Chronic disease high-risk population health management system

Abstract

The invention discloses a chronic disease high-risk population health management system, and the system comprises a task management module which is used for creating a target task and distributing the task to a basic medical institution; the task management module is used for collecting information of risk personnel for the first time and dividing the risk personnel to corresponding risk levels; the intervention management module is used for periodically collecting various behavior data and health data of the risk personnel and feeding back intervention suggestions in real time according to the collected data, and meanwhile, the intervention management module is used for combining and comparing the data in various stages and dynamically evaluating the intervention suggestions so as to continuously optimize the guidance suggestion library; and the follow-up management module is used for periodically reminding risk personnel to fill corresponding data and reminding personnel with different risk levels to perform offline follow-up with different frequencies so as to further provide personalized intervention suggestions. Management of the system can help risk personnel to gradually change bad living habits and behavior modes, and therefore the purpose of preventing or delaying development of chronic diseases is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of health management, and in particular to a health management system for people at high risk of chronic diseases. Background Art

[0002] Chronic disease high-risk population refers to people who are identified as having a higher risk of chronic diseases. Among them, those with one or more of the following characteristics can be considered as chronic disease high-risk population: 1. Blood pressure level is 130-139 / 85-89 mmHg; 2. Current smokers; 3. Fasting blood glucose level is 6.1≤FBG<7.0mmol / L; 4. Serum total cholesterol level is 5.2≤TC<6.2 mmol / L; 5. Male waist circumference ≥ 90cm, female waist circumference ≥ 85cm.

[0003] The health status of people at high risk of chronic diseases (hereinafter referred to as risk persons) requires closer monitoring and management to prevent or delay the development of chronic diseases.

[0004] In order to cope with the increasingly severe situation of chronic disease prevention and control, health management of people at high risk of chronic diseases is an important link in the advancement of chronic disease prevention and control, and exploring appropriate health management measures and models for people at high risk of chronic diseases is one of the important tasks of current chronic disease prevention and control. To this end, the present invention provides a health management system for people at high risk of chronic diseases. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a health management system for people at high risk of chronic diseases, so as to manage and monitor people at high risk of chronic diseases throughout the process, provide personalized health management guidance and suggestions, and thus prevent or delay the development of chronic diseases.

[0006] The technical solution adopted by the present invention is as follows: The health management system for people at high risk of chronic diseases includes a task management module, which is used to create goals and assign them to primary medical institutions, which carry out health management tasks. Creating goals includes collecting identity information and health index information of risk personnel, and the task management module divides personnel into risk levels by statistical health index information for hierarchical management. The intervention management module is used to collect various behavioral data and health data of risk personnel in stages, and evaluate the corresponding behavioral stage and health status of the personnel in each stage according to the data of each stage; the intervention management module stores a guidance opinion library, and the intervention management module provides real-time feedback on corresponding intervention suggestions based on the assessed behavioral stage, health status and guidance opinion library. After the stage intervention is completed, the behavioral data and health data of the next stage are collected and compared with the data of the previous stage to evaluate the intervention effect of the intervention suggestions of the previous stage, so as to optimize the guidance opinion library; The follow-up management module is used to periodically remind risk personnel to report behavioral data and health data during the intervention management period, and to remind personnel of different risk levels to conduct offline follow-up visits of different frequencies. Public health physicians in primary medical institutions fill in follow-up records after offline follow-up visits, and provide and feedback personalized intervention suggestions.

[0007] The health management system of the present invention has the following beneficial effects: 1. The present invention divides the risk groups into risk levels. By adopting a hierarchical management model, it is possible to achieve refined management of risk personnel, pay appropriate attention to low-risk groups, strengthen attention to high-risk groups, reasonably allocate management resources, and make effective use of resources.

[0008] 2. The intervention management module of the present invention analyzes the specific behavioral stage and health data of each risk person, and tailors personalized guidance for each risk person to ensure that the intervention measures are more in line with the individual's actual situation. It is highly targeted and can significantly improve the management effect. Moreover, by adopting this intelligent intervention method, it can also effectively reduce the labor cost of medical institutions and can be widely promoted.

