Online medical appointment method and device, computer storage medium and program product

Abstract

The invention relates to an online medical appointment method and device, a computer storage medium and a program product. The online medical appointment method comprises the following steps: acquiring patient information and doctor information in response to an appointment request of a patient terminal; determining a recommended doctor list according to the patient information and the doctor information, and pushing the recommended doctor list to the recommended doctor list; according to a plurality of doctors selected from the recommended doctor list by the patient terminal, forming an appointment doctor list; queuing and reserving a plurality of reserved doctors in the reserved doctor list according to the patient information; and under the condition that the first reservation doctor in the reservation doctor list succeeds in reservation, queuing reservation of second reservation doctors in the reservation doctor list is ended, and the second reservation doctors are other doctors except the first reservation doctor in the reservation doctor list. According to the invention, the patient is supported to queue up for reception of multiple doctors, the doctor-seeing sequence can be flexibly arranged, and the waiting time is optimized.

Description

Technical Field

[0001] The present disclosure relates to the fields of artificial intelligence and Internet medical technology, and particularly to an online medical appointment method, an apparatus, a computer storage medium, and a program product. Background Art

[0002] Currently, online medical treatment (also known as Internet medical treatment) has been well developed. In the medical service mode of related technologies, whether a user actively seeks a designated doctor for consultation or relies on the triage algorithm of the platform to recommend a doctor, usually only one doctor can be selected for placing an order. This single-selection method is particularly inconvenient when patients are in urgent need of medical services, because patients still need to wait for the doctor to accept the order, resulting in low medical treatment efficiency. Summary of the Invention

[0003] In view of at least one of the above technical problems, the present disclosure provides an online medical appointment method, an apparatus, a computer storage medium, and a program product, which support patients to queue up for diagnosis and treatment by multiple doctors, can flexibly arrange the medical treatment order, and optimize the waiting time.

[0004] According to one aspect of the present disclosure, there is provided an online medical appointment method, including:

[0005] Responding to a reservation request from a patient terminal, obtaining patient information and doctor information;

[0006] Determining a recommended doctor list according to the patient information and the doctor information, and pushing it to the recommended doctor list;

[0007] Forming a reserved doctor list according to multiple doctors selected by the patient terminal from the recommended doctor list;

[0008] For multiple reserved doctors in the reserved doctor list, queuing and making a reservation according to the patient information;

[0009] When the first reserved doctor in the reserved doctor list makes a successful reservation, ending the queuing reservation of the second reserved doctor in the reserved doctor list, where the second reserved doctor is other doctors in the reserved doctor list except the first reserved doctor.

[0010] In some embodiments of the present disclosure, the determining a recommended doctor list according to the patient information and the doctor information includes:

[0011] Determining the matching degree between the patient's symptoms and each doctor according to the patient information and the doctor information;

[0012] Determining the sorting priority of each doctor according to the matching degree between the patient's symptoms and each doctor;

[0013] Determine multiple recommended doctors according to the sorting priority of each doctor to form the list of recommended doctors.

[0014] In some embodiments of the present disclosure, the determining the sorting priority of each doctor according to the matching degree between the patient's symptoms and each doctor includes:

[0015] Determine the estimated consultation time of each doctor according to the doctor information;

[0016] Determine the sorting priority of each doctor according to at least one of the matching degree between the patient's symptoms and each doctor and the estimated consultation time of each doctor.

[0017] In some embodiments of the present disclosure, the determining the estimated consultation time according to the doctor information includes:

[0018] Extract at least one of the current queuing situation and the doctor's consultation efficiency from the doctor information;

[0019] Calculate the estimated consultation time according to at least one of the current queuing situation and the doctor's consultation efficiency.

[0020] In some embodiments of the present disclosure, the determining the estimated consultation time according to the doctor information further includes:

[0021] Adjust the estimated consultation time according to the actual situation of the doctor at a predetermined time interval, where the actual situation of the doctor includes at least one of the doctor's overtime situation and the patient's appointment cancellation.

[0022] In some embodiments of the present disclosure, the determining the sorting priority of each doctor according to at least one of the matching degree between the patient's symptoms and each doctor and the estimated consultation time of each doctor includes:

[0023] Extract the urgency of the patient's symptoms from the patient information;

[0024] Determine the sorting priority of each doctor according to at least one of the urgency of the patient's symptoms, the matching degree between the patient's symptoms and each doctor, and the estimated consultation time of each doctor.

[0025] In some embodiments of the present disclosure, the determining the sorting priority of each doctor according to at least one of the urgency of the patient's symptoms, the matching degree between the patient's symptoms and each doctor, and the estimated consultation time of each doctor includes:

[0026] Extract the patient's historical medical records from the patient information;

[0027] Extract the online status of each doctor from the doctor information;

[0028] Determine the sorting priority of each doctor according to at least one of the emergency degree of the patient's symptoms, the patient's historical medical records, the matching degree between the patient's symptoms and each doctor, the online status of each doctor, and the estimated consultation time of each doctor.

[0029] In some embodiments of the present disclosure, the determining the sorting priority of each doctor according to at least one of the emergency degree of the patient's symptoms, the patient's historical medical records, the matching degree between the patient's symptoms and each doctor, the online status of each doctor, and the estimated consultation time of each doctor includes:

[0030] Determine the sorting priority of each doctor according to the emergency degree of the patient's symptoms, the emergency degree weight, the patient's historical medical records, the medical record weight, the matching degree between the patient's symptoms and each doctor, the matching degree weight, the online status of each doctor, the online status weight, the estimated consultation time of each doctor, and the consultation time weight.

[0031] In some embodiments of the present disclosure, the online medical appointment method further includes:

[0032] When the emergency degree weight is greater than a predetermined value, increase the consultation time weight.

[0033] In some embodiments of the present disclosure, the determining the matching degree between the patient's symptoms and the doctor according to the patient information and the doctor information includes:

[0034] Extract the patient's symptoms from the patient information;

[0035] Extract the disease labels that the doctor is good at from the doctor information;

[0036] Process the patient's symptoms and the disease labels that the doctor is good at using a standard disease classification system;

[0037] Determine the matching degree between the patient's symptoms and the doctor according to the processed patient's symptoms and the disease labels that the doctor is good at.

[0038] In some embodiments of the present disclosure, the determining the matching degree between the patient's symptoms and the doctor according to the processed patient's symptoms and the disease labels that the doctor is good at includes:

[0039] Convert the processed patient's symptoms and the disease labels that the doctor is good at into a patient symptom vector and a disease label vector that the doctor is good at;

[0040] Calculate the cosine similarity between the patient symptom vector and the disease label vector that the doctor is good at;

[0041] Use the cosine similarity as the matching degree between the patient's symptoms and the doctor.

[0042] In some embodiments of the present disclosure, the online medical appointment method further includes:

[0043] Displaying the recommended doctor list and the reserved doctor list through the patient terminal;

[0044] Adjusting the recommended doctor list and the reserved doctor list according to the input of the patient terminal.

