CN114242231A - Remote medical consultation method and system based on block chain - Google Patents
Remote medical consultation method and system based on block chain Download PDFInfo
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Abstract
The invention discloses a remote medical consultation method and a system based on a block chain, which solve the problems of untimely remote medical consultation and uneven medical resource distribution in the prior art by adopting an advanced remote medical technology; through the combination of technologies such as a database, terminals and application programs, the convenience, the intelligence and the humanization of the remote medical consultation technology are improved, and a consultation request is initiated at a consultation patient terminal by a consultation patient to upload medical data of the consultation patient to a consultation guide; and the consultation guide terminal performs data matching with the remote consultation doctor according to the medical data of the consultation patient, screens out the consultation doctor and determines consultation time to perform remote consultation.
Description
Technical Field
The invention relates to the technical field of remote medical consultation methods, in particular to a block chain-based remote medical consultation method and system.
Background
At present, with the rapid development of networks and the shortage of medical resources, remote medical treatment can be supported by computer technology, remote sensing, remote measuring and remote control technology, the advantages of medical technology and medical equipment of large hospitals or special medical centers are fully exerted, and remote diagnosis, treatment and consultation are carried out on patients in remote areas, islands or ships with poor medical conditions.
The block chain can integrate various traditional technologies and combine together with a new structure to form a new information recording, storing and expressing mode, has the characteristics of 'unforgeability', 'trace in the whole process', 'traceability', 'public transparency', 'collective maintenance' and the like, and ensures the safety of on-line data transmission and access.
In the on-line consultation, a hospital can be provided with a consultation platform, help the consultation patients to recommend a registration department according to the data provided by the consultation patients, and arrange a diagnosis separating platform at each department to reasonably distribute the queued consultation patients and guide the consultation patients to the corresponding diagnosis rooms.
However, in the remote medical consultation, the situation that the consultation patient selects a wrong department doctor or enters a doctor consultation channel in a bundle mode often occurs, so that the consultation is not timely, and the medical resource allocation is not uniform.
Disclosure of Invention
In order to overcome the defects of the prior art, an object of the present invention is to provide a block chain-based remote medical consultation method and system, which solve the problems of untimely remote medical consultation and uneven medical resource distribution.
One of the purposes of the invention is realized by adopting the following technical scheme:
a remote medical consultation method based on a blockchain comprises the following steps: step 1: the consultation patient initiates a consultation request at a consultation patient terminal and uploads the medical data of the consultation patient to the consultation guide terminal;
step 2: the consultation guide terminal performs data matching with the remote consultation doctor according to the medical data of the consultation patient, screens out the consultation doctor and determines consultation time;
and step 3: the consultation guide terminal respectively sends the consultation time and relevant pre-consultation matters to a consultation patient and a remote sitting doctor;
and 4, step 4: after the remote doctor in consultation knows the medical data of the patient, the remote doctor in consultation sends a group building request to the consultation guide terminal through the remote doctor in consultation terminal, and the consultation guide terminal builds a remote medical consultation group for the patient in consultation and the remote doctor in consultation;
and 5: after the remote consultation is finished, the consultation guide terminal uploads the consultation record to the database.
Further, the method for screening the remote doctor for sitting in a doctor by the consultation guide terminal is as follows:
step S100: obtaining consultation patient data, a list directory of remote sitting doctors and historical diagnosis records of consultation patients in a database;
step S200: extracting the characteristics of the historical diagnosis record of the consultation patient to obtain a historical diagnosis data characteristic set;
step S300: performing characteristic classification on the historical diagnostic data characteristic set to obtain historical diagnostic data characteristic classification data;
step S400: counting the number of the historical diagnostic data and the characteristic data in each category in the historical diagnostic data characteristic classification data;
step S500: obtaining a first preset number threshold;
step S600: obtaining first statistical information meeting the first preset number threshold from a statistical result;
step S700: and extracting corresponding remote doctor data from the list directory of the remote doctors according to the first statistical information.
Step S800: according to the number of the first statistical information, obtaining a first remote doctor sitting set with the same number as the first statistical information from a list directory of remote doctor sitting doctors;
step S900: assigning the respective remote clinician data to each remote clinician in the first set of remote clinicians;
step S1000: obtaining a first diagnosis task;
step S1100: according to the first consultation task, arranging a specific remote sitting doctor to conduct consultation on the corresponding consultation patient;
step S1200: identify the remote sitting doctor.