[0009] 3. The present invention adopts a staged intervention model for risk personnel. This method increases the frequency of intervention for risk personnel, and can gradually adapt and change bad living habits and behaviors through a step-by-step guidance method, and ultimately achieve the purpose of maintaining healthy behaviors for a long time. Management in this way not only makes risk personnel adaptable, but also gradually builds confidence and improves compliance, thereby promoting the formation of a healthy lifestyle. In addition, by adopting a staged intervention model, the data of each stage can be combined and compared to achieve dynamic evaluation, so as to continuously optimize the guidance opinion library, and then timely make adaptive adjustments to the intervention opinions in the subsequent stages to improve the management effect.

[0010] 4. The follow-up management module provided by the present invention can provide full-process health management reminders to risk personnel during the intervention management period to avoid poor management results due to forgetfulness; in addition, the module can also remind relevant personnel to do offline follow-up visits of different frequencies according to different risk level groups. Offline follow-up visits are mainly conducted by public health physicians in primary medical institutions to conduct health examinations and track lifestyle changes. After each offline follow-up visit, doctors in primary medical institutions combine the system's automatic matching opinions, further propose personalized intervention suggestions, and send the intervention suggestions to the risk personnel's mobile phone for viewing. The present invention realizes the combined management of online and offline through follow-up visits, which can not only allow doctors to have a more comprehensive understanding of the situation and thus provide personalized guidance, but also enhance the connection between risk personnel and medical personnel, and improve the compliance of risk personnel to intervention measures, thereby improving the overall intervention effect.

[0011] As a preferred embodiment of the present invention, the health indicator information includes blood sugar, blood pressure, blood lipids, waist circumference, and smoking status. The task management module stores the danger range value of the corresponding indicator information. The task management module compares the collected indicator information with the danger range value. If the indicator value is within the danger range value, the indicator is judged to be a danger indicator. The task management module counts the number of danger indicators and divides the personnel according to the statistical number. If the number of danger indicators is greater than or equal to three, it is divided into a high-risk level. If the number of danger indicators is greater than zero and less than three, it is divided into a low-risk level.

[0012] Beneficial effects: By dividing people into high-risk and low-risk levels, the present invention can take different intervention measures according to different risk levels. High-risk people can receive more frequent and more in-depth interventions, while low-risk people can receive routine monitoring and health guidance, thereby improving the utilization rate of medical resources.

[0013] As a preferred embodiment of the present invention, the identity information includes at least name, telephone number and home address, and the task management module assigns the corresponding personnel to the primary medical institutions in the corresponding area for management according to the home address.

[0014] Beneficial effects: The present invention adopts a local management approach, which can not only reduce the cost of cross-regional management and coordination, but also make follow-up work more convenient and efficient.

[0015] As a preferred embodiment of the present invention, the intervention management module stores a health education video library, and the intervention management module is used to push personalized health education videos in a targeted manner according to the collected health indicator information, and count the number of views of each person.

[0016] Beneficial effects: The present invention can help risk personnel to continue to pay attention to and learn relevant health knowledge through targeted push of personalized health education videos, improve their health awareness and self-management ability, and gradually change bad living habits and behaviors to reduce the risk of illness. Moreover, by counting the number of views and setting a reward mechanism for the number of views, their initiative and enthusiasm can be improved, thereby improving the effect of education.

[0017] As a preferred embodiment of the present invention, the behavioral data includes smoking cessation behavior, exercise behavior and low-salt and low-oil behavior. The intervention management module stores questionnaires for different behaviors. The intervention management module determines the behavioral stage of the person based on the statistical results of the questionnaire. The behavioral stages include pre-intention stage, intention stage, preparation stage, action stage and maintenance stage. The intervention management module provides real-time feedback on intervention suggestions based on the person's behavioral stage and the guidance opinion library.

[0018] Beneficial effects: The present invention achieves more personalized and comprehensive intervention by managing different behaviors and setting intervention suggestions for different stages of different behaviors. From the pre-intention stage to the maintenance stage, corresponding intervention suggestions are set for each stage, which can gradually guide risk personnel from unconsciousness to actual action, and ultimately achieve the purpose of maintaining healthy behaviors for a long time. This approach not only improves the pertinence and effectiveness of intervention measures, but also enhances the compliance and confidence of risk personnel, which is conducive to the formation of a healthy lifestyle.

