Emergency green channel information management method, system and storage medium

Abstract

The present invention discloses an emergency green channel information management method, system and storage medium, which belongs to the field of medical data processing technology. The method includes: deploying multiple first sensors in the hospital area, wearing a second sensor on the patient's body, the first sensor generating the stay information of the second sensor; generating a processing node based on the stay information, and generating the patient's first processing timeline based on the processing node; recording a first video based on a portable recorder, recording a second video based on a fixed camera, segmenting the first video and the second video to obtain a first sub-video and a second sub-video corresponding to the processing node; analyzing the first sub-video and the second sub-video to obtain operation process information and coordination process information, and mapping them to the first processing timeline respectively to obtain a second processing timeline; finally, evaluating the treatment process of each second processing timeline based on the electronic medical record. The present invention makes the management and evaluation of the emergency treatment process more accurate and comprehensive.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical data processing, and in particular relates to an emergency green channel information management method, system and storage medium. Background Art

[0002] The emergency green channel provides a fast, orderly diagnosis and treatment channel for critically ill patients, aiming to shorten treatment time and improve the success rate of treatment. In this context, how to optimize the emergency treatment process through technological means and improve the work efficiency of medical staff has become an important research direction in the field of medical information management.

[0003] For example, Chinese patent publication number CN109003664A discloses an emergency green channel information management method and device. This method manages the emergency green channel's first aid process through intelligent terminals A and B, positioning devices, an information management platform, and a server. Intelligent terminal A collects patient identity information and user account information, while intelligent terminal B obtains the patient's identity information and binds the patient. It also reads location tag information and the current server time. The server then stores and processes the data to intelligently collect information such as the full-process treatment time of emergency green channel patients and the arrival time of medical staff, thereby improving the management efficiency and accuracy of the emergency treatment process.

[0004] However, the solution in the aforementioned document primarily focuses on information collection and storage, with limited capabilities for real-time monitoring of the treatment process and integrated analysis of video data. Furthermore, when processing timeline information about the patient's treatment process, the solution lacks in-depth mining and analysis of video data, making it impossible to comprehensively assess the efficiency and effectiveness of each link in the treatment process. Summary of the Invention

[0005] To solve the above problems, the present invention provides an emergency green channel information management method, system and storage medium to solve the problems existing in the prior art.

[0006] In order to achieve the above-mentioned object of the invention, the present invention proposes an emergency green channel information management method, comprising:

[0007] A plurality of first sensors are arranged in the hospital area, a second sensor is worn on the patient's body, and when the second sensor reaches the detection range of the first sensor, the first sensor generates stay information;

[0008] generating a processing node based on the stay information, and generating a first processing timeline of the patient based on the processing node;

[0009] Recording a first video with a portable recorder and a second video with a fixed camera, segmenting the first video and the second video according to the first processing timeline to obtain a first sub-video and a second sub-video corresponding to the processing node;

[0010] Analyze the first sub-video and the second sub-video to obtain operation process information and coordination process information, respectively, and map the first sub-video, the second sub-video, the operation process information, and the coordination process information to the first processing timeline to obtain a second processing timeline;

[0011] The electronic medical record of the patient is obtained, a disease manifestation is assigned to each second treatment timeline based on the electronic medical record, and the second treatment timelines with the same disease manifestation are analyzed to evaluate the treatment process.

[0012] Furthermore, analyzing the first sub-video and the second sub-video includes the following steps:

[0013] Defining the first sub-video and the second sub-video as target videos, splitting the target video into multiple static frames, identifying each static frame based on a deep neural network to obtain an operation event contained therein, merging the static frames corresponding to the same operation event to obtain an operation video corresponding to the operation event, determining an occurrence time period of the operation event based on the position of the operation video in the target video, dividing the video length of the target video into multiple time windows based on the occurrence time period, and constructing a first feature matrix with the time windows as row features and the operation events as column features;

[0014] A preset arrangement rule is set, and the first feature matrix is ​​rearranged based on the arrangement rule to obtain a second feature matrix. Feature recognition is performed on the second feature matrix based on a convolutional neural network to extract feature events from the second feature matrix, where the feature events include the operation process information and the coordination process information.

