Intelligent management system for medication compliance of patients with heart failure

Abstract

The invention relates to the technical field of medical AI, in particular to an intelligent management system for medication compliance of heart failure patients, which comprises an intelligent medicine box, wearable equipment and an electronic health record module, acquires basic information of the patients in real time, constructs an evaluation module, extracts medication behaviors, vital signs and historical records in a cloud database, and stores the medication behaviors, vital signs and historical records in the cloud database. Personalized medication education data is pushed through an intelligent platform, the medication compliance rate, the vital sign trend and symptom improvement data of a patient are integrated in animation, image-text and interactive game forms, the patient is incentive through milestone awards, and treatment confidence enhancement and active report problem proportion statistics are obtained. The problems that the medication data of the heart failure patient is not comprehensively collected in real time, the compliance risk is difficult to accurately assess, the medication education lacks individuation, follow-up management is not graded according to the risk, social support lacks, and treatment feedback is not visual and poor in excitation, so that the medication compliance of the patient is low are solved.

Description

Technical Field

[0001] This invention belongs to the field of medical AI technology, specifically relating to an intelligent management system for medication adherence in heart failure patients. Background Technology

[0002] Heart failure (HF), a chronic cardiovascular disease that seriously threatens human health, is characterized by high morbidity, high disability rate, and high mortality rate. In the treatment of HF, patient medication adherence is a key factor affecting treatment effectiveness and prognosis. However, the current situation regarding medication adherence among HF patients is far from satisfactory, and many problems urgently need to be addressed. In the traditional model, doctors mainly rely on verbal feedback from patients during outpatient follow-up visits to understand their medication adherence, lacking real-time and accurate data support. Patients may fail to strictly follow medical advice due to forgetfulness, misunderstanding, or intentional negligence, making it difficult for doctors to detect and intervene in a timely manner. This not only significantly reduces treatment effectiveness but may also lead to relapses, increasing hospitalization rates and medical costs. Simultaneously, existing patient education methods are relatively limited, relying mainly on paper materials or verbal explanations, which are insufficient to meet the needs of patients with varying levels of education, resulting in inconsistent levels of understanding of medication. In terms of follow-up management, the lack of a scientific stratification strategy prevents the rational allocation of medical resources based on patient risk levels, leading to high-risk patients not receiving timely attention and low-risk patients wasting resources. Furthermore, the treatment process for HF patients often lacks effective external support. Family members often lack the professional knowledge and skills to assist patients in monitoring medication use; patients also lack a platform for communication, making it difficult for them to share treatment experiences and insights. These problems can easily lead to feelings of loneliness and helplessness in patients during treatment, affecting their confidence and adherence to treatment.

[0003] Existing technologies have shortcomings such as incomplete and real-time collection of patient medication data, lack of accurate risk stratification assessment of adherence, lack of personalized education methods, failure to follow up management based on risk stratification, lack of social support and linkage, and unintuitive and unmotivating treatment feedback. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides an intelligent management system for medication adherence in heart failure patients. This system resolves problems such as incomplete and real-time medication data collection, difficulty in accurately assessing adherence risk, lack of personalized medication education, failure to categorize follow-up management according to risk levels, lack of social support, and unintuitive and unmotivating treatment feedback, all of which contribute to low patient medication adherence. To achieve the above objectives, this invention adopts the following technical solution: The intelligent management system for medication adherence in heart failure patients includes: a data acquisition module, used to collect basic patient information in real time using smart pillboxes, wearable devices, and electronic health record modules, and synchronize the data to a cloud platform via IoT technology to obtain a multidimensional health database of patients; an assessment module, used to extract medication behavior, vital signs, and historical records from the cloud database, and train an adherence prediction module using machine learning algorithms to stratify patients according to risk, obtaining high, medium, and low adherence risk classification results; and a tiered education module, used to push personalized medication education materials through the intelligent platform based on the patient's education level and risk level, using animation, graphics, and interactive games. The system provides several key features: a report on improved patient medication knowledge; a remote follow-up module that allocates follow-up frequencies based on risk level, verifies medication records via telephone, home visits, and outpatient checkups, dynamically adjusts medication regimens based on feedback, and provides individualized treatment optimization suggestions; a social linkage module that trains family members to use smart devices to assist in monitoring medication, organizes patient communities to share experiences, and invites doctors to give regular lectures, providing an assessment report on the strength of external support for patients through family participation and mutual assistance; and a visual feedback module that integrates patient medication adherence rates, vital sign trends, and symptom improvement data, incentivizes patients through milestone rewards, and provides statistics on increased treatment confidence and the proportion of patients proactively reporting problems.

