Medical consultation system based on intelligent terminal

Abstract

The present invention discloses a medical consultation system based on an intelligent terminal, comprising a login and registration module, a medical consultation and registration module, a medical consultation and queuing module, an intelligent auxiliary diagnosis module, a doctor consultation module, a pharmaceutical service module, an evaluation module and a database. The login and registration module is used to register and log in to an account, and at the same time authenticate and verify the patient's identity information. The medical consultation and registration module is used to find doctor information and register with the doctor. The medical consultation and queuing module can view the number of people queuing for each doctor and the registered queue position. At the same time, during this period, the patient can also apply for a refund or apply for a backward adjustment of the queue position. The present invention can match the database to confirm the patient's condition, save the doctor's consultation time, and improve the consultation efficiency.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a medical consultation system based on an intelligent terminal. Background Art

[0002] With the development of society and the improvement of people's living standards, people pay more and more attention to their own health. It has become a consensus among people to seek medical treatment when they are sick. However, with the quickening pace of life, people are hindered by work or busy with other things, resulting in people having no time to go to the hospital for treatment.

[0003] In response to the above situation, the present invention provides a medical consultation system based on smart terminals, which can conduct medical consultations with patients through audio and video, and conduct intelligent medical consultations and collect medical data on patients while they are waiting in line, greatly shortening the consultation time of the doctors and improving the consultation efficiency of the doctors. Through electronic prescriptions and pharmaceutical services, door-to-door drug dispensing is achieved, and the needs of residents for common disease consultations / chronic disease renewal prescriptions are met in real time, making medical services within reach. Summary of the Invention

[0004] The purpose of the present invention is to provide a medical consultation system based on an intelligent terminal to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a medical consultation system based on an intelligent terminal, comprising a login and registration module, a medical consultation registration module, a medical consultation queuing module, an intelligent auxiliary diagnosis module, a doctor consultation module, a pharmaceutical service module, an evaluation module and a database.

[0006] The login and registration module is used to register and log in to the account, and authenticate and verify the patient's identity information. The login and registration module registers the account through the mobile phone number.

[0007] The doctor registration module is used to search for doctor information and register with the doctor.

[0008] The consultation queue module can check the number of people in each doctor's queue and the queue position of the registered doctor. During this period, the patient can also apply for a refund or adjust the queue position.

[0009] The intelligent auxiliary diagnosis module extracts keywords from the detailed description of the patient's condition, matches the database, estimates the condition and confirms it with the patient. After the patient confirms, the intelligent auxiliary diagnosis module prescribes a prescription based on the patient's condition.

[0010] The doctor consultation module conducts consultation with the patient through audio or video connection.

[0011] In the pharmaceutical service module, the pharmacist can confirm the rationality of the prescription through online review and issue the corresponding CA signature. The reviewed prescription will be sent to the patient's registered mobile phone number via SMS, and the consultation is over.

[0012] The evaluation module is used to obtain patient evaluations and save the evaluation information to the database.

[0013] The tracing module is used to determine whether it is a misdiagnosis by the doctor based on the discomfort reaction caused by the patient taking the prescription prescribed by the doctor within a unit time.

[0014] The database is used to store data information generated by each module.

[0015] The modules of the present invention are closely connected, and each module can exchange information with the database. Each module can obtain the required information from the database in real time, and also save the data information generated by each module to the database, further strengthening the information exchange between the modules.

[0016] Furthermore, the login and registration module includes a registration module and a login module.

[0017] The registration module needs to obtain the registered mobile phone number filled in by the patient, and the registration module will send a mobile phone verification code to the patient's registered mobile phone number. The registration module receives the mobile phone verification code filled in by the patient and automatically checks whether the filled-in mobile phone verification code is correct. If the filled-in mobile phone verification code is incorrect, the patient needs to fill it in again. If the filled-in mobile phone verification code is correct, the registration module will authenticate the patient's identity, that is, compare the patient's name and ID number through the provided database interface. If the name and ID number match, the account registration is successful and the patient logs in successfully. If the name and ID number do not match, the identity authentication fails and the patient needs to re-authenticate.

[0018] The login module includes two methods: mobile phone number login and face recognition login. The mobile phone number login method requires obtaining the registered mobile phone number filled in by the patient. The login module will send a mobile phone verification code to the patient's registered mobile phone number. The login module receives the mobile phone verification code filled in by the patient and automatically checks whether the filled mobile phone verification code is correct. If the filled mobile phone verification code is incorrect, the patient needs to fill it in again. If the filled mobile phone verification code is correct, the login module will automatically match the patient's login mobile phone number in the database of the system. If the login mobile phone number cannot be matched in the database, the patient is not registered, and the login module automatically jumps to the registration module. If the database successfully matches the login mobile phone number, the account is successfully logged in.

[0019] The face recognition login method requires obtaining the patient's face information through the camera in the device used by the patient. The login module compares the obtained patient's face information with the face information on the patient's ID card through the provided database interface. If the face information of the two matches, the login is successful. If the face information of the two does not match, the login fails, and feedback is given to the patient whether to re-acquire the face information. When the patient confirms to re-acquire the face information, the login module re-acquires the patient's face information through the camera in the device used by the patient and compares it with the data in the provided database. When the patient confirms not to re-acquire the face information, it automatically jumps to the mobile phone number login method;

[0020] The patient who logs in through face recognition can be a patient who has not registered an account, and after successful login, the account will be automatically registered and the identity authentication will be completed, but the mobile phone number needs to be bound.

[0021] In the login and registration module, the account that successfully logs in can query historical medical consultation records.

[0022] In the login and registration module of the present invention, the patient's identity authentication is completed directly when the patient registers an account, and the patient does not need to repeat the authentication in the subsequent process, which greatly saves the patient's time. At the same time, after the patient successfully logs in to the account, the patient can directly query the historical consultation records. If there is a situation with the same manifestation of the current symptoms in the historical consultation records, the patient can directly refer to the content of the historical consultation records to treat the current symptoms without having to register for consultation again. This not only saves time for the patient, but also saves money. The login and registration module of the present invention also includes a login method of scanning a code to log in, which supports interconnection with the Internet hospital system. The mobile patient terminal of the Internet hospital can scan the code to directly log in to the consultation terminal to initiate a consultation.

[0023] Furthermore, the doctor-seeking and registration module is used to help patients find doctors and register.

[0024] The doctor search method includes department search and doctor list search. The department search is to search for doctors by selecting a specific department. Different departments correspond to different doctors. The doctor list search is to search for a specific doctor by browsing the doctor list. The doctor list contains all doctors in each department. After finding a specific doctor, the patient can view the doctor's homepage.

[0025] The doctor's homepage includes the doctor's historical number of patients, patient evaluations, diseases he or she is good at treating, and the number of patients currently waiting to be seen.

[0026] There is an upper limit on the number of patients waiting to be seen by the doctor in the same department. When the upper limit is reached, the patient cannot continue to register with the doctor. The calculation process of the upper limit of the number of patients waiting to be seen by the doctor is as follows:

[0027] The first step is to count the number of doctors n who are seeing patients in the department and the time T until they get off work.

[0028] The second step is to multiply the number of patients waiting to be seen by each doctor in the current department by the average time t that the corresponding doctor takes to see one patient, and get the waiting time of each doctor.

[0029] Step 3 is to sort the doctors by their waiting time from largest to smallest and mark their serial numbers, i.e. the doctor with serial number n currently has x number of patients waiting to be seen. n The average time it takes for a doctor with serial number n to see a patient is t n ,

[0030] The fourth step is to set the current number of patients waiting to be seen by the doctor with the shortest waiting time as the initial number of patients waiting to be seen x0, and the average time for the doctor with the shortest waiting time to see one patient as the initial waiting time t0. The initial waiting time is the product of the initial number of patients waiting to be seen x0 and the initial waiting time t0, that is, x0t0. The workload of each doctor is divided and limited. The waiting time of each doctor does not exceed the off-duty time. In addition, compared with the doctor with the shortest waiting time, the waiting time of each doctor does not exceed one third of the minimum waiting time.

[0031] Step 5 is to calculate the upper limit of the number of patients waiting to be seen by each doctor, that is, when the initial waiting time x0t0 Less than or equal to the time T from get off work, that is, When the number of patients waiting to be seen by the doctor with serial number n is Right now is the upper limit of the number of patients waiting to be seen by the doctor. Greater than the time T until leaving get off work, that is, When the number of patients waiting to be seen by the doctor with serial number n is Right now The maximum number of patients the doctor currently wants to see.

[0032] After the patient has viewed the doctor's homepage and is satisfied with the doctor, he or she can register for the doctor. When the registration is successful, the doctor registration module will automatically generate a registration number and a queue number, and send them to the patient's registered mobile phone number via SMS.

[0033] The registration number is automatically generated according to the current registration order, and the queue number is automatically generated according to the current queue order.

[0034] The patient's registration number is different from the queuing number. The patient's registration number will not change, but the patient's queuing number will change due to changes in the patient's queuing position. The patient's queuing number will be updated in real time.

[0035] The medical registration module of the present invention monitors and adjusts the current number of patients waiting to be seen and the upper limit of the number of patients for each department's doctors through a calculation formula. It not only takes into account the average time t for each doctor to see a patient, the current number of patients waiting to be seen x for each doctor, the time to get off work T, the number of patients waiting to be seen x0 for the doctor with the shortest current waiting time, and the average time t0 for the doctor with the shortest current waiting time to see a patient, but also ensures that the current waiting time for each doctor does not exceed one-third of the minimum waiting time compared with the doctor with the shortest current waiting time. Taking these factors into consideration, the number of registered patients is effectively diverted to prevent the situation where each doctor is too busy and idle at the same time. At the same time, a distinction is made between the registration number and the queue number. The patient's identity is confirmed by the registration number, and the queue position is confirmed by the queue number. If no distinction is made, the patient may have a bad experience due to the change of the queue position.

[0036] Furthermore, in the consultation queue module, patients can view the number of people waiting in line for consultation with each doctor and their own queue position.

[0037] The patient can submit an application to the consultation queue module during the queuing period, and the application includes refund and adjustment of queue position.

[0038] The adjustment of the queue position can only be made backwards. The application for adjusting the queue position requires the patient to note the estimated consultation time.

[0039] The consultation queuing module calculates the time difference between the current time and the patient's estimated consultation time, and divides the obtained time difference by the average consultation time of each patient of the registered doctor to obtain the estimated number of patients h1 that the doctor will see from the current time to the patient's estimated consultation time.

[0040] The queue number for the doctor at the current time is h2.

[0041] The queue position after the consultation queue module is adjusted is h1+h2, and the queue numbers of all patients between the original queue position and the adjusted queue position are reduced by one.

[0042] When the patient's queue ends, that is, when the attending doctor calls the patient's number to see him, the consultation queuing module will send a text message notification to the patient's registered mobile phone number.