[0015] Furthermore, dividing the time window includes the following steps:

[0016] An initial window duration is set, and the video duration is divided into multiple equally spaced first windows based on the initial window duration. The proportion of the occurrence time period of the operation event in each first window is obtained. If the proportion is greater than a first threshold, the operation event is defined to occur in the corresponding first window. The event overlap rate of adjacent first windows including the operation event is counted. If the event overlap rate is greater than a second threshold, the two first windows are merged into a second window. The density of the operation event in the second window is counted. The second window with the density greater than a third threshold is located, and the window is split to obtain a third window and a fourth window. The unmerged first window, the undivided second window after the merger, the third window, and the fourth window are defined as the time window.

[0017] Furthermore, performing window segmentation on the second window includes the following steps:

[0018] The first window is selected from the second window as a split window, and the second window is split into a first sub-window and a second sub-window based on the split window. A first number and a second number of operation events included in the first sub-window and the second sub-window are counted, and a difference between the first number and the second number is calculated. Another first window is selected from the second window as the split window and re-split, and the difference obtained after the split is calculated. This step is repeated until the first windows within the second window are traversed, and the first sub-window and the second sub-window corresponding to the minimum difference value are located as the segmentation result of the second window.

[0019] Furthermore, projecting the first sub-video and the second sub-video into the first processing timeline includes the following steps:

[0020] defining the first sub-video and the second sub-video as target sub-videos, setting a tag in the target sub-video, the tag corresponding to the processing node, setting a first storage area and a second storage area, redundantly storing the target sub-video in the first storage area and the second storage area, losslessly compressing the target sub-video to obtain a first compressed file, and decompressing the first compressed file in the first storage area to obtain the corresponding target sub-video when checking the processing node;

[0021] If the existence time of the first compressed file reaches the first retention time, the first compressed file in the first storage area is deleted, and the second storage area performs lossy compression on the first compressed file to obtain a second compressed file. When viewing the processing node, the second compressed file in the second storage area is decompressed to obtain the corresponding target sub-video. If the existence time of the second compressed file reaches the second retention time, the target sub-video is deleted. When viewing the processing node, the corresponding operation process information or the cooperation process information is displayed.

[0022] Furthermore, a viewing time is preset, and the first compressed file or the second compressed file is restored before the viewing time arrives.

[0023] Furthermore, evaluating the treatment process includes the following steps:

[0024] The patient's disease manifestations are extracted based on the electronic medical record, and the disease manifestations are used as the treatment attributes of the second processing timeline. The second processing timeline is clustered based on the treatment attributes to obtain symptom clusters. Corresponding historical data are obtained based on the symptom clusters. The historical data are analyzed to obtain standard processing procedures. The standard processing procedures include time standards, mandatory operations and standard collaboration modes. Time efficiency, operation specification and team collaboration evaluations are performed on each second processing timeline based on the standard processing procedures. The treatment level is determined based on the evaluation results, and the treatment process is evaluated based on the treatment level.

[0025] Furthermore, the stay information includes arrival time, departure time and location information of the second sensor.

[0026] The present invention also provides an emergency green channel information management system, which is used to implement the above-mentioned emergency green channel information management method, and the system includes:

[0027] The sensing module includes a plurality of first sensors arranged in a hospital area, a second sensor worn on a patient's body, and the first sensor generates stay information when the second sensor reaches a detection range of the first sensor;

[0028] a first analysis module, generating a processing node based on the stay information, and generating a first processing timeline of the patient based on the processing node;

[0029] A second analysis module, based on the first video recorded by the body recorder and the second video recorded by the fixed camera, the second analysis module segments the first video and the second video according to the first processing timeline to obtain the first sub-video and the second sub-video corresponding to the processing node, analyzes the first sub-video and the second sub-video to obtain operation process information and coordination process information, and maps the first sub-video, the second sub-video, the operation process information, and the coordination process information to the first processing timeline to obtain a second processing timeline;

[0030] An evaluation module is used to obtain the patient's electronic medical record, assign disease manifestations to each second treatment timeline based on the electronic medical record, analyze the second treatment timelines with the same disease manifestations, and evaluate the treatment process.

[0031] The present invention also discloses a computer storage medium, wherein the computer storage medium stores program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute the above-mentioned method.

