Intelligent medical information pushing method and system applied to internet hospital

Abstract

The application discloses a kind of wisdom medical information push method and system applied to internet hospital, it is related to medical information push field, its method includes: obtaining the medical information of business party platform and the information type of medical information;Based on information type and preset information template, information configuration is carried out to medical information to determine medical information distribution channel and medical data template;The communication stability of medical information distribution channel is verified to determine stability result, and the information timeliness of medical data template is verified according to stability result;When the communication stability of medical information distribution channel and the information timeliness of medical data template are verified, medical information is pushed to the terminal equipment of target user by combining medical information distribution channel and medical data template using aggregation message sending process.The application can effectively improve the quality and efficiency of medical service.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the field of medical information pushing, in particular to a smart medical information pushing method and system applied to an internet hospital. BACKGROUND

[0002] With the rapid development of information technology and the increasing demand for medical services, hospital information systems are facing unprecedented challenges. Due to the limitations in design and implementation, traditional hospital information systems often lead to information island phenomenon, that is, different application systems cannot effectively share and exchange data. In particular, in the field of message services, there are multiple message sending channels within the hospital, such as WeChat, Alipay, SMS, and email, etc. These channels lack unified management and coordination, resulting in low efficiency of message sending, poor user experience, and reduced quality and efficiency of medical services.

[0003] Therefore, there is an urgent need for a smart medical information pushing method and system applied to an internet hospital to solve the above problems. SUMMARY

[0004] The embodiment of the present application provides a smart medical information pushing method and system applied to an internet hospital, which is used to improve the efficiency of medical information pushing and improve the quality and efficiency of medical services.

[0005] To achieve the above purpose, the embodiment of the present application adopts the following technical scheme:

[0006] In a first aspect, a smart medical information pushing method applied to an internet hospital is provided, which is applied to a message integration platform connected with a hospital information system and a plurality of business platform. The method comprises:

[0007] Obtaining medical information of the business platform and information type of the medical information;

[0008] Based on the information type and the preset information template, the medical information is configured to determine the medical information distribution channel and the medical data template;

[0009] The communication stability of the medical information distribution channel is verified to determine the stability result, and the information timeliness of the medical data template is verified according to the stability result;

[0010] When the communication stability of the medical information distribution channel and the information timeliness of the medical data template are both verified, the medical information is pushed to the terminal equipment of the target user by combining the medical information distribution channel and the medical data template using the aggregation message sending process.

[0011] In a possible implementation manner of the first aspect, the method further includes the following steps.

[0012] When the query instruction of the user is received, the keyword input by the user on the front-end platform is acquired, and a query condition is set based on the keyword;

[0013] The query condition is encapsulated into a fixed format, and a query request is sent to the back-end platform based on the encapsulated query condition;

[0014] When the query request is received by the back-end platform, a preset search engine is called to construct a query language;

[0015] Target information is searched in a preset database in combination of the query language and a weighted search strategy.

[0016] In a possible implementation manner of the first aspect, the determining of the medical information distribution channel and the medical data template based on the information type and the preset information template includes the following steps.

[0017] The information type is mapped to a preset classification label to determine a label type corresponding to the information type;

[0018] A medical data template is matched in a preset information template based on the label type;

[0019] Template information of the medical data template is edited according to the information type;

[0020] Configuration information is acquired in a preset database, and a subscription permission verification result is determined by performing subscription permission verification on the target user based on the configuration information, the configuration information including subscriber information and a subscription topic;

[0021] The medical information distribution channel is determined according to the subscription permission verification result and a preset channel priority.

[0022] In a possible implementation manner of the first aspect, the verifying of the information timeliness of the medical data template according to the stability result of the medical information distribution channel includes the following steps.

[0023] A real-time transmission rate of the medical information distribution channel, and a sending quantity and a receiving quantity of a plurality of data packets in a preset time period are acquired by using a preset data acquisition system, wherein the plurality of data packets include a medical text data packet and a medical image data packet, the medical text data packet is smaller than a preset threshold, and the medical image data packet is larger than the preset threshold;

[0024] The ratio between the real-time transmission rate and a preset reference transmission rate is calculated to determine a bandwidth fluctuation value of the medical information distribution channel;

[0025] Image packet loss rates and text packet loss rates corresponding to the medical image data packets and the medical text data packets are respectively calculated based on the sending quantity and the receiving quantity;

[0026] Medical text features and medical image features in the medical data template are extracted, and a medical scene corresponding to the medical text features and the medical image features is determined by using a preset scene recognition model;

[0027] A medical scene weight is determined according to the medical scene by using a preset weight rule table, and the medical text features and the medical image features are quantified to obtain medical text feature quantities and medical image feature quantities;

[0028] An emergency index is determined according to the medical scene by using a preset emergency evaluation model;

[0029] A template packet loss rate of the medical data template is calculated by combining the medical scene weight, the medical text feature quantities, the text packet loss rate, the medical image feature quantities and the image packet loss rate;

[0030] A stability result of the medical information distribution channel is determined by using a fuzzy comprehensive evaluation method in combination with the template packet loss rate, the bandwidth fluctuation value and the emergency index;

[0031] The information timeliness of the medical data template is verified according to the stability result.

[0032] In a possible implementation manner of the first aspect, verifying the information timeliness of the medical data template according to the stability result includes the following steps:

[0033] A difference value between a generation time of the medical data template and a current time is calculated;

[0034] The difference value is input into a preset timeliness decay function to determine a current emergency index of the medical data template;

[0035] The current emergency index is mapped to a preset weight rule table to determine an information emergency weight;

[0036] The information emergency weight and the stability result are input into a preset timeliness index formula to obtain a timeliness index of the medical data template;

[0037] When the timeliness index is greater than a preset timeliness threshold, the medical data template passes the information timeliness verification.

[0038] In a possible implementation manner of the first aspect, the pushing the medical information to the terminal device of the target user by adopting the aggregated message sending procedure combined with the medical information distribution channel and the medical data template comprises the following steps:

[0039] performing sensitive word checking and parameter checking on the pushed information in the medical data template;

[0040] when the sensitive word checking and the parameter checking on the pushed information pass, determining whether the medical information distribution channel is a system message channel;

[0041] if the medical information distribution channel is a system message channel, storing the medical information into a preset database by using a preset system message logic;

[0042] if the medical information distribution channel is a common message channel, determining whether there is a unique user identifier of the target user in the hospital information system;

[0043] if there is the unique user identifier of the target user in the hospital information system, pushing the medical information to the terminal device of the target user according to a preset priority;

[0044] if there is no user identifier of the target user in the hospital information system, determining whether the medical information is pushed only to the terminal device of the target user;

[0045] if the medical information is not pushed only to the terminal device of the target user, obtaining a family identifier corresponding to the target user in the hospital information system, and pushing the medical information to the terminal device of a family member corresponding to the target user based on the family identifier.

[0046] In a possible implementation manner of the first aspect, the method further comprises the following steps:

[0047] when there are multiple users, obtaining a user attribute and a unique user identifier of each of the users;

[0048] constructing a user message queue corresponding to each of the target users based on each of the unique user identifiers, and arranging each of the user message queues according to each of the user attributes to determine an initial user pushing list;

[0049] extracting an information attribute of the medical information in each of the user message queues;

[0050] arranging each of the medical information according to each of the information attributes determines a message push list of each of the users and adds each of the medical information into a message queue of each of the users according to the message push list;

[0051] determining a message queue length of each of the user message queues by using a preset message queue system, and taking the user message queue with the message queue length greater than a preset queue length as a backlog message queue;

[0052] determining a priority value of each of the medical information in the backlog message queue;

[0053] calculating an average priority value of each of the backlog message queues by using the priority value, and rearranging the initial user push list according to the average priority value to determine a final user push list;

[0054] pushing the medical information to each of the users based on the final user push list.

[0055] In a possible implementation manner of the first aspect, the user attributes include user rights and user credits, and arranging each of the user message queues according to each of the user attributes to determine an initial user push list includes the following steps:

[0056] weighting and summing the user rights and the user credits to determine a comprehensive score of each of the users;

[0057] arranging each of the user message queues according to the comprehensive score to determine the initial user push list.

[0058] In a second aspect, the present application provides a machine readable storage medium, characterized in that the machine readable storage medium has instructions stored thereon, the instructions being used to cause a machine to execute the above-mentioned smart medical information push method applied to an internet hospital.

[0059] In a third aspect, the present application provides a smart medical information push system applied to an internet hospital, including:

[0060] a memory configured to store instructions; and

[0061] a processor configured to call the instructions from the memory and capable of implementing the above-mentioned smart medical information push method applied to an internet hospital when executing the instructions.

