Method and system for tracking and analyzing chronic disease diagnosis and treatment data based on cloud computing

Abstract

The invention belongs to the technical field of chronic disease diagnosis and treatment data management, and particularly relates to a tracking analysis method and system for chronic disease diagnosis and treatment data based on cloud computing. Comprising the steps that a data layer carries out unified fusion processing on public diagnosis and treatment data of chronic diseases to form a new medical record database, a new treatment database and a new follow-up visit database; the data layer is provided with a multi-source data acquisition module, a multi-source data management module, a data storage module and a capability opening module; importing the data of the data layer into an application layer; a risk assessment model module, a medical record management module, a follow-up visit management module and a statistical analysis module are arranged in the application layer; the service layer is interconnected and communicated with the application layer; a taking management module, a remote diagnosis and treatment module, a chronic disease evaluation module and a health knowledge module are loaded in the service layer. The problems that data cannot be shared, system functions are not comprehensive enough, and chronic disease work development is affected in existing chronic disease data management are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of chronic disease diagnosis and treatment data management, and more specifically, to a tracking and analysis method and system for chronic disease diagnosis and treatment data based on cloud computing. Background Art

[0002] Chronic diseases have become an important focus in the field of global public health. With the advancement of medical technology, the treatment methods for chronic diseases have gradually become richer. In addition to on-site diagnosis and treatment services, the development of remote diagnosis and treatment and self-management technology for chronic diseases is of great significance to promoting the health management of chronic diseases. Realizing health self-management through remote diagnosis and treatment and online consultation can help patients better understand the measures to control the disease, slow down the progression of the disease, reduce the incidence of severe diseases, avoid disability and death, and improve the quality of life, thereby reducing the public health hazards and disease burden of the disease. Remote diagnosis and treatment of chronic diseases and self-health management need to be based on a systematic approach to achieve comprehensive tracking and analysis of multi-dimensional data, evaluate the condition and risk factors, manage the condition and prevent recurrence.

[0003] Chinese patent document CN113239279A discloses a chronic disease medical data collection, analysis and management method and cloud platform based on data analysis and cloud computing. The method constructs a chronic disease medical management platform to collect, process and analyze the disease parameters corresponding to chronic disease patients who have been treated in various hospitals in the region, and obtains the average medical expenses and average medical effect coefficients of chronic disease patients with various chronic disease types and various medical history durations in each hospital. At the same time, the hospitals in the region are statistically classified to obtain a hospital set with various chronic disease types and various medical history durations, and the hospitals in the hospital set are comprehensively ranked, and the ranking results are stored in the chronic disease medical management platform to provide chronic disease patients with chronic disease retrieval needs.

[0004] However, the current multi-dimensional data management of chronic diseases has the following technical problems:

[0005] (1) Chronic disease data exist in different information systems: such as the comprehensive prevention and treatment basic information system, the prevention and treatment work information system, the hospital information system, etc., resulting in the inability to share data between systems, between institutions, and between different departments of the same institution.

[0006] (2) Although there are many systems or platforms for collecting data, they still cannot meet the actual work needs. This is mainly reflected in the following aspects: First, due to the long duration of chronic disease patients and the many changes in the diagnosis, treatment and management process, long-term monitoring of medical information is required throughout the course of the disease. At the same time, it is also necessary to record the patient's and family's detailed general sociological demographic information, family history, medical history, medical history, etc., and regular follow-up of chronic disease patients is required to improve the quality of life of patients. The current data platforms can only simply enter demographic sociological information and diagnosis and treatment information of a certain period of time / institution, and the information collected is relatively simple. A large amount of information useful for chronic disease management obtained from on-site surveys exists in paper form and cannot be entered. or there is an input system, but the systems exist in different institutions; secondly, with the development of the concept of big health and big wellness and the growing demand for health among patients with chronic diseases, chronic disease management is not only limited to prolonging life, but also to improve the quality of life and make patients live a better life. Special attention should be paid to the comorbidities and psychological problems of patients with chronic diseases. The current information system cannot obtain the above information, which is not conducive to doctors' comprehensive grasp of patient information and timely scientific and accurate diagnosis and treatment; finally, it is impossible to flexibly set or modify the relevant parameters of the recorded information, which hinders data mining and analysis and affects the development of chronic disease management and monitoring.