[0032] Beneficial effects:

[0033] The present invention achieves real-time monitoring of the patient's movement path by deploying multiple first sensors and second sensors worn by the patient within the hospital area. It then generates a first processing timeline based on the patient's stay information at various locations. Video data is recorded by a portable recorder and a fixed camera, and identified to obtain information about the operation process and coordination process. This information is then mapped onto the processing timeline to form a second processing timeline, making it easier to view the process nodes of the emergency treatment process. Furthermore, by obtaining the patient's electronic medical record and assigning disease manifestations, it is possible to perform cluster analysis on the processing timelines of the same disease manifestations, evaluate the efficiency and effectiveness of the treatment process, and thus optimize the emergency treatment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flowchart of the steps of an emergency green channel information management method of the present invention;

[0035] Figure 2 A schematic diagram of the principle of dividing the second window of the present invention;

[0036] Figure 3 This is a structural diagram of an emergency green channel information management system of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0038] It is understood that the terms "first," "second," etc., used herein may be used to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script without departing from the scope of this application.

[0039] like Figure 1 As shown, the present invention provides an emergency green channel information management method, which specifically includes the following steps:

[0040] S1: Multiple first sensors are deployed in the hospital area, and the patient wears a second sensor. When the second sensor reaches the detection range of the first sensor, the first sensor generates stay information.

[0041] Specifically, the stay information includes the arrival time, departure time and location information of the second sensor.

[0042] The first sensor includes an RFID sensor or a Bluetooth gateway, and the second sensor includes a beacon or a Bluetooth sensor. When an emergency patient arrives at the hospital, they can wear the second sensor or directly install the second sensor on the transfer bed. The first sensor is deployed at the entrance of each department, ward, operating room, etc. The first sensor itself has a location tag, such as the emergency entrance. When the second sensor enters the monitoring range of the first sensor, the second sensor establishes a connection with the first sensor. When the second sensor leaves the monitoring range of the first sensor, the second sensor disconnects from the first sensor. The first sensor can generate stop information based on the entry and exit time of the second sensor and send the stop information to the server for storage.

[0043] S2: Generate a processing node based on the stay information, and generate a first processing timeline of the patient based on the processing node.

[0044] For example, processing node 1 is at the emergency entrance from 8:00 to 8:02, processing node 2 is at triage registration from 8:05 to 8:07, and processing node 3 is at preliminary diagnosis in emergency room A from 8:08 to 8:12, then the above processing nodes are arranged in order as the first processing timeline.

[0045] S3: Record a first video based on a portable recorder, record a second video based on a fixed camera, split the first video and the second video according to a first processing timeline, and obtain a first sub-video and a second sub-video corresponding to the processing node.

[0046] S4: Analyze the first sub-video and the second sub-video to obtain operation process information and coordination process information respectively, and map the first sub-video, the second sub-video, the operation process information and the coordination process information respectively to the first processing timeline to obtain the second processing timeline.

[0047] During an emergency, medical staff wear video recorders to record a first video of the emergency treatment process. Surveillance cameras installed throughout the hospital capture a second video of the entire emergency area. The first and second videos are then segmented based on the processing nodes described above. For example, if processing node 1 is 8:00-8:02, the segment between 8:00-8:02 in the first video is captured as the first sub-video, and the segment between 8:00-8:02 in the second video is captured as the second sub-video.

[0048] Image recognition is then performed on the first and second sub-videos to obtain information about the operation process and coordination process. The operation process information includes the medical staff's treatment operation procedures, such as wound treatment, injections, and equipment use. The coordination process information includes communication and the arrival time of the consulting doctor. Finally, the first and second sub-videos, the operation process information, and the coordination process information are mapped to the processing nodes corresponding to the first processing timeline. When viewing the second processing timeline later, not only the process nodes of the entire emergency treatment process can be viewed, but also the specific scene videos corresponding to the nodes, which are convenient for review and quality inspection.

[0049] S5: Obtain the patient's electronic medical record, assign disease manifestations to each second treatment timeline based on the electronic medical record, analyze the second treatment timelines for the same disease manifestations to evaluate the treatment process.

[0050] Electronic medical records contain a patient's personal information, diagnosis, complications, and other information. This information is extracted from the patient's electronic medical record and used as the disease manifestation corresponding to the second treatment timeline. After vectorizing the disease manifestations, a K-means or DBSCAN algorithm is used for clustering. This clusters the second treatment timelines with the same disease manifestations into a single category, essentially grouping patients with the same physical condition and disease manifestations into a single category. Finally, by comparing and analyzing the various emergency treatment processes within this category, each treatment process is evaluated, identifying any problems and providing a data foundation for subsequent optimization of the emergency process.