[0062] By the above technical solution, by acquiring the medical information and its type of the business platform, the standardization classification of medical data is realized, the accuracy and pertinence of information are ensured, the comprehensiveness of information is ensured, and the omissions or errors caused by manual intervention can be effectively reduced. By integrating information from different business platforms, breaking the information silos, and forming a comprehensive patient health portrait, it helps doctors to better understand the patient's condition and make more accurate diagnosis and treatment decisions, improving the user experience. Based on the information type and the preset information template, the medical information distribution channel and the medical data template are configured, the medical information can be sent to the target user through the most suitable channel and format, and the readability of the information is improved. The communication stability of the medical information distribution channel is verified to ensure that the information can be reliably sent to the target user, and by monitoring the communication quality and other indicators of the channel in real time, potential problems can be found and handled in time to avoid information loss or delay. According to the communication stability result, the information timeliness of the medical data template is further verified, which can ensure that the medical information reaches the user at the most needed time, and helps to improve the user experience. The medical information is pushed to the terminal device of the target user by combining the medical information distribution channel and the medical data template using the aggregation message sending process, which can select the optimal pushing mode and format according to network conditions and other factors to ensure that the information reaches the user terminal accurately, improve the accuracy of information pushing, and improve the quality of medical services.

[0063] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific embodiments section. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 A flowchart of a smart medical information pushing method applied to an Internet hospital provided by an embodiment of the present application;

[0065] Figure 2 A structural diagram of a smart medical information pushing method applied to an Internet hospital provided by an embodiment of the present application;

[0066] Figure 3 A flowchart of a medical information pushing method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0067] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. It should be understood that the specific embodiments described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present application.

[0068] It should be noted that if the application embodiments involve directionality indication (such as up, down, left, right, front, back, …), the directionality indication is only used to explain the relative position relationship, motion condition, etc. between components in a certain posture (as shown in the drawings), if the specific posture changes, the directionality indication will also change accordingly.

[0069] In addition, if the application embodiments involve "first", "second" and the like, the "first", "second" and the like are only for description purposes, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of technical features indicated. Therefore, the features limited by "first", "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the realization of ordinary skilled in the art, when the combination of technical solutions appears contradictory or cannot be realized, it should be considered that the combination of technical solutions does not exist, nor within the scope of protection claimed by the present application.

[0070] Figure 1 The flowchart of the smart medical information pushing method applied to the Internet hospital according to the embodiments of the present application is schematically shown. As shown in Figure 1 The embodiments of the present application provide a smart medical information pushing method applied to an Internet hospital, which is applied to a message integration platform, the message integration platform is connected with a hospital information system and a plurality of business platform, and the method can include the following steps.

[0071] S110, obtaining medical information of the business platform and information type of the medical information;

[0072] S120, information configuration of the medical information based on the information type and the preset information template, determination of the medical information distribution channel and the medical data template;

[0073] S130, verification of the communication stability of the medical information distribution channel to determine the stability result, and verification of the information timeliness of the medical data template according to the stability result;

[0074] S140, when the communication stability of the medical information distribution channel and the information timeliness of the medical data template are both verified, the medical information is pushed to the terminal equipment of the target user by combining the medical information distribution channel and the medical data template and using the aggregation message sending process.

[0075] The medical information of the service provider platform and the information type of the medical information are acquired. The service provider platform is various medical service systems connected with the hospital information system. In the embodiment, the service provider platform includes the hospital information system, the inpatient medical service system, the emergency patient medical service system, and the like. The hospital information system is responsible for integrating various information in the hospital, such as patient information, medical records, drug inventory, and financial data, to provide a unified information platform for the hospital, and to support the daily operation and management decision of the hospital. In the embodiment, the information type refers to the information type sent by the service provider platform to the message integration platform. For example, the information type sent by the inpatient medical service system is inpatient information, and the information type sent by the emergency patient medical service system to the message integration platform is emergency information.

[0076] Based on the information type and the preset information template, the medical information is configured and determined, the medical information distribution channel and the medical data template are determined, the preset information template is a template designed in advance to standardize the message format and content. The template defines the title, content, format, parameter placeholder, and the like of the message. Different information templates are preset for different medical information types. For example, the test report template can include the patient's name, test items, test results, reference range, and abnormal prompt fields; the drug reminder template can include the drug name, drug dosage, drug time, drug method, and precautions fields. According to the specific medical information content, the information is filled into the preset template to generate the final message content to be pushed. According to the type of medical information, the most suitable medical information distribution channel is selected according to the preset rules or strategies. The selection basis can be the urgency of the information, the availability of the channel, and the like. In the embodiment, the stability of the channel and the timeliness of the medical data template are determined as the medical information distribution channel. The stability of the channel refers to the reliability and communication quality of the medical information distribution channel, which ensures that the medical information can be accurately and timely sent to the terminal device of the target user, and avoids information loss, delay or error caused by channel problems. The timeliness of the medical data template refers to whether the content of the medical data template is timely and effective, whether it can meet the current information needs of the user, for example, the timeliness of emergency information. The emergency information needs to be timely and accurately pushed to the terminal device of the target user and the doctor. If the emergency information is not timely pushed, it will affect the understanding of the current actual situation of the target user and the doctor, and further affect the treatment of the patient.

[0077] The communication stability of the medical information distribution channel is verified to determine a stability result, and the information timeliness of the medical data template is verified according to the stability result. In this embodiment, the communication stability of the medical information distribution channel is verified by packet loss rate, bandwidth fluctuation value and emergency index. The packet loss rate refers to the proportion of lost data packets in the total sent data packets in the data transmission process. The bandwidth fluctuation value is used to measure the change of network bandwidth. Excessive bandwidth fluctuation may affect the stability and speed of information transmission. The emergency index is used to quantify the emergency degree of medical information, which can be set according to the type and content of medical information and the specific situation of the patient. The fuzzy comprehensive evaluation method is used to determine the stability result by combining the packet loss rate, bandwidth fluctuation value and emergency index. In this embodiment, the stability result refers to the stability performance of the medical information distribution channel in the communication process. After obtaining the stability result, the information timeliness of the medical data template is verified according to the stability result. Medical information has timeliness, such as test results and medication reminders, which need to be pushed to the user in time. The timeliness verification of the medical data template depends on the stability verification result of the medical information distribution channel. If the channel is stable, the medical information will be pushed to the user in time according to the preset medical data template. If the channel is unstable, it may be delayed to send to the user, resulting in expired medical information and affecting the timeliness.

[0078] When the communication stability of the medical information distribution channel and the information timeliness of the medical data template are both verified, the aggregated message sending process is used to efficiently and accurately push the medical information to the terminal device of the target user. In this embodiment, the aggregated message sending process refers to the process of integrating multiple associated medical information of the same target user into one comprehensive message according to the preset rules, and then adapting the characteristics of the medical information distribution channel for pushing. The preset rules can be determined according to the actual situation, for example, integrating multiple test results of the same patient into one report abstract, or summarizing appointment reminders of multiple departments into one travel notice before pushing. According to the content and emergency degree of the aggregated message, the appropriate medical information distribution channel and medical data template are matched, for example, for an aggregated message containing critical value notification, a channel with high stability and strong timeliness and a template are selected for pushing.

[0079] Figure 2 A structural schematic diagram of a smart medical information pushing method applied to an Internet hospital is provided for the embodiments of the present application, which includes a business platform, a message platform, a message channel and a user terminal.

[0080] By obtaining medical information and its types from the business platform, the standardization of medical data classification is realized, ensuring the accuracy and pertinence of the information, ensuring the comprehensiveness of the information, and effectively reducing the omission or errors caused by manual intervention. By integrating information from different business platforms, breaking the information silos, and forming a comprehensive patient health portrait, it helps doctors to better understand the patient's condition and make more accurate diagnosis and treatment decisions, improving the user experience. Based on the information type and the preset information template, the medical information distribution channel and the medical data template are configured, which can send medical information to the target user through the most suitable channel and format, improving the readability of the information. The communication stability of the medical information distribution channel is verified to ensure that the information can be reliably sent to the target user, and by monitoring the communication quality and other indicators of the channel in real time, potential problems can be found and handled in a timely manner to avoid information loss or delay. According to the communication stability result, the information timeliness of the medical data template is further verified to ensure that the medical information is delivered to the user when it is most needed, which helps to improve the user's experience. Combined with the medical information distribution channel and the medical data template, the medical information is pushed to the target user's terminal device using the aggregation message sending process, which can select the optimal pushing method and format according to network conditions and other factors to ensure that the information is accurately delivered to the user terminal, improve the accuracy of information pushing, and improve the quality of medical services.

[0081] In one embodiment of the present embodiment, the method further comprises the following steps:

[0082] S210, when receiving the user's query instruction, obtaining the keywords input by the user on the front-end platform, and setting the query condition based on the keywords;

[0083] S220, encapsulating the query condition into a fixed format, and sending a query request to the back-end platform based on the encapsulated query condition;

[0084] S230, when the back-end platform receives the query request, invoking a preset search engine to construct a query language;

[0085] S240, searching for target information in a preset database in combination with the query language and the weighted search strategy.

[0086] When receiving the user's query instruction, the keywords input by the user on the front-end platform are obtained, and the query condition is set based on the keywords. The front-end platform is the interface for user interaction with the system, and the user inputs keywords through the front-end platform to initiate a query request. That is, when the user inputs keywords in the search box of the front-end platform, the system receives the user's query instruction, and the process starts. The keywords are extracted from the user input, and the database query rule generated according to the keywords is used to retrieve matching information from the back-end data source, which can be implemented using the WHERE clause in the SQL statement, API parameters, etc.

[0087] Subsequently, the query conditions are packaged into a fixed format, and a query request is sent to the backend platform based on the packaged query conditions. In this embodiment, the fixed format can be JSON, XML, etc. Various query conditions are organized into a standard data format according to certain rules and structures, so that the backend system can accurately parse and process these conditions. Next, the keywords and related query conditions are integrated into structured data conforming to the packaging format, and the packaged query conditions are sent to the backend platform through the HTTP protocol.