Further, step S200 specifically includes:
step S210: according to the list directory of the remote sitting-consultation doctors, acquiring the adequacy field of each remote sitting-consultation doctor data in the list directory of the remote sitting-consultation doctors;
step S220: creating a qualified area convolution comparison database according to the qualified area of each remote sitting doctor data;
step S230: and traversing and performing convolution comparison on each adept field in the adept field convolution comparison database and the real-time consultation data to obtain a real-time consultation data set.
Further, step S300 specifically includes:
step S310: establishing a consultation information characteristic strategy model;
step S320: and according to the consultation information characteristic strategy model, characteristic classification is carried out on the real-time consultation data set to obtain real-time consultation information classification data.
Further, step S1100 includes:
step S1110: creating a multi-level consultation model, wherein the multi-level consultation model comprises a primary consultation layer and a secondary consultation layer;
step S1120: skipping from the primary consultation layer to the secondary consultation layer according to the first consultation task, wherein the secondary consultation layer comprises a multi-line parallel consultation interface;
step S1130: and synchronously displaying each consultation doctor to perform consultation on the corresponding consultation patient through the multi-line parallel consultation interface in the secondary consultation layer in a split screen mode.
Further, step S1200 includes:
step 1210: obtaining real-time remote doctor consultation amount data through a database;
step S1220: picking up a first remote doctor set with the real-time remote doctor consultation amount meeting a preset consultation amount threshold according to the real-time remote doctor consultation amount data;
step S1230: obtaining characteristic data of each remote doctor in the first set of remote doctors;
step S1240: inputting the characteristic data into a connection model for practice to obtain a consultation amount evaluation model of the remote sitting-consultation doctor;
step S1250: inputting the characteristic data of each remote doctor in the list of remote doctors in the first consulting room into a remote doctor consultation amount evaluation model to obtain a first consultation amount evaluation result of each remote doctor;
step S1260: and identifying each remote doctor according to the first consultation amount evaluation result.
Further, the characteristic data of each remote doctor in the list of remote doctors in the first consulting room is input into the remote doctor consultation volume evaluation model to obtain a first consultation volume evaluation result of each remote doctor, and step S1450 includes:
step S1251: inputting the characteristic data of each remote doctor in the list of remote doctors in the first consulting room into a remote doctor consultation amount evaluation model;
step S1252: the remote doctor consultation volume evaluation model is obtained through practice of multiple groups of practice data, wherein each group of data in the multiple groups of practice data comprises characteristic data of each remote doctor in the first remote doctor set and marking data used for marking a first consultation volume evaluation result of each remote doctor;
step S1253: and obtaining derived data of the remote doctor consultation quantity evaluation model, wherein the derived data comprises a first consultation quantity evaluation result of each remote doctor.
Further, step S800 includes:
step S810: obtaining a second preset number threshold;
step S820: determining whether the number of the first statistical information exceeds the second preset number threshold;
step S830: and if the number of the first statistical information exceeds the second preset number threshold, obtaining a first diagnosis task.
Furthermore, a network module is arranged for providing a medical data interaction interface, and the database uploads the data to the block chain through the network module to perform coverage updating on the stored medical data.
Another object of the present invention is to provide a block chain-based remote medical consultation system, including a consultation patient terminal, a consultation guide terminal, a remote doctor-sitting terminal, and a database, wherein:
the consultation patient terminal is used for initiating a consultation request at the consultation patient terminal by the consultation patient and uploading the medical data of the consultation patient terminal to the consultation guide terminal;
the consultation guide terminal is used for carrying out data matching with the remote consultation doctor according to the medical data of the consultation patient, screening out the consultation doctor and determining consultation time; the consultation guide terminal respectively sends the consultation time and relevant pre-consultation matters to a consultation patient and a remote sitting doctor;
the remote doctor-sitting terminal is used for sending a group establishing request to the consultation guide terminal through the remote doctor-sitting terminal after the remote doctor-sitting knows the medical data of the consultation patient, and the consultation guide terminal establishes a remote medical consultation group for the consultation patient and the remote doctor-sitting;
and the database is used for uploading the consultation records to the database by the consultation guide terminal after the remote consultation is finished.
Compared with the prior art, the invention has the advantages that:
the remote medical consultation adopts an advanced remote medical technology, and solves the problems that the existing remote medical consultation is not timely and the medical resource distribution is not uniform.
