundation for subsequent lesion evaluation and grade calculation, and provides more accurate information for subsequent information service decisions. Secondly, by performing lesion probability statistical analysis on the patient's trauma site regional imaging data, the degree and probability of trauma lesions can be quantified, providing a scientific basis for medical staff. In addition, based on the probability data of lesions at the patient's trauma site, the trauma lesions of the clinical imaging data of the trauma patients are evaluated and analyzed, so as to obtain the influencing factors of the lesions of the trauma patients. This process not only helps to detect the trauma lesions of the patients at an early stage, but also provides important information for the subsequent treatment process. Then, by combining the abnormal physiological indicators of the patients in the bedside equipment monitoring information data, the physiological indicator loss analysis of the desensitized clinical records of the trauma patients is carried out. The corresponding physiological indicator loss can be calculated by real-time monitoring of the physiological condition of the trauma patients, so as to obtain the clinical physiological indicator loss status data of the patients. This helps medical staff to understand the physiological condition of the patients more comprehensively, discover abnormal changes in time, and provide a basis for clinical service decision-making. Next, by combining the influencing factors of the lesions of the trauma patients and the clinical physiological indicator loss status data of the patients, a suitable trauma grade scoring calculation formula is used to score and calculate the desensitized clinical records of the trauma patients. This calculation formula not only comprehensively considers the lesions of the patients and the probability of loss of physiological indicators, but also quantifies them into trauma grade scoring values ​​according to medical standards. The calculation of this scoring value can provide objective and comparable indicators for medical staff, which helps to quickly evaluate the severity of the patient's trauma. Finally, the desensitized data of the trauma patients' clinical records were evaluated for trauma grade using the calculated patient trauma grade score. The evaluation results can provide medical staff with detailed information about the severity of the patient's trauma, which helps to formulate a more accurate department allocation management strategy. In summary, the implementation of the above steps provides the clinical team with a comprehensive and scientific patient assessment tool, which helps to improve the quality and efficiency of medical information services.

[0085] Preferably, step S24 includes the following steps:

[0086] Step S241: Acquire the heart rate oscillation amplitude data and blood pressure and blood oxygen reduction ratio data of the trauma patient during clinical treatment through the bedside equipment monitoring information data;

[0087] The embodiment of the present invention collects and obtains physiological indicator data from the bedside equipment monitoring information data, including heart rate data, blood pressure status data, blood oxygen status data, etc., and then uses mathematical statistical analysis methods to perform statistical analysis on the acquired data to analyze the corresponding cardiac rhythmic change amplitude and the patient's blood pressure and blood oxygen decrease ratio, and finally obtains the heart rate oscillation amplitude data and the blood pressure and blood oxygen decrease ratio data.

[0088] Step S242: performing physiological index abnormality prediction analysis on the desensitized clinical record data of the trauma patient according to the heart rate oscillation amplitude data and the blood pressure and blood oxygen drop ratio data, and obtaining the abnormal condition data of the clinical physiological indexes of the trauma patient;

[0089] The embodiment of the present invention extracts corresponding features, such as mean value, standard deviation, change trend, etc., from the heart rate oscillation amplitude data and the blood pressure and blood oxygen decrease ratio data. Secondly, based on the extracted features, a suitable machine learning algorithm (such as support vector machine (SVM), random forest) is used to establish a physiological indicator abnormality prediction model. Then, the desensitized data of the clinical records of trauma patients are predicted by using the established model to predict and analyze the abnormal conditions of the patients' physiological indicators, including but not limited to disorders of the cardiovascular system, insufficient oxygenation, etc., and finally the abnormal condition data of clinical physiological indicators of trauma patients are obtained.

[0090] Step S243: Statistically analyzing the abnormal condition data of clinical physiological indicators of trauma patients to obtain abnormal probability data of physiological indicators;

[0091] The embodiment of the present invention summarizes and compiles the predicted abnormal condition data of clinical physiological indicators of trauma patients by using mathematical statistical methods (such as frequency statistics, probability density function, etc.) to calculate the probability of occurrence of each abnormal physiological indicator, and finally obtains the abnormal probability data of physiological indicators.

[0092] Step S244: Perform physiological indicator loss analysis on the desensitized data of the trauma patient's clinical records according to the physiological indicator abnormality probability data to obtain the patient's clinical physiological indicator loss status data.

[0093] The embodiment of the present invention analyzes the desensitized data of clinical records of trauma patients by combining the abnormal probability data of physiological indicators obtained by analysis, so as to consider the degree of impact of different abnormal physiological indicators on the health of patients, and analyze the risks and losses of trauma patients in terms of physiological indicators, and finally obtain the loss status data of clinical physiological indicators of patients.

