Informatization management system based on medical conjunct nursing education platform

Abstract

The invention relates to the technical field of medical education, and provides an informatization management system based on a medical conjunct nursing education platform, and the system comprises a user management module which is used for enabling nursing personnel and researchers in a medical conjunct to log in the system, carrying out the intelligent research, and distributing corresponding permissions according to different roles of users; the intelligent research module is used for creating a highly vivid clinical medical care space by using a VR technology, and combining the nursing research data with the generated medical care space through an AR technology; and the intelligent analysis module analyzes the research tendency and formulates a personalized research process line and a research mode by means of an AI algorithm according to the research performance and the evaluation effect of the nursing personnel. Resources are pushed in a targeted mode according to the research conditions of nursing personnel, waste is avoided, the resource utilization efficiency and the education return on investment rate are improved, the research route is updated in time according to new knowledge in the nursing field, the research route quality is improved, and the research route effect is evaluated and improved by integrating multi-dimensional data.

Description

Technical Field

[0001] The present invention relates to the field of medical education technology, and in particular to an information management system based on a medical alliance nursing education platform. Background Art

[0002] In the field of modern medical nursing education, with the rapid development of information technology and the widespread application of the medical alliance model, the demand for intelligent and personalized nursing education platforms is growing. Traditional nursing education methods often adopt unified teaching content and schedule arrangements, which cannot fully meet the individual differences of different nursing staff, resulting in uneven learning results.

[0003] Knowledge and technology in the field of nursing are in a state of constant updating and development. New clinical guidelines, nursing technologies, and research results continue to emerge, requiring nursing education content to keep up with these changes in a timely manner. For example, with the introduction of new medical equipment and improvements in disease treatment methods, nursing operations and nursing plans also need to be adjusted accordingly. The traditional static study route system cannot adapt to this rapidly changing environment. The effect of the study route is affected by many factors, and it cannot be fully and accurately measured by a single assessment method. Multi-dimensional data such as study route visit rate, study time distribution, and assessment pass rate all reflect the performance of the study route in different aspects. In addition, the nursing staff in the medical alliance come from different levels and backgrounds, and their study needs will also change at different stages.

[0004] Therefore, we made improvements to this and proposed an information management system based on the medical alliance nursing education platform. Summary of the invention

[0005] (I) The technical problem to be solved by the present invention is: to push resources in a targeted manner according to the study situation of nursing staff, to avoid waste, to improve resource utilization efficiency and return on education investment, to update the study route in a timely manner based on new knowledge in the nursing field, to improve the quality of the study route, to evaluate the effect of the study route based on comprehensive multi-dimensional data and to make targeted improvements.

[0006] (II) Technical solution

[0007] In order to achieve the above-mentioned purpose of the invention, the present invention provides an information management system based on a medical alliance nursing education platform, comprising:

[0008] The user management module is used for nursing staff and learners within the medical alliance to log into the system, conduct intelligent learning, and assign corresponding permissions according to different user roles;

[0009] The intelligent learning module uses VR technology to create a highly realistic clinical medical and nursing space, and uses AR technology to combine nursing learning materials with the generated medical and nursing space;

[0010] Intelligent analysis module, with the help of AI algorithm, analyzes learning tendencies based on nursing staff’s learning performance and evaluation results, and formulates personalized learning progress lines and learning methods;

[0011] Dynamic adjustment module, which counts the reading popularity and study time of nursing staff's study route, summarizes the assessment status, and weightedly feeds back the evaluation of the teaching effect of the study route, dynamically optimizing the study route data of nursing staff;

[0012] The blockchain technology module establishes a blockchain distributed ledger, stores the nursing staff's learning tracks and teaching materials on multiple nodes, and uses AES encryption to encrypt the data stored on the blockchain.

[0013] Preferably, the intelligent analysis module assesses the nursing staff's learning tendency by the following steps:

[0014] Ⅰ. Assume that the collection of learning performance data samples is , where each sample Including but not limited to study frequency, study route type preference, answering speed and accuracy;

[0015] II. Random Selection Center Points As the initial cluster center;

[0016] III. Calculate each sample Distance to cluster center ,in is the number of features;

[0017] IV. Sample Assigned to the category of the closest cluster center ;

[0018] V. Update cluster centers ;

[0019] VI. Repeat the above assignment and update steps until the cluster center no longer changes significantly;

[0020] VII. Finally, we get There are cluster categories, each category represents a learning tendency.

