Chronic disease tracking management system

Abstract

The invention relates to the technical field of medical health informatization, and discloses a chronic disease tracking management system, which is characterized in that a data acquisition module, a data analysis module, a health monitoring module, a user interaction module, a control module and a tracking management optimization module are established, key factors of physiology, life and environment influencing chronic diseases are comprehensively covered; the data analysis module is used for deeply mining data values based on a scientific calculation formula and accurately identifying potential health risks; the health monitoring module realizes automatic and personalized health early warning and management suggestion pushing according to an analysis result and threshold setting, all the modules cooperate closely, and finally the system has the advantages of data comprehensiveness, risk pre-judgment accuracy, management personalization and service continuity. The problems of data dispersion, risk assessment lagging and single intervention measure in traditional chronic disease management are solved, and a whole-process and intelligent health management service is provided for chronic disease patients.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of medical and health information technology, in particular to a chronic disease tracking management system. BACKGROUND

[0002] With the acceleration of population aging and the change of lifestyle, chronic diseases have become a major public health problem threatening human health. Traditional chronic disease management mode relies on patients to regularly go to medical institutions for examination, and medical staff to diagnose and intervene according to limited examination data. However, this mode has many drawbacks: first, the data source is single and scattered, and only physiological indicators of patients at a specific time node can be obtained, which is difficult to fully reflect the long-term impact of living habits, environmental factors and other factors on chronic disease development; second, the risk assessment lacks timeliness and accuracy, and the patient's physical condition cannot be monitored in real time, and the disease is often discovered after it has developed to a certain extent, delaying the best intervention opportunity; third, the intervention measures are highly homogeneous, and it is difficult to develop individualized programs for individual differences, resulting in poor management effect. In the face of the problems of data dispersion, risk assessment lag and single intervention measures in traditional chronic disease management, there is an urgent need for a chronic disease tracking management system that can realize multi-dimensional data collection, accurate risk prediction, individualized management and continuous service, in order to improve the efficiency and quality of chronic disease management and meet the growing demand for medical and health care. SUMMARY

[0003] (0) Technical problems solved In view of the shortcomings of the prior art, the present application provides a chronic disease tracking management system, which has the advantages of data comprehensiveness, risk prediction accuracy, management individualization and service continuity, and solves the problems of data dispersion, risk assessment lag and single intervention measures in traditional chronic disease management.

[0004] (II) Technical solutions To achieve the above purpose, the present application provides the following technical solutions: a chronic disease tracking management system, comprising a data collection module, a data analysis module, a health monitoring module, a user interaction module, a control module and a tracking management optimization module; The data collection module collects user physiological data, lifestyle information and external chronic disease data sets in real time through user case, home environment, exercise and diet records, mobile application or wearable device, and transmits the data to the data analysis module after invalid data reduction processing; The data analysis module calculates the collected data set and identifies the user's potential health risk level; The health monitoring module automatically reminds the user to perform chronic disease medical intervention or reexamination according to the calculation result and the set threshold value, and generates individualized health report and suggestions in combination with the user's current physiological data; The user interaction module provides a convenient interface to support real-time communication between the user and the doctor, and to share user health data; The control module is used to coordinate the data transmission and function operation between the modules; The tracking management optimization module optimizes the tracking management strategy according to the system operation and user feedback.

[0005] Preferably, the data acquisition module includes a physiological data unit, a lifestyle data unit, a historical case data unit, and an external influencing factor data unit, and the data analysis module includes a lifestyle influence analysis unit, a genetic disease influencing factor analysis unit, a work pressure and home environment factor influence analysis unit.

[0006] Preferably, the physiological data unit collects user physiological data in real time through wearable devices and mobile applications, including blood glucose, blood pressure, heart rate, body weight, blood oxygen saturation, and other physiological parameters.

[0007] Preferably, the lifestyle data unit collects user lifestyle data through mobile applications and manual input, including eating habits, exercise frequency and intensity, sleep quality, medication records, and other lifestyle data.

[0008] Preferably, the historical case data unit obtains user historical case data through user input and electronic medical record systems of medical institutions, including past medical history, diagnosis results, treatment process, family medical history, and other related data.

[0009] Preferably, the external influencing factor data unit collects external influencing factor data through mobile applications, environmental sensors, and user input, including home environment, work pressure, work environment, family genetic disease history, social activities, psychological stress, and other related factors.

