KR20240013020A - Method and apparatus for providing management information of a medical facility to a terminal according to information related to filing an insurance claim using a neural network - Google Patents

Abstract

Embodiments present a method and apparatus in which a server provides information on the management status of a medical institution to a terminal related to the medical institution using a neural network. The method according to one embodiment obtains financial information about the medical institution and tax information about the medical institution based on business information and authentication information entered into the server, and the financial information about the medical institution is related to the tax invoice. Information, information related to invoices, information related to cash receipts, information related to card sales, information related to insurance claims, information related to corporate card use details, information related to deposit and withdrawal details by account, and tax information for the above-mentioned medical institutions. It includes information related to corporate tax, information related to value-added tax, and information related to withholding tax, and provides information about the business status of the medical institution based on the business information, financial information predicted for the medical institution, and tax information predicted for the medical institution. The score is determined, and the predicted financial information for the medical institution and the predicted tax information for the medical institution are calculated using an evaluation model using a neural network based on the business operator information, financial information for the medical institution, and tax information for the medical institution. Generated through, and based on the fact that the score for the management status of the medical institution is lower than a preset score, requesting information about the prescription of the medical institution and information about the medical record of the medical institution from the computer server related to the medical institution, By comparing information related to the insurance claim with information about the medical institution's prescription received from a computer server related to the medical institution and information about the medical record of the medical institution, first claim details and second claim details are determined, and the first claim details are determined. 1 Claim details are insurance claim details omitted from the information related to the insurance claim, and the second claim details are insurance claim details added to the information related to the insurance claim, financial information about the medical institution, and information about the medical institution. It may include transmitting information on the management status of the medical institution, including tax information, a score on the management status of the medical institution, the first claim details, and the second claim details, to a terminal related to the medical institution.

Description

Method and device for providing management information of a medical institution to a terminal according to information related to insurance claims using a neural network NEURAL NETWORK}

Embodiments of the present disclosure relate to technology for providing management information of a medical institution to a terminal according to information related to insurance claims, and technology for providing management information of a medical institution to a terminal according to information related to insurance claims using a neural network. It is about.

In general, it is difficult for small and medium-sized medical institutions, rather than large medical institutions, to have the means to analyze management performance, so accounting analysis and management analysis are mostly handled using only the know-how of managers.

Meanwhile, as the use of smartphones has become popular, managers of medical institutions can check the management details of medical institutions in real time through smartphones. However, because existing accounting-related applications are provided for general managers, there is a problem in that management details are provided without considering the special characteristics of medical institutions.

Accordingly, based on the medical institution's income, expenditure, and tax amount, which reflects the details of medical expenses covered by health insurance, the future income, expenditure, and tax amount are predicted through a neural network, and the financial status and tax amount of the medical institution are calculated along with the predicted information. A method is needed to provide the tax status of medical institutions to medical institution managers.

Embodiments of the present disclosure may provide a method and device for providing management information of a medical institution to a terminal according to information related to insurance claims using a neural network.

The technical challenges to be achieved in the embodiments are not limited to the matters mentioned above, and other technical challenges not mentioned may be considered by those skilled in the art from the various embodiments described below. You can.

A method in which a server according to an embodiment provides information about the management status of a medical institution to a terminal related to the medical institution using a neural network is based on the business operator information and authentication information input to the server. Obtain financial information and tax information about the medical institution, and the financial information about the medical institution includes information related to tax invoices, information related to invoices, information related to cash receipts, information related to card sales, and information related to insurance claims. , includes information related to corporate card usage details, information related to deposit and withdrawal details by account, tax information for the medical institution includes information related to corporate tax, information related to value-added tax, and information related to withholding tax, and the business information, Determine a score for the management status of the medical institution based on the financial information predicted for the medical institution and the tax information predicted for the medical institution, and the financial information predicted for the medical institution and the tax information predicted for the medical institution are the Generated through an evaluation model using a neural network based on business operator information, financial information about the medical institution, and tax information about the medical institution, and based on the score for the management status of the medical institution being lower than a preset score, the medical institution Request information about the prescription of the medical institution and information about the medical record of the medical institution from the computer server related to the medical institution, information related to the insurance claim, information about the prescription of the medical institution received from the computer server related to the medical institution, and the above. By comparing information on the medical record of the medical institution, first claim details and second claim details are determined, wherein the first claim details are insurance claim details omitted from the information related to the insurance claim, and the second claim details are It is insurance claim details added to the information related to the insurance claim, and includes financial information about the medical institution, tax information about the medical institution, a score on the management status of the medical institution, the first claim details, and the second claim details. It may include transmitting information about the management status of the medical institution to a terminal related to the medical institution.

According to one embodiment, the method includes a first preset method based on information related to the tax invoice, information related to the invoice, information related to the cash receipt, information related to the card sales, and information related to the insurance claim. It may further include determining the income of the medical institution within the period and determining the expenditure of the medical institution within the first preset period based on information related to the corporate card use history and information related to deposit/withdrawal details for each account. there is. For example, a first difference value may be determined by subtracting the expenditures of the medical institution from the income of the medical institution. For example, the group that includes the medical institution may be determined among a plurality of groups established based on information on the management status of the plurality of medical institutions. For example, a second difference value may be determined by subtracting average expenses from average income within the preset first period for a plurality of medical institutions included in the group. For example, based on the fact that the first difference value is smaller than the second difference value, information related to marketing may be transmitted to a terminal related to the medical institution.

According to one embodiment, the score for the management status of the medical institution may be determined by the following equation.

