Information management system and method for relaxation training in perioperative period

Abstract

The invention belongs to the technical field of medical management systems, and particularly relates to an information management system and method for perioperative relaxation training, the system comprises a patient end system and a doctor and nurse end system, the patient end system is composed of a scale system, a training system, an integral system and a registration and login system, the scale system forms a complete evaluation result on the basis that a patient can receive and complete a personalized psychological health state self-evaluation scale issued by a nurse; the training system uses an intelligent algorithm to recommend a relaxation training guidance video most suitable for the current state to the patient according to the evaluation result and automatically records the training condition, the score system accumulates scores according to the training condition of the patient and motivates the patient to actively participate, and the registration and login system can manage the data and evaluation result of the patient. The doctor and nurse end system is used for checking and managing the patient end system. According to the invention, an electronic platform with high integration, user friendliness and comprehensive functions can be established, and the psychological management efficiency and quality of patients in the perioperative period are effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical management systems, and in particular relates to an information management system and method for perioperative relaxation training. Background Art

[0002] In the current medical environment, the psychological management of perioperative patients is increasingly valued. The perioperative period (perioperative period) is a period of time before, during and after surgery. Relaxation training during this period is of great significance for the patient's psychological adjustment and physical recovery, especially relaxation training and mental health assessment for postoperative patients.

[0003] However, most of the existing perioperative information management systems focus on process management, data recording and other aspects, and lack information management of relaxation training, and mostly adopt traditional paper-based tables and manual recording methods, which are not only inefficient, but also easy to lose data and difficult to track. In recent years, although some electronic platforms have begun to try to introduce online evaluation and training functions, these platforms often have problems such as single functions, poor user experience, and insufficient data processing capabilities. In particular, most of the existing platforms lack in-depth mining of patients' personalized needs, as well as professional training guidance for the postoperative recovery stage. In addition, the integration and scalability of the front-end and back-end systems are insufficient, which limits the platform's potential in cross-platform applications, performance optimization, and future functional expansion. Therefore, the present invention provides an information management system and method for perioperative relaxation training, which can establish a highly integrated, user-friendly, and fully functional electronic platform to effectively improve the efficiency and quality of psychological management of perioperative patients. Summary of the invention

[0004] The present invention provides an information management system and method for perioperative relaxation training, which are used to solve the technical problems that the existing information management platform lacks in-depth mining of patients' personalized needs and is unable to provide professional training guidance for the postoperative recovery stage.

[0005] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present invention is as follows: an information management system for perioperative relaxation training, the management system includes a patient-side system and a doctor-nurse-side system, the patient-side system is composed of a scale system, a training system, a scoring system and a registration and login system, the scale system is based on the patient's ability to receive and complete the personalized mental health status self-assessment scale issued by the nurse to form a complete evaluation result; the training system uses an intelligent algorithm to recommend the most suitable relaxation training guidance video for the patient's current state according to the evaluation result, and automatically records the training situation, the scoring system accumulates points according to the patient's training situation, and encourages the patient to actively participate, the registration and login system can manage the patient's data and evaluation results, and can provide data statistics and export, and display the patient's training situation and scoring situation, and the doctor-nurse-side system is used to view and manage the patient-side system.

[0006] Furthermore, the personalized mental health status self-assessment scale is presented in the form of multiple-choice questions, and by analyzing the patient's historical data, medical record information, and multi-dimensional data of living habits, scale questions that meet the patient's current mental state and needs are dynamically generated.

[0007] Furthermore, the scale system performs data processing and model selection on the complete assessment results, thereby obtaining the results of the patient's mental health, that is, healthy or abnormal.

[0008] Furthermore, the data processing process includes three processes: outlier removal, minimum-maximum normalization, and missing value filling.

[0009] Compared with the prior art, the present invention has the following beneficial effects:

[0010] (1) The present invention meets the individual needs of patients and improves the training effect through customized scales and training plans; and greatly improves the efficiency of data collection and processing through electronic systems, reducing the workload of medical staff.

[0011] (2) The present invention ensures the security of data during transmission and storage through strict patient authorization management, realizes the front-end and back-end separation design, and combines cloud computing and big data technology to achieve efficient processing and secure storage of data.

