Medical personnel medical operation standard monitoring and reminding system

Abstract

The invention relates to the technical field of medical quality control, and particularly discloses a medical staff medical operation standard monitoring and reminding system. The intelligent analysis engine identifies operation and tracking steps, detects deviation and evaluates risks; the grading reminding intervention module performs multi-mode reminding according to risk grades; the system management and feedback module establishes a file and generates a report; and the security and privacy protection module ensures data security. Through the multi-modal data acquisition module, data is acquired from multiple dimensions of images, actions, environments, equipment interaction and the like, and comprehensive monitoring of operation of medical staff is realized. The intelligent analysis engine can accurately identify the operation type, track the operation steps, detect the operation deviation and evaluate the risk level, and provides an accurate basis for subsequent reminding intervention. According to the invention, whole-process standard monitoring, real-time reminding and personalized feedback of medical operation are realized, and the operation standardization and safety are improved.

Description

Technical Field

[0001] This invention relates to the field of medical quality control technology, specifically to a system for monitoring and reminding medical staff of their medical operating procedures. Background Technology

[0002] In the medical field, the standardization of medical staff's operations directly affects the treatment outcome and the safety of patients. However, due to the wide variety and complexity of medical procedures, and the possibility of operational deviations by medical staff due to fatigue, lack of experience, or negligence, these deviations, if not corrected in time, may lead to medical risks or even medical accidents.

[0003] Currently, monitoring of medical staff's operational procedures largely relies on manual supervision, which suffers from problems such as poor real-time performance, limited coverage, and inconsistent evaluation standards. Furthermore, traditional reminder methods are rather simplistic, making it difficult to provide targeted interventions based on risk levels, and there is also a risk of privacy breaches for patients and medical staff during data collection and management.

[0004] Therefore, a system for monitoring and reminding medical staff of standardized medical procedures has become an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a monitoring and reminder system for medical staff's medical operation procedures, which solves the problems of insufficient real-time performance, poor intervention targeting, and lack of privacy protection in existing monitoring systems for medical staff's operation procedures.

[0006] To achieve the above objectives, the technical solution provided by this invention is: a medical operation standard monitoring and reminder system for medical personnel, comprising:

[0007] The multimodal data acquisition module is used to collect image data, motion data, environmental parameters, and device interaction data during the operation process of medical staff;

[0008] The data preprocessing module is used to clean, fuse, and extract features from the collected data;

[0009] The intelligent operation specification database stores structured medical operation standards, step sequences, and parameter thresholds;

[0010] The intelligent analysis engine is used to identify operation types, track operation steps, detect operation deviations, and assess risk levels.

[0011] The tiered alert and intervention module issues alerts and provides corrective guidance in a multimodal manner based on the risk level.

[0012] The system management and feedback module is used to establish user operation specification files, generate evaluation reports, and recommend training content.

[0013] The security and privacy protection module encrypts, de-identifies, and controls access to system data.

[0014] Furthermore, the multimodal data acquisition module includes an image acquisition unit, a motion capture unit, an environment perception unit, a device interaction unit, and an identity recognition unit;

[0015] The image acquisition unit consists of a 4K high-definition camera, an infrared night vision camera, and a depth sensor, covering the entire operating area.

[0016] The motion capture unit uses an inertial measurement wristband to collect three-dimensional acceleration, angular velocity, and attitude angle data of the hand;

[0017] The environmental sensing unit includes a temperature and humidity sensor, an air quality sensor, a light sensor, and a sound sensor deployed in the operating area.

[0018] The device interaction unit communicates with the medical device via an Internet of Things (IoT) interface to collect device operation records, parameter settings, and operating status.

[0019] The identification unit uses UHF RFID to identify the identity and operating permissions of medical personnel.

[0020] Furthermore, the intelligent analysis engine includes:

[0021] The operation type identification unit uses the YOLOv5 algorithm to identify the current operation type.

[0022] The step tracking unit tracks operation steps in real time based on a temporal convolutional network.

[0023] The deviation detection unit compares the differences between real-time operation and standard specifications through a twin network;

[0024] The risk assessment unit uses fuzzy comprehensive evaluation to classify operational risks into three levels: low, medium, and high.

[0025] Furthermore, the tiered alert and intervention module dynamically adjusts the alert method according to the risk level, specifically as follows:

[0026] Low-risk level: detected through vibration of the smart bracelet and text prompts on the terminal screen;

[0027] Medium risk level: Detected via continuous vibration of the wristband, voice prompts, and pop-up operation instructions;

[0028] High-risk level: Implement audible and visual alarms and remotely notify supervisors for emergency intervention.

[0029] Furthermore, the intelligent operation specification database includes a basic specification library, a specialized operation library, a step sequence library, a parameter threshold library, and a variation adaptation library, wherein the parameter threshold library stores the quantitative parameter ranges for each operation step.

[0030] Furthermore, the security and privacy protection module includes a data encryption unit, a privacy desensitization unit, an access control unit, and an emergency response unit. The privacy desensitization unit automatically desensitizes the image data.