In the above equation, p is a score for the management status of the medical institution, z is a weight related to the medical department of the medical institution, n is the number of medical institutions included in the group, and a is the evaluation. is the revenue of the medical institution predicted for a second period preset by the model, where b is the expenditure of the medical institution predicted for the second period preset by the evaluation model, and c is the is the tax amount of the medical institution predicted for the set second period, where a _i is the average income of the ith medical institution within the preset first period, and b _i is the average expenditure of the ith medical institution within the preset first period; , the c _i may be the average tax amount of the ith medical institution within the first preset period.

For example, the preset score may be determined by the following equation.

In the above equation, p _th is the preset score, n _t is the number of the plurality of groups, z is a weight related to the medical department of the medical institution, and r _a is the group among the plurality of groups. is the ranking of income, r _b may be the ranking of expenditures of the group among the plurality of groups, and r _c may be the ranking of the tax amount of the group among the plurality of groups.

Here, the number of the plurality of groups may be the number of medical subjects determined through the web crawling described above. Here, the ranking of the income of the group may indicate the order in which the average income is high based on the average income within the preset first period for each of the plurality of groups. Here, the ranking of the expenditures of the groups may indicate the order in which the average expenditures are low based on the average expenditures within the preset first period for each of the plurality of groups. Here, the ranking of the tax amount of the group may indicate the order in which the average tax amount is lower based on the average tax amount within the preset first period for each of the plurality of groups.

According to one embodiment, the amount of tax paid within the first preset period may be determined based on the information related to the corporate tax, the information related to the value-added tax, and the information related to the withholding amount. For example, the average tax amount paid within the first preset period for the plurality of medical institutions included in the group may be determined. For example, based on the difference between the tax amount and the average tax amount being greater than a preset value, tax-related information may be transmitted to a terminal related to the medical institution.

Additionally, for example, the preset value may be set by the following equation.

In the above equation, V is the preset value, z is a weight related to the medical department of the medical institution, n is the number of medical institutions included in the group, and c _i is the i th number included in the group. It is the average tax amount of the medical institution within the preset first period, and c _avg may be an average value of the average tax amount of the medical institutions included in the group within the preset first period.

According to one embodiment, the neural network may include an input layer, one or more hidden layers, and an output layer. For example, each learning data consisting of multiple business information, financial information about multiple medical institutions, tax information about multiple medical institutions, correct answer predicted financial information, and predicted tax information is input to the input layer of the neural network. , passes through the one or more hidden layers and the output layer to output an output vector, and the output vector is input to a loss function layer connected to the output layer, and the loss function layer provides the correct answer for the output vector and each learning data. The evaluation model can be created based on outputting a loss value using a loss function that compares vectors, and learning the parameters of the neural network in a direction that reduces the loss value.

According to embodiments, the server acquires information related to insurance claims based on business information and authentication information entered into the server, so that when determining information on the management of a medical institution, in addition to general income such as cash receipts and card sales, Information related to the insurance claim may be reflected.

According to embodiments, the server provides predicted financial information about the medical institution and predicts the medical institution through an evaluation model using a neural network based on the business operator information, financial information about the medical institution, and tax information about the medical institution. By generating tax information, you can check the management status of medical institutions from various angles.

According to embodiments, based on the fact that the score for the management status of the medical institution is lower than a preset score, the server provides information related to the insurance claim, information about the medical institution's prescription received from a computer server related to the medical institution, and By comparing information on the medical records of the medical institutions, the current medical institution's insurance claim details can be adaptively reviewed.

The effects that can be obtained from the examples are not limited to the effects mentioned above, and other effects not mentioned can be clearly derived and understood by those skilled in the art based on the detailed description below. It can be.

The accompanying drawings, which are included as part of the detailed description to aid understanding of the embodiments, provide various embodiments and together with the detailed description describe technical features of the various embodiments.
1 is a diagram showing the configuration of an electronic device according to an embodiment.
Figure 2 is a diagram showing the configuration of a program according to one embodiment.
Figure 3 shows a method in which a server provides information on the management status of a medical institution to a terminal according to an embodiment.
Figure 4 shows an example of an evaluation model according to an embodiment.
Figure 5 is a diagram illustrating a procedure in which a server transmits marketing-related information and tax-related information to a terminal according to an embodiment.
Figure 6 is a block diagram showing the configuration of a server according to one embodiment.

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. Additionally, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may change. Some features or features of one embodiment may be included in other embodiments or may be replaced with corresponding features or features of other embodiments.

In the description of the drawings, procedures or steps that may obscure the gist of the various embodiments are not described, and procedures or steps that can be understood at the level of a person with ordinary knowledge in the relevant technical field are not described. did.

Throughout the specification, when a part is said to “comprise or include” a certain element, this means that it does not exclude other elements but may further include other elements, unless specifically stated to the contrary. do. In addition, terms such as "... unit", "... unit", and "module" used in the specification refer to a unit that processes at least one function or operation, which refers to hardware, software, or a combination of hardware and software. It can be implemented as: Additionally, the terms “a or an,” “one,” “the,” and similar related terms are used herein in the context of describing various embodiments (particularly in the context of the claims below). Unless otherwise indicated or clearly contradicted by context, it may be used in both singular and plural terms.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the attached drawings. The detailed description set forth below in conjunction with the accompanying drawings is intended to illustrate exemplary embodiments of various embodiments and is not intended to represent the only embodiment.

In addition, specific terms used in various embodiments are provided to aid understanding of the various embodiments, and the use of such specific terms may be changed to other forms without departing from the technical spirit of the various embodiments. .

1 is a diagram showing the configuration of an electronic device according to an embodiment.