[0045] In some embodiments of the present disclosure, the displaying the recommended doctor list and the reserved doctor list through the patient terminal includes at least one of the following steps:

[0046] For each recommended doctor in the recommended doctor list, displaying the doctor information of the recommended doctor, where the doctor information includes at least one of the current queuing number, the estimated consultation time, the historical consultation situation, the doctor level, the doctor consultation price, and the reservation option;

[0047] For each reserved doctor in the reserved doctor list, displaying the doctor information of the reserved doctor, where the doctor information includes at least one of the reservation queuing progress, the estimated consultation time, the historical consultation situation, the doctor level, and the doctor consultation price.

[0048] In some embodiments of the present disclosure, the adjusting the recommended doctor list and the reserved doctor list according to the input of the patient terminal includes at least one of the following steps:

[0049] Adding a doctor from the recommended doctor list to the reserved doctor list according to the input of the patient terminal;

[0050] Deleting a doctor from at least one of the recommended doctor list and the reserved doctor list according to the input of the patient terminal;

[0051] Adjusting the order of doctors in at least one of the recommended doctor list and the reserved doctor list according to the input of the patient terminal.

[0052] In some embodiments of the present disclosure, the adjusting the order of doctors in at least one of the recommended doctor list and the reserved doctor list according to the input of the patient terminal includes at least one of the following steps:

[0053] Sorting the recommended doctors in the recommended doctor list according to the input of the patient terminal in the order of any one of the sorting priority, the current queuing number, the estimated consultation time, the historical consultation situation, the doctor level, and the doctor consultation price;

[0054] According to the input of the patient terminal, sort the reserved doctors in the reserved doctor list in the order of sorting priority, appointment queuing progress, the expected consultation time, the historical consultation situation, the doctor level, and the doctor consultation price, any one of them.

[0055] In some embodiments of the present disclosure, the obtaining patient information in response to a reservation request of a patient terminal includes:

[0056] Obtain the natural language description input by the patient terminal through an artificial intelligence interaction model;

[0057] Obtain the patient information corresponding to the natural language description through natural language processing, where the patient information includes at least one of patient symptoms, the urgency of patient symptoms, and the patient's historical medical records.

[0058] According to another aspect of the present disclosure, there is provided an online medical reservation device, including:

[0059] An information acquisition module, configured to obtain patient information and doctor information in response to a reservation request of a patient terminal;

[0060] A first list determination module, configured to determine a recommended doctor list according to the patient information and the doctor information, and push it to the recommended doctor list;

[0061] A second list determination module, configured to form a reserved doctor list according to multiple doctors selected by the patient terminal from the recommended doctor list;

[0062] A queuing reservation module, configured to queue and reserve multiple reserved doctors in the reserved doctor list according to the patient information; in the case where the first reserved doctor in the reserved doctor list reserves successfully, end the queuing reservation of the second reserved doctor in the reserved doctor list, where the second reserved doctor is other doctors in the reserved doctor list except the first reserved doctor.

[0063] According to another aspect of the present disclosure, there is provided an online medical reservation device, including:

[0064] A memory, configured to store instructions;

[0065] A processor, configured to execute the instructions, so that the online medical reservation device implements the online medical reservation method as described in any of the above embodiments.

[0066] According to another aspect of the present disclosure, there is provided a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the online medical reservation method as described in any of the above embodiments is implemented.

[0067] According to another aspect of the present disclosure, there is provided a computer program product including a computer program, wherein when the computer program is executed by a processor, it implements the online medical appointment method as described in any of the above embodiments.

[0068] The present disclosure supports patients in queuing for consultations with multiple doctors, enabling flexible arrangement of the medical treatment order and optimizing the waiting time. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0070] Figure 1 It is a schematic diagram of some embodiments of the online medical appointment method of the present disclosure.

[0071] Figure 2 It is a schematic diagram of other embodiments of the online medical appointment method of the present disclosure.

[0072] Figure 3 It is a schematic diagram of the recommended doctor list in some embodiments.

[0073] Figure 4 It is a schematic diagram of still other embodiments of the online medical appointment method of the present disclosure.

[0074] Figure 5 It is a schematic structural diagram of some embodiments of the online medical appointment device of the present disclosure.

[0075] Figure 6 It is a schematic diagram of other embodiments of the online medical appointment device of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0076] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some embodiments of the present disclosure, rather than all embodiments. The following description of at least one exemplary embodiment is actually only illustrative and in no way limits the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present disclosure.

[0077] Unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure.

[0078] Meanwhile, it should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn in actual proportional relationship.

[0079] Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the said technologies, methods, and devices should be regarded as part of the authorization specification.

[0080] In all the examples shown and discussed here, any specific values should be construed as merely exemplary, not as a limitation. Thus, other examples of the exemplary embodiments may have different values.

[0081] It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0082] The inventors have found through research that: in the related art, patients often hope to quickly and effectively solve health problems, but in the process of choosing a doctor, it is often difficult to find the most suitable medical service for themselves, resulting in low medical treatment efficiency. This limitation not only affects the patients' medical treatment experience, but also makes them feel more anxious when facing health problems. Therefore, improving the flexibility and efficiency of doctor selection has become an important direction for improving the patients' medical treatment experience.

[0083] In view of at least one of the above technical problems, the present disclosure provides an online medical appointment method and device, a computer storage medium, and a program product. The present disclosure will be described below through specific embodiments.

[0084] Figure 1 It is a schematic diagram of some embodiments of the online medical appointment method of the present disclosure. Preferably, this embodiment can be executed by the online medical appointment device of the present disclosure. As Figure 1 shown, Figure 1 The method of the embodiment may include at least one step among step 100 to step 500.

[0085] In some embodiments of the present disclosure, the online medical appointment method of the present disclosure may be at least one of a medical service online medical appointment method, a consultation online medical appointment method, and an online consultation online medical appointment method.

[0086] Step 100, in response to a reservation request from a patient terminal, obtain patient information and doctor information.

[0087] In some embodiments of the present disclosure, step 100 may include at least one step among step 110 to step 120.

[0088] Step 110: Obtain the natural language description input by the patient terminal through the artificial intelligence interaction model.

[0089] Step 120: Obtain the patient information corresponding to the natural language description through natural language processing (NLP), where the patient information includes at least one of patient symptoms, the urgency of patient symptoms, and the patient's historical medical records.

[0090] In some embodiments of the present disclosure, the patient information may include basic information such as the patient's chief complaint symptoms, duration, frequency, severity, and location, as well as details, characteristics, and causes of the symptoms.

[0091] In some embodiments of the present disclosure, the patient information may further include relevant information such as the patient's past medical history, medication use, mental state, the urgency of patient symptoms, and the patient's historical medical records.

[0092] In some embodiments of the present disclosure, step 100 may include: using a large model to analyze the natural language description input by the patient to identify patient information, where the patient information includes key symptoms and disease labels. The above embodiments of the present disclosure can match the doctor's area of expertise according to the patient information, and combine historical data and the current queuing situation to predict the reception efficiency and estimated reception time of each doctor. The above embodiments of the present disclosure can provide personalized doctor recommendations for patients by considering the patient's historical medical records and preferences.

