阅读说明：本技术 网络问诊方法、计算机装置和存储介质 (Network inquiry method, computer device and storage medium ) 是由 成刚 于 2021-08-30 设计创作，主要内容包括：本发明公开了一种网络问诊方法、装置和存储介质。网络问诊方法包括：当进行在线问诊,排队轮至求诊信息,根据求诊信息匹配具有相应职业属性信息和空闲度信息的医务人员,建立第一终端与医务人员所使用的第二终端之间的在线会话；当进行离线问诊,缓存求诊信息,向第三终端发送求诊信息,接收第三终端的反馈信息,将反馈信息发送至第一终端等步骤。本发明可以实现网络在线问诊,能够避免患者与医生面对面接触,避免医生的精力受到患者影响,从而提高诊断效率、改善诊断效果以及避免医患摩擦；通过离线问诊模式可以将求诊信息缓存起来,待有诊断结果后再通知患者,无需患者花时间排队候诊,节约患者的时间。本发明可广泛应用于数字医疗技术领域。(The invention discloses a network inquiry method, a device and a storage medium. The network inquiry method comprises the following steps: when on-line inquiry is carried out, queuing is carried out until inquiry information is obtained, medical staff with corresponding occupational attribute information and vacancy information are matched according to the inquiry information, and an on-line session between a first terminal and a second terminal used by the medical staff is established; and when off-line inquiry is carried out, the inquiry information is cached, the inquiry information is sent to the third terminal, the feedback information of the third terminal is received, the feedback information is sent to the first terminal, and the like. The invention can realize online inquiry on network, can avoid the face-to-face contact between the patient and the doctor, and avoid the influence of the energy of the doctor on the patient, thereby improving the diagnosis efficiency, improving the diagnosis effect and avoiding the doctor-patient friction; the diagnosis information can be cached through the off-line inquiry mode, the patient is informed after the diagnosis result is available, the patient does not need to spend time waiting for a diagnosis in a queue, and the time of the patient is saved. The invention can be widely applied to the technical field of digital medical treatment.)

1. A method for on-line interrogation, comprising:

acquiring diagnosis information sent by a first terminal;

acquiring an inquiry mode selected by the first terminal; the inquiry mode comprises online inquiry and offline inquiry;

when the first terminal selects on-line inquiry, executing the following steps:

mapping the diagnosis information to a first queue for queuing;

acquiring occupation attribute information and vacancy information of medical personnel;

when the queue in the first queue takes turns to the diagnosis information, matching medical staff with corresponding professional attribute information and vacancy information according to the diagnosis information;

sending the diagnosis information to a second terminal; the second terminal is a terminal matched with one side of the medical staff;

when the second terminal is detected to confirm the diagnosis information, establishing an online session between the first terminal and the second terminal;

or, when the first terminal selects off-line inquiry, executing the following steps:

caching the diagnosis information;

sending the diagnosis information to a third terminal;

when receiving the feedback information of the third terminal, sending the feedback information to the first terminal; the third terminal is a terminal at one side of the medical staff.

2. The network interrogation method of claim 1, wherein after the step of establishing an online session between the first terminal and the second terminal, the network interrogation method further comprises:

setting a time limit of the online session;

maintaining the online session until the time limit is reached;

when the time length from the time limit is less than a preset threshold value, sending an expiration reminder to the first terminal;

and when the first terminal is detected to finish the delay operation, delaying the time limit.

3. The network interrogation method of claim 2, wherein the first terminal completes the delay operation, comprising:

sending a delayed fee order to the first terminal; the amount of the delayed fee order is positively correlated with the delay duration of the time limit;

the first terminal pays the delayed fee order;

alternatively, the first and second electrodes may be,

the first terminal sends a delay request to at least one fourth terminal; the fourth terminal is a sending terminal corresponding to other diagnosis seeking information queued in the first queue;

and the fourth terminal confirms that the delay request is approved.

4. The network interrogation method according to any one of claims 1 to 3, wherein the network interrogation method further comprises:

acquiring an artificial intelligence model;

inputting part or all of the diagnosis information into the artificial intelligence model for processing;

acquiring auxiliary diagnosis information output by the artificial intelligence model;

and sending the auxiliary diagnosis information to the second terminal.

5. The network interrogation method of claim 4, further comprising:

setting a plurality of medical staff groups; different groups of the medical staff have different high and low levels;

mapping each medical staff to a corresponding medical staff group according to the occupational attribute information of each medical staff;

acquiring artificial diagnosis information made by the medical staff aiming at the diagnosis information and auxiliary diagnosis information obtained by processing the same diagnosis information by the artificial intelligence model;

acquiring the coincidence degree between the artificial diagnosis information made by the medical staff and the auxiliary diagnosis information processed by the artificial intelligent model;

and adjusting the mapping relation between the medical staff and the medical staff group according to the goodness of fit.

6. The network interrogation method of claim 5, further comprising:

selecting a plurality of the medical staff members from the group of the medical staff members having the high and low levels higher than a preset threshold;

acquiring the manual diagnosis information made by the selected medical staff and the diagnosis information according to which the manual diagnosis information is made;

establishing a training data set by taking the obtained artificial diagnosis information as label data and taking the diagnosis information corresponding to the artificial diagnosis information as training input data;

training the artificial intelligence model using the training data set.

7. The network interrogation method according to any one of claims 1 to 3, wherein the acquiring of the interrogation mode selected by the first terminal comprises:

acquiring current length information of the first queue;

calculating to obtain the remaining queuing time information according to the current length information;

sending the remaining queuing time information to the first terminal;

controlling the first terminal to display the remaining queuing time information and displaying options for selecting the inquiry mode;

receiving a selection operation detected by the first terminal;

and determining the inquiry mode selected by the first terminal according to the selection operation.