[0012] (3) The present invention realizes the synergistic mechanism of the scale system, the training system, the points system and the registration and login system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a structural diagram of the information management system for perioperative relaxation training of the present invention;

[0014] Figure 2It is a flow chart of the information management system for perioperative relaxation training of the present invention;

[0015] Figure 3 Validation diagram of an information management system for perioperative relaxation training;

[0016] Figure 4 A diagram for opening permissions for the information management system for perioperative relaxation training;

[0017] Figure 5 A historical record chart for the information management system for perioperative relaxation training;

[0018] Figure 6 This is the clock-in record chart of the information management system for perioperative relaxation training. DETAILED DESCRIPTION

[0019] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0020] like Figure 1 As shown, an information management system for perioperative relaxation training, the management system includes a patient-side system and a doctor-nurse side system, the patient-side system consists of a scale system, a training system, a scoring system and a registration and login system, the scale system is based on the patient's ability to receive and complete a personalized mental health status self-assessment scale issued by a nurse to form a complete evaluation result; the training system uses an intelligent algorithm to recommend the most suitable relaxation training guidance video for the patient's current state according to the evaluation result, and automatically records the training situation, the scoring system accumulates points according to the patient's training situation, and encourages the patient to actively participate, the registration and login system can manage the patient's data and evaluation results, and can provide data statistics and exports, and display the patient's training situation and scoring situation, and the doctor-nurse side system is used to view and manage the patient-side system.

[0021] The scale system can receive the assessment scale issued by the research nurse. Through the assessment tool screening, the postoperative patient problems are targeted. The personalized mental health status self-assessment scale is presented in the form of multiple-choice questions. By analyzing the patient's (surgical history, family medical history), medical record information (examination report, outpatient medical record), and lifestyle (psychological status, smoking and drinking habits, exercise habits) multi-dimensional data, the scale questions that meet the patient's current mental state and needs are dynamically generated.

[0022] The scale system processes data and selects models for the complete assessment results to obtain the patient's mental health results, that is, healthy or abnormal.

[0023] The data processing process includes three steps: outlier removal, minimum-maximum normalization, and missing value filling.

[0024] Outlier removal: For each feature x i (Surgical history i , Family history i 、Inspection report i , outpatient medical records i 、Psychological condition B i , smoking and drinking habits i , exercise habits i

[0025] x i =λ Q Q i +λ a ·a i +λ f ·f i +λ P ·P i +λ B ·B i +λ q ·q i +λ M ·M i , i∈{1,2,3...,N},)

[0026] , where λ xx is the normalized weight value. Calculate its average value The difference χ i =x i -u, and with standard deviation If the difference χ i If the value of -s exceeds the threshold m=3s (3 times the standard deviation), the data point is considered an outlier and is removed or replaced.

[0027] Normalization: Convert data into dimensionless values ​​between 0 and 1 to facilitate processing and analysis by subsequent algorithms. Use the minimum-maximum normalization formula:

[0028] Among them, x is the normalized data, x min and x max are the minimum and maximum values ​​in the data respectively. After normalization, the data value range is between 0 and 1.

[0029] Missing value filling: Using linear interpolation, for missing values ​​x m , whose two non-missing values ​​are x a and x b , and a<m<b, then

[0030] After the above data processing, a new data feature quantity S, x is formed. i ∈S. After extracting the running data feature quantity, it is used as a training sample to construct a logistic regression model. The specific process is as follows:

[0031] (1) Suppose there are features, 1 bias term b, and corresponding weights. Then the logistic regression model can be expressed as: where Sigmoid is the logarithmic domain activation function used to convert the output into a probability value; is the dot product of the weight vector and the feature vector.

[0032] (2) Set a threshold K. The Sigmoid function maps the output of the patient's mental state feature quantity to the interval [0, 1], and can identify whether the patient's mental state is healthy according to different values. If H > K, it indicates that there is an abnormality in the patient's mental health, and at this time, a self-assessment scale for mental health status needs to be pushed to the patient; if H < K, it indicates that the patient's mental health is normal.