[0093] The patient chief complaint collection method in the related art usually relies on text box form submission. Although key information in the chief complaint is prompted, it is still difficult to avoid information omission. The above embodiments of the present disclosure introduce a large model for collecting chief complaint information, which can communicate more deeply with users through natural language to ensure that important medical information is obtained.

[0094] The medical large model of the above embodiments of the present disclosure can effectively process the diseases described by patients, optimize the process of collecting key information, and minimize the need for users to fill in redundant information. The above embodiments of the present disclosure use a large model to improve the integrity and accuracy of information collection, thereby improving the patient's medical experience.

[0095] Through natural language processing technology (NLP), the online medical appointment device in the above embodiments of the present disclosure can understand and parse the patient's language input, including symptom descriptions, medical histories, and other relevant information. This enables patients to express their chief complaints in natural language, and the online medical appointment device can accurately extract key information to ensure that the medical team obtains comprehensive background information.

[0096] In the above embodiments of the present disclosure, the large model can continuously optimize the understanding and classification of the patient's chief complaint, improve the accuracy of information extraction, adapt to new medical knowledge and changing patient needs, and ensure the provision of efficient and accurate services in practical applications. This intelligent information processing ability not only improves the efficiency of medical consultations but also enhances the ability to handle complex cases.

[0097] Step 200: Determine a recommended doctor list based on the patient information and the doctor information, and push it to the recommended doctor list.

[0098] In some embodiments of the present disclosure, step 200 may include at least one of steps 210 to 230.

[0099] Step 210: Determine the matching degree between the patient's symptoms and each doctor based on the patient information and the doctor information.

[0100] In some embodiments of the present disclosure, step 210 may include at least one of steps 211 to 214.

[0101] Step 211: Extract the patient's symptoms from the patient information.

[0102] Step 212: Extract the disease labels that the doctor is good at from the doctor information.

[0103] Step 213: Process the patient's symptoms and the disease labels that the doctor is good at using a standard disease classification system.

[0104] Step 214: Determine the matching degree between the patient's symptoms and the doctor based on the processed patient's symptoms and the disease labels that the doctor is good at.

[0105] In some embodiments of the present disclosure, step 214 may include at least one of steps 2141 to 2143.

[0106] Step 2141: Convert the processed patient's symptoms and the disease labels that the doctor is good at into a patient symptom vector and a disease label vector that the doctor is good at.

[0107] Step 2142: Calculate the cosine similarity between the patient symptom vector and the disease label vector that the doctor is good at.

[0108] In some embodiments of the present disclosure, cosine similarity is an index for measuring the similarity between two vectors, and is particularly commonly used in text analysis to calculate the similarity between documents. The basic idea of cosine similarity is to judge their similarity degree by calculating the cosine value of the included angle between two vectors.

[0109] In some embodiments of the present disclosure, step 214 may include: calculating the cosine similarity between the patient symptom vector A and the doctor's disease expertise label vector B according to formula (1). .

[0110] (1)

[0111] In some embodiments of the present disclosure, the value range of the cosine similarity is between [-1, 1], and the larger the value, the higher the similarity.

[0112] Step 2143: Use the cosine similarity as the matching degree between the patient's symptoms and the doctor.

[0113] Step 220: Determine the sorting priority of each doctor according to the matching degree between the patient's symptoms and each doctor.

[0114] The above embodiments of the present disclosure can set priorities according to the degree of matching between the doctor's expertise field and the patient's symptoms.

[0115] The above embodiments of the present disclosure collect the symptoms described by the patient and the doctor's professional fields and disease expertise labels (using ICD-10), and perform vectorization processing on the text. Among them, ICD-10 (International Classification of Diseases, 10th Revision) is an international disease classification system formulated by the World Health Organization (WHO). It is used to standardize the recording and reporting of diseases and other health conditions, and is widely used in medical statistics, insurance claims, public health monitoring and other fields.

[0116] In terms of data collection and management, the above embodiments of the present disclosure adopt a standardized disease classification system (such as medical classification standards like ICD-10) to ensure the consistency and accuracy of disease labels.

[0117] The above embodiments of the present disclosure match the symptoms described by the patient with the disease labels that the doctor is good at through similarity calculation. The above embodiments of the present disclosure use cosine similarity to calculate the similarity between the patient symptom vector and the doctor's disease expertise label vector.

[0118] The above embodiments of the present disclosure use TF-IDF (Term Frequency-Inverse Document Frequency) to calculate the importance of each word, and convert the symptoms input by the patient and the disease labels that the doctor is good at into TF-IDF vectors. Among them, TF-IDF is a commonly used text analysis technique for evaluating the importance of a word in a document collection. TF-IDF combines the two concepts of term frequency (TF) and inverse document frequency (IDF).

[0119] In some embodiments of the present disclosure, step 220 may include at least one of steps 221 to 222.

[0120] Step 221, determine the estimated consultation time for each doctor according to the doctor information.

[0121] In some embodiments of the present disclosure, step 221 may include: extracting at least one of the current queuing situation and the doctor's consultation efficiency from the doctor information; calculating the estimated consultation time according to at least one of the current queuing situation and the doctor's consultation efficiency.

[0122] In some embodiments of the present disclosure, step 221 may further include: adjusting the estimated consultation time according to the actual situation of the doctor at a predetermined time interval, where the actual situation of the doctor includes at least one of the doctor's overtime situation and the patient's appointment cancellation.

[0123] In some embodiments of the present disclosure, step 221 may include: dynamically calculating the estimated consultation time by combining the current number of people in the queue and the doctor's consultation efficiency, and adjusting the estimated consultation time in real time according to the actual situation (such as the doctor's overtime, the patient's appointment cancellation).

[0124] Step 222, determine the sorting priority of each doctor according to at least one of the matching degree between the patient's symptoms and each doctor and the estimated consultation time of each doctor.

[0125] In some embodiments of the present disclosure, step 222 may include: extracting the urgency of the patient's symptoms from the patient information; determining the sorting priority of each doctor according to at least one of the urgency of the patient's symptoms, the matching degree between the patient's symptoms and each doctor, and the estimated consultation time of each doctor.

[0126] In some embodiments of the present disclosure, the urgency of the patient's symptoms is the urgency of the patient's condition that can be selected (such as urgent, general, chronic). For example: for a disease, such as fever, there are low fever, high fever, etc., and high fever is more urgent.

[0127] In some embodiments of the present disclosure, step 222 may include: extracting the urgency of the patient's symptoms from the patient information; extracting the patient's historical medical records from the patient information; extracting the online status of each doctor from the doctor information; determining the sorting priority of each doctor according to at least one of the urgency of the patient's symptoms, the patient's historical medical records, the matching degree between the patient's symptoms and each doctor, the online status of each doctor, and the estimated consultation time of each doctor.

[0128] In some embodiments of the present disclosure, the matching degree between the patient's symptoms and each doctor is determined based on the similarity calculation result.

[0129] In some embodiments of the present disclosure, the doctor's online status: Check the availability of the doctor (such as whether currently receiving patients).

[0130] In some embodiments of the present disclosure, the patient's historical medical records: Prioritize recommending doctors who the patient has seen before; or doctors who are good at handling similar cases. For example: If a doctor is good at handling the disease label "fever", being good at handling similar cases means that the doctor has treated patients with the fever disease many times, that is, similar cases.

