CN112185533A

CN112185533A - Remote medical system, remote medical doctor resource allocation method and storage medium

Info

Publication number: CN112185533A
Application number: CN202011143332.7A
Authority: CN
Inventors: 白先兵
Original assignee: Taikang Health Industry Investment Holdings Co ltd; Taikang Insurance Group Co Ltd
Current assignee: Taikang Health Industry Investment Holdings Co ltd; Taikang Insurance Group Co Ltd
Priority date: 2020-10-23
Filing date: 2020-10-23
Publication date: 2021-01-05
Anticipated expiration: 2040-10-23

Abstract

The present disclosure provides a remote medical system, a doctor resource allocation method for remote medical, an electronic device and a computer readable storage medium, and relates to the field of computer technologies. The telemedicine system includes: the first terminal is used for receiving the symptom description, the real-time physical sign data and the medical consultation request of the user and uploading the symptom description, the real-time physical sign data and the medical consultation request to the server; the second terminal is used for displaying the historical consultation data and the real-time physical sign data of the user, receiving a diagnosis result input by a doctor, sending the diagnosis result to a server and receiving an appointment return visit operation of the doctor; the server side comprises: the portrait module is used for obtaining portrait data of a doctor and portrait data of a user; the prediction module is used for predicting the number of the medical consultation requests in a first preset time period; and the distribution module is used for realizing the distribution of the doctor according to the doctor portrait data, the user portrait data and the prediction data obtained by the prediction module. The present disclosure may enable reasonable assignment of doctors.

Description

Remote medical system, remote medical doctor resource allocation method and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a remote medical system, a method for allocating doctor resources for remote medical, an electronic device, and a computer-readable storage medium.

Background

The development and popularization of computer and network technologies bring great convenience to production and life of people, people can enjoy various services without leaving home, and video inquiry is one of the services.

In order to meet the remote medical requirements of users such as video inquiry, doctors need to be arranged to receive a diagnosis. However, as each physician is skilled in the field differently, the time required to treat different patients varies greatly. The existing remote medical service cannot reasonably distribute doctors, and has the problems that the quantity of medical consultation does not correspond to the quantity of doctors, a user cannot match with a proper doctor and the like, so that the resource waste or the inquiry efficiency is low and the like.

Therefore, there is a need to provide a remote medical system, by which the number of doctors on duty can be reasonably arranged, and a suitable doctor can be matched for a user based on a global optimal idea, so that the processing efficiency of medical consultation requests is improved, and medical resources are saved.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

An object of the embodiments of the present disclosure is to provide a remote medical system, a method for allocating doctor resources for remote medical treatment, an electronic device, and a computer-readable storage medium, by which the processing efficiency of medical consultation requests can be improved and medical resources can be saved.

According to a first aspect of the present disclosure, there is provided a telemedicine system comprising:

the first terminal is used for receiving the symptom description, the real-time physical sign data and the medical consultation request of a user and uploading the symptom description, the real-time physical sign data and the medical consultation request to a server;

the second terminal is used for displaying the historical consultation data and the real-time physical sign data of the user, receiving a diagnosis result input by a doctor, sending the diagnosis result to a server and receiving an appointment return visit operation of the doctor;

a server, the server comprising:

the portrait module is used for obtaining corresponding portrait data of the doctor and portrait data of the user according to the basic information of each user and each doctor and the historical consultation data;

the prediction module is used for predicting the number of the medical consultation requests in a first preset time period according to the real-time sign data, the historical consultation data and the environment data of each user;

and the distribution module is used for realizing the distribution of the doctors according to the doctor portrait data, the user portrait data and the prediction data obtained by the prediction module so as to ensure that the first terminal and the second terminal realize medical consultation service according to the distribution result.

In an exemplary embodiment of the present disclosure, the first terminal includes a data acquisition module, a request response module, and a result receiving module, wherein:

the data acquisition module is used for acquiring the real-time physical sign data of the user through Internet of things equipment and uploading the real-time physical sign data to a server;

the request response module is used for receiving the medical consultation request and the symptom description of the user and uploading the medical consultation request and the symptom description to the server;

the result receiving module is used for receiving the diagnosis result and the callback request.

In an exemplary embodiment of the present disclosure, the first terminal further comprises a doctor selection module:

the doctor selection module is used for receiving the candidate doctor list sent by the distribution module, responding to the selection operation of the user on the candidate doctor list, uploading the result of the selection operation to the server, and sending the medical consultation request to the corresponding second terminal through the server.

In an exemplary embodiment of the present disclosure, the second terminal includes a receiving and displaying module, a diagnosing module and a sending module, and is provided with an interactive interface, wherein:

the receiving and displaying module is used for receiving the medical consultation request forwarded by the server and responding to the medical consultation request to display the symptom description, the historical consultation data and the real-time sign data of the user in the interactive interface;

the diagnosis module is used for receiving a diagnosis result input by the doctor;

the sending module is used for sending the diagnosis result to the server so as to complete the medical consultation service through the server.