[0088] When the backend platform receives the query request, a preset search engine is called to build a query language. In this embodiment, the preset search engine is Elasticsearch, which is a search engine based on Lucene and provides distributed, RESTful search and analysis engine functions. The query language of the preset search engine is a JSON format language used to define complex query conditions. In this embodiment, the query language is Query DSL. Through the query language, users can express various query requirements in a structured manner, such as exact match, range query, fuzzy query, and combined query. When the user inputs the keywords and initiates the query request on the front-end platform, the backend platform will receive this request. The backend platform will parse the received query request and extract the keywords and query conditions. The backend platform will call the APIs provided by Elasticsearch, which allow the backend platform to interact with Elasticsearch. According to the parsed keywords and query conditions, the backend platform will build a JSON object conforming to the preset search engine query language specification.

[0089] Target information is searched in the preset database in combination with the query language and the weighted search strategy. In the query language, not only are the keywords and fields to be searched specified, but also the fields are assigned weights. The weighted search strategy is a method of sorting search results according to the importance or relevance of fields. In the search process, field weight distribution is allocated, that is, different fields are assigned different weights to reflect their importance in the search. The search engine sorts the search results according to the field weight, so that the results matched by more important fields are placed in front. In this embodiment, the preset database can be determined according to actual conditions, that is, the target information is determined in the preset database using the query language and the weighted search strategy. In this embodiment, the target information refers to the medical related data that needs to be pushed to the user after being filtered by the query conditions and sorted by the weight.

[0090] By determining the keywords of the user and querying the target information based on the keywords, the medical information demand of the user can be matched more accurately, the target data with high relevance is screened out, the precision and business adaptability of medical information retrieval are improved, and it is ensured that the user can query the target information in time and accurately.

[0091] In one of the embodiments of the present embodiment, determining the medical information distribution channel and the medical data template based on the information type and the preset information template includes the following steps:

[0092] S310, mapping the information type to a preset classification label to determine the label type corresponding to the information type;

[0093] S320, matching the medical data template in the preset information template based on the label type;

[0094] S330, editing the template information of the medical data template according to the information type;

[0095] S340, obtaining configuration information in a preset database, and determining a subscription permission verification result based on the configuration information, the configuration information including subscriber information and subscription topics;

[0096] S350, determining the medical information distribution channel according to the subscription permission verification result and a preset channel priority.

[0097] Mapping the information type to a preset classification label to determine the label type corresponding to the information type. In the present embodiment, the information type refers to the information type sent by the business platform to the message integration platform, for example, the information type sent by the inpatient medical service system is inpatient information, the information type sent by the emergency patient medical service system to the message integration platform is emergency information, etc. The preset classification label is a set of standardized labels defined in advance, used to identify the characteristics of information. The correspondence between the information type and the label can be set according to the actual situation. Mapping the information type to a preset classification label to determine the label type corresponding to the information type can be realized by using a rule engine mapping, and then the label type corresponding to the information type is determined.

[0098] The medical data template is matched with the preset information template based on the label type, and the preset information template in this embodiment refers to an information template that is designed in advance and used to generate a framework template of a specific format information, such as a medical report template, a medication reminder template, and the like. The medical data template is one of the preset information templates and is used to generate information to be sent to a user, and the medical data template can include specific titles, content of a body, contact information, and the like. According to the label type of the information and the preset information template, a mapping relationship between the label and the template is established, for example, a medical report label is mapped to a medical report sending template, and the mapping relationship can be configured and managed through a configuration file, a database table, or a management interface. When information needs to be sent, the system first queries the mapping relationship according to the label type of the information to find the corresponding medical data template. For example, the label type of the information is a medical report, and the corresponding medical data template of the medical report label is found by querying the mapping relationship. The final information to be sent to the user is generated in combination with the specific content of the information.

[0099] Subsequently, template information of the medical data template is edited according to the information type, and a suitable medical data template is selected according to the information type. According to the specific requirements of the information type, the template information in the medical data template is edited. For example, the information type is a medication reminder template, and the medication reminder template includes fields such as a drug name, a medication dose, a medication time, a medication method, and matters needing attention, and the content in the above fields is filled in according to the actual situation. After the template information is edited, the medical data template is saved.

[0100] Configuration information is obtained from a preset database, and subscription permission verification is performed on a target user based on the configuration information to determine a subscription permission verification result. In this embodiment, the configuration information includes subscriber information and subscription topics, and the preset database can be determined according to the actual situation. The subscriber information refers to information recording which users subscribe to which topics, such as a user ID, a user name, a subscription state, and the like; and the subscription topic refers to a defined information category or topic in the system that can be subscribed to. After the configuration information is obtained from the preset database, the subscription permission verification is performed on the target user according to the parsed subscriber information and the subscription topic, and specifically refers to confirming whether the target user exists in the subscriber information and confirming whether the target user has subscribed to a specified topic. According to the process of the subscription permission verification, it is determined whether the target user has the permission to subscribe to the specified topic, and then the subscription permission verification result is determined, which is recorded in the preset database.

[0101] According to the subscription permission verification result and the preset channel priority, a medical information distribution channel is determined. After obtaining the subscription permission verification result, the verification result can be permission to subscribe, refusal to subscribe, etc. According to the subscription permission verification result and the configuration of the system, the medical information distribution channel available to the target user is determined. For example, if the user has the subscription permission and the three channels of email, short message and in-application notification are configured, then the three channels can be available. According to the preset channel priority, the available medical information distribution channels are sorted. In this embodiment, the preset channel priority can be determined according to the actual situation. For example, short message > email > in-application notification. According to the preset channel priority, the medical information distribution channel with the highest priority is selected. If the channel with the highest priority is unavailable or fails to send, the channels with lower priorities can be tried in turn until the information is successfully sent to the user.

[0102] By determining the medical information distribution channel and the medical data template, and matching the channel priority according to the information urgency, the information real-time performance can be ensured, the information processing efficiency can be improved, the information can be timely conveyed to the user, the user can quickly obtain the required information, and the user experience and service satisfaction can be improved.

[0103] In one of the embodiments of the present embodiment, the communication stability of the medical information distribution channel is verified to determine a stability result, and the information timeliness of the medical data template is verified according to the stability result, including the following steps:

[0104] S410, using a preset data acquisition system to obtain the real-time transmission rate of the medical information distribution channel and the sending quantity and receiving quantity of a plurality of data packets in a preset time period, wherein the plurality of data packets include medical text data packets and medical image data packets, the medical text data packets are less than a preset threshold, and the medical image data packets are greater than the preset threshold;

[0105] S420, calculating the ratio between the real-time transmission rate and a preset reference transmission rate to determine the bandwidth fluctuation value of the medical information distribution channel;

[0106] S430, calculating the image packet loss rate and the text packet loss rate corresponding to the medical image data packets and the medical text data packets respectively based on the sending quantity and the receiving quantity;

[0107] S440, extracting medical text features and medical image features in the medical data template, and using a preset scene recognition model to determine the medical scene corresponding to the medical text features and the medical image features;

[0108] S450, determining the medical scene weight according to the preset weight rule table according to the medical scene, and quantifying the medical text features and the medical image features to obtain the medical text feature quantity and the medical image feature quantity;

[0109] S460, determining an emergency index according to the medical scene by using a preset emergency assessment model;

[0110] S470, calculating a template packet loss rate of the medical data template in combination with the medical scene weight, the medical text feature quantity, the text packet loss rate, the medical image feature quantity, and the image packet loss rate;

[0111] S480, determining a stability result of the medical information distribution channel by using a fuzzy comprehensive evaluation method in combination with the template packet loss rate, the bandwidth fluctuation value, and the emergency index;

[0112] S490, verifying information timeliness of the medical data template according to the stability result.

[0113] Firstly, a preset data acquisition system is used to obtain a real-time transmission rate of the medical information distribution channel and a sending quantity and a receiving quantity of a plurality of data packets in a preset time period. In this embodiment, the plurality of data packets include medical text data packets and medical image data packets, wherein the medical text data packets are smaller than a preset threshold, and the medical image data packets are larger than the preset threshold. The preset threshold can be determined according to actual conditions. The preset data acquisition system is a network monitoring tool deployed in advance, which is used to capture channel transmission data in real time. The real-time transmission rate refers to the amount of data transmitted by the channel per unit time. The sending quantity refers to the total number of data packets sent to the channel. The receiving quantity refers to the number of data packets successfully received by the target terminal. The data amount of the medical text data packet is smaller than that of the medical image data packet. The medical image data packet can contain more content or larger file fragments, such as X-ray films, CT scans, etc. The medical text data packet is relatively small, which can be some simple instructions, short messages, or smaller file parts. The medical text data packet represents a data packet with a small data amount, and the medical text data packet can contain information such as text notifications, such as appointment notifications. The medical image data packet represents a data packet with a large data amount, and the medical image data packet can contain attachment transmission, such as CT images, complete medical records, etc. Different data packets have different pressures on the channel and need to be monitored differently. For example, the medical image data packet is prone to cause bandwidth congestion. By using the preset data acquisition system, the transmission rate of the data packet is monitored in real time, so that the current transmission efficiency of the channel can be understood. For example, when the network is congested or under high load, the transmission rate can decrease. When the network condition is good, the transmission rate can be higher. In the preset time period, the sending quantity and the receiving quantity of the plurality of data packets transmitted through the medical information distribution channel are counted. By comparing the sending quantity and the receiving quantity, the integrity and reliability of data transmission can be understood. For example, if the sending quantity is greater than the receiving quantity, there can be data loss.