[0131] In some embodiments of the present disclosure, based on the patient's historical medical records, doctors who the patient has seen before can be preferentially recommended.

[0132] In some embodiments of the present disclosure, according to the urgency of the patient's symptoms and the patient's historical medical records and the matching degree between the patient's symptoms and each doctor and the doctor's online status of each doctor and the estimated consultation time of each doctor At least one of the above, the step of determining the sorting priority T of each doctor may include: According to the urgency of the patient's symptoms and the patient's historical medical records and the matching degree between the patient's symptoms and each doctor and the doctor's online status of each doctor and the estimated consultation time of each doctor At least one of the above, call a large model to determine the sorting priority T of each doctor.

[0133] In some embodiments of the present disclosure, according to the urgency of the patient's symptoms and the patient's historical medical records and the matching degree between the patient's symptoms and each doctor and the doctor's online status of each doctor and the estimated consultation time of each doctor At least one of the above, the step of determining the sorting priority T of each doctor may include: According to the urgency of the patient's symptoms and the urgency weight and the patient's historical medical records and the medical record weight and the matching degree between the patient's symptoms and each doctor and the matching degree weight , the online status of each doctor , the online status weight , the estimated consultation time of each doctor and the consultation time weight , determine the sorting priority T of the patient's queuing request for each doctor according to formula (2).

[0134] (2)

[0135] In some embodiments of the present disclosure, the urgency weight , the urgency weight , the matching degree weight , the online status weight , the consultation time weight and other weight coefficients can be adjusted according to the actual situation.

[0136] In some embodiments of the present disclosure, the online medical appointment method may further include: when the urgency weight is greater than a predetermined value, increase the consultation time weight . That is, for patients with a higher urgency level, increase the sorting priority of doctors with a faster estimated consultation time, and preferentially arrange doctors with a faster estimated consultation time to receive patients.

[0137] In some other embodiments of the present disclosure, step 222 may include: according to the patient's historical medical records , the medical record weight , the matching degree between the patient's symptoms and each doctor , the matching degree weight , the online status of each doctor , the online status weight , determine the sorting priority T of the patient's queuing request for each doctor according to formula (3).

[0138] (3)

[0139] In some embodiments of the present disclosure, the priority sorting can consider the matching degree, the availability of doctors, and the patient's historical medical records to recommend the most suitable doctor for the patient. Thus, the patient can easily browse the doctor list, view the expertise areas and patient evaluations of each doctor, thereby optimizing the medical experience.

[0140] Step 230, determine multiple recommended doctors according to the sorting priority of each doctor to form the recommended doctor list.

[0141] In some embodiments of the present disclosure, steps 220 and 230 may include: according to the urgency level of the patient's symptoms , the historical medical records of the patient , the matching degree between the patient's symptoms and each doctor , the online status of each doctor , and the estimated consultation time of each doctor , at least one of the above, call the large model to determine the sorting priority T of each doctor, and then determine multiple recommended doctors according to the sorting priority of each doctor to form the recommended doctor list.

[0142] In some embodiments of the present disclosure, steps 220 and 230 may include: after the patient inputs symptoms or selects disease labels, the online medical appointment device calls the large model to analyze the patient's needs; the large model recommends the most suitable doctor according to the doctor's expertise labels and the current load situation.

[0143] In some embodiments of the present disclosure, the online medical appointment method may further include: updating the sorting priority of each doctor at a predetermined time interval.

[0144] Step 300, form a list of doctors to be appointed according to the multiple doctors selected by the patient terminal from the recommended doctor list.

[0145] Step 400, queue up and make an appointment for the multiple doctors to be appointed in the list of doctors to be appointed according to the patient information.

[0146] In some embodiments of the present disclosure, the online medical appointment method may further include: sorting the recommended doctors in the recommended doctor list according to the sorting priority of each doctor; sorting the doctors to be appointed in the list of doctors to be appointed according to the sorting priority of each doctor.

[0147] In some embodiments of the present disclosure, the online medical appointment method may further include: updating the sorting of the recommended doctors in the recommended doctor list and the sorting of the doctors to be appointed in the list of doctors to be appointed at a predetermined time interval.

[0148] Step 500, when the appointment of the first doctor to be appointed in the list of doctors to be appointed is successful, end the queuing appointment of the second doctor to be appointed in the list of doctors to be appointed, where the second doctor to be appointed is other doctors in the list of doctors to be appointed except the first doctor to be appointed.

[0149] The above embodiments of the present disclosure allow patients to select multiple doctors for queuing simultaneously and update the queuing status of each doctor in real time. In the above embodiments of the present disclosure, after the patient selects a doctor, the online medical appointment device will send an appointment request and record the queuing information, generate a unique queuing ID for each doctor for subsequent tracking, and regularly check the reception status of each doctor to update the patient's queuing information, including the waiting time and the current number of people in the queue. In the above embodiments of the present disclosure, on the patient interface, the queuing status and the estimated waiting time of each doctor are displayed. In addition, the above embodiments of the present disclosure can set priority rules according to factors such as the urgency of the patient, the patient's historical medical records, and the expertise of the doctor, and use a weighted algorithm to comprehensively consider multiple factors to determine the queuing order.

[0150] The above embodiments of the present disclosure can support patients to queue for multiple doctors for reception simultaneously. The above embodiments of the present disclosure support patients to queue for multiple doctors for reception simultaneously.

[0151] In some embodiments of the present disclosure, the present disclosure allows patients to select doctors in different specialties according to their own needs and symptoms. For example, patients with chest pain can select doctors in different specialties such as cardiology, cardiac surgery, and coronary heart disease specialty.

[0152] During the waiting period, in the above embodiments of the present disclosure, patients can view the availability and matching degree of doctors in real time through intelligent algorithms, flexibly adjust their priority selections to ensure more comprehensive medical advice and services in complex cases. This flexible queuing mechanism in the above embodiments of the present disclosure can add the doctors to be reserved. Patients can make dynamic adjustments among multiple candidate doctors, select the best-matched medical resources, utilize real-time data analysis and recommendation engines, intelligently recommend the most suitable doctors according to the patient's symptom description and historical medical records, and continuously update the priorities during the queuing process. The above embodiments of the present disclosure not only improve the medical treatment efficiency but also provide more choices for patients and optimize the overall medical treatment experience. In this way, in the above embodiments of the present disclosure, patients can effectively manage their time and medical needs to ensure timely medical services at critical moments.

[0153] Figure 2 It is a schematic diagram of some other embodiments of the online medical appointment method of the present disclosure. Preferably, this embodiment can be executed by the online medical appointment device of the present disclosure. As Figure 2 shown, Figure 2 In addition to the method steps of the Figure 1 embodiment, the method of the embodiment may further include at least one step from step 600 to step 700.

[0154] Step 600, display the recommended doctor list (i.e., the recommended interface) and the reserved doctor list (i.e., the reservation interface) through the patient terminal.

[0155] In some embodiments of the present disclosure, step 600 may include at least one of steps 610 to 620.