[0021] Preferably, the intelligent analysis module extracts features and generates rules for nursing staff's learning tendency, including the following steps:

[0022] A. Set training data set , which includes career effectiveness traits , and study tendency ;

[0023] B. Calculate each attribute Information gain rate ;

[0024] C. Select the attribute with the largest information gain rate as the root node for partitioning and recursively construct a decision tree.

[0025] Preferably, in step B, the information gain rate The calculation of includes the following steps:

[0026] B1. Calculation of data set Information confusion index ,in, Yes Category In the dataset proportion;

[0027] B2. Calculated properties Information confusion index after dividing the data set under different values ,in For attributes The value is Sample sharding;

[0028] B3. Calculate information gain , and calculate the attributes Split information ,but .

[0029] Preferably, the intelligent analysis module recommends the nursing staff's learning progress line including the following steps:

[0030] a. Let the set of nursing staff be , the study route set is , build a user-study route rating matrix ;

[0031] b. Calculate the similarity between users ,in, For users Study route Rating, For users For study routes with high similarity Ratings;

[0032] c. Find target users The most similar Users ;

[0033] d. Calculate the study route For Users Recommended score ;

[0034] e. Sort the study routes according to the recommendation scores to provide users with The top ranked study routes are recommended as part of the study progression line.

[0035] Preferably, the dynamic adjustment module weighted feedback study route teaching effect evaluation includes the following steps:

[0036] Ⅰ. Computing Study Route Visitation rate ,in, For study route In the shadow period The number of visits within The total number of visits to all learning routes for nursing staff during this time period;

[0037] II. Calculating the standard deviation of study time ,in For Nurses Study route Study time, is the total number of nursing staff studying this study route, For study route Average duration of

[0038] III. Network Study Route The pass rate ,in, For study route The total number of nursing staff who participated in the assessment, The number of people who passed the assessment;

[0039] IV. Comprehensive evaluation of study route effects based on feedback ,in, for Study route visitation rate, The maximum visit rate among all study routes. is the minimum visitation rate among all study routes. To balance study time, To study the maximum value in time, To study the minimum value in time, is the weight of the study route visit rate, To study the weight of time distribution, is the weight value of the assessment pass rate, and , It is the test pass rate, and its value range is 0-1.

[0040] Preferably, the dynamic adjustment module dynamically adjusts the content of the nursing staff's study route, including the following steps:

[0041] A. According to the study point Finding the beat ,when When the set threshold is exceeded, it is considered that there is a knowledge gap and the relevant study route content is generated, among which: To provide nursing staff with a comprehensive understanding of the learning points during the learning process The number of beats to be found, A summary of all study points;

[0042] B. Nursing Guide Included study points collection , study route Included study points collection ;

[0043] C. Computing study route and Care Guide Relevance of study points ,in, is a similarity function, which is used to judge the similarity between two study points. Its value range is 0-1. When the set threshold is exceeded, the study route to update.

[0044] Preferably, in step C, the study route When updating, according to the study points Corresponding study routes Priority when dynamically generated , the higher the priority, the more relevant study points Corresponding study routes Priority is given to generation, where For study point The importance rating of Rate the urgency, and All are preset values. and are the weights of importance and urgency, respectively, and .

[0045] The information management system based on the medical alliance nursing education platform provided by the present invention has the following beneficial effects:

[0046] 1. Analyze the learning situation of nursing staff through the intelligent analysis module, customize the learning process line for nursing staff, and push video and picture material learning routes to visual learners, so that they can quickly understand the knowledge and avoid wasting time. This enhances the learning motivation of nursing staff, enables nursing staff to gain a sense of accomplishment, stimulates learning enthusiasm, forms a virtuous circle, and fully considers individual differences. Whether new or experienced nurses, they can obtain learning content and rhythm that suits their own situation, which helps to optimize the use of educational resources, and recommends accurate allocation of resources based on learning tendencies and paths to avoid waste, improve resource utilization efficiency and return on investment.