[0019] As a preferred embodiment of the present invention, the health data includes height, weight, waist circumference, blood pressure and blood sugar. The guidance library is provided with different intervention suggestions corresponding to each health data in different range values. The intervention management module matches the collected data to the corresponding range value and makes corresponding intervention suggestions in combination with the guidance library.

[0020] Beneficial effects: The present invention sets precise intervention suggestions for different data, can propose targeted solutions to specific problems of individuals, and can achieve more accurate and comprehensive health management.

[0021] As a preferred embodiment of the present invention, the intervention management module includes a phase one submodule, a phase two submodule, a phase three submodule, a phase four submodule, a phase five submodule, and a phase six submodule. Each phase submodule is used to collect the current behavioral data and health data of the risk personnel, and make current intervention recommendations based on the corresponding data; wherein, the data collected by the phase two submodule, the phase three submodule, the phase four submodule, the phase five submodule, and the phase six submodule are compared with the data collected by the previous phase submodule to obtain the intervention effect of the previous intervention recommendation, and optimize the guidance opinion library based on the intervention effect.

[0022] Beneficial effects: The present invention divides the intervention management module into multiple sub-modules, and starting from the second-phase sub-module, each phase sub-module compares the collected behavioral data and health data with the data of the previous phase sub-module to evaluate the intervention effect and optimize the guidance opinion library. This can realize dynamic evaluation of intervention suggestions, so as to continuously optimize the guidance opinion library and improve the intervention management effect.

[0023] As a preferred embodiment of the present invention, the intervention management module evaluates multiple health conditions corresponding to the person based on the collected information, and the guidance opinion library is provided with intervention suggestions corresponding to each health condition, wherein each intervention suggestion is provided with multiple intervention contents, and the intervention management module randomly provides one of the multiple intervention contents each time for the corresponding situation evaluated, and the intervention management module determines the applicability level of the intervention content by counting the intervention effects of different persons after using the intervention content, and the applicability levels include excellent, good, medium, and poor, and the intervention management module makes corresponding optimization of the intervention suggestion according to the applicability level.

[0024] Beneficial effects: Due to differences in individual situations, different people may have different feedback on the same intervention content. For example, different people have different acceptance of exercise time, exercise type, exercise intensity, etc., so the final intervention effect will also be different. Therefore, in order to ensure that the guidance opinions meet the current needs of individuals to a greater extent, the present invention collects and analyzes data on intervention effects, and continuously optimizes the guidance opinion library, eliminates inappropriate intervention content, and ensures that each individual can obtain the most effective personalized suggestions. The specific optimization process is to judge the applicability level of the intervention content, and the ranking of the applicability level increases step by step from poor to excellent. When the applicability level of the intervention content is higher, it indicates that the population has a higher degree of applicability to the intervention content, and it is a guidance opinion that is more acceptable to most people and has better effects. Finally, by retaining the intervention content with a higher applicability level, the optimization of the intervention suggestions is achieved.

[0025] As a preferred embodiment of the present invention, the intervention management module compares the collected health data with the normal reference range value to obtain a deviation value. If the absolute value of the current deviation value is less than the absolute value of the previous deviation value, it indicates that the intervention is an effective intervention. The intervention management module counts the number n of effective interventions after using the intervention suggestion. If n≥100, it is excellent; if 100>n≥50, it is good; if 50>n≥20, it is medium; if n<20, it is poor.

[0026] Beneficial effects: The deviation value of the present invention is the difference between a certain data and the normal reference range. If the data is higher than the maximum value of the normal reference range, it is a positive deviation value. If the data is lower than the minimum value of the normal reference range, it is a negative deviation value. The absolute value of the positive deviation value or the negative deviation value indicates the difference degree. If the absolute value is smaller, it indicates that the difference degree is smaller, which means that the person is healthier in this indicator. In the present invention, if the absolute value of the current deviation value is less than the absolute value of the previous deviation value, it indicates that after the intervention of the previous stage, it is gradually returning to the normal range, which is an effective intervention. The intervention management module can judge the effectiveness of the intervention suggestion based on actual data by counting the effects of different personnel using the intervention suggestion, and the judgment method is scientific and reliable.