1 is a block diagram of an electronic device 101 in a network environment 100, according to various embodiments. Referring to FIG. 1, in the network environment 100, the electronic device 101 communicates with the electronic device 102 through a first network 198 (e.g., a short-range wireless communication network) or a second network 199. It is possible to communicate with at least one of the electronic device 104 or the server 108 through (e.g., a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108. According to one embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or may include an antenna module 197. In some embodiments, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added to the electronic device 101. In some embodiments, some of these components (e.g., sensor module 176, camera module 180, or antenna module 197) are integrated into one component (e.g., display module 160). It can be. The electronic device 101 may also be referred to as a client, terminal, or peer.

The processor 120, for example, executes software (e.g., program 140) to operate at least one other component (e.g., hardware or software component) of the electronic device 101 connected to the processor 120. It can be controlled and various data processing or calculations can be performed. According to one embodiment, as at least part of data processing or computation, the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132. The commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134. According to one embodiment, the processor 120 includes a main processor 121 (e.g., a central processing unit or an application processor) or an auxiliary processor 123 that can operate independently or together (e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device 101 includes a main processor 121 and a secondary processor 123, the secondary processor 123 may be set to use lower power than the main processor 121 or be specialized for a designated function. You can. The auxiliary processor 123 may be implemented separately from the main processor 121 or as part of it.

The auxiliary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or while the main processor 121 is in an active (e.g., application execution) state. ), together with the main processor 121, at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190) At least some of the functions or states related to can be controlled. According to one embodiment, co-processor 123 (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module 180 or communication module 190). there is. According to one embodiment, the auxiliary processor 123 (eg, neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models.

Artificial intelligence models can be created through machine learning. For example, such learning may be performed in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (e.g., server 108). Learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but It is not limited. An artificial intelligence model may include multiple artificial neural network layers. Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above. In addition to hardware structures, artificial intelligence models may additionally or alternatively include software structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101. Data may include, for example, input data or output data for software (e.g., program 140) and instructions related thereto. Memory 130 may include volatile memory 132 or non-volatile memory 134.

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142, middleware 144, or application 146.

The input module 150 may receive commands or data to be used in a component of the electronic device 101 (e.g., the processor 120) from outside the electronic device 101 (e.g., a user). The input module 150 may include, for example, a microphone, mouse, keyboard, keys (eg, buttons), or digital pen (eg, stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101. The sound output module 155 may include, for example, a speaker or a receiver. Speakers can be used for general purposes such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 can visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector, and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of force generated by the touch.

The audio module 170 can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device (e.g., directly or wirelessly connected to the electronic device 101). Sound may be output through the electronic device 102 (e.g., speaker or headphone).

The sensor module 176 detects the operating state (e.g., power or temperature) of the electronic device 101 or the external environmental state (e.g., user state) and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 includes, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that can be used to connect the electronic device 101 directly or wirelessly with an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 can convert electrical signals into mechanical stimulation (e.g., vibration or movement) or electrical stimulation that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 can capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 can manage power supplied to the electronic device 101. According to one embodiment, the power management module 188 may be implemented as at least a part of, for example, a power management integrated circuit (PMIC).

The battery 189 may supply power to at least one component of the electronic device 101. According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

Communication module 190 is configured to provide a direct (e.g., wired) communication channel or wireless communication channel between electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108). It can support establishment and communication through established communication channels. Communication module 190 operates independently of processor 120 (e.g., an application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., : LAN (local area network) communication module, or power line communication module) may be included. Among these communication modules, the corresponding communication module is a first network 198 (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (e.g., legacy It may communicate with an external electronic device 104 through a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN). These various types of communication modules may be integrated into one component (e.g., a single chip) or may be implemented as a plurality of separate components (e.g., multiple chips). The wireless communication module 192 uses subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 can be confirmed or authenticated.

The wireless communication module 192 may support 5G networks after 4G networks and next-generation communication technologies, for example, NR access technology (new radio access technology). NR access technology provides high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low latency). -latency communications)) can be supported. The wireless communication module 192 may support high frequency bands (eg, mmWave bands), for example, to achieve high data rates. The wireless communication module 192 uses various technologies to secure performance in high frequency bands, for example, beamforming, massive array multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. It can support technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module 192 may support various requirements specified in the electronic device 101, an external electronic device (e.g., electronic device 104), or a network system (e.g., second network 199). According to one embodiment, the wireless communication module 192 supports Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mmTC, or U-plane latency (e.g., 164 dB or less) for realizing URLLC. Example: Downlink (DL) and uplink (UL) each of 0.5 ms or less, or round trip 1 ms or less) can be supported.

The antenna module 197 may transmit or receive signals or power to or from the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator made of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected to the plurality of antennas by, for example, the communication module 190. can be selected Signals or power may be transmitted or received between the communication module 190 and an external electronic device through the at least one selected antenna. According to some embodiments, in addition to the radiator, other components (eg, radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module 197.

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, a mmWave antenna module includes: a printed circuit board, an RFIC disposed on or adjacent to a first side (e.g., bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., mmWave band); And a plurality of antennas (e.g., array antennas) disposed on or adjacent to the second side (e.g., top or side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (e.g., bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( (e.g. commands or data) can be exchanged with each other.