[0047] 2. Through the dynamic adjustment module, the content of nursing staff training can be adjusted in real time to maintain the timeliness of educational content and keep up with the knowledge updates in the nursing field. For example, the new wound care standards can be incorporated into the training route in a timely manner to ensure that what nursing staff learn is consistent with clinical practice. This is crucial to improving the quality of the training route. Comprehensive multi-dimensional data can be used to evaluate the effectiveness of the training route, and accurate analysis of problem training routes can be conducted, such as content difficulty or explanation method problems, and then optimized and improved. It can also adapt to the dynamic changes of learners. When nursing staff change positions, the algorithm can automatically recommend suitable training routes to meet new needs. It can also promote the sustainable development of the education platform, attract more nursing staff and enhance the competitiveness of the platform by optimizing the training route and training experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 An overall system diagram of an information management system based on a medical alliance nursing education platform provided for this application. DETAILED DESCRIPTION

[0050] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples of the specification. The following examples are only used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0051] like Figure 1 As shown, this embodiment proposes an information management system based on a medical alliance nursing education platform, including:

[0052] The user management module is used for nursing staff and learners within the medical alliance to log into the system, conduct intelligent learning, and assign corresponding permissions according to different user roles;

[0053] The intelligent learning module uses VR technology to create a highly realistic clinical medical and nursing space, and uses AR technology to combine nursing learning materials with the generated medical and nursing space;

[0054] Intelligent analysis module, with the help of AI algorithm, analyzes learning tendencies based on nursing staff’s learning performance and evaluation results, and formulates personalized learning progress lines and learning methods;

[0055] Dynamic adjustment module, which counts the reading popularity and study time of nursing staff's study route, summarizes the assessment status, and weightedly feeds back the evaluation of the teaching effect of the study route, dynamically optimizing the study route data of nursing staff;

[0056] The blockchain technology module establishes a blockchain distributed ledger, stores the nursing staff's learning tracks and teaching materials on multiple nodes, and uses AES encryption to encrypt the data stored on the blockchain.

[0057] In this embodiment, the intelligent analysis module evaluates the nursing staff's learning tendency by the following steps:

[0058] Ⅰ. Assume that the collection of learning performance data samples is , where each sample Including but not limited to study frequency, study route type preference, answering speed and accuracy;

[0059] II. Random Selection Center Points As the initial cluster center;

[0060] III. Calculate each sample Distance to cluster center ,in is the number of features;

[0061] IV. Sample Assigned to the category of the closest cluster center ;

[0062] V. Update cluster centers ;

[0063] VI. Repeat the above assignment and update steps until the cluster center no longer changes significantly;

[0064] VII. Finally, we get There are cluster categories, each category represents a learning tendency.

[0065] In this embodiment, the intelligent analysis module extracts features and generates rules for nursing staff's learning tendency, including the following steps:

[0066] A. Set training data set , which includes career effectiveness traits , and study tendency ;

[0067] B. Calculate each attribute Information gain rate ;

[0068] C. Select the attribute with the largest information gain rate as the root node for partitioning and recursively construct a decision tree.

[0069] In this embodiment, in step B, the information gain rate The calculation of includes the following steps:

[0070] B1. Calculation of data set Information confusion index ,in, Yes Category In the dataset proportion;

[0071] B2. Calculated properties Information confusion index after dividing the data set under different values ,in For attributes The value is Sample sharding;

[0072] B3. Calculate information gain , and calculate the attributes Split information ,but .

[0073] In this embodiment, the intelligent analysis module recommends the learning progress line for the nursing staff including the following steps:

[0074] a. Let the set of nursing staff be , the study route set is , build a user-study route rating matrix ;

[0075] b. Calculate the similarity between users ,in, For users Study route Rating, For users For study routes with high similarity Ratings;

[0076] c. Find target users The most similar Users ;

[0077] d. Calculate the study route For Users Recommended score ;

[0078] e. Sort the study routes according to the recommendation scores to provide users with The top ranked study routes are recommended as part of the study progression line.