[0051] The present invention achieves real-time monitoring of the patient's movement path by deploying multiple first sensors and second sensors worn by the patient within the hospital area. A processing timeline is then generated based on the patient's stay information at various locations. Video data is recorded by a portable recorder and a fixed camera, and identified to obtain information about the operation process and coordination process. This information is then mapped onto the processing timeline to form a second processing timeline, making it easier to view the process nodes of the emergency treatment process. In addition, by obtaining the patient's electronic medical record and assigning disease manifestations, cluster analysis can be performed on the processing timelines of the same disease manifestations to evaluate the efficiency and effectiveness of the treatment process, thereby optimizing the emergency treatment process.

[0052] This method not only intelligently captures the full treatment time of emergency green channel patients, but also comprehensively evaluates the efficiency and effectiveness of each step in the treatment process through video analysis technology. This method overcomes the shortcomings of existing technologies in real-time monitoring of the treatment process and video data analysis capabilities, making the management and evaluation of the emergency treatment process more accurate and comprehensive.

[0053] In this embodiment, analyzing the first sub-video and the second sub-video includes the following steps:

[0054] Define the first sub-video and the second sub-video as target videos, split the target video into multiple static frames, identify each static frame based on a deep neural network, obtain the operation events contained in it, merge the static frames of the same operation event to obtain the operation video of the corresponding operation event, determine the occurrence time period of the operation event according to the position of the operation video in the target video, divide the video length of the target video into multiple time windows based on the occurrence time period, and construct a first feature matrix with the time window as the row feature and the operation event as the column feature.

[0055] For ease of description, the first and second sub-videos are collectively defined as target videos. When analyzing the target video, the target video is first split into multiple static frames, each of which is then identified using a deep neural network. For the first sub-video, which is strongly associated with medical tools, this embodiment uses YOLOv8 to detect the tools in the static frames and obtain the included operation events, such as handheld syringes and cotton swabs. For the second sub-video, which requires target tracking, this embodiment uses the DeepSORT target tracking algorithm to identify and track the target, thereby obtaining the following operation events, such as Doctor A arriving at the target department for consultation at 6:00 PM.

[0056] For an injection operation, it may last for 30 seconds, and there may be hundreds of frames of images with the operation event of holding a syringe. Therefore, the static frames of the same hand-held syringe are merged to obtain a complete operation video. Then, based on the time tags of each static frame, the position of the operation video in the target video is further determined. For example, if the entire video length of the target video is 30 minutes, and the operation event of holding a syringe occurs between 10 and 11 minutes, then the time period of the operation video is between 10 and 11 minutes. Then, multiple time windows are generated based on the time period of each operation video. For example, after division, there are time window 1, time window 2, and time window 3. Time window 1 includes multiple operation events such as holding a syringe and doctor A coming for consultation. The purpose of constructing the first feature matrix is ​​to facilitate subsequent computer processing.

[0057] A preset arrangement rule is set, and the first feature matrix is ​​rearranged based on the arrangement rule to obtain a second feature matrix. Feature recognition is performed on the second feature matrix based on a convolutional neural network to extract feature events from the second feature matrix. The feature events include operation process information and coordination process information.

[0058] The arrangement rules are set manually. For example, the disinfection procedure includes handheld syringes, handheld cotton swabs, and handheld disinfectant. However, in the generated first feature matrix, the above elements may be far apart in the matrix. In order to facilitate the recognition of the convolutional neural network, their positions need to be adjusted. For example, the arrangement rule 1 is to group the handheld cotton swabs, handheld disinfectant, and handheld syringes. If the current first feature matrix is , where elements 1, 2, and 3 represent handheld cotton swabs, handheld disinfectant, and handheld syringes, respectively. By translating the handheld syringe and handheld disinfectant upward, the elements of handheld syringes, handheld cotton swabs, and handheld disinfectant are more concentrated, and the second characteristic matrix obtained is , so that the convolution kernel of the convolutional neural network can be easily identified and extracted, that is, it is convenient for the convolutional neural network to extract co-occurrence features. The present invention uses a 3*3 convolution kernel for feature extraction and determines feature events. For example, by extracting the three features of handheld syringe, handheld cotton swab and handheld disinfectant in time windows 1, 2, and 3, it is determined that there is an injection event in time windows 1-3, that is, operation process information. The number of convolution kernels of the convolutional neural network, the size of each convolution kernel, the setting of the pooling layer, and the selection of the activation function can be determined by those skilled in the art based on actual experience and will not be described here.

