An operating room medical staff behavior management system

Abstract

This invention provides an operating room medical staff behavior management system, belonging to the field of information technology management consulting technology. It includes: a process configuration module for determining the corresponding surgical list for each medical staff member based on their job permissions and configuring the behavior monitoring process; a process parsing module for setting monitoring targets for medical staff based on the parsing results of each monitoring step; a personnel analysis module for identifying non-standard behaviors of medical staff based on the behavior monitoring results of each monitoring step and analyzing all non-standard behaviors of each medical staff member involved in the current surgery to obtain comprehensive behavioral norms; and a behavior management module for determining the list of permitted dispatch personnel and behavioral norms for all hospital areas based on the comprehensive behavioral norms and the analysis results of medical staff in the corresponding surgical scenarios, storing this list on a cloud platform for managers of different hospital areas to view. This effectively achieves efficient, standardized, and managed operating rooms.

Description

Technical Field

[0001] This invention relates to the field of behavioral information technology management consulting technology, and in particular to a behavioral management system for medical staff in operating rooms. Background Technology

[0002] With the rapid development of medical technology and the continuous improvement of surgical quality, the operating room, as a core department of the hospital, directly affects patient safety and the overall operational level of the hospital through its management efficiency and standardization. Existing cloud platforms generally only store surgical information, such as patient names, surgical types, and the names of medical staff involved in the surgery. This leads to uncertainty about whether operating room staff adhere to proper behavioral standards, creating an information gap. Furthermore, the existing operating room management system lacks integrated information system integration services such as implementation services and information system design services. It also lacks supporting information processing and storage services such as business-to-consumer (B2C) e-commerce services and professional data storage and backup services. Moreover, it lacks information technology consulting services such as information planning and information technology management consulting. This results in a fragmented operating room information management system, with data storage lacking professional security guarantees, system design and implementation lacking professional integration service support, and management decision-making lacking professional information technology consulting guidance. This further exacerbates management problems such as low efficiency, poor standardization, and information silos in the operating room, making it difficult to meet the management needs of modern hospitals with multiple campuses and information technology requirements.

[0003] Therefore, this invention proposes a behavior management system for medical staff in the operating room. Summary of the Invention

[0004] This invention provides an operating room medical staff behavior management system, which configures monitoring processes for different surgical situations based on the permissions of each medical staff member, so as to ensure targeted monitoring of the behavior of each medical staff member. Furthermore, by identifying and analyzing non-standard behaviors, it determines and stores the standardization of the surgery, ensuring comprehensive information and effectively achieving efficient standardization and management of the operating room.

[0005] This invention provides an operating room medical staff behavior management system, comprising: The process configuration module is used to determine the surgical list of each medical staff member based on their job permissions, and to configure a behavior monitoring process for each surgical procedure in the surgical list. The process parsing module is used to retrieve the required monitoring process from all configuration results and perform process parsing when a surgery related to the medical staff is triggered, and to set monitoring targets for the medical staff to monitor their behavior based on the parsing results of each monitoring step. The personnel analysis module is used to determine the non-standard behaviors of the medical staff based on the behavior monitoring results of each monitoring step, and to analyze all non-standard behaviors of each medical staff involved in the on-site surgery to obtain the comprehensive behavioral standardization of the corresponding on-site surgery. The behavior management module is used to determine the list of personnel allowed to be dispatched and their behavior norms for all hospital areas based on the comprehensive behavioral norms and the analysis results of medical staff in the corresponding surgical scenarios, and then store them on the cloud platform for managers of different hospital areas to view.

[0006] Preferably, the process configuration module includes: An initial construction unit is used to retrieve the medical staff’s historical surgical involvement and their responsibilities in the historical surgical involvement from the historical database, and to construct an initial list; The permission acquisition unit is used to acquire the medical position of each medical staff member and the position permissions assigned based on the medical position; The field mapping unit is used to extract fields from the job permissions, and establish a matching distribution of the operation attribute field set with each extracted field as a cluster center and the corresponding surgical operation process. Using the cluster center as the center point of the corresponding matching distribution, and dividing the area into circles with a preset radius from the center point, a first circle is obtained; The outermost field in the corresponding matching distribution is regarded as a point and connected sequentially to obtain the contour boundary. The longest opposite edge of the contour boundary is locked. At the same time, the contour boundary is divided horizontally and vertically to obtain the first line and the second line respectively. Based on the longest opposite edge, the first line, and the second line, and in combination with the preset radius and the first circle, the matching coefficient of the corresponding extracted field is calculated; Fields with a matching coefficient greater than 0 are considered as matching fields. The coefficient calculation unit is used to determine the sensitivity coefficient of the corresponding surgery based on the matching fields and matching coefficients under the same surgery. The list acquisition unit is used to regard surgeries with a sensitivity coefficient greater than a preset coefficient as first surgeries. When there is no first surgery in the initial list, the corresponding first surgery and its corresponding responsibilities are added to the initial list to obtain the surgery list of the corresponding medical staff.