[0005] Furthermore, the data acquisition module includes: a health acquisition submodule, used to collect the patient's daily medication time, dosage, and missed doses using a smart pillbox with medication recording and reminder functions, equipped with wearable devices to continuously monitor vital signs data such as heart rate, blood pressure, and weight, combined with an electronic health record module; an encrypted transmission submodule, used to integrate basic information such as the patient's age, gender, comorbidities, and renal function, and through IoT technology, to encrypt and transmit the medication data from the smart pillbox, the vital signs data from the wearable device, and the basic information from the electronic health record in real time and synchronize them to the cloud platform; and a data cleaning submodule, used to extract health indicators after data cleaning and structuring processing, to obtain a multidimensional health database of the patient covering medication behavior, physiological parameters, and basic information.

[0006] Furthermore, the assessment module includes: a medication data submodule, used to accurately extract patient medication behavior data from a cloud database using a data interface, including medication time, dosage accuracy, and number of missed doses; a medical record submodule, used to synchronously acquire vital sign data, including dynamic changes in heart rate, blood pressure, and weight, integrating the patient's previous medical records, medication adjustment history, and adverse event information; and a compliance prediction submodule, used to perform in-depth analysis of multi-dimensional data using machine learning algorithms to construct a compliance prediction module. Based on the output of the compliance prediction module, patients are divided into three levels—high, medium, and low—according to their risk level, resulting in a scientifically quantified compliance risk classification.

[0007] Furthermore, the tiered education module includes: a precise matching submodule, used to accurately extract and match patients' educational level and adherence risk level using an intelligent platform; an operation guide submodule, used to select targeted content from a pre-set educational resource library through algorithm analysis, the targeted content including drug action mechanisms, common side effect coping strategies, and correct medication practice operation guidelines; and a data analysis submodule, used to use dynamic animation to demonstrate key steps, graphic cards to reinforce memory points, and interactive games to simulate medication scenarios, and through online testing and behavioral data analysis, to assess patients' mastery of drug knowledge, side effect management, and operation skills, and generate a quantitative report on the improvement of medication knowledge mastery.

[0008] Furthermore, the remote follow-up module includes: a follow-up frequency submodule, used to allocate differentiated follow-up frequencies according to the patient's high, medium, and low risk levels of compliance; high-risk patients receive weekly telephone follow-ups plus monthly home visits; medium-risk patients receive bi-weekly telephone follow-ups plus quarterly outpatient check-ups; and low-risk patients receive monthly telephone follow-ups plus semi-annual outpatient check-ups; a medication status submodule, used to obtain actual medication status by verifying the smart pillbox records and patient self-reports during follow-ups, and simultaneously assessing symptoms of dyspnea and edema, as well as vital signs such as heart rate and blood pressure; and a dynamic adjustment submodule, used to dynamically adjust medication dosage and type based on feedback data using clinical decision support tools, and generate individualized treatment optimization suggestions including medication adjustment plans, monitoring priorities, and follow-up visit plans.

[0009] Furthermore, the aforementioned social linkage module includes: a training and communication sub-module, used to conduct family training using a combination of online and offline methods, build online patient communities, set up medication check-in and experience exchange areas, and regularly invite attending physicians to conduct special lectures to answer questions; an improvement feedback sub-module, used to extract data on the frequency of family supervision, the depth of patient interaction, and the effectiveness of doctor guidance through community activity analysis, family member device operation logs, and patient symptom improvement feedback; and a quantitative assessment sub-module, used to analyze the impact of external support on patient medication adherence using a quantitative assessment module, and obtain an external support strength assessment report that includes support strength scores and improvement directions.