[0094] The present invention obtains corresponding physiological parameters, including heart rate oscillation amplitude and blood pressure and blood oxygen reduction ratio, etc. by using bedside equipment monitoring information data. Among them, the heart rate oscillation amplitude reflects the rhythmic changes of the heart, and the blood pressure and blood oxygen reduction ratio is related to the patient's oxygenation status and blood pressure regulation. The acquisition of these data can provide an important basis for the subsequent prediction and analysis of abnormal physiological indicators, so that medical staff can monitor the patient's physiological state more comprehensively and in real time. Secondly, by combining the heart rate oscillation amplitude data and the blood pressure and blood oxygen reduction ratio data to predict and analyze the abnormal physiological indicators of trauma patients' clinical records, it is possible to identify the abnormal physiological indicators of patients, including but not limited to cardiovascular system disorders, insufficient oxygenation, etc. This analysis result provides timely physiological status warning for medical staff, which helps to discover and deal with potential health risks at an early stage, and improve the safety of patients and the quality of nursing services. Then, by statistically analyzing the abnormal status data of clinical physiological indicators of trauma patients, the probability of occurrence of various abnormal conditions can be quantified, which helps the medical clinical service team to more comprehensively understand the fluctuation trend of the patient's physiological state and the frequency of abnormal events. The generation of abnormal probability data of physiological indicators can provide a basis for subsequent loss analysis of physiological indicators and a scientific basis for the development of personalized information service plans and intervention measures. Finally, by using the abnormal probability data of physiological indicators to analyze the loss of physiological indicators of desensitized clinical records of trauma patients, this analysis process aims to quantify the risk and loss probability of patients in terms of physiological indicators, provide medical staff with detailed information about the physiological health status of patients, improve the effective use of medical resources, and thus improve the prognosis and rehabilitation of patients. In addition, it can make the physiological status monitoring of trauma patients more accurate and timely, and provide effective decision-making support for medical staff.

[0095] Preferably, the trauma grade score calculation formula in step S25 is specifically:

[0096]

[0097] Where C is the patient's trauma grade score, t1 is the lower limit of the clinical observation time range of trauma patients, t2 is the upper limit of the clinical observation time range of trauma patients, t is the time variable parameter of the clinical observation of trauma patients, α is the overall adjustment coefficient of trauma grade, P(t) is the patient's blood pressure value at time t, P0 is the daily average blood pressure value of trauma patients, β is the patient's blood pressure adjustment coefficient, R(t) is the patient's serum lactate concentration value at time t, γ is the patient's serum lactate concentration adjustment coefficient, i is the index item of the trauma patient's lesion influencing factor, n is the total number of trauma patient's lesion influencing factors, q i is the trauma lesion impact measure of the lesion impact factor of the i-th trauma patient, η iis the trauma level influence weight parameter of the lesion influencing factor of the i-th trauma patient, j is the index item of the patient's physiological index, m is the total number of the patient's physiological indexes, s j is the clinical physiological index loss status data of the jth patient’s physiological index, θ j is the trauma grade influence weight parameter of the jth patient’s physiological index, and ε is the correction coefficient of the patient’s trauma grade score.

[0098] The present invention obtains a trauma grade scoring calculation formula by using a specific mathematical model and after verification, which is used to score and calculate the desensitized data of the clinical records of trauma patients. The trauma grade scoring calculation formula defines the time range of clinical observation of trauma patients by using the lower limit and upper limit of the clinical observation time range of trauma patients, and by comprehensively evaluating the data within this time range, it can more accurately reflect the patient's condition. By using the time variable parameter of the clinical observation of trauma patients to represent different time points within the entire observation time range, the evaluation is made more dynamic and timely. By using the overall adjustment coefficient of the trauma grade, the entire trauma grade score can be flexibly adjusted as a whole to adapt to different situations and needs. By using the patient's blood pressure value to reflect the patient's circulatory system status, and by using the daily average blood pressure value as a reference value for comparative analysis, the patient's blood pressure adjustment coefficient is also used to adjust its influence on the trauma grade score. By using the patient's serum lactate concentration value to reflect the index of the patient's tissue perfusion and oxygenation status, and by using the patient's serum lactate concentration adjustment coefficient to adjust its influence on the trauma grade score. Secondly, the impact of different lesion influencing factors and patients' physiological indicators is comprehensively considered by combining the comprehensive items of trauma patients' lesion influencing factors and patients' physiological indicators. In addition, the correction coefficient is introduced to fine-tune the overall score value to consider the impact of possible additional factors on the trauma grade score. Therefore, this trauma grade score calculation formula takes into account multiple parameters to flexibly adapt to different patient conditions, thereby calculating the trauma grade score of trauma patients. This score provides a comprehensive reference for doctors and medical staff, which helps to more comprehensively understand the patient's condition and assign the corresponding department to provide clinical information services. In summary, this formula fully considers the patient trauma grade score C, the lower limit of the clinical observation time range of trauma patients t1, the upper limit of the clinical observation time range of trauma patients t2, the time variable parameter t of the clinical observation of trauma patients, the overall adjustment coefficient of trauma grade α, the patient blood pressure value P(t) of trauma patients at time t, the daily average blood pressure value P0 of trauma patients, the patient blood pressure adjustment coefficient β, the patient serum lactate concentration value R(t) of trauma patients at time t, the patient serum lactate concentration adjustment coefficient γ, the index item i of the trauma patient lesion influencing factor, the total number n of trauma patient lesion influencing factors, and the trauma lesion influence measurement value q of the i-th trauma patient lesion influencing factori , the trauma level influence weight parameter η of the lesion influencing factor of the i-th trauma patient i , index item number j of the patient's physiological index, total number m of the patient's physiological index, patient clinical physiological index loss status data s of the jth patient's physiological index j , the trauma level influence weight parameter θ of the jth patient's physiological index j , the correction coefficient ε of the patient's trauma grade score value, forms a functional relationship based on the correlation between the patient's trauma grade score value C and the above parameters:

[0099]

[0100] The formula can realize the scoring calculation process of desensitized data of trauma patients' clinical records. At the same time, by introducing the correction coefficient ε of the patient's trauma grade score value, it can be adjusted according to the errors occurring in the calculation process, thereby improving the accuracy and applicability of the trauma grade score calculation formula.