[0007] Preferably, the process configuration module further includes: The process matching unit is used to match the corresponding behavior monitoring process from the type-responsibility-process comparison table according to the surgical type and responsibilities of each surgery in the surgical list.

[0008] Preferably, the process parsing module includes: The retrieval unit is used to determine when the medical staff receives a surgical preparation notification to trigger a surgery. At this time, the process that is consistent with the triggered surgery is retrieved from the configuration result corresponding to the medical staff's surgery list and is regarded as the required monitoring process. The equipment determination unit is used to match the required monitoring process with the process equipment lookup table to obtain the monitoring equipment for each monitoring step before, during and after the operation for the medical staff. The behavior monitoring unit is used to control the monitoring equipment to monitor the behavior of medical staff at different stages.

[0009] Preferably, the monitoring equipment includes: cameras and radio frequency identification devices for predefined standard management nodes under different monitoring steps.

[0010] Preferably, the personnel analysis module includes: The comparative analysis unit is used to retrieve standard monitoring results consistent with the monitoring steps from the standard database, and compare and analyze them with the behavioral monitoring results of the corresponding monitoring steps to identify non-standard nodes. The extraction unit is used to extract the monitoring comparison results of the remaining personnel under the non-standard node to obtain the first adjustment coefficient, and update the comparison analysis coefficient under the non-standard node; The state determination unit is used to re-determine whether the non-standard node is still in a non-standard state based on the update coefficient. If so, the non-standard node is retained. If not, the non-standard nodes will be removed. The coefficient analysis unit is used to obtain and analyze the update coefficients of all retained non-standard nodes of the same medical staff during the on-site surgery. The comprehensive analysis unit is used to obtain the comprehensive behavioral norms of the corresponding surgery based on the analysis results of all medical staff in the on-site surgery and in combination with the node distribution of the non-standard nodes retained by each medical staff member.

[0011] Preferably, the comprehensive analysis unit includes: The distribution determination subunit is used to statistically analyze all the retained non-standard nodes of all medical staff during the on-site surgery to obtain the node distribution for the on-site surgery. The node distribution includes the step number of the retained non-standard node and the statistical count of the corresponding retained non-standard node. A single deterministic subunit is used to place all update coefficients according to the order of node appearance to obtain the analysis vector of the corresponding medical staff, and input the analysis vector into the normative analysis model to obtain the single normativity of the corresponding medical staff. The analysis result is the corresponding first normativity. The comprehensive sub-unit is used to determine the comprehensive behavioral norms of the corresponding surgery based on the node distribution and the individual norms of each medical staff member in the current surgery.

[0012] Preferably, the behavior management module includes: The storage unit is used to store the individual specifications of each medical staff member involved and the comprehensive specifications of the corresponding surgery to the cloud platform after each surgery is completed. The normative list generation unit is used to automatically generate behavioral normative lists for different surgical procedures and construct behavioral normative lists for different medical staff when managers from different hospital areas view them, based on the cloud platform to obtain viewing instructions. The scheduling judgment unit is used to determine whether the corresponding medical staff are allowed to be scheduled based on the list of behavioral norms of the corresponding medical staff. If they are allowed, the unit marks the corresponding medical staff as scheduled and stores the information.

[0013] Compared with the prior art, the beneficial effects of this application are as follows: Based on the permissions of each medical staff member, monitoring procedures for different surgical situations are configured to ensure targeted monitoring of the behavior of each medical staff member. Then, by identifying and analyzing non-standard behaviors, the standardization of the surgery is determined and stored, ensuring comprehensive information and effectively achieving efficient standardization and management of the operating room. Meanwhile, this system can integrate information system integration services such as implementation services and information system design services, providing professional integration technical support for the system's deployment, debugging, and optimization in different hospital areas, ensuring the system's adaptability and stability in multi-hospital environments. It also provides supporting information processing and storage services such as business-to-consumer (B2C) e-commerce services, data storage, and backup services, achieving professional, secure, and long-term storage and backup of surgical behavior management-related data. Furthermore, it can connect with the B2C e-commerce needs of medical staff for information services, expanding the system's service dimensions. Integrating information technology consulting services such as information planning and information technology management consulting, it provides professional consulting guidance for the information management planning of hospital operating rooms, the technical optimization of system operation, and the construction of multi-hospital management systems. This achieves full-process information service support from behavior monitoring to data management to system integration to technical consulting, completely breaking down information silos, building an integrated operating room information management system, and further enhancing the professionalism, systematicness, and efficiency of operating room management in a multi-hospital model.