[0007] (3) As personal privacy awareness increases and personnel mobility increases, the management model of chronic disease patients faces new challenges.

[0008] In summary, how to efficiently track and analyze chronic disease diagnosis and treatment data and provide actual feedback to both patients to achieve optimal diagnosis and treatment management is the primary technical problem that needs to be urgently solved in this field. In view of this, the present invention designs a method and system for tracking and analyzing chronic disease diagnosis and treatment data based on cloud computing. Summary of the invention

[0009] The present invention aims to overcome at least one defect of the above-mentioned prior art and provide a method for tracking and analyzing chronic disease diagnosis and treatment data based on cloud computing, so as to solve the problems existing in chronic disease data management that data cannot be shared, system functions are not comprehensive enough to meet actual work needs, and the development of chronic disease management and monitoring work is affected.

[0010] The invention also discloses a system for realizing the method.

[0011] The detailed technical scheme of the present invention is as follows:

[0012] A method for tracking and analyzing chronic disease diagnosis and treatment data based on cloud computing, the method comprising:

[0013] S1: The data layer integrates and processes the public diagnosis and treatment data of chronic diseases into a new medical record database, a new treatment database, and a new follow-up database; the data layer is loaded with a multi-source data acquisition module, a multi-source data governance module, a data storage module, and a capability exposure module for data collection, governance, storage, and exposure;

[0014] S2: Import the new medical record database, the new treatment database and the new follow-up database into the application layer respectively; the application layer is loaded with a risk assessment model module, a medical record management module, a follow-up management module and a statistical analysis module for medical record management, medication management, epidemiological management, basic treatment management and follow-up management;

[0015] S3: The service layer and the application layer are interconnected and communicated;

[0016] The service layer is used to implement medication supervision, remote diagnosis and treatment, chronic disease assessment and provision of health knowledge.

[0017] Preferably, according to the present invention, the new medical record database is obtained by merging the National Special Report Network Medical Record Table Card and the National Special Report Network Treatment Basic Table with the patient's ID number and name as link variables.

[0018] Preferably, according to the present invention, the new treatment database is obtained by merging the new medical record database and the treatment follow-up table of the National Special Report Network with the patient's ID number, name, and treatment code ptid as link variables.

[0019] Preferably, according to the present invention, the new follow-up database is obtained by merging the new treatment database and the follow-up information table of the National Special Report Network with the patient treatment code ptid as a link variable.

[0020] A system for implementing the above method, comprising a data layer, an application layer and a service layer;

[0021] The data layer is used to realize data collection, management, storage and opening: including multi-source data collection module, multi-source data processing module, data storage module and capability opening module;

[0022] The application layer provides a one-stop patient management platform for disease control centers and hospitals, which is used to realize the informatization and process-based management of patient referral, tracking, visiting and supervision, and to realize risk assessment medical record management, follow-up management and statistical analysis: including risk assessment model module, medical record management module, follow-up management module and statistical analysis module;

[0023] The service layer is used for patients to implement medication supervision, chronic disease assessment, and auxiliary consultation: it includes medication management module, remote diagnosis and treatment module, chronic disease assessment module and health knowledge module.