In an exemplary embodiment of the present disclosure, the second terminal further includes a callback module;

the callback module is used for carrying out callback operation on the user in the reserved callback list; and the reserved callback list is determined by the server according to the symptom description and the user grade of the user.

In an exemplary embodiment of the present disclosure, the portrait module includes a doctor portrait unit and a user portrait unit, wherein:

the doctor portrait unit is used for obtaining the doctor portrait data according to the basic information of the doctor and the historical consultation data;

the user portrait unit is used for obtaining the user portrait data according to the basic information of the user and the historical consultation data.

In an exemplary embodiment of the present disclosure, the prediction module includes an analysis unit, a prediction unit, and a shift scheduling unit, wherein:

the analysis unit is used for analyzing and obtaining the physical condition of the user according to the real-time sign data;

the prediction unit is used for predicting the number of the medical consultation requests in a first preset time period according to the physical condition of the user, the historical consultation data and the environment data, and the environment data comprises weather conditions;

the scheduling unit is used for scheduling the doctor on duty in the first preset time period according to the prediction data obtained by the prediction unit and the current state of each doctor.

In an exemplary embodiment of the present disclosure, the allocation module includes an analysis unit, a matching unit, and a connection unit, wherein:

the analysis unit is used for analyzing and obtaining the number of newly added medical consultation requests and the number of idle doctors in a second preset time period and the current medical consultation requests and the number of idle doctors based on a preset supply and demand prediction model and the doctor scheduling result obtained by the prediction module;

the matching unit is used for matching doctors for each user according to the newly added medical consultation requests, the number of idle doctors, the current medical consultation requests, the number of idle doctors, the doctor portrait data and the user portrait data;

the connection unit is used for sending the medical consultation request to the second terminal corresponding to the matched doctor according to the matching result obtained by the matching unit.

In an exemplary embodiment of the present disclosure, the allocation module further includes a return unit:

the return unit is used for sending the matched candidate doctor list to the first terminal so that the user can select the doctor establishing the connection.

According to a second aspect of the present disclosure, there is provided a doctor resource allocation method for remote medical treatment, applied to a server in the remote medical treatment system, including:

receiving a symptom description, real-time sign data and a medical consultation request of a user uploaded by a first terminal, and analyzing to obtain the physical condition of the user based on the real-time sign data;

obtaining corresponding doctor portrait data and user portrait data according to the basic information of each user and each doctor and the historical consultation data;

predicting the number of the medical consultation requests in a first preset time period based on the physical condition of the user, historical consultation data and environmental data, and scheduling doctor duty in the first preset time period based on the predicted data;

analyzing and obtaining the newly added medical consultation requests and the number of idle doctors in a second preset time period and the current medical consultation requests and the number of idle doctors based on a preset supply and demand prediction model and the doctor scheduling results obtained by the prediction module;

and matching the doctor for each user according to the newly added medical consultation request, the number of the idle doctors, the current medical consultation request, the number of the idle doctors, the doctor portrait data and the user portrait data.

According to a third aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the above-described medical advice method via execution of the executable instructions.

According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described medical consultation method.

Exemplary embodiments of the present disclosure may have some or all of the following benefits:

the disclosed exemplary embodiment provides a remote medical system which comprises a first terminal, a second terminal and a server side. The first terminal is used for receiving the symptom description, the real-time physical sign data and the medical consultation request of the user and uploading the received symptom description, the real-time physical sign data and the medical consultation request to the server; the second terminal is used for displaying historical consultation data and real-time physical sign data of the user, receiving a diagnosis result input by a doctor, sending the diagnosis result to the server and receiving an appointment return visit operation of the doctor; the server side comprises a portrait module, a prediction module and a distribution module. The portrait module is used for obtaining corresponding portrait data of doctors and user portrait data according to basic information of users and doctors and historical consultation data; the prediction module is used for predicting the number of the medical consultation requests in a first preset time period according to the real-time sign data, the historical consultation data and the environment data of each user; and the distribution module is used for realizing the distribution of doctors according to the doctor portrait data, the user portrait data and the prediction data obtained by the prediction module so as to ensure that the first terminal and the second terminal realize the medical consultation service according to the distribution result. On one hand, the remote medical system provided by the exemplary embodiment can collect real-time sign data of users through the first terminal, and can predict the medical consultation number in a preset time period through the real-time sign data of each user in combination with historical consultation data and environmental data, so that a doctor can be reasonably scheduled to be on duty according to the predicted number, and supply and demand balance between the doctor and the users is realized. The medical consultation system can ensure that the medical consultation request of the user can be quickly and timely processed when the consultation amount is large, and can reduce the number of on-duty doctors, reduce the labor cost and save medical resources when the consultation amount is small. On the other hand, the server side of the remote medical system can also obtain portrait data of doctors and users through the portrait module, so that medical service can be provided for each user to match with proper doctors on the basis of ensuring global optimum according to the portrait data of doctors and users and the forecast result of the forecast module. On the other hand, in the above remote medical system, the doctor can also perform the scheduled return visit operation through the second terminal, that is, when the consultation amount is large, the doctor can perform the scheduled return visit on some users. In addition, the consultation amount can be pre-judged according to the prediction result of the prediction module, and emergency scheduling can be carried out, so that emergency situations can be well dealt with.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.