[0059] In this embodiment, dividing the time window includes the following steps:

[0060] Set the initial window duration, divide the video duration into multiple equally spaced first windows based on the initial window duration, obtain the proportion of the time period of the operation event in each first window, if the proportion is greater than the first threshold, define the operation event as occurring in the corresponding first window, count the event overlap rate of adjacent first windows including operation events, if the event overlap rate is greater than the second threshold, merge the two first windows into a second window, count the density of operation events included in the second window, locate the second window with a density greater than the third threshold, split it into windows to obtain the third window and the fourth window, and define the unmerged first window, the undivided second window after the merger, the third window and the fourth window as time windows.

[0061] For example, if the initial window length is set to 10s and the video length is 10 minutes, the video length will be divided into 60 first time windows, and then the proportion of the time period of the operation event in each first window will be obtained. For example, the time span of operation event A is within the first windows 1-4, and it appears in the first window 1 for 5s, then the proportion in the first window 1 is 6S. If the proportion in the first windows 1-4 exceeds the first threshold, for example, the first threshold is 5s, then it is defined that operation event A appeared in the first windows 1-4. The judgment process for other operation events is the same.

[0062] Then, the event overlap rate of the operation events that appear in the adjacent first windows is counted. If operation events A, B, C, and E appear in the first window 1 and operation events A, B, C, D, and E appear in the first window 2, the event overlap rate is calculated using the first formula. The first formula is: ,in, is to compare the event overlap rate of the two first windows, X is the number of the same operation events in the two first windows, To obtain the maximum number of operation events in the two first windows, are the number of operation events included in the two compared first windows; the event overlap rate of the above example is 4 / 5*100%=80%, and the second threshold is set to 78%. Then, the first window 1 and the first window 2 are merged into the second window 1. Then, the event overlap rate of the second window 1 and the first window 3 are compared. If the event overlap rate is greater than the second threshold, the first window 3 is merged into the second window 1. Otherwise, the second window 1 is retained, and the first window 3 and the first window 4 are compared. This process is repeated until the last first window is processed.

[0063] After merging, the density of the operation events included in each second window is counted. If the density is greater than a third threshold (set to 5), it indicates that the second window includes too many operation events, which is not conducive to feature recognition by the deep learning model. Therefore, the second window needs to be segmented again. The specific segmentation method is described later. After defining the segmentation of the second window, the third and fourth windows are obtained. Finally, the existing first, second, third, and fourth windows are defined as time windows.

[0064] In this embodiment, splitting the second window includes the following steps:

[0065] A first window is selected from the second window as a split window, and the second window is split into a first sub-window and a second sub-window based on the split window. A first number and a second number of operation events included in the first sub-window and the second sub-window are counted, and a difference between the first number and the second number is calculated. Another first window is selected from the second window as a split window and re-split, and a difference value obtained after the split is calculated. This step is repeated until the first windows in the second window are traversed, and the first sub-window and the second sub-window corresponding to the minimum difference value are located as the segmentation result of the second window.

[0066] like Figure 2 As shown, since the second window is formed by merging multiple first windows, each second window will include multiple first windows. In particular, if the number of first windows included in the second window is less than 3, it will not be split. In actual use, if the number of first windows included is less than 3, the density will not be greater than the third threshold. When splitting, select in reverse chronological order, first select Figure 2 The first window 9 in the example is split with the first window 9 as the split window to obtain the first sub-window (first window 1-8) and the second sub-window (first window 9-10). The difference value is the absolute value of the difference between the operation events included in the two sub-windows. The first sub-window includes 12 operation events (AL) and the second sub-window includes 3 operation events (H, J, L). Therefore, the difference value is 9.

[0067] When splitting the first window 6, the first sub-window includes six operation events (AF) and the second sub-window includes six operation events (GL). The difference value is 0, which is the smallest. Therefore, the first sub-window (first windows 1-5) and the second sub-window (first windows 6-10) are used as the final segmentation result of the second window. This method can reasonably segment the second window, so that the split windows can include multiple complete operation events.