[0031] The advantages of this invention compared to the prior art are:

[0032] This invention uses a multimodal data acquisition module to collect data from multiple dimensions such as images, actions, environment, and device interactions, thereby achieving comprehensive monitoring of medical staff's operations.

[0033] The intelligent analysis engine of this invention can accurately identify operation types, track operation steps, detect operation deviations, and assess risk levels, providing a precise basis for subsequent reminders and interventions.

[0034] The graded reminder intervention module of this invention adopts different reminder methods according to the risk level, which can not only remind medical staff to correct operational deviations in a timely manner, but also avoid excessive reminders from interfering with normal operations.

[0035] The system management and feedback module of this invention establishes user operation specification files, generates evaluation reports and recommends training content, which helps medical staff understand their own operational deficiencies and conduct targeted training to improve them, thereby enhancing the overall level of operational standardization.

[0036] The security and privacy protection module of this invention encrypts, de-identifies, and controls access to system data, effectively protecting data security and the privacy of medical staff and patients. Attached Figure Description

[0037] Figure 1 This is a system block diagram of a medical operation standard monitoring and reminder system for medical staff according to the present invention.

[0038] Figure 2 This is a block diagram showing the unit composition of the multimodal data acquisition module.

[0039] Figure 3 This is a block diagram of the architecture components of the intelligent analysis engine. Detailed Implementation

[0040] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0041] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0042] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0043] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0044] The following is a detailed description of the medical operation standard monitoring and reminder system for medical personnel according to the present invention, with reference to the accompanying drawings.

[0045] Combined with appendix Figure 1-3 This invention will be described in detail below.

[0046] A medical operation standard monitoring and reminder system for medical staff, through the collaborative work of multimodal data collection, intelligent analysis, and hierarchical reminders, can achieve full-process standard monitoring and intervention of medical staff's operations.

[0047] The system includes a multimodal data acquisition module, a data preprocessing module, an intelligent operation specification database, an intelligent analysis engine, a tiered reminder and intervention module, a system management and feedback module, and a security and privacy protection module.

[0048] The multimodal data acquisition module is used to collect image data, motion data, environmental parameters, and device interaction data during the operation process of medical staff. It includes an image acquisition unit, a motion capture unit, an environmental perception unit, a device interaction unit, and an identity recognition unit. The image acquisition unit consists of a 4K high-definition camera, an infrared night vision camera, and a depth sensor, covering the entire operating area. The motion capture unit uses an inertial measurement wristband to collect three-dimensional acceleration, angular velocity, and attitude angle data of the hand. The environmental perception unit includes temperature and humidity sensors, air quality sensors, light sensors, and sound sensors deployed in the operating area. The device interaction unit communicates with medical devices via an IoT interface, collecting device operation records, parameter settings, and operating status. The identity recognition unit uses UHF RFID to identify the medical staff and their operating permissions.

[0049] The data preprocessing module is used to clean, fuse, and extract features from the collected data, providing a high-quality data foundation for subsequent intelligent analysis.

[0050] The intelligent operation specification database stores structured medical operation standards, step sequences, and parameter thresholds, including a basic specification library, a specialty operation library, a step sequence library, a parameter threshold library, and a variation adaptation library. The parameter threshold library stores the quantitative parameter ranges for each operation step.

[0051] The intelligent analysis engine is used to identify operation types, track operation steps, detect operation deviations, and assess risk levels. It includes an operation type identification unit, a step tracking unit, a deviation detection unit, and a risk assessment unit. The operation type identification unit acquires real-time images of the operation area through the image acquisition unit of the multimodal data acquisition module, uses the YOLOv5 algorithm to extract frame-level features from the images, and combines this with medical equipment operation status data acquired by the device interaction unit to construct a multi-feature input vector. This vector is then matched with basic operation feature templates in the intelligent operation specification database to complete the operation type identification. The step tracking unit, based on operation type identification, builds a step tracking model based on a temporal convolutional network. It uses hand movement temporal data acquired by the motion capture unit as the core input, integrates the temporal features of the operation motion images from the image acquisition unit, and compares them in real-time with the corresponding step temporal library in the intelligent operation specification database to generate a step progress tracking curve. The deviation detection unit constructs a Siamese network model, using real-time operation data and standard specification data as two input branches, where real-time operation data and standard specification data are used as input branches respectively. The data includes hand posture angle sequences from the motion capture unit, key motion image features from the image acquisition unit, and standard specification data from the intelligent operation specification database, which calls upon standard motion templates and parameter thresholds for corresponding operation steps. The cosine similarity of the feature vectors of these two data points is calculated using a Siamese network. When the similarity is below a preset threshold, it is considered an operational deviation, and the deviation location and type are marked. The risk assessment unit uses a fuzzy comprehensive evaluation method, taking the severity of the deviation, the duration of the deviation, and the risk weight of the current operation step (taken from the intelligent operation specification database) from the deviation detection results as evaluation factors to establish a risk level evaluation matrix. A comprehensive risk value is calculated using a membership function. A risk value ≤ 0.3 is classified as low risk, 0.3 < risk value ≤ 0.7 as medium risk, and risk value > 0.7 as high risk, generating a risk assessment report.