According to one embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199. Each of the external electronic devices 102 or 104 may be of the same or different type as the electronic device 101. According to one embodiment, all or part of the operations performed in the electronic device 101 may be executed in one or more of the external electronic devices 102, 104, or 108. For example, when the electronic device 101 needs to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 may perform the function or service instead of executing the function or service on its own. Alternatively, or additionally, one or more external electronic devices may be requested to perform at least part of the function or service. One or more external electronic devices that have received the request may execute at least part of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device 101. The electronic device 101 may process the result as is or additionally and provide it as at least part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology can be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of Things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199. The electronic device 101 may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and can provide services to the connected electronic device 101. In addition, the server 108 may perform a membership registration process, store and manage various information of users who have registered as members, and provide various purchase and payment functions related to the service. Additionally, the server 108 may share execution data of service applications running on each of the plurality of electronic devices 101 in real time so that services can be shared between users. This server 108 may have the same hardware configuration as a typical web server or service server. However, in terms of software, it may be implemented through any language such as C, C++, Java, Python, Golang, and Kotlin and may include program modules that perform various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, and receives work performance requests from clients or other servers and derives and provides work results in response. It refers to a computer system and the computer software (server program) installed for it. In addition, in addition to the server program described above, the server 108 includes a series of application programs running on the server 108 and, in some cases, various databases (DBs) built internally or externally, hereinafter " It should be understood as a broad concept including “DB”). Accordingly, the server 108 classifies membership registration information and various information and data about games, stores them in a DB, and manages this DB, which may be implemented inside or outside the server 108. In addition, the server 108 can be implemented using a variety of server programs provided on general server hardware and operating systems such as Windows, Linux, UNIX, and Macintosh. , Representative examples include IIS (Internet Information Server) used in a Windows environment and CERN, NCSA, APPACH, and TOMCAT used in a Unix environment, etc., to implement web services. Additionally, the server 108 may be linked with an authentication system and payment system for user authentication of the service or payment for purchases related to the service.

The first network 198 and the second network 199 are a connection structure that allows information exchange between each node, such as terminals and servers, or a network connecting the server 108 and the electronic devices 101 and 104. It means (Network). The first network 198 and the second network 199 are the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), and 3G. , 4G, LTE, 5G, Wi-Fi, etc., but are not limited to these. The first network 198 and the second network 199 may be closed, such as a LAN or WAN, but are preferably open, such as the Internet. The Internet includes protocols such as TCP/IP protocol, TCP, and UDP (user datagram protocol), as well as various services that exist at the upper layer, such as HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), and DNS (Domain Name System). ), a worldwide open computer primary network (198) and secondary network (199) that provides Simple Mail Transfer Protocol (SMTP), Simple Network Management Protocol (SNMP), Network File Service (NFS), and Network Information Service (NIS). ) refers to the structure.

A database can have a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). A database may have a data storage form that allows for free search (extraction), deletion, editing, addition, etc. of data. Databases are relational database management systems (RDBMS) such as Oracle, Infomix, Sybase, and DB2, or object-oriented database management such as Gemston, Orion, and O2. It can be implemented according to the purpose of an embodiment of the present disclosure using a system (OODBMS) and an XML native database such as Excelon, Tamino, and Sekaiju, and has its own functions. To achieve this, you can have appropriate fields or elements.

Figure 2 is a diagram showing the configuration of a program according to one embodiment.

Figure 2 is a block diagram 200 illustrating program 140 according to various embodiments. According to one embodiment, the program 140 includes an operating system 142, middleware 144, or an application 146 executable on the operating system 142 for controlling one or more resources of the electronic device 101. It can be included. Operating system 142 may include, for example, AndroidTM, iOSTM, WindowsTM, SymbianTM, TizenTM, or BadaTM. At least some of the programs 140 are preloaded into the electronic device 101, for example, at the time of manufacture, or are stored in an external electronic device (e.g., the electronic device 102 or 104, or a server) when used by a user. It can be downloaded or updated from 108)). All or part of the program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or retrieval) of one or more system resources (eg, process, memory, or power) of the electronic device 101 . Operating system 142 may additionally or alternatively operate on other hardware devices of electronic device 101, such as input module 150, audio output module 155, display module 160, and audio module 170. , sensor module 176, interface 177, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196, or It may include one or more driver programs for driving the antenna module 197.

The middleware 144 may provide various functions to the application 146 so that functions or information provided from one or more resources of the electronic device 101 can be used by the application 146. The middleware 144 includes, for example, an application manager 201, a window manager 203, a multimedia manager 205, a resource manager 207, a power manager 209, a database manager 211, and a package manager 213. ), connectivity manager (215), notification manager (217), location manager (219), graphics manager (221), security manager (223), call manager (225), or voice recognition manager (227). You can.

The application manager 201 may, for example, manage the life cycle of the application 146. The window manager 203 may, for example, manage one or more GUI resources used on the screen. For example, the multimedia manager 205 identifies one or more formats required for playing media files, and encodes or decodes the corresponding media file using a codec suitable for the selected format. It can be done. The resource manager 207 may, for example, manage the source code of the application 146 or the memory space of the memory 130. The power manager 209 manages, for example, the capacity, temperature, or power of the battery 189, and may use this information to determine or provide related information necessary for the operation of the electronic device 101. . According to one embodiment, the power manager 209 may interface with a basic input/output system (BIOS) (not shown) of the electronic device 101.

Database manager 211 may create, search, or change a database to be used by application 146, for example. The package manager 213 may, for example, manage the installation or update of applications distributed in the form of package files. The connectivity manager 215 may manage, for example, a wireless connection or direct connection between the electronic device 101 and an external electronic device. For example, the notification manager 217 may provide a function for notifying the user of the occurrence of a designated event (eg, an incoming call, message, or alarm). The location manager 219 may, for example, manage location information of the electronic device 101. The graphics manager 221 may, for example, manage one or more graphic effects to be provided to the user or a user interface related thereto.