[0024] Preferably, according to the present invention, the method for using the data layer includes:

[0025] S11: The multi-source data collection module collects the medical record form Card, the basic treatment form, the treatment follow-up form and the follow-up information form from the national special report network;

[0026] S12: The multi-source data governance module processes the data collected from the National Special Report Network. The processing flow is as follows:

[0027] First, the National Special Report Network medical record table Card and the National Special Report Network treatment basic table were merged using the patient's ID number and name as link variables to obtain a new medical record database and imported into the system;

[0028] Secondly, the new medical record database and the treatment follow-up table of the National Special Report Network were merged with the patient ID number, name, and patient treatment code ptid as link variables to obtain a new treatment database and imported into the system;

[0029] Finally, the new treatment database and the follow-up information table of the national special report network are merged with the patient treatment code ptid as a link variable to obtain a new follow-up database and imported into the system;

[0030] S13: The data storage module stores and manages the collected and managed data;

[0031] S14: The capability exposure module provides external services and applications with data interfaces and protocols that are easy to access and use, as follows:

[0032] Use RESTful API design to provide a unified resource access interface, unified resource access and data query methods; communicate through HTTP protocol, use GET, POST, PUT and DELETE methods to perform data addition, deletion, modification and query operations;

[0033] Design an interface version management mechanism, use version numbers in URLs to distinguish different versions of interfaces, create a new version when an interface upgrade or modification is required, and gradually migrate users to the new version;

[0034] Design authentication and authorization mechanisms to ensure that only authorized external systems or applications can access and use system resources;

[0035] Design a unified exception handling and error code mechanism to classify and handle abnormal situations that may occur in the connected system or application, and return appropriate error codes and error information.

[0036] Preferably, according to the present invention, the method for using the application layer includes:

[0037] S21: The risk assessment model module implements the construction of a chronic disease risk assessment model based on R language, and uploads and uploads the risk assessment model to the system after the construction is completed; when a risk assessment of a chronic disease is required, the risk assessment model in the system is called, and relevant parameters of the patient are input to perform a chronic disease risk assessment;

[0038] S22: The specific management contents of the medical record management module include:

[0039] Categorize and manage patients; add screening conditions to find specific patient populations to view detailed information about specific patient populations; filter some statistical indicators to export reports and compare disease management situations horizontally;

[0040] Based on the patient's health data, use the risk assessment model to conduct risk assessment, monitoring and early warning;

[0041] Real-time statistics of patient compliance through follow-up data; Providing active and accurate intervention to patients; Providing online medical services to reduce the number of times patients go to the hospital;

[0042] Real-time monitoring of patients' examination and medication data, automatic generation of case closing data; online reminders are set to remind patients to undergo regular examinations and medications, and timely alerts are given to management personnel when patients are found to have not undergone regular examinations and medications;

[0043] Through the system big data, doctors can push health education materials to their patients’ APP according to their conditions, conduct regular health education, increase patients’ initiative in diagnosis and treatment, and set up AI robots to be online 24 hours a day to answer patients’ questions.

[0044] S23: The specific management contents of the follow-up management module include:

[0045] Set up a patient contact list, conduct follow-up visits using mini-programs, text messages, or phone calls, and perform statistical analysis on the follow-up results;

[0046] Provide follow-up information to the patients for reference, including the patient's disease history, medical history and health behavior;

[0047] Conduct psychological scale tests regularly, including assessment of anxiety, depression, sleep disorders, suicidal tendencies, compliance, and cognitive assessment, to understand the patient's psychological condition and provide timely psychological counseling for patients with abnormal psychological conditions;

[0048] S24: The analysis contents of the statistical analysis module include: statistical description, parameter estimation, hypothesis testing and correlation regression.

[0049] Preferably, according to the present invention, the method for using the service layer includes:

[0050] S31: The medication management module manages the patient's medication behavior, sets an automatic medication reminder, and the patient uploads medication data in real time after taking the medication;

[0051] S32: The remote diagnosis and treatment module implements hierarchical authorization control and provides doctor Q&A supervision and appointment outpatient diagnosis and treatment services;

[0052] The hierarchical authorization control means setting different levels of usage permissions for users of different levels. The higher the level, the more data can be viewed. At the same time, a patient privacy data protection mechanism is set to reduce privacy leakage;

[0053] The doctor's Q&A supervision refers to enabling patients to consult doctors online, upload imaging reports, scan codes with doctors, confirm visits, and provide service evaluations;

[0054] The appointment for outpatient treatment means that the patient makes an appointment for treatment in advance on the mobile terminal, and the doctor reserves the time in advance;

[0055] S33: The chronic disease assessment module uses the risk assessment model in the system to perform chronic disease assessment;

[0056] S34: The health knowledge module stores authoritative health knowledge to provide patients with authoritative, practical and easy-to-understand health knowledge; and a search box is set for patients to perform keyword searches.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] (1) The present invention establishes a standardized analysis model, the system basic database is downloaded from the National Special Report Network, and data processing is performed based on the model to ensure the authority, integrity, availability and universality of the data.