[0027] As a preferred embodiment of the present invention, the follow-up management module takes the first collection time as the starting point during the intervention management period, and reminds personnel to fill in behavioral data and health data once every two months; reminds high-risk personnel to follow up offline every six months, and reminds low-risk personnel to follow up offline every year. Public health doctors in primary medical institutions fill in follow-up records after offline follow-up, and provide and feedback personalized intervention suggestions.

[0028] Beneficial effects: The present invention can realize continuous monitoring by regularly reminding personnel to fill in behavioral data and health data, and conduct stratified follow-up according to the risk level, so as to avoid the situation where the management effect is poor due to forgetting to fill in the data. At the same time, by reasonably arranging the follow-up frequency according to the risk level, the medical cost can be reduced while ensuring the intervention effect; wherein, offline follow-up is mainly carried out by public health physicians in primary medical institutions, and health examinations and lifestyle changes are tracked. After each offline follow-up, the doctor of the primary medical institution fills in the follow-up record, and combines the automatic matching opinions of the system to further propose personalized intervention suggestions, and send the intervention suggestions to the risk personnel's mobile phone for viewing. Through regular follow-up feedback, the combined management of online and offline is realized, and the doctor can further optimize the suggestion in combination with the specific situation of the risk personnel, so that the intervention is more accurate and effective, which is conducive to improving the intervention effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a structural frame of the embodiment of the present invention Figure 1 ; Figure 2 It is a structural frame of the embodiment of the present invention Figure 2 . DETAILED DESCRIPTION

[0030] Typical embodiments that embody the features and advantages of the present invention will be described in detail in the following description. It should be understood that the present invention can have various changes in different embodiments without departing from the scope of the present invention, and the descriptions and illustrations therein are essentially used for illustration purposes rather than for limiting the present invention.

[0031] See also Figure 1 and Figure 2 As shown, this embodiment discloses a health management system for people at high risk of chronic diseases, including a task management module, an intervention management module and a follow-up management module.

[0032] The task management module is used to create targets and assign tasks to primary medical institutions, which carry out health management tasks. Specifically, when medical institutions at all levels discover high-risk groups during outpatient clinics and physical examinations and are willing to manage risk personnel, the corresponding units create information about the high-risk groups in the health management system, that is, fill in the corresponding information from the mobile phone or computer to the health management system. The created target information includes identity information and health indicator information. The identity information includes at least name, phone number and home address. The task management module is used to assign the corresponding personnel to the primary medical institutions in the corresponding area for management based on the collected home address. The primary medical institutions create tasks and assign risk personnel to reduce the cost of cross-regional management and coordination, and also make subsequent follow-up work more convenient. The health indicator information includes five indicator information: blood sugar, blood pressure, blood lipids, waist circumference, and smoking status. The task management module stores the danger range value of the corresponding indicator information. The task management module compares the collected indicator information with the danger range value. If the indicator value is within the danger range, the indicator is judged to be a danger indicator. The task management module counts the number of danger indicators and divides the personnel according to the statistical number. If the number of danger indicators is greater than or equal to three, it is divided into a high-risk level. If the number of danger indicators is greater than zero and less than three, it is divided into a low-risk level. By dividing the personnel into risk levels, it is convenient to take different intervention measures for people of different risk levels in the future, thereby improving the utilization rate of medical resources.

[0033] Intervention management module, the intervention management module is used to manage high and low risk groups. The intervention management module includes a phase one submodule, a phase two submodule, a phase three submodule, a phase four submodule, a phase five submodule, and a phase six submodule, wherein the management time of each phase submodule is two months. Each phase submodule stores different health education video libraries, each health education video library includes different health education videos corresponding to different risk indicators, and each phase submodule pushes personalized health education videos to risk personnel at a push frequency of once a month. Specifically, each phase submodule identifies the risk indicators in the five health indicator information, and pushes one or more videos corresponding to the risk indicators to the person at one time. Each phase submodule is also used to count the number of views per person, and the number of views is used as the number of points. The higher the number of points, the more offline rewards can be redeemed according to the number of points, so as to encourage them to continue to pay attention to and learn health knowledge, improve their health awareness and self-management ability, thereby gradually changing bad living habits and behaviors, and reducing the risk of disease.