[0101] Preferably, step S3 comprises the following steps:

[0102] Step S31: Obtaining trauma department information data;

[0103] The embodiment of the present invention collects information related to the trauma department by using the trauma clinical information service platform to establish a comprehensive understanding of the operation status of the trauma department, including information data on the number of patients in the department, patient care information, equipment resources, medical staff configuration, etc., and organizes and cleans the collected trauma department information to ensure the accuracy and completeness of the data, and finally obtains trauma department information data.

[0104] Step S32: Obtaining trauma patient flow data and trauma patient sharing frequency data of each trauma department through the trauma department information data, and calculating cross-work loss on the trauma department information data according to the trauma patient flow data to obtain cross-work loss probability data of the trauma department;

[0105] The embodiment of the present invention uses a mathematical statistical method to analyze the information data of the trauma department to obtain the patient flow information of each trauma department, that is, the number of trauma patients received by each department, and at the same time, obtain the frequency of joint care of trauma patients by medical staff in each trauma department, that is, the frequency information of multiple departments jointly performing clinical information services on trauma patients, thereby obtaining trauma patient flow data and trauma patient sharing frequency data. Then, by combining the trauma patient flow data to analyze the information data of the trauma department, the work loss probability between different departments is calculated and quantified, and potential cooperation bottlenecks and the efficiency of collaborative work between departments are identified to improve, and finally the cross-work loss probability data of the trauma department is obtained.

[0106] Step S33: performing department association analysis on the trauma department information data based on the trauma patient sharing frequency data to obtain trauma department association relationship data; constructing a relationship network on the trauma department information data according to the trauma department association relationship data to obtain an association relationship network between trauma departments;

[0107] The embodiment of the present invention uses the analyzed trauma patient sharing frequency data to perform correlation analysis on the trauma department information to identify the correlation between different trauma departments, thereby obtaining trauma department correlation data. Then, different trauma departments are connected and constructed by using the analyzed correlation between trauma departments to construct a correlation network showing the correlation between trauma departments, and finally obtain the correlation network between trauma departments.

[0108] Step S34: Based on the cross-work loss probability data of the trauma departments, the coupling strength of the correlation network between the trauma departments is calculated using the department coupling strength calculation formula to obtain the coupling strength between the trauma departments;

[0109] The embodiment of the present invention combines the index item number, total number, cross-work loss probability data of the trauma department, work association distance parameter, association correlation measurement parameter, coupling influence weight parameter, coupling influence attenuation parameter, time range parameter for coupling strength calculation, time variable parameter, time influence attenuation parameter and related parameters of the trauma department to form a suitable department coupling strength calculation formula to calculate the coupling strength of the association relationship network between trauma departments, so as to quantify the coupling strength between different trauma departments, and finally obtain the coupling strength between trauma departments. In addition, the department coupling strength calculation formula can also use any coupling detection algorithm in the art to replace the coupling strength calculation process, and is not limited to the department coupling strength calculation formula.

[0110] Step S35: performing coupling evaluation and analysis on the trauma department information data according to the coupling strength between trauma departments to obtain coupling influencing factors between trauma departments;

[0111] The embodiment of the present invention uses the calculated coupling strength between trauma departments to evaluate and analyze the relevant trauma departments in the trauma department information data, so as to evaluate the collaborative working relationship between different departments and identify potential factors affecting collaborative obstacles, and finally obtain the coupling influencing factors between trauma departments.

[0112] Step S36: Perform departmental classification and allocation of the patient's clinical trauma grade status data to obtain the patient's graded trauma department initial allocation result data; perform influencing collaborative operations on the patient's graded trauma department initial allocation result data based on the coupling influencing factors between trauma departments to obtain the trauma patient department collaborative allocation result data.

[0113] The embodiment of the present invention allocates corresponding departments for service inspection according to the patient's trauma level in the patient's clinical trauma level status data, so that trauma patients with different trauma levels can be allocated to corresponding departments, thereby obtaining the initial allocation result data of the patient's graded trauma department. Then, by using the coupling influencing factors between the trauma departments obtained by analysis, the department allocation results in the initial allocation result data of the patient's graded trauma department are reallocated to further optimize the patient's departmental grade matching and ensure that the trauma patient can get better medical information service operations in the department with synergistic effects, and finally obtain the department collaborative allocation result data of the trauma patient.