[0059] (2) The present invention sorts out the number and name of patients to be tested and the number and name of patients to be followed up and receive medication based on the number list and contact information of all managed patients, and reminds patients to take medication and receive regular medical treatment online on a daily basis to improve patient compliance; combined with the client's management method of supervising patients' medication, the management rate and regular medication rate are implemented.

[0060] (3) The various physical examination results of the present invention are timely entered into the system, which is convenient for doctors and patients to query the test results in time and ensure data sharing. Through statistical analysis of work indicators such as online follow-up diagnosis and treatment and follow-up treatment, doctors can grasp the patient's health data and medication data in real time. The risk assessment model can be used to conduct risk assessment on patients, and real-time and accurate intervention can be carried out on patients, providing a new management model for chronic diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1It is a flow chart of the method for tracking and analyzing chronic disease diagnosis and treatment data based on cloud computing described in the present invention.

[0062] Figure 2 This is a module diagram of the tracking and analysis system for chronic disease diagnosis and treatment data based on cloud computing described in the present invention. DETAILED DESCRIPTION

[0063] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0064] Embodiment 1,

[0065] like Figure 1 This embodiment provides a method for tracking and analyzing chronic disease diagnosis and treatment data based on cloud computing, the method comprising:

[0066] S1: The data layer integrates and processes the public diagnosis and treatment data of chronic diseases into a new medical record database, a new treatment database, and a new follow-up database; the data layer is loaded with a multi-source data acquisition module, a multi-source data governance module, a data storage module, and a capability exposure module for data collection, governance, storage, and exposure;

[0067] S2: Import the new medical record database, the new treatment database and the new follow-up database into the application layer respectively; the application layer is loaded with a risk assessment model module, a medical record management module, a follow-up management module and a statistical analysis module for medical record management, medication management, epidemiological management, basic treatment management and follow-up management;

[0068] S3: The service layer and the application layer communicate with each other:

[0069] The service layer is used to implement medication supervision, remote diagnosis and treatment, chronic disease assessment and provision of health knowledge.

[0070] The new medical record database is obtained by merging the medical record table Card of the National Special Report Network and the basic treatment table of the National Special Report Network, using the patient's ID number and name as link variables.

[0071] The new treatment database is obtained by merging the new medical record database and the treatment follow-up table of the National Special Report Network with the patient's ID number, name, and treatment code ptid as link variables.

[0072] The new follow-up database is obtained by merging the new treatment database and the follow-up information table of the National Special Report Network with the patient treatment code ptid as a link variable.

[0073] Embodiment 2,

[0074] like Figure 2 , a system for implementing the method as described in Example 1, comprising a data layer, an application layer and a service layer;

[0075] The data layer is used to realize data collection, management, storage and opening: including multi-source data collection module, multi-source data processing module, data storage module and capability opening module;

[0076] The application layer provides a one-stop patient management platform for CDCs and hospitals, which is used to realize the informatization and process of patient referral, tracking, visiting and supervision, and to realize risk assessment medical record management, follow-up management and statistical analysis: including risk assessment model module, medical record management module, follow-up management module and statistical analysis module; it is convenient for medical staff to follow up the disease conditions of chronic disease patients in real time, understand the patient's diagnosis and treatment compliance, and prevent risks in time. Through multi-dimensional data tracking and analysis, the data is deeply mined to help medical staff and prevention and control personnel conduct scientific research and guide the formulation and implementation of relevant policies and measures for chronic disease management.