[0034] Each sub-module is also used to collect the current behavioral data and health data of risk personnel, and make current intervention recommendations based on the corresponding data. Specifically, risk personnel fill in the current behavioral data and health data from the mobile phone. The behavioral data includes smoking cessation behavior, exercise behavior, and low-salt and low-oil behavior. Each sub-module stores a guidance library and questionnaires for different behaviors. Each sub-module determines the behavioral stage of the person based on the statistical results of the questionnaire. The behavioral stage includes the pre-intention stage, intention stage, preparation stage, action stage, and maintenance stage. The guidance library has corresponding intervention recommendations for each stage from the pre-intention stage to the maintenance stage. Each sub-module provides real-time feedback on intervention recommendations based on the person's behavioral stage and the guidance library.

[0035] Health data includes height, weight, waist circumference, blood pressure and blood sugar. The guidance database has different intervention suggestions corresponding to different ranges of data. Each sub-module will match the collected data to the corresponding range and make corresponding intervention suggestions in combination with the guidance database. By superimposing and combining the corresponding intervention suggestions of behavioral data and health data, the final guidance can be obtained, and risk personnel can implement it according to the guidance.

[0036] Further, starting from the second phase submodule, including the second phase submodule, the third phase submodule, the fourth phase submodule, the fifth phase submodule, and the sixth phase submodule, each phase submodule compares the collected behavioral data and health data with the data of the previous phase submodule to evaluate the intervention effect and optimize the guidance library. Specifically, the intervention management module evaluates the various health conditions corresponding to the person according to the collected information, and the guidance library is provided with intervention suggestions corresponding to each health condition, wherein each intervention suggestion is provided with multiple intervention contents, and each phase submodule randomly provides one of the multiple intervention contents for the corresponding situation evaluated each time. The intervention management module can judge the applicability level of the intervention content by statistically analyzing the effects of different personnel after using the intervention content. The applicability levels include excellent, good, medium, and poor, and the ranking of the applicability levels increases from poor to excellent. When the applicability level of the intervention suggestion is higher, it indicates that the population has a higher applicability to the intervention content, and it is a guidance that is more acceptable to the majority of the population and has better effects. By optimizing the guidance library, timely identifying and eliminating intervention contents with poor effects, ineffective interventions can be reduced.

[0037] The specific judgment process of the applicable level is as follows: each submodule compares the collected health data with the normal reference range value to obtain the deviation value. The deviation value is the degree of difference between a certain data and the normal reference range. If the data is higher than the maximum value of the normal reference range, it is a positive deviation value. If the data is lower than the minimum value of the normal reference range, it is a negative deviation value. The absolute value of the positive deviation value or the negative deviation value indicates the degree of difference. If the absolute value is smaller, it indicates that the degree of difference is smaller, which means that the person is healthier in this indicator. In the present invention, if the absolute value of the current deviation value is less than the absolute value of the previous deviation value, it indicates that after the intervention of the previous stage, it is gradually returning to the normal range, which is an effective intervention. Each submodule counts the number of effective interventions n after using the intervention suggestion. If n≥100, it is excellent; if 100>n≥50, it is good; if 50>n≥20, it is medium; if n<20, it is poor. This judgment process is based on actual data, and the judgment method is scientific and reliable.

[0038] The follow-up management module is used to remind the risk personnel to fill in the behavioral data and health data every two months, starting from the collection time of the first phase of the sub-module during the intervention management period. The frequency of once every two months corresponds to the data reporting of each sub-module. By regularly reminding risk personnel to fill in the behavioral data and health data during the intervention management period, poor management results can be avoided due to forgetting to fill in the data.