FIG. 1 illustrates a system architecture diagram of a telemedicine system to which embodiments of the present disclosure may be applied;

FIG. 2 shows a flow diagram of a process of generating doctor profile data according to one embodiment of the present disclosure;

FIG. 3 illustrates a flow diagram of a process of generating user representation data in accordance with one embodiment of the present disclosure;

FIG. 4 shows a flow chart of a physician scheduling process according to one embodiment of the present disclosure;

FIG. 5 shows a flow diagram of a physician-to-user matching process according to one embodiment of the present disclosure;

FIG. 6 shows a flow chart of a method for allocating physician resources for telemedicine, to which embodiments of the present disclosure may be applied;

FIG. 7 illustrates a schematic structural diagram of a computer system suitable for use with the electronic device to implement embodiments of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

However, as each physician is skilled in the field differently, the time required to treat different patients varies greatly. The existing video inquiry service cannot reasonably distribute doctors, and has the problems that the quantity of medical consultation does not correspond to the quantity of doctors, a user cannot match with a proper doctor, and the like, so that the resource waste or the inquiry efficiency is low, and the like.

In order to solve the above problem, the present exemplary embodiment first provides a remote medical system, and referring to fig. 1, an architecture diagram of the remote medical system provided by the present exemplary embodiment is shown, and as shown in the figure, the system architecture 100 may include a first terminal 110, a second terminal 120, and a server 130. Wherein:

the first terminal 110 may be configured to receive a symptom description, real-time physical sign data, and a medical consultation request of a user, and upload the symptom description, the real-time physical sign data, and the medical consultation request to a server;

the second terminal 120 may be configured to display historical consultation data and real-time physical sign data of the user, receive a diagnosis result input by a doctor, send the diagnosis result to the server, and receive an appointment return visit operation of the doctor;

the server 130 may include a rendering module 131, a prediction module 132, and an assignment module 133. Wherein the content of the first and second substances,

the portrait module 131 can be used for obtaining corresponding doctor portrait data and user portrait data according to the basic information of each user and each doctor and the historical consultation data;

the prediction module 132 may be configured to predict the number of medical consultation requests in a first preset time period according to the real-time sign data, the historical consultation data, and the environmental data of each user;

the distribution module 133 may be configured to implement distribution of doctors according to the doctor profile data, the user profile data, and the prediction data obtained by the prediction module, so that the first terminal and the second terminal implement medical consultation services according to the distribution result.

In the remote medical system provided by the exemplary embodiment of the present disclosure, on one hand, the remote medical system provided by the exemplary embodiment can collect real-time sign data of users through the first terminal, and predict the medical consultation number in a preset time period through the real-time sign data of each user in combination with historical consultation data and environmental data, so that a doctor can be reasonably scheduled to be on duty according to the predicted number, and supply and demand balance between the doctor and the users is realized. The medical consultation system can ensure that the medical consultation request of the user can be quickly and timely processed when the consultation amount is large, and can reduce the number of on-duty doctors, reduce the labor cost and save medical resources when the consultation amount is small. On the other hand, the server side of the remote medical system can also obtain portrait data of doctors and users through the portrait module, so that medical service can be provided for each user to match with proper doctors on the basis of ensuring global optimum according to the portrait data of doctors and users and the forecast result of the forecast module. On the other hand, in the above remote medical system, the doctor can also perform the scheduled return visit operation through the second terminal, that is, when the consultation amount is large, the doctor can perform the scheduled return visit on some users. In addition, the consultation amount can be pre-judged according to the prediction result of the prediction module, and emergency scheduling can be carried out, so that emergency situations can be well dealt with.

The details of the telemedicine system described above are further explained below:

in the present exemplary embodiment, the first terminal 110 may be configured as a user terminal, and the first terminal may include a data collection module, a request response module, and a result receiving module.

Specifically, the data acquisition module can be used for acquiring real-time physical sign data of the user through the internet of things device and uploading the real-time physical sign data to the server. The data acquisition module can be used for monitoring the body state of the user in real time to obtain real-time physical sign data of the user, the arrangement of a doctor can be improved based on the real-time physical sign data, the processing efficiency of medical consultation requests is improved, and the user experience is improved. For example, in the data acquisition process, the first terminal starts the internet of things device and acquires physical sign data of a user; or may be triggered and initiated by the user at any time during the medical consultation, which is not particularly limited in this example embodiment.

