Medical online consultation and evaluation system

Abstract

The invention discloses a medical online consultation evaluation system, and the system comprises an obtaining module which is used for obtaining the historical treatment information and basic information of a current patient, and building an electronic health record according to the historical treatment information; the matching module is used for acquiring doctor information, generating a doctor label table and distributing a corresponding doctor to the current patient according to the historical doctor seeing information and the basic information of the current patient; the consultation module is used for the current patient to select a consultation mode for consultation, providing suggestions according to the consultation content of the current patient, and storing the consultation content of the current patient and the doctor in the database; the evaluation module is used for evaluating the doctor to obtain a doctor evaluation result; according to the method and the system, the patient can consult the doctor again according to the binding relationship between the patient and the doctor after the consultation is finished, and a corresponding health management scheme can be formulated for the patient after the consultation is finished, so that more comprehensive and continuous attention is provided, and better service experience is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of online medical consultation, and particularly to an online medical consultation and evaluation system. Background Art

[0002] Most of the existing medical consultations are that patients go to the hospital or clinic in person to queue up for registration and receive face-to-face diagnosis and treatment by doctors, which requires a lot of waiting time; with the rapid development of Internet technology, online medical services have gradually become an important way for patients to obtain medical information and treatment suggestions. By using Internet and mobile communication technologies to provide users with medical consultation, health assessment and other services, it can reduce the on-site queuing waiting time and is more convenient.

[0003] However, when the existing medical consultation and evaluation system is applied, it is usually set to a single and instant consultation mode. When patients consult, they often can only ask about the current symptoms or problems. After the consultation ends, they cannot obtain comprehensive and continuous medical attention. Summary of the Invention

[0004] In view of the above-mentioned prior art, the present invention aims to provide an online medical consultation and evaluation system, mainly to solve the technical problems existing in the above-mentioned background art.

[0005] To achieve the above object, the technical solution of the embodiment of the present invention is implemented as follows:

[0006] An online medical consultation and evaluation system, comprising:

[0007] An acquisition module, configured to acquire the historical medical record information, basic information and intended consultation direction of the current patient, and establish an electronic health record according to the historical medical record information and the basic information;

[0008] A matching module, configured to acquire doctor information, generate a doctor label table, and allocate a corresponding doctor to the current patient according to the historical medical record information, basic information, intended consultation direction of the current patient and the doctor label table;

[0009] A consultation module, configured to allow the current patient to select a consultation method for consultation, provide suggestions according to the consultation content of the current patient, and save the consultation content between the current patient and the doctor to a database;

[0010] A personalization module, configured to provide a health management plan for the current patient;

[0011] An evaluation module, configured to evaluate the doctor and obtain a doctor evaluation result.

[0012] Optionally, the historical medical record information includes the past medical history, medical record, and electronic examination results of the current patient;

[0013] The past medical history includes the history of diseases, surgeries, blood transfusions, drug allergies, and trauma that the current patient has had, as well as the time, cause, and recovery status of the past medical history;

[0014] The medical record includes the time of the current patient's previous medical visits, the departments visited, and the doctor's treatment opinions on the patient;

[0015] The electronic examination results include the results of medical imaging examinations performed on the current patient;

[0016] The basic information includes the name, age, gender, and address of the current patient;

[0017] The intended consultation direction includes the intended department and the consultation topic.

[0018] Optionally, the method for obtaining doctor information, generating a doctor label table, and assigning a corresponding doctor to the current patient based on the current patient's historical medical visit information, basic information, intended consultation direction, and the doctor label table includes:

[0019] Obtain doctor information, where the doctor information includes the doctor's professional background, area of expertise, and past treatment success rate. Add labels to the doctors based on the doctor information to generate a doctor label table, and store the doctor label table in a database;

[0020] Obtain the current patient feature vector based on the current patient's historical medical visit information, basic information, and intended consultation direction. Obtain the first doctor feature vector by converting based on the doctor information and the doctor label table. Calculate the correlation between the current patient feature vector and the first doctor feature vector, sort the doctors from highest to lowest in terms of matching degree to obtain a sorting result;

[0021] The patient selects a corresponding doctor according to the sorting result and binds the patient identity information to the doctor.