[0010] Furthermore, the visualization feedback module includes: a dynamic chart submodule, which uses data visualization technology to extract patient medication adherence rate, vital sign trends of heart rate and blood pressure, and symptom improvement data of dyspnea from a cloud database, converting medication adherence rate into a progress bar, generating dynamic curves of vital sign data, color-coding symptom changes, and generating a personal health dashboard for patients to view; an abnormal indicator submodule, which integrates group data to generate a doctor management dashboard, marks abnormal indicators, sets targets for continuous 30-day achievement and symptom improvement, and incentivizes patients through virtual badges and points rewards; and a feedback questionnaire submodule, which obtains treatment confidence scores and problem reporting frequency through patients actively clicking the feedback button and conducting regular questionnaires, and obtains a quantitative statistical analysis report on the increase in treatment confidence and the proportion of proactively reported problems.

[0011] Furthermore, the precise matching submodule is used to use an intelligent platform to accurately extract and match patients' educational level and compliance risk level. The educational level includes primary school, middle school, university and above, and the compliance risk level includes high, medium and low.

[0012] Furthermore, the training and communication sub-module is used to conduct family training in a combination of online and offline methods. Online, video tutorials explain the key points of operating smart pillboxes and wearable devices. Offline, practical exercises are organized to ensure mastery of medication reminders and data viewing skills. An online patient community is built, with dedicated areas for medication check-ins and experience sharing. Attending physicians are invited regularly to conduct special lectures to answer questions.

[0013] In the technical solution provided by this invention, a data acquisition module is used to collect basic patient information in real time using a smart pillbox, wearable device, and electronic health record module. The data is then synchronized to a cloud platform via IoT technology to obtain a multidimensional health database of the patient. An assessment module is used to extract medication behavior, vital signs, and historical records from the cloud database. A compliance prediction module is trained using machine learning algorithms to stratify patients according to risk, resulting in high, medium, and low compliance risk classifications. A tiered education module is used to push personalized medication education materials to patients through a smart platform based on their education level and risk level, using animation, graphics, and interactive games to provide patients with effective medication education. The invention includes a report on improved medication knowledge among patients; a remote follow-up module that assigns follow-up frequencies based on patient risk levels, verifies medication records via telephone, home visits, and outpatient checkups, dynamically adjusts medication regimens based on feedback, and provides individualized treatment optimization suggestions; a social linkage module that trains family members to use smart devices to assist in monitoring medication, organizes patient communities to share experiences, and invites doctors to give regular lectures, providing an assessment report on the strength of external support for patients through family participation and mutual assistance among patients; and a visual feedback module that integrates data on patient medication adherence, vital sign trends, and symptom improvement, incentivizes patients through milestone rewards, and provides statistics on increased treatment confidence and the proportion of patients proactively reporting problems. This invention addresses the problems of incomplete and real-time medication data collection in heart failure patients, difficulty in accurately assessing adherence risk, lack of personalized medication education, non-risk-based follow-up management, lack of social support, and unintuitive and unmotivating treatment feedback, all of which contribute to low patient medication adherence. Attached Figure Description

[0014] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0015] Figure 1 This is a schematic diagram of the first embodiment of the intelligent management system for medication adherence in heart failure patients according to the present invention.

[0016] Figure 2 This is a schematic diagram of the second embodiment of the intelligent management system for medication adherence in heart failure patients according to the present invention.

[0017] Figure 3 This is a schematic diagram of the third embodiment of the intelligent management system for medication adherence in heart failure patients according to the present invention.

[0018] Figure 4 This is a schematic diagram of the fourth embodiment of the intelligent management system for medication adherence in heart failure patients according to the present invention.

[0019] Figure 5This is a schematic diagram of the fifth embodiment of the intelligent management system for medication adherence in heart failure patients according to the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0022] Intelligent management system for medication adherence in heart failure patients, such as Figure 1 As shown, it includes: a data acquisition module, used to collect basic patient information in real time using smart pillboxes, wearable devices, and electronic health record modules, and synchronize the data to a cloud platform via IoT technology to obtain a multi-dimensional health database of patients; an assessment module, used to extract medication behavior, vital signs, and historical records from the cloud database, and train an adherence prediction module using machine learning algorithms to stratify patients according to risk, obtaining high, medium, and low adherence risk classification results; and a tiered education module, used to push personalized medication education materials through the intelligent platform based on the patient's education level and risk level, using animation, graphics, and interactive games to help patients understand medication use. The system includes: a report on improved patient understanding; a remote follow-up module to allocate follow-up frequencies based on risk levels, verify medication records via telephone, home visits, and outpatient checkups, dynamically adjust medication regimens based on feedback, and provide individualized treatment optimization suggestions; a social linkage module to train family members to use smart devices to assist in monitoring medication, organize patient communities to share experiences, and invite doctors to give regular lectures, providing an assessment report on the strength of external support for patients through family participation and mutual assistance among patients; and a visual feedback module to integrate patient medication adherence rates, vital sign trends, and symptom improvement data, motivating patients through milestone rewards, and providing statistics on increased treatment confidence and the proportion of patients proactively reporting problems.