[0039] The follow-up management module is also used to remind relevant personnel to do offline follow-up at different frequencies according to different risk levels. Among them, taking the collection time of the first phase sub-module as the starting point, for high-risk personnel, offline follow-up is reminded every six months, and the time of the second offline follow-up is after the monitoring and management of each phase sub-module is completed; for low-risk personnel, offline follow-up is reminded once a year, and the time of offline follow-up is also after the monitoring and management of each phase sub-module is completed. Offline follow-up is mainly carried out by public health physicians in primary medical institutions to conduct health examinations and track lifestyle changes. After each offline follow-up, doctors in primary medical institutions can enter from the mobile phone or computer terminal, fill in the follow-up record form, further propose personalized intervention suggestions, and send the intervention suggestions to the risk personnel's mobile phone terminal for viewing. After the full-term management is completed, the primary medical institution will make a comprehensive judgment on the personnel during the follow-up to determine whether the personnel needs to continue management. If management is required, the system cycle will be followed. If management is not required, it will end.

[0040] By using the health management system of the present invention, there are many beneficial effects: 1. The present invention divides the risk groups into risk levels. By adopting a hierarchical management model, it is possible to achieve refined management of risk personnel, pay appropriate attention to low-risk groups, strengthen attention to high-risk groups, reasonably allocate management resources, and make effective use of resources.

[0041] 2. The present invention can provide personalized guidance in real time by comprehensively analyzing the behavioral stages and health data of risk personnel. This is not only highly targeted for intervention in risk personnel and can significantly improve management results, but also, by adopting this intelligent intervention method, it can effectively reduce the labor costs of medical institutions and can be widely promoted.

[0042] 3. The present invention adopts a staged intervention model for risk personnel. This model increases the frequency of intervention for risk personnel, and can gradually adapt and change bad living habits and behaviors through a step-by-step guidance method, and ultimately achieve the purpose of maintaining healthy behaviors for a long time. Management in this way not only makes risk personnel adaptable, but also gradually builds confidence and improves compliance, thereby promoting the formation of a healthy lifestyle. In addition, by adopting a staged intervention model, the data of each stage can be combined and compared to achieve dynamic evaluation, so as to continuously optimize the guidance opinion library, and then timely make adaptive adjustments to the intervention opinions in subsequent stages to improve management effects.

[0043] 4. The follow-up management module provided in the present invention can provide full-process health management reminders to risk personnel during the intervention management period to avoid poor management results due to forgetfulness, which is beneficial to improving the utilization rate of the system by risk personnel; in addition, the module can also remind relevant personnel to do offline follow-ups of different frequencies according to people of different risk levels. Through offline follow-up, doctors can further optimize the suggestions based on the specific circumstances of risk personnel, making the intervention more accurate and effective, which is beneficial to improving the intervention effect. At the same time, it can also enhance the connection between risk personnel and medical personnel, improve the compliance of risk personnel to intervention measures, and thus improve the overall intervention effect.

[0044] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within the scope of protection required by the present invention.

Claims

1. Health management system for people at high risk of chronic diseases, characterized by: It includes a task management module, which is used to create goals and assign them to primary medical institutions, so that primary medical institutions can carry out health management tasks; creating goals includes collecting identity information and health index information of risk personnel, and the task management module divides personnel into risk levels by statistical health index information for hierarchical management; The intervention management module is used to collect various behavioral data and health data of risk personnel in stages, and evaluate the corresponding behavioral stage and health status of the personnel in each stage according to the data of each stage; the intervention management module stores a guidance opinion library, and the intervention management module provides real-time feedback on corresponding intervention suggestions based on the assessed behavioral stage, health status and guidance opinion library. After the stage intervention is completed, the behavioral data and health data of the next stage are collected and compared with the data of the previous stage to evaluate the intervention effect of the intervention suggestions of the previous stage, so as to optimize the guidance opinion library; The follow-up management module is used to periodically remind risk personnel to report behavioral data and health data during the intervention management period, and to remind personnel of different risk levels to conduct offline follow-up visits of different frequencies. Public health physicians in primary medical institutions fill in follow-up records after offline follow-up visits, and provide and feedback personalized intervention suggestions.

