Cholecystitis patient health science popularization education system

Abstract

The invention discloses a cholecystitis patient health science popularization education system, and relates to the technical field of medical health science popularization education, and the technical key points are that the system comprises the following modules: a user interaction layer used for the login, registration, science popularization content display, interaction function and feedback channel of patients and medical personnel; the business logic layer is responsible for processing business processes of the system and comprises a content recommendation algorithm, an interaction mechanism and feedback processing; the data management layer is used for storing and managing data of the system, including popular science content, user information and interaction records; and the external interface layer interacts with an external system and a platform, and the external system does not open but is not limited to a social media platform and a medical database. Through interesting, interesting, interactive and personalized science popularization contents and education modes, the health knowledge level of the cholecystitis patient is improved, the self-management ability and treatment compliance of the patient are enhanced, the incidence rate of cholecystitis is reduced, and the overall effect of health science popularization education is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical and health science popularization education, and particularly relates to a health science popularization education system for patients with cholecystitis. Background Art

[0002] With the improvement of living standards and the change of diet structure, biliary tract diseases such as gallstones and cholecystitis are highly prevalent. Behavior and living habits are the main factors affecting the occurrence of cholecystitis, such as overeating, excessive dieting, or irregular diet.

[0003] Health science popularization education in our country is in the development stage, and the policy support is continuously increasing. The public has various ways to obtain health science popularization education, including online video websites, APPs, etc. and offline periodicals, books, etc., but there are problems such as a large number of incorrect knowledge and inaccurate docking of popular science content with the needs of the audience. The 4I theory was proposed by Professor Don Schultz of Northwestern University in the United States, including four principles: interest, benefit, interaction, and individuality, emphasizing consumer-centered, paying attention to needs and integrated marketing.

[0004] The understanding of basic health knowledge of cholecystitis among related populations is uneven, and there are misunderstandings, resulting in an increase in the incidence rate. The low educational level, poor medical compliance, and insufficient understanding ability of patients, as well as the insufficient communication ability, single education method, and uninteresting content of medical staff, result in poor health science popularization education effects. The credibility of health science popularization education on Internet platforms is insufficient, and the audit and supervision threshold is low, leading to many misunderstandings in popular science knowledge and even spreading incorrect knowledge, causing public panic. The educational levels of inpatients are uneven, and some elderly patients cannot understand the text. It is difficult to produce health science popularization works suitable for this type of population. It is necessary to accurately draw a "user portrait", judge the actual needs of the audience, and plan targeted and personalized health science popularization education content. Summary of the Invention

[0005] The purpose of the present invention is to solve the above problems, and provide a health science popularization education system for patients with cholecystitis, so as to solve the problems of poor health science popularization education effects for patients with cholecystitis, inaccurate docking of popular science content with the needs of the audience, insufficient credibility of health science popularization education on Internet platforms, and poor applicability of popular science works caused by uneven educational levels of inpatients in the prior art.

[0006] In order to achieve the above purpose, the technical solution of the present invention is as follows: A health science popularization education system for patients with cholecystitis, the system includes the following modules:

[0007] A user interaction layer, which is used for the login, registration, popular science content display, interaction function, and feedback channel of patients and medical staff;

[0008] A business logic layer, which is responsible for processing the business processes of the system, including content recommendation algorithms, interaction mechanisms, and feedback processing;

[0009] The data management layer stores and manages the data of the system, including science popularization content, user information, and interaction records;

[0010] The external interface layer interacts with external systems and platforms, and the external systems include but are not limited to social media platforms and medical databases.

[0011] Furthermore, the content management module includes:

[0012] The content upload and review sub-module allows medical staff or professional content creators to upload science popularization content, and the system automatically conducts preliminary reviews;

[0013] The content classification and tagging sub-module manages the classification and tagging of science popularization content;

[0014] The content recommendation algorithm sub-module, based on the 4I theory, combines user portraits, interaction records, and feedback data to intelligently recommend science popularization content.