Security manager 223 may provide, for example, system security or user authentication. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101. For example, the voice recognition manager 227 transmits the user's voice data to the server 108 and provides a command corresponding to a function to be performed in the electronic device 101 based at least in part on the voice data, Alternatively, text data converted based at least in part on the voice data may be received from the server 108. According to one embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to one embodiment, at least a portion of the middleware 144 may be included as part of the operating system 142 or may be implemented as separate software different from the operating system 142.

The application 146 includes, for example, home 251, dialer 253, SMS/MMS (255), instant message (IM) 257, browser 259, camera 261, and alarm 263. , Contacts (265), Voice Recognition (267), Email (269), Calendar (271), Media Player (273), Album (275), Watch (277), Health (279) (such as exercise amount or blood sugar) It may include applications that measure biometric information) or environmental information 281 (e.g., measure atmospheric pressure, humidity, or temperature information). According to one embodiment, the application 146 may further include an information exchange application (not shown) that can support information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to deliver designated information (e.g., calls, messages, or alarms) to an external electronic device, or a device management application configured to manage the external electronic device. there is. The notification relay application, for example, transmits notification information corresponding to a specified event (e.g., mail reception) generated in another application (e.g., email application 269) of the electronic device 101 to an external electronic device. You can. Additionally or alternatively, the notification relay application may receive notification information from an external electronic device and provide it to the user of the electronic device 101.

The device management application, for example, controls the power (e.g., turn-on or turn-off) of an external electronic device or some component thereof (e.g., a display module or camera module of the external electronic device) that communicates with the electronic device 101. ) or functions (such as brightness, resolution, or focus). A device management application may additionally or alternatively support installation, deletion, or update of applications running on external electronic devices.

Throughout this specification, neural network, neural network, and network function may be used with the same meaning. A neural network may consist of a set of interconnected computational units, which may generally be referred to as “nodes.” These “nodes” may also be referred to as “neurons.” A neural network is composed of at least two or more nodes. Nodes (or neurons) that make up neural networks may be interconnected by one or more “links.”

Within a neural network, two or more nodes connected through a link can relatively form a relationship as an input node and an output node. The concepts of input node and output node are relative, and any node in an output node relationship with one node may be in an input node relationship with another node, and vice versa. As described above, input node to output node relationships can be created around links. One or more output nodes can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, nodes connecting the input node and the output node may have weights. Weights may be variable and may be varied by a user or algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the weight.

As described above, in a neural network, two or more nodes are interconnected through one or more links to form an input node and output node relationship within the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. For example, if there are two neural networks with the same number of nodes and links and different weight values between the links, the two neural networks may be recognized as different from each other.

Figure 3 shows a method in which a server provides information on the management status of a medical institution to a terminal according to an embodiment. One embodiment of FIG. 3 may be combined with various embodiments of the present disclosure.

Referring to FIG. 3, in step S301, the server may obtain financial information about the medical institution and tax information about the medical institution based on business information and authentication information entered into the server.

Here, the business information may be information about the business necessary to obtain financial information about the medical institution and tax information about the medical institution. For example, business information includes business registration number, name of medical institution, information on medical treatment, information related to the National Tax Service, information related to Credit Finance Association, information related to health insurance, information related to national pension, and information related to labor welfare. may include. For example, information related to the National Tax Service may include information necessary to access the National Tax Service website. For example, information related to the Credit Finance Association may include information necessary to access the Credit Finance Association website. For example, information related to health insurance may include information necessary to access the health insurance homepage (e.g., health insurance management number). For example, information related to the National Pension may include information necessary to access the National Pension website (e.g., National Pension management number). For example, information related to labor welfare may include information required to access the employment insurance website (e.g., labor welfare management number).

Here, the authentication information may be information about authentication necessary to obtain financial information of the medical institution and tax information of the medical institution. For example, authentication information may include information related to electronic signatures. For example, authentication information includes information about joint certificates, information about financial certificates, information about telecommunication company-affiliated certificates (e.g. PASS), and corporate-affiliated certificates (e.g. Naver, Kakao, Toss, Payco, Samsung Pass). It may contain information about

Here, the financial information about the medical institution may be information about the income and expenses of the medical institution. For example, financial information for medical institutions includes information related to tax invoices, information related to invoices, information related to cash receipts, information related to card sales, information related to insurance claims, information related to corporate card use history, and deposits and withdrawals by account. It may contain information related to the details. For example, information related to tax invoices, information related to invoices, and information related to cash receipts may be received from a server related to the National Tax Service through business information and authentication information. For example, information related to card sales may be received from a server related to the Credit Finance Association through business information and authentication information. For example, information related to card sales may include information such as card approval details, card purchase details, and deposit details. For example, information related to insurance claims may be received from a server related to health insurance through business information and authentication information. For example, information related to corporate card usage details may be received from a server related to the corporate card through business information and authentication information. For example, deposit/withdrawal details for each account may be received from a server related to the bank corresponding to the account through business information and authentication information.

Here, tax information about the medical institution may be information about the tax of the medical institution. For example, tax information for a medical institution may include information related to corporate tax, information related to value-added tax, and information related to withholding tax. For example, information related to corporate tax, information related to value-added tax, and information related to withholding tax may be received from a server related to the National Tax Service through authentication information.

For example, business information, authentication information, financial information about medical institutions, and tax information about medical institutions may be encrypted using various encryption methods and stored on the server.

In step S302, the server may determine a score for the management status of the medical institution based on the business operator information, financial information predicted for the medical institution, and tax information predicted for the medical institution. For example, the financial information predicted for the medical institution and the tax information predicted for the medical institution are generated through an evaluation model using a neural network based on the business information, financial information about the medical institution, and tax information about the medical institution. can be created.