[0023] like Figure 2As shown in this embodiment, the health collection submodule is used to collect the patient's daily medication time, dosage, and missed doses using a smart pillbox with medication recording and reminder functions. It is equipped with wearable devices to continuously monitor vital signs data such as heart rate, blood pressure, and weight, and is integrated with the electronic health record module. The encrypted transmission submodule is used to integrate basic information such as the patient's age, gender, comorbidities, and kidney function. Through IoT technology, it transmits the medication data from the smart pillbox, the vital signs data from the wearable device, and the basic information from the electronic health record in real time with encryption and synchronizes them to the cloud platform. The data cleaning submodule is used to extract health indicators after data cleaning and structuring processing, obtaining a multidimensional health database of the patient covering medication behavior, physiological parameters, and basic information.

[0024] The health data collection submodule uses a smart pillbox to accurately record patient medication usage, while wearable devices monitor key vital signs in real time, providing a comprehensive understanding of patient health dynamics. The encrypted transmission submodule integrates basic information and uses IoT technology to encrypt and synchronize it to the cloud in real time, ensuring data security and timeliness. The data cleaning submodule cleans and structures the collected data, extracts key health indicators, and constructs a multi-dimensional health database. This series of operations enables accurate and comprehensive collection of patient information, facilitating subsequent precise assessment of medication adherence risk.

[0025] like Figure 3 As shown in this embodiment, the medication data submodule is used to accurately extract patient medication behavior data from a cloud database using a data interface. This medication behavior data includes medication time, dosage accuracy, and the number of missed doses. The medical record submodule is used to synchronously acquire vital sign data, including dynamic changes in heart rate, blood pressure, and weight, integrating the patient's previous medical records, medication adjustment history, and adverse event information. The adherence prediction submodule is used to perform in-depth analysis of multi-dimensional data using machine learning algorithms to construct an adherence prediction module. Based on the output of the adherence prediction module, patients are divided into three levels—high, medium, and low—according to their risk level, resulting in a scientifically quantified adherence risk classification.

[0026] The medication data submodule uses a data interface to accurately extract patient medication behavior data from the cloud, clearly identifying key information such as medication timing, dosage accuracy, and the number of missed doses. The medical record submodule synchronously acquires dynamic changes in vital signs, integrating past medical visits, medication adjustments, and adverse event records to comprehensively understand the patient's health history. The adherence prediction submodule utilizes machine learning algorithms to deeply analyze multi-dimensional data, constructing a prediction module that scientifically and quantitatively classifies patient adherence risk into high, medium, and low levels. This provides a precise basis for subsequently developing personalized intervention strategies.

[0027] like Figure 4As shown in this embodiment, the precise matching submodule is used to accurately extract and match patients' educational level and adherence risk level using an intelligent platform; the operation guide submodule is used to select targeted content from a preset educational resource library through algorithm analysis, including drug action mechanisms, common side effect coping strategies, and correct medication practice operation guidelines; the data analysis submodule is used to use dynamic animation to demonstrate key steps, graphic cards to reinforce memory points, and interactive games to simulate medication scenarios, and through online testing and behavioral data analysis, to assess patients' mastery of drug knowledge, side effect management, and operation skills, and generate a quantitative report on the improvement of medication knowledge mastery.

[0028] The precise matching submodule accurately extracts and matches patients' educational level and adherence risk level, ensuring that educational content aligns with individual needs. The operation guide submodule uses algorithms to select targeted content from a pre-set database, covering various aspects of medication knowledge to provide comprehensive guidance to patients. The data analysis submodule employs diverse educational methods such as dynamic animations, graphic cards, and interactive games to enhance learning engagement and retention. Online tests and behavioral data analysis assess patient mastery, generating quantitative reports that help doctors understand patient learning progress, adjust educational strategies, effectively improve patients' medication knowledge and operational skills, and ultimately enhance medication adherence.