The convenience, the intellectualization and the humanization of the remote medical consultation technology are improved through the combination of technologies such as a database, terminals and application programs.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented according to the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic block diagram of a consultation system according to the embodiment.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or intervening components may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or intervening components may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
A remote medical consultation method based on a blockchain is characterized in that the method is based on the medical blockchain of medical data interaction, the medical blockchain comprises a database used for storing medical data and an application program used for remote consultation, and each terminal is communicated with the database through a network module (preferably 5G) based on the built-in application program. The database can be provided with a plurality of servers according to the requirements of data quantity and the like, data connection is established among the servers, the network module provides a medical data interaction interface, medical data interaction is carried out on the medical data interaction module and the medical block chain at regular time, and the stored medical data is updated in a covering mode.
The method comprises the following steps:
step 1: the consultation patient initiates a consultation request at a consultation patient terminal and uploads the medical data of the consultation patient to the consultation guide terminal;
step 2: the consultation guide terminal performs data matching with the remote consultation doctor according to the medical data of the consultation patient, screens out the consultation doctor and determines consultation time;
and step 3: the consultation guide terminal respectively sends the consultation time and relevant pre-consultation matters to a consultation patient and a remote sitting doctor;
and 4, step 4: after the remote doctor in consultation knows the medical data of the patient, the remote doctor in consultation sends a group building request to the consultation guide terminal through the remote doctor in consultation terminal, and the consultation guide terminal builds a remote medical consultation group for the patient in consultation and the remote doctor in consultation;
and 5: the remote doctor and the patient with a consultation are in video consultation in the remote medical consultation group, and after the video consultation is finished, the consultation guide terminal uploads the consultation record to the database.
The method for screening the remote sitting doctor by the consultation guide terminal comprises the following steps:
step S100: obtaining consultation patient data, a list directory of remote sitting doctors and historical diagnosis records of consultation patients in a database;
specifically, the medical data system based on the database and artificial intelligence can be entered into the database to see the historical consultation data of the consultation patient, including the serial number of the remote doctor who has participated in the consultation, the department, the title, the number of the historical consultation persons, the work experience, the consultation time and the like. The consultation patient data of the database can be combined, so that a consultation guide or a consultation patient can more intuitively see the optional or matched remote doctor in the consultation.
The remote doctor in sitting consultation is responsible for participating in medical diagnosis and other work in consultation process, and can communicate with and interact with the patients in real time. The list directory of the remote sitting-consultation doctors in the database comprises the names, areas of excellence and the like of the remote sitting-consultation doctors. For example, the database-based remote clinician excellence field enables the consultation guide or the patient to select the remote clinician for more intuitive comparison and select the most suitable remote clinician for the patient.
The historical diagnosis record refers to a diagnosis record popped up when a real-time video is displayed on the consultation terminal. The historical diagnosis record of the database refers to the fact that the consultation patient uses a keyboard to knock and send the consultation data to the remote doctor-sitting terminal when the database interacts with the remote doctor-sitting in real time, the diagnosis data can provide the consultation patient with real-time interaction consultation experience, although the sending time of different diagnosis data is different, the consultation data can only appear at a specific time point in a video, therefore, the diagnosis data sent at the same time basically have the same theme, and the consultation experience of simultaneously exchanging discussions with other consultation patients can be achieved when the consultation patient participates in the exchange discussions. The history diagnosis records are wide in coverage, so that the remote doctor can see various requirements of the consultation patient, for example, the question diagnosis data encountered by the remote doctor can explain the consultation patient in detail, so that the consultation patient can know the consultation data more.
Step S200: extracting the characteristics of the historical diagnosis record of the consultation patient to obtain a historical diagnosis data characteristic set;
step S300: performing characteristic classification on the historical diagnostic data characteristic set to obtain historical diagnostic data characteristic classification data;
in particular, feature extraction refers to feature extraction starting from an initial set of measurement data and establishing features intended to provide data and non-redundancy in program learning, pattern recognition and image processing, thereby facilitating subsequent learning and generalization steps and in some cases leading to better interpretability. The characteristic extraction of the diagnosis record is that each item of the historical diagnosis record sent by the patient to be diagnosed is extracted with the most obvious characteristic, and the most important characteristic is only one. The historical diagnosis data characteristic set is a set formed by sorting the characteristics of historical diagnosis data after the system finishes characteristic extraction analysis processing on the historical diagnosis data sent by patients to be diagnosed. Further, when a consultation patient carries out diagnosis data communication discussion, the system extracts the characteristics of the historical diagnosis records of the consultation patient, the extracted characteristics of the historical diagnosis data are collected to form a data set, on the basis, the characteristic classification is carried out on the characteristic set of the historical diagnosis data, the characteristics of the historical diagnosis data with the same characteristics are divided into one block, all the characteristics of the historical diagnosis data are subjected to classification processing, and the characteristic classification data of the historical diagnosis data are obtained. The characteristics of the historical diagnosis data are classified, so that the number of the historical diagnosis data of each category can be more conveniently counted.