[0043] The consultation queuing module of the present invention has the function that patients can adjust their queue position by applying. This function provides convenience for patients who cannot see the doctor on time due to temporary matters without affecting the consultation experience of other patients, and also reduces the refund rate of registration.

[0044] Furthermore, the intelligent auxiliary diagnosis module includes a condition filling module, a keyword extraction module, a symptom estimation module, a drug matching module and an estimated symptom confirmation module.

[0045] The medical condition filling module will actively send a request to the patient to fill in the detailed medical condition description, and after the patient submits the completed detailed medical condition description;

[0046] The keyword extraction module automatically extracts keywords from the patient's condition details, and then intelligently compares the original hospital medical records or the Internet hospital medical record database based on the keywords to determine whether the patient is a follow-up patient or a chronic disease patient.

[0047] If the patient is neither a returning patient nor a chronic disease patient, the keyword extraction module will transmit the keywords extracted from the patient's condition details to the symptom estimation module.

[0048] If the patient is a follow-up patient or a patient with a chronic disease, the keyword extraction module will judge whether the patient's condition has changed significantly or a critical value has appeared based on the keywords extracted from the patient's condition description. When the patient's condition has changed significantly or a critical value has appeared, the keyword extraction module will make a note on the patient's condition description, mark the patient's condition change, and upload the patient's condition description and notes to the database. When the patient's condition has not changed significantly or a critical value has appeared, the patient will automatically jump to the drug matching module;

[0049] The drug matching module requires returning patients or patients with chronic diseases to submit information about the drugs they need to buy and that are in the prescriptions previously issued by the attending physician for the disease. The drug matching module automatically compares the drug library and the store inventory library based on the drug information submitted by the patient, and searches for drugs of the same type, brand and specification. If a drug that meets the requirements is found, the store inventory library closest to the patient's location where the drug is stored will be searched and displayed to the patient. If a drug that meets the requirements is not found, other drugs of different types, brands or specifications with the same efficacy will be searched according to the drug information submitted by the patient, and presented to the patient through the drug matching module after review by the attending physician, and the nearest store inventory library with the drug reviewed by the attending physician will be presented based on the patient's location. If the attending physician fails to pass the review, other drugs of different types, brands or specifications with the same efficacy will be searched again according to the drug information submitted by the patient, and submitted to the attending physician for review.

[0050] The disease prediction module will compare the acquired keywords with the database, match the corresponding symptoms, and search the database for the corresponding disease, thereby making an estimate of the patient's condition.

[0051] The estimated symptom confirmation module searches the database for other symptoms corresponding to the estimated symptom and confirms with the patient. The other symptoms corresponding to the estimated symptom are symptoms that the estimated symptom has but that the patient has not described in the detailed description of the condition.

[0052] The intelligent auxiliary diagnosis module confirms the patient's symptoms in two ways: picture selection and text judgment. The two confirmation methods are available for patients to choose. Each confirmation method has 5 questions. The picture selection method is that the intelligent auxiliary diagnosis module retrieves the appearance pictures, B-ultrasound pictures and color ultrasound pictures in the database that are consistent with the estimated symptoms for patients to choose. In addition to the pictures, each multiple-choice question also has an option that does not match the answer. The text judgment method is that the intelligent auxiliary diagnosis module retrieves the symptom manifestations in the database that are consistent with the estimated symptoms. The patient only needs to judge whether they have them.

[0053] If the results confirmed by the patient are consistent with the symptoms of the predicted disease, the intelligent auxiliary diagnosis module will determine that the patient has the disease. The intelligent auxiliary diagnosis module will match the treatment prescription corresponding to the disease in the database, automatically generate a case and treatment prescription, and submit it to the attending doctor for review. If the review is passed, the attending doctor will sign the case and treatment prescription submitted by the intelligent auxiliary diagnosis module. If the review is not passed, the intelligent auxiliary diagnosis module will regenerate the case and new treatment prescription and submit it to the attending doctor for review again. After the attending doctor signs the submitted case and treatment prescription, the intelligent auxiliary diagnosis module will pass the prescription to the pharmaceutical service module, and the patient will no longer be in line.

[0054] If the patient's confirmed result does not match the symptoms of the estimated disease, the intelligent auxiliary diagnosis module cannot confirm that the patient has the estimated disease. The estimated disease can only serve as a reference. At the same time, the intelligent auxiliary diagnosis module requests the patient to upload pictures of their own symptoms. The pictures of their own symptoms uploaded by the patient include symptom appearance pictures, B-ultrasound pictures and color ultrasound pictures. The number of pictures of their own symptoms uploaded by the patient is 2 to 10.

[0055] The intelligent auxiliary diagnosis module saves the patient's detailed description of the condition, estimated symptoms and uploaded symptom pictures into the database. At the same time, the patient will automatically exit the intelligent auxiliary diagnosis module and return to the medical queuing module to continue queuing.

[0056] When the intelligent auxiliary diagnosis module of the present invention confirms the patient's estimated symptoms, if the confirmation is successful, the intelligent auxiliary diagnosis module directly retrieves the prescription information of the disease in the database, automatically generates a case and treatment prescription, and ends the consultation. The attending doctor does not need to continue to see the patient. This not only saves the attending doctor's consultation time, but also improves the attending doctor's work efficiency. If the intelligent auxiliary diagnosis module fails to confirm the patient's estimated symptoms, it will save the patient's detailed description of the condition, the estimated symptoms and the symptom pictures uploaded by the patient into the database, providing reference materials for the attending doctor when he sees the patient next, which can effectively improve the attending doctor's consultation efficiency.

[0057] Furthermore, the doctor consultation module automatically retrieves the detailed description and notes of the patient's condition, the estimated symptoms determined by the intelligent auxiliary diagnosis module, and the symptom pictures uploaded by the patient from the database, and transmits them to the corresponding doctor who received the patient when he registered, so that the doctor can analyze the patient's condition.

[0058] The doctor who receives the patient establishes an audio or video connection with the patient through the doctor consultation module to conduct a consultation with the patient. During the consultation, the patient can check the doctor's current situation at any time.

[0059] The attending doctor confirms the patient's condition by asking the patient a question.

[0060] After confirming the patient's condition, the attending doctor can jump to the medical order interface and fill in the medical order information on the medical order interface. The medical order information includes the patient's medical history, prescription information and CA signature. The medical order information is synchronized in real time, and the patient can simultaneously see the medical history, prescription information and CA signature filled in by the attending doctor. After the attending doctor issues the medical order, it will flow to the pharmaceutical service module.

[0061] The doctor consultation module of the present invention automatically retrieves from the database the detailed medical condition description filled out by the patient, the estimated symptoms determined by the intelligent auxiliary diagnosis module, and the symptom images uploaded by the patient. With this information, the attending doctor can more clearly understand the patient's condition, saving consultation time. At the same time, during the consultation, the patient can check the doctor's current status at any time through the doctor consultation module, supervise the doctor, and prevent the doctor from being distracted.

[0062] Furthermore, the evaluation module is used to obtain the patient's evaluation of the doctor after the consultation. The evaluation content includes a five-star rating and a text-edited evaluation. After the patient submits the evaluation, the evaluation module will process the patient's evaluation content and automatically save the processed and valid evaluation content into the database.

[0063] The specific processing method of the evaluation module for patient evaluation is as follows: the evaluation module extracts the time from the time the patient submits the evaluation to the end of the consultation, the keywords of the evaluation content and the proportion of invalid evaluations in the historical evaluations.

[0064] The evaluation module compares the time from the patient submitting the evaluation to the end of the consultation with a first preset value.

[0065] When the time from the patient's submission of the evaluation to the end of the visit is greater than the first preset value, the time from the end of each evaluation of the patient's history to the end of the visit is retrieved, and the average time from the end of each evaluation of the patient's history to the end of the visit is calculated. The average time is multiplied by the tolerance range factor to obtain a third preset value. The tolerance range factor is variable and is obtained by dividing the average time from the end of the visit of all patients who submitted the evaluation on the previous day by the average time from the end of the visit of all patients who submitted the evaluation on the previous two days.

[0066] When the time between the patient's submission of the evaluation and the end of the consultation is greater than the first preset value and less than or equal to the third preset value, or when the time between the patient's submission of the evaluation and the end of the consultation is less than or equal to the first preset value, the keywords of the patient's evaluation are matched with the pre-made evaluation data matching library to determine whether the keywords in the evaluation contain uncivilized words or the matching degree with the department to which the corresponding doctor belongs. The matching degree includes three types, namely, mismatch, neutral and match.

[0067] If the keywords in the review contain uncivilized words or the matching degree with the department of the doctor is mismatched or neutral, the evaluation module will automatically determine that the review is invalid.

[0068] If the keywords in the evaluation do not contain any uncivilized words and the corresponding doctor's department is a match, the evaluation module will automatically determine that the evaluation is valid.

[0069] When the time between the patient submitting the evaluation and the end of the consultation is greater than the first preset value and greater than the third preset value,

[0070] Match the keywords of the patient's evaluation with the pre-made evaluation database to determine whether there are any uncivilized words in the keywords of the evaluation or the matching degree with the department of the corresponding doctor.

[0071] If the keywords in the review contain uncivilized words or the matching degree with the department of the doctor is not matched, the review module will automatically determine that the review is invalid.

[0072] If the keywords in the evaluation do not contain any uncivilized words and the corresponding doctor's department is a match, the evaluation module will automatically determine that the evaluation is valid.

[0073] If the keywords in the evaluation do not contain any uncivilized words and the matching degree of the corresponding doctor's department is neutral, the evaluation module will compare the proportion of invalid evaluations in the historical evaluations with the second estimated value.

[0074] If the total proportion of invalid evaluations in historical evaluations is greater than or equal to the second estimated value and the proportion of invalid evaluations due to the presence of uncivilized words and the mismatch with the department of the attending doctor is greater than or equal to the fourth estimated value, the evaluation module automatically determines that the evaluation is invalid.

[0075] If the total proportion of invalid evaluations in historical evaluations is greater than or equal to the second estimated value and the proportion of invalid evaluations due to the presence of uncivilized words and the mismatch with the department of the attending doctor is less than the fourth estimated value, the evaluation module automatically determines that the evaluation is valid.

[0076] If the proportion of invalid evaluations in the historical evaluations is less than the second estimated value, the evaluation module automatically determines that the evaluation is valid.