For example, a neural network may include an input layer, one or more hidden layers, and an output layer. For example, each learning data consisting of multiple business information, financial information about multiple medical institutions, tax information about multiple medical institutions, correct answer predicted financial information, and predicted tax information is input to the input layer of the neural network. , passes through the one or more hidden layers and the output layer to output an output vector, and the output vector is input to a loss function layer connected to the output layer, and the loss function layer provides the correct answer for the output vector and each learning data. The evaluation model can be created based on outputting a loss value using a loss function that compares vectors, and learning the parameters of the neural network in a direction that reduces the loss value.

For example, the group that includes the medical institution may be determined among a plurality of groups established based on information on the management status of the plurality of medical institutions. For example, information on the management status of multiple medical institutions may be pre-entered into the server. For example, the server may determine a plurality of groups into n groups through a clustering technique based on information about the management status of a plurality of medical institutions. For example, clustering may refer to unsupervised learning that groups data with similar properties into a certain number of clusters.

Additionally, for example, information on the management status of multiple medical institutions can be primarily classified through values related to medical departments. Here, information about medical departments may be included in information about the management status of the plurality of medical institutions. For example, medical departments include orthopedics, otolaryngology, ophthalmology, obstetrics and gynecology, urology, plastic surgery, internal medicine, surgery, pediatrics, neurology, psychiatry, dermatology, dentistry, anesthesiology, radiology, laboratory medicine, It may be a variety of medical specialties such as preventive medicine, thoracic surgery, and rehabilitation medicine. For example, the types of medical departments may be updated periodically through web crawling. Here, web crawling may be an operation to collect information through a plurality of websites. For example, based on information collected through web crawling, the server determines a preset number of medical subjects in order of high frequency of words related to the preset medical subjects, and sets values related to the preset number of medical subjects. You can decide. For example, a value related to a medical subject may be a value corresponding to the medical subject. That is, if the medical department is orthopedics, the value corresponding to the medical department may be '1', and if the medical department is dermatology, the value corresponding to the medical department may be '10'. At this time, for example, the related medical subject may be set to a value that falls within a preset range.

For example, the server may classify information on the management status of a plurality of medical institutions into each medical subject according to values corresponding to the medical subjects. That is, for example, the server may classify a plurality of medical institutions into a plurality of first groups, and in this case, the first group may be a group for a plurality of medical institutions corresponding to the same medical subject.

Alternatively, for example, the server may classify a plurality of medical institutions into related medical subjects according to values corresponding to the medical subjects based on information on the management status of the plurality of medical institutions. That is, for example, the server may classify a plurality of medical institutions into a plurality of first groups, and in this case, the first group may be a group for a plurality of medical institutions corresponding to related medical subjects.

For example, the server may generate a vector through data preprocessing for each of the plurality of primary classified first groups. For example, the server may generate a plurality of sales vectors based on information about the management status of a plurality of medical institutions included in the first group. For example, the sales vector may be a vector composed of [sales, number of employees].

For example, for one first group, n second groups may be determined through a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) technique based on a plurality of sales vectors. For example, DBSCAN assumes that if a specific element (point) belongs to a cluster, it must be close to many other elements in the cluster, and for this calculation, the radius and minimum points are required. can be used For example, the diameter may be a radius based on a specific data element, which may be referred to as a dense area. For example, the minimum element can indicate how many elements are needed around the core point to specify it. Additionally, each element in the data set can be divided into core, border, and outlier points. For example, an element may be a sales vector corresponding to one medical institution.

For example, the server can check the size of the diameter of each element and discover how many surrounding elements there are. Afterwards, if there are more than m elements within the diameter range, the server may determine that element to be a key element. Additionally, the server may determine an element included within the diameter range from the core element as a boundary element. Additionally, the server may determine elements that are not included within the diameter range from the core element as outlier elements, and the outlier elements may be excluded from the corresponding cluster. Additionally, the server can classify core elements into the same cluster if the distance between them is less than the diameter.

Because of this, the number of plural groups is not determined to be fixed, and the number of plural groups and types of medical subjects can be changed flexibly according to information about the management status currently input to the server.

According to one embodiment, the server operates within a first preset period based on information related to the tax invoice, information related to the invoice, information related to the cash receipt, information related to the card sales, and information related to the insurance claim. The income of the above-mentioned medical institution can be determined. For example, the server may determine the expenditure of the medical institution within the first preset period based on information related to the corporate card usage details and information related to deposit/withdrawal details for each account. For example, the income of the medical institution and the expenditures of the medical institution may be calculated in various ways. For example, the income of the medical institution may be determined as the sum of tax invoices and invoice details, card sales details, cash receipt details, and insurance claim details included within a preset first period. For example, the expenditure of the medical institution may be determined as the sum of the expenditure details of the corporate card and the expenditure details of each account included within the preset first period.

For example, a first difference value may be determined by subtracting the expenditures of the medical institution from the income of the medical institution. For example, the group that includes the medical institution may be determined among a plurality of groups established based on information on the management status of the plurality of medical institutions. For example, a second difference value may be determined by subtracting average expenses from average income within the preset first period for a plurality of medical institutions included in the group. For example, based on the fact that the first difference value is smaller than the second difference value, information related to marketing may be transmitted to the terminal. Here, information related to marketing may include information about the advertising company, marketing strategy, and cost estimate according to the marketing strategy. For example, the server may determine a preset number of medical institutions in order of highest sales for a plurality of medical institutions that have the same medical specialty as the medical institution, and determine the marketing strategies of the preset number of medical institutions through web crawling. For example, marketing strategies may include SEO (search engine optimization), online advertising services of portal sites, cafe/blog experience groups, influencer or model sponsorship advertisements, advertisements through SNS, and advertisements through news. Here, SEO may be a marketing strategy that allows advertisements to appear at the top of search results by organizing web pages according to the way search engines collect data and determine rankings. For example, a portal site's online advertising service may include services such as Naver's Smart Place. For example, the server may determine the estimated cost according to the marketing strategy based on the income of the medical institution, and determine an advertising company corresponding to the estimated cost according to the marketing strategy among a plurality of pre-stored advertising companies.