[0022] Optionally, the calculating the correlation between the current patient feature vector and the first doctor feature vector and sorting the doctors from highest to lowest in terms of matching degree includes:

[0023] Construct a random forest model, input the first doctor feature vector into the random forest model, calculate the Gini index, and screen the feature information with the highest correlation based on the Gini index to obtain a second feature vector;

[0024] Construct an LSTM model, input the second feature vector into the LSTM model for processing to obtain an output result, and sort the doctors from highest to lowest in terms of matching degree based on the output result.

[0025] Optionally, the consultation methods include:

[0026] Text and image consultation: The patient uploads text descriptions and pictures to communicate with the doctor online, and the chat record is saved to the database.

[0027] Phone consultation: The patient has a real-time voice communication with the doctor, and the call content is recorded and saved to the database.

[0028] Video consultation: The patient has a face-to-face video communication online, and the video content is recorded and saved to the database.

[0029] Optionally, it further includes a personalized module for providing a health management plan for the current patient.

[0030] The personalized module includes an exercise recommendation unit, a diet management unit, a science popularization unit, and an interactive communication unit.

[0031] The exercise recommendation unit is used to recommend exercise videos suitable for the patient's training level according to the patient's specific situation, collect the health data information during the patient's exercise, and analyze the collected health data information to evaluate the patient's exercise effect.

[0032] The diet management unit is used to formulate a scientific diet plan that meets the patient's nutritional needs according to the patient's health condition and personal preferences, display the nutrient intake, food types and portions included in the diet plan, and record the patient's daily diet.

[0033] The science popularization unit is used to display the symptoms, prevention methods, and daily care knowledge of basic diseases to the patient in the form of pictures, texts or videos, providing guidance for the patient's health management.

[0034] The interactive communication unit is used for the patient to ask questions online and wait for the doctor or other patients to reply; it is also used for the patient to share experiences and discuss and interact with other patients.

[0035] Optionally, the method for evaluating the doctor to obtain an evaluation result specifically includes the following steps:

[0036] Construct an evaluation index set and an evaluation set, and the evaluation index set includes a first-level evaluation index subset and a second-level evaluation index subset.

[0037] Determine the weight vectors of the first-level evaluation index subset and the second-level evaluation index subset to obtain the weight vector of the evaluation index.

[0038] Calculate the membership degree of each evaluation index for different evaluation levels to construct a fuzzy relation matrix.

[0039] The evaluation matrix is combined with the weights of each evaluation index to synthesize an evaluation result vector, and the one with the highest value is determined as the evaluation result.

[0040] Optionally, the first-level evaluation index subset includes doctors' professional knowledge and communication level; the second-level evaluation index subset includes the accuracy of doctors' disease diagnosis, the effectiveness of suggestions, the response time of doctors, and the convenience of communication.

[0041] The evaluation set includes good, fine, average, and poor.

[0042] The beneficial effects of the present invention are as follows: A medical online consultation evaluation system provided by the present invention obtains relevant information of users through an acquisition module, establishes an electronic health record, and obtains doctor information through a matching module to generate a doctor label table. Based on the relevant information of users, doctor information, and the doctor label table, corresponding doctors are assigned to patients, and the relationship between doctors and patients is bound; through an evaluation module, doctors can be evaluated, and according to the scoring results, it can provide a reference for patients when choosing doctors; through a consultation module, doctors provide suggestions according to the consultation content of patients, and provide corresponding health management plans for patients through a personalized module; after the consultation ends, according to the binding relationship between patients and doctors, patients can consult doctors again, and after this consultation ends, a corresponding health management plan can be formulated for patients, so as to provide more comprehensive and continuous attention and obtain a better service experience. Description of the Drawings

[0043] Figure 1 It is a schematic structural diagram of a medical online consultation evaluation system provided in an embodiment of the present invention. Detailed Embodiments

[0044] The technical solutions of the present invention will be further elaborated in detail below in conjunction with the accompanying drawings of the specification and specific embodiments. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. In the following description, the expression "some embodiments" describes a subset of all possible embodiments, but it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0045] In the following description, numerous specific details are given to provide a more thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that the present invention may be practiced without one or more of these details. In other instances, well-known features of the art are not described in order to avoid obscuring the present invention.

[0046] It should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present invention to those skilled in the art. The terms used herein are for the purpose of describing particular embodiments only and are not intended to be limiting of the present invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "including", when used in this specification, specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term "and / or" includes any and all combinations of the associated listed items.