[0015] Furthermore, the interaction module includes:

[0016] The question and answer sub-module sets questions in the science popularization content. Patients can answer the questions during the viewing or reading process, and the system provides real-time feedback on the correctness of the answers and offers explanations;

[0017] The comment and feedback sub-module allows patients to leave comments below the science popularization content to share their views and experiences;

[0018] The bullet screen interaction sub-module enables patients to have real-time interactions by sending bullet screens for the science popularization videos published on the platform.

[0019] Furthermore, the data analysis module includes:

[0020] The data collection sub-module collects the basic information of patients, interaction records, feedback data, and data from Internet platforms;

[0021] The data analysis sub-module uses statistical methods to conduct descriptive statistics, correlation analysis, and difference tests on the collected data;

[0022] The data visualization sub-module displays the analysis results in the form of charts, reports, etc. in the system background.

[0023] Furthermore, the system also includes an effect evaluation mechanism for evaluating the effect of science popularization education, including patients' knowledge mastery, interaction frequency, and satisfaction, etc., and optimizing the science popularization content and education strategies according to the evaluation results.

[0024] Furthermore, the system is based on the 4I theory, namely Interesting, Interests, Interaction, and Individuality. Through intelligent algorithms, it realizes effective interaction between patients and the system, and between patients and medical staff, improves user participation and learning effects, and ensures the interestingness and personalization of recommended content.

[0025] Compared with the prior art, the beneficial effects of this solution are as follows: The system of the present invention provides convenient login, registration, science popularization content display, interaction functions, and feedback channels for patients and medical staff through the user interaction layer, greatly improving the user experience. The intelligent processing capabilities of the business logic layer, including content recommendation algorithms, interaction mechanisms, and feedback processing, ensure that the system can provide personalized services according to user needs and behaviors. The efficient storage and management functions of the data management layer guarantee the security and accuracy of science popularization content, user information, and interaction records. The extensive connectivity of the external interface layer enables the system to seamlessly connect with external systems such as social media platforms and medical databases, expanding the dissemination channels of science popularization content and data sources, and providing users with more comprehensive and timely health information.

[0026] Furthermore, the content management module includes content upload and review, content classification and tagging, and content recommendation algorithms, which not only improve the quality and management efficiency of content but also achieve precise push of science popularization content through intelligent recommendation algorithms, combined with user portraits, interaction records, and feedback data, improving user participation and learning effects. The diverse functions of the interaction module, such as question and answer, comment and feedback, and bullet screen interaction, enhance the interaction between patients and the system, and between patients and medical staff, enabling patients to obtain immediate feedback and communication during the learning process, and enhancing the fun and effects of learning. The comprehensiveness of the data analysis module, including data collection, data analysis, and data visualization, provides strong data support for the system. Through in-depth analysis of the collected data using statistical methods, the system can intuitively display the effects of science popularization education, including patients' knowledge mastery, interaction frequency, and satisfaction, etc., thus providing a scientific basis for content optimization and strategy adjustment.

[0027] In addition, the system of the present invention is based on the 4I theory, namely, Interesting, Interests, Interaction, and Individuality. Through intelligent algorithms, it realizes effective interaction between patients and the system, as well as between patients and medical staff. The application of this theory not only improves user participation and learning effects but also ensures the interestingness and personalization of recommended content, making science popularization education more in line with the needs and interests of users. Compared with traditional health science popularization education methods, the system of the present invention can significantly improve the health knowledge level of patients with cholecystitis, enhance the patients' self-management ability and treatment compliance, and is of great significance for reducing the incidence of cholecystitis and improving the prognosis of patients. Description of the Drawings

[0028] Figure 1 is the system architecture diagram of the present invention in the embodiments of the present invention;

[0029] Figure 2 is the implementation flowchart of the interestingness module in the embodiments of the present invention;

[0030] Figure 3 is the implementation flowchart of the interests module in the embodiments of the present invention;

[0031] Figure 4 is the implementation flowchart of the interaction module in the embodiments of the present invention;

[0032] Figure 5 is the implementation flowchart of the individuality module in the embodiments of the present invention. Detailed Embodiments

[0033] In order to enable those skilled in the art of the present technology to better understand the solution of the present invention, the technical solution of the present invention will be further described in detail below in conjunction with the embodiments and drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0034] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below in conjunction with the embodiments.