Wherein, above-mentioned thing networking device can be intelligent bracelet. For example, each user may be equipped with an intelligent bracelet, the physical sign data of the user is collected in real time through the intelligent bracelet, and when the user initiates a medical consultation request, the collected physical sign data of the user is uploaded to the server together with the medical consultation request. In addition, the internet of things device may also be another device that can collect real-time physical sign data of the user, and this is not particularly limited in this example embodiment.

The request response module can be used for receiving the medical consultation request and symptom description of the user and uploading the medical consultation request and the symptom description to the server. Wherein, the medical consultation request is a remote request initiated by the user for seeking medical diagnosis or suggestion. For example, the medical consultation request may be a video request, the user may initiate a video request to the server through the first terminal, and after receiving the video request, the server matches a suitable doctor for the user and establishes a video connection between the first terminal corresponding to the user and the second terminal corresponding to the doctor, so that the doctor provides medical service for the user through the video connection. In addition, the medical consultation request may be other remote requests such as a voice request, and this example embodiment is not particularly limited thereto.

In the present exemplary embodiment, in order to better match doctors to the user and improve the efficiency of medical services, the user also needs to input a symptom description when sending a medical consultation request through the first terminal. The symptom description refers to a description of the physical condition of the user, for example, the symptom description may be a clinical symptom representation of a disease, such as fever, cough, etc., and the symptom description may also be other expressions of the physical state according to the above definition, which is not limited by the present exemplary embodiment.

The result receiving module can be used for receiving the diagnosis result and the callback request. For example, when the medical consultation requests in the system in the same time period are too many, the server can screen out a part of users to be added into the reservation callback list according to actual conditions, a doctor can call back the users in the reservation callback list at the reservation time after the current time period, and the users receive the callback requests from the doctor through the result receiving module. It should be noted that the above scenario is only an exemplary illustration, and the scope of protection of the exemplary embodiment is not limited thereto.

In addition, the result receiving module can be used for receiving diagnosis results. The diagnosis result may be a medical condition judgment and medical advice obtained by a doctor based on the collected real-time physical sign data of the user and the symptom description input by the user, or may be other advice for improving the health condition of the user, which is not particularly limited in this exemplary embodiment. For example, the result receiving module may be a display device for displaying the diagnosis result to the user, or a voice device for the doctor to inform the user of the diagnosis result in real time during the medical consultation period. In addition, the result receiving module may also notify the diagnosis result to the user through other manners, which all belong to the protection scope of the present exemplary embodiment.

In this example embodiment, the server may reasonably assign doctors to the user through the matching module. For each user, the matching result of the matching module may be a doctor, and at this time, the server may directly forward the medical consultation request of the user to the matched doctor. In addition, the matching module can match one user to a plurality of doctors for the user to select, and under the situation, the server returns the candidate doctor list containing the plurality of doctors to the first terminal corresponding to the user for the user to select and consult doctors by self.

Specifically, in the above process, the first terminal may further include a doctor selection module, and the doctor selection module may be configured to receive a candidate doctor list sent by the server, and in response to a selection operation of the user on the received candidate doctor list, upload a result of the selection operation to the server, so as to send the medical consultation request to the corresponding second terminal through the server.

For example, after obtaining the candidate doctor list, the server may send the candidate doctor list to the first terminal, and display the candidate doctor list in a display interface of the first terminal. The user can obtain the medical service of the intended doctor by clicking the intended doctor in the candidate doctor list in the display interface and uploading the selection result to the server. It should be noted that the above scenario is only an exemplary illustration, and does not limit the protection scope of the exemplary embodiment. For example, the user may select his/her intended doctor from the doctor candidate list by other selection operations such as long pressing and voice control, which also belongs to the scope of protection of the present exemplary embodiment.

In the present exemplary embodiment, the second terminal 120 may be configured as a doctor terminal, and the second terminal may include a receiving and presenting module, a diagnosing module and a transmitting module. Meanwhile, in order to enable a doctor to visually and conveniently interact with the user side and the server side so as to complete medical service, the second terminal is further provided with an interactive interface, and the interactive interface can provide a convenient working operation interface for the doctor.

Specifically, the receiving and displaying module may be configured to receive a medical consultation request forwarded by the server, and display, in response to the medical consultation request, a symptom description, historical consultation data, and real-time sign data of the user in the interactive interface. For example, after the server side realizes the optimal matching between the users and the doctors in the system through the distribution module, the server side may send the medical consultation request initiated by each user to the second terminal corresponding to each matched doctor. The second terminal receives the medical consultation request sent by the server through the receiving and displaying module, and the symptom description, the historical consultation data and the real-time physical sign data input by the user who initiates the medical consultation request are displayed in the interactive interface, so that a doctor can check the symptom description, the historical consultation data and the real-time physical sign data and provide medical service for the user. The historical consultation data can include the history diagnosis records and the health record information of the user, so that a doctor can quickly know the medical history of the user through the historical consultation data, the misdiagnosis probability is reduced, and the quality and the efficiency of medical service are improved.