[0010] Preferably, the lifestyle influence analysis unit calculates the influence degree of lifestyle on the user's body , and the calculation formula is: ; In the formula, represents the influence degree of lifestyle on the user's body, represents the health score of each index of eating habits, represents the number of eating habit indexes; represents the score of each index of exercise amount, represents the number of exercise amount indexes; represents the score of each index of medication records, represents the number of medication record indexes; γ, α, β respectively represent the weight coefficients of each factor.

[0011] Preferably, the genetic disease affecting factor analysis unit calculates a risk value of the genetic disease leading to chronic disease aggravation , and the calculation formula is as follows: ; In the formula, represents the risk value of the genetic disease leading to chronic disease aggravation, represents the genetic risk coefficient of different chronic diseases in the family, represents the number of chronic diseases involved in the family; represents the incidence condition weight of each chronic disease in the family.

[0012] Preferably, the work pressure and home environment factor analysis unit calculates a risk value of the work pressure and home environment factor leading to chronic disease aggravation , and the calculation formula is as follows: ; In the formula, represents the risk value of the work pressure and home environment factor leading to chronic disease aggravation; represents the score of each index of the work environment, represents the number of work environment indexes; represents the score of each index of the home environment, represents the number of home environment indexes; represents the work pressure score; , , respectively represent the weight coefficients of each factor.

[0013] Preferably, the health monitoring module determines the influence degree of the life habit on the user's body , sets a threshold value in combination with the lifestyle risk threshold value standard corresponding to different chronic diseases in medical research, performs risk level determination and trend analysis in combination with the current physiological data of the user, and generates personalized health management suggestions covering lifestyle adjustment and medical guidance contents; according to the risk value of the genetic disease leading to chronic disease aggravation , sets a threshold value in reference to the risk assessment standard in the field of disease genetics, performs genetic risk warning and disease development prediction in combination with the current physiological data of the user, and generates targeted prevention suggestions including regular screening plan and genetic consultation arrangement contents; according to the risk value of the work pressure and home environment factor leading to chronic disease aggravation , sets a threshold value in accordance with the evaluation indexes in the fields of environmental medicine and occupational health, performs environmental risk assessment and disease influence analysis in combination with the current physiological data of the user, and generates environmental intervention suggestions involving environmental improvement scheme and work pressure adjustment contents.

[0014] Compared with the prior art, the chronic disease tracking management system has the following beneficial effects: 1、The present application calculates the degree of influence of living habits on the user's body , as a quantitative indicator for assessing the health risk of the user's lifestyle, and plays a key role in the early warning mechanism of the health monitoring module. When the degree of influence of living habits on the user's body is in the interval (0-30), it is determined to be low risk, and the system reminds the user of a daily healthy lifestyle, such as pushing health recipes and exercise suggestions; when the degree of influence of living habits on the user's body is in the interval (31-60), it is determined to be medium risk, and the system warns of lifestyle improvement and provides personalized dietary adjustment and exercise plan; when the degree of influence of living habits on the user's body is in the interval (61-100), it is determined to be high risk, and the system automatically reminds the user to seek medical treatment in time, and the doctor formulates a strengthened intervention plan, so that the system solves the problem of being unable to accurately assess the influence of lifestyle on chronic diseases and being difficult to provide targeted health management solutions.

[0015] 2、The present application calculates the risk value of genetic diseases leading to chronic diseases , as an important basis for predicting the possibility of chronic disease deterioration caused by genetic factors, and plays a guiding role in the long-term health tracking and prevention system. When the risk value of genetic diseases leading to chronic diseases is in the interval (0-20), it is determined to be low risk, and the system reminds the user to have a regular health examination, and suggests that the user have a basic physical examination every year; when the risk value of genetic diseases leading to chronic diseases is in the interval (21-50), it is determined to be medium risk, and the system performs targeted screening warning, such as suggesting that users with a family history of heart disease have regular cardiac ultrasound examinations; when the risk value of genetic diseases leading to chronic diseases is in the interval (51-100), it is determined to be high risk, and the system performs close medical monitoring and notifies the user to develop a high-frequency special examination plan with the medical institution, and provides genetic counseling services, so that the system solves the problem of ignoring the influence of genetic factors on the development of chronic diseases and being unable to layout effective preventive measures in advance.