[0114] Subsequently, a ratio between the real-time transmission rate and a preset reference transmission rate is calculated to determine the bandwidth fluctuation value of the medical information distribution channel. In this embodiment, the preset reference transmission rate can be determined according to actual conditions. The real-time transmission rate refers to the actual transmission speed of the current medical information distribution channel, measured by the amount of data transmitted per second. It reflects the transmission capacity of the channel at the current time. The ratio obtained by dividing the real-time transmission rate by the reference transmission rate is used to quantify the relative relationship between the real-time transmission rate and the reference transmission rate. A value determined according to the ratio of the real-time transmission rate to the reference transmission rate is used to measure the bandwidth fluctuation degree of the medical information distribution channel. If the ratio exceeds the set threshold range, the corresponding alarm is triggered.

[0115] The image packet loss rate and the text packet loss rate corresponding to the medical image data packet and the medical text data packet are calculated based on the sending quantity and the receiving quantity, respectively. In this embodiment, the sending quantity refers to the number of data packets sent through the medical information distribution channel. The receiving quantity refers to the number of data packets actually successfully received by the target end. In this embodiment, the image packet loss rate refers to the ratio between the number of image data packets that fail to successfully arrive at the receiving end and the total number of image data packets sent in the transmission of medical image data packets; the text packet loss rate refers to the ratio between the number of text data packets that fail to successfully arrive at the receiving end and the total number of text data packets sent in the transmission of medical text data packets. The packet loss rate calculation formula is to divide the value obtained by subtracting the receiving quantity from the sending quantity by the sending quantity by percentage. By separately calculating the packet loss rate of the medical text data packet and the medical image data packet, for example, medical text data packet packet loss rate = (medical text data packet sending number - medical text data packet receiving number) / medical text data packet sending number x 100%; medical image data packet packet loss rate = (medical image data packet sending number - medical image data packet receiving number) / medical image data packet sending number x 100%. In this embodiment, the distinction between large data and small data can be realized by a preset data quantity threshold, that is, the packet loss rate of data packets at different data quantity thresholds is calculated. Medical text data packets (such as medical images and complete medical record files) occupy high bandwidth and take a long time to transmit, and are more likely to be lost due to network congestion and bandwidth fluctuation; medical image data packets (such as text notifications and simple status updates) are transmitted quickly, but may be lost due to channel fragmentation or protocol overhead. In medical information, the loss of medical text data packets will result in incomplete diagnosis information, and the loss of medical image data packets may also affect timeliness. Classification monitoring of packet loss rate can optimize the transmission strategy, such as enabling retransmission mechanism for medical text data packets and optimizing channel priority for medical image data packets.

[0116] The medical text features and medical image features in the medical data template are extracted, and a preset scene recognition model is used to determine the medical scene corresponding to the medical text features and medical image features. In this embodiment, the medical text features refer to elements extracted from medical text data that can represent key information such as text content and semantics. Medical text data can include patient medical records, examination reports, diagnosis conclusions, treatment plan descriptions, and various other textual information. The medical text features can be extracted using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The medical image features refer to key information extracted from medical image data that can describe image content, structure, texture, and other characteristics. Medical images such as X-ray films, CT images, and MRI images contain rich visual information. By extracting features from these images, the key parts and key information in the images can be highlighted. The image feature extraction method can be based on edge detection, such as Canny edge detection. The preset scene recognition model is a pre-trained and pre-set model used to recognize different medical scenes, and is trained based on a large amount of data with medical scene annotations. The preset scene recognition model learns the mapping relationship between features and scenes in the data, so that it can analyze new medical text features and image features and determine which medical scene they belong to. Medical scenes can be understood as different stages or different types of medical activity scenes that patients are in during the medical process, such as outpatient diagnosis scenes, inpatient treatment scenes, surgery scenes, and rehabilitation treatment scenes. By inputting the extracted medical text features and image features into the preset scene recognition model for processing and analysis, the model will output one or more most likely corresponding medical scene results for each set of features based on its internal decision mechanism and learned knowledge.

[0117] According to the medical scene, the weight of the medical scene is determined by using a preset weight rule table, and the medical text features and medical image features are quantified to obtain medical text feature quantities and medical image feature quantities. The preset weight rule table in this embodiment is a table formulated in advance according to professional knowledge, clinical experience and related data analysis, which clearly defines the relative importance of different medical scenes in the overall medical decision, diagnosis or service process. For example, in judging the emergency degree of the patient, the emergency scene may be given a higher weight, and the weight of the routine physical examination scene is relatively low. According to the identified medical scene, the corresponding weight value is found in the preset weight rule table. For example, if it is determined that the medical scene is a surgical treatment scene, according to the weight rule table, the weight corresponding to this scene may be 0.8, indicating that the surgical treatment scene has a high importance when considering various factors. Next, according to the type and importance of the text features, a suitable method is used to convert them into numerical values. For example, for the disease name, symptom description and other features appearing in the text, a dictionary can be constructed to vectorize the text according to the words in the dictionary. If the text contains words such as "angina", "chest tightness", "electrocardiogram abnormality", etc., each word corresponds to a specific numerical value. By counting the frequency of occurrence of these words and other information, a numerical vector is generated as the medical text feature quantity. The TF-IDF method can also be used to calculate the importance score of each feature word in the text, thereby obtaining the text feature quantity. The quantification method of image features mainly depends on the feature extraction algorithm of the image. For example, for the edge features in medical images, the length and curvature of the edge can be calculated to quantify; for texture features, the energy, entropy and contrast of the gray level co-occurrence matrix can be calculated as the quantized image feature quantity. Based on the deep learning method, the deep feature vector of the image can be extracted, which contains the key information of the image and can be used as the medical image feature quantity.

[0118] According to the medical scene, the urgency index is determined by using a preset urgency evaluation model, and in this embodiment, the preset urgency evaluation model is a pre-set rule or algorithm system for evaluating the urgency of information; the urgency index represents the quantitative value of the information urgency. The medical scene and other information are input into the preset urgency evaluation model, and a quantitative value reflecting the information urgency is calculated by using internal rules or algorithms. According to the model calculation result, the urgency index of the medical scene is obtained.

[0119] The template packet loss rate of the medical data template is calculated in combination with the medical scene weight, the medical text feature quantity, the text packet loss rate, the medical image feature quantity and the image packet loss rate. The medical text feature quantity and the text packet loss rate are first weighted and multiplied, and the medical image feature quantity and the image packet loss rate are also weighted and multiplied. Then, the calculation results of the text part and the image part are weighted and summed again according to the medical scene weight, to obtain the final template packet loss rate. In this embodiment, the template packet loss rate refers to the packet loss rate of the medical data template.

[0120] The stability result of the medical information distribution channel is determined by using the fuzzy comprehensive evaluation method in combination with the template packet loss rate, the bandwidth fluctuation value and the urgency index. The template packet loss rate, the bandwidth fluctuation value and the urgency index are taken as three key factors influencing the stability of the medical information distribution channel, to construct a factor set. According to the importance of each factor to the stability of the channel, the weight of each factor is determined. For example, the weight of the packet loss rate is 0.4, the weight of the bandwidth fluctuation value is 0.3, and the weight of the urgency index is 0.3. The weight distribution needs to be determined according to the actual application scene and expert experience. Subsequently, an evaluation set is constructed, such as high stability, medium stability and low stability. For each factor, according to the actual value and the preset evaluation standard, the membership degree of the factor to each evaluation grade is determined, to construct a fuzzy relationship matrix. The weight set and the fuzzy relationship matrix are synthesized and operated by using the fuzzy comprehensive evaluation formula, to obtain the comprehensive membership degree of each evaluation grade. According to the evaluation grade corresponding to the maximum value of the comprehensive membership degree, the stability result of the medical information distribution channel is determined.

[0121] Finally, the information timeliness of the medical data template is verified according to the stability result. The stability result refers to the stability grade of the medical information distribution channel determined by the fuzzy comprehensive evaluation method. The information timeliness refers to the characteristic that the information can accurately and timely reach the receiving end within a specified time after being sent. For some information with high real-time requirements, such as emergency notification and real-time monitoring data, the timeliness is crucial. That is, whether the information timeliness of the medical data template can be verified through the stability result. If the medical data template cannot meet the timeliness requirement of the emergency notification, measures need to be taken for optimization, such as adjusting the sending strategy, increasing the bandwidth, optimizing the network configuration, etc., to improve the stability of the channel or shorten the information sending time.