[0029] like Figure 5 As shown in this embodiment, the follow-up frequency submodule is used to allocate differentiated follow-up frequencies according to the patient's high, medium, and low risk levels of compliance. High-risk patients are followed up by phone once a week and by home visit once a month; medium-risk patients are followed up by phone every two weeks and by outpatient visit every quarter; and low-risk patients are followed up by phone every month and by outpatient visit every six months. The medication status submodule is used to obtain the actual medication status by checking the smart pillbox records and the patient's self-report during follow-up, and to simultaneously assess symptoms such as dyspnea and edema, as well as vital signs such as heart rate and blood pressure. The dynamic adjustment submodule is used to dynamically adjust the dosage and type of medication based on the feedback data using clinical decision support tools, and to generate individualized treatment optimization suggestions that include medication adjustment plans, monitoring priorities, and follow-up visit plans.

[0030] The follow-up frequency submodule allocates differentiated follow-up arrangements based on patients' compliance risk levels. High-, medium-, and low-risk patients receive different frequencies of telephone calls, home visits, and outpatient follow-ups, ensuring the rational allocation of medical resources and close monitoring of high-risk patients. The medication status submodule comprehensively monitors actual medication use by verifying smart pillbox records and patient self-reports, simultaneously assessing symptoms and vital signs to provide a basis for treatment adjustments. The dynamic adjustment submodule utilizes clinical decision support tools to dynamically optimize medication regimens based on feedback data, generating individualized recommendations that help improve the accuracy and effectiveness of heart failure patient treatment.

[0031] In this embodiment, the training and communication submodule is used to conduct family training in a combination of online and offline methods, build an online patient community, set up a medication check-in and experience exchange area, and regularly invite attending physicians to conduct special lectures to answer questions; the improvement feedback submodule is used to extract data on the frequency of family supervision, the depth of patient interaction, and the effectiveness of doctor guidance through community activity analysis, family member device operation logs, and patient symptom improvement feedback; the quantitative assessment submodule is used to analyze the degree of impact of external support on patient medication adherence using the quantitative assessment module, and obtain an external support strength assessment report that includes support strength score and improvement direction.

[0032] The training and communication submodule combines online and offline training for family members, establishes an online patient community with various dedicated sections, and invites doctors to conduct lectures to improve family members' monitoring capabilities, promote experience sharing and mutual support among patients, and enhance patients' confidence in treatment. The feedback improvement submodule extracts key data from community activity, family member operation logs, and patient symptom feedback to comprehensively understand family monitoring, patient interaction, and doctor guidance. The quantitative assessment submodule uses quantitative methods to analyze the impact of external support on medication adherence, generating assessment reports to provide a basis for optimizing external support strategies and contributing to a positive treatment support environment.

[0033] In this embodiment, the dynamic chart submodule uses data visualization technology to extract patient medication adherence rate, vital sign trends of heart rate and blood pressure, and symptom improvement data of dyspnea from a cloud database. Medication adherence rate is converted into a progress bar, vital sign data is generated into a dynamic curve graph, symptom changes are indicated by color, and a personal health dashboard is generated for patients to view. The abnormal indicator submodule integrates group data to generate a doctor management dashboard, marks abnormal indicators, sets targets for continuous 30-day achievement and symptom improvement, and incentivizes patients through virtual badges and points rewards. The feedback questionnaire submodule obtains treatment confidence scores and problem reporting frequency through patients actively clicking the feedback button and conducting regular questionnaires, resulting in a quantitative statistical analysis report on the increase in treatment confidence and the proportion of proactively reported problems.

[0034] The dynamic charts submodule utilizes data visualization technology to transform complex data into intuitive progress bars, dynamic graphs, and color-coded indicators, generating a personal health dashboard that allows patients to clearly understand their medication and health status, enhancing their self-management awareness. The abnormal indicators submodule integrates group data to generate a doctor's management dashboard, marking abnormalities and setting up reward mechanisms to incentivize patients to maintain good treatment behaviors and improve medication adherence. The feedback questionnaire submodule obtains treatment confidence and problem report data through proactive patient feedback and regular questionnaires, generating quantitative analysis reports that help doctors promptly understand patients' psychological state and adjust treatment plans.