[0052] The tiered alert and intervention module uses a multimodal approach to issue alerts and provide corrective guidance based on the risk level, and dynamically adjusts the alert method according to the risk level. For low-risk levels, alerts are sent via smart bracelet vibration and text prompts on the terminal screen; for medium-risk levels, alerts are sent via continuous bracelet vibration, voice prompts, and operation guidance pop-ups; for high-risk levels, alerts are sent via sound and light alarms and remote notifications to supervisors for emergency intervention.

[0053] The system management and feedback module is used to establish user operation standard files, generate evaluation reports, and recommend training content, helping hospitals to comprehensively manage and target the improvement of medical staff's operation standards.

[0054] The security and privacy protection module encrypts, de-identifies, and controls access to system data. It includes a data encryption unit, a privacy de-identification unit, an access control unit, and an emergency response unit. The privacy de-identification unit automatically de-identifies image data to ensure data security and personnel privacy.

[0055] The specific implementation process of the medical staff's medical operation standard monitoring and reminder system of the present invention is as follows:

[0056] Each unit in the multimodal data acquisition module collects relevant data, while the identity recognition unit identifies the identity and operating permissions of medical staff to ensure the legality and traceability of the operation.

[0057] The data preprocessing module cleans the collected data of various types, removes noise and outliers, and then performs data fusion to integrate data from different sources and extract key features.

[0058] The intelligent analysis engine's operation type identification unit uses the YOLOv5 algorithm to identify the current operation type; the step tracking unit tracks operation steps in real time based on a temporal convolutional network; the deviation detection unit compares the differences between real-time operations and standard specifications in the intelligent operation specification database using a Siamese network; and the risk assessment unit uses the fuzzy comprehensive evaluation method to assess the risk level.

[0059] The tiered alert and intervention module issues alerts and provides corrective guidance using corresponding multimodal methods based on the assessed risk level.

[0060] The system management and feedback module establishes user operation specification files, generates evaluation reports based on operation status, and recommends training content based on the evaluation results.

[0061] The security and privacy protection module encrypts, de-identifies, and controls access to system data throughout the entire data flow process, ensuring data security and privacy.

[0062] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A monitoring and reminder system for medical staff's standardized medical procedures, characterized in that, include: The multimodal data acquisition module is used to collect image data, motion data, environmental parameters, and device interaction data during the operation process of medical staff; The data preprocessing module is used to clean, fuse, and extract features from the collected data; The intelligent operation specification database stores structured medical operation standards, step sequences, and parameter thresholds; The intelligent analysis engine is used to identify operation types, track operation steps, detect operation deviations, and assess risk levels. The tiered alert and intervention module issues alerts and provides corrective guidance in a multimodal manner based on the risk level. The system management and feedback module is used to establish user operation specification files, generate evaluation reports, and recommend training content. The security and privacy protection module encrypts, de-identifies, and controls access to system data.

2. The medical operation standard monitoring and reminder system for medical staff according to claim 1, characterized in that: The multimodal data acquisition module includes an image acquisition unit, a motion capture unit, an environment perception unit, a device interaction unit, and an identity recognition unit; The image acquisition unit consists of a 4K high-definition camera, an infrared night vision camera, and a depth sensor, covering the entire operating area. The motion capture unit uses an inertial measurement wristband to collect three-dimensional acceleration, angular velocity, and attitude angle data of the hand; The environmental sensing unit includes a temperature and humidity sensor, an air quality sensor, a light sensor, and a sound sensor deployed in the operating area. The device interaction unit communicates with the medical device via an Internet of Things (IoT) interface to collect device operation records, parameter settings, and operating status. The identification unit uses UHF RFID to identify the identity and operating permissions of medical personnel.

3. The medical operation standard monitoring and reminder system for medical staff according to claim 2, characterized in that: The intelligent analysis engine includes: The operation type identification unit uses the YOLOv5 algorithm to identify the current operation type. The step tracking unit tracks operation steps in real time based on a temporal convolutional network. The deviation detection unit compares the differences between real-time operation and standard specifications through a twin network; The risk assessment unit uses fuzzy comprehensive evaluation to classify operational risks into three levels: low, medium, and high.

4. The medical operation standard monitoring and reminder system for medical staff according to claim 3, characterized in that: The tiered alert and intervention module dynamically adjusts the alert method according to the risk level, specifically as follows: Low-risk level: detected through vibration of the smart bracelet and text prompts on the terminal screen; Medium risk level: Detected via continuous vibration of the wristband, voice prompts, and pop-up operation instructions; High-risk level: Implement audible and visual alarms and remotely notify supervisors for emergency intervention.

5. The medical operation standard monitoring and reminder system for medical personnel according to claim 4, characterized in that: The intelligent operation specification database includes a basic specification library, a specialized operation library, a step sequence library, a parameter threshold library, and a variation adaptation library. The parameter threshold library stores the quantitative parameter ranges for each operation step.

6. The medical operation standard monitoring and reminder system for medical personnel according to claim 5, characterized in that: The security and privacy protection module includes a data encryption unit, a privacy desensitization unit, an access control unit, and an emergency response unit. The privacy desensitization unit automatically desensitizes the image data.

Images