[0122] For example, there is currently a patient with sudden hemiplegia and aphasia, and the emergency system determines that it is an “acute ischemic stroke (onset <4.5 hours)”, and needs to transmit a head CT text report (exclude hemorrhage, text volume 5KB, lower than the preset text threshold 10KB) and a CT image data package (DICOM format, size 12MB, higher than the preset image threshold 8MB), the target is to complete the thrombolytic decision within 30 minutes. Subsequently, the data acquisition system is used to monitor the distribution channel, wherein the transmission rate is 100Mbps, the real-time rate is 90Mbps, the bandwidth fluctuation value = 90 / 100 = 0.9; the number of data packets, wherein the number of image data packets sent is 100, the number of received is 96, the image packet loss rate = 4%; the number of text data packets sent is 20, the number of received is 19, the text packet loss rate = 5%; Next, identify the medical scene and urgency, the text report contains “cerebral sulcus disappearance, gray-white matter boundary blur”, and the image shows “left MCA blood supply area low density shadow” and other information, therefore, the scene recognition model is used to determine the medical scene of acute ischemic stroke intravenous thrombolysis; and the preset urgency evaluation model outputs the urgency index = 0.95. Subsequently, the template packet loss rate is calculated, template packet loss rate = scene weight × (text feature amount × text packet loss rate + image feature amount × image packet loss rate), scene weight (stroke thrombolysis) = 0.9; text feature amount (diagnostic value of CT report) = 0.7; image feature amount (lesion positioning value of CT image) = 0.8; template packet loss rate = 0.9 × (0.7 × 5% + 0.8 × 4%) = 5.97%. Input template packet loss rate 5.97%, bandwidth fluctuation 0.9, urgency index 0.95, use fuzzy rule to determine, packet loss rate ≤ 6% (low), bandwidth fluctuation ≤ 1.0 (relatively stable), urgency ≥ 0.9 (extremely high), stability result = “excellent”. Finally, according to the stability result of the current medical information distribution channel, it is determined whether the text (CT report) and image (CT image) can be completely and timely transmitted to the neurologist terminal, and the current stability result is excellent, indicating that the channel is stable, and the data can arrive within 10 minutes, and the doctor has 20 minutes to analyze and make decisions. By verifying the timeliness, it can be ensured that the medical data can still meet the “time window” requirement of clinical decision-making in emergency scenarios. For example, for every 1 minute of delay in treating a cerebral infarction patient, the number of brain cell deaths increases by about 1.9 million, so the system must ensure the timeliness and integrity of data transmission through parameter monitoring (such as bandwidth fluctuation, packet loss rate, etc.).

[0123] By verifying the timeliness of the information, it can ensure that the user receives important information in a timely manner when needed, meet their real-time information needs, improve the user experience, improve the reliability and stability of the channel, and reduce information transmission interruptions or delays. And by considering different data volume types, packet loss rates, and other factors, it better adapts to different network environments and information sending needs, and improves the ability to handle complex situations.

[0124] In one of the embodiments of the present embodiment, verifying the timeliness of the information of the medical data template according to the stability result comprises the following steps:

[0125] S510, extracting the generation time of the medical data template and the current time, and calculating the difference between the generation time and the current time;

[0126] S520, inputting the difference into a preset timeliness decay function to determine the current urgency index of the medical data template;

[0127] S530, mapping the current urgency index to a preset weight rule table to determine the information urgency weight;

[0128] S540, inputting the information urgency weight and the stability result into a preset timeliness index formula to obtain the timeliness index of the medical data template;

[0129] S550, when the timeliness index is greater than a preset timeliness threshold, the medical data template passes the information timeliness verification.

[0130] First, the generation time of the medical data template and the current time are extracted, and the generation time of the medical data template refers to the time point when the template is created or defined. The generation time of the template is obtained by reading the metadata, attributes or other storage information of the template. The difference between the generation time and the current time is calculated.

[0131] Second, input the difference into a preset timeliness decay function to determine the current urgency index of the medical data template. In the present embodiment, the timeliness decay function is a function preset in advance, which is used to describe the law of the decay of the information urgency index with time, and reflects the timeliness of the information, that is, the value and urgency of the information will gradually decrease with the passage of time. The current urgency index refers to the quantitative value reflecting the urgency of the medical data template at the current time calculated according to the timeliness decay function. The preset timeliness decay function can be an exponential decay function, that is, the urgency index decreases exponentially with time. The exponential decay function is as follows:

[0132]

[0133] Where λ is the decay constant, which determines the speed of the decay of the urgency index, I(t) represents the current urgency index, and Δt represents the time difference.

[0134] The current emergency index is mapped to a preset weight rule table to determine the information emergency weight. In the embodiment, the preset weight rule table is a pre-set rule table which stipulates the weight values corresponding to different emergency index ranges. The information emergency weight refers to the corresponding weight value found in the weight rule table according to the current emergency index, indicating the importance of the emergency degree of the information in the comprehensive evaluation. According to business needs and experience, the weight values corresponding to different emergency index ranges are pre-defined. For example, when the emergency index is between 0 and 30, the weight is 0.1; when the emergency index is between 31 and 60, the weight is 0.4; and when the emergency index is between 61 and 100, the weight is 0.8. The current emergency index is compared with the weight rule table to find the corresponding emergency index range and determine the corresponding information emergency weight.

[0135] After obtaining the information emergency weight, the information emergency weight and the stability result are input into a preset timeliness index formula to obtain the timeliness index of the medical data template. In the embodiment, the preset timeliness index formula is a mathematical expression containing the emergency weight, which is used to comprehensively calculate the time sensitivity of the information. The timeliness index is an index used to determine the effectiveness of information transmission, etc. By substituting the previously determined information emergency weight into a pre-set timeliness index calculation formula, the timeliness index of the medical data template is calculated. In the embodiment, the timeliness index is used to comprehensively reflect the influence of the emergency degree and timeliness of the information on the medical data template. The preset timeliness index formula is as follows:

[0136] Timeliness index = information emergency weight × e -λ×(1-稳定性结果)

[0137] where λ is the decay constant, used to control the decay rate.

[0138] According to the preset weight rule table and the information emergency index, the corresponding emergency weight is found. According to actual needs and business scenarios, a timeliness index formula is pre-set, which usually comprehensively considers the emergency weight and other factors affecting timeliness. The information emergency weight and the stability result are substituted into the preset timeliness index formula to calculate the timeliness index. Channels with high stability can more reliably transmit information, reducing the risk of delay and interruption, making it more likely for information to be delivered within the expected time, thereby improving the timeliness index. Channels with low stability may frequently experience delays, interruptions and other problems, affecting the timely delivery of information, thereby reducing the timeliness index.

[0139] When the timeliness index is greater than the preset timeliness threshold, the medical data template passes the information timeliness verification. In the embodiment, the preset timeliness threshold can be determined according to actual conditions. According to the business requirements and the information type requirements, a reasonable timeliness threshold is determined. Then, according to the preset timeliness index formula, the information urgency weight and other related factors are input into the calculation to obtain the timeliness index of the current medical data template. The calculated timeliness index is compared with the preset timeliness threshold. If the timeliness index is greater than the threshold, it is considered that the medical data template passes the timeliness verification; otherwise, it is considered that the verification fails. When the information timeliness verification is passed, it means that the timeliness index of the medical data template exceeds the preset timeliness threshold, which means that the information under the template still has high timeliness and value at the current time, and can be effectively conveyed to the target user within the specified time.

[0140] By verifying the information timeliness of the medical data template according to the stability result, it can be ensured that the information received by the user is timely and valuable, thereby improving the user experience and satisfaction, and ensuring that the information is sent in time, and the stability and reliability of the sending channel are also ensured.

[0141] Figure 3 An exemplary flowchart of pushing medical information according to an embodiment of the present application is shown. As shown in the embodiment, in one of the embodiments, the medical information is pushed to the terminal device of the target user by combining the medical information distribution channel and the medical data template using the aggregated message sending process, which includes the following steps: Figure 3

[0142] S610, sensitive word verification and parameter verification are performed on the push information in the medical data template;

[0143] S620, when the sensitive word verification and parameter verification of the push information pass, it is judged whether the medical information distribution channel is a system message channel;

[0144] S630, if the medical information distribution channel is a system message channel, the medical information is stored in the preset database by using the preset system message logic;

[0145] S640, if the medical information distribution channel is a general message channel, it is judged whether there is a unique user identifier of the target user in the hospital information system;

[0146] S650, if there is a unique user identifier of the target user in the hospital information system, the medical information is pushed to the terminal device of the target user according to the preset priority;

[0147] S660, if there is no user identifier of the target user in the hospital information system, it is judged whether the medical information is only pushed to the terminal device of the target user;​

[0148] S670、If the medical information is not only pushed to the target user, the family identifier corresponding to the target user is obtained in the hospital information system, and the medical information is pushed to the terminal device of the family corresponding to the target user based on the family identifier.

[0149] First, the push information in the medical data template is checked for sensitive words and parameters. Specifically, a sensitive word library is established, and prohibited words are defined in advance to form a dynamically updated word library. The text content of the push information is scanned through a text matching algorithm (such as regular expressions, word segmentation matching, semantic analysis, etc.), and it is determined whether the sensitive words are included. Then, different strategies are taken according to the risk level of the sensitive words. In this embodiment, the sensitive word check refers to checking whether the information content contains illegal words, such as medical terminology sensitive words, privacy information, etc.; the parameter check verifies whether the key parameters in the information are complete or the format is correct, to prevent push failure due to parameter errors. The text content of the push information is extracted from the medical data template, and then matched with the words in the sensitive word library. If it is found that the push information contains sensitive words, it is processed according to the pre-set rules.