[0077] The evaluation module of the present invention screens the patient's evaluation content, determines whether the patient's evaluation is valid, saves the valid evaluation, and updates it in real time on the doctor's homepage. At the same time, the evaluation also feeds back the patient's recognition level of the attending doctor, and provides a reference value for other registered patients who view the attending doctor. In the process of evaluating the patient, the evaluation module has a mismatch in the matching degree, which means that the keywords in the patient's evaluation are different from the department to which the attending doctor belongs, and the patient has made random or wrong evaluations. Neutral means that the department to which the attending doctor belongs cannot be judged only by the keywords in the patient's evaluation. Match means that the department to which the attending doctor belongs can be judged by the keywords in the patient's evaluation. The first preset value is set to prevent the patient from submitting an evaluation too close to the end of the consultation, resulting in inaccurate evaluations and failure to truly reflect the patient's own situation. The second estimated value is set to prevent the proportion of invalid evaluations in the patient's historical evaluations from being too high, which may result in the current evaluation being a random evaluation by the patient and having little reference value for other patients. The third preset value is set to take into account the average time from the end of each evaluation in the patient's historical consultation to the end of the consultation. When the time from the patient submitting the evaluation to the end of the consultation is less than the average time, it means that the patient may not take the evaluation seriously. The fourth estimated value is set to determine the proportion of invalid evaluations in the patient's invalid evaluations due to the presence of uncivilized words and the mismatch with the department to which the attending doctor belongs. If the proportion is high, it means that there is a high probability that the patient's evaluation contains uncivilized words or random evaluations. Therefore, the evaluation of this patient is of little reference value to other patients.

[0078] Furthermore, after the consultation, the patient takes the medicine according to the prescription issued by the intelligent auxiliary diagnosis module or the attending doctor. If any physical discomfort occurs within a unit of time, the patient can provide feedback on his or her condition through the traceability module. The patient's condition feedback includes two aspects: the time and dosage of each medication, and the description of the physical discomfort reaction.

[0079] The tracing module compares the time and amount of each medication taken by the patient in the patient's condition feedback with the prescription information issued by the intelligent auxiliary diagnosis module or the attending doctor to determine whether the type, time and amount of the patient's medication are consistent with the description in the prescription information.

[0080] If the descriptions are consistent, then directly compare the symptoms of physical discomfort.

[0081] If the descriptions are inconsistent, the comparison results are saved and then compared with the symptoms of physical discomfort reactions;

[0082] The physical discomfort symptom comparison extracts keywords based on the description of the physical discomfort in the patient's condition feedback. The tracing module retrieves the adverse reaction content corresponding to each drug in the database prescribed by the intelligent auxiliary diagnosis module or the attending doctor, and compares the adverse reaction content corresponding to the drug retrieved by the tracing module with the keywords extracted based on the description of the patient's physical discomfort.

[0083] If the adverse reaction content corresponding to the drug retrieved by the tracing module contains the extracted keyword, the tracing module saves the drug name and adverse reaction corresponding to the keyword.

[0084] If the adverse reaction content corresponding to the drug retrieved by the traceability module does not contain the extracted keywords, the traceability module determines that the patient's adverse reaction is not caused by drug allergy;

[0085] The traceability module transmits the patient's condition feedback, the comparison results of the patient's medication time and dosage with the prescription information, and the comparison results of the patient's physical discomfort symptoms to the corresponding attending physician. The attending physician determines whether the patient's discomfort reaction is caused by the patient himself or by misdiagnosis based on the received information, and provides feedback on the patient's condition through the traceability module within the specified time.

[0086] The tracing module of the present invention compares the time and dosage of each medication with the prescription information issued by the intelligent auxiliary diagnosis module or the attending doctor, in order to provide a basis for the attending doctor to make judgments.

[0087] When the types of drugs are different, determine whether the patient's discomfort is caused by mistakenly taking other types of drugs.

[0088] When the type of medication is the same but the patient's medication time and dosage are different each time, determine whether the patient's adverse reaction is caused by incorrect medication and worsening of the condition;

[0089] The comparison of symptoms of physical discomfort reaction in the traceability module is to determine whether the patient is allergic to the prescribed medicine, which leads to physical discomfort;

[0090] Finally, the attending doctor determines the cause of the patient's physical discomfort based on the information transmitted by the traceability module and provides feedback on the patient's condition through the traceability module within the specified time.

[0091] If the patient is suffering from the disease due to taking other drugs by mistake, it is recommended that the patient take the correct medication.

[0092] If the patient's condition worsens due to improper medication, it is recommended that the patient take the correct medication and re-register for diagnosis and treatment.

[0093] If the patient is allergic to a drug, then according to the efficacy of the drug to which the patient is allergic, other drugs with the same efficacy should be substituted.

[0094] If the patient's condition worsens, it is recommended that the patient seek diagnosis and treatment as soon as possible.

[0095] If the patient's condition was caused by a misdiagnosis by the attending physician, an apology will be made to the patient and a new prescription will be issued.

[0096] The present invention stipulates that patients can only provide feedback on their condition through the tracing module within a unit time. On the one hand, the longer the time, the more difficult it is to analyze the cause of the patient's discomfort, and it may also be caused by other factors. At the same time, limiting patients who exceed the unit time from providing feedback on their condition can reduce the burden on the database and avoid database bloat.

[0097] The present invention sets the attending doctor to give feedback on the patient's condition within a specified time, which has the effect of timely information and avoids the patient's condition from worsening due to the attending doctor's untimely response.

[0098] Furthermore, the patient may only apply for adjustment of queue position twice at most.

[0099] The consultation queuing module of the present invention limits the number of times a patient may apply to adjust their queue position, in order to prevent patients from maliciously disrupting the queue order, thereby causing a bad consultation experience for other patients.

[0100] Compared with the existing technology, the beneficial effects achieved by the present invention are: the present invention takes many aspects into consideration and sets the upper limit of the number of patients to be seen by doctors in each department, which can effectively divert the number of patients. At the same time, the patient's application for adjusting the queue position can effectively enhance the patient's sense of identity and reduce the registration refund rate. The intelligent auxiliary diagnosis module can not only improve the efficiency of the doctor's consultation, but also provide the doctor with reference materials during the consultation, saving the doctor's consultation time. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0102] Figure 1 Schematic diagram of the composition of the medical consultation system based on the intelligent terminal of the present invention;

[0103] Figure 2 This is a flow chart of the login and registration module of the medical consultation system based on the intelligent terminal of the present invention;

[0104] Figure 3 This is a flow chart of the medical consultation module of the smart terminal-based medical consultation system of the present invention;

[0105] Figure 4 This is a flow chart of the consultation queuing module of the medical consultation system based on the intelligent terminal of the present invention;

[0106] Figure 5 This is a flow chart of the intelligent auxiliary diagnosis module of the medical consultation system based on the intelligent terminal of the present invention;

[0107] Figure 6 This is a flow chart of the doctor consultation module of the intelligent terminal-based consultation system of the present invention. DETAILED DESCRIPTION

[0108] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0109] See also Figure 1-6 The present invention provides a technical solution: a medical consultation system based on an intelligent terminal, including a login and registration module, a medical consultation registration module, a medical consultation queuing module, an intelligent auxiliary diagnosis module, a doctor consultation module, an evaluation module and a database.

[0110] The login and registration module is used to register and log in to the account, and authenticate and verify the patient's identity information. The login and registration module registers the account through the mobile phone number.

[0111] The doctor registration module is used to search for doctor information and register with the doctor.

[0112] The consultation queue module can check the number of people in each doctor's queue and the queue position of the registered doctor. During this period, the patient can also apply for a refund or adjust the queue position.

[0113] The intelligent auxiliary diagnosis module extracts keywords from the detailed description of the patient's condition, matches the database, estimates the condition and confirms it with the patient. After the patient confirms, the intelligent auxiliary diagnosis module prescribes a prescription based on the patient's condition.

[0114] The doctor consultation module conducts consultation with the patient through audio or video connection.

[0115] In the pharmaceutical service module, the pharmacist can confirm the rationality of the prescription through online review and issue the corresponding CA signature. The reviewed prescription will be sent to the patient's registered mobile phone number via SMS, and the consultation is over.

[0116] The evaluation module is used to obtain patient evaluations and save the evaluation information to the database.

[0117] The tracing module is used to determine whether it is a misdiagnosis by the doctor based on the discomfort reaction caused by the patient taking the prescription prescribed by the doctor within a unit time.

[0118] The database is used to store data information generated by each module.

[0119] The login and registration module includes a registration module and a login module.

[0120] The registration module needs to obtain the registered mobile phone number filled in by the patient, and the registration module will send a mobile phone verification code to the patient's registered mobile phone number. The registration module receives the mobile phone verification code filled in by the patient and automatically checks whether the filled-in mobile phone verification code is correct. If the filled-in mobile phone verification code is incorrect, the patient needs to fill it in again. If the filled-in mobile phone verification code is correct, the registration module will authenticate the patient's identity, that is, compare the patient's name and ID number through the provided database interface. If the name and ID number match, the account registration is successful and the patient logs in successfully. If the name and ID number do not match, the identity authentication fails and the patient needs to re-authenticate.

[0121] The login module includes two methods: mobile phone number login and face recognition login. The mobile phone number login method requires obtaining the registered mobile phone number filled in by the patient. The login module will send a mobile phone verification code to the patient's registered mobile phone number. The login module receives the mobile phone verification code filled in by the patient and automatically checks whether the filled mobile phone verification code is correct. If the filled mobile phone verification code is incorrect, the patient needs to fill it in again. If the filled mobile phone verification code is correct, the login module will automatically match the patient's login mobile phone number in the database of the system. If the login mobile phone number cannot be matched in the database, the patient is not registered, and the login module automatically jumps to the registration module. If the database successfully matches the login mobile phone number, the account is successfully logged in.

[0122] The face recognition login method requires obtaining the patient's face information through the camera in the device used by the patient. The login module compares the obtained patient's face information with the face information on the patient's ID card through the provided database interface. If the face information of the two matches, the login is successful. If the face information of the two does not match, the login fails, and feedback is given to the patient whether to re-acquire the face information. When the patient confirms to re-acquire the face information, the login module re-acquires the patient's face information through the camera in the device used by the patient and compares it with the data in the provided database. When the patient confirms not to re-acquire the face information, it automatically jumps to the mobile phone number login method;

[0123] The patient who logs in through face recognition can be a patient who has not registered an account, and the account will be automatically registered and the identity authentication will be completed after the face information is successfully compared, but the mobile phone number needs to be bound.

[0124] In the login and registration module, the account that successfully logs in can query historical medical consultation records.

[0125] The doctor-seeking and registration module is used to help patients find doctors and register.

[0126] The doctor search method includes department search and doctor list search. The department search is to search for doctors by selecting a specific department. Different departments correspond to different doctors. The doctor list search is to search for a specific doctor by browsing the doctor list. The doctor list contains all doctors in each department. After finding a specific doctor, the patient can view the doctor's homepage.

[0127] The doctor's homepage includes the doctor's historical number of patients, patient evaluations, diseases he or she is good at treating, and the number of patients currently waiting to be seen.