[0047] It should be further noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there can be intervening elements. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there can be intervening elements at the same time. The terms "vertical", "horizontal", "inner", "outer", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only embodiments.

[0048] To thoroughly understand the present invention, detailed structures will be presented in the following description to illustrate the technical solutions proposed by the present invention. The alternative embodiments of the present invention are described in detail as follows. However, in addition to these detailed descriptions, the present invention can also have other embodiments.

[0049] Embodiment

[0050] Please refer to the appended Figure 1 , this application provides a medical online consultation and evaluation system, including:

[0051] An acquisition module, configured to acquire the historical medical record information, basic information, and intended consultation direction of the current patient, and establish an electronic health record according to the historical medical record information and the basic information;

[0052] A matching module, configured to obtain doctor information, generate a doctor label table, and allocate a corresponding doctor for the current patient according to the historical medical records, basic information, and intended consultation direction of the current patient, as well as the doctor label table;

[0053] A consultation module, configured to enable the current patient to select a consultation method for consultation, provide suggestions based on the consultation content of the current patient, and save the consultation content between the current patient and the doctor to a database;

[0054] A personalization module, configured to provide a health management plan for the current patient;

[0055] An evaluation module, configured to evaluate a doctor to obtain a doctor evaluation result.

[0056] Specifically, when a patient registers or logs in for the first time, basic information and the intended consultation direction are collected through a form, and the patient can modify the intended consultation direction during subsequent consultations; this online medical consultation evaluation system is docked with the hospital information system to obtain the patient's historical medical records and electronic examination results; and the collected information is stored in a database to establish an electronic health record; facilitating the doctor to quickly obtain the health status information of the patient during subsequent online consultations, and also facilitating the subsequent tracking of the patient's health status; through the matching module, according to the patient's historical medical records, basic information, intended consultation direction, and doctor label table, a suitable doctor can be matched for the current patient, and the patient's problems can be more targeted solved; through the consultation module, a consultation link can be established between the doctor and the current patient, so as to provide suggestions for the patient; the personalization module can formulate a corresponding health management plan according to the suggestions provided by the doctor, and the evaluation module is used for the patient to evaluate the doctor of this consultation; by establishing an online electronic record to obtain the patient's historical medical records and matching the most suitable doctor for the patient, enabling the doctor to give suggestions based on the problems consulted by the current patient and viewing the patient's historical medical records, and saving the suggestions to the database, facilitating the subsequent health management of the patient, so as to provide comprehensive and continuous medical attention to the patient.

[0057] As an optional implementation manner, the historical medical records include the current patient's past medical history, medical records, and electronic examination results;

[0058] The past medical history includes the disease history, surgery history, blood transfusion history, drug allergy history, and trauma history that the current patient has ever had, as well as the time, cause, and recovery situation of the past medical history;

[0059] The medical records include the time of the current patient's historical medical treatment, the department of medical treatment, and the doctor's treatment opinions on the patient;

[0060] The electronic inspection results include the medical imaging inspection results and laboratory inspection results of the current patient;

[0061] The basic information includes the name, age, gender, and address of the current patient;

[0062] The intended consultation direction includes the intended department and consultation topic;

[0063] Exemplarily, the medical imaging inspection results are X-ray results, CT results, etc., and the laboratory inspection results are blood routine results, etc.; the intended departments are internal medicine, surgery, etc., and the consultation topics are cold symptoms, chronic diseases, etc.

[0064] As an optional implementation manner, the method for obtaining doctor information, generating a doctor label table, and allocating a corresponding doctor to the current patient according to the historical medical record information, basic information, intended consultation direction of the current patient, and the doctor label table includes:

[0065] Obtain doctor information, where the doctor information includes the professional background, expertise field, and past treatment success rate of the doctor. Add labels to the doctor based on the doctor information to generate a doctor label table, and store the doctor label table in the database;

[0066] Obtain the feature vector of the current patient based on the historical medical record information, basic information, and intended consultation direction of the current patient, convert the first doctor feature vector based on the doctor information and the doctor label table, calculate the correlation between the feature vector of the current patient and the first doctor feature vector, sort the doctors from high to low according to the matching degree, and obtain a sorting result;

[0067] The patient selects a corresponding doctor according to the sorting result and binds the patient identity information to the doctor.