Step S400: counting the number of the historical diagnostic data characteristics in each category in the historical diagnostic data characteristic classification data;
specifically, after the characteristics of the historical diagnosis data are extracted, the system classifies the historical diagnosis data into various large categories according to the characteristic similarity of the historical diagnosis data, counts the number of the historical diagnosis records in each category based on the data of each category, checks the love degree of the consultation patient to a certain remote doctor, and counts the result.
Step S500: obtaining a first preset number threshold;
step S600: obtaining first statistical information meeting a first preset number threshold from the statistical result;
in particular, a threshold, also called a critical value, refers to the lowest or highest value at which an effect can be produced. The first preset number threshold is a preset number value, and can be adjusted according to actual conditions. Based on the first preset number threshold, the system compares the statistical results of the number of the historical diagnostic data features in each category in the historical diagnostic data feature classification data with the statistical results of the number of the historical diagnostic data features to obtain the result which best meets the first preset number threshold, and the result is recorded as first statistical information. The first statistical information may be one or more. For example, five statistical results of the historical diagnosis data of the consultation patient are obtained, the number of the historical diagnosis data features in the five categories is compared with a first preset number threshold, and if three of the five categories are met, the first statistical information is three.
Step S700: and extracting corresponding remote doctor data from the list directory of the remote doctors according to the first statistical information.
Specifically, the data of the corresponding remote referring doctor is a general term of data, profession, job title, or adept diseases, etc. related to the remote referring doctor and its history, experience, or ability, which can be received by the receiver and meet some special consultation requirement. The remote doctor data includes remote doctor supply and demand data, remote doctor technical data, remote doctor management data, remote doctor consultation time data, remote doctor seniority data and the like. The system extracts corresponding remote doctor data from the list of remote doctors according to the first statistical information, for example, three pieces of the first statistical information, and extracts the serial number of the corresponding remote doctor, the department of the remote doctor, the picture data of the remote doctor, the number of the remote doctor, the job title of the remote doctor, the work experience, the number of the consultants, the number of the residual consultants of the remote doctor, and the like from the list of the remote doctor according to the statistical data of the three remote doctors.
Step S800: according to the number of the first statistical information, obtaining a first remote doctor sitting set with the same number as the first statistical information from the list directory of the remote doctor sitting doctors;
step S900: assigning the respective remote clinician data to each remote clinician in the first set of remote clinicians;
specifically, the list directory of the remote doctor sitting consults is a list mode established by the database for all the relevant data of the remote doctor sitting consults, so that the consultation patients can conveniently check the data of the remote doctor sitting consults. The first set of remote doctors is a set of data of all the remote doctors in the list of the remote doctors, and is recorded as the first set of remote doctors. The corresponding remote doctor data refers to the remote doctor data of the first statistical information meeting a first preset number threshold. The system distributes the number of the first remote sitting and consulting doctor set meeting the first statistical information in the list directory of the remote sitting and consulting doctors according to the number of the first statistical information, distributes corresponding data of the remote sitting and consulting doctors to each remote sitting and consulting doctor in the first remote sitting and consulting doctor set respectively, and displays the distributed consultation for everybody, so that the time of consulting patients is saved, and the requirements of the consulting patients are met.
Step S1000: obtaining a first diagnosis task;
step S1100: and according to the first consultation task, arranging a specific remote doctor to consult the corresponding consultation patient.
Specifically, when the first statistical information is more than one, two or more remote doctors can meet the needs of the consultation patient, and one remote doctor can only meet one consultation patient at the same time, the consultation patient can be met by two or more remote doctors at the same time under the same consultation room lens, so that the consultation patient looks confused, and at the moment, the system can send out a first consultation task. The first diagnosis task is a task which can obtain a split screen when the statistical result is more than one, and is recorded as a first split screen task. After receiving the first screen split task, the screen split consultation devices of other remote doctors can be opened to carry out consultation on the corresponding consultation patient data distributed by the remote doctors.
Step S1200: identify the remote sitting doctor.
Wherein, step S200 specifically includes:
step S210: according to the list directory of the remote sitting-consultation doctors, acquiring the adequacy field of each remote sitting-consultation doctor data in the list directory of the remote sitting-consultation doctors;
step S220: creating a qualified area convolution comparison database according to the qualified area of each remote sitting doctor data;
step S230: and traversing and performing convolution comparison on each adept field in the adept field convolution comparison database and the real-time consultation data to obtain a real-time consultation data set.