8. The network interrogation method according to any one of claims 1 to 3, wherein the acquiring of professional attribute information and idleness information of medical staff comprises:

acquiring department information, professional direction information, working age information, job level information, expert scoring information, patient scoring information and assistant level information of medical personnel;

acquiring the historical number of receiving visits, the current number of receiving visits, the maximum number of receiving visits and the number of assistant persons of medical staff;

using the department information, professional direction information, working age information, job level information, expert scoring information, patient scoring information and assistant level information as the professional attribute information;

and determining the idleness information according to the weighted sum of the historical number of receiving diagnoses, the current number of receiving diagnoses, the maximum number of receiving diagnoses and the number of assistant persons.

9. A computer device comprising a memory for storing at least one program and a processor for loading the at least one program to perform the method of any one of claims 1-8.

10. A storage medium in which a processor-executable program is stored, wherein the processor-executable program, when executed by a processor, is configured to perform the network interrogation method of any one of claims 1 to 8.

Technical Field

The invention relates to the technical field of digital medical treatment, in particular to a network inquiry method, a computer device and a storage medium.

Background

At present, when a patient asks for a doctor, the patient needs to visit a medical institution such as a hospital and the like, and a great deal of time is spent for operations such as appointment registration, queue waiting and the like before the doctor visits; on the doctor side, because the number of patients who need to be met by doctors is large, the working time and the energy of the doctors are limited, and the visiting time and the energy distributed to each patient are also limited, the phenomena of waiting for two hours and seeing a doctor for five minutes widely exist, the doctor is tired, but the treatment effect obtained by the patient is not necessarily good. On the other hand, the patient is ill-conditioned by the disease, the doctor is difficult to provide a perfect service due to long-time mental work, and in the current treatment process, even the slight disease requires the patient and the doctor to be in face-to-face contact, so that the friction between the doctor and the patient is easily caused, and even the malignant event occurs.

In the prior inquiry informatization related technologies, the mature technologies are mainly hospital registration and call sign systems, but the systems only solve the problem of patient queuing, and the inquiry process after the patients finish queuing is the same as the conventional process and informatization is not realized. Some medical institutions provide a channel for the patient to communicate with the doctor through video chat, but are limited to the procedures of preliminary communication between the patient and the medical institution to know the medical level of the patient, limited preliminary consultation between the patient and the doctor before consultation, and the like, and the information exchange between the doctor and the patient is not sufficient. In summary, the problems of the related art include limited inquiry flow covered by informatization, and limited informatization degree of the specific inquiry flow.

Disclosure of Invention

In view of the above technical problems of low informatization degree of the inquiry flow, the present invention provides a network inquiry method, a computer device and a storage medium.

In one aspect, an embodiment of the present invention provides a method for online inquiry, including:

acquiring diagnosis information sent by a first terminal;

acquiring an inquiry mode selected by the first terminal; the inquiry mode comprises online inquiry and offline inquiry;

when the first terminal selects on-line inquiry, executing the following steps:

mapping the diagnosis information to a first queue for queuing;

acquiring occupation attribute information and vacancy information of medical personnel;

when the queue in the first queue takes turns to the diagnosis information, matching medical staff with corresponding professional attribute information and vacancy information according to the diagnosis information;

sending the diagnosis information to a second terminal; the second terminal is a terminal matched with one side of the medical staff;

when the second terminal is detected to confirm the diagnosis information, establishing an online session between the first terminal and the second terminal;

or, when the first terminal selects off-line inquiry, executing the following steps:

caching the diagnosis information;

sending the diagnosis information to a third terminal;

when receiving the feedback information of the third terminal, sending the feedback information to the first terminal; the third terminal is a terminal at one side of the medical staff.

Further, after the step of establishing the online session between the first terminal and the second terminal, the network inquiry method further includes:

setting a time limit of the online session;

maintaining the online session until the time limit is reached;

when the time length from the time limit is less than a preset threshold value, sending an expiration reminder to the first terminal;

and when the first terminal is detected to finish the delay operation, delaying the time limit.

Further, the first terminal completes the delay operation, including:

sending a delayed fee order to the first terminal; the amount of the delayed fee order is positively correlated with the delay duration of the time limit;

the first terminal pays the delayed fee order;

alternatively, the first and second electrodes may be,

the first terminal sends a delay request to at least one fourth terminal; the fourth terminal is a sending terminal corresponding to other diagnosis seeking information queued in the first queue;

and the fourth terminal confirms that the delay request is approved.

Further, the network inquiry method further comprises the following steps:

acquiring an artificial intelligence model;

inputting part or all of the diagnosis information into the artificial intelligence model for processing;

acquiring auxiliary diagnosis information output by the artificial intelligence model;

and sending the auxiliary diagnosis information to the second terminal.

Further, after the step of transmitting the auxiliary diagnosis information to the second terminal, the network inquiry method further includes:

acquiring manual diagnosis information returned by the second terminal;

according to the manual diagnosis information, confirming or adjusting the auxiliary diagnosis information;

and sending the confirmed or adjusted auxiliary diagnosis information to the first terminal.

Further, the network inquiry method further comprises the following steps:

setting a plurality of medical staff groups; different groups of the medical staff have different high and low levels;

mapping each medical staff to a corresponding medical staff group according to the occupational attribute information of each medical staff;

acquiring artificial diagnosis information made by the medical staff aiming at the diagnosis information and auxiliary diagnosis information obtained by processing the same diagnosis information by the artificial intelligence model;

acquiring the coincidence degree between the artificial diagnosis information made by the medical staff and the auxiliary diagnosis information processed by the artificial intelligent model;

and adjusting the mapping relation between the medical staff and the medical staff group according to the goodness of fit.

Further, the network inquiry method further comprises the following steps:

selecting a plurality of the medical staff members from the group of the medical staff members having the high and low levels higher than a preset threshold;

acquiring the manual diagnosis information made by the selected medical staff and the diagnosis information according to which the manual diagnosis information is made;

establishing a training data set by taking the obtained artificial diagnosis information as label data and taking the diagnosis information corresponding to the artificial diagnosis information as training input data;

training the artificial intelligence model using the training data set.