[0068] The calculation of the correlation between the feature vector of the current patient and the first doctor feature vector, and the sorting of doctors from high to low according to the matching degree includes:

[0069] Construct a random forest model, input the first doctor feature vector into the random forest model, calculate the Gini index, and screen the feature information with the highest correlation based on the Gini index to obtain a second feature vector;

[0070] Construct an LSTM model, input the second feature vector into the LSTM model for processing to obtain an output result, and sort the doctors from high to low according to the matching degree based on the output result.

[0071] Specifically, for the historical medical visit information and basic information of patients, preprocessing is required. An exemplary preprocessing method is data cleaning and feature selection to remove abnormal data and generate patient feature vectors. For doctor information, tags are added to doctors. Exemplarily, the tags are cardiovascular specialists, pediatricians, etc., to generate a doctor tag table. The doctor tag table is stored in a database, and then the doctor tag table, the professional background of the doctor, the area of expertise, and the past treatment success rate are converted into a first doctor feature vector. The first doctor feature vector and the patient feature vector are input into a random forest model. At each decision tree node of the random forest, the Gini index is calculated to evaluate the importance of features. The smaller the Gini index, the greater the contribution of the feature. According to the calculated Gini index, the features that contribute the most to the matching degree between doctors and patients are selected to obtain a second feature vector:

[0072] The second feature vector obtained after being screened by the random forest is input into an LSTM model for further processing. The LSTM model can capture the long-term dependencies in the second feature vector, which helps to more accurately match patients with physicians. The LSTM will output the matching scores between the current patient and each doctor. According to the matching scores, the physicians can be ranked. The higher the score of a physician, the higher the matching degree with the patient's characteristics, so they should be ranked in the front. The current patient can select the corresponding doctor according to their own needs. After the patient selects a physician, the patient's identity information is bound to the physician so that when the patient needs to consult again later, the previous doctor can be quickly matched, avoiding the patient having to rematch a doctor and saving time.

[0073] As an optional implementation manner, the consultation method includes:

[0074] Graphic and text consultation: The patient uploads text descriptions and pictures to communicate with the doctor online, and the chat records are saved in the database;

[0075] Phone consultation: The patient has a real-time voice communication with the doctor, and the call content is recorded and saved in the database;

[0076] Video consultation: The patient has a face-to-face video communication online, and the video content is recorded and saved in the database.

[0077] As an optional implementation manner, it further includes a personalized module for providing a health management plan for the current patient;

[0078] The personalized module includes an exercise recommendation unit, a diet management unit, a science popularization unit, and an interactive communication unit;

[0079] The exercise recommendation unit is used to recommend exercise videos suitable for the patient's training level according to the patient's specific conditions, collect the health data information of the patient during exercise, analyze the collected health data information, and evaluate the patient's exercise effect;

[0080] Specifically, the exercise recommendation unit can recommend appropriate exercise videos according to the patient's condition and preferences, and can collect the patient's exercise data (such as heart rate, exercise duration, calories burned, etc.) through wearable devices (such as smart bracelets, smart watches) or mobile applications, store and analyze the collected data, and evaluate the exercise effect;

[0081] The diet management unit is used to formulate a scientific diet plan that meets the patient's nutritional needs according to the patient's health condition and personal preferences, display the nutrient intake, food types and portions included in the diet plan, and record the patient's daily diet;

[0082] The popular science unit is used to display the symptoms, prevention methods, and daily care knowledge of basic diseases to the patient in the form of pictures, texts or videos, and provide guidance for the patient's health management;

[0083] The interactive communication unit is used for the patient to ask questions online and wait for the doctor or other patients to reply; it is also used for the patient to share experiences and discuss and interact with other patients.

[0084] Through the exercise recommendation unit, diet management unit, popular science unit, and interactive communication unit, a more comprehensive health management plan can be provided for the patient, so that the patient can obtain continuous medical attention after online consultation.