[0156] Step 610, for each recommended doctor in the recommended doctor list, display the doctor information of the recommended doctor, where the doctor information includes at least one of the current queue number, estimated consultation time, historical consultation situation, doctor level, doctor consultation price, and appointment option.

[0157] In some embodiments of the present disclosure, the appointment option is whether to make an appointment with the doctor or whether to add an appointment with the doctor.

[0158] In some embodiments of the present disclosure, step 610 may include: displaying a list of all available doctors, including doctor information such as the doctor's name, area of expertise, current queue number, estimated consultation time, etc.

[0159] In some embodiments of the present disclosure, step 610 may further include: displaying detailed information after clicking on a certain doctor, including the doctor's profile, patient evaluations, detailed disease tags of expertise, etc.

[0160] Figure 3 Schematic diagram of the recommended doctor list in some embodiments. As Figure 3 shown, the doctor information of each recommended doctor may include the doctor's name, doctor consultation price, current queue number, estimated consultation time, and whether there is an appointment option. As Figure 3 shown, the recommended doctor list includes system-recommended doctors A and B, historical consultation doctors C and D, resident doctors (doctors E and F), attending doctors (doctors G and H), deputy chief physicians (doctors I and J), and chief physicians (doctors K and L).

[0161] Figure 3 For the recommended doctor list shown, display the system-recommended doctors on the patient-side interface, and display the doctor information for multiple dimensions (such as supporting faster consultation, higher relevance to the consultation situation, lower price, etc.), for example: doctor consultation price, current queue number, estimated waiting duration. In addition, the present disclosure also sorts the doctors who the patient has historically consulted and doctors of different levels from low to high, giving the patient more room for choice. The patient can choose whether to add an appointment and select the additional doctors.

[0162] In some embodiments of the present disclosure, as Figure 3 shown, display the shortest consultation doctor duration selected at the bottom, display the highest price selected, and the total number of additional doctors.

[0163] Step 620: For each reserved doctor in the reserved doctor list, display the doctor information of the reserved doctor, where the doctor information includes at least one of the appointment queuing progress, the estimated consultation time, the historical consultation situation, the doctor level, and the doctor consultation price.

[0164] In some embodiments of the present disclosure, step 600 may include: The patient can select a doctor for reservation, and the online medical reservation device provides an estimated consultation time according to the current queuing situation.

[0165] In some embodiments of the present disclosure, step 600 may include: providing a function of displaying the reference sorting of consultation prices and waiting durations to help patients make more informed decisions when selecting doctors; collecting data such as the average consultation time of each doctor and the time distribution of patient arrivals; dynamically calculating the estimated consultation time in combination with the current number of queuing patients and the consultation efficiency of the doctor, and adjusting the estimated consultation time in real time according to the actual situation (such as doctor overtime, patient cancellation of reservation).

[0166] Step 700: Adjust the recommended doctor list and the reserved doctor list according to the input of the patient terminal.

[0167] In some embodiments of the present disclosure, step 700 may include at least one of steps 710 to 730.

[0168] Step 710: Add a doctor from the recommended doctor list to the reserved doctor list according to the input of the patient terminal.

[0169] Step 720: Delete a doctor from at least one of the recommended doctor list and the reserved doctor list according to the input of the patient terminal.

[0170] Step 730: Adjust the order of doctors in at least one of the recommended doctor list and the reserved doctor list according to the input of the patient terminal.

[0171] In some embodiments of the present disclosure, step 730 may include at least one of steps 731 to 732.

[0172] Step 731: Sort the recommended doctors in the recommended doctor list according to the order of any one of the sorting priority, the current number of queuing patients, the estimated consultation time, the historical consultation situation, the doctor level, and the doctor consultation price according to the input of the patient terminal.

[0173] Step 732: Sort the reserved doctors in the reserved doctor list according to the input of the patient terminal in the order of any one of the sorting priority, the appointment queuing progress, the expected consultation time, the historical consultation situation, the doctor level, and the doctor consultation price.

[0174] Through the above embodiments of the present disclosure, patients can view the consultation fees of different doctors according to their personal budgets and time arrangements and sort them by price. The transparent pricing mechanism in the above embodiments of the present disclosure increases patients' trust in medical services. At the same time, the above embodiments of the present disclosure display the expected waiting time of each doctor in real time. Thus, patients can reasonably arrange their appointment times according to the current number of people in the queue and the doctor's consultation speed. This information in the above embodiments of the present disclosure combines historical consultation data and real-time queuing situations to ensure that patients can quickly evaluate whether to select a certain doctor, thereby optimizing the medical experience. Through the dual display of the consultation price and the waiting time in the above embodiments of the present disclosure, patients can not only better manage their medical costs but also find the best balance between time and medical quality, thereby enhancing the overall satisfaction of patients.

[0175] Figure 4 It is a schematic diagram of some embodiments of the online medical appointment method of the present disclosure. Preferably, this embodiment can be executed by the online medical appointment device of the present disclosure. As Figure 4 shown, Figure 4 In addition to the method steps of Embodiment or Embodiment, the method of this embodiment may further include at least one of the steps from step 401 to step 410. Figure 1 Embodiment or Figure 2 Embodiment,

[0176] Step 401: Display all available doctor lists, where the information of each available doctor in the list includes the doctor's name, expertise field, current number of people in the queue, expected consultation time, and other information.

[0177] Step 402: When the patient clicks on a certain doctor through the patient terminal, display the detailed information of the doctor, where the detailed information includes the doctor's profile, patient evaluations, detailed disease expertise labels, etc.

[0178] Step 403: When the patient selects to reserve a certain doctor through the patient terminal, the online medical appointment device provides an expected consultation time according to the current queuing situation.

[0179] In some embodiments of the present disclosure, step 403 can be implemented as Figure 1 Steps 300 to 500 of Embodiment, and Figure 2 Step 600 of Embodiment.

[0180] Step 404, manage the queuing situation of doctors in real time, where the doctor queuing situation includes new appointments, appointment cancellations, updated estimated consultation times, etc.

[0181] Step 405, when the patient inputs symptoms or selects disease tags through the patient terminal, the online medical appointment device invokes a large model to analyze the patient's needs.

[0182] In some embodiments of the present disclosure, step 405 can be implemented as Figure 1 Step 100 of the embodiment.

[0183] Step 406, use the large model to analyze the patient's symptoms and needs, recommend suitable doctors; display the recommended doctor list on the interface so that the patient can view the detailed information of each doctor through the patient terminal.

[0184] In some embodiments of the present disclosure, step 406 may include: the large model recommends the most suitable doctor based on the doctor's expertise tags and current load situation.

[0185] In some embodiments of the present disclosure, step 406 can be implemented as Figure 1 Step 200 of the embodiment.

[0186] Step 407, when the patient selects a doctor through the patient terminal, the online medical appointment device displays the current number of people in the queue and the estimated consultation time.

[0187] In some embodiments of the present disclosure, step 407 can be implemented as Figure 1 Steps 300 to 500 of the embodiment, and Figure 2 Step 600 of the embodiment.

[0188] Step 408, when the patient confirms the appointment through the patient terminal, the online medical appointment device updates the queuing management system and adjusts the doctor's queuing situation and estimated consultation time.