[0016] 3、The present application calculates the risk value of work pressure and home environment factors leading to chronic diseases , as a reference standard for assessing the influence of external environment on chronic diseases, and provides decision support in the development of environmental intervention and health adjustment strategies, when the risk value of work pressure and home environment factors leading to chronic diseases When in the interval of (0-30), it is determined as low risk, the system carries out environment maintenance and pressure adjustment prompts, such as pushing home environment cleaning knowledge, and stress relief skills; when the risk value of chronic disease caused by work pressure and home environment factors is When in the interval of (31-60), it is determined as medium risk, the system carries out environment improvement and stress management suggestions, such as recommending air purification equipment for poor air quality, and providing professional psychological counseling channels for high work pressure; when the risk value of chronic disease caused by work pressure and home environment factors is When in the interval of (61-100), it is determined as high risk, the system carries out emergency environment intervention and medical intervention reminders, and suggests that the user temporarily leaves the bad working environment, and contacts the doctor to assess the condition and adjust the treatment plan, so that the system finally solves the problem that the influence of external environmental factors on chronic diseases is difficult to quantify and effective environmental intervention measures cannot be taken in time. BRIEF DESCRIPTION OF DRAWINGS

[0017] Fig. 1 The system flowchart of the present application is shown in the figure; Fig. 2 The system use flowchart of the present application embodiment 1 is shown in the figure; Fig. 3 The system use flowchart of the present application embodiment 2 is shown in the figure. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0019] Please refer to Figs. 1-3 A chronic disease tracking management system, comprising a data collection module, a data analysis module, a health monitoring module, a user interaction module, a control module and a tracking management optimization module; The data collection module collects user physiological data, lifestyle information and external influence chronic disease data sets in real time through user cases, home environment, exercise and diet records, mobile applications or wearable devices, performs invalid data reduction processing, and transmits to the data analysis module; The data analysis module calculates the collected data set and identifies the user's potential health risk level; The health monitoring module automatically reminds the user to perform chronic disease medical intervention or recheck according to the calculation result and the set threshold value, in combination with the current physiological data of the user, and generates a personalized health report and suggestion; The user interaction module provides a convenient interface to support real-time communication between the user and the doctor, and to share user health data; The control module is used to coordinate the data transmission and function operation between the modules; The tracking management optimization module optimizes the tracking management strategy according to the system operation and user feedback.

[0020] Advantages: By establishing a data acquisition module, a data analysis module, a health monitoring module, a user interaction module, a control module, and a tracking management optimization module, a chronic disease tracking management system is formed. The data acquisition module realizes multi-dimensional and real-time data acquisition, covering key factors affecting chronic diseases in physiology, life, and environment. The data analysis module deeply mines data value based on scientific calculation formulas and accurately identifies potential health risks. The health monitoring module realizes automatic and personalized health warning and management suggestion pushing based on analysis results and threshold settings. The modules work closely together, and the system ultimately has the advantages of data comprehensiveness, risk prediction accuracy, management personalization, and service sustainability, effectively solving the problems of data dispersion, risk assessment lag, and single intervention measures in traditional chronic disease management, and providing full-process and intelligent health management services for chronic disease patients.

[0021] The data acquisition module includes a physiological data unit, a lifestyle habit data unit, a historical case data unit, and an external influencing factor data unit. The data analysis module includes a lifestyle habit influence analysis unit, a genetic disease influencing factor analysis unit, a work pressure and home environment factor influence analysis unit.

[0022] The physiological data unit collects user physiological data in real time through wearable devices and mobile applications, including blood glucose, blood pressure, heart rate, body weight, blood oxygen saturation, and other physiological parameters.

[0023] The lifestyle habit data unit collects user lifestyle habit data through mobile applications and user manual input, including dietary habits, exercise frequency and intensity, sleep quality, medication records, and other lifestyle habit data.

[0024] The historical case data unit obtains user historical case data through user input and medical institution electronic medical record system, including past medical history, diagnosis results, treatment process, family medical history, and other related data.

[0025] The external influencing factor data unit collects external influencing factor data through mobile applications, environmental sensors, and user input, including home environment (such as air quality, noise level), work pressure, work environment (such as occupational exposure risk), family genetic disease history, social activities, psychological stress, and other related factors.