[0085] As an optional implementation method, the method for evaluating a doctor to obtain an evaluation result specifically includes the following steps:

[0086] Construct an evaluation index set and an evaluation set. The evaluation index set includes a first-level evaluation index subset and a second-level evaluation index subset; the first-level evaluation index subset includes doctor's professional knowledge and communication level; the second-level evaluation index subset includes the accuracy of the doctor's diagnosis of the condition, the effectiveness of the suggestions, the response time of the doctor's reply, and the convenience of communication; the evaluation set includes good, good, general, and poor;

[0087] U=(U1,U2)=(doctor's professional knowledge, communication level)

[0088] U1=(u 11 ,u 12 )=(the accuracy of the doctor's diagnosis of the condition, the effectiveness of the suggestions)

[0089] U2=(u 21 ,u 22) = (Doctor response timeliness, communication convenience)

[0090] V = (v1, v2, v3, v4) = (Good, Fine, Average, Poor)

[0091] Where U is the evaluation index set, U1 is the first-level evaluation index subset, and U2 is the second-level evaluation index subset; V is the evaluation set;

[0092] Determine the weight vectors of the first-level evaluation index subset and the second-level evaluation index subset to obtain the weight vector of the evaluation index;

[0093] Specifically, the analytic hierarchy process is used for weight setting to obtain the weight vectors of the first-level evaluation index subset and the second-level evaluation index subset, and then determine the weight vector of the evaluation index;

[0094] A = (A1, A2)

[0095] A1 = (a 11 , a 12 )

[0096] A2 = (a 21 , a 22 )

[0097] Where A is the weight vector of the evaluation index, A1 is the weight vector of the first-level evaluation index subset, and A2 is the weight vector of the second-level evaluation index subset;

[0098] Calculate the membership degree of each evaluation index for different evaluation levels to construct a fuzzy relation matrix;

[0099] Use the membership function to construct a fuzzy evaluation matrix R. The fuzzy evaluation matrix R is composed of the membership degrees of each evaluation index factor U i to the evaluation levels (R / U i ). The expression of the fuzzy evaluation matrix is:

[0100]

[0101] Synthesize the evaluation matrix with the weights of each evaluation index to obtain an evaluation result vector, and determine the one with the highest value as the evaluation result;

[0102]

[0103] Specifically, the membership function is used to determine the membership degree of each evaluation index factor to each evaluation level; the fuzzy evaluation matrix is a matrix composed of membership degrees, where each element represents the membership degree of the evaluation index factor to the evaluation level. The rows of the fuzzy evaluation matrix correspond to the evaluation index factors, and the columns correspond to the evaluation levels;

[0104] In the present invention, there are two levels of evaluation indicators. First, a single-factor evaluation is carried out on the subset of secondary evaluation indicators to construct an evaluation matrix of the secondary evaluation indicators, and the evaluation result vectors B1 and B2 of the subset of secondary evaluation indicators are obtained. Then, based on B1 and B2, a fuzzy evaluation matrix R of the subset of primary evaluation indicators is constructed. Through the fuzzy evaluation matrix and weight vector of the primary evaluation indicators, the evaluation result B is finally obtained. Each element in the evaluation result represents the membership degree of the entire evaluation system to a certain evaluation level, that is, the score of the evaluation level corresponding to the evaluation of the doctor. According to the scores of each level, the evaluation level with the highest membership degree (score) is used as the final evaluation result;

[0105] The process of constructing a fuzzy evaluation matrix using the membership function is a process of converting qualitative evaluation into quantitative evaluation. By hierarchically dividing and single-factor evaluating the evaluation indicators, a fuzzy judgment matrix is gradually constructed, and finally the evaluation result is obtained; this has significant advantages when dealing with complex and fuzzy evaluation problems.

[0106] Through the evaluation module, doctors can be evaluated to obtain scoring results. Based on the evaluation results, it can also provide a reference for patients to select doctors. At the same time, according to the scoring results, doctors can also improve their professional skills and service quality.

[0107] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should be covered within the protection scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claimed rights.

Claims

1. A medical online consultation and evaluation system, characterized in that: include: The acquisition module is used to obtain the historical medical information, basic information and intended consultation direction of the current patient, and establish an electronic health record based on the historical medical information and the basic information; A matching module, used to obtain doctor information, generate a doctor label table, and assign a corresponding doctor to the current patient according to the current patient's historical medical information, basic information, intended consultation direction, and the doctor label table; A consultation module is used for the current patient to select a consultation method for consultation, provide suggestions based on the consultation content of the current patient, and save the consultation content between the current patient and the doctor to a database; A personalized module, used to provide a health management plan for the current patient; The evaluation module is used to evaluate doctors and obtain doctor evaluation results.