The diagnosis module can be used for receiving diagnosis results input by doctors. In the present exemplary embodiment, after the doctor obtains the diagnosis result based on the above-mentioned historical consultation data, symptom description and real-time sign data, the doctor can input the obtained diagnosis result into the diagnosis module. For example, the diagnostic module may include an input control, such as an input window, into which the physician may enter the diagnostic results. The diagnosis result may include information such as disease judgment, treatment suggestion, and medication suggestion, which is not particularly limited in this exemplary embodiment.

The sending module can be used for sending the diagnosis result to the server so as to complete medical consultation service through the server. For example, after the doctor inputs the input result into the diagnosis module, the sending module may upload the diagnosis result to the server, and send the diagnosis result to the user side from the server. In addition, the sending module can also directly send the diagnosis result to the user side, for example, when the medical consultation request is a video or telephone request, the sending module can be a voice collecting and sending device, correspondingly, the voice collecting and receiving device can be arranged at the user side, and a doctor can inform the diagnosis result to the user in real time by establishing voice connection between the doctor side and the user side. It should be noted that the above scenario is only an exemplary illustration, the scope of protection of the present exemplary embodiment is not limited thereto, and the sending module may send the diagnosis result to the corresponding user in other manners.

The second terminal may further include a callback module. The callback module can be used for carrying out callback operation on users in the reserved callback list; the reservation callback list is determined by the server according to symptom description and the user grade of the user. In the present exemplary embodiment, when the number of users initiating medical consultation requests is too large in a certain period of time, the system automatically screens out part of the clients according to the symptom description and user level of the users and puts the clients into an appointment callback list. In the appointment time, a doctor can actively dial back the users in the appointment callback list through the callback module, so that the user flow can be balanced, and the user satisfaction can be improved. In addition, the callback module can also be used for visiting and knowing the illness state of the user. For example, when the condition of a certain user needs to be tracked and known or guided by analysis, the user can also be added into the appointment callback list, and a doctor mainly dials back to the user at the appointed time according to the actual situation and the condition development condition so as to provide medical service.

In the exemplary embodiment, the server 130 of the remote medical system may further include a data information base, in addition to the image module, the prediction module and the assignment module, wherein the data information base may be used to store health files and historical consultation information of all users, and data such as basic information of users and doctors, and data information in the data base may be used to obtain information related to a certain user during a diagnosis process, and may also be used to generate user image data and doctor image data, which all belong to the protection scope of the exemplary embodiment.

Specifically, the portrait module may include a doctor portrait unit and a user portrait unit. The doctor portrait unit can be used for obtaining doctor portrait data according to the basic information and historical consultation data of a doctor; the user portrait unit can be used for obtaining user portrait data according to the basic information of the user and the historical consultation data.

The basic information of the doctor may include information indicating the identity and the specialty of the doctor, such as the name, age, sex, physical condition, field of attack, and medical age of the doctor. For example, the doctor profile unit may obtain doctor profile data by performing the method shown in fig. 2, and as shown in the figure, the method may include the following steps:

in step S210, historical advisory data is acquired.

In this step, the server can obtain historical consulting data in the system. For example, when the system is started for the first time, the server may obtain all current historical consulting data of the system from the data information base. After that, the newly added historical consultation data in the data information base can be acquired periodically so as to update the doctor portrait data and the user portrait data in real time. In addition, the step can also preferably acquire basic information of each doctor so as to obtain more accurate doctor portrait data. It should be noted that the above scenario is only an exemplary illustration, and the scope of protection of the exemplary embodiment is not limited thereto.

In step S220, the inquiry condition of the doctor is obtained according to the above historical consultation data.

In this step, after obtaining the above-mentioned historical consultation data, the inquiry condition of the doctor can be obtained based on the historical consultation data. For example, the treatment time and treatment effect required by the doctor for different diseases can be extracted from the historical consultation data. It should be noted that the above scenario is only an exemplary illustration, and the scope of protection of the exemplary embodiment is not limited thereto.

In step S230, the physician' S adequacy area is obtained according to the above analysis of the inquiry situation.

In this step, the physician' S field of expertise is analyzed according to the inquiry situation obtained through the history of consultation data at step S220. For example, the treatment time and treatment effect may be given a score according to a certain weighting rule, and the disease condition and area that each doctor is skilled in treating may be obtained according to the score. It should be noted that the above scenario is only an exemplary illustration, and the scope of protection of the exemplary embodiment is not limited thereto.