[0189] In some embodiments of the present disclosure, step 407 can be implemented as Figure 1 Steps 300 to 500 of the embodiment, and Figure 2 Step 600 of the embodiment.

[0190] Step 409, the online medical appointment device regularly updates the doctor's queuing situation and estimated consultation time to ensure the accuracy of the information.

[0191] Step 410, display the patient's queuing progress and estimated consultation time in real time on the appointment interface through the patient terminal, so that the patient can view the real-time queuing progress and estimated consultation time on the appointment interface.

[0192] In some embodiments of the present disclosure, step 410 can be implemented as Figure 2 step 600 of the embodiment.

[0193] In the above embodiments of the present disclosure, the chief complaint information is collected through large model technology, whereby patients can express their symptoms more conveniently, reduce the barriers to information transmission, improve the medical treatment efficiency, and thus ensure that doctors obtain an accurate overview of the patient's condition when receiving patients.

[0194] In addition, through the above embodiments of the present disclosure, patients can queue up for multiple doctors at the same time, optimize the medical treatment order, and reduce the waiting time. The above embodiments of the present disclosure also support selecting the doctor to receive patients according to the disease labels of expertise, enabling patients to find the most suitable professional doctor and improving the accuracy of diagnosis and treatment.

[0195] Meanwhile, the above embodiments of the present disclosure can provide personalized doctor recommendations and medical treatment suggestions according to the patient's historical medical records and preferences, meeting the needs of different patients. This personalized service not only improves the patient satisfaction, but also enhances the pertinence of medical services.

[0196] Finally, the above embodiments of the present disclosure enhance the patient's trust in medical services through transparent price information display and patient evaluations, promoting a good relationship between doctors and patients. When choosing a doctor, patients can clearly understand the fees and the feedback of other patients, thus making a more informed decision and enhancing their trust in the medical system. This sense of trust helps to improve the doctor-patient relationship and promote more efficient communication and cooperation.

[0197] Figure 5 It is a schematic structural diagram of some embodiments of the online medical appointment device of the present disclosure. As Figure 5 shown, the online medical appointment device includes a memory 51 and a processor 52.

[0198] The memory 51 is used to store instructions. The processor 52 is coupled to the memory 51 and is configured to execute the online medical appointment method described in any embodiment of the present disclosure based on the instructions stored in the memory.

[0199] As Figure 5 shown, the online medical appointment device further includes a communication interface 53 for information interaction with other devices. Meanwhile, the online medical appointment device further includes a bus 54, and the processor 52, the communication interface 53, and the memory 51 complete mutual communication through the bus 54.

[0200] The memory 51 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory. The memory 51 may also be a memory array. The memory 51 may also be partitioned, and the partitions may be combined into virtual volumes according to certain rules.

[0201] In addition, the processor 52 can be a central processing unit (CPU), or can be an application specific integrated circuit (ASIC), or can be one or more integrated circuits configured to implement the embodiments of the present disclosure.

[0202] Figure 6 It is a schematic diagram of some other embodiments of the online medical appointment device of the present disclosure. As Figure 6 shown, the online medical appointment device of the present disclosure may include an information acquisition module 61, a first list determination module 62, a second list determination module 63, and a queuing appointment module 64.

[0203] The information acquisition module 61 is configured to acquire patient information and doctor information in response to an appointment request from a patient terminal.

[0204] In some embodiments of the present disclosure, the information acquisition module 61 can be configured to obtain a natural language description input by the patient terminal through an artificial intelligence interaction model; and obtain the patient information corresponding to the natural language description through natural language processing, where the patient information includes at least one of patient symptoms, the urgency of patient symptoms, and the patient's historical medical records.

[0205] The first list determination module 62 is configured to determine a recommended doctor list according to the patient information and the doctor information, and push it to the recommended doctor list.

[0206] In some embodiments of the present disclosure, the first list determination module 62 can be configured to determine the matching degree between the patient symptoms and each doctor according to the patient information and the doctor information; determine the sorting priority of each doctor according to the matching degree between the patient symptoms and each doctor; and determine a plurality of recommended doctors according to the sorting priority of each doctor to form the recommended doctor list.

[0207] In some embodiments of the present disclosure, when the first list determination module 62 determines the matching degree between the patient symptoms and the doctor according to the patient information and the doctor information, it can be configured to extract the patient symptoms from the patient information; extract the disease labels that the doctor is good at from the doctor information; process the patient symptoms and the disease labels that the doctor is good at using a standard disease classification system; and determine the matching degree between the patient symptoms and the doctor according to the processed patient symptoms and the disease labels that the doctor is good at.

[0208] In some embodiments of the present disclosure, when the first list determination module 62 determines the matching degree between the patient's symptoms and the doctor based on the processed patient's symptoms and the doctor's expertise disease tags, it may be configured to convert the processed patient's symptoms and the doctor's expertise disease tags into a patient symptom vector and a doctor's expertise disease tag vector; calculate the cosine similarity between the patient symptom vector and the doctor's expertise disease tag vector; and use the cosine similarity as the matching degree between the patient's symptoms and the doctor.

[0209] In some embodiments of the present disclosure, when the first list determination module 62 determines the sorting priority of each doctor based on the matching degree between the patient's symptoms and each doctor, it may be configured to determine the estimated consultation time of each doctor according to the doctor information; and determine the sorting priority of each doctor based on at least one of the matching degree between the patient's symptoms and each doctor and the estimated consultation time of each doctor.

[0210] In some embodiments of the present disclosure, when the first list determination module 62 determines the estimated consultation time according to the doctor information, it may be configured to extract at least one of the current queuing situation and the doctor's consultation efficiency from the doctor information; and calculate the estimated consultation time based on at least one of the current queuing situation and the doctor's consultation efficiency.

[0211] In some embodiments of the present disclosure, when the first list determination module 62 determines the estimated consultation time according to the doctor information, it may further be configured to adjust the estimated consultation time according to the actual situation of the doctor at a predetermined time interval, where the actual situation of the doctor includes at least one of the doctor's overtime situation and the patient's cancellation of the appointment.

[0212] In some embodiments of the present disclosure, when the first list determination module 62 determines the sorting priority of each doctor based on at least one of the matching degree between the patient's symptoms and each doctor and the estimated consultation time of each doctor, it may be configured to extract the urgency degree of the patient's symptoms from the patient information; and determine the sorting priority of each doctor based on at least one of the urgency degree of the patient's symptoms, the matching degree between the patient's symptoms and each doctor, and the estimated consultation time of each doctor.

[0213] In some embodiments of the present disclosure, when the first list determination module 62 determines the sorting priority of each doctor according to at least one of the urgency level of the patient's symptoms, the matching degree between the patient's symptoms and each doctor, and the estimated consultation time of each doctor, it may be configured to extract the patient's historical medical records from the patient information; extract the online status of each doctor from the doctor information; and determine the sorting priority of each doctor according to at least one of the urgency level of the patient's symptoms, the patient's historical medical records, the matching degree between the patient's symptoms and each doctor, the online status of each doctor, and the estimated consultation time of each doctor.