According to one embodiment, the amount of tax paid within the first preset period may be determined based on the information related to the corporate tax, the information related to the value-added tax, and the information related to the withholding amount. For example, the average tax amount paid within the first preset period for the plurality of medical institutions included in the group may be determined. For example, based on the difference between the tax amount and the average tax amount being greater than a preset value, tax-related information may be transmitted to the terminal. Here, tax-related information may include information about the tax corporation and estimated costs for tax agency. For example, the tax-related information may be pre-stored on the server. For example, the server may determine the estimated cost according to the tax agent based on the tax amount of the medical institution, and determine a tax corporation corresponding to the estimated cost according to the tax agent among a plurality of pre-stored tax agencies.

Additionally, for example, the server may change the financial information of the medical institution and the tax information of the medical institution obtained based on the first claim details and the second claim details. For example, the server may modify the information related to the insurance claim by adding the first claim details to the information related to the insurance claim and excluding the second claim details from the information related to the insurance claim. For example, the server may change the medical institution's expenses and the medical institution's tax amount based on information related to the revised insurance claim. For example, the server may transmit changed financial information of the medical institution and changed tax information of the medical institution to a terminal related to the medical institution.

For example, the server may determine a score for the medical institution's management status based on financial information and predicted tax information predicted by the evaluation model. For example, the forecasted financial information may be financial information forecast for a second preset period of time. For example, the predicted tax information may be tax information predicted for a preset second period. Here, the preset second period may be a period after the current point in time. That is, the preset first period may be a period before the current time, and the preset second period may be a period after the current time.

For example, the score for the management status of the medical institution can be determined by Equation 1 below.

In Equation 1, p is a score for the management status of the medical institution, z is a weight related to the medical department of the medical institution, n is the number of medical institutions included in the group, and a is is the revenue of the medical institution predicted for the second period preset by the evaluation model, b is the expenditure of the medical institution predicted by the evaluation model for the second period preset, and c is the expenditure of the medical institution predicted by the evaluation model for the second period preset. is the tax amount of the medical institution predicted for the preset second period, a _i is the average revenue of the ith medical institution within the preset first period, and b _i is the average income of the ith medical institution within the preset first period. Expenses, and c _i may be the average tax amount of the ith medical institution within the first preset period. Here, by the max function, the abc value and the a ₀ -b ₀ -c ₀ value can always have a value of 1 or more. Here, z may have a value between 0 and 1. For example, z may have different values depending on the treatment department and may be preset in the server. For example, the z value may have a larger value as the number of medical institutions corresponding to the medical field increases.

Since the score for the management status of the medical institution described above reflects the predicted income, expenses, and taxes of the medical institution, and also reflects information related to insurance claims, the server reports the management status of the medical institution from various angles based on information specific to the medical institution. You can review.

In step S303, the server may request information about the medical institution's prescription and information about the medical record of the medical institution from a computer server related to the medical institution, based on the score for the management status of the medical institution being lower than a preset score. there is. Here, information about the medical institution's prescription may be information about the prescription stored by an order communication system (OCS) in a computer server related to the medical institution. For example, here, information about the medical record of a medical institution may be information about a medical record (chart) stored by an electronic medical record (EMR) system in a computer server related to the medical institution.

In step S304, the server compares information related to the insurance claim with information about the medical institution's prescription received from a computer server related to the medical institution and information about the medical record of the medical institution, thereby creating the first claim details and the second claim. You can decide the details. For example, the first claim details may be insurance claim details omitted from the information related to the insurance claim. For example, the second claim details may be insurance claim details added to the information related to the insurance claim.

For example, the server may determine a first list for which insurance money was claimed within a preset first period based on information about the medical institution's prescription and information about the medical institution's medical record. For example, the server may determine a second list of insurance claims within a preset first period based on information related to insurance claims. For example, based on the first list and the second list, the server may determine insurance claim details included only in the first list as the first claim details. For example, based on the first list and the second list, the server may determine insurance claim details included only in the second list as the second claim details.

In step S305, the server provides information on the management status of the medical institution, including financial information on the medical institution, tax information on the medical institution, a score on the management status of the medical institution, the first claim details, and the second claim details. Information can be transmitted to a terminal related to the medical institution. Here, information about the management status of the medical institution may be information indicating the overall financial and tax status of the medical institution. For example, the server may transmit interface information indicating the management status of the medical institution to the terminal along with information on the management status of the medical institution. Here, the terminal related to the medical institution may be a terminal used by the manager of the medical institution. For example, a terminal related to the medical institution may be a terminal with an established wireless connection with the server.

Figure 4 shows an example of an evaluation model according to an embodiment. The embodiments of FIG. 4 can be combined with various embodiments of the present disclosure.

Referring to FIG. 4, the neural network used in the evaluation model may include an input layer 410, one or more hidden layers 420, and an output layer 430.