[0114] The present invention firstly acquires information data of trauma departments, and can establish a comprehensive understanding of the operation status of trauma departments, including information on the number of patients in the department, patient care information, equipment resources, and medical staff configuration. It can also fully understand the current status of the department, which can provide a basis for subsequent trauma patient flow analysis and department association modeling. At the same time, by using the information data of trauma departments to obtain the trauma patient flow data and trauma patient sharing frequency data of each trauma department, and performing cross-work loss calculation on the information data of trauma departments based on the trauma patient flow data, the probability of work loss between different departments is quantified, which helps to identify potential cooperation bottlenecks and improve the efficiency of collaborative work between departments, thereby improving the overall clinical information service level. Secondly, by using the shared frequency data of trauma patients to perform department association analysis on the information data of trauma departments, the association relationship between departments is determined. Moreover, through these association relationship data, an association relationship network between trauma departments is constructed, thereby providing key information for subsequent coupling strength calculation and coupling evaluation. Then, by combining the cross-work loss probability data of trauma departments, a suitable department coupling strength calculation formula was used to calculate the coupling strength of the association network between trauma departments. This calculation process can quantify the coupling strength between different departments based on the cross-work loss probability data, thus providing a scientific basis for the evaluation of collaborative work between departments. Next, the coupling evaluation and analysis of trauma department information data was performed by using the calculated coupling strength between trauma departments, which helps to understand the collaborative work relationship between different departments, identify potential collaborative obstacles, and thus optimize the health service process and improve the overall efficiency of trauma clinical information services. Finally, by grading the department data of the patient's clinical trauma grade status, trauma patients with different trauma grades can be assigned to the corresponding department. In addition, the initial allocation result data of the patient's graded trauma department is affected by the coupling influencing factors between trauma departments. This step aims to further optimize the patient's departmental grade matching, thereby ensuring that patients can get better medical information services in departments with synergistic effects.

[0115] Preferably, the department coupling strength calculation formula in step S34 is specifically:

[0116]

[0117] In the formula, I ab is the coupling strength between trauma departments a and b, a and b are index items of trauma departments, N is the total number of trauma departments, is the probability data of cross-work loss in the trauma department, D ab is the working association distance parameter between trauma departments a and b, ρ1 is the coupling influence weight parameter of the working association distance of trauma departments, ρ2 is the coupling influence attenuation parameter of the working association distance of trauma departments, σ ab is the correlation measurement parameter between trauma departments a and b, ρ3 is the coupling influence weight parameter of the correlation, ρ4 is the coupling influence attenuation parameter of the correlation, T is the time range parameter for coupling strength calculation, τ is the time variable parameter for coupling strength calculation, λ is the time influence attenuation parameter, and ∈ is the correction value of the coupling strength between trauma departments.

[0118] The present invention obtains a department coupling strength calculation formula by using a specific mathematical model and after verification, which is used to calculate the coupling strength of the association relationship network between trauma departments. The department coupling strength calculation formula takes into account the cross-work loss between departments by using the probability data of cross-work loss of trauma departments, that is, the potential loss caused by uncoordinated working relationships or poor information flow, so that the formula can flexibly reflect the impact of such loss on coupling strength. The spatial relationship between trauma departments, that is, the distance of work collaboration between them, is measured by using the working association distance parameter, which helps to more accurately evaluate the actual degree of collaboration between departments. The influence of the working association distance on the coupling strength is also adjusted by using the coupling influence weight and coupling influence attenuation parameters of the working association distance of the trauma department, that is, the farther the distance, the weaker the synergy effect. By adjusting these parameters, the different effects of distance on collaborative work can be better reflected. Secondly, the strength and direction of the association between departments are measured by using the association correlation measurement parameter, which enables the formula to take into account the quality and closeness of the working relationship between departments. The effect of the correlation on the coupling strength was also adjusted by using the coupling influence weight and coupling influence decay parameters of the correlation, that is, the stronger the correlation, the more significant the synergy. By adjusting these parameters, the contribution of the correlation to the collaborative work can be better captured. Then, the time limit of the coupling strength calculation was defined by using the time range parameter, so that the formula can take into account the time-varying nature of the relationship between departments and provide a suitable assessment for both long-term and short-term collaborative work. The time factor was also taken into account by using the time variable parameter and time influence decay parameter of the coupling strength calculation, so that the formula can model the evolution of the collaborative work between departments. By introducing these parameters, the calculation formula is made more dynamic and can adapt to the collaborative work status at different time points. In addition, the introduction of correction values ​​allows the overall coupling strength to be fine-tuned to include factors that may not be explicitly considered in the formula. This adjustment can improve the flexibility and adaptability of the model. Therefore, this formula can flexibly adapt to the relationship between different trauma departments by comprehensively considering multiple parameters, thereby calculating the coupling strength between trauma departments. This strength value provides information about the degree of collaboration between departments to medical institution managers, which helps to optimize resource allocation and improve the collaboration of patient department allocation. In summary, this formula fully considers the coupling strength I between trauma department a and trauma department b. ab , index items a and b of trauma departments, total number of trauma departments N, probability data of cross-work loss in trauma departments Working association distance parameter D between trauma department a and trauma department b ab, the coupling influence weight parameter ρ1 of the trauma department work association distance, the coupling influence attenuation parameter ρ2 of the trauma department work association distance, the association correlation measurement parameter σ between trauma department a and trauma department b ab , the coupling influence weight parameter ρ3 of the correlation correlation, the coupling influence attenuation parameter ρ4 of the correlation correlation, the time range parameter T for coupling strength calculation, the time variable parameter τ for coupling strength calculation, the time influence attenuation parameter λ, the correction value ∈ of the coupling strength between trauma departments, according to the coupling strength I between trauma departments a and b ab The correlation between the above parameters forms a functional relationship:

[0119]

[0120] This formula can realize the calculation process of the coupling strength of the correlation relationship network between trauma departments. At the same time, by introducing the correction value ∈ of the coupling strength between trauma departments, it can be adjusted according to the errors occurring in the calculation process, thereby improving the accuracy and applicability of the calculation formula for department coupling strength.