[0068] In this embodiment, projecting the first sub-video and the second sub-video into the first processing timeline includes the following steps:

[0069] Define the first sub-video and the second sub-video as target sub-videos, set a label in the target sub-video, the label corresponds to the processing node, set a first storage area and a second storage area, redundantly store the target sub-video in the first storage area and the second storage area, losslessly compress the target sub-video to obtain a first compressed file, and when checking the processing node, decompress the first compressed file in the first storage area to obtain the corresponding target sub-video.

[0070] For the sake of simplicity, the first sub-video and the second sub-video are collectively referred to as the target sub-video. When storing the target sub-video, a label corresponding to the processing node is marked in the target sub-video so that when accessing the processing node, the corresponding target sub-video can be located. This embodiment also provides a first storage area and a second storage area, and stores the target sub-video in both storage areas, thereby achieving redundant storage and preventing accidental video loss. In order to save storage space, the target sub-video is losslessly compressed into a first compressed file for storage. When viewing, the first compressed file in the first storage area is decompressed to obtain the target sub-video. If the first compressed file is not in the first storage area, the target sub-video is searched for in the second storage area.

[0071] If the existence time of the first compressed file reaches the first retention time, the first compressed file in the first storage area is deleted, and the second storage area performs lossy compression on the first compressed file to obtain a second compressed file. When viewing the processing node, the second compressed file in the second storage area is decompressed to obtain the corresponding target sub-video. If the existence time of the second compressed file reaches the second retention time, the target sub-video is deleted. When viewing the processing node, the corresponding operation process information or cooperation process information is displayed.

[0072] The first retention time is set to 6 years. In order to further save storage space, after the existence time of the first compressed file exceeds 6 years, if it is in the first storage space, it will be deleted. If it is in the second storage space, it will be decompressed first to obtain the target sub-video, and then the target sub-video will be lossily compressed to obtain the second compressed file. In this way, the storage space of the first storage area and the second storage area can be further released. Finally, if the existence time of the second compressed file reaches the second retention time, the second retention time is 2 years, that is, the target sub-video has been stored for 8 years, then the second storage space will be deleted. However, the operation process information or coordination process information corresponding to the target sub-video is retained, so that when viewing the processing node, the processing status at that time can still be obtained based on the text information.

[0073] In this embodiment, a viewing time is also preset, and the first compressed file or the second compressed file is restored before the viewing time arrives.

[0074] The viewing time is manually set. For example, if you set the system to review the emergency treatment process of the previous week at 3:00 PM every Wednesday, the system will decompress the first compressed file of the previous week in the first storage area at 2:40 PM every Wednesday, further speeding up the video display. The specific viewing time setting rules are determined according to actual needs.

[0075] The evaluation and treatment process in this embodiment includes the following steps:

[0076] The patient's disease manifestations are extracted based on the electronic medical record, and the disease manifestations are used as the treatment attributes of the second processing timeline. The second processing timeline is clustered based on the treatment attributes to obtain symptom clusters. The corresponding historical data are obtained based on the symptom clusters. The historical data are analyzed to obtain the standard processing procedure. The standard processing procedure includes time standards, mandatory operations and standard collaboration modes. Based on the standard processing procedure, each second processing timeline is evaluated in terms of time efficiency, operation specifications and team collaboration. The treatment level is determined based on the evaluation results, and the treatment process is evaluated based on the treatment level.

[0077] Specifically, disease manifestations also include the patient's chief complaint, such as squeezing pain behind the sternum accompanied by cold sweats, and physical signs, such as systolic blood pressure >140 and heart rate >100. The second processing timeline is then clustered based on the treatment attributes. Suppose the second processing timeline is clustered with the 3,000 chest pain patients in the database, and the patient's symptom cluster is determined to be myocardial infarction. For myocardial infarction, 200 similar cases from the same cluster that have been successfully treated in the past are obtained. By analyzing these similar cases, the time standard for treatment, required operations, and standard collaboration models are determined. For time standards, the median of historical data can be extracted, such as the standard time for electrocardiogram is 8 minutes. For mandatory operations, treatment operations that will be performed in 90% of cases are selected, such as electrocardiogram and myocardial infarction tests. For standard collaboration models, the proportion of various cross-department handovers is obtained, such as the direct handover between cardiology and catheterization room accounts for 85%, thereby obtaining a standard handover model. For example, handovers between the emergency department, laboratory department, cardiology department, and catheterization room are required. In addition, the average response time of each department is obtained as the standard response time, such as the average response time for cardiology consultation is 15 minutes.