[0077] The service layer is used for patients to implement medication supervision, chronic disease assessment, and auxiliary consultation: it includes medication management module, remote diagnosis and treatment module, chronic disease assessment module and health knowledge module.

[0078] Preferably, according to the present invention, the method for using the data layer includes:

[0079] S11: The multi-source data collection module collects the medical record form Card, the basic treatment form, the treatment follow-up form and the follow-up information form from the national special report network;

[0080] S12: The multi-source data governance module processes the data collected from the National Special Report Network. The processing flow is as follows:

[0081] First, the National Special Report Network medical record table Card and the National Special Report Network treatment basic table are merged with the patient's ID number and name as link variables to obtain a new medical record database and import it into the system to achieve the association between the patient's basic information and the patient's treatment code ptid;

[0082] Secondly, the new medical record database and the treatment follow-up table of the National Special Report Network were merged with the patient ID number, name, and patient treatment code ptid as link variables to obtain a new treatment database and imported into the system to achieve treatment data integrity;

[0083] Finally, the new treatment database and the follow-up information table of the National Special Report Network are merged with the patient treatment code ptid as a link variable to obtain a new follow-up database and imported into the system to achieve the integrity of the follow-up data and screen out useless follow-up data;

[0084] S13: The data storage module designs a suitable data storage and processing solution to store and manage the collected and managed data; the design of a suitable data storage and processing solution refers to selecting a relational database, a time series database or a distributed storage system according to customer resources and data storage needs.

[0085] Choose to use a relational database, time series database, or distributed storage system for data storage.

[0086] S14: The capability exposure module provides external services or applications with data interfaces and protocols that are easy to access and use, as follows:

[0087] Use RESTful API design to provide a unified resource access interface, unified resource access and data query methods; communicate through HTTP protocol, use GET, POST, PUT and DELETE methods to perform data addition, deletion, modification and query operations;

[0088] To ensure the stability and compatibility of the interface, an interface version management mechanism is designed. Version numbers, such as / v1 / and / v2 / , are used in URLs to distinguish different versions of interfaces. When an interface upgrade or modification is required, a new version is created and users are gradually migrated to the new version to avoid affecting existing users.

[0089] Design authentication and authorization mechanisms to ensure that only authorized external systems or applications can access and use system resources; use authentication protocols and token mechanisms such as OAuth and JWT to authenticate and control permissions of connected systems or applications, and restrict access to and operations on data based on user roles and permissions;

[0090] Design a unified exception handling and error code mechanism to classify and handle abnormal situations that may occur in the connected system or application, and return appropriate error codes and error information so that users can quickly locate and solve problems, providing a good user experience and troubleshooting capabilities.

[0091] Preferably, according to the present invention, the method for using the application layer includes:

[0092] S21: The risk assessment model module implements the construction of a chronic disease risk assessment model based on R language, and uploads and uploads the risk assessment model to the system after the construction is completed; when a risk assessment of a chronic disease onset is required, the risk assessment model in the system is called, and relevant parameters of the patient are input to perform a chronic disease risk assessment;

[0093] The risk assessment models include but are not limited to: Bayes model, meta model, Logistic model, rules model and trees model, and new risk assessment models can be added at any time; for example, when calling the Bayes model, the patient's relevant parameters include whether there is brain infection, mental system disease, psychological injury, physical disease, whether aluminum tableware is used, hobbies, mother's education level and whether he has been engaged in heavy physical labor recently;

[0094] S22: The specific management contents of the medical record management module include:

[0095] Categorize and manage patients; add screening conditions to find specific patient populations to view detailed information about specific patient populations; filter some statistical indicators to export reports and compare disease management situations horizontally;

[0096] Based on the patient's health data, a risk assessment model is used to conduct risk assessment, monitoring and early warning; the patient's health data is obtained from the data layer;

[0097] Real-time statistics of patient compliance are collected through follow-up data, including the number of drugs issued and collected, to see whether patients take their medications on time and whether medication records are filled out on time and as required; proactive and precise intervention is provided to patients; online medical services are provided to reduce the number of times patients go to the hospital;