In step S231, the physician' S input of the excel label is received.

In step S240, doctor image data is obtained.

In this step, the areas where the doctors are skilled in the process, which are obtained by analyzing the historical consultation data in step S230, and the areas and features of the doctors, which are strong in the process, which are received by the doctors in step S231, may be comprehensively analyzed. And acquiring doctor image data corresponding to each doctor based on the comprehensive analysis result and the basic information of the doctor.

The basic information of the user may include the name, age, sex, occupation, medical history and the like of the user, which are used to represent the identity and the information related to the physical condition of the user. For example, the user representation unit may obtain user representation data by performing the method shown in FIG. 3, which may include the following steps:

in step S310, historical consultation data is acquired.

In this step, historical consulting data corresponding to the user may be obtained. For example, the step may obtain all or updated historical consulting data of the system, so as to generate user image data according to the historical consulting data. In addition, the process of acquiring the historical consulting data can also be as follows: for example, the identity information of the corresponding user, such as the user telephone, the user IP address, etc., may be obtained from the current medical consultation request of the system, and the historical consultation data corresponding to each user may be obtained from the data information base according to the obtained identity information. Further, basic information of the user can be inquired in a data information base. It should be noted that the above scenario is only an exemplary illustration, and the scope of protection of the exemplary embodiment is not limited thereto.

In step S320, a health profile of the user is obtained.

In this step, after the historical consulting data is obtained, the health profile of the user can be extracted from the historical consulting data. The historical consultation data can comprise historical consultation records, inquiry records, health files and the like of the user.

In step S330, user image data is obtained.

In this step, user image data corresponding to each user is obtained based on the obtained health file and the basic information of the user.

The above scenario is only an exemplary illustration, and the doctor image unit and the user image unit may obtain the doctor image data and the user image data in other manners, which also belongs to the protection scope of the present exemplary embodiment.

In order to reasonably arrange the on-duty of doctors and avoid the problems of medical resource waste, untimely processing, reduced service quality and the like caused by less doctors on duty when the consultation amount is large or more doctors on duty when the consultation amount is small, the service end can predict the number of medical consultation requests in a period of time through the prediction module. Specifically, the prediction module may analyze the unit, predict the unit and schedule the unit:

the analysis unit can be used for analyzing and obtaining the physical condition of the user according to the real-time physical sign data. For example, the data such as the heart rate, the blood pressure, the body temperature, etc. of the user can be obtained according to the real-time physical sign data, and the real-time physical condition of the user can be obtained according to the data and by combining the historical consultation data of the user.

The prediction unit may be configured to predict the number of medical consultation requests in the first preset time period according to the physical condition of the user analyzed by the analysis unit, and the historical consultation data and the environmental data. The environmental data is an objective environmental factor that may affect the state of a disease or a physical condition, for example, the environmental data may include a weather condition, or may include other environmental factors that meet the above definition, which fall within the scope of the present exemplary embodiment. The first preset time may be determined according to actual situations, for example, when a certain hospital is a shift on a monday, the first preset time may be set to be one week, and depending on different situations, the first preset time may also be set to be other time lengths, which also belongs to the protection scope of the present exemplary embodiment.

The above process of predicting the number of medical consultation requests in the first preset time period according to the physical condition of the user, the historical consultation data and the environmental data can be as follows, for example: and analyzing the number of users who are possibly suffered from diseases and are aggravated by the existing diseases in the current environment according to the current physical condition of the users and the historical consultation data and the environmental data, and deducing the number of the generated medical consultation requests. It should be noted that the above scenario is only an exemplary illustration, and the scope of protection of the exemplary embodiment is not limited thereto.

The scheduling unit may be configured to schedule the doctor on duty in the first preset time period according to the prediction data obtained by the prediction unit and the current status of each doctor. The prediction data is the number of the medical consultation requests in the first preset time period predicted by the prediction unit. The current status of each doctor may include health status of the doctor, recent attendance, sex characteristics, and the like.

Taking the first preset time period as one week as an example, the process of implementing shift scheduling may be as follows: the number of the medical consultation requests in the future week predicted by the prediction unit is obtained, the number of doctors in each field is predicted, and the duty schedule of the doctors in the future week is obtained by combining the current state of each doctor, such as whether the doctor is in the late shift (the doctor cannot be in the early shift on the next day), whether the doctor is in the leave, whether the doctor likes and identifies the doctor in the shift, whether the doctor is in the male or female, whether the doctor is pregnant, whether the doctor is uncomfortable, and the like.

The process of the prediction module for predicting the consultation amount and scheduling the doctor is further described below with reference to the flow shown in fig. 4, and as shown in the figure, the process includes the following steps:

in step S410, real-time physical sign data of the user is acquired.