[0150] After the sensitive word check and parameter check of the push information pass, it is determined whether the medical information distribution channel is a system message channel. In this embodiment, the system message channel refers to a dedicated channel of the hospital information system. First, it is ensured that the push information passes the sensitive word check and parameter check, and then information related to the medical information distribution channel, such as channel identifier, configuration parameters, sending authority, etc. is collected. According to the pre-set rules and conditions, it is determined whether the current medical information distribution channel is a system message channel.

[0151] If the medical information distribution channel is a system message channel, the medical information is stored in the pre-set database using the pre-set system message logic. In this embodiment, the pre-set system message logic is a pre-defined rule and process for processing system messages, including how to store medical information in the database; the pre-set database is a pre-configured database for storing medical information; when the medical information distribution channel is a system message channel, the storage operation is performed according to the pre-set system message logic, and the medical information is stored in the pre-set database.

[0152] If the medical information distribution channel is a general message channel, it is determined whether the unique user identifier of the target user exists in the hospital information system. In this embodiment, the general message channel refers to a channel for sending non-system-level general messages, such as a short message notification channel and the like. The target user refers to the object of the message to be sent, that is, a specific user who needs to receive the medical information. The unique user identifier refers to specific information for uniquely identifying a user in the hospital information system, such as a user ID, a work number, a medical record number, and the like. The relevant information of the target user is obtained from the message sending request or related data. In the database of the hospital information system, it is determined whether the corresponding unique user identifier exists according to the obtained target user information, to determine whether the unique user identifier of the target user exists.

[0153] If the unique user identifier of the target user exists in the hospital information system, the medical information is pushed to the terminal device of the target user according to the preset priority. In this embodiment, the preset priority can be determined according to actual conditions. After the unique user identifier of the target user is queried and confirmed to exist in the hospital information system, the emergency degree and importance of the current medical information are evaluated according to the preset priority rule, to determine the corresponding priority. If there are multiple medical information to be pushed to the same target user, the information is sorted according to the preset priority rule. The medical information is pushed to the terminal device of the target user one by one according to the sorted order. The information with high priority is pushed first, to ensure that the user can receive important information in time.

[0154] If the unique user identifier of the target user does not exist in the hospital information system, it is determined whether the medical information is only pushed to the terminal device of the target user. In the hospital information system, the unique user identifier of the target user is queried. If the identifier is not found, it means that the target user cannot be determined through the user identification mechanism inside the hospital information system, and it needs to be further determined whether the medical information only needs to be pushed to the target user himself, that is, whether it is limited to point-to-point pushing.

[0155] If the medical information is not only pushed to the target user, the corresponding family identifier of the target user is obtained in the hospital information system, and the medical information is pushed to the terminal device of the family corresponding to the target user based on the family identifier. In this embodiment, the family identifier refers to specific information used to identify and locate the family members of the patient in the hospital information system, such as the family member's mobile phone number, email address, user ID, etc. It is judged whether the medical information is only pushed to the target user himself / herself. If the medical information needs to be pushed not only to the target user but also to other related personnel, in the hospital information system, the corresponding family identifier of the patient is queried according to the unique user identifier of the target user. The hospital information system usually records the contact information of the patient and his / her family members. According to the family identifier, the terminal device contact information of the family members, such as the mobile phone number, email address, etc. is obtained. The medical information is sent to the terminal device of the family members through appropriate means.

[0156] By checking and explaining the sensitive words, it can be ensured that the medical information does not contain illegal or sensitive content, and the accuracy and safety of information sending are improved. And it can ensure that the user receives important medical information in time, which can effectively improve the user experience and satisfaction.

[0157] In one of the embodiments of the present embodiment, the method further comprises the following steps:

[0158] S710, when there are multiple users, obtaining the user attribute and unique user identifier of each user;

[0159] S720, constructing a user message queue corresponding to each target user based on each unique user identifier, and arranging each user message queue according to each user attribute to determine an initial user push list;

[0160] S730, extracting the information attribute of the medical information in each user message queue;

[0161] S740, arranging the medical information according to each information attribute to determine the message push list of each user, and adding each medical information to each user message queue according to the message push list;

[0162] S750, determining the message queue length of each user message queue by using a preset message queue system, and taking the user message queue with a message queue length greater than a preset queue length as a backlog message queue;

[0163] S760, determining the priority value of each medical information in the backlog message queue by using a preset priority evaluation model;

[0164] S770, calculating the average priority value of each backlog message queue by using the priority value, and rearranging the initial user push list according to the average priority value to determine a final user push list;

[0165] S780, push medical information to each user based on the final user push list.

[0166] When there are multiple users, obtain the user attributes and unique user identification of each user, that is, when there are multiple users and multiple pieces of medical information need to be pushed, determine the user attributes and unique user identification of each user. In this embodiment, the user attributes refer to a set of information used to describe the characteristics and information of the user, including but not limited to user roles (such as doctors, nurses, patients, etc.), contact information, department affiliation, etc. The unique user identification refers to specific information used to uniquely identify a user in the system. The user attributes and unique user identification can be obtained in the medical information system.

[0167] Based on each unique user identification, build a user message queue corresponding to each target user, and arrange each user message queue according to each user attribute to determine an initial user push list. In this embodiment, the user message queue refers to a dedicated queue created for each user, used to temporarily store and manage the messages of the user. The initial user push list is a list generated according to the arrangement results of all user message queues, which determines the priority order of user push. First, a message queue is created for each user to store and manage the messages of the user. When a new message is generated, the message is placed in the corresponding message queue according to the unique identification of the target user. The message queues are sorted according to the attributes of each user (such as role, urgency, etc.) to determine the priority of message push. For example, the message queue of a doctor may have a higher priority than that of an ordinary patient. According to the arrangement results of all user message queues, an initial user push list is generated.

[0168] Extract the information attributes of the medical information in each user message queue. In this embodiment, the information attributes can be information types such as test reports, diagnosis results, medication reminders, etc., and can also be urgency levels, privacy information permissions, etc. Access each user's message queue, parse each piece of medical information in the queue, extract its attributes, and store the extracted attributes. The extraction of information attributes can be implemented using data structures in programming languages, such as SQL queries, etc.

[0169] Subsequently, the medical information is arranged according to each information attribute to determine a message push list of each user, and each medical information is added to the message queue of each user according to the message push list. In this embodiment, the message push list refers to a list of medical information arranged in a certain order, which is used to guide the push order of the message. The information attribute is extracted from the medical information in the message queue of each user, and the medical information is sorted according to the extracted information attribute to determine the message push list. For example, the messages can be arranged in descending order of urgency, and the message with high urgency is pushed first. For example, the queue of patient A: [M01 (critical value) -> M02 (follow-up reminder) -> M03 (popular article)] is pushed, and the critical value message at the head of the queue is processed first, and then the subsequent messages are processed. According to the generated message push list, the medical information is added back to the user message queue to ensure that the message order in the queue is consistent with the push list.

[0170] The message queue length of each user message queue is determined by using a preset message queue system, and the user message queue with a message queue length greater than a preset queue length is regarded as a backlog message queue. In this embodiment, the message queue length refers to the number of messages in the message queue of each user. For example, if there are 10 pieces of unpushed medical information in the message queue of user A, the queue length is 10; the preset queue length is a threshold value set in advance, which is used to judge whether the queue is overloaded. The backlog message queue is a message queue that is considered to have message backlog when the message queue length exceeds the preset queue length and needs special processing. When using the preset message queue system, the number of messages in each queue can be obtained through the management interface or API provided by the system. That is, when the message queue length exceeds the preset queue length, it is considered that the message queue has message backlog and needs special processing.

[0171] The priority value of each medical information in the backlog message queue is determined, and first the factors affecting the priority are determined, and each factor is assigned a weight. Common factors include information type, urgency, generation time, state of related patients, etc. For each piece of medical information in the backlog message queue, its related attributes such as type, urgency, generation time, etc. are extracted. The extracted attribute values are input into a priority evaluation model to calculate the priority value of each piece of medical information. Determining the priority value of each medical information in the backlog message queue includes the following steps:

[0172] Determine the diagnosis and treatment stage of each medical information and the diagnosis and treatment data corresponding to the diagnosis and treatment stage;

[0173] Determine the age characteristics of the user through the medical information;

[0174] Based on the historical diagnosis and treatment stage and the historical diagnosis and treatment data corresponding to the historical diagnosis and treatment stage, a time effectiveness decay function corresponding to each diagnosis and treatment stage is constructed;

[0175] The diagnosis and treatment stage and diagnosis and treatment data are input into the timeliness decay function to obtain a primary timeliness score;

[0176] The final timeliness score is calculated according to the age characteristics;

[0177] The diagnosis and treatment stage is matched with a preset diagnosis and treatment path to obtain a clinical emergency coefficient;

[0178] A medical risk value of the diagnosis and treatment stage is determined by using a preset medical risk level model;

[0179] When the age characteristics are greater than preset age characteristics, an amplification multiple of the medical risk value is determined by using a preset risk amplification formula;

[0180] The final medical risk value is obtained by multiplying the amplification multiple and the medical risk value;

[0181] The final medical risk value, the clinical emergency coefficient and the final timeliness score are combined for weighted fusion to obtain a priority value of each medical information.