[0098] Set up and remind patients of reexamination, sputum examination, medication, etc., grasp medication data in real time, and automatically generate case closing data such as medication rate; set up online reminders to remind patients to undergo regular examinations and medication, such as daily blood pressure measurement, medication, and blood biochemistry, immunology and other tests for half a year; remind patients through WeChat applets, text messages, voice calls and other methods. When it is detected that patients do not undergo regular examinations and medication, timely alert management personnel to improve compliance;

[0099] Assisted health education: Through system big data, doctors can push health education materials to their patients’ APP according to their conditions, conduct regular health education, increase patients’ initiative in diagnosis and treatment, and set up AI robots to be online 24 hours a day to answer patients’ questions, improve the timeliness of diagnosis and treatment services, and reduce doctors’ workload;

[0100] S23: The specific management contents of the follow-up management module include:

[0101] (1) It is convenient to call up the patient's address book and conduct follow-up through mini-programs, text messages, phone calls, etc.;

[0102] (2) Provide follow-up information reference to understand the patient's medical history and medical history, such as the comorbidity of chronic diseases; understand the patient's health behavior, including smoking history, drinking history, diet, etc.;

[0103] (3) Psychological support and intervention for patients, including assessment of anxiety, depression, sleep disorders, suicidal tendencies, compliance, and cognitive assessment. Regular psychological scale tests are conducted to understand psychological conditions such as anxiety and depression, and psychological counseling is provided in a timely manner;

[0104] S24: The analysis contents of the statistical analysis module include: statistical description, parameter estimation, hypothesis testing and correlation regression;

[0105] The statistical description includes: mean, standard deviation, median, percentile, probability of binomial distribution and Poisson distribution; the parameter estimation includes: credible interval estimation of population mean, credible interval estimation of population variance, credible interval estimation of population standard deviation, credible interval estimation of binomial distribution population rate, credible interval estimation of Poisson distribution population mean; the hypothesis test includes: group and paired t test, u test, variance analysis and pairwise comparison of multiple sample means in group design, variance analysis and pairwise comparison of compatible group design, rank sum test and pairwise comparison of two or more groups, paired rank sum test, compatible group rank sum test The related regression includes: calculation and hypothesis test of product-moment correlation coefficient, calculation and hypothesis test of rank correlation coefficient, comparison of sample correlation coefficient with overall correlation coefficient, comparison and combination of two sample correlation coefficients, calculation and hypothesis test of linear regression equation, and statistical prediction and statistical control using regression equation;

[0106] The statistical analysis module can help scientific researchers who are not statistics professionals to complete statistical analysis of data, and can complete various statistical analyses of common data, such as timely counting the number of people who have taken medication and have been tested, the number of people who have not taken medication and have not been tested, and their demographic characteristics; it is convenient for medical staff to follow up on the disease conditions of chronic disease patients in real time, understand the patient's compliance with diagnosis and treatment, and prevent risks in a timely manner; through multi-dimensional data tracking and analysis, the data is deeply mined to help medical staff and prevention and control personnel carry out scientific research and guide the formulation and implementation of relevant policies and measures for chronic disease management.

[0107] The method of using the service layer includes:

[0108] S31: The medication management module manages the patient's medication behavior, sets automatic medication reminders, and uploads medication data in real time; such as reminders one week before medication collection, reminders three days before medication collection, reminders one hour before medication is taken at the prescribed time, reminders half an hour after medication is not taken at the prescribed time, and reminders one hour after medication is not taken at the prescribed time.

[0109] The automatic medication reminder relies on online reminders from a mobile terminal, or offline reminders from a smart electronic medicine box, to achieve medication behavior management; the reminder can be set as a sound or light reminder and a repeated reminder mechanism can be set.