In this step, the real-time physical sign data may be uploaded by the first terminal. For example, the first terminal can be used for monitoring the physical sign data of the user in real time, the real-time monitoring can be realized by configuring an intelligent bracelet for each user in the system, and the intelligent bracelet can be used for monitoring the physical sign data of the user in real time and uploading the physical sign data to the server.

In step S411, the real-time physical sign data is analyzed to obtain the physical condition of the user.

In this step, the physical condition of the user is analyzed based on the obtained real-time physical sign data. For example, whether the user has fever or not may be determined based on the body temperature of the user, and the present exemplary embodiment is not particularly limited thereto.

In step S420, the history consultation data of the user is acquired.

In this step, historical consulting data of the user is acquired. Specifically, the information may be obtained by querying the data information base.

In step S430, environment data is acquired.

In this step, environmental data that may have an influence on the physical condition of the user is acquired. For example, weather conditions within a first preset time.

In step S440, the service volume is predicted.

In this step, the service volume within the first preset time, that is, the number of medical consultation requests, may be predicted based on big data and machine learning techniques according to the analyzed physical condition of the user, and historical consultation data and environmental data. For example, a prediction model may be obtained by training the historical data of the parameters, and the service volume in the first preset time is obtained by prediction of the prediction model. The prediction may be performed in other ways, and the scope of protection of the present exemplary embodiment is not limited thereto.

In step S450, a traffic constraint is acquired.

In this step, the service constraint conditions may be determined according to the current status of the doctor in the system, such as whether the previous day is late shift (next day is not early shift), whether the doctor asks for leave, shift preference, identity, whether the doctor is male or female, whether the doctor is pregnant, whether the doctor is uncomfortable, and the like.

In step S460, a doctor' S schedule of attendance within a first preset time is obtained.

In addition, in the exemplary embodiment, after the doctor reaches a certain amount, in the case that the doctor cannot process the disease, the doctor can reasonably arrange some users to reserve a period of time from the time when the doctor is not busy to the time when the doctor is not busy, and large-area queuing is reduced.

Meanwhile, the remote medical system provided by the embodiment of the invention can predict the upcoming large flow in advance through the prediction module, and intelligently and emergently send a doctor to watch.

The purpose of optimal allocation is to match the user with the most suitable doctor, so that the doctor has high treatment efficiency and the user satisfaction is high. But standing at a global perspective, the optimal doctor for multiple users may be the same at the same time. Therefore, in order to improve the treatment efficiency and ensure that as many users are treated as possible, the distribution module adopts a global optimal distribution strategy, and distributes the most suitable doctor to each user while ensuring the global optimal. Specifically, the distribution module may include an analysis unit, a matching unit, and a connection unit:

the analysis unit can be used for analyzing and obtaining the number of newly added medical consultation requests and the number of idle doctors in a second preset time period and the current medical consultation requests and the number of idle doctors based on a preset supply and demand prediction model and the doctor scheduling result obtained by the prediction module. The supply and demand forecasting model is a pre-trained model representing the supply and demand relationship between a doctor and a user. The second preset time may be a user waiting time expected by the system, and the system is expected to ensure that the user in the system obtains medical services or is added to the appointment callback list during the waiting time, or may be other time lengths set according to actual situations, which is not particularly limited in this exemplary embodiment.

The matching unit can be used for matching doctors for each user according to the newly added medical consultation requests and the number of idle doctors, the current medical consultation requests and the number of idle doctors, and the doctor portrait data and the user portrait data.

The connecting unit can send the medical consultation request to the second terminal corresponding to the matched doctor according to the matching result obtained by the matching unit.

The process of the above-mentioned assignment module to achieve the optimal assignment between the user and the doctor is further described below with reference to the flow shown in fig. 5, as shown in the figure, the process includes the following steps:

in step S510, a medical consultation request of the user is received.

In step S520, the waiting number of users is calculated.

In this step, the system will query the number of users currently waiting to answer and predict the number of users that will appear within a second preset time. For example, taking the second preset time as 30 seconds as an example, the process may find the number of users currently waiting for listening and predict the number of users who will dial in within 30 seconds, so as to obtain the waiting number of users.

In step S530, it is determined whether the number relationship between the user and the doctor satisfies a first preset condition.

In this step, the first preset condition is used to limit the number of users in the same time period in the system according to the number relationship between the users and the doctor, and determine to add the users into the waiting answering list or the reserved callback list. If the first preset condition is not satisfied, the process goes to step S531, and if so, the process goes to step S532.

Taking the first preset condition as an example whether the waiting number of the user exceeds 120% of the number of doctors, the process may be: and judging the relation between the user waiting number and the doctor number, if the user waiting number waiting for answering does not exceed 120% of the doctor number, skipping to step S531, and otherwise, skipping to step S532.

In step S531, a waiting listening list is entered.

In step S532, it is determined whether the user satisfies a second preset condition.