[0182] The diagnosis and treatment stage of each medical information and diagnosis and treatment data corresponding to the diagnosis and treatment stage are determined. In this embodiment, the diagnosis and treatment stage refers to different stages divided according to the development of the patient's condition and the treatment needs in the medical process, which can include emergency, acute phase, chronic disease phase, rehabilitation phase, etc. The diagnosis and treatment data can be initial diagnosis stage data, examination stage data, treatment stage data, etc., such as basic vital signs, family medical history, etc. Subsequently, the age characteristics of the user are determined through the medical information, and age-related information is extracted from the medical information to understand the age range or age distribution of the user, which helps to better understand the medical needs of each stage.

[0183] Based on the historical diagnosis and treatment stages and the historical diagnosis and treatment data corresponding to the historical diagnosis and treatment stages, a timeliness decay function corresponding to each diagnosis and treatment stage is constructed. In this embodiment, the historical diagnosis and treatment stages and the historical diagnosis and treatment data include detailed records of different diagnosis and treatment stages (such as initial diagnosis, examination, treatment, and rehabilitation). For the historical diagnosis and treatment data of each diagnosis and treatment stage, the change of data value with time is analyzed, and according to the data analysis result, a suitable mathematical function model is selected to describe the decay law of data value. An exponential decay function can be selected to construct the timeliness decay function, and the selected function model is parameterized by using historical data. The exponential decay function needs to determine the decay coefficient by fitting historical data, and an optimization algorithm such as least squares method can be used to minimize the error between the model prediction value and the actual value. According to the parameter estimation result, a timeliness decay function corresponding to each diagnosis and treatment stage is constructed. That is, for the historical data of each stage, the interval between the data generation time and the processing time (i.e. the delay time) is calculated, and the corresponding diagnosis and treatment result (such as “effective treatment”, “risk increase due to delay”, “missed optimal treatment time”, etc.) is associated. According to the timeliness of diagnosis and treatment, a timeliness decay function corresponding to each diagnosis and treatment stage is constructed.

[0184] The diagnosis and treatment stage and the diagnosis and treatment data are input into the timeliness decay function to obtain a primary timeliness score. Different diagnosis and treatment stages (such as initial diagnosis, examination, treatment, and rehabilitation) have different requirements for timeliness. For example, in the initial diagnosis stage, timely and accurate understanding of the patient's symptoms and medical history is crucial for developing subsequent examination and treatment plans; while in the rehabilitation stage, although information is still important, the timeliness requirement is relatively low. In this embodiment, the primary timeliness score is obtained by calculating the timeliness decay function, which is a quantitative value representing the importance or relevance of the diagnosis and treatment data at the current time. The timeliness decay function is used to determine the primary timeliness score of the current diagnosis and treatment stage.

[0185] The final timeliness score is calculated according to the age characteristics. When considering the timeliness of medical information, the patient's age is taken as a key factor in the calculation, because patients of different ages have significantly different requirements for the timeliness of medical information. First, based on the diagnosis and treatment stage and type of medical information, an initial timeliness score is determined. For example, the importance of a blood test report in the initial diagnosis stage can be initially scored as 90 points. The timeliness adjustment coefficient is determined according to the patient's age characteristics. It is assumed that the timeliness requirement for medical information of infants is increased, and the adjustment coefficient is 1.2; the timeliness requirement for chronic disease follow-up information of the elderly is reduced, and the adjustment coefficient is 0.8. The initial timeliness score is multiplied by the adjustment coefficient. For example, the final timeliness score of the blood test report of the infant is 90 x 1.2 = 108 points, and the final timeliness score of the examination report of the elderly chronic disease follow-up is 90 x 0.8 = 72 points.

[0186] The matching of the diagnosis and treatment stage with the preset diagnosis and treatment path, and obtaining the clinical emergency coefficient refers to judging the emergency degree of the patient's condition by comparing the actual diagnosis and treatment stage of the patient with the standard diagnosis and treatment path. The preset diagnosis and treatment path is a standardized diagnosis and treatment process, and the essence is to model the time-event sequence of the “ideal diagnosis and treatment process” of a certain disease. For example, the standard path of ischemic stroke requires that thrombolysis be completed within 3 hours of onset, and image review be completed within 24 hours; the clinical emergency coefficient is a numerical index quantifying the degree of deviation of the current diagnosis and treatment process from the standard path. Compare the current diagnosis and treatment stage with the keywords in the preset diagnosis and treatment path, check whether the current diagnosis and treatment stage conforms to the order specified in the preset path, and evaluate whether the current diagnosis and treatment stage is completed within the time range specified in the preset path. For example, the preset path requires that a certain examination be completed within 3 days after the initial diagnosis, and if the actual completion time is within the specified range, the time matching degree is high.

[0187] The preset medical risk level model is used to determine the medical risk value of the diagnosis and treatment stage. In this embodiment, the preset medical risk level model is a standardized model for evaluating medical risk based on medical knowledge, historical case data, evidence-based medicine, etc. The collected data is input into the preset medical risk level model, and the model analyzes and processes the data according to the pre-set algorithm and rules to calculate a numerical value representing the medical risk degree of the patient's current diagnosis and treatment stage, i.e. the medical risk value. According to the obtained medical risk value, medical staff can have a clear understanding of the medical risk level of the patient, so as to make more reasonable diagnosis and treatment decisions. For example, if the risk value is high, it indicates that the patient may have a high medical risk, and medical staff may adjust the treatment plan, strengthen monitoring measures, and make emergency preparations in advance to reduce the risk and improve medical safety and treatment success rate.

[0188] When the age characteristic is greater than the preset age characteristic, the amplification multiple of the medical risk value is determined by using the preset risk amplification formula. The preset age characteristic can be determined according to the actual situation, that is, by using the preset risk amplification formula, the basic risk value is dynamically adjusted according to the age characteristic of the patient to quantify the additional impact of the age factor on the medical risk. The preset risk amplification formula can be amplification multiple = 1 + k × (actual age - preset age), k is the risk coefficient, which can be set through clinical data. For example, the preset age characteristic is 60 years old, k = 0.05 (i.e. the risk increases by 5% for every 1 year over). The patient is 65 years old, and the amplification multiple = 1 + 0.05 × (65-60) = 1.25.

[0189] The final medical risk value is obtained by multiplying the amplification factor and the medical risk value, and is a medical risk value obtained by combining the basic medical risk value and the amplification factor. When the age characteristic of the patient is greater than the preset age characteristic, the amplification factor calculated according to the preset risk amplification formula is used to quantify the degree of influence of the age factor on the medical risk. The final medical risk value is obtained by multiplying the basic medical risk value by the amplification factor. This final value more objectively reflects the actual medical risk of the patient after being affected by the age factor. For example, if the basic medical risk value is 10 and the amplification factor is 1.5, then the final medical risk value is 10 x 1.5 = 15.

[0190] The priority value of each medical information is obtained by weighted fusion of the final medical risk value, the clinical emergency coefficient and the final timeliness score. The clinical emergency coefficient represents the emergency degree of the patient's condition, and the final timeliness score reflects the timeliness of the medical information, i.e. the timeliness and effectiveness of the information. The priority value of each medical information is determined by weighted fusion, for example, the weight of the final medical risk value is 0.5, the weight of the clinical emergency coefficient is 0.3, and the weight of the final timeliness score is 0.2. Then, each factor is multiplied by its corresponding weight, and the results are added to obtain the priority value of each medical information. That is: priority value = final medical risk value x weight 1 + clinical emergency coefficient x weight 2 + final timeliness score x weight 3; the priority value is used to measure the processing priority of the medical information. The higher the priority value, the more important, urgent and timely the medical information is, and it needs to be processed in priority.

[0191] The average priority value of each backlog message queue is calculated by the priority value, and the final user push list is determined by rearranging the initial user push list according to the average priority value. First, the average value of the priority values of all medical information in the backlog queue of a single user is calculated, for example, the backlog queue of user A has 3 messages with priority values of 90, 80 and 70, and the average priority is 80. Second, the initial user push list is reordered according to the average priority of each backlog queue, i.e. the users in the initial user push list are reordered from high to low according to the average priority value of each backlog message queue to determine the final user push list. In this embodiment, the final user push list refers to the user processing order after the initial user push list is reordered according to the average priority of each backlog queue, ensuring that users with high average priority are processed first.

[0192] Based on the final user push list, medical information is pushed to each user according to the average priority of each backlog message queue. The initial user push list is reordered to obtain the final user push list. According to the order of the final user push list, medical information is pushed to each user in turn.

[0193] By acquiring the attributes and unique identification of each user, a user message queue is constructed, and an initial push list is arranged according to the user attributes, so as to ensure that information is pushed to the correct user and improve the pertinence and priority of information push. By calculating the length of the message queue, a backlog message queue is identified, the message backlog problem in the system is found in time, the information processing efficiency is enhanced, and the user experience is improved.

[0194] In one of the embodiments of the present embodiment, the user attributes include user authority and user credit, and arranging each user message queue according to each user attribute to determine the initial user push list includes the following steps:

[0195] S810, the user authority and the user credit are weighted and summed to determine the comprehensive score of each user;

[0196] S820, arranging each user message queue according to the comprehensive score to determine the initial user push list.