[0079] Specifically, the intelligent analysis module is used to analyze the learning situation of nursing staff, and a learning progress line is customized for them. For example, video and picture material learning routes are pushed to visual learners, allowing them to quickly understand knowledge and avoid wasting time. This enhances the learning motivation of nursing staff, enables them to gain a sense of accomplishment, stimulates their enthusiasm for learning, and forms a virtuous circle. In addition, individual differences are fully taken into consideration, so that both new and experienced nurses can obtain learning content and rhythm that suits their own situation, which helps to optimize the use of educational resources, and accurately allocate resources based on learning tendencies and paths to avoid waste, thereby improving resource utilization efficiency and return on investment.

[0080] In this embodiment, the dynamic adjustment module weighted feedback study route teaching effect evaluation includes the following steps:

[0081] Ⅰ. Computing Study Route Visitation rate ,in, For study route In the shadow period The number of visits within The total number of visits to all learning routes for nursing staff during this time period;

[0082] II. Calculating the standard deviation of study time ,in For Nurses Study route Study time, is the total number of nursing staff studying this study route, For study route Average duration of

[0083] III. Network Study Route The pass rate ,in, For study route The total number of nursing staff who participated in the assessment, The number of people who passed the assessment;

[0084] IV. Comprehensive evaluation of study route effects based on feedback ,in, for Study route visitation rate, The maximum visit rate among all study routes. is the minimum visitation rate among all study routes. To balance study time, To study the maximum value in time, To study the minimum value in time, is the weight of the study route visit rate, To study the weight of time distribution, is the weight value of the assessment pass rate, and , It is the test pass rate, and its value range is 0-1.

[0085] In this embodiment, the dynamic adjustment module dynamically adjusts the content of the nursing staff's study route, including the following steps:

[0086] A. According to the study point Finding the beat ,when When the set threshold is exceeded, it is considered that there is a knowledge gap and the relevant study route content is generated, among which: To provide nursing staff with a comprehensive understanding of the learning points during the learning process The number of beats to be found, A summary of all study points;

[0087] B. Nursing Guide Included study points collection , study route Included study points collection ;

[0088] C. Computing study route and Care Guide Relevance of study points ,in, is a similarity function, which is used to judge the similarity between two study points. Its value range is 0-1. When the set threshold is exceeded, the study route to update.

[0089] In this embodiment, in step C, the study route When updating, according to the study points Corresponding study routes Priority when dynamically generated , the higher the priority, the more relevant study points Corresponding study routes Priority is given to generation, where For study point The importance rating of Rate the urgency, and All are preset values. and are the weights of importance and urgency, respectively, and .

[0090] Specifically, the dynamic adjustment module can be used to adjust the content of nursing training in real time, keep the educational content timely, and keep up with the update of nursing knowledge. For example, the new wound care standards can be incorporated into the training route in a timely manner to ensure that what nursing staff learns is consistent with clinical practice. This is crucial to improving the quality of the training route. Comprehensive multi-dimensional data can be used to evaluate the effectiveness of the training route, and accurate analysis can be conducted on problem-oriented training routes, such as content difficulty or explanation method problems, and then optimized and improved. It can also adapt to the dynamic changes of learners. When nursing staff change their positions, the algorithm can automatically recommend suitable training routes to meet new needs. It can also promote the sustainable development of the education platform, attract more nursing staff and enhance the competitiveness of the platform by optimizing the training route and training experience.

[0091] The above embodiments are only used to illustrate the present invention, but not to limit the present invention. Although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that various combinations, modifications or equivalent substitutions of the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and should be included in the scope of the claims of the present invention.

Claims

1. An information management system based on a medical alliance nursing education platform, characterized in that: include: The user management module is used for nursing staff and learners within the medical alliance to log into the system, conduct intelligent learning, and assign corresponding permissions according to different user roles; The intelligent learning module uses VR technology to create a highly realistic clinical medical and nursing space, and uses AR technology to combine nursing learning materials with the generated medical and nursing space; Intelligent analysis module, with the help of AI algorithm, analyzes learning tendencies based on nursing staff’s learning performance and evaluation results, and develops personalized learning progress lines and learning methods; Dynamic adjustment module, which counts the reading popularity and study time of nursing staff's study route, summarizes the assessment status, and weightedly feeds back the evaluation of the teaching effect of the study route, dynamically optimizing the study route data of nursing staff; The blockchain technology module establishes a blockchain distributed ledger, stores the nursing staff's learning tracks and teaching materials on multiple nodes, and uses AES encryption to encrypt the data stored on the blockchain.