[0035] In this embodiment, the precise matching submodule is used to use an intelligent platform to accurately extract and match patients' educational level and compliance risk level. Educational level includes primary school, middle school, university and above, and compliance risk level includes high, medium and low.

[0036] By using an intelligent platform to accurately extract patients' educational level and adherence risk level and then matching them accordingly, the platform fully considers the differences in patients' ability to accept and understand medical information across different educational backgrounds. For patients with only a primary school education, simple, easy-to-understand, and visually appealing educational content can be provided; for patients with a secondary school education or above, more in-depth professional content can be appropriately added. Simultaneously, based on adherence risk levels, high-risk patients receive closer attention and enhanced education. This precise matching ensures that patients receive the most suitable medication education and guidance, effectively improving their understanding and adherence to treatment plans.

[0037] In this embodiment, the training and communication submodule is used to conduct family training in a combination of online and offline methods. Online, video tutorials explain the key points of operating smart pillboxes and wearable devices. Offline, practical exercises are organized to ensure that patients master the skills of medication reminders and data viewing. An online patient community is built, and a medication check-in and experience exchange area is set up. Attending physicians are invited regularly to conduct special lectures to answer questions.

[0038] We combine online and offline training for family members. Online video tutorials clearly explain the key points of equipment operation, allowing family members to quickly grasp the basics. Offline practical exercises reinforce skills mastery, ensuring family members can skillfully remind patients of medication and view data, effectively assisting patients in managing their medication. We have established online patient communities with dedicated areas for medication tracking and experience sharing, fostering a positive supportive atmosphere and promoting mutual encouragement and learning among patients. We regularly invite attending physicians to conduct specialized lectures, providing authoritative answers to questions and offering professional guidance to patients and their families.

[0039] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart management system for medication adherence in heart failure patients, characterized in that, The intelligent management system for medication adherence in heart failure patients includes: The data acquisition module is used to collect patients' basic information in real time using smart pillboxes, wearable devices and electronic health record modules, and synchronize the data to the cloud platform through Internet of Things technology to obtain a multi-dimensional health database of patients; An assessment module was constructed to extract medication behavior, vital signs and historical records from the cloud database. The adherence prediction module was trained through machine learning algorithms to stratify patients according to risk and obtain high, medium and low adherence risk classification results. The tiered education module is used to push personalized medication education materials through an intelligent platform based on the patient's education level and risk level. It uses animation, graphics and interactive games to generate a report on the improvement of the patient's knowledge of medication. The remote follow-up module is used to allocate follow-up frequencies to patients according to their risk levels, verify medication records through telephone, home visits and outpatient follow-up, dynamically adjust medication plans based on feedback, and obtain individualized treatment optimization suggestions; The social linkage module is used to train family members to use smart devices to assist in monitoring medication, organize patient communities to share experiences and invite doctors to give regular lectures, and obtain an assessment report on the strength of external support for patients through family participation and mutual assistance among patients. The visualization feedback module integrates data on patient medication adherence, vital sign trends, and symptom improvement. It motivates patients through milestone rewards, resulting in increased treatment confidence and statistics on the proportion of patients proactively reporting problems.

2. The intelligent management system for medication adherence in heart failure patients according to claim 1, characterized in that, The data acquisition module includes: The health data collection submodule is used to collect patients' daily medication time, dosage, and missed doses using a smart pillbox with medication recording and reminder functions. It is equipped with wearable devices to continuously monitor vital signs data such as heart rate, blood pressure, and weight, in conjunction with the electronic health record module. The encrypted transmission submodule is used to integrate basic information such as patient age, gender, comorbidities, and renal function. Through IoT technology, it transmits medication data from the smart pillbox, vital sign data from wearable devices, and basic information from electronic health records in real time in encrypted form and synchronizes them to the cloud platform. The data cleaning submodule is used to extract health indicators after data cleaning and structuring, resulting in a multidimensional health database of patients covering medication behavior, physiological parameters, and basic information.

3. The intelligent management system for medication adherence in heart failure patients according to claim 1, characterized in that, The construction evaluation module includes: The medication data submodule is used to accurately extract patient medication behavior data from a cloud database using a data interface. The medication behavior data includes medication time, dosage accuracy, and number of missed doses. The medical record submodule is used to synchronously acquire vital sign data, which includes dynamic changes in heart rate, blood pressure, and weight, and integrates the patient's previous medical records, medication adjustment history, and adverse event information. The adherence prediction submodule is used to perform in-depth analysis of multi-dimensional data through machine learning algorithms to build an adherence prediction module. Based on the output of the adherence prediction module, patients are divided into three levels of risk: high, medium and low, to obtain a scientifically quantified adherence risk classification result.