[0121] Preferably, step S4 comprises the following steps:

[0122] Step S41: based on the trauma patient department collaborative allocation result data, the patient physiological index monitoring information data of the trauma patient in the allocated department is obtained through the bedside equipment monitoring information data, and the patient physiological index monitoring information data of the trauma patient in the allocated department is analyzed for change trends to obtain the patient physiological index change trend data;

[0123] The embodiment of the present invention first determines the assigned department of a trauma patient by using the trauma patient department collaborative assignment result data, and secondly, obtains the physiological indicator monitoring information data of the trauma patient in the assigned department in real time from the bedside equipment monitoring information data, and then analyzes the patient physiological indicator monitoring information data of the trauma patient in the assigned department by using a change trend analysis method to understand the changes in the physiological condition of the trauma patient, and finally obtains the patient physiological indicator change trend data.

[0124] Step S42: monitoring abnormal conditions of patients based on the trend data of changes in physiological indicators of patients, and obtaining abnormal condition information data of trauma patients;

[0125] The embodiment of the present invention monitors the patient's physiological indicator change trend data by using appropriate abnormality monitoring algorithms and models to monitor whether there are abnormal conditions in the physiological indicator information in the patient's physiological indicator change trend data, and identifies potential abnormal conditions, and finally obtains abnormal information data on the condition of trauma patients.

[0126] Step S43: performing abnormal event prediction processing on the abnormal information data of the trauma patient's condition to obtain abnormal event information data of the trauma patient;

[0127] The embodiment of the present invention uses an event prediction method to analyze abnormal information data on the condition of trauma patients to predict more serious medical information service problems that trauma patients may face, and combines the changing trends of the physiological indicators of trauma patients to identify abnormal events occurring in trauma patients, including abnormal changes in various physiological indicators of trauma clinical patients, and finally obtains abnormal event information data of trauma patients.

[0128] Step S44: Perform alarm notification analysis on the abnormal event information data of trauma patients, obtain the alarm notification decision of abnormal events of trauma patients, and upload it to the trauma clinical information service platform for department notification to execute the corresponding trauma patient clinical notification service task.

[0129] The embodiment of the present invention firstly makes an alarm notification for abnormal changes in the patient's abnormal event information data of trauma patients, so as to formulate a corresponding notification strategy for clinical abnormal event alarms, and provides specific decision-making suggestions for the clinical medical service team, thereby obtaining a decision on the alarm notification of abnormal events for trauma patients. Then, the obtained decision on the alarm notification of abnormal events for trauma patients is uploaded to the trauma clinical information service platform for department notification, so as to achieve timely notification between departments and ensure that trauma patients can quickly obtain professional clinical medical information services when abnormal events occur, and finally trigger the execution of corresponding clinical notification service tasks for trauma patients, such as notifying the relevant departments that handle abnormal changes in patients to make necessary notifications to perform corresponding clinical treatments and intervention measures.

[0130] The present invention first obtains the physiological index monitoring information data of trauma patients in the assigned department from the bedside equipment monitoring information data in real time according to the collaborative allocation result data of the trauma patient department, which helps the clinical medical team to understand the patient's physiological condition in real time and provides data support for personalized clinical information services. At the same time, by analyzing the change trend of the patient's physiological index monitoring information data in the assigned department, the patient's physiological index change trend data is obtained, which provides important information for medical staff to identify the change of the patient's condition in the early stage, so that appropriate medical intervention measures can be taken more quickly. Secondly, by monitoring the abnormal condition of the patient's physiological index change trend data, the potential abnormal condition can be identified by comprehensively analyzing the change trend of the patient's physiological index. The key to this step is that it can provide an early warning of the patient's condition, so that the medical staff can pay attention to the patient in time and take appropriate medical information service measures, thereby minimizing the patient's health risks. Then, by performing abnormal event prediction processing on the abnormal condition information data of the trauma patient, more serious medical information service problems that the patient may face are predicted. By comprehensively analyzing the change trend of the patient's physiological index, abnormal events that may occur in the patient can be identified in advance, providing the medical team with more sufficient preparation time to effectively respond to potential emergencies. Finally, by analyzing the alarm notification of abnormal event information data of trauma patients, specific decision-making suggestions can be provided to the clinical medical service team. In addition, by uploading the abnormal event alarm notification decisions of trauma patients obtained by analysis to the trauma clinical information service platform, timely notification between departments can be achieved, which helps to promote collaborative work between different departments and ensure that patients can quickly obtain professional clinical medical information services when abnormal events occur. The key to this step is that it can improve the response speed of medical information services, reduce the medical risks of patients, and thus improve the overall quality of clinical information services.