[0014] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a structural diagram of an operating room medical staff behavior management system according to an embodiment of the present invention; Figure 2 This is a structural diagram of the hospital management system in an embodiment of the present invention; Figure 3 This is a structural diagram of the contour boundary in an embodiment of the present invention; Figure 4 This is a structural diagram of identity recognition in an embodiment of the present invention. Detailed Implementation

[0017] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0018] This invention provides a behavior management system for operating room medical staff, such as... Figure 1 As shown, it includes: The process configuration module is used to determine the surgical list of each medical staff member based on their job permissions, and to configure a behavior monitoring process for each surgical procedure in the surgical list. The process parsing module is used to retrieve the required monitoring process from all configuration results and perform process parsing when a surgery related to the medical staff is triggered, and to set monitoring targets for the medical staff to monitor their behavior based on the parsing results of each monitoring step. The personnel analysis module is used to determine the non-standard behaviors of the medical staff based on the behavior monitoring results of each monitoring step, and to analyze all non-standard behaviors of each medical staff involved in the on-site surgery to obtain the comprehensive behavioral standardization of the corresponding on-site surgery. The behavior management module is used to determine the list of personnel allowed to be dispatched and their behavior norms for all hospital areas based on the comprehensive behavioral norms and the analysis results of medical staff in the corresponding surgical scenarios, and then store them on the cloud platform for managers of different hospital areas to view.

[0019] In this embodiment, in modern medical systems, hospitals are gradually expanding in size, and operating rooms are no longer limited to a single hospital area, but rather form a multi-site medical network. To eliminate information barriers, management on a single platform is required, while each operating room operates independently, and business data must also operate independently. Therefore, the following must be achieved: One platform login, but different access permissions for each hospital district, because different hospital districts target different patient groups. For example, hospital district A targets pregnant women, while hospital district B targets newborns.

[0020] Furthermore, the basic information of medical staff is updated once and is valid across multiple hospital campuses. Generally, campus-level administrators only manage their own campus. The specific implementation method is as follows: Figure 2 As shown.

[0021] In this embodiment, medical positions include, for example, operating room nurses, operating room surgeons, and operating room assistants. The authority of medical staff in different positions is different. For example, the authority of an operating room nurse is to pay attention to the cleanliness of the operating room, adjust the lighting and room temperature, add the necessary medications, and clean the instruments.

[0022] For example, the job authority of operating room nurse A1 includes: cleaning all instruments in surgery 01 and cleaning some instruments in surgery 02. In this case, the surgery list includes surgery 01 and surgery 02, as well as the responsibilities of the medical staff and the surgery.

[0023] In this embodiment, the behavior monitoring process is pre-set. For example, if operating room nurse A1 cleans all instruments for surgery 01, the corresponding monitoring process includes: identifying nurse A1, monitoring handwashing clothing, operating room slippers, and changing room access, etc., to regulate behavior based on the surgery. Specifically, identification can be achieved using biometrics, such as... Figure 4 As shown.

[0024] In this embodiment, non-standard behavior refers to behavior that does not meet the set standards. For example, the monitoring step is to monitor the handwashing process of medical staff, and the monitoring result is that the medical staff did not use disinfection tools to disinfect their hands during the handwashing process. The set standard is that disinfection tools should be used to disinfect hands. In this case, the behavior can be regarded as non-standard behavior.

[0025] In this embodiment, the analysis results refer to a single norm for healthcare workers.

[0026] In this embodiment, the comprehensive behavioral norms are derived from the analysis results of each medical staff member.

[0027] In this embodiment, "allowed dispatchers" refers to medical staff who can be dispatched to other hospital areas.

[0028] In this embodiment, the code of conduct includes guidelines for both medical staff and surgical procedures.

[0029] In this embodiment, the surgery-related information stored on the cloud platform includes, but is not limited to, the behavior management information of medical staff. It can effectively output and display information related to the surgical behavior management system, such as the consumption of clothing and footwear materials, the number of people entering and leaving, and can also effectively output and display comprehensive statistical information such as the turnover rate of changing room lockers, the peak distribution of personnel entering and leaving the operating room, and surgical information.