[0026] The lifestyle habit influence analysis unit calculates the impact of lifestyle habits on the user's body , whose calculation formula is: ; In the formula, represents the degree of influence of living habits on the user's body, represents the health score of each indicator of eating habits (such as diet balance score, salt intake score, and other eating habit scores), represents the number of indicators of eating habits; represents the score of each indicator of exercise amount (such as exercise intensity score, exercise frequency score, etc.), represents the number of indicators of exercise amount; represents the score of each indicator of medication record (such as on-time medication rate score, drug effectiveness score), represents the number of indicators of medication record; γ, α, β respectively represent the weight coefficients of each factor, and reasonable weights are set according to medical research, such as setting the weight proportion of eating habits to be the largest because eating habits have a greater impact on chronic diseases; The advantage is that the degree of influence of living habits on the user's body is calculated , which is used as a quantitative indicator for assessing the health risk of the user's lifestyle, and plays a key role in the early warning mechanism of the health monitoring module. When the degree of influence of living habits on the user's body is in the (0-30) interval, it is determined to be low risk, and the system performs daily health lifestyle reminders, such as pushing health recipes and exercise suggestions; when the degree of influence of living habits on the user's body is in the (31-60) interval, it is determined to be medium risk, and the system performs lifestyle improvement warnings and provides personalized diet adjustment, exercise plan solutions; when the degree of influence of living habits on the user's body is in the (61-100) interval, it is determined to be high risk, and the system automatically performs emergency health intervention reminders, suggests the user to seek medical treatment in time, and simultaneously links with doctors to develop intensive intervention plans, so that the system ultimately solves the problem of being unable to accurately assess the impact of lifestyle on chronic diseases and being difficult to provide targeted health management solutions.

[0027] The genetic disease influence factor analysis unit calculates the risk value of genetic diseases causing chronic diseases to be severe , whose calculation formula is: ; In the formula, represents the risk value of genetic diseases causing chronic diseases to be severe, represents the genetic risk coefficient of different chronic diseases in the family (such as high blood pressure genetic risk coefficient, diabetes genetic risk coefficient, etc., which can be set according to medical research data), represents the number of chronic diseases involved in the family; Indicates the incidence of each chronic disease in the family (e.g., the incidence of direct relatives is higher, and the incidence of collateral relatives is lower); Advantages: By calculating the risk value of chronic disease caused by genetic diseases , as an important basis for predicting the possibility of chronic disease deterioration caused by genetic factors, and plays a guiding role in long-term health tracking and prevention system. When the risk value of chronic disease caused by genetic diseases is in the (0-20) interval, it is determined as low risk, and the system performs regular health check reminders, suggesting that users perform annual basic physical examinations; when the risk value of chronic disease caused by genetic diseases is in the (21-50) interval, it is determined as medium risk, and the system performs targeted screening and early warning, such as recommending regular cardiac color Doppler ultrasound examinations for users with a family history of heart disease; when the risk value of chronic disease caused by genetic diseases is in the (51-100) interval, it is determined as high risk, and the system performs close medical monitoring and notification, and links medical institutions to develop high-frequency special examination programs for users, while providing genetic counseling services. Ultimately, the system solves the problem of ignoring the impact of genetic factors on chronic disease development and the inability to layout effective preventive measures in advance.

[0028] The work pressure and home environment factor influence analysis unit calculates the risk value of chronic disease caused by work pressure and home environment factors , and the calculation formula is: ; In the formula, indicates the risk value of chronic disease caused by work pressure and home environment factors; indicates the score of each index of the work environment (such as the score of harmful substance exposure and the score of workplace noise, etc.), indicates the number of work environment indexes; indicates the score of each index of the home environment (such as the score of air quality and the score of humidity, etc.), indicates the number of home environment indexes; indicates the work pressure score; , , respectively indicate the weight coefficients of each factor, which are set according to the actual impact degree, for example, when working in the chemical industry, the work environment has a greater impact on chronic diseases, so the weight of the work environment is increased; Advantages: By calculating the risk value of chronic disease caused by work pressure and home environment factors , it is used as a reference standard for evaluating the impact of external environment on chronic diseases, and provides decision support in environmental intervention and health adjustment strategy development. When the risk value of chronic disease caused by work pressure and home environment factors is When the interval is (0-30), it is determined to be low risk, and the system performs environmental maintenance and stress adjustment prompts, such as pushing home environment cleaning tips and stress relief techniques; when the risk value of chronic disease aggravation caused by work stress and home environment factors is When the interval is (31-60), it is determined to be medium risk, and the system performs environmental improvement and stress management suggestions, such as recommending air purification equipment for poor air quality and providing professional psychological counseling channels for high work stress; when the risk value of chronic disease aggravation caused by work stress and home environment factors is When the interval is (61-100), it is determined to be high risk, and the system performs emergency environmental intervention and medical intervention reminders, suggesting that the user temporarily leave the bad working environment, and contacting the doctor to assess the condition and adjust the treatment plan, so that the system solves the problem of difficult quantification of external environmental factors on chronic diseases and inability to take effective environmental intervention measures in time.