[0035] Embodiment: A Health Science Popularization Education System for Cholecystitis Patients Based on the 4I Theory

[0036] I. System Architecture and Module Design

[0037] (I) System Architecture

[0038] This system is guided by the 4I theory and adopts a layered architecture design to ensure the flexibility, scalability, and user-friendliness of the system. The system architecture is divided into four main layers: the user interaction layer, the business logic layer, the data management layer, and the external interface layer.

[0039] User Interaction Layer: This is the interface where the system directly contacts users, including the patient side and the medical staff side. The patient side provides personalized science popularization content display, interactive functions, and feedback channels; the medical staff side is used for content management, interactive monitoring, and effect evaluation. This layer focuses on interestingness and individuality, attracting users and meeting the needs of different users through friendly interface design and personalized content recommendations.

[0040] Business Logic Layer: Responsible for processing the business processes of the system, including content recommendation algorithms, interactive mechanisms, feedback processing, etc. This layer integrates the interaction principle in the 4I theory, realizing effective interaction between patients and the system, and between patients and medical staff through intelligent algorithms, improving user participation and learning effects.

[0041] Data Management Layer: Stores and manages the data of the system, including science popularization content, user information, interaction records, etc. Data management follows the interests principle in the 4I theory to ensure the accuracy and security of data, while providing valuable information and services for users.

[0042] External Interface Layer: Interacts with external systems and platforms, such as social media platforms, medical databases, etc. Through this layer, the system can obtain the latest medical information and user feedback, continuously optimize the science popularization content, and reflect the interestingness and individuality of the 4I theory.

[0043] (2) Module Design

[0044] 1. User Interaction Module

[0045] Patient Side:

[0046] Personalized Science Popularization Content Display: According to the user profile, display science popularization comics, animations, and videos that match the user's interests and needs. For example, for young patients, give priority to showing content in the form of animations and comics; for middle-aged and elderly patients, provide content combining videos and texts.

[0047] Interactive Functions: Include asking questions, answering, commenting, and giving feedback. Patients can participate in interactions while watching science popularization content, and the system records and feedbacks the interaction results in real time. For example, set questions in the science popularization video, and patients can get the correct answers and analyses immediately after answering.

[0048] Feedback Channel: Patients can submit feedback on the popular science content through the system, including satisfaction ratings, suggestions, and questions. The system optimizes content recommendations and interaction mechanisms based on the feedback.

[0049] Medical Staff Side:

[0050] Content Management: Medical staff can upload, review, and manage popular science content to ensure its scientificity and accuracy. At the same time, adjust the content strategy according to patient feedback and interaction data.

[0051] Interaction Monitoring: Real-time monitor the interaction of patients, promptly reply to patients' questions and comments, and enhance patients' sense of participation and learning effect.

[0052] Effect Evaluation: Through the data analysis function, evaluate the effect of popular science education, including patients' knowledge mastery, interaction frequency, and satisfaction, etc. Optimize popular science content and education strategies according to the evaluation results.

[0053] 2. Content Management Module

[0054] Content Upload and Review: Medical staff or professional content creators can upload popular science content, and the system automatically conducts a preliminary review to ensure that the content meets scientific and policy requirements. After passing the review, the content enters the content library.

[0055] Content Classification and Tagging: Manage popular science content through classification and tagging, which facilitates the system to make accurate recommendations based on user portraits and interaction data. The classification includes videos, comics, texts, etc.; the tags include causes of illness, symptoms, treatment methods, dietary precautions, etc.