4. The intelligent management system for medication adherence in heart failure patients according to claim 1, characterized in that, The tiered education module includes: The precise matching submodule is used to accurately extract and match patients' educational level and compliance risk level using an intelligent platform; The operation guide submodule is used to filter targeted content from a preset educational resource database through algorithm analysis. The targeted content includes drug action mechanism, common side effect coping strategies and correct medication practice operation guidelines. The data analysis submodule is used to demonstrate key steps with dynamic animations, reinforce memory points with graphic cards, and simulate medication scenarios with interactive games. Through online testing and behavioral data analysis, it assesses patients' mastery of drug knowledge, side effect management, and operational skills, and generates a quantitative report on the improvement of medication knowledge.

5. The intelligent management system for medication adherence in heart failure patients according to claim 1, characterized in that, The remote follow-up module includes: The follow-up frequency submodule is used to allocate differentiated follow-up frequencies based on the patient's risk level of high, medium, and low compliance. High-risk patients are followed up by telephone once a week and by home visit once a month; medium-risk patients are followed up by telephone every two weeks and by outpatient check-up every quarter; and low-risk patients are followed up by telephone every month and by outpatient check-up every six months. The medication status submodule is used to obtain the actual medication status by checking the smart pillbox records and the patient's self-report during follow-up visits, and to simultaneously assess symptoms such as dyspnea and edema, as well as vital signs such as heart rate and blood pressure. The dynamic adjustment submodule is used to dynamically adjust the dosage and type of medication based on feedback data and clinical decision support tools, generating individualized treatment optimization suggestions that include medication adjustment plans, monitoring priorities, and follow-up visit plans.

6. The intelligent management system for medication adherence in heart failure patients according to claim 1, characterized in that, The linked social module includes: The training and exchange sub-module is used to conduct family training in a combination of online and offline methods, build online patient communities, set up medication check-in and experience exchange areas, and regularly invite attending physicians to conduct special lectures to answer questions. The feedback improvement submodule is used to extract data on the frequency of family supervision, the depth of patient interaction, and the effectiveness of doctor guidance by analyzing community activity, family member device operation logs, and patient symptom improvement feedback. The quantitative assessment submodule is used to analyze the impact of external support on patient medication adherence and obtain an external support strength assessment report that includes a support strength score and directions for improvement.

7. The intelligent management system for medication adherence in heart failure patients according to claim 1, characterized in that, The visualization feedback module includes: The dynamic chart submodule is used to extract patient medication adherence rate, vital sign trends of heart rate and blood pressure, and symptom improvement data of dyspnea from cloud database using data visualization technology. Medication adherence rate is converted into a progress bar, vital sign data is generated into dynamic curves, symptom changes are marked with colors, and a personal health dashboard is generated for patients to view. The abnormal indicators submodule is used to integrate group data to generate a doctor management dashboard, mark abnormal indicators, set targets for continuous 30-day achievement and symptom improvement, and incentivize patients through virtual badges and points rewards. The feedback questionnaire submodule is used to obtain treatment confidence scores and problem reporting frequency by allowing patients to actively click the feedback button and complete regular questionnaires. This results in a quantitative statistical analysis report on the increase in treatment confidence and the proportion of patients who actively report problems.

8. The intelligent management system for medication adherence in heart failure patients according to claim 4, characterized in that, The precise matching submodule is used to accurately extract and match patients' educational level and adherence risk level using an intelligent platform. The educational level includes primary school, middle school, university, and above. The adherence risk level includes... High, medium, and low.

9. The intelligent management system for medication adherence in heart failure patients according to claim 6, characterized in that, The training and communication submodule is used to conduct family training in a combination of online and offline methods. Online, video tutorials explain the key points of operating smart pillboxes and wearable devices. Offline, practical exercises are organized to ensure mastery of medication reminders and data viewing skills. An online patient community is built, with dedicated areas for medication check-ins and experience sharing. Attending physicians are invited regularly to conduct special lectures to answer questions.

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