Specifically, to obtain a real-time consultation data set, a skilled area convolution comparison must be performed on the real-time bullet screen. The field of excellence refers to the most prominent feature of things, and things are conveniently classified according to the field of excellence. The expert field convolution is conducted convolution picking in the expert field of the remote doctor data, and further convolution can be used as a characteristic picker in machine learning, so that the picked characteristic data is centralized and representative, and further convolution characteristics of the remote doctor data are obtained. A database is a "warehouse that organizes, stores, and manages data according to a data structure," which is an organized, sharable, uniformly managed collection of large amounts of data that is stored in a computer for a long period of time. The domain-adept convolution comparison database refers to a set of domain-adept convolutions organized according to the remote sitter data. Firstly, acquiring adequacy field data of each remote doctor in a list directory of the remote doctors according to the data of the list of the remote doctors; according to the adequacy field of each remote doctor sitting data, gathering the adequacy fields of all the remote doctors, creating an adequacy field convolution comparison database, and performing traversal convolution comparison on each adequacy field in the adequacy field convolution comparison database and the real-time consultation data to obtain a real-time consultation data set. The real-time consultation data set can make the characteristic classification simpler and more convenient.
Step S300 specifically includes:
step S310: establishing a consultation information characteristic strategy model;
step S320: and according to the consultation information characteristic strategy model, characteristic classification is carried out on the real-time consultation data set to obtain real-time consultation information classification data.
Specifically, a consultation characteristic strategy tree is created, and classification is performed based on a strategy tree structure according to consultation characteristics, so that a complete characteristic strategy tree is created. When a consultation patient sends real-time data to interact with a consultation doctor, the system classifies the characteristics of the real-time consultation data set based on the real-time consultation information and consultation characteristic strategy tree, and then the real-time consultation information classification data can be obtained.
Step S1100 specifically includes:
step S1110: creating a multi-level consultation model, wherein the multi-level consultation model comprises a primary consultation layer and a secondary consultation layer;
step S1120: according to the first consultation task, jumping from the first consultation layer to a second consultation layer, wherein the second consultation layer comprises a multi-line parallel consultation interface;
step S1130: and synchronously displaying each consultation doctor to consult the corresponding consultation patient through the multi-line parallel consultation interface in the secondary consultation layer in a split screen mode.
In particular, it is possible how each of the consultants can perform a split-screen consultation of the respective consultation initiator or of the patient data, even if the first consultation task is available. The multi-level consultation model refers to the consultation layering of a consultation room, the consultation doctors are graded according to numbers, the first-level consultation doctors, the second-level consultation doctors, the third-level consultation doctors and the like, the first-level consultation doctors are main consultation persons in the first consultation room and are positioned in a first consultation layer, and all the consultation doctors in the first consultation room are layered according to the hierarchy. The multiline parallel mode is that the multiline parallel mode is composed of a data receiving unit and a plurality of parallel data acquisition units, the data acquisition units are communicated with the receiving unit in a wireless mode, and each acquisition unit can acquire parallel data of a plurality of channels. The synchronous split screen of the consultation interface means that when the first consultation task is received, the consultation interface can be divided into a plurality of equal parts, and the consultation doctor at each level can conduct consultation on data of a consultation initiator or a consultation patient.
Specifically, a multi-level consultation model is firstly created, wherein the multi-level consultation model comprises a primary consultation layer and a secondary consultation layer, and the parallel number of the secondary consultation lines is changed according to the number of the first statistical information and is always consistent with the number of the first statistical information. When the system receives a first consultation task, the consultation interface jumps from the first consultation layer to the second consultation layer, and the second consultation layer comprises a multi-line parallel consultation interface; each consultation doctor can conduct consultation on corresponding consultation patient data through synchronous split screen display of the multi-line parallel consultation interfaces in the secondary consultation layer, and a consultation patient can select one of the consultation interfaces to interact according to the requirements of the consultation doctor, so that convenience is brought to each consultation patient, and time is saved.