[0056] Content Recommendation Algorithm: Based on the 4I theory, combined with user portraits, interaction records, and feedback data, intelligently recommend popular science content. The algorithm takes into account users' interests, needs, interaction frequency, and learning effects to ensure the interestingness and personalization of the recommended content.

[0057] 3. Interaction Module

[0058] Question and Answer: Set questions in the popular science content. Patients can answer the questions during the viewing or reading process, and the system provides real-time feedback on the correctness of the answers and offers explanations. For example, set "What are the main symptoms of cholecystitis?" in a popular science video. After the patient selects an answer, the system immediately displays the correct answer and a detailed explanation.

[0059] Comment and Feedback: Patients can leave comments below the popular science content to share their views and experiences. The system collects comment data for optimizing content recommendations and interaction mechanisms. At the same time, medical staff can reply to patients' comments to enhance interactivity.

[0060] Bullet screen interaction: For popular science videos released on platforms such as Bilibili, patients can interact in real time by sending bullet screens. The system background monitors the bullet screen content in real time, and medical staff can reply in a timely manner to increase the patients' sense of participation.

[0061] 4. Data analysis module

[0062] Data collection: Collect patients' basic information, interaction records, feedback data, and data from Internet platforms (such as click-through rate, reading volume, interaction volume, etc.). Data collection is carried out through the user interaction layer and the external interface layer to ensure the comprehensiveness and accuracy of the data.

[0063] Data analysis: Use statistical methods, such as SPSS 25.0 software, to conduct descriptive statistics, correlation analysis, and difference tests on the collected data. The analysis results include patients' acceptance, interest, personalization degree, demand degree, and innovation degree of popular science content, etc.

[0064] Data visualization: Display the analysis results in the form of charts, reports, etc. in the system background, so that medical staff and content creators can intuitively understand the effects and existing problems of popular science education. According to the analysis results, the system automatically adjusts the recommendation algorithm and content optimization strategy.

[0065] II. Specific implementation steps

[0066] (I) Patient registration and login

[0067] 1.1 The patient opens the system user interface and clicks the "Patient Login" button to enter the patient login interface.

[0068] 1.2 The patient clicks the "Register" button, fills in personal information, including name, gender, age, educational level, hobbies, contact information, etc., and sets a login password to complete the registration.

[0069] 1.3 The patient uses the registered account and password to log in to the system and enters the patient main interface.

[0070] (II) Questionnaire survey and user portrait

[0071] 2.1 After the patient logs in, the system automatically pops up a questionnaire survey interface, asking the patient to fill in a questionnaire about the health knowledge needs and interests of cholecystitis.

[0072] 2.2 The questionnaire content includes:

[0073] Your degree of knowledge demand for aspects such as the cause of cholecystitis, symptoms, treatment methods, and dietary precautions (1-5 points, 1 point means very uninterested, 5 points means very interested).

[0074] Which form of popular science content do you prefer (videos, comics, texts, etc.).

[0075] The channels through which you usually obtain health knowledge (Internet, TV, books, doctor's explanations, etc.).

[0076] Your expectations for the interestingness, personalization level, and interactivity of health science popularization content (rated from 1 to 5 points, where 1 point means very low expectation and 5 points means very high expectation).

[0077] 2.3 After the patient completes the questionnaire survey and clicks the "Submit" button, the system draws a "user portrait" based on the questionnaire results, records information such as the patient's gender, age, educational level, hobbies, knowledge needs, and content preferences, and stores it in the user information database in the data management layer.

[0078] (III) Content Recommendation

[0079] 3.1 The system calls the recommendation algorithm of the content management module according to the information in the "user portrait" to recommend personalized science popularization content for the patient.

[0080] 3.2 The recommendation algorithm comprehensively considers factors such as the patient's age, gender, educational level, hobbies, knowledge needs, and content preferences, and screens out science popularization comics, animations, and interactive videos that meet the patient's needs from the science popularization content database.