[0131] Preferably, the present invention further provides a trauma clinical information service platform construction system, which is used to execute the trauma clinical information service platform construction method as described above, and the trauma clinical information service platform construction system comprises:

[0132] The information service platform construction and data processing module is used to obtain trauma clinical information service demand data, and use cloud computing technology to construct a network model framework for the trauma clinical information service demand data to obtain a trauma clinical information service network model framework; upload the trauma clinical information service network model framework to the cloud platform to build a trauma clinical information service platform, wherein the trauma clinical information service platform includes a data processing unit; obtain trauma patient clinical record data, trauma patient clinical imaging data, and bedside equipment monitoring information data through the trauma clinical information service platform and upload them to the data processing unit of the trauma clinical information service platform;

[0133] The trauma grade assessment module for trauma patients is used to perform desensitization processing on the clinical record data of trauma patients by using the data processing unit to obtain desensitized clinical record data of trauma patients; perform trauma grade assessment on the desensitized clinical record data of trauma patients based on the clinical imaging data of trauma patients and the monitoring information data of bedside equipment, thereby obtaining the clinical trauma grade status data of patients;

[0134] The trauma patient department collaborative allocation module is used to obtain trauma department information data and perform inter-department coupling analysis on the trauma department information data to obtain coupling influencing factors between trauma departments; based on the coupling influencing factors between trauma departments, the patient's clinical trauma grade status data is collaboratively allocated to departments to obtain trauma patient department collaborative allocation result data;

[0135] The department patient abnormal condition prediction and notification module is used to monitor the abnormal patient condition of the bedside equipment monitoring information data based on the trauma patient department collaborative allocation result data, and obtain the trauma patient condition abnormal information data; perform abnormal event prediction and processing on the trauma patient condition abnormal information data, obtain the trauma patient abnormal event alarm notification decision, and upload it to the trauma clinical information service platform for department notification, so as to perform the corresponding trauma patient clinical notification service tasks.

[0136] In summary, the present invention provides a trauma clinical information service platform construction system, which consists of an information service platform construction and data processing module, a trauma patient trauma level assessment module, a trauma patient department collaborative allocation module, and a department patient abnormal condition prediction notification module. It can realize any trauma clinical information service platform construction method described in the present invention, and is used to realize a trauma clinical information service platform construction method by combining the operations between the computer programs running on each module. The internal structure of the system cooperates with each other. The information service platform construction and data processing module is a key component in the trauma clinical information service system. The module is responsible for obtaining trauma clinical information service demand data and using cloud computing technology to construct a network model framework to form a trauma clinical information service network model framework. The establishment of this model framework provides theoretical support and technical basis for subsequent information services. In addition, by uploading the network model framework to the cloud platform to construct a trauma clinical information service platform, which contains a data processing unit, the construction of this platform enables the processing and service of trauma clinical information to be centrally managed and efficiently implemented. Through this platform, clinical record data, clinical imaging data, and bedside equipment monitoring information data of trauma patients can be obtained, and uploaded to the data processing unit for further analysis and processing. Secondly, the trauma grade assessment module for trauma patients is designed to assess and grade the clinical conditions of trauma patients. The clinical record data of trauma patients is desensitized by using the data processing unit to ensure the privacy of patients. Based on the clinical imaging data and bedside equipment monitoring information data of trauma patients, the trauma grade of the desensitized clinical record data is assessed to obtain the clinical trauma grade status data of the patients. This assessment result is of great significance for the clinical medical team to formulate information service plans and predict the patient's recovery. Then, the department collaborative allocation module for trauma patients aims to achieve the reasonable allocation of trauma patient resources and the collaborative operation between departments. By obtaining the information data of the trauma department and conducting coupling analysis on it, the coupling influencing factors between departments are obtained. Based on these factors, the clinical trauma grade status data of patients is collaboratively allocated to departments to achieve the optimal allocation of patient department resources, which helps to improve the utilization efficiency of clinical medical resources and the quality of information services.Finally, the department patient abnormal condition prediction and notification module aims to promptly detect abnormal conditions of patient conditions and take corresponding early warning measures. It monitors abnormal patient conditions by combining the result data of the collaborative allocation of trauma patient departments with the bedside equipment monitoring information data. It also predicts and processes abnormal events on these abnormal information data to generate abnormal event alarm notification decisions for trauma patients, and uploads them to the trauma clinical information service platform for department notification to implement the corresponding trauma patient clinical notification service tasks. The establishment of this module helps to improve the effectiveness and safety of clinical information services for trauma patients and ensure the health and safety of patients. This can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient trauma clinical information service platform construction process, thereby simplifying the operating procedures of the trauma clinical information service platform construction system.