In this step, the second preset condition may be whether the user level score reaches a preset threshold. Wherein, the user grade score can be obtained according to the user portrait data. For example, taking the preset threshold as 10 minutes as an example, the process of this step may be: judging whether the grade score of the user exceeds 10 points according to the user portrait data, if so, jumping to the step S531; if not, go to step S540. In addition, the second preset condition may also be considered comprehensively, such as the urgency of the condition of the user, which falls within the scope of protection of the present exemplary embodiment.

In step S540, the reservation call-back list is entered.

In this step, the user who is judged to be added to the reservation call-back list in steps S530 and S532 is added to the list, and a reservation time period is recommended to the user according to actual conditions.

In step S550, a current idle doctor list and a new idle doctor list added within a second preset time are obtained.

In this step, after the user enters the waiting list, the system calculates the number of doctors currently available and the number of doctors available within a second preset time. Taking the second preset time as 30 seconds as an example, the system calculates the number of doctors who are currently idle and the number of doctors who will be idle within 30 seconds.

In step S560, a list of doctors and a list of users who need matching are acquired.

In this step, the system lists the list of doctors and users that need to be matched, for example, m clients joining the waiting list and n doctors that are currently idle and will idle for a second preset time.

In step S570, the time required for each doctor to process the medical consultation request of each user is calculated based on the user image data and the doctor image data.

In step S580, an optimal matching list is calculated.

In this step, taking the above scenario as an example, the problem of calculating the optimal matching list may be analogized to calculating m customers, n doctors, and how to obtain the optimal solution for how long each doctor processes a certain customer is x. For example, an annealing algorithm may be simulated to obtain an optimal solution to the problem.

In step S590, the medical consultation request of the user is transmitted to the corresponding doctor according to the matching result obtained by the matching unit.

In this exemplary embodiment, the assignment module may further include a return unit, and the return unit may be configured to send the matched candidate doctor list to the first terminal, so that the user selects a doctor who establishes a connection. For example, when the system matches more than one doctor to the user, the matched candidate doctor list can be sent to the first terminal through the returning unit for the user to select the intended doctor.

Further, the present exemplary embodiment also provides a method for allocating doctor resources for remote medical treatment, which is applied to the server in the remote medical treatment system, and as shown in fig. 6, the method may include the following steps:

step S610: and receiving the symptom description, the real-time physical sign data and the medical consultation request of the user uploaded by the first terminal, and analyzing the real-time physical sign data to obtain the physical condition of the user.

Step S620: and acquiring corresponding doctor portrait data and user portrait data according to the basic information of each user and each doctor and the historical consultation data.

Step S630: predicting the number of the medical consultation requests in a first preset time period based on the physical condition of the user, historical consultation data and environmental data, and scheduling doctor duty in the first preset time period based on the prediction data.

Step S640: and analyzing to obtain the newly added medical consultation requests and the number of idle doctors in a second preset time period and the current medical consultation requests and the number of idle doctors based on a preset supply and demand prediction model and the doctor scheduling results obtained by the prediction module.

Step S650: and matching the doctor for each user according to the newly added medical consultation request, the number of the idle doctors, the current medical consultation request, the number of the idle doctors, the doctor portrait data and the user portrait data.

The details of the method for allocating doctor resources for remote medical treatment are described in detail in the corresponding modules and units of the remote medical treatment system, and therefore are not described herein again.

It should be noted that the computer system 700 of the electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments of the present disclosure.

As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for system operation are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.

As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method as described in the embodiments below. For example, the electronic device may implement the steps shown in fig. 2 to 6, and the like.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

It should be noted that the computer readable media shown in the present disclosure may be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A telemedicine system, comprising:

a server, the server comprising:

2. The telemedicine system of claim 1, wherein the first terminal comprises a data acquisition module, a request response module, and a result receiving module, wherein:

3. The telemedicine system of claim 2, wherein the first terminal further comprises a physician selection module:

4. The telemedicine system of claim 1, wherein the second terminal comprises a receiving and presenting module, a diagnosis module and a sending module, and is provided with an interactive interface, wherein:

5. The telemedicine system of claim 4, wherein the second terminal further comprises a callback module;

6. The telemedicine system of claim 1, wherein the representation module comprises a doctor representation unit and a user representation unit, wherein:

7. The telemedicine system of claim 1, wherein the prediction module comprises an analysis unit, a prediction unit, and a shift unit, wherein:

8. The telemedicine system of claim 1, wherein the assignment module comprises an analysis unit, a matching unit, and a connection unit, wherein:

9. The telemedicine system of claim 8, wherein the dispensing module further comprises a return unit:

10. A remote medical doctor resource allocation method applied to the server in the remote medical system according to claim 1, comprising:

11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the doctor assignment method as claimed in claim 10.

12. An electronic device, comprising:

a processor;

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the doctor assignment method of claim 10 via execution of the executable instructions.