[0128] There is an upper limit on the number of patients waiting to be seen by the doctor in the same department. When the upper limit is reached, the patient cannot continue to register with the doctor. The calculation process of the upper limit of the number of patients waiting to be seen by the doctor is as follows:

[0129] The first step is to count the number of doctors n who are seeing patients in the department and the time T until they get off work.

[0130] The second step is to multiply the number of patients waiting to be seen by each doctor in the current department by the average time t that the corresponding doctor takes to see one patient, and get the waiting time of each doctor.

[0131] Step 3 is to sort the doctors by their waiting time from largest to smallest and mark their serial numbers, i.e. the doctor with serial number n currently has x number of patients waiting to be seen. n The average time it takes for a doctor with serial number n to see a patient is t n ,

[0132] The fourth step is to set the current number of patients waiting to be seen by the doctor with the shortest waiting time as the initial number of patients waiting to be seen x0, and the average time for the doctor with the shortest waiting time to see one patient as the initial waiting time t0. The initial waiting time is the product of the initial number of patients waiting to be seen x0 and the initial waiting time t0, that is, x0t0. The workload of each doctor is divided and limited. The waiting time of each doctor does not exceed the off-duty time. In addition, compared with the doctor with the shortest waiting time, the waiting time of each doctor does not exceed one third of the minimum waiting time.

[0133] Step 5 is to calculate the upper limit of the number of patients waiting to be seen by each doctor, that is, when the initial waiting time x0t0 Less than or equal to the time T from get off work, that is, When the number of patients waiting to be seen by the doctor with serial number n is Right now is the upper limit of the number of patients waiting to be seen by the doctor. Greater than the time T until leaving get off work, that is, When the number of patients waiting to be seen by the doctor with serial number n is Right now The maximum number of patients the doctor currently wants to see.

[0134] For example, there are three doctors A, B, and C in the department. There are still 4 hours until the end of get off work time T. The current number of patients waiting to be seen by A is 11, and the average time it takes for A to see a patient is 0.3 hours. The current number of patients waiting to be seen by B is 9, and the average time it takes for B to see a patient is 0.4 hours. The current number of patients waiting to be seen by C is 6, and the average time it takes for C to see a patient is 0.45 hours.

[0135] The waiting time for A, B, and C were 3.3 hours, 3.6 hours, and 2.7 hours, respectively.

[0136] Sort the waiting time for A, B, and C in descending order and mark the serial number. The doctor with serial number 1 is B, the doctor with serial number 2 is A, and the doctor with serial number 3 is C.

[0137] The current number of patients waiting to be seen by B is x1, and the average time t1 for B to see a patient is 0.4 hours. The current number of patients waiting to be seen by A is x2, and the average time t2 for A to see a patient is 0.3 hours. The current number of patients waiting to be seen by C is x3, and the average time t3 for C to see a patient is 0.45 hours. The initial number of patients waiting to be seen by x0 is 6, and the initial seeing time t0 is 0.45 hours.

[0138] The initial number of patients waiting to be seen x0 is 6 and the initial treatment time t0 is 0.45 hours. The result is 3.6 hours. Compare 3.6 hours with the time T until the end of get off work, which is 4 hours. That is, 3.6 < 4. Then substitute the values ​​of t1, t2, and t3 into The upper limit of the number of patients waiting to be seen for B, A, and C is calculated to be 9, 12, and 8 respectively. That is, the upper limit has been reached for the number of patients waiting to be seen for B, and patients cannot continue to register for B. The upper limit has not been reached for the number of patients waiting to be seen for A and C, and patients can continue to register for A and C.

[0139] After the patient has viewed the doctor's homepage and is satisfied with the doctor, he or she can register for the doctor. When the registration is successful, the doctor registration module will automatically generate a registration number and a queue number, and send them to the patient's registered mobile phone number via SMS.

[0140] The registration number is automatically generated according to the current registration order, and the queue number is automatically generated according to the current queue order.

[0141] The patient's registration number is different from the queuing number. The patient's registration number will not change, but the patient's queuing number will change due to changes in the patient's queuing position. The patient's queuing number will be updated in real time.

[0142] In the consultation queue module, patients can view the number of people waiting in line for consultation with each doctor and their own queue position.

[0143] The patient can submit an application to the consultation queue module during the queuing period, and the application includes refund and adjustment of queue position.

[0144] The adjustment of the queue position can only be made backwards. The application for adjusting the queue position requires the patient to note the estimated consultation time.

[0145] The consultation queuing module calculates the time difference between the current time and the patient's estimated consultation time, and divides the obtained time difference by the average consultation time of each patient of the registered doctor to obtain the estimated number of patients h1 that the doctor will see from the current time to the patient's estimated consultation time.

[0146] The queue number for the doctor at the current time is h2.

[0147] The queue position after the consultation queue module is adjusted is h1+h2, and the queue numbers of all patients between the original queue position and the adjusted queue position are reduced by one.

[0148] For example, the patient's registration number is 28, and the current time is 14:00. The corresponding queue number of the attending doctor is 25. The average time it takes for the attending doctor to see a patient is 0.25 hours. However, the patient cannot see the doctor on time due to some urgent matters. The patient can apply to the system and note that the estimated consultation time is 16:00. The consultation queue module will then automatically calculate the time difference between the patient's estimated consultation time and the current time, that is, 16-14=2, and then divide the obtained time difference by the average time it takes for the attending doctor to see a patient to obtain the estimated number of patients that the doctor will see from the current time to the patient's estimated time, that is, 2 / 0.25=8. Finally, the registration number that the doctor sees at the current time is added to the estimated number of patients that the attending doctor will see, that is, 25+8=33, and the patient's adjusted queue position is 33, and the patient queue number between the patient's original queue position and the adjusted queue position is reduced by one.

[0149] The patient can only apply for adjustment of queue position twice at most.

[0150] When the patient's queue ends, that is, when the attending doctor calls the patient's number to see him, the consultation queuing module will send a text message notification to the patient's registered mobile phone number.

[0151] The intelligent auxiliary diagnosis module includes a condition filling module, a keyword extraction module, a symptom estimation module, a drug matching module and an estimated symptom confirmation module.

[0152] The medical condition filling module will actively send a request to the patient to fill in the detailed medical condition description, and after the patient submits the completed detailed medical condition description;

[0153] The keyword extraction module automatically extracts keywords from the patient's condition details, and then intelligently compares the original hospital medical records or the Internet hospital medical record database based on the keywords to determine whether the patient is a follow-up patient or a chronic disease patient.

[0154] If the patient is neither a returning patient nor a chronic disease patient, the keyword extraction module will transmit the keywords extracted from the patient's condition details to the symptom estimation module.

[0155] If the patient is a follow-up patient or a patient with a chronic disease, the keyword extraction module will judge whether the patient's condition has changed significantly or a critical value has appeared based on the keywords extracted from the patient's condition description. When the patient's condition has changed significantly or a critical value has appeared, the keyword extraction module will make a note on the patient's condition description, mark the patient's condition change, and upload the patient's condition description and notes to the database. When the patient's condition has not changed significantly or a critical value has appeared, the patient will automatically jump to the drug matching module;

[0156] The drug matching module requires patients with follow-up visits or chronic diseases to submit information about the drugs they need to buy and that are in the prescriptions previously issued by the attending physician for the disease. The drug matching module automatically compares the drug library and the store inventory library based on the drug information submitted by the patient, and searches for drugs of the same type, brand and specification. If a drug that meets the requirements is found, the store inventory library closest to the patient's location where the drug is stored will be searched and displayed to the patient. If a drug that meets the requirements is not found, other drugs of different types, brands or specifications with the same efficacy will be searched according to the drug information submitted by the patient, and presented to the patient through the drug matching module after review by the attending physician, and the nearest store inventory library with the drugs reviewed by the attending physician will be presented based on the patient's location. If the attending physician fails to pass the review, other drugs of different types, brands or specifications with the same efficacy will be searched again according to the drug information submitted by the patient, and the drug will be submitted to the attending physician for review.

[0157] Users can choose to have the medicine delivered or purchased at the nearest store based on the store's inventory. However, whether it is delivered or purchased at the store, the patient needs to have the medicine reviewed by the attending doctor when purchasing.

[0158] The estimated symptom confirmation module searches the database for other symptoms corresponding to the estimated symptom and confirms with the patient. The other symptoms corresponding to the estimated symptom are symptoms that the estimated symptom has but that the patient has not described in the detailed description of the condition.

[0159] The intelligent auxiliary diagnosis module confirms the patient's symptoms in two ways: picture selection and text judgment. The two confirmation methods are available for patients to choose. Each confirmation method has 5 questions. The picture selection method is that the intelligent auxiliary diagnosis module retrieves the appearance pictures, B-ultrasound pictures and color ultrasound pictures in the database that are consistent with the estimated symptoms for patients to choose. In addition to the pictures, each multiple-choice question also has an option that does not match the answer. The text judgment method is that the intelligent auxiliary diagnosis module retrieves the symptom manifestations in the database that are consistent with the estimated symptoms. The patient only needs to judge whether they have them.

[0160] If the results confirmed by the patient are consistent with the symptoms of the predicted disease, the intelligent auxiliary diagnosis module will determine that the patient has the disease. The intelligent auxiliary diagnosis module will match the treatment prescription corresponding to the disease in the database, automatically generate a case and treatment prescription, and submit it to the attending doctor for review. If the review is passed, the attending doctor will sign the case and treatment prescription submitted by the intelligent auxiliary diagnosis module. If the review is not passed, the intelligent auxiliary diagnosis module will regenerate the case and new treatment prescription and submit it to the attending doctor for review again. After the attending doctor signs the submitted case and treatment prescription, the intelligent auxiliary diagnosis module will pass the prescription to the pharmaceutical service module, and the patient will no longer be in line.

[0161] If the patient's confirmed result shows symptoms that do not match the predicted symptoms, the intelligent auxiliary diagnosis module cannot confirm that the patient has the predicted symptoms. The predicted symptoms can only serve as a reference. At the same time, the intelligent auxiliary diagnosis module requests the patient to upload pictures of their own symptoms. The pictures of their own symptoms uploaded by the patient include symptom appearance pictures, B-ultrasound pictures and color ultrasound pictures. The number of pictures of their own symptoms uploaded by the patient is 2 to 10.

[0162] The intelligent auxiliary diagnosis module saves the patient's detailed description of the condition, estimated symptoms and uploaded symptom pictures into the database. At the same time, the patient will automatically exit the intelligent auxiliary diagnosis module and return to the medical queuing module to continue queuing.

[0163] The doctor consultation module automatically retrieves the patient's detailed description and notes of the condition to be seen from the database, the estimated symptoms determined by the intelligent auxiliary diagnosis module, and the symptom pictures uploaded by the patient, and transmits them to the corresponding doctor who saw the patient when he registered, so that the doctor can analyze the patient's condition.