2. According to claim 1, an information management system based on a medical alliance nursing education platform is characterized in that: The intelligent analysis module evaluates the nursing staff's learning tendency by the following steps: Ⅰ. Assume that the collection of learning performance data samples is , where each sample Including but not limited to study frequency, study route type preference, answering speed and accuracy; II. Random Selection Center Points As the initial cluster center; III. Calculate each sample Distance to cluster center ,in is the number of features; IV. Sample Assigned to the category of the closest cluster center ; Ⅴ. Update cluster centers ; VI. Repeat the above assignment and update steps until the cluster center no longer changes significantly; VII. Finally, we get There are cluster categories, each category represents a learning tendency.

3. According to claim 2, an information management system based on a medical alliance nursing education platform is characterized in that: The intelligent analysis module extracts features and generates rules for nursing staff's learning tendency, including the following steps: A. Set training data set , which includes career effectiveness traits , and study tendency ; B. Calculate each attribute Information gain rate ; C. Select the attribute with the largest information gain rate as the root node for partitioning and recursively construct a decision tree.

4. According to claim 3, an information management system based on a medical alliance nursing education platform is characterized in that: In step B, the information gain rate The calculation of includes the following steps: B1. Calculation of data set Information confusion index ,in, Yes Category In the dataset proportion; B2. Calculated properties Information confusion index after dividing the data set under different values ,in For attributes The value is Sample sharding; B3. Calculate information gain , and calculate the attributes Split information ,but .

5. According to claim 2, an information management system based on a medical alliance nursing education platform is characterized in that: The intelligent analysis module recommends the nursing staff's learning progress line including the following steps: a. Let the set of nursing staff be , the study route set is , build a user-study route rating matrix ; b. Calculate the similarity between users ,in, For users Study route Rating, For users For study routes with high similarity Ratings; c. Find target users The most similar Users ; d. Calculate the study route For Users Recommended score ; e. Sort the study routes according to the recommendation scores to provide users with The top ranked study routes are recommended as part of the study progression line.

6. The information management system based on the medical alliance nursing education platform according to claim 1 is characterized in that: The dynamic adjustment module weighted feedback study route teaching effect evaluation includes the following steps: Ⅰ. Computing Study Route Visitation rate ,in, For study route In the shadow period The number of visits within The total number of visits to all learning routes for nursing staff during this time period; II. Calculating the Standard Deviation of Study Time ,in For Nurses Study route Study time, is the total number of nursing staff studying this study route, For study route Average duration of III. Network Study Route The pass rate ,in, For study route The total number of nursing staff who participated in the assessment, The number of people who passed the assessment; IV. Comprehensive evaluation of study route effects based on feedback ,in, for Study route visitation rate, The maximum visit rate among all study routes. is the minimum visitation rate among all study routes. To balance study time, To study the maximum value in time, To study the minimum value in time, is the weight of the study route visit rate, To study the weight of time distribution, is the weight value of the assessment pass rate, and , It is the exam pass rate, and its value range is 0-1.

7. According to claim 1, an information management system based on a medical alliance nursing education platform is characterized in that: The dynamic adjustment module dynamically adjusts the content of the nursing staff's study route, including the following steps: A. According to the study point Finding the beat ,when When the set threshold is exceeded, it is considered that there is a knowledge gap and the relevant study route content is generated, among which: To provide nursing staff with a comprehensive understanding of the learning points during the learning process The number of beats to be found, A summary of all study points; B. Nursing Guide Included study points collection , study route Included study points collection ; C. Computing study route and Care Guide Relevance of study points ,in, is a similarity function, which is used to judge the similarity between two study points. Its value range is 0-1. When the set threshold is exceeded, the study route to update.

8. According to claim 7, an information management system based on a medical alliance nursing education platform is characterized in that: In step C, the study route When updating, according to the study points Corresponding study routes Priority when dynamically generated , the higher the priority, the more relevant study points Corresponding study routes Priority is given to generation, where For study point The importance rating of Rate the urgency, and All are preset values. and are the weights of importance and urgency, respectively, and .

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