Step S1200 specifically includes:
step 1210: obtaining real-time remote doctor consultation amount data through a database;
step S1220: picking a first remote doctor set with the real-time remote doctor consultation amount meeting a preset consultation amount threshold according to the real-time remote doctor consultation amount data;
step S1230: obtaining characteristic data of each remote sitting doctor in a first remote sitting doctor set;
step S1240: inputting the characteristic data into the connection model for practice to obtain a consultation amount evaluation model of the remote sitting-consultation doctor;
step S1250: inputting the characteristic data of each remote doctor in the list of remote doctors in the first consulting room into the remote doctor consultation amount evaluation model to obtain a first consultation amount evaluation result of each remote doctor;
step S1260: and identifying each remote doctor according to the first consultation amount evaluation result.
Specifically, the amount of consultation of the remote referring doctor refers to the number of consultation of the remote referring doctor. The higher the consultation amount of one remote doctor, the better the medical level of the remote doctor. The connection model is a complex network system formed by a large number of simple processing units (called neurons) widely connected with each other, reflects many basic features of human brain functions, and is a highly complex nonlinear dynamical learning system. The connection model has large-scale parallel, distributed storage and processing, self-organization, self-adaptation and self-learning capabilities, and is particularly suitable for processing inaccurate and fuzzy data processing problems which need to consider many factors and conditions simultaneously.
Real-time remote doctor consultation amount data are obtained through database analysis, a preset consultation amount threshold value is set for a consultation patient, the real-time remote doctor consultation amount data are compared with the preset consultation amount threshold value, the real-time remote doctor consultation amount data meeting the preset consultation amount threshold value are picked out and aggregated to form a first remote doctor set. Picking up characteristic data of each remote doctor in the first remote doctor set, inputting the characteristic data serving as input information into the connection model for practice to obtain a remote doctor consultation volume evaluation model, inputting the characteristic data of each remote doctor in the list of remote doctors in the first consultation room serving as input information into the remote doctor consultation volume evaluation model, evaluating the first consultation volume of each remote doctor to obtain a first consultation volume evaluation result of each remote doctor, and identifying each remote doctor according to the first consultation volume evaluation result. The obtained first consultation amount evaluation result can enable the consultation patient or the consultation guide to compare and select the remote doctor according to the data, and the most satisfactory remote doctor is selected.
The characteristic data of each remote doctor in the list of remote doctors in the first consulting room is input into the remote doctor consultation amount evaluation model to obtain a first consultation amount evaluation result of each remote doctor, and step S1250 specifically includes:
step S1251: inputting the characteristic data of each remote doctor in the list of remote doctors in the first consulting room into the consultation quantity evaluation model of the remote doctors;
step S1252: the remote doctor consultation amount evaluation model is obtained through practice of multiple groups of practice data, wherein each group of data in the multiple groups of practice data comprises characteristic data of each remote doctor in the first remote doctor set and marking data used for marking the first consultation amount evaluation result of each remote doctor;
step S1253: and obtaining derived data of the remote doctor consultation amount evaluation model, wherein the derived data comprises a first consultation amount evaluation result of each remote doctor.
Specifically, in order to obtain a more accurate first consultation volume evaluation result for each remote referring doctor, it is necessary to practice through a plurality of sets of exercise data. Firstly, the characteristic data of each remote doctor in the list of remote doctors in the first consulting room is used as input quantity and is input into a remote doctor consultation quantity evaluation model, the remote doctor consultation quantity evaluation model is obtained through practice of multiple groups of practice data, and the derivation result has certainty and accuracy. Each group of data in the multiple groups of exercise data comprises characteristic data of each remote doctor in the first remote doctor set and marking data used for marking the first consultation amount evaluation result of each remote doctor. And the derived data of the remote doctor consultation amount evaluation model can be obtained through a plurality of groups of exercise data, and the derived data comprises a first consultation amount evaluation result of each remote doctor. The first consultation amount evaluation result obtained in the way is more accurate, and basically no error is generated.
Step S1000 specifically includes:
step S1010: obtaining a second preset number threshold;
step S1020: judging whether the number of the first statistical information exceeds a second preset number threshold value or not;
step S1030: and if the number of the first statistical information exceeds a second preset number threshold, obtaining a first diagnosis task.
Specifically, the system can only split the screen when the first consultation task is obtained, so that the consultation patient can select the split screen to be consulted, and the aim is that treatment of different diseases needs consultation with different doctors. To obtain the first diagnostic task, the number of first statistical messages must exceed a second preset number threshold. Firstly, a second preset number threshold value is determined, the number of the first statistical information is compared with the second preset number threshold value, whether the number of the first statistical information exceeds the second preset number threshold value or not is judged, if the number of the first statistical information exceeds the second preset number threshold value, a first diagnosis task can be obtained, and the system carries out screen-splitting consultation operation.