[0078] When conducting time efficiency evaluation, operation standard evaluation and team collaboration evaluation, the time efficiency score, operation standard score and team collaboration score are calculated based on the following second formula, third formula and fourth formula respectively. The second formula is ,in, Score for time efficiency, is the number of processing nodes included in the second processing timeline, is the deviation between the actual processing time of the nth processing node in the second processing timeline and the corresponding standard processing time, is the standard processing time of the nth processing node. For example, if the total deviation value of each processing node is 30 minutes (numerator) and the total standard processing time is 120 minutes (denominator), the time efficiency score is 1-30 / 120=0.75.

[0079] The third formula is ,in, Rate the operating specifications. is the number of treatment operations actually performed in the second treatment timeline, is the number of standard treatment operations. For example, if 3 operations were actually performed and there were 6 standard treatment operations, the operation standard score would be 3 / 6=0.5.

[0080] The fourth formula is ,in, Score teamwork. is the number of actual department handovers in the second processing timeline, The number of handovers in standard handover mode, The number of departments that conducted the consultation, is the deviation between the actual response time of the i-th department and the corresponding standard response time, is the standard response time of the i-th department. If the number of actual department handovers is 3, the number of handovers in the standard handover model is 2, the total deviation of the response of each department is 7 minutes (numerator), and the total standard response time is 30 minutes (denominator), then the teamwork score is 0.635.

[0081] Finally, the time efficiency score, operation standard score and teamwork score are weighted and summed to obtain a comprehensive score. Then, multiple numerical ranges are set, and each numerical range corresponds to a level of treatment, such as 0.6-0.7 points corresponding to level D, thus completing the evaluation of the treatment process.

[0082] like Figure 3 As shown, the present invention also provides an emergency green channel information management system, which is used to implement the above-mentioned emergency green channel information management method, and the system includes:

[0083] The sensing module includes a plurality of first sensors arranged in the hospital area, a second sensor worn on the patient's body, and the first sensor generates stay information when the second sensor reaches the detection range of the first sensor;

[0084] a first analysis module, generating a processing node based on the stay information, and generating a first processing timeline of the patient based on the processing node;

[0085] A second analysis module, based on the first video recorded by the portable recorder and the second video recorded by the fixed camera, the second analysis module segments the first video and the second video according to the first processing timeline to obtain first sub-videos and second sub-videos corresponding to the processing nodes, analyzes the first sub-video and the second sub-video to obtain operation process information and coordination process information, respectively, and maps the first sub-video, the second sub-video, the operation process information, and the coordination process information to the first processing timeline to obtain a second processing timeline;

[0086] The evaluation module is used to obtain the patient's electronic medical record, assign disease manifestations to each second treatment timeline based on the electronic medical record, analyze the second treatment timelines of the same disease manifestations, and evaluate the treatment process.

[0087] The present invention also discloses a computer storage medium, wherein the computer storage medium stores program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute the above-mentioned method.