[0033] The specific process of the training system using the intelligent algorithm is as follows:

[0034] (1) Based on the collaborative filtering recommendation algorithm for patients, first analyze patients with similar effects according to the patient's historical training data and effect feedback, and then recommend corresponding guidance videos to the patient.

[0035] (2) Based on the content-based collaborative filtering recommendation algorithm, first extract the features of the training video or content to find the relevance, and then recommend similar guidance videos to the patient based on the patient's past preference records.

[0036] (3) Through weight analysis, calculate the proportion α of video A liked by the patient recommended by the collaborative filtering recommendation algorithm based on the patient, and the proportion β of similar video B liked by the patient recommended by the video content filtering recommendation algorithm. Finally, through combined recommendation, num represents the number of videos.

[0037] The specific implementation process of the training system to recommend the most suitable relaxation training guidance video for the patient according to the evaluation result and automatically record the training situation is as follows:

[0038] (1) Extract various data of the patient information and video information into vectors (through embedding, respectively obtain the feature vectors of ID, gender, mental status, outpatient medical records, etc.).

[0039] (2) Perform full connection integration calculation on the feature vectors of each category to comprehensively obtain the feature vector of each patient and the feature vector of each training video.

[0040] (3) Compare the two feature vectors by cosine similarity to obtain the predicted score;

[0041] (4) Compare the predicted score with the actual score and make the score difference converge, thus completing the network training;

[0042] (5) When the model training is completed, the feature vectors of all patients and training videos will be obtained. Based on the above feature vectors, the feature similarity of the patients and training videos is compared to make recommendations.

[0043] The registration and login system supports WeChat authorization for quick registration and login, simplifying user operations.

[0044] like Figure 2 As shown, if the system user needs to start using the system, he must first scan the QR code provided by the research nurse in WeChat and follow the official account. Then he will authorize WeChat. After verifying the mobile phone number and entering the relevant information, he can use the system normally after the research nurse completes the review and authorization in the background.

[0045] First, open WeChat and scan the hospital's QR code. After entering the platform, you will see the authorization application interface. If you use the platform for the first time, you need to complete the registration process. Figure 3 , 4 As shown in Figure 5, the platform needs to identify and verify the mobile phone number. Verifying the mobile phone number also requires clarifying the identity of the registrant. After applying for permission, the platform will send the application to the backend. After the research nurse opens the permission in the backend, it can be used normally. Figure 6 shown.

[0046] The doctor and nurse system consists of a patient authorization system, a patient management system, and a data system.

[0047] The patient authorization system manages the patient's access rights and ensures data security. Figure 6 As shown, the patient management system can view the evaluation results of all patients in real time and conduct patient classification management. The data system provides data statistics and export functions, supports in-depth analysis of evaluation and training data, and can see the sleep conditions and mood curves recorded in the daily records. 28-day task check-in records: It will record the patient's daily training completion status 28 days after surgery and display the score curve based on each evaluation result.

[0048] A method comprising an information management system for perioperative relaxation training comprises the following steps:

[0049] (1) Assessment: Based on the patient's ability to receive and complete the personalized mental health status self-assessment scale issued by the nurse, a complete assessment result is formed;

[0050] (2) Training: using an intelligent algorithm to recommend the most suitable relaxation training instruction video for the patient's current condition based on the evaluation results, and automatically record the training status;

[0051] (3) Points: The points system accumulates points based on the patient's training status to encourage patients to actively participate;

[0052] (4) System login management: manage patient data and assessment results, provide data statistics and export, and display the patient's training status and score status.

[0053] The present invention separates the front and back ends to improve local performance, supports multiple platforms across platforms, and has good openness and scalability.

[0054] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. An information management system for perioperative relaxation training, characterized in that: The management system includes a patient - side system and a doctor - nurse - side system. The patient - side system consists of a scale system, a training system, a point system, and a registration and login system. The scale system is based on the patient's ability to receive and complete a personalized mental health status self - assessment scale issued by nurses, and forms a complete assessment result; The training system uses intelligent algorithms to recommend the most suitable relaxation training guidance video for the patient according to the assessment result, and automatically records the training situation. The point system accumulates points according to the patient's training situation to encourage the patient to actively participate. The registration and login system can manage the patient's data and assessment results, and can provide data statistics and export, and display the patient's training situation and point situation. The doctor - nurse - side system is used to view and manage the patient - side system.