Further, the acquiring the inquiry mode selected by the first terminal includes:

acquiring current length information of the first queue;

calculating to obtain the remaining queuing time information according to the current length information;

sending the remaining queuing time information to the first terminal;

controlling the first terminal to display the remaining queuing time information and displaying options for selecting the inquiry mode;

receiving a selection operation detected by the first terminal;

and determining the inquiry mode selected by the first terminal according to the selection operation.

Further, the acquiring of the occupational attribute information and the vacancy information of the medical staff comprises:

acquiring department information, professional direction information, working age information, job level information, expert scoring information, patient scoring information and assistant level information of medical personnel;

acquiring the historical number of receiving visits, the current number of receiving visits, the maximum number of receiving visits and the number of assistant persons of medical staff;

using the department information, professional direction information, working age information, job level information, expert scoring information, patient scoring information and assistant level information as the professional attribute information;

and determining the idleness information according to the weighted sum of the historical number of receiving diagnoses, the current number of receiving diagnoses, the maximum number of receiving diagnoses and the number of assistant persons.

In another aspect, an embodiment of the present invention further provides a computer apparatus, including a memory and a processor, where the memory is used to store at least one program, and the processor is used to load the at least one program to perform the network inquiry method in the embodiment of the present invention.

In another aspect, an embodiment of the present invention further provides a storage medium, in which a processor-executable program is stored, and the processor-executable program is used to execute the network inquiry method in the embodiment of the present invention when executed by a processor.

The beneficial effects of the invention include: the online inquiry method in the embodiment can provide an online inquiry mode and an offline inquiry mode for a first terminal used by a patient, wherein the online inquiry mode can match the patient to a doctor with proper occupational attribute information and vacancy information according to the inquiry information of the patient, so that an online session between the patient and the doctor is established, online inquiry of a network is realized, the face-to-face contact opportunity between the patient and the doctor can be reduced, the influence of the patient on the energy of the doctor is relieved, the diagnosis efficiency is improved, the diagnosis effect is improved, and the doctor-patient friction is reduced; the diagnosis information can be cached for the doctor to process through the off-line inquiry mode, the patient is informed after the diagnosis result exists, and the patient does not need to spend time queuing for waiting for diagnosis, so that the time of the patient is saved, and the shortage degree of medical resources is relieved; the network inquiry method in the embodiment realizes informatization in the inquiry main links such as patient queuing, doctor and patient distribution communication and the like, and carries out interactive matching of the inquiry information of the patient, the professional attribute information and the vacancy information of the doctor in the process of queuing, doctor distribution and the like, so that the information interaction between the doctor and the patient is more sufficient, informatization is realized in the processes of on-line inquiry and off-line inquiry, and digital medical treatment with higher coverage degree is realized.

Drawings

FIG. 1 is a schematic view of an embodiment of a network inquiry method;

FIG. 2 is a flowchart of an embodiment of a method for web interrogation;

FIG. 3 is a schematic diagram illustrating a display effect of the first terminal when the inquiry mode is selected in the embodiment;

FIG. 4 is a diagram illustrating a display effect of a second terminal when a queuing queue is displayed in the embodiment;

FIG. 5 is a schematic diagram illustrating a display effect of a first terminal in an online session with a second terminal in the embodiment;

FIG. 6 is a diagram illustrating a display effect of a second terminal in an online session with a first terminal in the embodiment;

fig. 7 is a schematic diagram illustrating a display effect of a fourth terminal after receiving a delay request sent by a first terminal in the embodiment;

fig. 8 is a schematic diagram illustrating a display effect of the fourth terminal after receiving the delay request in the embodiment;

FIG. 9 is a schematic diagram illustrating a display effect of a third terminal when performing an offline inquiry in the embodiment;

FIG. 10 is a schematic diagram of an embodiment in which an artificial intelligence model is applied;

FIG. 11 is a schematic structural diagram of a computer device for performing a web-based inquiry method according to an embodiment of the present invention;

in the figure, 101 denotes a first terminal, 102 denotes a second terminal, 103 denotes a third terminal, and 104 denotes a fourth terminal.

Detailed Description

To make the objects, technical solutions and advantages of the present application more apparent, embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.

In this embodiment, the network inquiry method can be applied to the system shown in fig. 1. In fig. 1, a server may be provided by a medical institution such as a hospital or a third-party institution that provides a network service, and the server may be provided in the hospital or in the cloud. The server can be accessed by different terminals such as the first terminal, the second terminal, the third terminal and the fourth terminal, and performs data interaction with the terminals, so that the server can execute the network inquiry method in the embodiment.

In this embodiment, the medical staff may refer to a doctor, a nurse, a practice staff, a medical student, a nurse, a doctor assistant, or the like. The users of the first terminal and the fourth terminal may be patients, or may be persons who do not have a disease but wish to consult a doctor.

Referring to fig. 1, the first terminal and the fourth terminal are patient-side terminals used by a patient a, a patient b, a patient c, and the like, respectively. The second terminal and the third terminal are terminals on the doctor side, and are used by medical staff such as a doctor in charge of on-line inquiry and a doctor in charge of off-line inquiry. Specifically, the first terminal, the second terminal, the third terminal and the fourth terminal may be general mobile terminals such as a mobile phone, a tablet computer, a notebook computer and a smart watch, wherein the first terminal and the second terminal may be terminals and other devices set at a registration place of a hospital, and the third terminal and the fourth terminal may also be special terminals specially equipped for doctors by the hospital.

In this embodiment, for a general mobile terminal, a server may open APPs of different versions for terminal installation, for example, the server may open an APP of a patient side and an APP of a doctor side, and when the general mobile terminal installs the APP of the patient side, the mobile terminal may serve as a first terminal or a fourth terminal in this embodiment; when the general mobile terminal is installed with the doctor side APP, the mobile terminal may serve as the second terminal or the third terminal in this embodiment.