[0030] The beneficial effects of the above technical solution are: based on the permissions of each medical staff member, a monitoring process for different surgical situations is configured to ensure targeted monitoring of the behavior of each medical staff member. Then, by identifying and analyzing non-standard behaviors, the standardization of the surgery is determined and stored, ensuring comprehensive information and effectively achieving efficient standardization and management of the operating room.

[0031] This invention provides an operating room medical staff behavior management system, wherein the process configuration module includes: An initial construction unit is used to retrieve the medical staff’s historical surgical involvement and their responsibilities in the historical surgical involvement from the historical database, and to construct an initial list; The permission acquisition unit is used to acquire the medical position of each medical staff member and the position permissions assigned based on the medical position; The field mapping unit is used to extract fields from the job permissions, and establish a matching distribution of the operation attribute field set with each extracted field as a cluster center and the corresponding surgical operation process. Using the cluster center as the center point of the corresponding matching distribution, and dividing the area into circles with a preset radius from the center point, a first circle is obtained; The outermost field in the corresponding matching distribution is regarded as a point and connected sequentially to obtain the contour boundary. The longest opposite edge of the contour boundary is locked. At the same time, the contour boundary is divided horizontally and vertically to obtain the first line and the second line respectively. Based on the longest opposite edge, the first line, and the second line, and in combination with the preset radius and the first circle, the matching coefficient of the corresponding extracted field is calculated; ; Where X represents the matching coefficient of the corresponding extracted field; This indicates the number of fields whose corresponding matching distribution falls within the first circle; This indicates the number of fields present in the corresponding matching distribution; Lmax, Ld1, and Ld2 represent the lengths of the longest opposite edge, the first line, and the second line, respectively; r represents the preset radius; max represents the maximum value sign; Fields with a matching coefficient greater than 0 are considered as matching fields. The coefficient calculation unit is used to determine the sensitivity coefficient of the corresponding surgery based on the matching fields and matching coefficients under the same surgery. ; in, This indicates the sensitivity coefficient of the corresponding surgery; This indicates the number of matching fields involved in the corresponding surgery; This represents the matching coefficient of the i1th matching field under the corresponding surgery; This indicates the number of fields in the corresponding surgical operation attribute field set; This represents the sum of the field weights of all operation attribute fields involved in the matching distribution of the i1th matching field; The list acquisition unit is used to regard surgeries with a sensitivity coefficient greater than a preset coefficient as first surgeries. When there is no first surgery in the initial list, the corresponding first surgery and its corresponding responsibilities are added to the initial list to obtain the surgery list of the corresponding medical staff.

[0032] Preferably, the process configuration module further includes: The process matching unit is used to match the corresponding behavior monitoring process from the type-responsibility-process comparison table according to the surgical type and responsibilities of each surgery in the surgical list.

[0033] In this embodiment, the historical database includes surgeries that different medical staff have participated in and their responsibilities in the corresponding surgeries, thus obtaining an initial list.

[0034] In this embodiment, each job permission has its own unique field symbol for unique representation. Therefore, subsequent analysis is carried out by field extraction. For example, the job permission of medical staff A1 is: &&100%. At this time, the extracted fields are "&&" and "%%". Then, the fields in the operation attribute field set of the operation process are clustered and classified using this field as the cluster center.

[0035] In this embodiment, the operation process refers to the surgical process, such as the process of coronary artery bypass surgery. At this time, each operation step involved in the process has its corresponding operation attributes, such as intervention attributes, construction attributes, suturing attributes, etc., and different attributes have their corresponding fields.

[0036] In this embodiment, using the extracted fields as cluster centers is achieved through a clustering algorithm to realize the clustering distribution results of the operational attribute field set, which are then regarded as a matching distribution. Specifically: The set of operational attribute fields in the surgical procedure is vectorized to obtain the feature vector of each field. Using the extracted field of job permissions as the cluster center, and setting the number of clusters to 1, calculate the cosine distance between the feature vector of each operation attribute field and the feature vector of the cluster center; Operation attribute fields with a cosine distance less than a preset threshold (0.8) are assigned to the matching distribution of the cluster center, while those with a cosine distance greater than 0.8 are removed. Finally, the matching distribution results of the extracted fields and the set of operation attribute fields are obtained.

[0037] In this embodiment, the preset radius is pre-set, mainly for dividing the circle. It is a pixel value set based on the number of fields in the operation attribute field set, and the preset radius = 10 × Where n is the number of fields in the corresponding surgical operation attribute field set, and the preset radius ranges from [20, 100] pixels; when the number of fields is less than 4, the preset radius is fixed at 20 pixels, and when it is greater than 100, the preset radius is fixed at 100 pixels.