2. The health management system for people at high risk of chronic diseases according to claim 1, characterized in that: The health indicator information includes blood sugar, blood pressure, blood lipids, waist circumference, and smoking status. The task management module stores the danger range value of the corresponding indicator information. The task management module compares the collected indicator information with the danger range value. If the indicator value is within the danger range value, the indicator is judged to be a danger indicator. The task management module counts the number of danger indicators and divides personnel according to the statistical number. If the number of danger indicators is greater than or equal to three, it is divided into a high-risk level. If the number of danger indicators is greater than zero and less than three, it is divided into a low-risk level.

3. The health management system for people at high risk of chronic diseases according to claim 1, characterized in that: The identity information includes at least name, telephone number and home address. The task management module assigns the corresponding personnel to the primary medical institutions in the corresponding area for management according to the home address.

4. The health management system for people at high risk of chronic diseases according to claim 1, characterized in that: The intervention management module stores a health education video library, and is used to push personalized health education videos in a targeted manner according to the collected health indicator information, and count the number of views of each person.

5. The health management system for people at high risk of chronic diseases according to claim 1, characterized in that: The behavioral data include smoking cessation behavior, exercise behavior and low-salt and low-oil behavior. The intervention management module stores questionnaires for different behaviors. The intervention management module determines the behavioral stage of the person based on the statistical results of the questionnaire. The behavioral stages include pre-intention stage, intention stage, preparation stage, action stage and maintenance stage. The intervention management module provides real-time feedback on intervention suggestions based on the person's behavioral stage and the guidance opinion library.

6. The health management system for people at high risk of chronic diseases according to claim 1, characterized in that: The health data includes height, weight, waist circumference, blood pressure and blood sugar. The guidance database provides different intervention suggestions corresponding to each health data in different range values. The intervention management module matches the collected data to the corresponding range value and makes corresponding intervention suggestions in combination with the guidance database.

7. The health management system for people at high risk of chronic diseases according to claim 1, characterized in that: The intervention management module includes a phase one submodule, a phase two submodule, a phase three submodule, a phase four submodule, a phase five submodule, and a phase six submodule. Each phase submodule is used to collect the current behavioral data and health data of the risk personnel, and make current intervention suggestions based on the corresponding data; among them, the data collected by the phase two submodule, the phase three submodule, the phase four submodule, the phase five submodule, and the phase six submodule are compared with the data collected by the previous phase submodule to obtain the intervention effect of the previous intervention suggestion, and optimize the guidance opinion library based on the intervention effect.

8. The health management system for people at high risk of chronic diseases according to claim 7, characterized in that: The intervention management module evaluates multiple health conditions corresponding to the person based on the collected information, and intervention suggestions corresponding to each health condition are provided in the guidance opinion library, wherein each intervention suggestion is provided with multiple intervention contents, and the intervention management module randomly provides one of the multiple intervention contents each time according to the corresponding situation evaluated, and the intervention management module determines the applicability level of the intervention content by counting the intervention effects of different persons after using the intervention content, and the applicability levels include excellent, good, medium, and poor, and the intervention management module makes corresponding optimization of the intervention suggestion according to the applicability level.

9. The health management system for people at high risk of chronic diseases according to claim 8, characterized in that: The intervention management module compares the collected health data with the normal reference range value to obtain a deviation value. If the absolute value of the current deviation value is less than the absolute value of the previous deviation value, it indicates that the intervention is an effective intervention. The intervention management module counts the number of effective interventions n after using the intervention suggestion. If n≥100, it is excellent; if 100>n≥50, it is good; if 50>n≥20, it is fair; If n<20, it is a difference.

10. The health management system for people at high risk of chronic diseases according to claim 1, characterized in that: During the intervention management period, the follow-up management module takes the first collection time as the starting point, and reminds personnel to fill in behavioral data and health data every two months; reminds high-risk personnel to follow up offline every six months, and reminds low-risk personnel to follow up offline every year. Public health doctors in primary medical institutions fill in follow-up records after offline follow-up, and provide and feedback personalized intervention suggestions.

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