[0029] The health monitoring module determines the degree of influence of the user's body according to the life habits , combined with the lifestyle risk threshold standard corresponding to different chronic diseases in medical research, combined with the user's current physiological data, risk level determination and trend analysis are performed, and personalized health management suggestions covering lifestyle adjustment and medical guidance content are generated; according to the risk value of chronic disease aggravation caused by genetic diseases , reference the risk assessment standard in the field of disease genetics to set the threshold, combined with the user's current physiological data, genetic risk warning and disease development prediction are performed, and targeted prevention suggestions including regular screening plan and genetic counseling arrangement content are generated; according to the risk value of chronic disease aggravation caused by work stress and home environment factors , according to the evaluation index of environmental medicine and occupational health field to set the threshold, combined with the user's current physiological data, environmental risk assessment and disease influence analysis are performed, and environmental intervention suggestions involving environmental improvement scheme and work stress adjustment content are generated.

[0030] The user interaction module actively pushes related information to the user according to the risk level determination result and personalized health management suggestions of the health monitoring module, and builds a communication bridge for the user and the doctor, for example, when the health monitoring module determines that the user is in high risk, the user interaction module immediately reminds the user to check the health report and medical advice through pop-up windows, message push and other ways, and automatically generates a communication appointment channel with the attending doctor, making it convenient for the user to consult the condition in time; when in low risk, it pushes daily health knowledge to encourage users to maintain good living habits, while supporting users to actively upload their feelings and health questions, realizing efficient interaction between doctors and patients.

[0031] The tracking management optimization module optimizes the algorithm, adjusts the threshold value, improves the interface, and reconstructs the process according to the running data of the data acquisition module, the data analysis module, the health monitoring module, the user interaction module and the control module, the user feedback information, the calculation result and the function execution situation. Specifically, the data acquisition frequency and data integrity of the data acquisition module are analyzed to optimize the data acquisition strategy; the weight coefficient in the calculation formula is adjusted according to the calculation accuracy of the data analysis module; the threshold setting is optimized according to the timeliness and effectiveness of the health monitoring module; the interactive design is improved by referring to the feedback of the user on the interface operation and communication function in the user interaction module; the coordination process of the control module is optimized by comprehensively considering the running efficiency of each module, so as to further optimize the system.

[0032] Advantages: through the cooperation of the health monitoring module, the user interaction module and the tracking management optimization module, the precise, personalized and dynamic chronic disease management beneficial effects are achieved, wherein the health monitoring module realizes precise risk assessment and early warning based on multi-dimensional data analysis, the user interaction module promotes efficient communication between doctors and patients and improves the self-management ability of users, and the tracking management optimization module continuously optimizes the system function and service according to the actual running situation, so that the system solves the problems of lack of real-time monitoring, insufficient individualization and difficulty in continuous optimization in the traditional chronic disease management mode, effectively improves the scientificity and effectiveness of chronic disease management, and provides more comprehensive and high-quality health management services for users.