[0164] The doctor who receives the patient establishes an audio or video connection with the patient through the doctor consultation module to conduct a consultation with the patient. During the consultation, the patient can check the current status of the doctor at any time.

[0165] The attending doctor confirms the patient's condition by asking the patient a question.

[0166] After confirming the patient's condition, the attending doctor can jump to the medical order interface and fill in the medical order information on the medical order interface. The medical order information includes the patient's medical history, prescription information and CA signature. The medical order information is synchronized in real time, and the patient can simultaneously see the medical history, prescription information and CA signature filled in by the attending doctor. After the attending doctor issues the medical order, it will flow to the pharmaceutical service module.

[0167] The evaluation module is used to obtain the patient's evaluation of the doctor after the consultation. The evaluation content includes a five-star rating and a text-edited evaluation. After the patient submits the evaluation, the evaluation module will process the patient's evaluation content and automatically save the processed and valid evaluation content to the database.

[0168] The specific processing method of the evaluation module for patient evaluation is as follows: the evaluation module extracts the time from the time the patient submits the evaluation to the end of the consultation, the keywords of the evaluation content and the proportion of invalid evaluations in the historical evaluations.

[0169] The evaluation module compares the time from the patient submitting the evaluation to the end of the consultation with a first preset value.

[0170] When the time from the patient's submission of the evaluation to the end of the visit is greater than the first preset value, the time from the end of each evaluation of the patient's history to the end of the visit is retrieved, and the average time from the end of each evaluation of the patient's history to the end of the visit is calculated. The average time is multiplied by the tolerance range factor to obtain a third preset value. The tolerance range factor is variable and is obtained by dividing the average time from the end of the visit of all patients who submitted the evaluation on the previous day by the average time from the end of the visit of all patients who submitted the evaluation on the previous two days.

[0171] When the time between the patient's submission of the evaluation and the end of the consultation is greater than the first preset value and less than or equal to the third preset value, or when the time between the patient's submission of the evaluation and the end of the consultation is less than or equal to the first preset value, the keywords of the patient's evaluation are matched with the pre-made evaluation data matching library to determine whether the keywords in the evaluation contain uncivilized words or the matching degree with the department to which the corresponding doctor belongs. The matching degree includes three types, namely, mismatch, neutral and match.

[0172] If the keywords in the review contain uncivilized words or the matching degree with the department of the doctor is mismatched or neutral, the evaluation module will automatically determine that the review is invalid.

[0173] In this embodiment, the patient visited the ophthalmology department. The time between the patient submitting the evaluation and the end of the consultation is less than or equal to a first preset value. The word "bone" appears as a keyword extracted from the patient's evaluation. Then the evaluation module automatically determines that the evaluation does not match the department to which the corresponding doctor belongs, and the evaluation is invalid.

[0174] If the keywords in the evaluation do not contain any uncivilized words and the corresponding doctor's department is a match, the evaluation module will automatically determine that the evaluation is valid.

[0175] When the time between the patient submitting the evaluation and the end of the consultation is greater than the first preset value and greater than the third preset value,

[0176] Match the keywords of the patient's evaluation with the pre-made evaluation database to determine whether there are any uncivilized words in the keywords of the evaluation or the matching degree with the department of the corresponding doctor.

[0177] If the keywords in the review contain uncivilized words or the matching degree with the department of the doctor is not matched, the review module will automatically determine that the review is invalid.

[0178] If the keywords in the evaluation do not contain any uncivilized words and the corresponding doctor's department is a match, the evaluation module will automatically determine that the evaluation is valid.

[0179] If the keywords in the evaluation do not contain any uncivilized words and the matching degree of the corresponding doctor's department is neutral, the evaluation module will compare the proportion of invalid evaluations in the historical evaluations with the second estimated value.

[0180] If the total proportion of invalid evaluations in historical evaluations is greater than or equal to the second estimated value and the proportion of invalid evaluations due to the presence of uncivilized words and the mismatch with the department of the attending doctor is greater than or equal to the fourth estimated value, the evaluation module automatically determines that the evaluation is invalid.

[0181] In this embodiment, the patient visited the ophthalmology department. The time between the patient submitting the evaluation and the end of the consultation is greater than the first preset value and the third preset value and is greater than or equal to the second estimated value and the fourth estimated value. The keywords extracted from the patient's evaluation are all neutral words, and there are no uncivilized words or words that do not match the department to which the corresponding doctor belongs. Then the evaluation module automatically determines that the proportion of invalid evaluations caused by uncivilized words and the mismatch with the department to which the doctor belongs in the patient's invalid evaluation is too large. At the same time, the evaluation has little reference value for other patients, so the evaluation is invalid.

[0182] If the total proportion of invalid evaluations in historical evaluations is greater than or equal to the second estimated value and the proportion of invalid evaluations due to the presence of uncivilized words and the mismatch with the department of the attending doctor is less than the fourth estimated value, the evaluation module automatically determines that the evaluation is valid.

[0183] In this embodiment, the patient visited the orthopedics department. The time between the patient submitting the evaluation and the end of the consultation was greater than the first preset value and the third preset value, greater than or equal to the second estimated value and less than the fourth estimated value. The keywords extracted from the patient's evaluation were all neutral words, and no uncivilized words or words that did not match the department to which the corresponding doctor belonged appeared. Then the evaluation module automatically determined that although the patient's invalid evaluations accounted for a high proportion, the invalid evaluations caused by uncivilized words and the mismatch with the department to which the doctor belonged accounted for a small proportion. At the same time, there were no uncivilized words or words that did not match the department to which the corresponding doctor belonged in the evaluation, so the evaluation was valid.

[0184] If the proportion of invalid evaluations in the historical evaluations is less than the second estimated value, the evaluation module automatically determines that the evaluation is valid.

[0185] The evaluation module of the present invention screens the patient's evaluation content, determines whether the patient's evaluation is valid, saves the valid evaluation, and updates it in real time on the doctor's homepage. At the same time, the evaluation also feeds back the patient's recognition level of the attending doctor, and provides a reference value for other registered patients who view the attending doctor. In the process of evaluating the patient, the evaluation module has a mismatch in the matching degree, which means that the keywords in the patient's evaluation are different from the department to which the attending doctor belongs, and the patient has made random or wrong evaluations. Neutral means that the department to which the attending doctor belongs cannot be judged only by the keywords in the patient's evaluation. Match means that the department to which the attending doctor belongs can be judged by the keywords in the patient's evaluation. The first preset value is set to prevent the patient from submitting an evaluation too close to the end of the consultation, resulting in inaccurate evaluations and failure to truly reflect the patient's own situation. The second estimated value is set to prevent the proportion of invalid evaluations in the patient's historical evaluations from being too high, which may result in the current evaluation being a random evaluation by the patient and having little reference value for other patients. The third preset value is set to take into account the average time from the end of each evaluation in the patient's historical consultation to the end of the consultation. When the time from the patient submitting the evaluation to the end of the consultation is less than the average time, it means that the patient may not take the evaluation seriously. The fourth estimated value is set to determine the proportion of invalid evaluations in the patient's invalid evaluations due to the presence of uncivilized words and the mismatch with the department to which the attending doctor belongs. If the proportion is high, it means that there is a high probability that the patient's evaluation contains uncivilized words or random evaluations. Therefore, the evaluation of this patient is of little reference value to other patients.

[0186] After the consultation, the patient takes the medicine according to the prescription issued by the intelligent auxiliary diagnosis module or the attending doctor. If any physical discomfort occurs within a unit of time, the patient can provide feedback on his or her condition through the traceability module. The patient's condition feedback includes two aspects: the time and dosage of each medication and the description of the physical discomfort reaction.

[0187] The tracing module compares the time and amount of each medication taken by the patient in the patient's condition feedback with the prescription information issued by the intelligent auxiliary diagnosis module or the attending doctor to determine whether the type, time and amount of the patient's medication are consistent with the description in the prescription information.

[0188] If the descriptions are consistent, then directly compare the symptoms of physical discomfort.

[0189] If the descriptions are inconsistent, the comparison results are saved and then compared with the symptoms of physical discomfort reactions;

[0190] The physical discomfort symptom comparison extracts keywords based on the description of the physical discomfort in the patient's condition feedback. The tracing module retrieves the adverse reaction content corresponding to each drug in the database prescribed by the intelligent auxiliary diagnosis module or the attending doctor, and compares the adverse reaction content corresponding to the drug retrieved by the tracing module with the keywords extracted based on the description of the patient's physical discomfort.

[0191] If the adverse reaction content corresponding to the drug retrieved by the tracing module contains the extracted keyword, the tracing module saves the drug name and adverse reaction corresponding to the keyword.

[0192] If the adverse reaction content corresponding to the drug retrieved by the traceability module does not contain the extracted keywords, the traceability module determines that the patient's adverse reaction is not caused by drug allergy;

[0193] The traceability module transmits the patient's condition feedback, the comparison results of the patient's medication time and dosage with the prescription information, and the comparison results of the patient's physical discomfort symptoms to the corresponding attending physician. The attending physician determines whether the patient's discomfort reaction is caused by the patient himself or by misdiagnosis based on the received information, and provides feedback on the patient's condition through the traceability module within the specified time.

[0194] The tracing module of the present invention compares the time and dosage of each medication with the prescription information issued by the intelligent auxiliary diagnosis module or the attending doctor, in order to provide a basis for the attending doctor to make judgments.

[0195] When the types of drugs are different, determine whether the patient's discomfort is caused by mistakenly taking other types of drugs.

[0196] When the type of medication is the same but the patient's medication time and dosage are different each time, determine whether the patient's adverse reaction is caused by incorrect medication and worsening of the condition;

[0197] The comparison of symptoms of physical discomfort reaction in the traceability module is to determine whether the patient is allergic to the prescribed medicine, which leads to physical discomfort;

[0198] Finally, the attending doctor determines the cause of the patient's physical discomfort based on the information transmitted by the traceability module and provides feedback on the patient's condition through the traceability module within the specified time.

[0199] If the patient is suffering from the disease due to taking other drugs by mistake, it is recommended that the patient take the correct medication.

[0200] If the patient's condition worsens due to improper medication, it is recommended that the patient take the correct medication and re-register for diagnosis and treatment.

[0201] If the patient is allergic to a drug, then according to the efficacy of the drug to which the patient is allergic, other drugs with the same efficacy should be substituted.

[0202] If the patient's condition worsens, it is recommended that the patient seek diagnosis and treatment as soon as possible.

[0203] If the patient's condition was caused by a misdiagnosis by the attending physician, an apology will be made to the patient and a new prescription will be issued.

[0204] The present invention stipulates that patients can only provide feedback on their condition through the tracing module within a unit time. On the one hand, the longer the time, the more difficult it is to analyze the cause of the patient's discomfort, and it may also be caused by other factors. At the same time, limiting patients who exceed the unit time from providing feedback on their condition can reduce the burden on the database and avoid database bloat.