[0197] The user authority and the user credit are weighted and summed to determine the comprehensive score of each user. In the present embodiment, the user authority refers to the identity and operation range of the user in the system, such as the authority level of administrator, doctor, nurse, patient, etc. Different authorities correspond to different responsibilities and data access ranges, which can be quantified as a certain score value. The user credit refers to the behavior standard and reliability of the user in the system, such as the historical operation record of the user, whether the user complies with the platform regulations, etc. A user with good credit can obtain a higher score value. The user authority and credit are respectively converted into specific score values, the weights of the user authority and credit are set according to business requirements, and the weighted sum formula is used to calculate the comprehensive score, as shown below:

[0198] Comprehensive score = authority score value × authority weight + credit score value × credit weight

[0199] For example, the user authority score value is 80, and the credit score value is 90. The authority weight is set as 0.6, and the credit weight is set as 0.4. Then the comprehensive score is: comprehensive score = 80 × 0.6 + 90 × 0.4 = 84.

[0200] Arranging each user message queue according to the comprehensive score to determine the initial user push list, the scores obtained by weighting and summing the user authority and credit are sorted in descending order according to the comprehensive scores of all user message queues. The sorted list is the initial user push list, and the messages of the user with a high comprehensive score are preferentially pushed.

[0201] By weighting and summing the user authority and credit, the comprehensive priority of the user can be accurately determined, the overall efficiency and response speed are improved, and the user experience is enhanced.

[0202] The embodiment of the present application further provides a machine readable storage medium, which has instructions stored thereon, and the instructions are used to cause a machine to execute the smart medical information pushing method applied to the Internet hospital.

[0203] The embodiment of the present application further provides an electronic device, which comprises:

[0204] a memory configured to store instructions; and

[0205] a processor configured to call the instructions from the memory and enable the smart medical information pushing method applied to the Internet hospital to be implemented when the instructions are executed.

[0206] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can adopt a form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0207] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems), computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be realized by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams. These computer program instructions can be provided to a general purpose computer, a special purpose computer, an embedded processor or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices produce a device implemented in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus configured to perform the functions specified in one or more flows or blocks.

[0208] These computer program instructions can also be stored in a computer readable storage medium capable of guiding the computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a product including instruction apparatus, which implements the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus configured to perform the functions specified in one or more flows or blocks.

[0209] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer implemented process, so that the instructions executed on the computer or other programmable device provide a means for implementing the flowcharts and / or block diagrams.Figure 1 one or more processes and / or functions specified in one or more blocks Figure 1 one or more processes and / or functions specified in one or more blocks

[0210] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0211] Memory can include non-persistent memory and / or volatile memory, random access memory (RAM), and / or non-volatile memory, e.g., read only memory (ROM) or flash memory. Memory is an example of computer readable media.

[0212] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0213] It should also be noted that the terms "comprising," "including," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0214] The above merely provides an embodiment of the present application and is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A smart medical information pushing method applied to an Internet hospital, characterized in that, The method is applied to a message integration platform connected with a hospital information system and a plurality of business platforms, and comprises the following steps: Obtaining medical information of the business platform and an information type of the medical information; Based on the information type and a preset information template, the medical information is configured to determine a medical information distribution channel and a medical data template; The communication stability of the medical information distribution channel is verified to determine a stability result, and the information timeliness of the medical data template is verified according to the stability result; When the communication stability of the medical information distribution channel and the information timeliness of the medical data template are both verified, the medical information is pushed to a terminal device of a target user by combining the medical information distribution channel and the medical data template using an aggregated message sending process; The information timeliness of the medical data template is verified according to the stability result, which comprises the following steps: Extracting the generation time of the medical data template and the current time, and calculating the difference between the generation time and the current time; The difference is input into a preset timeliness decay function to determine the current urgency index of the medical data template; The current urgency index is mapped to a preset weight rule table to determine an information urgency weight; The information urgency weight and the stability result are input into a preset timeliness index formula to obtain the timeliness index of the medical data template; When the timeliness index is greater than a preset timeliness threshold, the medical data template passes the information timeliness verification.

2. The method of claim 1, wherein, The method further comprises the following steps: After receiving a user's query instruction, the user's input keyword on the front-end platform is obtained, and a query condition is set based on the keyword; The query condition is packaged into a fixed format, and a query request is sent to the back-end platform based on the packaged query condition; When the back-end platform receives the query request, a preset search engine is called to build a query language; Target information is searched in a preset database by combining the query language and a weighted search strategy.

3. The method of claim 1, wherein, Based on the information type and a preset information template, the medical information is configured to determine a medical information distribution channel and a medical data template, which comprises the following steps: The information type is mapped to a preset classification label to determine the label type corresponding to the information type; Based on the label type, a medical data template is matched in a preset information template; The template information of the medical data template is edited according to the information type; Configuration information is obtained in a preset database, and a subscription permission verification result is determined by performing subscription permission verification on the target user based on the configuration information, wherein the configuration information includes subscriber information and subscription topics; A medical information distribution channel is determined according to the subscription permission verification result and a preset channel priority.

4. The method of claim 1, wherein, The communication stability of the medical information distribution channel is verified to determine a stability result, and the information timeliness of the medical data template is verified according to the stability result, which comprises the following steps: acquiring a real-time transmission rate of the medical information distribution channel and a sending quantity and a receiving quantity of a plurality of data packets in a preset time period by using a preset data acquisition system, wherein the plurality of data packets comprise medical text data packets and medical image data packets, the medical text data packets are smaller than a preset threshold, and the medical image data packets are larger than the preset threshold; calculating a bandwidth fluctuation value of the medical information distribution channel by calculating a ratio between the real-time transmission rate and a preset reference transmission rate; calculating an image packet loss rate and a text packet loss rate corresponding to the medical image data packets and the medical text data packets respectively based on the sending quantity and the receiving quantity; extracting medical text features and medical image features in the medical data template, and determining a medical scene corresponding to the medical text features and the medical image features by using a preset scene recognition model; determining a medical scene weight by using a preset weight rule table according to the medical scene, and obtaining medical text feature quantities and medical image feature quantities by quantifying the medical text features and the medical image features; determining an emergency index by using a preset emergency evaluation model according to the medical scene; calculating a template packet loss rate of the medical data template by combining the medical scene weight, the medical text feature quantities, the text packet loss rate, the medical image feature quantities and the image packet loss rate; determining a stability result of the medical information distribution channel by using a fuzzy comprehensive evaluation method in combination with the template packet loss rate, the bandwidth fluctuation value and the emergency index; verifying information timeliness of the medical data template according to the stability result.

5. The method of claim 1, wherein, The method of combining the medical information distribution channel and the medical data template and using an aggregated message sending process to push the medical information to a terminal device of a target user comprises the following steps: performing sensitive word verification and parameter verification on push information in the medical data template; when the sensitive word verification and the parameter verification of the push information pass, determining whether the medical information distribution channel is a system message channel; if the medical information distribution channel is a system message channel, storing the medical information into a preset database by using a preset system message logic; if the medical information distribution channel is a common message channel, determining whether a unique user identifier of the target user exists in the hospital information system; if the unique user identifier of the target user exists in the hospital information system, pushing the medical information to the terminal device of the target user according to a preset priority; if the unique user identifier of the target user does not exist in the hospital information system, determining whether the medical information is only pushed to the terminal device of the target user; if the medical information is not only pushed to the terminal device of the target user, acquiring a family identifier corresponding to the target user in the hospital information system, and pushing the medical information to a terminal device of a family member of the target user based on the family identifier.

6. The method of claim 5, wherein, The method further comprises the following steps: when there are a plurality of users, acquiring a user attribute and a unique user identifier of each of the users; constructing a user message queue corresponding to each of the target users based on each of the unique user identifiers, and determining an initial user push list according to arrangement of each of the user message queues based on each of the user attributes; extracting information attributes of the medical information in each of the user message queues; determining a message push list of each of the users according to arrangement of the medical information based on each of the information attributes, and adding each of the medical information into each of the user message queues according to the message push list; determining a message queue length of each of the user message queues by using a preset message queue system, and taking the user message queue with a message queue length greater than a preset queue length as a backlog message queue; determining a priority value of each of the medical information in the backlog message queue; calculating an average priority value of each of the backlog message queues by using the priority value, and determining a final user push list by re-arranging the initial user push list according to the average priority value; pushing the medical information to each of the users based on the final user push list.

7. The method of claim 6, wherein, The user attributes include user permissions and user credits, and the determining of the initial user push list according to arrangement of each of the user message queues based on each of the user attributes includes the following steps: determining a comprehensive score of each of the users by weighted sum of the user permissions and the user credits; determining the initial user push list according to arrangement of each of the user message queues based on the comprehensive score.

8. A machine-readable storage medium, characterized in that, The machine-readable storage medium has instructions stored thereon for causing a machine to execute the smart medical information push method applied to the internet hospital according to any one of claims 1 to 7.

9. A smart medical information pushing system applied to an Internet hospital, characterized in that, comprising: a memory configured to store instructions; and a processor configured to call the instructions from the memory and capable of implementing the smart medical information push method applied to the internet hospital according to any one of claims 1 to 7 when executing the instructions.

Images