[0214] In some embodiments of the present disclosure, when the first list determination module 62 determines the sorting priority of each doctor according to at least one of the urgency level of the patient's symptoms, the patient's historical medical records, the matching degree between the patient's symptoms and each doctor, the online status of each doctor, and the estimated consultation time of each doctor, it may be configured to determine the sorting priority of each doctor according to the urgency level of the patient's symptoms, the urgency level weight, the patient's historical medical records, the medical record weight, the matching degree between the patient's symptoms and each doctor, the matching degree weight, the online status of each doctor, the online status weight, the estimated consultation time of each doctor, and the consultation time weight.

[0215] In some embodiments of the present disclosure, the online medical appointment device of the present disclosure may also be configured to increase the consultation time weight when the urgency level weight is greater than a predetermined value.

[0216] The second list determination module 63 is configured to form a list of doctors to be appointed according to multiple doctors selected by the patient terminal from the list of recommended doctors.

[0217] The queuing appointment module 64 is configured to queue and make an appointment for multiple doctors to be appointed in the list of doctors to be appointed according to the patient information; when the appointment of the first doctor to be appointed in the list of doctors to be appointed is successful, end the queuing appointment of the second doctor to be appointed in the list of doctors to be appointed, where the second doctor to be appointed is other doctors in the list of doctors to be appointed except the first doctor to be appointed.

[0218] In some embodiments of the present disclosure, the online medical appointment device of the present disclosure may also be configured to display the list of recommended doctors and the list of doctors to be appointed through the patient terminal; and adjust the list of recommended doctors and the list of doctors to be appointed according to the input of the patient terminal.

[0219] In some embodiments of the present disclosure, when the online medical appointment device of the present disclosure displays the recommended doctor list and the reserved doctor list through the patient terminal, it may be configured to perform at least one of the following operations: for each recommended doctor in the recommended doctor list, display the doctor information of the recommended doctor, where the doctor information includes at least one of the current queuing number, the estimated consultation time, the historical consultation situation, the doctor level, the doctor consultation price, and the reservation option; for each reserved doctor in the reserved doctor list, display the doctor information of the reserved doctor, where the doctor information includes at least one of the reservation queuing progress, the estimated consultation time, the historical consultation situation, the doctor level, and the doctor consultation price.

[0220] In some embodiments of the present disclosure, when the online medical appointment device of the present disclosure processes the recommended doctor list and the reserved doctor list according to the input of the patient terminal, it may be configured to perform at least one of the following operations: add a doctor from the recommended doctor list to the reserved doctor list according to the input of the patient terminal; delete a doctor in at least one of the recommended doctor list and the reserved doctor list according to the input of the patient terminal; adjust the order of doctors in at least one of the recommended doctor list and the reserved doctor list according to the input of the patient terminal.

[0221] In some embodiments of the present disclosure, when the online medical appointment device of the present disclosure adjusts the order of doctors in at least one of the recommended doctor list and the reserved doctor list according to the input of the patient terminal, it may be configured to perform at least one of the following operations: sort the recommended doctors in the recommended doctor list according to the order of any one of the sorting priority, the current queuing number, the estimated consultation time, the historical consultation situation, the doctor level, and the doctor consultation price according to the input of the patient terminal; sort the reserved doctors in the reserved doctor list according to the order of any one of the sorting priority, the reservation queuing progress, the estimated consultation time, the historical consultation situation, the doctor level, and the doctor consultation price according to the input of the patient terminal.

[0222] In some embodiments of the present disclosure, the online medical appointment device of the present disclosure may also be configured to perform the online medical appointment method described in any of the above embodiments of the present disclosure.

[0223] The above embodiments of the present disclosure can efficiently collect the patient's chief complaint information by using large model technology to ensure that doctors can comprehensively understand the patient's background. Through the above embodiments of the present disclosure, patients can make selections based on the doctor's expertise disease labels to ensure professional diagnosis and treatment.

[0224] The above embodiments of the present disclosure support patients to queue up for consultations with multiple doctors at the same time, flexibly arrange the order of seeing a doctor, and optimize the waiting time.

[0225] In addition, to help patients make informed decisions, the above embodiments of the present disclosure provide a reference ranking of consultation prices and a real-time display of waiting durations, enabling patients to comprehensively evaluate professionalism and consultation efficiency when choosing a doctor. The integration of these functions aims to provide patients with efficient, convenient, and personalized medical services.

[0226] According to another aspect of the present disclosure, there is provided a computer program product including a computer program, wherein when the computer program is executed by a processor, it implements the online medical appointment method as described in any of the above embodiments.

[0227] According to another aspect of the present disclosure, there is provided a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, they implement the online medical appointment method as described in any of the above embodiments of the present disclosure.

[0228] In some embodiments of the present disclosure, the computer-readable storage medium may be a non-transitory computer-readable storage medium.

[0229] The solutions of the above embodiments of the present disclosure guide users to describe their chief complaints as detailed as possible through a large model, and support patients to queue up for consultations with multiple doctors. In the above embodiments of the present disclosure, during the process of waiting for a doctor's consultation, patients can select the consulting doctor according to the actual consultation situation. This series of improvements in the above embodiments of the present disclosure can not only help patients find a suitable doctor faster, but also significantly improve the overall user experience and enhance patients' trust and satisfaction with the medical platform.

[0230] Those skilled in the art should understand that the embodiments of the present disclosure can be provided as a method, an apparatus, or a computer program product. Therefore, the present disclosure can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can adopt the form of a computer program product implemented on one or more computer-usable non-transitory storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0231] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate a means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0232] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction means that implements the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0233] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0234] The online medical appointment device, information acquisition module, first list determination module, second list determination module, and queuing appointment module described above can be implemented as a general-purpose processor, programmable logic controller (PLC), digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or any suitable combination thereof for performing the functions described in this application.

[0235] So far, the present disclosure has been described in detail. To avoid obscuring the concept of the present disclosure, some details well known in the art have not been described. Those skilled in the art can clearly understand how to implement the technical solutions disclosed herein based on the above description.

[0236] Those of ordinary skill in the art can understand that all or part of the steps to implement the above embodiments can be completed by hardware or can be completed by a program instructing relevant hardware. The program can be stored in a non-transitory computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a magnetic disk, an optical disc, etc.

[0237] The description of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or to limit the disclosure to the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments were chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure and design various embodiments with various modifications suitable for the particular purpose.

Claims

1. An online medical appointment method, comprising: Responding to an appointment request from a patient terminal, obtaining patient information and doctor information; Determine a recommended doctor list based on the patient information and the doctor information, and push the recommended doctor list to the patient; composing a reservation doctor list according to a plurality of doctors selected by the patient terminal from the recommended doctor list; For multiple appointment doctors in the appointment doctor list, queuing for appointments according to the patient information; When the first doctor in the doctor's appointment list is successfully booked, the queuing appointment of the second doctor in the doctor's appointment list is terminated, wherein the second doctor is a doctor in the doctor's appointment list other than the first doctor.

2. The online medical appointment method according to claim 1, wherein: Determining a recommended doctor list according to the patient information and the doctor information includes: Determining the matching degree between the patient's symptoms and each doctor according to the patient information and the doctor information; Determine the ranking priority of each doctor according to the matching degree between the patient's symptoms and each doctor; According to the ranking priority of each doctor, a plurality of recommended doctors are determined to form the recommended doctor list.