[0033] The chronic disease tracking management system of the application is applied to actual scenes, as follows: Example 1 (personalized management of diabetic patients) Scene one: Mr. Li is a type 2 diabetes patient, who uses the chronic disease tracking management system for daily health management. The data acquisition module collects his blood glucose, heart rate and exercise data in real time through a smart blood glucose meter and a smart bracelet, and he manually records daily diet content and medication through a mobile application. The data analysis module calculates the influence of Mr. Li's living habits on the body according to the formula of the living habit influence analysis unit, and determines that the influence degree is 65 points, which is in high risk. The health monitoring module immediately pushes an emergency health intervention reminder to Mr. Li through the user interaction module, suggests him to see a doctor as soon as possible, and automatically makes an appointment with an endocrinologist. At the same time, the system generates personalized health management suggestions, including a diet plan for strictly controlling carbohydrate intake and a 30-minute aerobic exercise plan every day. The tracking management optimization module optimizes the blood glucose collection frequency of the data acquisition module from 3 times a day to 5 times a day according to this warning situation, so as to ensure real-time monitoring of Mr. Li's blood glucose changes and help him effectively control the disease.

[0034] Example 2 (genetic risk prevention and control of hypertensive patients) Scenario two: Ms. Wang has a family history of hypertension. The system's genetic disease impact factor analysis unit calculates that her risk of developing chronic disease due to genetic factors is 45 points, which is considered medium risk. The health monitoring module pushes targeted screening warnings to Ms. Wang through the user interaction module, recommending that she undergo dynamic blood pressure monitoring and cardiac ultrasound examination every six months, and providing professional genetic counseling appointment services. Ms. Wang communicates with the cardiovascular doctor through the user interaction module, sharing her family medical history and daily blood pressure data. The doctor develops a preventive medication plan for Ms. Wang based on the information provided by the system. The follow-up management optimization module optimizes the calculation formula of the genetic disease impact factor analysis unit based on Ms. Wang's usage feedback, increasing the weight of the age factor in the family medical history, making the risk assessment more accurate, and helping Ms. Wang prevent hypertension from worsening in advance.

[0035] Example 3 (Environmental intervention for patients with chronic respiratory diseases) Scenario three: Mr. Zhang works in the decoration industry and is exposed to dust for a long time, suffering from chronic bronchitis. The system's work pressure and home environment factor analysis unit calculates that his risk of developing chronic disease due to work pressure and home environment factors is 78 points, which is considered high risk. The health monitoring module sends an urgent environmental intervention and medical intervention reminder to Mr. Zhang through the user interaction module, recommending that he temporarily change his job to reduce dust exposure and seek medical treatment promptly. At the same time, the system generates an environmental improvement plan, recommending that he install a high-efficiency air purifier at home and maintain indoor ventilation. It also provides professional respiratory rehabilitation training videos to help him alleviate symptoms. After adjusting his work and living environment according to the system's recommendations, Mr. Zhang feeds back his condition improvement to the doctor through the user interaction module. The follow-up management optimization module optimizes the work environment index scoring standard based on Mr. Zhang's case, increasing the evaluation weight of the decoration dust concentration, and provides more accurate risk assessment and more effective intervention measures for similar professional patients.

[0036] Summary: Through the actual scene of the examples, the effectiveness and innovation of the present application are comprehensively verified, wherein, example 1 demonstrates how the system accurately identifies high-risk states and provides timely medical appointments, personalized diet and exercise programs, and optimizes data collection frequency based on multi-dimensional data collection and life habit influence analysis unit formula through Mr. Li's diabetes management case, which embodies the advantages of the system in personalized lifestyle management and real-time health monitoring; Example 2 takes Ms. Wang's genetic risk prevention and control of hypertension as an example to highlight the system's ability to scientifically assess genetic risk, promote efficient communication between doctors and patients, and optimize calculation formulas by using the genetic disease influence factor analysis unit, which verifies the system's ability in genetic risk prediction and precise medical decision support; Example 3 focuses on Mr. Zhang's environmental intervention for chronic respiratory diseases, which shows that the system can accurately determine high-risk states and provide environmental improvement and medical intervention suggestions through the work pressure and home environment factor influence analysis unit, and can also optimize the evaluation criteria according to the actual case, which confirms the system's effectiveness in environmental factor-related health management and dynamic optimization, thus verifying that the present application can effectively solve the traditional chronic disease management problems and has the significant advantages of data comprehensiveness, risk prediction accuracy, management personalization, and service sustainability, providing a full-process, intelligent, and dynamically optimized health management solution for chronic disease patients.