2. An information management system for perioperative relaxation training according to claim 1, characterized in that: The personalized mental health status self - assessment scale is presented in the form of multiple - choice questions. By analyzing the patient's historical data, medical record information, and multi - dimensional data of living habits, it dynamically generates scale questions that conform to the patient's current mental state and needs.

3. The information management system for perioperative relaxation training according to claim 1, characterized in that: The scale system performs data processing and model selection on the complete assessment result to obtain the result of the patient's mental health, that is, healthy or abnormal.

4. The information management system for perioperative relaxation training according to claim 3, characterized in that: The data - processing process includes three processes: outlier removal, min - max normalization, and missing - value filling.

5. The information management system for perioperative relaxation training according to claim 3, characterized in that: The processed data forms new data feature quantities. After extracting the running - data feature quantities, they are used as training samples to construct a logistic - regression model. The specific process is as follows: (1) Suppose there are n features [x1, x2, ..., x n ], a bias term b and the corresponding weights [ω1,ω2,...,ω n ], then set the model of logistic regression; (2) Set a threshold K. The Sigmoid function maps the output of the patient's mental - state feature quantity to the interval [0,1], and can identify whether the patient's mental health is normal according to different values. If H > K, it indicates that there is an abnormality in the patient's mental health, and at this time, a mental - health status self - assessment scale needs to be pushed to the patient; if H < K, it indicates that the patient's mental health is normal.

6. The information management system for perioperative relaxation training according to claim 1, characterized in that: The specific process of the training system using intelligent algorithms is as follows: (1) Based on the collaborative - filtering recommendation algorithm for patients, first analyze patients with similar effects according to the patient's historical training data and effect feedback, and then recommend corresponding guidance videos to the patient; (2) Based on the content - based collaborative - filtering recommendation algorithm, first extract the features of the training video or content to find the correlation, and then recommend similar guidance videos to the patient based on the patient's past preference records; (3) Through weight analysis, calculate the proportion α of video A liked by the patient recommended by the collaborative - filtering recommendation algorithm for patients, and the proportion β of similar video B liked by the patient recommended by the video - content filtering recommendation algorithm. Finally, perform combined recommendation.

7. An information management system for perioperative relaxation training according to claim 6, characterized in that: The specific implementation process of the training system recommending the most suitable relaxation training guidance video for the patient according to the assessment result and automatically recording the training situation is as follows: (1) Extract various data of patient information and video information into vectors (2) Perform a fully - connected integration calculation on the feature vectors of each category to comprehensively obtain the feature vector ν of each patient and the feature vector ψ of each training video; (3) Compare the two feature vectors using cosine similarity to obtain a predicted score; (4) Compare the predicted score with the actual score and make the score difference converge to complete network training; (5) When the model training is completed, the feature vectors of all patients and training videos will be obtained. Based on the above feature vectors, the feature similarity of the patients and training videos is compared to make recommendations.

8. The information management system for perioperative relaxation training according to claim 1, characterized in that: The registration and login system supports WeChat authorization for quick registration and login, simplifying user operations.

9. The information management system for perioperative relaxation training according to claim 1, characterized in that: The doctor and nurse system consists of a patient authorization system, a patient management system, and a data system. The data system provides data statistics and export functions, supports in-depth analysis of assessment and training data, and can see the sleep status and mood curve graph recorded in the daily log. 28-day task punch-in record: It will record the patient's daily training completion status 28 days after surgery and display the score curve graph based on each assessment result.

10. A method for implementing an information management system for perioperative relaxation training as described in any one of claims 1 to 9, characterized in that: The specific steps include: (1) Assessment: Based on the patient's ability to receive and complete the personalized mental health status self-assessment scale issued by the nurse, a complete assessment result is formed; (2) Training: using an intelligent algorithm to recommend the most suitable relaxation training instruction video for the patient's current condition based on the evaluation results, and automatically record the training status; (3) Points: The points system accumulates points based on the patient's training status to encourage patients to actively participate; (4) System login management: manage patient data and assessment results, provide data statistics and export, and display the patient's training status and score status.

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