[0038] In this embodiment, such as Figure 3 As shown, the outermost field refers to the field involved in the outermost boundary of the corresponding matching distribution. For example, the matching distribution of cluster center a1 includes: b1, b2, b3, b4, b5. In this case, the connected contour C1 is the contour boundary.

[0039] In this embodiment, the longest opposite line refers to the longest line extracted from the line segment formed by any two points on the contour boundary.

[0040] In this embodiment, the first line and the second line are C2 and C3, respectively.

[0041] In this embodiment, the preset coefficient is obtained by statistically analyzing the matching data of job authority and surgical operation attribute fields of 100 groups of medical staff in different positions. By calculating the average sensitivity coefficient (1.0) + standard deviation (0.3) of the 100 groups of data, the critical value of 1.3 is obtained, which is statistically significant. If the hospital is a tertiary hospital or other institution with higher requirements for surgical management, the preset coefficient is adjusted to 1.5, and the preset coefficient is adjusted to 1.0 for primary hospitals.

[0042] In this embodiment, the weights of the operational attribute fields are assigned according to the importance of the surgical operation. Core operational fields (such as surgical instrument operation and disinfection) have a weight of 0.6, auxiliary operational fields (such as operating room environment adjustment and material preparation) have a weight of 0.3, and basic operational fields (such as identity verification and dress code) have a weight of 0.1. The matching distribution of each extracted field involves the weights of the operational attribute fields and... , which is the sum of the weights of each field.

[0043] In this embodiment, the type-responsibility-process lookup table is a three-dimensional data table containing six core fields: surgery type, medical staff responsibilities, monitoring process number, monitoring steps, monitoring nodes, and standard behavioral requirements. Each record in the table corresponds to a monitoring step of a certain medical staff member with a certain responsibility in a certain type of surgery. It is pre-stored and can be directly matched after the permissions of the medical staff member are determined.

[0044] The beneficial effects of the above technical solution are: an initial list is constructed based on a historical database, and then the outline boundary is constructed based on the matching distribution results of the fields of job permissions and operation attribute fields of medical staff. Furthermore, the matching coefficient and sensitivity coefficient are obtained through line analysis, which provides a basis for the subsequent expansion of the list. All possibilities that need to be monitored for behavior are taken into account to ensure the efficiency of subsequent behavior management.

[0045] This invention provides a behavior management system for operating room medical staff, wherein the process parsing module includes: The retrieval unit is used to determine when the medical staff receives a surgical preparation notification to trigger a surgery. At this time, the process that is consistent with the triggered surgery is retrieved from the configuration result corresponding to the medical staff's surgery list and is regarded as the required monitoring process. The equipment determination unit is used to match the required monitoring process with the process equipment lookup table to obtain the monitoring equipment for each monitoring step before, during and after the operation for the medical staff. The behavior monitoring unit is used to control the monitoring equipment to monitor the behavior of medical staff at different stages.

[0046] Preferably, the monitoring equipment includes: cameras at predefined standardized management nodes for different monitoring steps and radio frequency identification (RFID) devices. The cameras are deployed at predefined standardized management nodes such as the preoperative preparation area, surgical operation area, and postoperative cleanup area of ​​the operating room. Each node deploys 1-2 high-definition infrared cameras. Cameras in the preoperative preparation area cover the handwashing station, changing area, and instrument disinfection area; cameras in the surgical operation area cover the perimeter of the operating table and the instrument transfer area; and cameras in the postoperative cleanup area cover the instrument recycling area and medical waste disposal area. The cameras have a 45° downward shooting angle, a resolution of no less than 1080P, and a frame rate of no less than 25fps. The RFID devices consist of RFID tags and RFID readers. RFID tags are worn on the medical staff's name tags and contain their name, position, and permissions. RFID readers are deployed at the entrance of each standardized management node and work in conjunction with the cameras to achieve real-time identification of medical staff. The working logic is as follows: The RFID reader identifies the identity information on the medical staff's ID cards and uploads the information to the system's identity verification unit. After successful verification, the system sends a start command to the camera at the corresponding node. Once the camera is activated, it captures the actions of medical staff at that point, and binds the behavioral video data with the identity information identified by RFID to form "personnel identity-behavioral action" related data; The system analyzes the associated data in real time, extracts behavioral features, and compares them with the standard behavioral features of the monitoring step to achieve behavioral monitoring; if the RFID does not identify the identity information, the camera only collects images and does not analyze behavioral features.

[0047] In this embodiment, the surgical preparation notification refers to the notification that surgery is required and preparations need to be made in advance; this is considered a trigger.