[0037] Although embodiments of the present application have been shown and described, it is to be understood that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A chronic disease tracking management system, characterized by, The system comprises a data collection module, a data analysis module, a health monitoring module, a user interaction module, a control module, and a tracking management optimization module. The data collection module collects user physiological data, lifestyle information, and external factors affecting chronic diseases in real time through user cases, home environment, exercise and diet records, mobile applications, or wearable devices, and transmits the data to the data analysis module after invalid data reduction processing. The data analysis module calculates the collected data set and identifies the user's potential health risk level. The health monitoring module automatically reminds the user to perform chronic disease medical intervention or recheck according to the calculation results and the set threshold value, combined with the user's current physiological data, and generates a personalized health report and suggestion. The user interaction module provides a convenient interface to support real-time communication between users and doctors and share user health data. The control module is used to coordinate data transmission and function operation between modules. The tracking management optimization module optimizes the tracking management strategy according to the system operation and user feedback.

2. The chronic disease tracking management system according to claim 1, wherein: The data collection module includes a physiological data unit, a lifestyle data unit, a historical case data unit, and an external influencing factor data unit.

3. The chronic disease tracking and management system of claim 2, wherein: The physiological data unit collects user physiological data in real time through wearable devices and mobile applications, including blood glucose, blood pressure, heart rate, body weight, blood oxygen saturation, and other physiological parameters.

4. The chronic disease tracking and management system of claim 2, wherein: The lifestyle data unit collects user lifestyle data through mobile applications and manual input, including eating habits, exercise frequency and intensity, sleep quality, medication records, and other lifestyle data.

5. The chronic disease tracking and management system of claim 2, wherein: The historical case data unit obtains user historical case data through user input and electronic medical record systems, including past medical history, diagnosis results, treatment process, family medical history, and other related data.

6. The chronic disease tracking and management system of claim 2, wherein: The external influencing factor data unit collects external influencing factor data through mobile applications, environmental sensors, and user input, including home environment, work pressure, work environment, family genetic disease history, social activities, psychological stress, and other related factors.

7. The chronic disease tracking and management system of claim 2, wherein: The lifestyle habit influence analysis unit calculates the degree of influence of the lifestyle habits on the user's body The calculation formula is: ； In the formula, represents the degree of influence of the living habits on the user's body, represents the health score of each index of the eating habits, represents the number of indexes of the eating habits; represents the score of each index of the exercise amount, represents the number of indexes of the exercise amount; represents the score of each index of the medication record, represents the number of indexes of the medication record; γ, α, β respectively represent the weight coefficients of each factor.

8. The chronic disease tracking and management system of claim 2, wherein: The genetic disease affecting factor analysis unit calculates a risk value of a genetic disease causing a chronic lesion The formula is: ； In the formula, represents the risk value of chronic lesions caused by genetic diseases, represents the genetic risk coefficient of different chronic diseases in the family, represents the number of chronic diseases involved in the family; represents the incidence of each chronic disease in the family.

9. The chronic disease tracking and management system of claim 2, wherein: The working pressure and home environment factor influence analysis unit calculates a risk value of chronic disease exacerbation caused by the working pressure and home environment factors The calculation formula is: ； In the formula, represents the risk value of chronic disease aggravation caused by working pressure and home environment factors; represents the score of each index of working environment, represents the number of indexes of working environment; represents the score of each index of home environment, represents the number of indexes of home environment; represents the score of working pressure; , , respectively represents the weight coefficient of each factor.

10. The chronic disease tracking and management system of claim 1, wherein: The health monitoring module sets a threshold according to the degree of influence of the living habits on the user's body , combines the lifestyle risk threshold standard corresponding to different chronic diseases in medical research, combines the current physiological data of the user, performs risk level determination and trend analysis, and generates personalized health management suggestions covering lifestyle adjustment and medical guidance content; According to the risk value of chronic lesions caused by genetic diseases , a threshold is set according to the risk assessment standard in the field of disease genetics, combined with the current physiological data of the user, a genetic risk warning and disease development prediction are carried out, and targeted prevention suggestions including regular screening plans and genetic counseling arrangements are generated again; According to the risk value of chronic disease aggravation caused by working pressure and home environment factors , according to the threshold of evaluation index in the field of environmental medicine and occupational health, combined with the current physiological data of the user, environmental risk assessment and disease influence analysis are carried out, and environmental intervention suggestions involving environmental improvement scheme and working pressure adjustment content are generated again.

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