In this embodiment, for a general mobile terminal, the server may also open an APP of the same version for terminal installation, and identify the user type of the mobile terminal through different login accounts. For example, a patient may register a patient account in the server in advance, and use the patient account to log in the APP at the mobile terminal, then the mobile terminal may serve as the first terminal or the fourth terminal in this embodiment, that is, the patient side terminal; a doctor may register a patient account in the server in advance, and use the patient account to log in the APP at the mobile terminal, so that the mobile terminal may serve as the second terminal or the third terminal in this embodiment, that is, the doctor-side terminal.

In this embodiment, the network inquiry method is executed by the server. However, the method for performing the web inquiry by the server is only an exemplary illustration, and does not constitute a limitation to the subject matter of the present application.

Referring to fig. 2, the network inquiry method includes the steps of:

s1, acquiring diagnosis information sent by a first terminal;

s2, acquiring an inquiry mode selected by the first terminal; wherein the inquiry mode comprises on-line inquiry and off-line inquiry;

when the first terminal selects the on-line inquiry, the following steps are executed:

S301A, mapping the diagnosis information to a first queue for queuing;

S302A, acquiring occupational attribute information and vacancy information of medical staff;

S303A, when the queuing in the first queue is in turn to the diagnosis information, matching medical staff with corresponding professional attribute information and vacancy information according to the diagnosis information;

S304A, sending the diagnosis information to a second terminal;

S305A, when the second terminal is detected to confirm the diagnosis information, establishing an online session between the first terminal and the second terminal;

when the first terminal selects off-line inquiry, the following steps are executed:

S301B, caching the information of seeking diagnosis;

S302B, sending diagnosis information to a third terminal;

and S303B, when the feedback information of the third terminal is received, the feedback information is sent to the first terminal.

Before step S1 is executed, the patient may be required to register, log in, select a medical package, and pre-pay for medical expenses.

In step S1, the first terminal can be operated by the patient to input the information for diagnosis, wherein the information for diagnosis can include the name, identification number, age, sex, and other personal identification information of the patient, and the information for chief complaints and medical records. The complaint information can be represented by text, voice, picture, video and other data formats. The chief complaint information in the text format can be the text edited by the patient according to the disease symptoms and feelings of the patient; the complaint information in the voice format can be the words spoken by the patient according to the disease symptoms and feelings of the patient, or the recorded sound such as cough and the like which can describe the disease symptoms; the chief complaint information in picture and video format can be pictures or video clips obtained by shooting and recording the focus of a patient; the chief information in other data formats can be the body temperature and blood sugar and other data measured by the patient by using the terminal with the body temperature measuring function, the blood sugar detecting function and the like.

In step S2, the server may perform the following steps S201-S206:

s201, obtaining the current length information of a first queue;

s202, calculating to obtain the remaining queuing time information according to the current length information;

s203, sending the remaining queuing time information to a first terminal;

s204, instructing the first terminal to display the remaining queuing time information and displaying options for selecting an inquiry mode;

s205, receiving a selection operation detected by a first terminal;

s206, according to the selection operation, determining the inquiry mode selected by the first terminal.

The server may create at least one queuing sequence for queuing different types of patients, respectively. For example, the queue into which a patient is to be queued may be determined based on whether the patient is in an emergency, a medical package purchased by the patient, and the like. For example, two queuing queues, namely a first queue and a second queue, can be arranged in the server, wherein the first queue is a conventional queue and is used for queuing patients suffering from non-emergency diseases; the second queue is an emergency queue for queuing patients suffering from emergency diseases. In this embodiment, the user of the first terminal is taken as an example of a patient suffering from non-emergency diseases, and the patient is arranged in the first queue.

Therefore, in step S201, the server obtains the current length information of the first queue, i.e. how many individuals are currently queued in the first queue. In step S202, the server estimates the remaining queuing time information according to the current length information of the first queue and the change speed of the length of the first queue in the previous hour. In step S203, the server sends the current length information and the remaining queuing time information of the first queue to the first terminal, and instructs the first terminal to display the remaining queuing time information and display an option for selecting an inquiry mode in step S204, so that the display interface of the first terminal has a display effect as shown in fig. 3. Referring to fig. 3, the patient using the first terminal may view the estimated remaining queuing time, and in combination with his/her own needs, may click an "exit" button if he/she does not wish to perform the present inquiry, may click an "offline queuing" button if he/she wishes to perform offline queuing, and may click an "online queuing" button if he/she wishes to perform online queuing.

The first terminal detects the operation of the patient, generates an instruction according to the operation of the patient, and sends the instruction to the server. In step S205, the server receives an instruction from the first terminal, and recognizes a selection operation of the patient. In step S206, the server determines the inquiry mode selected by the patient according to the selection operation of the first terminal.

If the inquiry mode selected by the patient is online inquiry in step S2, the server may perform the following steps S301A-S305A:

S301A, mapping the diagnosis information to a first queue for queuing;

S302A, when the queue in the first queue takes turns to find the information of the diagnosis, acquiring professional attribute information and vacancy information of medical staff;

S303A, according to the diagnosis information, matching medical staff with corresponding professional attribute information and vacancy information;

S304A, sending the diagnosis information to a second terminal;

S305A, when the second terminal is detected to confirm the diagnosis information, establishing an online session between the first terminal and the second terminal;

in this embodiment, the second terminal is a terminal used by a doctor who is responsible for online inquiry, and therefore, in steps S301A to S305A, the second terminal receives the relevant information.

In step S301A, the server maps the diagnosis information to a first queue for queuing. Specifically, the server may write all or a critical part of the diagnosis information, such as the identity information, into the first queue for queuing, or the server may assign a number to the diagnosis information, and write the number into the first queue for queuing.