3. The online medical appointment method according to claim 2, wherein: Determining the ranking priority of each doctor according to the matching degree between the patient's symptoms and each doctor includes: Determine the estimated consultation time of each doctor based on the doctor information; The ranking priority of each doctor is determined according to at least one of the matching degree between the patient's symptoms and each doctor and the estimated consultation time of each doctor.

4. The online medical appointment method according to claim 3, wherein: Determining the estimated consultation time according to the doctor information includes: Extracting at least one of the current queuing situation and the doctor's reception efficiency from the doctor information; The estimated consultation time is calculated based on at least one of the current queuing situation and the doctor's consultation efficiency.

5. The online medical appointment method according to claim 4, wherein: Determining the estimated consultation time according to the doctor information also includes: The estimated consultation time is adjusted at predetermined time intervals according to the actual situation of the doctor, wherein the actual situation of the doctor includes at least one of the doctor's overtime and the patient's appointment cancellation.

6. The online medical appointment method according to any one of claims 3 to 5, wherein: Determining the sorting priority of each doctor according to at least one of the matching degree between the patient's symptoms and each doctor and the estimated consultation time of each doctor comprises: extracting the urgency of the patient's symptoms from the patient information; The ranking priority of each doctor is determined according to at least one of the urgency of the patient's symptoms, the matching degree between the patient's symptoms and each doctor, and the expected consultation time of each doctor.

7. The online medical appointment method according to claim 6, wherein: Determining the sorting priority of each doctor according to at least one of the urgency of the patient's symptoms, the matching degree between the patient's symptoms and each doctor, and the expected consultation time of each doctor comprises: Extracting the patient's historical medical records from the patient information; Extracting the doctor online status of each doctor from the doctor information; The sorting priority of each doctor is determined based on at least one of the urgency of the patient's symptoms, the patient's historical medical records, the matching degree of the patient's symptoms with each doctor, the online status of each doctor, and the estimated consultation time of each doctor.

8. The online medical appointment method according to claim 7, wherein: Determining the sorting priority of each doctor according to at least one of the urgency of the patient's symptoms, the patient's historical medical records, the matching degree between the patient's symptoms and each doctor, the online status of each doctor, and the expected consultation time of each doctor comprises: The sorting priority of each doctor is determined according to the urgency of the patient's symptoms, the urgency weight, the patient's historical medical records, the medical record weight, the matching degree of the patient's symptoms with each doctor, the matching weight, the online status of each doctor, the online status weight, the estimated consultation time of each doctor and the consultation time weight.

9. The online medical appointment method according to claim 8, further comprising: When the urgency weight is greater than a predetermined value, the consultation time weight is increased.

10. The online medical appointment method according to any one of claims 2 to 5, wherein: Determining the matching degree between the patient's symptoms and the doctor's symptoms according to the patient information and the doctor's information includes: extracting patient symptoms from the patient information; Extracting disease labels that doctors are good at from the doctor information; The patient's symptoms and the doctor's expertise in disease labeling are processed using a standard disease classification system; According to the processed patient symptoms and the disease labels that the doctor is proficient in, the matching degree between the patient symptoms and the doctor is determined.

11. The online medical appointment method according to claim 10, wherein: Determining the matching degree between the patient's symptoms and the doctor's disease labels after processing the patient's symptoms and the doctor's expertise includes: Convert the processed patient symptoms and the doctor's specialty disease labels into a patient symptom vector and a doctor's specialty disease label vector; Calculate the cosine similarity between the patient's symptom vector and the doctor's disease label vector; The cosine similarity is used as the matching degree between the patient's symptoms and the doctor's symptoms.

12. The online medical appointment method according to any one of claims 1 to 5, further comprising: Displaying the recommended doctor list and the scheduled doctor list through the patient terminal; The recommended doctor list and the scheduled doctor list are adjusted according to the input of the patient terminal.

13. The online medical appointment method according to claim 12, wherein: The displaying of the recommended doctor list and the scheduled doctor list by the patient terminal comprises at least one of the following steps: For each recommended doctor in the recommended doctor list, display the doctor information of the recommended doctor, wherein the doctor information includes at least one of the current number of people in the queue, the expected consultation time, the historical consultation situation, the doctor's grade, the doctor's consultation price and the appointment option; For each scheduled doctor in the scheduled doctor list, the doctor information of the scheduled doctor is displayed, wherein the doctor information includes at least one of the appointment queue progress, the expected consultation time, the historical consultation situation, the doctor grade and the doctor consultation price.

14. The online medical appointment method according to claim 12, wherein: The step of adjusting the recommended doctor list and the scheduled doctor list according to the input of the patient terminal includes at least one of the following steps: According to the input of the patient terminal, adding doctors from the recommended doctor list to the scheduled doctor list; Deleting doctors from at least one of the recommended doctor list and the scheduled doctor list according to input from the patient terminal; According to the input of the patient terminal, the order of doctors in at least one of the recommended doctor list and the scheduled doctor list is adjusted.

15. The online medical appointment method according to claim 14, wherein: The step of adjusting the order of doctors in at least one of the recommended doctor list and the scheduled doctor list according to the input of the patient terminal comprises at least one of the following steps: According to the input of the patient terminal, the recommended doctors in the recommended doctor list are sorted in the order of any one of sorting priority, current number of people in queue, expected consultation time, historical consultation situation, doctor level and doctor consultation price; According to the input of the patient terminal, the appointment doctors in the appointment doctor list are sorted in the order of any one of the sorting priority, the appointment queuing progress, the expected consultation time, the historical consultation situation, the doctor level and the doctor consultation price.

16. The online medical appointment method according to any one of claims 1 to 5, wherein: The step of obtaining patient information in response to the appointment request from the patient terminal includes: Obtain the natural language description of the patient's terminal input through the artificial intelligence interaction model; The patient information corresponding to the natural language description is obtained by natural language processing, wherein the patient information includes at least one of the patient's symptoms, the urgency of the patient's symptoms, and the patient's historical medical records.

17. An online medical appointment device, comprising: an information acquisition module, configured to acquire patient information and doctor information in response to an appointment request from a patient terminal; A first list determination module is configured to determine a recommended doctor list according to the patient information and the doctor information, and push the recommended doctor list to the patient; A second list determination module is configured to form a reservation doctor list according to a plurality of doctors selected by the patient terminal from the recommended doctor list; A queuing appointment module is configured to queue appointments for multiple appointment doctors in the appointment doctor list according to the patient information; When the first doctor in the doctor's appointment list is successfully booked, the queuing appointment of the second doctor in the doctor's appointment list is terminated, wherein the second doctor is a doctor in the doctor's appointment list other than the first doctor.

18. An online medical appointment device, comprising: a memory configured to store instructions; The processor is configured to execute the instruction so that the user equipment performs the method according to any one of claims 1 to 16.

19. A computer-readable storage medium, wherein: The computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the method according to any one of claims 1 to 16 is implemented.

20. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 16 is implemented.

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