[0088] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] The above embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0090] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An emergency green channel information management method, characterized in that: include: A plurality of first sensors are arranged in the hospital area, and a second sensor is worn on the patient's body. When the second sensor reaches the detection range of the first sensor, the first sensor generates stay information, and the stay information includes the arrival time, departure time and location information of the second sensor; generating a processing node based on the stay information, and generating a first processing timeline of the patient based on the processing node; Recording a first video with a portable recorder and a second video with a fixed camera, segmenting the first video and the second video according to the first processing timeline to obtain a first sub-video and a second sub-video corresponding to the processing node; Analyze the first sub-video and the second sub-video to obtain operation process information and coordination process information, respectively, and map the first sub-video, the second sub-video, the operation process information, and the coordination process information to the first processing timeline to obtain a second processing timeline; Obtaining the patient's electronic medical record, assigning each second treatment timeline disease manifestation based on the electronic medical record, and analyzing the second treatment timelines with the same disease manifestation to evaluate the treatment process; Analyzing the first sub-video and the second sub-video includes the following steps: Define the first sub-video and the second sub-video as target videos, split the target video into multiple static frames, identify each static frame in the first sub-video based on the YOLOv8 algorithm, identify each static frame in the second sub-video based on the DeepSORT algorithm, obtain the operation events contained therein, merge the static frames with the same operation event to obtain an operation video corresponding to the operation event, determine the occurrence time period of the operation event based on the position of the operation video in the target video, divide the video length of the target video into multiple time windows based on the occurrence time period, and construct a first feature matrix with the time windows as row features and the operation events as column features; Preset an arrangement rule, rearrange the first feature matrix based on the arrangement rule to obtain a second feature matrix, perform feature recognition on the second feature matrix based on a convolutional neural network to extract feature events from the second feature matrix, the feature events including the operation process information and the coordination process information; Dividing the time window comprises the following steps: Setting an initial window duration, dividing the video duration into a plurality of equally spaced first windows based on the initial window duration, obtaining a proportion of the occurrence time period of the operation event in each of the first windows, defining the operation event as occurring in the corresponding first window if the proportion is greater than a first threshold, counting an event overlap rate of adjacent first windows including the operation event, merging two first windows into a second window if the event overlap rate is greater than a second threshold, counting a density of the operation event included in the second window, locating the second window having the density greater than a third threshold, performing window segmentation on the second window to obtain a third window and a fourth window, and defining the unmerged first window, the undivided second window after merging, the third window, and the fourth window as the time window; Performing window segmentation on the second window includes the following steps: The first window is selected from the second window as a split window, and the second window is split into a first sub-window and a second sub-window based on the split window. A first number and a second number of operation events included in the first sub-window and the second sub-window are counted, and a difference between the first number and the second number is calculated. Another first window is selected from the second window as the split window and re-split, and the difference obtained after the split is calculated. This step is repeated until the first windows within the second window are traversed, and the first sub-window and the second sub-window corresponding to the minimum difference value are located as the segmentation result of the second window.

2. The method according to claim 1, characterized in that Mapping the first sub-video and the second sub-video to the first processing timeline includes the following steps: defining the first sub-video and the second sub-video as target sub-videos, setting a tag in the target sub-video, the tag corresponding to the processing node, setting a first storage area and a second storage area, redundantly storing the target sub-video in the first storage area and the second storage area, losslessly compressing the target sub-video to obtain a first compressed file, and decompressing the first compressed file in the first storage area to obtain the corresponding target sub-video when checking the processing node; If the existence time of the first compressed file reaches the first retention time, the first compressed file in the first storage area is deleted, and the second storage area performs lossy compression on the first compressed file to obtain a second compressed file. When viewing the processing node, the second compressed file in the second storage area is decompressed to obtain the corresponding target sub-video. If the existence time of the second compressed file reaches the second retention time, the target sub-video is deleted. When viewing the processing node, the corresponding operation process information or the cooperation process information is displayed.

3. The method according to claim 2, characterized in that A viewing time is preset, and the first compressed file or the second compressed file is restored before the viewing time arrives.

4. The method according to claim 1, wherein The evaluation process includes the following steps: The patient's disease manifestations are extracted based on the electronic medical record, and the disease manifestations are used as the treatment attributes of the second processing timeline. The second processing timeline is clustered based on the treatment attributes to obtain symptom clusters. Corresponding historical data are obtained based on the symptom clusters. The historical data are analyzed to obtain standard processing procedures. The standard processing procedures include time standards, mandatory operations and standard collaboration modes. Time efficiency, operation specification and team collaboration evaluations are performed on each second processing timeline based on the standard processing procedures. The treatment level is determined based on the evaluation results, and the treatment process is evaluated based on the treatment level.

5. An emergency green channel information management system, used to implement the method according to any one of claims 1 to 4, characterized in that: include, The sensing module includes a plurality of first sensors arranged in a hospital area, a second sensor worn on a patient's body, and the first sensor generates stay information when the second sensor reaches a detection range of the first sensor; a first analysis module, generating a processing node based on the stay information, and generating a first processing timeline of the patient based on the processing node; A second analysis module, based on the first video recorded by the body recorder and the second video recorded by the fixed camera, the second analysis module segments the first video and the second video according to the first processing timeline to obtain the first sub-video and the second sub-video corresponding to the processing node, analyzes the first sub-video and the second sub-video to obtain operation process information and coordination process information, and maps the first sub-video, the second sub-video, the operation process information, and the coordination process information to the first processing timeline to obtain a second processing timeline; An evaluation module is used to obtain the patient's electronic medical record, assign disease manifestations to each second treatment timeline based on the electronic medical record, analyze the second treatment timelines with the same disease manifestations, and evaluate the treatment process.

6. A computer storage medium, characterized in that The computer storage medium stores program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute the method according to any one of claims 1 to 4.

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