After step S301A is performed, the server may not send information about the queued queue, such as the first queue, to the first terminal used by the patient, so that the first terminal will only display the length of the first queue and the estimated remaining queuing time, and will not display information about other patients in the first queue. After step S301A, the server may send information about the queue, such as the first queue, to a second terminal used by the doctor, so that the first terminal will only display the length of the first queue and the estimated remaining queue time, but will not display information about other patients in the first queue

In step S302A, the server dynamically adjusts the first queue according to the diagnosis process of the doctor. Specifically, the server may queue the first queue using a first-in first-out method, and read out the diagnosis information in the first queue one by one according to the time written into the first queue, where the read-out diagnosis information is the diagnosis information that is turned round.

In step S302A, the server may also control the second terminal to display information of a queuing queue such as the first queue, and the display effect may be as shown in fig. 4. In fig. 4, the second terminal displays the patient information contained in the first queue in an avatar manner, and their order in the first queue. The doctor can operate in the second terminal, for example, drag the head portrait at any position in the first queue to the "quick reception" area, so that the diagnosis information corresponding to the dragged head portrait is the diagnosis information that is turned to. By the method, a doctor can autonomously select a patient for next inquiry according to the requirement of the current inquiry condition without being limited by queuing rules such as first-in first-out and the like.

In step S302A, if the doctor in charge of inquiry directly drags the head portrait of the patient in the first queue to the "quick visit" area, so that the doctor in charge of inquiry can directly perform inquiry on the patient selected by the doctor, the link of doctor assignment in step S303A and the link of doctor confirmation inquiry in step S304A can be skipped, and step S305A is directly executed to establish an online session between the first terminal and the second terminal. If the round of the diagnosis information in step S1 is naturally queued by the first queue "first in first out" or the like in step S302A, or the patient is selected by a general hospital administrator other than the referring physician by dragging the avatar, the diagnosis information of the round patient is not yet assigned to the corresponding referring physician, and step S303A is required to assign a physician to the round patient.

In step S303A, medical staff having corresponding professional attribute information and vacancy information are matched according to the diagnosis information uploaded by the patient through the first terminal.

In this embodiment, the professional attribute information of the doctor includes department information indicating which department the doctor works in the hospital, professional direction information indicating a medical history, a direction of academic research, a disease type mainly skilled in treatment, and the like, professional direction information indicating a medical history, a direction of academic research, and the like, professional direction information indicating a medical history of the doctor, a medical research direction, and a disease type mainly skilled in treatment, the professional year information indicating an accumulated medical life of the doctor, the medical grade information indicating a medical title of the doctor and a position assumed in the hospital, professional grade information indicating a medical history of the doctor or a score of the academic research evaluated by a peer expert, patient grade information indicating an evaluation of the doctor by a patient diagnosed by the doctor, and assistant grade information indicating a medical history, a medical grade, and the like of an assistant provided by the hospital for the doctor. This information reflects the professional literacy and professional competence of the doctor from different aspects, respectively.

In this embodiment, the idleness information of the doctor may be obtained by calculating data such as the historical number of received visits, the current number of received visits, the maximum number of received visits, and the number of assistant persons of the doctor. Specifically, the formula of the vacancy information may be:

idle＝10000-((0.3*queuesocre+0.2*consultscore+0.3*assstantsocre-0.2*occurtime socre+0.1)*10000)；

wherein, idle represents the idleness, queesocre represents the current number of the receiving diagnoses, consultscore represents the historical number of the receiving diagnoses, associatsecre represents the number of the assistant persons, and occurtimesocre represents the maximum number of the receiving diagnoses.

In step S303A, career attribute information and vacancy information that match the diagnosis information may be found in a matching manner item by item. For example, the chief complaint information in the information for consultation can be subjected to data cleaning, and keywords such as expression symptoms, severity and requirements for service attitudes can be screened out. Then, matching corresponding department information and professional direction information according to the keywords expressing the symptoms; matching corresponding working years information, job level information, expert scoring information and assistant level information according to the keywords expressing the severity degree, wherein for example, the more severe the symptoms reflected by the keywords are, the longer the working years, the higher the job level, the higher the expert scoring and the higher the assistant level are to be matched; the corresponding patient score information is matched according to keywords expressing requirements for service attitudes, e.g., higher patient scores are matched as requirements for service attitudes are higher.

According to the department information, professional direction information, working year information, job level information, expert scoring information, patient scoring information and assistant level information which are matched in the process, the professional attribute information to be matched can be determined, and doctors meeting the conditions can be screened out according to the matched professional attribute information.

And the server further matches doctors meeting the requirement of the vacancy information among the screened doctors meeting the condition of the professional attribute information. Specifically, the server selects a doctor with the highest idleness from the screened doctors meeting the condition of the professional attribute information as the finally matched doctor.

When the doctor assigned to the first terminal is determined in the step S303A, the second terminal used by the doctor is determined, and then the step S304A is performed to transmit the information for medical consultation to the second terminal. The second terminal may display a prompt to remind the doctor that there is a patient waiting for a call. The doctor may operate the second terminal to reject, ignore or confirm the referral information.

When the doctor operates the second terminal to confirm the information for medical consultation, the second terminal returns the confirmation operation of the doctor to the server, and the server executes step S305A to establish an online session between the first terminal and the second terminal.

In an online session between a first terminal and a second terminal, a display interface of the first terminal may be as shown in fig. 5, and a display interface of the second terminal may be as shown in fig. 6. Referring to fig. 5 and 6, a doctor and a patient can perform IM chat in an online session, rich communication tools and modes such as text input, video chat, image shooting and sign acquisition are provided in a chat interface, and the patient can upload information such as medical records and pictures of affected parts in the chat, so that the doctor can further diagnose. In the chat process, doctors can summarize the medical records of users, summarize and give out inquiry diagnosis, and can query the historical inquiry diagnosis and treatment information of patients in the chat session process, and in the treatment session process, doctors can provide diagnosis results, recommended medication suggestions and other diagnosis and treatment schemes for patients. When the online session is finished, the second terminal can automatically pop up a window to prompt a doctor to fill in a medical record summary for describing medical history, allergic history, contraindications, physical examination results and the like of a patient of the user, and the medical record summary is uploaded to the server and then stored in the cloud and can be sent to the first terminal for the patient to refer.