[0137] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0138] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing a trauma clinical information service platform, characterized in that: The following steps are involved: Step S1: Obtain trauma clinical information service demand data, and use cloud computing technology to construct a network model framework for the trauma clinical information service demand data to obtain a trauma clinical information service network model framework; upload the trauma clinical information service network model framework to the cloud platform to construct a trauma clinical information service platform, wherein the trauma clinical information service platform includes a data processing unit; obtain trauma patient clinical record data, trauma patient clinical imaging data, and bedside equipment monitoring information data through the trauma clinical information service platform and upload them to the data processing unit of the trauma clinical information service platform; Step S2: Desensitizing the clinical record data of the trauma patient by using the data processing unit to obtain desensitized clinical record data of the trauma patient; Based on the clinical imaging data of trauma patients and the monitoring information data of bedside equipment, the trauma grade of the desensitized clinical record data of trauma patients is evaluated to obtain the clinical trauma grade status data of the patients. Step S2 includes the following steps: Step S21: Desensitizing the clinical record data of the trauma patient by using the data processing unit to obtain desensitized clinical record data of the trauma patient; Step S22: extracting the trauma site from the clinical image data of the trauma patient to generate regional image data of the patient's trauma site; Step S23: performing a lesion probability statistical analysis on the patient's trauma site regional image data to obtain the patient's trauma site lesion probability data; performing a trauma lesion assessment analysis on the trauma patient's clinical image data based on the patient's trauma site lesion probability data to obtain the trauma patient's lesion influencing factors; Step S24: performing physiological index loss analysis on the desensitized clinical record data of the trauma patient according to the bedside equipment monitoring information data, and obtaining the patient's clinical physiological index loss status data; Step S25: Based on the trauma patient's lesion influencing factors and the patient's clinical physiological index loss status data, the trauma patient's clinical record desensitization data is scored and calculated using a trauma grade scoring calculation formula to obtain the patient's trauma grade score value; wherein the trauma grade scoring calculation formula is specifically: Where C is the patient's trauma grade score, t1 is the lower limit of the clinical observation time range of trauma patients, t2 is the upper limit of the clinical observation time range of trauma patients, t is the time variable parameter of the clinical observation of trauma patients, α is the overall adjustment coefficient of trauma grade, P(t) is the patient's blood pressure value at time t, P0 is the daily average blood pressure value of trauma patients, β is the patient's blood pressure adjustment coefficient, R(t) is the patient's serum lactate concentration value at time t, γ is the patient's serum lactate concentration adjustment coefficient, i is the index item of the trauma patient's lesion influencing factor, n is the total number of trauma patient's lesion influencing factors, q i is the trauma lesion impact measure of the lesion impact factor of the i-th trauma patient, η i is the trauma level influence weight parameter of the lesion influencing factor of the i-th trauma patient, j is the index item of the patient's physiological index, m is the total number of the patient's physiological indexes, s j is the clinical physiological index loss status data of the jth patient’s physiological index, θ j is the trauma grade influence weight parameter of the jth patient’s physiological index, and ε is the correction coefficient of the patient’s trauma grade score; Step S26: performing trauma grade assessment on the desensitized clinical record data of the trauma patient according to the patient's trauma grade score to obtain the patient's clinical trauma grade status data; Step S3: Obtain trauma department information data, and perform inter-department coupling analysis on the trauma department information data to obtain coupling influencing factors between trauma departments; perform department allocation collaborative operations on patient clinical trauma grade status data based on the coupling influencing factors between trauma departments to obtain trauma patient department collaborative allocation result data; Step S4: Based on the trauma patient department collaborative allocation result data, the bedside equipment monitoring information data is monitored for abnormal patient conditions to obtain abnormal information data on the trauma patient conditions; abnormal event prediction and processing is performed on the abnormal information data on the trauma patient conditions to obtain abnormal event alarm notification decisions for trauma patients, and the decisions are uploaded to the trauma clinical information service platform for department notification to perform corresponding trauma patient clinical notification service tasks.

2. The method for constructing a trauma clinical information service platform according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining trauma clinical information service demand data; Step S12: Designing a service network model architecture according to the trauma clinical information service demand data to generate trauma clinical information service network model architecture data; Step S13: constructing a network model framework for the trauma clinical information service network model architecture data using cloud computing technology to obtain a trauma clinical information service network model framework; Step S14: performing API service interface development processing on the trauma clinical information service network model framework to obtain the platform API service verification interface; uploading the trauma clinical information service network model framework to the cloud platform based on the platform API service verification interface to construct a trauma clinical information service platform, wherein the trauma clinical information service platform includes a data processing unit; Step S15: Obtain clinical record data of trauma patients, clinical imaging data of trauma patients, and bedside equipment monitoring information data through the trauma clinical information service platform and upload them to the data processing unit of the trauma clinical information service platform.

3. The method for constructing a trauma clinical information service platform according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: registering an account on the trauma clinical information service platform to obtain account data of the trauma clinical information service platform; Step S152: authenticating the account data of the trauma clinical information service platform based on the platform API service verification interface of the trauma clinical information service platform to obtain a platform API access key; Step S153: Based on the platform API access key, the trauma clinical information service platform is connected to the clinical information system, the imaging system, and the bedside monitoring device through API access and data structure conversion to obtain the clinical record data of trauma patients, the clinical imaging data of trauma patients, and the monitoring information data of bedside equipment; Step S154: Upload the trauma patient's clinical record data, trauma patient's clinical imaging data, and bedside equipment monitoring information data to the data processing unit of the trauma clinical information service platform using wireless transmission technology.

4. The method for constructing a trauma clinical information service platform according to claim 1, characterized in that: Step S24 includes the following steps: Step S241: Acquire the heart rate oscillation amplitude data and blood pressure and blood oxygen reduction ratio data of the trauma patient during clinical treatment through the bedside equipment monitoring information data; Step S242: performing physiological index abnormality prediction analysis on the desensitized clinical record data of the trauma patient according to the heart rate oscillation amplitude data and the blood pressure and blood oxygen drop ratio data, and obtaining the abnormal condition data of the clinical physiological indexes of the trauma patient; Step S243: Statistically analyzing the abnormal condition data of clinical physiological indicators of trauma patients to obtain abnormal probability data of physiological indicators; Step S244: Perform physiological indicator loss analysis on the desensitized data of the trauma patient's clinical records according to the physiological indicator abnormality probability data to obtain the patient's clinical physiological indicator loss status data.