[0205] The present invention sets the attending doctor to give feedback on the patient's condition within a specified time, which has the effect of timely information and avoids the patient's condition from worsening due to the attending doctor's untimely response.

[0206] The working principle of the present invention is as follows: the patient fills in the mobile phone number and the obtained mobile phone verification code in the login module of the login registration module, and the login module automatically checks whether the mobile phone verification code is correct.

[0207] If the mobile phone verification code is incorrect, the patient needs to fill it out again.

[0208] If the mobile phone verification code filled in is correct, the login module will automatically match the patient's login mobile phone number in the system's database. If the database successfully matches the login mobile phone number, the account is successfully logged in. If the login mobile phone number cannot be matched in the database, the patient is not registered, and the login module automatically jumps to the registration module.

[0209] The patient registers an account in the registration module of the login registration module. The patient needs to fill in the registered mobile phone number and the obtained mobile phone verification code. The login module automatically checks whether the filled mobile phone verification code is correct.

[0210] If the mobile phone verification code is incorrect, the patient needs to fill it out again.

[0211] If the mobile phone verification code filled in is correct, the registration module will authenticate the patient's identity, that is, compare the patient's name and ID number through the provided database interface. If the name and ID number match, the account registration is successful and the patient logs in successfully.

[0212] Patients can also directly obtain facial information through the face recognition module. The login and registration module will automatically compare the acquired patient's facial information with the facial information on the patient's ID card through the provided database interface. If the facial information of the two matches, the login is successful. If the facial information of the two does not match, the login fails, and the patient is fed back whether to re-acquire the facial information. When the patient confirms to re-acquire the facial information, the login module re-acquires the patient's facial information through the camera in the device used by the patient and compares it with the data in the provided database. When the patient confirms not to re-acquire the facial information, it automatically jumps to the mobile phone number login method.

[0213] After successfully logging into their account, patients can check their historical medical records.

[0214] When patients need to register for a consultation, they can search for doctors through departments or doctor lists in the medical consultation module. When they find a doctor they like, they can choose to enter the doctor's homepage to view the doctor's historical number of consultations, patient evaluations, diseases he or she is good at treating, and the number of patients currently waiting to be seen, and register. When the registration is successful, the registered mobile phone number will receive a text message notification of the successful registration.

[0215] If the number of patients currently waiting to see the doctor reaches the upper limit, the patient will no longer be able to register with the doctor and will need to select a new doctor of their choice to register with.

[0216] To calculate the upper limit of the number of patients waiting to be seen by the doctor, the doctor's waiting time in each department must be sorted first, and then the The value of is compared with the time T from get off work, when the initial waiting time x0t0 Less than or equal to the time T from get off work, that is, When the number of patients waiting to be seen by the doctor with serial number n is Right now is the upper limit of the number of patients waiting to be seen by the doctor. Greater than the time T until leaving get off work, that is, When the number of patients waiting to be seen by the doctor with serial number n is Right now The maximum number of patients the doctor currently has waiting to see.

[0217] After registering, the patient will enter the consultation queue module. In this module, the patient can view the number of people queuing for consultation with each doctor and the queue position of his or her own registration. At the same time, if there are special circumstances, the patient can also apply for a refund or adjust the queue position to the consultation queue module. When the patient's queue ends and the doctor calls the patient's number, the consultation queue module will send a text message notification to the patient's registered mobile phone number.

[0218] While patients are waiting in line, the intelligent auxiliary diagnosis module will proactively send a request to the patient to fill in a detailed description of the condition.

[0219] The keyword extraction module in the intelligent auxiliary diagnosis module will automatically extract keywords from the patient's condition details, and then intelligently compare the original hospital medical records or Internet hospital medical record database based on the keywords to determine whether the patient is a follow-up patient or a chronic disease patient.

[0220] If the patient is neither a returning patient nor a chronic disease patient, the keyword extraction module will transmit the keywords extracted from the patient's condition details to the symptom estimation module in the intelligent auxiliary diagnosis module.

[0221] If the patient is a returning patient or a chronic disease patient, the keyword extraction module will judge whether the patient's condition has changed significantly or a critical value has appeared based on the keywords extracted from the patient's condition description. When the patient's condition has changed significantly or a critical value has appeared, the keyword extraction module will make a note on the patient's condition description, mark the patient's condition change, and upload the patient's condition description and notes to the database. When the patient's condition has not changed significantly or a critical value has appeared, the patient will automatically jump to the drug matching module in the intelligent auxiliary diagnosis module;

[0222] The drug matching module requires patients with follow-up visits or chronic diseases to submit information about the drugs they need to buy and that are in the prescriptions previously issued by the attending physician for the disease. The drug matching module automatically compares the drug library and the store inventory library based on the drug information submitted by the patient, and searches for drugs of the same type, brand and specification. If a drug that meets the requirements is found, the store inventory library closest to the patient's location where the drug is stored will be searched and displayed to the patient. If a drug that meets the requirements is not found, other drugs of different types, brands or specifications with the same efficacy will be searched according to the drug information submitted by the patient, and presented to the patient through the drug matching module after review by the attending physician, and the nearest store inventory library with the drugs reviewed by the attending physician will be presented based on the patient's location. If the attending physician fails to pass the review, other drugs of different types, brands or specifications with the same efficacy will be searched again according to the drug information submitted by the patient, and the drug will be submitted to the attending physician for review.

[0223] Users can choose to have the medicine delivered or purchased at the nearest store based on the store's inventory. However, whether it is delivered or purchased at the store, the patient needs to have the medicine reviewed by the attending doctor when purchasing.

[0224] The intelligent auxiliary diagnosis module will automatically extract keywords from the patient's detailed description of the condition, compare the obtained keywords with the database, match the corresponding symptoms, and find the corresponding diseases in the database, so as to make an estimate of the patient's condition.

[0225] The intelligent auxiliary diagnosis module automatically searches the database for other symptoms corresponding to the estimated disease, and confirms with the patient through two methods: picture selection and text judgment.

[0226] If the results confirmed by the patient are consistent with the symptoms of the predicted disease, the intelligent auxiliary diagnosis module will determine that the patient has the disease. The intelligent auxiliary diagnosis module will match the treatment prescription corresponding to the disease in the database, automatically generate a case and treatment prescription, and submit it to the attending doctor for review. If the review is passed, the attending doctor will sign the case and treatment prescription submitted by the intelligent auxiliary diagnosis module. If the review is not passed, the intelligent auxiliary diagnosis module will regenerate the case and new treatment prescription and submit it to the attending doctor for review again. After the attending doctor signs the submitted case and treatment prescription, the intelligent auxiliary diagnosis module will pass the prescription to the pharmaceutical service module, and the patient will no longer be in line.

[0227] If the patient's confirmed result does not match the symptoms of the estimated disease, the intelligent auxiliary diagnosis module cannot confirm that the patient has the estimated disease. The estimated disease can only serve as a reference. At the same time, the intelligent auxiliary diagnosis module requires the patient to upload 2 to 10 pictures of their own symptoms.

[0228] The intelligent auxiliary diagnosis module saves the patient's detailed description of the condition, estimated symptoms and uploaded symptom pictures into the database. At the same time, the patient will automatically exit the intelligent auxiliary diagnosis module and return to the medical queuing module to continue queuing.

[0229] When the doctor sees the patient through audio or video, the doctor's consultation module automatically retrieves the detailed description of the patient's condition, the estimated symptoms determined by the intelligent auxiliary diagnosis module, and the symptom pictures uploaded by the patient from the database, and transmits them to the corresponding doctor when the patient registered, so that the doctor can make a brief analysis of the patient's condition.

[0230] During the consultation process, the patient can check the current situation of the doctor at any time.

[0231] After confirming the patient's condition, the attending doctor can jump to the medical order interface and fill in the medical order information on the medical order interface. The medical order information includes the patient's medical history, prescription information and CA signature. The medical order information is synchronized in real time, and the patient can simultaneously see the medical history, prescription information and CA signature filled in by the attending doctor. After the attending doctor issues the medical order, it will flow to the pharmaceutical service module.

[0232] After the consultation, the system automatically jumps to the evaluation module, where the patient can give the doctor a five-star rating and text-edited evaluation. The evaluation module will process the patient's evaluation content. The specific processing method of the evaluation module for the patient's evaluation is as follows: the evaluation module extracts the time between the patient's submission of the evaluation and the end of the consultation, the keywords of the evaluation content, and the proportion of invalid evaluations in the historical evaluations.

[0233] The evaluation module compares the time from the patient submitting the evaluation to the end of the consultation with a first preset value.

[0234] When the time from the patient's submission of the evaluation to the end of the visit is greater than the first preset value, the time from the end of each evaluation of the patient's history to the end of the visit is retrieved, and the average time from the end of each evaluation of the patient's history to the end of the visit is calculated. The average time is multiplied by the tolerance range factor to obtain a third preset value. The tolerance range factor is variable and is obtained by dividing the average time from the end of the visit of all patients who submitted the evaluation on the previous day by the average time from the end of the visit of all patients who submitted the evaluation on the previous two days.

[0235] When the time between the patient's submission of the evaluation and the end of the consultation is greater than the first preset value and less than or equal to the third preset value, or when the time between the patient's submission of the evaluation and the end of the consultation is less than or equal to the first preset value, the keywords of the patient's evaluation are matched with the pre-made evaluation data matching library to determine whether the keywords in the evaluation contain uncivilized words or the matching degree with the department to which the corresponding doctor belongs. The matching degree includes three types, namely, mismatch, neutral and match.

[0236] If the keywords in the review contain uncivilized words or the matching degree with the department of the doctor is mismatched or neutral, the evaluation module will automatically determine that the review is invalid.

[0237] If the keywords in the evaluation do not contain any uncivilized words and the corresponding doctor's department is a match, the evaluation module will automatically determine that the evaluation is valid.

[0238] When the time between the patient submitting the evaluation and the end of the consultation is greater than the first preset value and greater than the third preset value,

[0239] Match the keywords of the patient's evaluation with the pre-made evaluation database to determine whether there are any uncivilized words in the keywords of the evaluation or the matching degree with the department of the corresponding doctor.

[0240] If the keywords in the review contain uncivilized words or the matching degree with the department of the doctor is not matched, the review module will automatically determine that the review is invalid.

[0241] If the keywords in the evaluation do not contain any uncivilized words and the corresponding doctor's department is a match, the evaluation module will automatically determine that the evaluation is valid.

[0242] If the keywords in the evaluation do not contain any uncivilized words and the matching degree of the corresponding doctor's department is neutral, the evaluation module will compare the proportion of invalid evaluations in the historical evaluations with the second estimated value.