In this embodiment, the server may further set a time limit for the online session between the first terminal and the second terminal, start timing after the session is established, and maintain the online session between the first terminal and the second terminal before the time limit is reached; before the time limit is reached and the time length from the time limit is less than a preset threshold value, for example, 5 minutes before the time limit is reached, the server sends an expiration reminder to the first terminal, so that the patient is prompted that the online session is about to expire; after the time limit is reached, if the server does not detect that the first terminal completes the delay operation, the server ends the online session between the first terminal and the second terminal, and the patient using the first terminal is considered to end the inquiry process.

By setting the time limit of the online session, the inquiry process between the doctor and the patient can be properly limited in time, which is beneficial to improving the inquiry efficiency and enabling more patients to benefit.

In particular, the server may set a basic time limit for each patient, for example for patients with common diseases the basic time limit may be 20 minutes. The time limit of the basis can be adjusted according to the disease suffered by the patient, the disease belonging to the first or second diagnosis, the selected medical package and the like.

In this embodiment, the server may prompt the patient to complete the delay operation through the first terminal, thereby obtaining a longer time limit for the online session.

One of the delay operations is that the patient clicks "apply for delay" on the display interface shown in fig. 5, and then inputs the time desired to be extended in the pop-up input box, so as to trigger the server to generate a delay fee order, and the amount of the delay fee order is positively correlated with the time desired to be extended by the patient, for example, in a relationship of 5 yuan/minute or the like. After the patient operates the first terminal to pay the delayed fee order through the payment platform, the server prolongs the time limit of the online session between the first terminal and the second terminal.

Another delay operation is that the patient currently being asked operates the first terminal used by the patient, and sends a delay request to at least one fourth terminal through the server, where the fourth terminal refers to the terminal from which the other information for consultation that is queued in the first queue comes, that is, the terminal used by the other patient currently waiting for consultation who is queued behind the patient currently being asked. After the server forwards the delay request sent by the first terminal to the fourth terminal, the fourth terminal may display an interface as shown in fig. 7, and a patient using the fourth terminal may indicate agreement or disagreement with the delay request of the first terminal by clicking "accept" or "not accept" in the dialog box. When a fourth terminal grants the delayed request from the first terminal, the interface shown in fig. 8 may be further displayed at the fourth terminal, requiring the patient using the fourth terminal to input the inquiry time that is desirably assigned to the first terminal, and then subtracting the assigned inquiry time from the base time limit corresponding to the fourth terminal and adding the assigned inquiry time to the base time limit corresponding to the first terminal.

For example, if the patient of one of the fourth terminals wishes to transfer 5 minutes to the first terminal, then the inquiry time is subtracted from the basic time limit of this fourth terminal by 5 minutes, that is, the basic time limit of this fourth terminal is left for 15 minutes, and the online session time limit of the first terminal is extended by 5 minutes. If a patient with a plurality of fourth terminals wishes to assign the inquiry time to the first terminal, the server deducts the assigned time from the basic time period of each fourth terminal respectively, and the online conversation time period of the first terminal is prolonged by adjusting the sum of the inquiry times assigned by the fourth terminals.

Through a first time delay operation mode, the inquiry requirement of a patient using a first terminal can be met, and the benefit of a medical institution can be met. Through the second time delay operation mode, the inquiry demand of the patient using the first terminal can be ensured, the will of other patients can be respected, the treatment right of other patients is prevented from being influenced, the time limit transfer system can realize the balance demand among the patients, and the inquiry efficiency is improved.

If the inquiry mode selected by the patient is offline inquiry in step S2, the server may perform the following steps S301B, S302B, and S303B:

S301B, caching the information of seeking diagnosis;

S302B, sending diagnosis information to a third terminal;

and S303B, when the feedback information of the third terminal is received, the feedback information is sent to the first terminal.

After the patient using the first terminal selects off-line inquiry, the server performs step S301B to cache the inquiry information uploaded by the patient and store the information in the last or special queue of the first queue. After waiting for a special working hour or when the third terminal is turned on by a doctor who is specially responsible for off-line inquiry, the server performs step S302B to transmit the cached information for inquiry to the third terminal.

In this embodiment, a display interface of the third terminal is as shown in fig. 9, and the third terminal displays the diagnosis information uploaded by the patient to a doctor in charge of offline inquiry in a message-leaving manner. The doctor in charge of off-line inquiry can obtain a diagnosis result according to the diagnosis information according to professional knowledge, or think that the diagnosis information is not enough to determine the diagnosis result, the doctor can edit a feedback message according to the diagnosis result or a conclusion that the diagnosis result cannot be made, and upload the feedback message to the server. The server executes step S303B, and sends the received feedback information of the third terminal to the first terminal, and the patient can log in the APP again to check the feedback information, thereby obtaining the treatment result of the patient.

Through setting up the off-line inquiry mode, the patient can operate first terminal and withdraw from the APP after uploading the inquiry message, finishes the connection status between first terminal and the server, and the patient can wait to queue up and end beside first terminal without waiting for, can go to work or do other affairs to avoid spending long-time queuing, also can avoid robbing other really need queue up the medical resources of the patient who asks for.

In this embodiment, an artificial intelligence model is also introduced to assist in diagnosis. A convolutional neural network may be used as an artificial intelligence model.