[0081] 3.3 The system displays the recommended science popularization content in the "Recommended Content" area on the patient's main interface, including information such as the content title, introduction, form (video, comic, etc.), and duration. The patient can click on the science popularization content they are interested in to view it.

[0082] (IV) Patient Viewing and Interaction

[0083] 4.1 The patient clicks on the recommended science popularization content to enter the content playback or reading interface.

[0084] 4.2 For science popularization videos, the patient can watch the video content. During the video playback, the system will pop up questions according to the preset question session. The patient clicks on the options on the screen to answer the questions, and the system records the patient's answers in real-time and gives feedback, such as "Correct answer, keep it up!" or "Wrong answer, the correct answer is...".

[0085] 4.3 For science popularization comics, the patient can read the comic content page by page. During the reading process, the system will pop up interactive prompts, such as "Do you think this eating habit is harmful to patients with cholecystitis?" The patient clicks on the "Harmful" or "Harmless" button to answer, and the system records the patient's answer and shows the answers of other patients to increase interactivity.

[0086] 4.4 Patients can also express their opinions and questions in the comment area below the popular science content and communicate with other patients or medical staff. For popular science interactive videos published on video platforms such as Bilibili, patients can interact in real time by sending bullet comments, and medical staff can view the bullet comment content through the system background and reply in a timely manner to enhance the patients' sense of participation and learning experience.

[0087] (V) Medical staff feedback

[0088] 5.1 Medical staff log in to the system, enter the medical staff main interface, and click the "Interaction Record" button to view the interaction records of patients, including question answers, comment content, etc.

[0089] 5.2 Based on the interaction records of patients, medical staff evaluate the learning situation of patients. For questions answered incorrectly, give correct explanations and guidance; for questions raised by patients, reply and answer in a timely manner to help patients better understand and master knowledge related to cholecystitis.

[0090] 5.3 Medical staff can also send personalized learning suggestions and encouragement messages to patients through the feedback function of the system background, such as "You have a good grasp of the dietary precautions for cholecystitis. Keep up the good eating habits, have regular check-ups, and wish you a speedy recovery!" etc., to enhance the patients' self-management ability and treatment compliance.

[0091] (VI) Effect evaluation

[0092] 6.1 After patients watch or read popular science content, the system automatically pops up a questionnaire survey interface, asking patients to rate the acceptance, interestingness, personalization degree, demand degree, and innovation degree of the popular science content. The rating uses the Likert 5-level rating method, with 1 point indicating very dissatisfied and 5 points indicating very satisfied.

[0093] 6.2 The system collects the rating data of patients and, through the data analysis function of the Internet platform, obtains data such as the click-through rate, reading volume, and interaction volume (number of bullet comments, number of comments, etc.) of the popular science content on platforms such as Bilibili, Douyin, and Xiaohongshu.

[0094] 6.3 The data analysis module uses SPSS 25.0 software to process the collected data. Measurement data are described using and the t-test is used for comparison between groups. P < 0.5 indicates that the difference is statistically significant. The analysis results are presented in the form of charts, reports, etc. on the "Effect Evaluation" interface of the system background, and medical staff and content creators can intuitively understand the effect and existing problems of popular science education.

[0095] 6.4 Based on the results of the effect evaluation, the system automatically adjusts the recommendation algorithm and content optimization strategy to improve the quality and pertinence of popular science content, and further enhances the patients' health knowledge level and self-management ability.

[0096] Effect of the embodiment: Through the implementation of this system, the health knowledge level of patients with cholecystitis has been significantly improved. The scores of patients on the acceptance, interestingness, personalization degree, demand degree, and innovation degree of popular science content have all reached a relatively high level, with an average score of over 4 points. Internet platform data shows that the click-through rate and interaction volume of popular science content have increased significantly. The click-through rate of popular science animations on Douyin has reached over 100,000 times, and the number of bullet comments has exceeded 1,000; the reading volume of popular science comics on Xiaohongshu has reached over 50,000 times, and the number of comments has exceeded 500. The participation and satisfaction of patients have been significantly improved. The willingness of patients to actively obtain health knowledge has increased, and their self-management ability and treatment compliance for cholecystitis have been enhanced.