[0048] In this embodiment, the process equipment reference table includes the monitoring equipment involved in different behavior monitoring processes. It is pre-stored and is a two-dimensional data table containing six core fields: surgery type, medical staff responsibilities, monitoring steps, standardized management nodes, monitoring equipment type, and equipment deployment location. Each record in the table corresponds to the equipment configuration of a monitoring step.

[0049] The beneficial effects of the above technical solution are: by triggering surgery to retrieve the required monitoring process, and then by matching it with the process equipment reference table, effective behavior monitoring can be achieved.

[0050] This invention provides a behavior management system for operating room medical staff, wherein the personnel analysis module includes: The comparative analysis unit is used to retrieve standard monitoring results consistent with the monitoring steps from the standard database, and compare and analyze them with the behavioral monitoring results of the corresponding monitoring steps to identify non-standard nodes. The extraction unit is used to extract the monitoring comparison results of the remaining personnel under the non-standard node to obtain the first adjustment coefficient, and update the comparison analysis coefficient under the non-standard node; ; in, This represents the update coefficient of the corresponding medical staff at the j-th non-standard node; This represents the comparative analysis coefficient of the corresponding medical staff at the j-th non-standard node; This represents the monitoring results of the u-th remaining personnel under the j-th non-standard node. Compared with standard monitoring results The similarity function; This indicates that there exists a non-canonical node under the j-th node. Quantity; This represents the number of remaining individuals under the j-th non-normal node; Indicates the satisfaction The similarity function is used for cumulative summation calculation; The state determination unit is used to re-determine whether the non-standard node is still in a non-standard state based on the update coefficient. If so, the non-standard node is retained. If not, the non-standard nodes will be removed. The coefficient analysis unit is used to obtain and analyze the update coefficients of all retained non-standard nodes of the same medical staff during the on-site surgery. The comprehensive analysis unit is used to obtain the comprehensive behavioral norms of the corresponding surgery based on the analysis results of all medical staff in the on-site surgery and in combination with the node distribution of the non-standard nodes retained by each medical staff member.

[0051] In this embodiment, It is calculated based on the similarity function, namely sim (corresponding to the behavior monitoring results of medical staff, standard monitoring results).

[0052] In this embodiment, when the behavior monitoring result of a monitoring step does not match the standard monitoring result, its node is regarded as a non-standard node. It should be noted that each monitoring step corresponds to one node, and for ease of description, monitoring steps with unreasonable comparative analysis results are described as non-standard nodes.

[0053] In this embodiment, the first adjustment coefficient is: .

[0054] In this embodiment, if the update coefficient is not greater than the set threshold of the corresponding node, the non-standard node is retained; otherwise, it is removed. The set threshold of the node is pre-set and has a value of 0.5.

[0055] The beneficial effects of the above technical solution are: non-standard nodes are identified based on the comparative analysis of behavioral monitoring results and standard monitoring results, and then the first adjustment coefficient is determined by combining the comparative analysis results of other personnel under the node, so as to update the coefficient under the non-standard node. Subsequently, the distribution of medical staff and non-standard nodes is analyzed by comparing the size of the coefficients to obtain comprehensive behavioral standardization.

[0056] This invention provides a behavior management system for operating room medical staff, wherein the comprehensive analysis unit includes: The distribution determination subunit is used to statistically analyze all the retained non-standard nodes of all medical staff during the on-site surgery to obtain the node distribution for the on-site surgery. The node distribution includes the step number of the retained non-standard node and the statistical count of the corresponding retained non-standard node. A single deterministic subunit is used to place all update coefficients according to the order of node appearance to obtain the analysis vector of the corresponding medical staff, and input the analysis vector into the normative analysis model to obtain the single normativity of the corresponding medical staff. The analysis result is the corresponding first normativity. The comprehensive sub-unit is used to determine the comprehensive behavioral norms of the corresponding surgery based on the node distribution and the individual norms of each medical staff member in the current surgery.

[0057] In this embodiment, the node distribution is as follows: the non-standard node with stage number 01 has a count of 6, and the non-standard node with stage number 08 has a count of 2.

[0058] In this embodiment, the analysis vector = {the update coefficient of each retained non-standard node corresponding to the medical staff}.