Before using the convolutional neural network, a training data set is established to train the artificial intelligence model. Referring to fig. 10, the server first sets a plurality of medical staff members, and different medical staff members have different high and low levels. Specifically, three medical staff groups, i.e., a high-level medical staff group, a middle-level medical staff group, and a low-level medical staff group, may be set. The physicians are then initially grouped according to their professional attribute information such that each physician is mapped to a corresponding one of the groups of medical personnel. Specifically, different weights may be set for each attribute in the professional attribute information, each attribute in the professional attribute information is quantized and then a weighted average is obtained, then different numerical value ranges are set for each medical staff group, the numerical value range where the professional attribute information of a doctor is located is determined according to the weighted average corresponding to the professional attribute information of the doctor, the corresponding medical staff group is searched, and then the doctor is classified into the medical staff group during initial grouping.

Since the professional attribute information of the doctors in this embodiment is a standard for measuring the professional ability level of the doctors, the professional ability levels of different doctors can be distinguished through the professional attribute information, and the professional ability level difference of the doctors can be reflected by grouping the doctors. By grouping physicians according to the professional attribute information, the resulting physicians in the high-end medical staff group have a relatively highest level of expertise, the physicians in the medium-end medical staff group have a relatively medium level of expertise, and the physicians in the primary medical staff group have a relatively medium level of expertise.

Referring to fig. 10, in the present embodiment, it is considered that the professional ability level of the doctors in the medical staff group with the high and low levels higher than the preset threshold is high enough, so that the manual diagnosis information made based on the diagnosis request information independently is reliable medically. Therefore, the training data set can be constructed by searching the medical record, searching the manual diagnosis information made by doctors in the medical staff group with the high and low levels higher than the preset threshold value, and the diagnosis information based on the manual diagnosis information, using the searched manual diagnosis information as the label data, and using the diagnosis information corresponding to the searched manual diagnosis information as the training input data.

In this embodiment, the consultation information used for establishing the training data set may be the chief complaint information. Specifically, feature frame extraction can be performed on the video format in the complaint information, so that the complaint information in the video format is converted into an image format; for the chief complaint information in the text format and the image format, a feature vector can be extracted by using a feature extraction algorithm, so that the diagnosis information is converted into a corresponding feature vector. The feature vector is a data format that the convolutional neural network can handle. In this embodiment, since the diagnosis information can be converted to obtain the corresponding feature vector, the diagnosis information and the feature vector obtained by conversion thereof may not be distinguished.

Specifically, in the present embodiment, it is considered that the doctors initially grouped into the high-level medical staff group and the middle-level medical staff group have a sufficiently high level of expertise, and therefore, the manual diagnosis information made independently from the request information is medically reliable. Therefore, after the doctors in the high-level medical staff group and the middle-level medical staff group are selected, the artificial diagnosis information made by the doctors is searched, and the diagnosis information based on the artificial diagnosis information is made, so that the pairing relation of the diagnosis information 1-the artificial diagnosis information 1, the diagnosis information 2-the artificial diagnosis information 2, and … … diagnosis information n-the artificial diagnosis information n is formed, wherein the artificial diagnosis information n is the artificial diagnosis information independently made by one doctor in the high-level medical staff group or the middle-level medical staff group, and the diagnosis information n is the diagnosis information based on which the same doctor makes the artificial diagnosis information n. And forming a training data set by the diagnosis information, the artificial diagnosis information and the pairing relationship of the diagnosis information and the artificial diagnosis information.

When the convolutional neural network is trained by using a training data set, diagnosis information i in the convolutional neural network is used as input data of the convolutional neural network, and an output result i is obtained through convolution, pooling, nonlinear activation and other processing of the convolutional neural network; taking the artificial diagnosis information i corresponding to the diagnosis information i as expected output of the convolutional neural network, namely comparing the output result i with the artificial diagnosis information i to determine an error, when the error does not cause the convergence of the loss function, adjusting network parameters of a part or all layers of the convolutional neural network, and then performing next round of training, namely inputting the diagnosis information i +1 into the convolutional neural network, obtaining an output result i +1 obtained by processing the diagnosis information i +1, comparing the output result i +1 with the artificial diagnosis information i +1 to determine the error, and stopping training the convolutional neural network until the loss function converges.

After the convolutional neural network is trained, the convolutional neural network can be used to assist a doctor in diagnosis. Referring to fig. 6 and 9, both the second terminal used by the doctor in charge of online inquiry and the third terminal used by the doctor in charge of offline inquiry may display an "AI auxiliary diagnosis" button, and if the doctor clicks the button, the second terminal or the third terminal may transmit the diagnosis information transmitted by the patient to the server, and the server calculates and obtains a feature vector corresponding to the diagnosis information, and then inputs the feature vector to the trained convolutional neural network for processing, and uses the output result of the convolutional neural network as auxiliary diagnosis information.

And the server sends the auxiliary diagnosis information output by the convolutional neural network to the second terminal, and the second terminal displays the auxiliary diagnosis information for reference of a doctor. The auxiliary diagnosis information can be a text for recording prescriptions and medical orders, and doctors can directly operate the second terminal to confirm the auxiliary diagnosis information according to the professional knowledge of the doctors if the contents of the auxiliary diagnosis information are correct; if the basic content of the auxiliary diagnosis information is considered to be correct, but a place needing to be modified exists, the modification and the confirmation can be carried out on the basis of the auxiliary diagnosis information. And the second terminal uploads the confirmed or modified and adjusted auxiliary diagnosis information to the server, and the server sends the confirmed or modified and adjusted auxiliary diagnosis information to the first terminal for displaying.

In this embodiment, because the convolutional neural network is trained by the artificial diagnosis information independently made by the doctor with a higher professional ability level, the convolutional neural network obtains the classification processing ability of the diagnosis information, can process the diagnosis information uploaded by the first terminal operated by the patient, and outputs the auxiliary diagnosis information with medical diagnosis significance, and the doctor can refer to the auxiliary diagnosis information or even directly use the auxiliary diagnosis information as the diagnosis result, thereby assisting the doctor in diagnosing. Therefore, the inquiry efficiency of doctors can be improved by introducing artificial intelligence models such as a convolutional neural network for auxiliary diagnosis.