[0097] Based on the results of the effect evaluation, further optimize the recommendation algorithm to improve the accuracy and personalization degree of popular science content recommendation. Add dynamic recommendation factors based on patients' interaction records and learning effects, and adjust the recommended content in real time according to patients' feedback and mastery of different popular science content. Enrich the forms and themes of popular science content, and combine the latest medical research results and patients' needs to produce more diverse and rich popular science works, including virtual reality (VR) popular science experiences, interactive popular science games, etc., to improve the interestingness and attractiveness of popular science education.

[0098] Strengthen cooperation with Internet platforms to expand the dissemination channels and influence of popular science content. Establish cooperative relationships with more well-known Internet platforms, and put popular science content to a wider user group to improve the popularization rate of cholecystitis health knowledge. At the same time, utilize the big data analysis function of Internet platforms to further optimize the promotion strategy of popular science content and the accuracy of user portraits. Regularly organize training and exchanges for medical staff and content creators to improve their popular science creation ability and interactive communication skills. Through training and exchanges, share successful popular science cases and experiences, stimulate the innovative thinking of medical staff and content creators, and continuously optimize the quality and effect of popular science content.

[0099] The above specific embodiments are only explanations of the present invention, and they are not limitations of the present invention. Those skilled in the art can make modifications without creative contributions to this embodiment as needed after reading this specification, but as long as they are within the scope of the claims of the present invention, they are protected by the patent law.

Claims

1. A health science popularization education system for patients with cholecystitis, characterized in that: The system includes the following modules: The user interaction layer is used for the login, registration, science popularization content display, interactive functions, and feedback channels of patients and medical staff; The business logic layer is responsible for processing the business processes of the system, including content recommendation algorithms, interaction mechanisms, and feedback processing; The data management layer stores and manages the data of the system, including science popularization content, user information, and interaction records; The external interface layer interacts with external systems and platforms, and the external systems include but are not limited to social media platforms and medical databases.

2. The system according to claim 1, characterized in that: The content management module includes: The content upload and review sub-module allows medical staff or professional content creators to upload science popularization content, and the system automatically conducts preliminary reviews; The content classification and tagging sub-module manages the classification and tagging of science popularization content; The content recommendation algorithm sub-module, based on the 4I theory, combines user portraits, interaction records, and feedback data to intelligently recommend science popularization content.

3. The system according to claim 1, characterized in that: The interaction module includes: The question and answer sub-module sets questions in the science popularization content. Patients can answer the questions during the viewing or reading process. The system immediately feedbacks the correctness of the answers and provides explanations; The comment and feedback sub-module allows patients to post comments below the science popularization content to share their views and experiences; The bullet screen interaction sub-module enables patients to conduct real-time interactions by sending bullet screens for the science popularization videos published on the platform.

4. The system according to claim 1, characterized in that: The data analysis module includes: The data collection sub-module collects the basic information of patients, interaction records, feedback data, and data from Internet platforms; The data analysis sub-module uses statistical methods to conduct descriptive statistics, correlation analysis, and difference tests on the collected data; The data visualization sub-module displays the analysis results in the form of charts, reports, etc. in the system background.

5. The system according to claim 1, characterized in that: The system also includes an effect evaluation mechanism for evaluating the effect of science popularization education, including patients' knowledge mastery, interaction frequency, and satisfaction, etc., and optimizing the science popularization content and education strategies according to the evaluation results.

6. The system according to claim 1, wherein: The system is based on the 4I theory, namely Interesting, Interests, Interaction, and Individuality. Through intelligent algorithms, it realizes effective interactions between patients and the system, and between patients and medical staff, improves user participation and learning effects, and ensures the interestingness and personalization of the recommended content.

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