[0110] S32: The remote diagnosis and treatment module implements hierarchical authorization control and provides doctor Q&A supervision and appointment outpatient diagnosis and treatment services;

[0111] The hierarchical authorization control means setting different levels of usage permissions for users of different levels. The higher the level, the more data can be viewed. At the same time, a patient privacy data protection mechanism is set to reduce privacy leakage. For example, some examinations or follow-ups are considered private by patients and cannot be involved in the remote diagnosis and treatment process. Some sensitive data can only be known by the attending physician or the health authority, and ordinary assistants have no right to know.

[0112] The doctor's Q&A supervision refers to enabling patients to consult doctors online, upload imaging reports, scan codes with doctors, confirm visits, and provide service evaluations;

[0113] The appointment for outpatient treatment means that the patient makes an appointment for treatment in advance on the mobile terminal, and the doctor reserves the time in advance, so as to avoid the doctor being off duty and unable to get the medicine at the hospital, and save the patient the trouble of running around;

[0114] S33: The chronic disease assessment module uses the system risk assessment model to perform chronic disease assessment;

[0115] S34: The health knowledge module stores authoritative health knowledge to provide patients with authoritative, practical and easy-to-understand health knowledge; and sets a search box for patients to search for keywords: common symptoms, adverse reactions, medication instructions, etc.

[0116] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation methods of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A method for tracking and analyzing chronic disease diagnosis and treatment data based on cloud computing, characterized in that: The method comprises: S1: The data layer integrates and processes the public diagnosis and treatment data of chronic diseases into a new medical record database, a new treatment database, and a new follow-up database; the data layer is loaded with a multi-source data acquisition module, a multi-source data governance module, a data storage module, and a capability exposure module; S2: importing the new medical record database, the new treatment database and the new follow-up database into the application layer respectively; the application layer is loaded with a risk assessment model module, a medical record management module, a follow-up management module and a statistical analysis module; S3: The service layer is interconnected and communicated with the application layer; the service layer is loaded with a medication management module, a remote diagnosis and treatment module, a chronic disease assessment module and a health knowledge module.

2. According to the cloud computing-based tracking and analysis method for chronic disease diagnosis and treatment data according to claim 1, it is characterized in that: The new medical record database is obtained by merging the medical record table Card of the National Special Report Network and the basic treatment table of the National Special Report Network, using the patient's ID number and name as link variables.

3. The method for tracking and analyzing chronic disease diagnosis and treatment data based on cloud computing according to claim 2, characterized in that: The new treatment database is obtained by merging the new medical record database and the treatment follow-up table of the National Special Report Network with the patient's ID number, name, and treatment code ptid as link variables.

4. The method for tracking and analyzing chronic disease diagnosis and treatment data based on cloud computing according to claim 3 is characterized in that: The new follow-up database is obtained by merging the new treatment database and the follow-up information table of the National Special Report Network with the patient treatment code ptid as a link variable.

5. A tracking and analysis system for chronic disease diagnosis and treatment data based on cloud computing that implements the method according to any one of claims 1 to 4, characterized in that: Includes data layer, application layer and service layer; The data layer includes a multi-source data acquisition module, a multi-source data processing module, a data storage module and a capability exposure module; The application layer provides a one-stop patient management platform for disease control centers and hospitals, including a risk assessment model module, a medical record management module, a follow-up management module, and a statistical analysis module; The service layer is for patients and includes a medication management module, a remote diagnosis and treatment module, a chronic disease assessment module, and a health knowledge module.