5. The method for constructing a trauma clinical information service platform according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Obtaining trauma department information data; Step S32: Obtaining trauma patient flow data and trauma patient sharing frequency data of each trauma department through the trauma department information data, and calculating cross-work loss on the trauma department information data according to the trauma patient flow data to obtain cross-work loss probability data of the trauma department; Step S33: performing department association analysis on the trauma department information data based on the trauma patient sharing frequency data to obtain trauma department association relationship data; constructing a relationship network on the trauma department information data according to the trauma department association relationship data to obtain an association relationship network between trauma departments; Step S34: Based on the cross-work loss probability data of the trauma departments, the coupling strength of the correlation network between the trauma departments is calculated using the department coupling strength calculation formula to obtain the coupling strength between the trauma departments; Step S35: performing coupling evaluation and analysis on the trauma department information data according to the coupling strength between trauma departments to obtain coupling influencing factors between trauma departments; Step S36: Perform departmental classification and allocation of the patient's clinical trauma grade status data to obtain the patient's graded trauma department initial allocation result data; perform influencing collaborative operations on the patient's graded trauma department initial allocation result data based on the coupling influencing factors between trauma departments to obtain the trauma patient department collaborative allocation result data.

6. The method for constructing a trauma clinical information service platform according to claim 5, characterized in that: The calculation formula of department coupling strength in step S34 is specifically: In the formula, I ab is the coupling strength between trauma departments a and b, a and b are index items of trauma departments, N is the total number of trauma departments, is the probability data of cross-work loss in the trauma department, D ab is the working association distance parameter between trauma departments a and b, ρ1 is the coupling influence weight parameter of the working association distance of trauma departments, ρ2 is the coupling influence attenuation parameter of the working association distance of trauma departments, σ ab is the correlation measurement parameter between trauma department a and trauma department b, ρ3 is the coupling influence weight parameter of the correlation, ρ4 is the coupling influence attenuation parameter of the correlation, T is the time range parameter for coupling strength calculation, τ is the time variable parameter for coupling strength calculation, λ is the time influence attenuation parameter, ∈ is the correction value of the coupling strength between trauma departments.

7. The method for constructing a trauma clinical information service platform according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: based on the trauma patient department collaborative allocation result data, the patient physiological index monitoring information data of the trauma patient in the allocated department is obtained through the bedside equipment monitoring information data, and the patient physiological index monitoring information data of the trauma patient in the allocated department is analyzed for change trends to obtain the patient physiological index change trend data; Step S42: monitoring abnormal conditions of patients based on the trend data of changes in physiological indicators of patients, and obtaining abnormal condition information data of trauma patients; Step S43: performing abnormal event prediction processing on the abnormal information data of the trauma patient's condition to obtain abnormal event information data of the trauma patient; Step S44: Perform alarm notification analysis on the abnormal event information data of trauma patients, obtain the alarm notification decision of abnormal events of trauma patients, and upload it to the trauma clinical information service platform for department notification to execute the corresponding trauma patient clinical notification service task.

8. A trauma clinical information service platform construction system, characterized in that: Used to execute the method for constructing a trauma clinical information service platform as claimed in claim 1, the trauma clinical information service platform construction system comprises: The information service platform construction and data processing module is used to obtain trauma clinical information service demand data, and use cloud computing technology to construct a network model framework for the trauma clinical information service demand data to obtain a trauma clinical information service network model framework; upload the trauma clinical information service network model framework to the cloud platform to build a trauma clinical information service platform, wherein the trauma clinical information service platform includes a data processing unit; obtain trauma patient clinical record data, trauma patient clinical imaging data, and bedside equipment monitoring information data through the trauma clinical information service platform and upload them to the data processing unit of the trauma clinical information service platform; The trauma grade assessment module for trauma patients is used to perform desensitization processing on the clinical record data of trauma patients using the data processing unit to obtain desensitized clinical record data of trauma patients; perform trauma grade assessment on the desensitized clinical record data of trauma patients based on the clinical imaging data of trauma patients and the monitoring information data of bedside equipment, thereby obtaining the clinical trauma grade status data of patients; The trauma patient department collaborative allocation module is used to obtain trauma department information data and perform inter-department coupling analysis on the trauma department information data to obtain coupling influencing factors between trauma departments; based on the coupling influencing factors between trauma departments, the patient's clinical trauma grade status data is collaboratively allocated to departments to obtain trauma patient department collaborative allocation result data; The department patient abnormal condition prediction and notification module is used to monitor the abnormal patient condition of the bedside equipment monitoring information data based on the trauma patient department collaborative allocation result data, and obtain the trauma patient condition abnormal information data; perform abnormal event prediction and processing on the trauma patient condition abnormal information data, obtain the trauma patient abnormal event alarm notification decision, and upload it to the trauma clinical information service platform for department notification, so as to perform the corresponding trauma patient clinical notification service tasks.

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