[0059] The standard analysis model is a multilayer perceptron (MLP) neural network model with a four-layer structure: input layer - hidden layer 1 - hidden layer 2 - output layer. Specific parameters and training methods are as follows: Input layer: The number of neurons is consistent with the dimension of the analysis vector, that is, the number of non-canonical nodes retained by medical staff. The input value is the update coefficient of each non-canonical node. Hidden layer 1: 64 neurons, ReLU activation function; Hidden layer 2: 32 neurons, ReLU activation function; Output layer: The number of neurons is 1, the activation function is the Sigmoid function, and the output value is the single norm of medical staff, with a value range of (0,1). Training sample: 1000 sets of behavioral monitoring data of operating room medical staff were selected, including analysis vectors and the evaluation results of the degree of behavioral norms corresponding to the vectors by medical industry experts (single normativity). Training method: Gradient descent is used to train the model, the loss function is mean squared error (MSE), the learning rate is set to 0.01, the number of training iterations is 1000 rounds, and training is stopped when the loss function value is less than 0.001, thus obtaining the trained canonical analysis model; Model deployment: The trained model is deployed on the cloud platform. The management terminals of each campus can call the model through the interface, input the analysis vector, and output a single standardized result in real time.

[0060] In this embodiment, the overall normality is calculated as the average of all individual normalities × (1 + variance of all individual normalities × (the node weight of each retained non-normal node × the sum of the corresponding statistical counts) / the sum of all statistical counts). The weights of non-normal nodes are assigned according to the importance of the surgical stage, specifically: preoperative stage nodes (handwashing, disinfection, dressing) have a weight of 0.3, intraoperative stage nodes (instrument operation, surgical cooperation, aseptic operation) have a weight of 0.5, and postoperative stage nodes (instrument retrieval, environmental cleanup, medical waste disposal) have a weight of 0.2. For major surgeries (such as coronary artery bypass grafting or neurosurgery), the weights of intraoperative stage nodes are adjusted to 0.6, preoperative stage nodes to 0.3, and postoperative stage nodes to 0.1.

[0061] The beneficial effects of the above technical solution are: by statistically analyzing non-standard nodes to obtain node distribution, and combining this with analysis vectors to obtain single standardization, a foundation is provided for obtaining comprehensive behavioral standardization.

[0062] This invention provides a behavior management system for operating room medical staff, wherein the behavior management module includes: The storage unit is used to store the individual specifications of each medical staff member involved and the comprehensive specifications of the corresponding surgery to the cloud platform after each surgery is completed. The normative list generation unit is used to automatically generate behavioral normative lists for different surgical procedures and construct behavioral normative lists for different medical staff when managers from different hospital areas view them, based on the cloud platform to obtain viewing instructions. The scheduling judgment unit is used to determine whether the corresponding medical staff are allowed to be scheduled based on the list of behavioral norms of the corresponding medical staff. If they are allowed, the unit marks the corresponding medical staff as scheduled and stores the information.

[0063] In this embodiment, the list of surgical procedures includes: individual procedures for each medical professional involved, as well as the overall procedures for the surgery.

[0064] The list of healthcare worker behaviors includes: the individual norms that the healthcare worker follows in different surgeries.

[0065] In this embodiment, when each single normative in the list of behaviors of medical staff is greater than the set normative, it is determined that the corresponding medical staff can be dispatched and can work in other hospital areas. The dispatch mark is used to mark the staff to facilitate direct dispatch in the future.

[0066] The beneficial effects of the above technical solution are: by constructing lists for surgeries and medical staff, it is easy to view and provides convenience for management.

[0067] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A behavior management system for operating room medical staff, characterized in that, include: The process configuration module is used to determine the surgical list of each medical staff member based on their job permissions, and to configure a behavior monitoring process for each surgical procedure in the surgical list. The process parsing module is used to retrieve the required monitoring process from all configuration results and perform process parsing when a surgery related to the medical staff is triggered, and to set monitoring targets for the medical staff to monitor their behavior based on the parsing results of each monitoring step. The personnel analysis module is used to determine the non-standard behaviors of the medical staff based on the behavior monitoring results of each monitoring step, and to analyze all non-standard behaviors of each medical staff involved in the on-site surgery to obtain the comprehensive behavioral standardization of the on-site surgery. The behavior management module is used to determine the list of personnel allowed to be dispatched and their behavior norms for all hospital areas based on the comprehensive behavioral norms and the analysis results of medical staff in the corresponding surgical scenarios, and then store them on the cloud platform for managers of different hospital areas to view.