In addition, the embodiment is further provided with a network module for providing a medical data interaction interface while providing network communication, and the database uploads data to the block chain through the network module to perform coverage updating on the stored medical data.
As shown in fig. 1, the present embodiment further provides a remote medical consultation system based on a blockchain, including a consultation patient terminal, a consultation guide terminal, a remote doctor-sitting terminal, and a database, wherein:
the consultation patient terminal is used for initiating a consultation request at the consultation patient terminal by the consultation patient and uploading the medical data of the consultation patient terminal to the consultation guide terminal;
the consultation guide terminal is used for carrying out data matching with the remote consultation doctor according to the medical data of the consultation patient, screening out the consultation doctor and determining consultation time; the consultation guide terminal respectively sends the consultation time and relevant pre-consultation matters to a consultation patient and a remote sitting doctor;
the remote doctor-sitting terminal is used for sending a group establishing request to the consultation guide terminal through the remote doctor-sitting terminal after the remote doctor-sitting knows the medical data of the consultation patient, and the consultation guide terminal establishes a remote medical consultation group for the consultation patient and the remote doctor-sitting;
and the database is used for uploading the consultation records to the database by the consultation guide terminal after the remote consultation is finished.
The database in the embodiment is mainly read and written by a consultation guide, and a remote doctor in a sitting position can call related consultation information as required, so that the safety, traceability and the like of the database data are ensured,
The above embodiments are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby, and any insubstantial changes and substitutions made by those skilled in the art based on the present invention are within the protection scope of the present invention.
Claims (10)
1. A remote medical consultation method based on a block chain is characterized in that: the method comprises the following steps:
step 1: the consultation patient initiates a consultation request at a consultation patient terminal and uploads the medical data of the consultation patient to the consultation guide terminal;
step 2: the consultation guide terminal performs data matching with the remote consultation doctor according to the medical data of the consultation patient, screens out the consultation doctor and determines consultation time;
and step 3: the consultation guide terminal respectively sends the consultation time and relevant pre-consultation matters to a consultation patient and a remote sitting doctor;
and 4, step 4: after the remote doctor in consultation knows the medical data of the patient, the remote doctor in consultation sends a group building request to the consultation guide terminal through the remote doctor in consultation terminal, and the consultation guide terminal builds a remote medical consultation group for the patient in consultation and the remote doctor in consultation;
and 5: after the remote consultation is finished, the consultation guide terminal uploads the consultation record to the database.
2. The blockchain-based remote medical consultation method according to claim 1, characterized in that: the method for screening the remote sitting doctor by the consultation guide terminal comprises the following steps:
step S100: obtaining consultation patient data, a list directory of remote sitting doctors and historical diagnosis records of consultation patients in a database;
step S200: extracting the characteristics of the historical diagnosis record of the consultation patient to obtain a historical diagnosis data characteristic set;
step S300: performing characteristic classification on the historical diagnostic data characteristic set to obtain historical diagnostic data characteristic classification data;
step S400: counting the number of the historical diagnostic data and the characteristic data in each category in the historical diagnostic data characteristic classification data;
step S500: obtaining a first preset number threshold;
step S600: obtaining first statistical information meeting the first preset number threshold from a statistical result;
step S700: and extracting corresponding remote doctor data from the list directory of the remote doctors according to the first statistical information.
Step S800: according to the number of the first statistical information, obtaining a first remote doctor sitting set with the same number as the first statistical information from a list directory of remote doctor sitting doctors;
step S900: assigning the respective remote clinician data to each remote clinician in the first set of remote clinicians;
step S1000: obtaining a first diagnosis task;
step S1100: according to the first consultation task, arranging a specific remote sitting doctor to conduct consultation on the corresponding consultation patient;
step S1200: identify the remote sitting doctor.
3. The blockchain-based remote medical consultation method according to claim 2, characterized in that: step S200 specifically includes:
step S210: according to the list directory of the remote sitting-consultation doctors, acquiring the adequacy field of each remote sitting-consultation doctor data in the list directory of the remote sitting-consultation doctors;
step S220: creating a qualified area convolution comparison database according to the qualified area of each remote sitting doctor data;
step S230: and traversing and performing convolution comparison on each adept field in the adept field convolution comparison database and the real-time consultation data to obtain a real-time consultation data set.
4. The blockchain-based remote medical consultation method according to claim 2, characterized in that: step S300 specifically includes:
step S310: establishing a consultation information characteristic strategy model;
step S320: and according to the consultation information characteristic strategy model, characteristic classification is carried out on the real-time consultation data set to obtain real-time consultation information classification data.