6. The tracking and analysis system for chronic disease diagnosis and treatment data based on cloud computing according to claim 5 is characterized in that: The method of using the data layer includes: S11: The multi-source data collection module collects the medical record form Card, the basic treatment form, the treatment follow-up form and the follow-up information form from the national special report network; S12: The multi-source data governance module processes the data collected from the National Special Report Network. The processing flow is as follows: First, the National Special Report Network medical record table Card and the National Special Report Network treatment basic table were merged using the patient's ID number and name as link variables to obtain a new medical record database and imported into the system; Secondly, the new medical record database and the treatment follow-up table of the National Special Report Network were merged with the patient ID number, name, and patient treatment code ptid as link variables to obtain a new treatment database and imported into the system; Finally, the new treatment database and the follow-up information table of the national special report network are merged with the patient treatment code ptid as a link variable to obtain a new follow-up database and imported into the system; S13: The data storage module stores and manages the collected and managed data; S14: The capability exposure module provides external services and applications with data interfaces and protocols that are easy to access and use, as follows: Use RESTful API design to provide a unified resource access interface, unified resource access and data query methods; communicate through HTTP protocol, use GET, POST, PUT and DELETE methods to perform data addition, deletion, modification and query operations; Design an interface version management mechanism, use version numbers in URLs to distinguish different versions of interfaces, create a new version when an interface upgrade or modification is required, and gradually migrate users to the new version; Design authentication and authorization mechanisms so that only authorized external systems or applications can access and use system resources; Design a unified exception handling and error code mechanism to classify and handle abnormal situations that may occur in the connected system or application, and return appropriate error codes and error information.

7. The tracking and analysis system for chronic disease diagnosis and treatment data based on cloud computing according to claim 5 is characterized in that: The method for using the application layer includes: S21: The risk assessment model module implements the construction of a chronic disease risk assessment model based on R language, and uploads and uploads the risk assessment model to the system after the construction is completed; when a risk assessment of a chronic disease is required, the risk assessment model in the system is called, and relevant parameters of the patient are input to perform a chronic disease risk assessment; S22: The specific management contents of the medical record management module include: Categorize and manage patients; add screening conditions to find specific patient populations to view detailed information about specific patient populations; filter some statistical indicators to export reports and compare disease management situations horizontally; Based on the patient's health data, use the risk assessment model to conduct risk assessment, monitoring and early warning; Real-time statistics of patient compliance through follow-up data; provide active and accurate intervention for patients; provide online medical services to reduce the number of times patients go to the hospital; Real-time monitoring of patients' examination and medication data, automatic generation of case closing data; online reminders are set to remind patients to undergo regular examinations and medications, and timely alerts are given to management personnel when patients are found to have not undergone regular examinations and medications; Through the system big data, doctors can push health education materials to their patients’ APP according to their conditions, conduct regular health education, increase patients’ initiative in diagnosis and treatment, and set up AI robots to be online 24 hours a day to answer patients’ questions. S23: The specific management contents of the follow-up management module include: Set up a patient contact list, conduct follow-up visits using mini-programs, text messages, or phone calls, and perform statistical analysis on the follow-up results; Provide follow-up information for the patients being followed up for reference, including the patient's disease history, medical history and health behavior; Conduct psychological scale tests regularly, including assessment of anxiety, depression, sleep disorders, suicidal tendencies, compliance, and cognitive assessment, to understand the patient's psychological condition and provide timely psychological counseling for patients with abnormal psychological conditions; S24: The analysis contents of the statistical analysis module include: statistical description, parameter estimation, hypothesis testing and correlation regression.

8. The tracking and analysis system for chronic disease diagnosis and treatment data based on cloud computing according to claim 5 is characterized in that: The method of using the service layer includes: S31: The medication management module manages the patient's medication behavior, sets an automatic medication reminder, and the patient uploads medication data in real time after taking the medication; S32: The remote diagnosis and treatment module implements hierarchical authorization control and provides doctor Q&A supervision and appointment outpatient diagnosis and treatment services; The hierarchical authorization control means setting different levels of usage permissions for users of different levels. The higher the level, the more data can be viewed. At the same time, a patient privacy data protection mechanism is set up; The doctor's Q&A supervision refers to enabling patients to consult doctors online, upload imaging reports, scan codes with doctors, confirm visits, and provide service evaluations; The appointment for outpatient treatment means that the patient makes an appointment for treatment in advance on the mobile terminal, and the doctor reserves the time in advance; S33: The chronic disease assessment module uses the risk assessment model in the system to perform chronic disease assessment; S34: The health knowledge module stores authoritative health knowledge to provide patients with authoritative, practical and easy-to-understand health knowledge; and a search box is set for patients to perform keyword searches.

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