2. The operating room medical staff behavior management system according to claim 1, characterized in that, The process configuration module includes: An initial construction unit is used to retrieve the medical staff’s historical surgical involvement and their responsibilities in the historical surgical involvement from the historical database, and to construct an initial list; The permission acquisition unit is used to acquire the medical position of each medical staff member and the position permissions assigned based on the medical position; The field mapping unit is used to extract fields from the job permissions, and establish a matching distribution of the operation attribute field set with each extracted field as a cluster center and the corresponding surgical operation process. Using the cluster center as the center point of the corresponding matching distribution, and dividing the area into circles with a preset radius from the center point, a first circle is obtained; The outermost field in the corresponding matching distribution is regarded as a point and connected sequentially to obtain the contour boundary. The longest opposite edge of the contour boundary is locked. At the same time, the contour boundary is divided horizontally and vertically to obtain the first line and the second line respectively. Based on the longest opposite edge, the first line, and the second line, and in combination with the preset radius and the first circle, the matching coefficient of the corresponding extracted field is calculated; Fields with a matching coefficient greater than 0 are considered as matching fields. The coefficient calculation unit is used to determine the sensitivity coefficient of the corresponding surgery based on the matching fields and matching coefficients under the same surgery. The list acquisition unit is used to regard surgeries with a sensitivity coefficient greater than a preset coefficient as first surgeries. When there is no first surgery in the initial list, the corresponding first surgery and its corresponding responsibilities are added to the initial list to obtain the surgery list of the corresponding medical staff.

3. The operating room medical staff behavior management system according to claim 2, characterized in that, The process configuration module also includes: The process matching unit is used to match the corresponding behavior monitoring process from the type-responsibility-process comparison table according to the surgical type and responsibilities of each surgery in the surgical list.

4. The operating room medical staff behavior management system according to claim 1, characterized in that, The process parsing module includes: The retrieval unit is used to determine when the medical staff receives a surgical preparation notification to trigger a surgery. At this time, the process that is consistent with the triggered surgery is retrieved from the configuration result corresponding to the medical staff's surgery list and is regarded as the required monitoring process. The equipment determination unit is used to match the required monitoring process with the process equipment lookup table to obtain the monitoring equipment for each monitoring step before, during and after the operation for the medical staff. The behavior monitoring unit is used to control the monitoring equipment to monitor the behavior of medical staff at different stages.

5. The operating room medical staff behavior management system according to claim 4, characterized in that, The monitoring equipment includes cameras and radio frequency identification devices for predefined standard management nodes under different monitoring steps.

6. The operating room medical staff behavior management system according to claim 1, characterized in that, The personnel analysis module includes: The comparative analysis unit is used to retrieve standard monitoring results consistent with the monitoring steps from the standard database, and compare and analyze them with the behavioral monitoring results of the corresponding monitoring steps to identify non-standard nodes. The extraction unit is used to extract the monitoring comparison results of the remaining personnel under the non-standard node to obtain the first adjustment coefficient, and update the comparison analysis coefficient under the non-standard node; The state determination unit is used to re-determine whether the non-standard node is still in a non-standard state based on the update coefficient. If so, the non-standard node is retained. If not, the non-standard nodes will be removed. The coefficient analysis unit is used to obtain and analyze the update coefficients of all retained non-standard nodes of the same medical staff during the on-site surgery. The comprehensive analysis unit is used to obtain the comprehensive behavioral norms of the corresponding surgery based on the analysis results of all medical staff in the on-site surgery and in combination with the node distribution of the non-standard nodes retained by each medical staff member.

7. The operating room medical staff behavior management system according to claim 6, characterized in that, The comprehensive analysis unit includes: The distribution determination subunit is used to statistically analyze all the retained non-standard nodes of all medical staff during the on-site surgery to obtain the node distribution for the on-site surgery. The node distribution includes the step number of the retained non-standard node and the statistical count of the corresponding retained non-standard node. A single deterministic subunit is used to place all update coefficients according to the order of node appearance to obtain the analysis vector of the corresponding medical staff, and input the analysis vector into the normative analysis model to obtain the single normativity of the corresponding medical staff. The analysis result is the corresponding first normativity. The comprehensive sub-unit is used to determine the comprehensive behavioral norms of the corresponding surgery based on the node distribution and the individual norms of each medical staff member in the current surgery.

8. The operating room medical staff behavior management system according to claim 1, characterized in that, The behavior management module includes: The storage unit is used to store the individual specifications of each medical staff member involved and the comprehensive specifications of the corresponding surgery to the cloud platform after each surgery is completed. The normative list generation unit is used to automatically generate behavioral normative lists for different surgical procedures and construct behavioral normative lists for different medical staff when managers from different hospital areas view them, based on the cloud platform to obtain viewing instructions. The scheduling judgment unit is used to determine whether the corresponding medical staff are allowed to be scheduled based on the list of behavioral norms of the corresponding medical staff. If they are allowed, the unit marks the corresponding medical staff as scheduled and stores the information.

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