[0243] If the total proportion of invalid evaluations in historical evaluations is greater than or equal to the second estimated value and the proportion of invalid evaluations due to the presence of uncivilized words and the mismatch with the department of the attending doctor is greater than or equal to the fourth estimated value, the evaluation module automatically determines that the evaluation is invalid.

[0244] If the total proportion of invalid evaluations in historical evaluations is greater than or equal to the second estimated value and the proportion of invalid evaluations due to the presence of uncivilized words and the mismatch with the department of the attending doctor is less than the fourth estimated value, the evaluation module automatically determines that the evaluation is valid.

[0245] If the proportion of invalid evaluations in the historical evaluations is less than the second estimated value, the evaluation module automatically determines that the evaluation is valid.

[0246] The evaluation module will automatically save the processed and valid evaluation content into the database and synchronize it to the patient evaluation on the doctor's homepage in real time.

[0247] After the consultation, the patient takes the medicine according to the prescription issued by the intelligent auxiliary diagnosis module or the attending doctor. If any physical discomfort occurs within a unit of time, the patient can provide feedback on his or her condition through the traceability module. The patient's condition feedback includes two aspects: the time and dosage of each medication and the description of the physical discomfort reaction.

[0248] The tracing module compares the time and amount of each medication taken by the patient in the patient's condition feedback with the prescription information issued by the intelligent auxiliary diagnosis module or the attending doctor to determine whether the type, time and amount of the patient's medication are consistent with the description in the prescription information.

[0249] If the descriptions are consistent, then directly compare the symptoms of physical discomfort.

[0250] If the descriptions are inconsistent, the comparison results are saved and then compared with the symptoms of physical discomfort reactions;

[0251] The physical discomfort symptom comparison extracts keywords based on the description of the physical discomfort in the patient's condition feedback. The tracing module retrieves the adverse reaction content corresponding to each drug in the database prescribed by the intelligent auxiliary diagnosis module or the attending doctor, and compares the adverse reaction content corresponding to the drug retrieved by the tracing module with the keywords extracted based on the description of the patient's physical discomfort.

[0252] If the adverse reaction content corresponding to the drug retrieved by the tracing module contains the extracted keyword, the tracing module saves the drug name and adverse reaction corresponding to the keyword.

[0253] If the adverse reaction content corresponding to the drug retrieved by the traceability module does not contain the extracted keywords, the traceability module determines that the patient's adverse reaction is not caused by drug allergy;

[0254] The traceability module transmits the patient's condition feedback, the comparison results of the patient's medication time and dosage with the prescription information, and the comparison results of the patient's physical discomfort symptoms to the corresponding attending doctor. The attending doctor judges whether the patient's discomfort reaction is caused by the patient himself or a misdiagnosis based on the received information, and gives feedback on the patient's condition through the traceability module within the specified time. If it is caused by the patient's own reasons, targeted suggestions will be made. If it is caused by a misdiagnosis, the attending doctor will apologize and prescribe a new prescription.

[0255] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0256] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. The medical consultation system based on intelligent terminals is characterized by: It includes login and registration module, medical registration module, consultation queuing module, intelligent auxiliary diagnosis module, doctor consultation module, pharmaceutical service module, evaluation module, traceability module and database. The login and registration module is used to register and log in to the account, and authenticate and verify the patient's identity information. The login and registration module registers the account through the mobile phone number. The doctor registration module is used to find doctor information and register with the doctor. The registration number is automatically generated according to the current registration order, and the queue number is automatically generated according to the current queue order. If the patient's registration number is different from the queue number, the queue number will be updated in real time; The consultation queue module can check the number of people in each doctor's queue and the queue position of the registered doctor. During this period, the patient can also apply for a refund or adjust the queue position. The intelligent auxiliary diagnosis module extracts keywords from the detailed description of the patient's condition, matches it with the database, estimates the condition and confirms it with the patient. After the patient confirms, the intelligent auxiliary diagnosis module prescribes a prescription based on the patient's condition and submits it to the doctor for review based on the patient's confirmation. If the patient's confirmation result does not meet the estimated condition, the patient is asked to upload a picture of the symptom manifestation and save the relevant information, and the patient returns to the queue; The intelligent auxiliary diagnosis module includes a condition filling module, a keyword extraction module, a symptom estimation module, a drug matching module and an estimated symptom confirmation module; The estimated symptom confirmation module searches the database for other symptoms corresponding to the estimated symptom and confirms with the patient. The other symptoms corresponding to the estimated symptom are symptoms that the estimated symptom has but that the patient has not described in the detailed description of the condition. The intelligent auxiliary diagnosis module confirms the patient's symptoms in two ways: picture selection and text judgment. The two confirmation methods are available for patients to choose. Each confirmation method has 5 questions. The picture selection method is that the intelligent auxiliary diagnosis module retrieves the appearance pictures, B-ultrasound pictures and color ultrasound pictures in the database that are consistent with the estimated symptoms for patients to choose. In addition to the pictures, each multiple-choice question also has an option that does not match the options. The text judgment method is that the intelligent auxiliary diagnosis module retrieves the symptoms in the database that are consistent with the estimated symptoms. The patient only needs to judge whether they have them. If the results confirmed by the patient are consistent with the symptoms of the predicted disease, the intelligent auxiliary diagnosis module will determine that the patient has the disease. The intelligent auxiliary diagnosis module will match the treatment prescription corresponding to the disease in the database, automatically generate a case and treatment prescription, and submit it to the attending doctor for review. If the review is passed, the attending doctor will sign the case and treatment prescription submitted by the intelligent auxiliary diagnosis module. If the review is not passed, the intelligent auxiliary diagnosis module will regenerate the case and new treatment prescription and submit it to the attending doctor for review again. After the attending doctor signs the submitted case and treatment prescription, the intelligent auxiliary diagnosis module will pass the prescription to the pharmaceutical service module, and the patient will no longer queue. If the patient's confirmed result shows symptoms that do not match the predicted symptoms, the intelligent auxiliary diagnosis module cannot confirm that the patient has the predicted symptoms. The predicted symptoms can only serve as a reference. At the same time, the intelligent auxiliary diagnosis module requests the patient to upload pictures of their own symptoms. The pictures of their own symptoms uploaded by the patient include symptom appearance pictures, B-ultrasound pictures and color ultrasound pictures. The number of pictures of their own symptoms uploaded by the patient is 2 to 10. The intelligent auxiliary diagnosis module saves the patient's detailed description of the condition, estimated symptoms and uploaded symptom pictures into the database. At the same time, the patient will automatically exit the intelligent auxiliary diagnosis module and return to the doctor-seeking queue module to continue queuing; The doctor consultation module conducts consultation with the patient through audio or video connection. In the pharmaceutical service module, the pharmacist can confirm the rationality of the prescription through online review and issue the corresponding CA signature. The reviewed prescription will be sent to the patient's registered mobile phone number via SMS, and the consultation is over. The evaluation module is used to obtain patient evaluations and save the evaluation information to the database. The tracing module is used to determine whether the patient's discomfort reaction caused by taking the doctor's prescription within a unit time is a doctor's misdiagnosis. The tracing module extracts keywords and matches the adverse drug reaction content to determine whether the patient's discomfort reaction is caused by drug allergy. The doctor determines the cause of the patient's discomfort reaction based on the information provided by the tracing module and provides feedback on the patient's condition through the tracing module within a specified time. The database is used to store data information generated by each module.

2. The medical consultation system based on an intelligent terminal according to claim 1, characterized in that: The login and registration module includes a registration module and a login module. The registration module needs to obtain the registered mobile phone number filled in by the patient, and the registration module will send a mobile phone verification code to the patient's registered mobile phone number. The registration module receives the mobile phone verification code filled in by the patient and automatically checks whether the filled-in mobile phone verification code is correct. If the filled-in mobile phone verification code is incorrect, the patient needs to fill it in again. If the filled-in mobile phone verification code is correct, the registration module will authenticate the patient's identity, that is, compare the patient's name and ID number through the provided database interface. If the name and ID number match, the account registration is successful and the patient logs in successfully. If the name and ID number do not match, the identity authentication fails and the patient needs to re-authenticate. The login module includes two methods: mobile phone number login and face recognition login. The mobile phone number login method requires obtaining the registered mobile phone number filled in by the patient. The login module will send a mobile phone verification code to the patient's registered mobile phone number. The login module receives the mobile phone verification code filled in by the patient and automatically checks whether the filled mobile phone verification code is correct. If the filled mobile phone verification code is incorrect, the patient needs to fill it in again. If the filled mobile phone verification code is correct, the login module will automatically match the patient's login mobile phone number in the database of the system. If the login mobile phone number cannot be matched in the database, the patient is not registered, and the login module automatically jumps to the registration module. If the database successfully matches the login mobile phone number, the account is successfully logged in. The face recognition login method requires obtaining the patient's face information through the camera in the device used by the patient. The login module compares the obtained patient's face information with the face information on the patient's ID card through the provided database interface. If the face information of the two matches, the login is successful. If the face information of the two does not match, the login fails, and feedback is given to the patient whether to re-acquire the face information. When the patient confirms to re-acquire the face information, the login module re-acquires the patient's face information through the camera in the device used by the patient and compares it with the data in the provided database. When the patient confirms not to re-acquire the face information, it automatically jumps to the mobile phone number login method; The patient who uses the face recognition login method can be a patient who has not registered an account, and after successful login, the account will be automatically registered and the identity authentication will be completed, but the mobile phone number needs to be bound. In the login and registration module, the account that successfully logs in can query historical medical consultation records.