5. The blockchain-based remote medical consultation method according to claim 2, characterized in that: step S1100 includes:
step S1110: creating a multi-level consultation model, wherein the multi-level consultation model comprises a primary consultation layer and a secondary consultation layer;
step S1120: skipping from the primary consultation layer to the secondary consultation layer according to the first consultation task, wherein the secondary consultation layer comprises a multi-line parallel consultation interface;
step S1130: and synchronously displaying each consultation doctor to perform consultation on the corresponding consultation patient through the multi-line parallel consultation interface in the secondary consultation layer in a split screen mode.
6. The blockchain-based remote medical consultation method according to claim 2, characterized in that: step S1200 includes:
step 1210: obtaining real-time remote doctor consultation amount data through a database;
step S1220: picking up a first remote doctor set with the real-time remote doctor consultation amount meeting a preset consultation amount threshold according to the real-time remote doctor consultation amount data;
step S1230: obtaining characteristic data of each remote doctor in the first set of remote doctors;
step S1240: inputting the characteristic data into a connection model for practice to obtain a consultation amount evaluation model of the remote sitting-consultation doctor;
step S1250: inputting the characteristic data of each remote doctor in the list of remote doctors in the first consulting room into a remote doctor consultation amount evaluation model to obtain a first consultation amount evaluation result of each remote doctor;
step S1260: and identifying each remote doctor according to the first consultation amount evaluation result.
7. The blockchain-based remote medical consultation method according to claim 6, characterized in that: the characteristic data of each remote doctor in the list of remote doctors in the first consulting room is input into the remote doctor consultation amount evaluation model to obtain a first consultation amount evaluation result of each remote doctor, and step S1250 includes:
step S1251: inputting the characteristic data of each remote doctor in the list of remote doctors in the first consulting room into a remote doctor consultation amount evaluation model;
step S1252: the remote doctor consultation volume evaluation model is obtained through practice of multiple groups of practice data, wherein each group of data in the multiple groups of practice data comprises characteristic data of each remote doctor in the first remote doctor set and marking data used for marking a first consultation volume evaluation result of each remote doctor;
step S1253: and obtaining derived data of the remote doctor consultation quantity evaluation model, wherein the derived data comprises a first consultation quantity evaluation result of each remote doctor.
8. The blockchain-based remote medical consultation method according to claim 2, characterized in that: step S800 includes:
step S810: obtaining a second preset number threshold;
step S820: determining whether the number of the first statistical information exceeds the second preset number threshold;
step S830: and if the number of the first statistical information exceeds the second preset number threshold, obtaining a first diagnosis task.
9. The blockchain-based remote medical consultation method according to claim 1, characterized in that: the database uploads the data to the block chain through the network module, and the stored medical data is updated in a covering mode.
10. A remote medical consultation system based on a block chain is characterized in that:
including consultation patient terminal, consultation guide terminal, long-range doctor of sitting a doctor terminal and database, wherein:
the consultation patient terminal is used for initiating a consultation request at the consultation patient terminal by the consultation patient and uploading the medical data of the consultation patient terminal to the consultation guide terminal;
the consultation guide terminal is used for carrying out data matching with the remote consultation doctor according to the medical data of the consultation patient, screening out the consultation doctor and determining consultation time; the consultation guide terminal respectively sends the consultation time and relevant pre-consultation matters to a consultation patient and a remote sitting doctor;
the remote doctor-sitting terminal is used for sending a group establishing request to the consultation guide terminal through the remote doctor-sitting terminal after the remote doctor-sitting knows the medical data of the consultation patient, and the consultation guide terminal establishes a remote medical consultation group for the consultation patient and the remote doctor-sitting;
and the database is used for uploading the consultation records to the database by the consultation guide terminal after the remote consultation is finished.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114723181A (en) * | 2022-06-07 | 2022-07-08 | 常州云燕医疗科技有限公司 | Digital integrated operating room signal transmission system and method based on block chain |
| CN116013552A (en) * | 2023-03-27 | 2023-04-25 | 慧医谷中医药科技(天津)股份有限公司 | Remote consultation method and system based on blockchain |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114723181A (en) * | 2022-06-07 | 2022-07-08 | 常州云燕医疗科技有限公司 | Digital integrated operating room signal transmission system and method based on block chain |
| CN116013552A (en) * | 2023-03-27 | 2023-04-25 | 慧医谷中医药科技(天津)股份有限公司 | Remote consultation method and system based on blockchain |
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