2. A medical online consultation and evaluation system according to claim 1, characterized in that: The historical medical information includes the past medical history, medical records, and electronic examination results of the current patient; The past medical history includes the patient's past medical history, surgical history, blood transfusion history, drug allergy history, and trauma history, as well as the time, cause, and recovery status of the past medical history; The medical records include the time of the patient's previous medical visits, the medical department, and the doctor's treatment opinions on the patient; The electronic examination results include the medical imaging examination results of the current patient; The basic information includes the name, age, gender, and address of the current patient; The intended consultation direction includes the intended department and consultation topic.

3. A medical online consultation and evaluation system according to claim 2, characterized in that: The method for obtaining doctor information, generating a doctor label table, and assigning a corresponding doctor to the current patient according to the current patient's historical medical information, basic information, intended consultation direction, and the doctor label table includes: Obtaining doctor information, the doctor information including the doctor's professional background, areas of expertise, and past treatment success rate, adding tags to the doctors based on the doctor information, generating a doctor tag table, and storing the doctor tag table in a database; The current patient feature vector is obtained based on the historical medical information, basic information and intended consultation direction of the current patient, a first doctor feature vector is obtained based on the doctor information and the doctor label table, a correlation between the current patient feature vector and the first doctor feature vector is calculated, and the doctors are sorted from high to low according to the matching degree to obtain a sorting result; The patient selects a corresponding doctor according to the ranking result and binds the patient's identity information with the doctor.

4. A medical online consultation and evaluation system according to claim 3, characterized in that: The calculating the correlation between the current patient feature vector and the first doctor feature vector, and sorting the doctors from high to low according to the matching degree, includes: Constructing a random forest model, inputting the first doctor feature vector into the random forest model, calculating the Gini index, and screening the feature information with the highest correlation based on the Gini index to obtain a second feature vector; Construct an LSTM model, input the second feature vector into the LSTM model for processing, obtain an output result, and sort the doctors from high to low according to the matching degree based on the output result.

5. A medical online consultation and evaluation system according to claim 1, characterized in that: The consultation methods include: Graphics and text consultation: patients upload text descriptions and pictures, communicate with doctors online, and save chat records to the database; Telephone consultation: patients and doctors have real-time voice communication, and the content of the call is recorded and saved in the database; In video consultation, patients communicate face-to-face online via video, and the video content is recorded and saved in the database.

6. A medical online consultation and evaluation system according to claim 1, characterized in that: The personalized module includes an exercise recommendation unit, a diet management unit, a science popularization unit, and an interactive communication unit; The exercise recommendation unit is used to recommend an exercise video suitable for the patient's training level according to the patient's specific situation, collect the patient's health data information during exercise, analyze the collected health data information, and evaluate the patient's exercise effect; The diet management unit is used to formulate a scientific diet plan that meets the nutritional needs of the patient based on the patient's health status and personal preferences, and to display the nutrient intake, food types and portions included in the diet plan, and to record the patient's daily diet; The popular science unit is used to show patients the symptoms, prevention methods, and daily care knowledge of basic diseases in the form of pictures, texts, or videos, and provide guidance for patients' health management; The interactive communication unit is used for patients to ask questions online and wait for responses from doctors or other patients; it is also used for patients to share experiences and discuss and interact with other patients.

7. A medical online consultation and evaluation system according to claim 1, characterized in that: The method is used to evaluate doctors and obtain evaluation results, and the specific steps are: Constructing an evaluation indicator set and an assessment set, wherein the evaluation indicator set includes a primary evaluation indicator subset and a secondary evaluation indicator subset; Determine the weight vectors of the first-level evaluation indicator subset and the second-level evaluation indicator subset to obtain the weight vector of the evaluation indicator; Calculate the membership of each evaluation indicator to different evaluation levels and construct a fuzzy relationship matrix; The evaluation matrix is ​​synthesized with the weights of each evaluation index to obtain an evaluation result vector, and the one with the highest value is determined as the evaluation result.

8. A medical online consultation and evaluation system according to claim 7, characterized in that: The first-level evaluation indicator subset includes doctors' professional knowledge and communication level; the second-level evaluation indicator subset includes doctors' accuracy score of disease diagnosis, recommendation effectiveness score, doctor's response time, and patient repeat consultation rate; The evaluation set includes good, good, average, and poor.

Images