3. The medical consultation system based on an intelligent terminal according to claim 1, characterized in that: The doctor-seeking and registration module is used to help patients find doctors and register. Doctor search methods include department search and doctor list search. The department search searches for doctors by selecting a specific department. Different departments correspond to different doctors. The doctor list search searches for a specific doctor by browsing the doctor list. The doctor list contains all doctors in each department. After finding a specific doctor, the patient can view the doctor's homepage. The doctor's homepage includes the doctor's historical number of patients, patient evaluations, diseases he or she is good at treating, and the number of patients currently waiting to be seen. There is an upper limit on the number of patients waiting to be seen by doctors in the same department. When the upper limit is reached, patients will no longer be able to register with that doctor. The calculation process for the upper limit of the number of patients waiting to be seen by a doctor is as follows: The first step is to count the number of doctors n who are seeing patients in the department and the time T until they get off work. The second step is to multiply the number of patients waiting to be seen by each doctor in the current department by the average time t that the corresponding doctor takes to see one patient, and get the waiting time of each doctor. Step 3 is to sort the doctors by their waiting time from largest to smallest and mark their serial numbers, i.e. the doctor with serial number n currently has x number of patients waiting to be seen. n The average time it takes for the doctor with serial number n to see a patient is t n , The fourth step is to set the current number of patients waiting to be seen by the doctor with the shortest waiting time as the initial number of patients waiting to be seen x0, and the average time for the doctor with the shortest waiting time to see one patient as the initial waiting time t0. The initial waiting time is the product of the initial number of patients waiting to be seen x0 and the initial waiting time t0, that is, x0t0. The workload of each doctor is divided and limited. The waiting time of each doctor does not exceed the off-duty time. In addition, compared with the doctor with the shortest waiting time, the waiting time of each doctor does not exceed one third of the minimum waiting time. Step 5 is to calculate the upper limit of the number of patients waiting to be seen by each doctor, that is, when the initial waiting time x0t0 Less than or equal to the time T from get off work, that is, When the number of patients waiting to be seen by the doctor with serial number n is ,Right now is the upper limit of the number of patients waiting to be seen by the doctor. Greater than the time T until leaving get off work, that is, When the number of patients waiting to be seen by the doctor with serial number n is ,Right now The maximum number of patients the doctor currently wants to see. After the patient has viewed the doctor's homepage and is satisfied with the doctor, he or she can register for the doctor. When the registration is successful, the doctor registration module will automatically generate a registration number and a queue number, and send them to the patient's registered mobile phone number via SMS. The registration number is automatically generated according to the current registration order, and the queue number is automatically generated according to the current queue order. The patient's registration number is different from the queuing number. The patient's registration number will not change, but the patient's queuing number will change due to changes in the patient's queuing position. The patient's queuing number will be updated in real time.

4. The medical consultation system based on an intelligent terminal according to claim 1, characterized in that: In the consultation queue module, patients can view the number of people waiting in line for consultation with each doctor and their own queue position. In the consultation queue module, patients can view the number of people waiting in line for consultation with each doctor and their own queue position. The patient can submit an application to the consultation queue module during the queuing period, and the application includes refund and adjustment of queue position. The adjustment of the queue position can only be made backwards. The application for adjusting the queue position requires the patient to note the estimated consultation time. The consultation queuing module calculates the time difference between the current time and the patient's estimated consultation time, and divides the obtained time difference by the average consultation time of each patient of the registered doctor to obtain the estimated number of patients h1 that the doctor will see from the current time to the patient's estimated consultation time. The queue number for this doctor at this time is h2. The queue position after the consultation queue module is adjusted is h1+h2, and the queue numbers of all patients between the original queue position and the adjusted queue position are reduced by one. When the patient's queue ends, that is, when the attending doctor calls the patient's number to see him, the consultation queuing module will send a text message notification to the patient's registered mobile phone number.

5. The medical consultation system based on an intelligent terminal according to claim 1, characterized in that: The medical condition filling module will actively send a request to the patient to fill in the detailed medical condition description, and after the patient submits the completed detailed medical condition description; The keyword extraction module automatically extracts keywords from the patient's condition details, and then intelligently compares the original hospital medical records or the Internet hospital medical record database based on the keywords to determine whether the patient is a follow-up patient or a chronic disease patient. If the patient is neither a returning patient nor a chronic disease patient, the keyword extraction module will transmit the keywords extracted from the patient's condition details to the symptom estimation module. If the patient is a follow-up patient or a patient with a chronic disease, the keyword extraction module will judge whether the patient's condition has changed significantly or a critical value has appeared based on the keywords extracted from the patient's condition description. When the patient's condition has changed significantly or a critical value has appeared, the keyword extraction module will make a note on the patient's condition description, mark the patient's condition change, and upload the patient's condition description and notes to the database. When the patient's condition has not changed significantly or a critical value has appeared, the patient will automatically jump to the drug matching module; The drug matching module requires returning patients or patients with chronic diseases to submit information about the drugs they need to buy and that are in the prescriptions previously issued by the attending physician for the disease. The drug matching module automatically compares the drug library and the store inventory library based on the drug information submitted by the patient, and searches for drugs of the same type, brand and specification. If a drug that meets the requirements is found, the store inventory library closest to the patient's location where the drug is stored will be searched and displayed to the patient. If a drug that meets the requirements is not found, other drugs of different types, brands or specifications with the same efficacy will be searched according to the drug information submitted by the patient, and presented to the patient through the drug matching module after review by the attending physician, and the nearest store inventory library with the drug reviewed by the attending physician will be presented based on the patient's location. If the attending physician fails to pass the review, other drugs of different types, brands or specifications with the same efficacy will be searched again according to the drug information submitted by the patient, and submitted to the attending physician for review. The symptom prediction module compares the acquired keywords with the database, matches the corresponding symptoms, and searches the database for the symptoms corresponding to the corresponding symptoms, thereby making an estimate of the patient's condition.

6. The intelligent terminal-based medical consultation system according to claim 3, characterized in that: The doctor consultation module automatically retrieves the detailed description and notes of the patient's condition, the estimated symptoms determined by the intelligent auxiliary diagnosis module, and the symptom pictures uploaded by the patient from the database, and transmits them to the corresponding doctor who receives the patient when the patient registers, so that the doctor can analyze the patient's condition. The doctor who receives the patient establishes an audio or video connection with the patient through the doctor consultation module to conduct a consultation with the patient. During the consultation, the patient can check the current status of the doctor at any time. The attending doctor confirms the patient's condition by asking the patient a question. After confirming the patient's condition, the attending doctor can jump to the medical order interface and fill in the medical order information on the medical order interface. The medical order information includes the patient's medical history, prescription information and CA signature. The medical order information is synchronized in real time, and the patient can simultaneously see the medical history, prescription information and CA signature filled in by the attending doctor. After the attending doctor issues the medical order, it will flow to the pharmaceutical service module.

7. The intelligent terminal-based medical consultation system according to claim 1, characterized in that: The evaluation module is used to obtain the patient's evaluation of the doctor after the consultation. The evaluation content includes a five-star rating and a text-edited evaluation. After the patient submits the evaluation, the evaluation module will process the patient's evaluation content and automatically save the processed and valid evaluation content into the database. The specific processing method of the evaluation module for patient evaluation is as follows: the evaluation module extracts the time from the time the patient submits the evaluation to the end of the consultation, the keywords of the evaluation content and the proportion of invalid evaluations in the historical evaluations. The evaluation module compares the time from the patient submitting the evaluation to the end of the consultation with a first preset value. When the time from the patient's submission of the evaluation to the end of the visit is greater than the first preset value, the time from the end of each evaluation of the patient's history to the end of the visit is retrieved, and the average time from the end of each evaluation of the patient's history to the end of the visit is calculated. The average time is multiplied by the tolerance range factor to obtain a third preset value. The tolerance range factor is variable and is obtained by dividing the average time from the end of the visit of all patients who submitted the evaluation on the previous day by the average time from the end of the visit of all patients who submitted the evaluation on the previous two days. When the time between the patient's submission of the evaluation and the end of the consultation is greater than the first preset value and less than or equal to the third preset value, or when the time between the patient's submission of the evaluation and the end of the consultation is less than or equal to the first preset value, the keywords of the patient's evaluation are matched with the pre-made evaluation data matching library to determine whether the keywords in the evaluation contain uncivilized words or the matching degree with the department to which the corresponding doctor belongs. The matching degree includes three types, namely, mismatch, neutral and match. If the keywords in the review contain uncivilized words or the matching degree with the department of the doctor is mismatched or neutral, the evaluation module will automatically determine that the review is invalid. If the keywords in the evaluation do not contain any uncivilized words and the corresponding doctor's department is a match, the evaluation module will automatically determine that the evaluation is valid. When the time between the patient submitting the evaluation and the end of the consultation is greater than the first preset value and greater than the third preset value, Match the keywords of the patient's evaluation with the pre-made evaluation database to determine whether there are any uncivilized words in the keywords of the evaluation or the matching degree with the department of the corresponding doctor. If the keywords in the review contain uncivilized words or the matching degree with the department of the doctor is not matched, the review module will automatically determine that the review is invalid. If the keywords in the evaluation do not contain any uncivilized words and the corresponding doctor's department is a match, the evaluation module will automatically determine that the evaluation is valid. If the keywords in the evaluation do not contain any uncivilized words and the matching degree of the corresponding doctor's department is neutral, the evaluation module will compare the proportion of invalid evaluations in the historical evaluations with the second estimated value. If the total proportion of invalid evaluations in historical evaluations is greater than or equal to the second estimated value and the proportion of invalid evaluations due to the presence of uncivilized words and the mismatch with the department of the attending doctor is greater than or equal to the fourth estimated value, the evaluation module automatically determines that the evaluation is invalid. If the total proportion of invalid evaluations in historical evaluations is greater than or equal to the second estimated value and the proportion of invalid evaluations due to the presence of uncivilized words and the mismatch with the department of the attending doctor is less than the fourth estimated value, the evaluation module automatically determines that the evaluation is valid. If the proportion of invalid evaluations in the historical evaluations is less than the second estimated value, the evaluation module automatically determines that the evaluation is valid.

8. The medical consultation system based on an intelligent terminal according to claim 1, characterized in that: After the consultation, the patient takes the medicine according to the prescription issued by the intelligent auxiliary diagnosis module or the attending doctor. If any physical discomfort occurs within a unit of time, the patient can provide feedback on his or her condition through the traceability module. The patient's condition feedback includes two aspects: the time and dosage of each medication and the description of the physical discomfort reaction. The tracing module compares the time and amount of each medication taken by the patient in the patient's condition feedback with the prescription information issued by the intelligent auxiliary diagnosis module or the attending doctor to determine whether the type, time and amount of the patient's medication are consistent with the description in the prescription information. If the descriptions are consistent, then directly compare the symptoms of physical discomfort. If the descriptions are inconsistent, the comparison results are saved and then compared with the symptoms of physical discomfort reactions; The physical discomfort symptom comparison extracts keywords based on the description of the physical discomfort in the patient's condition feedback. The tracing module retrieves the adverse reaction content corresponding to each drug in the database prescribed by the intelligent auxiliary diagnosis module or the attending doctor, and compares the adverse reaction content corresponding to the drug retrieved by the tracing module with the keywords extracted based on the description of the patient's physical discomfort. If the adverse reaction content corresponding to the drug retrieved by the tracing module contains the extracted keyword, the tracing module saves the drug name and adverse reaction corresponding to the keyword. If the adverse reaction content corresponding to the drug retrieved by the traceability module does not contain the extracted keywords, the traceability module determines that the patient's adverse reaction is not caused by drug allergy; The tracing module transmits the patient's condition feedback, the comparison results of the patient's medication time and dosage with the prescription information, and the comparison results of the patient's physical discomfort symptoms to the corresponding attending physician. The attending physician determines whether the patient's discomfort reaction is caused by the patient himself or by misdiagnosis based on the received information, and provides feedback on the patient's condition through the tracing module within the specified time.

9. The medical consultation system based on an intelligent terminal according to claim 1, characterized in that: The patient may only apply for adjustment of queue position twice.

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