In this embodiment, referring to fig. 10, the mapping relationship between the medical staff and the medical staff group, that is, the medical staff group to which the doctor belongs, may be adjusted according to the matching degree between the artificial diagnosis information made by the doctor and the auxiliary diagnosis information output by the artificial intelligence model. Specifically, the server may input the diagnosis information looked by the doctor through the second terminal or the third terminal to the convolutional neural network no matter whether the doctor clicks the "AI auxiliary diagnosis" or not in the process of performing the online inquiry or the offline inquiry by the doctor, and acquire the auxiliary diagnosis information output by the convolutional neural network. Even if the doctor clicks the AI auxiliary diagnosis, the server does not immediately send the auxiliary diagnosis information to the second terminal or the third terminal, but prompts the doctor to diagnose according to the diagnosis information independently through the second terminal or the third terminal to obtain the manual diagnosis information, and inputs the manual diagnosis information to the second terminal or the third terminal to upload the manual diagnosis information to the server. And the server compares the artificial diagnosis information obtained by the doctor through independent diagnosis and the auxiliary diagnosis information output by the convolutional neural network aiming at the same diagnosis information, calculates the goodness of fit between the artificial diagnosis information and the auxiliary diagnosis information, and records the goodness of fit.

When the record of the goodness of fit between the manual diagnosis information and the auxiliary diagnosis information made by the same doctor is enough, the medical staff group to which the doctor belongs can be adjusted according to the goodness of fit. Specifically, an upper threshold value and a lower threshold value of the goodness of fit can be set, and if the goodness of fit between one piece of manual diagnosis information made by one doctor and the corresponding auxiliary diagnosis information is lower than the lower threshold value of the goodness of fit, the doctor is recorded as failing for one time; if the goodness of fit between the manual diagnosis information made by the doctor and the corresponding auxiliary diagnosis information is between the upper threshold value of goodness of fit and the lower threshold value of goodness of fit, recording that the doctor is qualified for one time; if the goodness of fit between one piece of manual diagnosis information made by the doctor and the corresponding auxiliary diagnosis information is higher than the goodness of fit upper limit threshold value, the doctor is recorded to be excellent. When the number of failures obtained by the doctor reaches a threshold value, the doctor can be downgraded, for example, the doctor originally belongs to the high-level medical staff group, and the doctor can be adjusted to belong to the middle-level medical staff group. When the number of excellences obtained by the doctor reaches a threshold, the doctor can be upgraded, for example, if the doctor originally belongs to the primary medical staff group, the doctor can be adjusted to belong to the intermediate medical staff group.

In addition, an upper threshold value and a lower threshold value of the goodness of fit can be set, the average value of all goodness of fit counted by a doctor in a period of time is calculated, and if the average value is higher than the upper threshold value of the goodness of fit, the doctor is upgraded; if the average value is lower than the lower threshold of the goodness of fit, performing degradation processing on the doctor; if the average value is between the upper threshold of goodness of fit and the upper threshold of goodness of fit, the group of medical staff to which this doctor belongs is maintained unchanged.

By adjusting the mapping relation between the medical staff and the medical staff group according to the goodness of fit between the artificial diagnosis information made by the doctor and the auxiliary diagnosis information output by the artificial intelligent model, the dynamic evaluation can be performed according to the work performance of the doctor in the process of performing the network inquiry by the doctor, the professional ability level of the doctor is improved, the growth of the doctor is facilitated, and the health of the patient is also facilitated. Moreover, because the medical staff in the original lower medical staff group is upgraded to the higher medical staff group because the matching degree of the manual diagnosis information made by the medical staff and the auxiliary diagnosis information made by the artificial intelligent network is high, and after upgrading to a higher level medical staff group, the manual diagnosis information made by the medical staff in the higher level medical staff group may be used to construct the training data set, so there are some medical staff, the process that 'the artificial diagnosis information made before is evaluated by the artificial intelligence network and the artificial diagnosis information made after is used for training the artificial intelligence network' exists between the artificial intelligence network and the artificial intelligence network, therefore, the effect of mutual training between the medical staff and the artificial intelligence network can be generated, the diagnosis capability level of the medical staff and the artificial intelligence network can be promoted, and the stable diagnosis standard is facilitated.

In an embodiment of the present invention, each step in the network inquiry method may be performed by using a computer device having the structure shown in fig. 11, where the computer device includes a memory 6001 and a processor 6002, where the memory 6001 is used for storing at least one program, and the processor 6002 is used for loading at least one program to perform the network inquiry method in the embodiment of the present invention. By operating the computer device, the same technical effect as the network inquiry method in the embodiment of the invention can be achieved.

In an embodiment of the present invention, there is provided a storage medium in which a processor-executable program is stored, wherein the processor-executable program, when executed by a processor, is configured to perform the network interrogation method in the embodiment of the present invention. By using the storage medium, the same technical effects as those of the network inquiry method in the embodiment of the present invention can be achieved.

It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Furthermore, the descriptions of upper, lower, left, right, etc. used in the present disclosure are only relative to the mutual positional relationship of the constituent parts of the present disclosure in the drawings. As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. In addition, unless defined otherwise, all technical and scientific terms used in this example have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description of the embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this embodiment, the term "and/or" includes any combination of one or more of the associated listed items.

It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element of the same type from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. The use of any and all examples, or exemplary language ("e.g.," such as "or the like") provided with this embodiment is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.

It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.

Further, operations of processes described in this embodiment can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described in this embodiment (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.

Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described in this embodiment includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein.

A computer program can be applied to input data to perform the functions described in the present embodiment to convert the input data to generate output data that is stored to a non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.

The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.