For example, the server preprocesses data based on information on multiple business operators, financial information on multiple medical institutions, and tax information on multiple medical institutions, including income data of medical institutions, expenditure data of medical institutions, and tax amount data of medical institutions by treatment subject. can be decided. For example, the evaluation model may include a first model for income data of the medical institution, a second model for expenditure data of the medical institution, and a third model for tax data of the medical institution. At this time, for example, income data, expenditure data, and tax data may be time-series data. For example, income data may be data on monthly income, expenditure data may be data on monthly expenditures, and tax amount data may be data on monthly tax amounts.

For example, each of the first model, second model, and third model may be a one direction convolutional neural network (1D-CNN) model. For example, CNN is a type of deep learning algorithm and is specialized for processing data arranged in a grid, so it can be effective in identifying patterns in data. Additionally, convolution is a process in which a CNN filter moves on original data, and in the 1D-CNN, the direction in which the CNN filter moves may be in one direction. Therefore, 1D-CNN can be effective when recognizing patterns for short sections of fixed length among all data.

For example, each learning data consisting of a medical institution's income data and correct answer prediction income data is input to the input layer of the neural network, passes through one or more hidden layers and an output layer to output an output vector, and the output vector is input to the output layer. It is input to the loss function layer connected to, and the loss function layer outputs the loss value using a loss function that compares the output vector with the correct answer vector for each learning data, and the parameters of the neural network are adjusted in the direction of decreasing the loss value. It can be learned.

For example, each learning data consisting of medical institution's expenditure data and correct answer prediction expenditure data is input to the input layer of the neural network, passes through one or more hidden layers and output layers to output an output vector, and the output vector is input to the output layer. It is input to the loss function layer connected to, and the loss function layer outputs the loss value using a loss function that compares the output vector with the correct answer vector for each learning data, and the parameters of the neural network are adjusted in the direction of decreasing the loss value. It can be learned.

For example, each learning data consisting of medical institution's tax amount data and correct answer predicted tax amount data is input to the input layer of the neural network, passes through one or more hidden layers and output layers to output an output vector, and the output vector is input to the output layer. It is input to the loss function layer connected to, and the loss function layer outputs the loss value using a loss function that compares the output vector with the correct answer vector for each learning data, and the parameters of the neural network are adjusted in the direction of decreasing the loss value. It can be learned.

For example, one or more hidden layers may include one or more convolutional layers 421 and one or more pooling layers 422.

For example, time series data for monthly data (e.g., income data, expenditure data, or tax data) may be vectorized, and data adjacent to the vectorized time series data may be filtered in a convolutional layer 421, and the convolutional layer A feature map of (421) may be formed.

For example, in the pooling layer 422, fixed vectors related to features are selected for dimensionality reduction based on the formed feature map, and sub-sampling is performed on the formed feature map to identify important features in the vectorized time series data. can be extracted. For example, the pooling layer 422 may be a max pooling layer that extracts the largest value. For example, the pooling layer 422 may be an average pooling layer that extracts an average value. For example, at this time, the parameters of the first neural network may include parameters (size of feature map, size of filter, depth, stride, zero padding) related to the convolutional layer 421 and the pooling layer 422. You can. Here, depth may be the number of consecutive convolutional layers. Here, the stride may be an interval moved when convolution is performed. Here, zero padding may be an operation of filling a specific value with 0.

Figure 5 is a diagram illustrating a procedure in which a server transmits marketing-related information and tax-related information to a terminal according to an embodiment. One embodiment of FIG. 5 may be combined with various embodiments of the present disclosure.

Referring to FIG. 5, in step S501, the server may obtain financial information about the medical institution and tax information about the medical institution based on the business information and authentication information entered into the server.

In step S502, the server may determine whether the score for the management status of the medical institution determined based on the financial information and predicted tax information predicted by the above-described evaluation model is less than a preset score.

For example, the server may determine the score for the management status of the medical institution using Equation 1 described above.

For example, the preset score may be determined by Equation 2 below.

In Equation 2, p _th is the preset score, n _t is the number of the plurality of groups, z is a weight related to the medical department of the medical institution, and r _a is the number of groups among the plurality of groups. The r _b may be a ranking of the group's income among the plurality of groups, and r _c may be a ranking of the group's tax amount among the plurality of groups. Here, the number of the plurality of groups may be the number of medical subjects determined through the web crawling described above. Here, the ranking of the income of the group may indicate the order in which the average income is high based on the average income within the preset first period for each of the plurality of groups. Here, the ranking of the expenditures of the groups may indicate the order in which the average expenditures are low based on the average expenditures within the preset first period for each of the plurality of groups. Here, the ranking of the tax amount of the group may indicate the order in which the average tax amount is lower based on the average tax amount within the preset first period for each of the plurality of groups.

Due to this, the server can determine the management status of the medical institution by considering the characteristics of the medical subject through grouping that classifies multiple medical institutions by medical subject. In addition, by using a standard value that also considers the ranking of the group, the server can more accurately determine the management state of the medical institution by applying different standard values to each group rather than determining the management state of the medical institution based on a uniform standard value. .

In step S503, based on the fact that the score for the management status of the medical institution is less than a preset score, the server stores information related to insurance claims, information about the medical institution's prescription received from the computer server related to the medical institution, and the medical institution's medical record. By comparing the information about the first claim details and the second claim details, it is possible to determine the first claim details and the second claim details.

For example, the server may determine the income of the medical institution within a first preset period based on information related to tax invoices, information related to invoices, information related to cash receipts, information related to card sales, and information related to insurance claims. there is. For example, the preset first period may be a period received from the terminal. For example, the first preset period may be a period from the current point in time to a point in time one year prior. For example, the first preset period may be a period during which the medical institution was operated. For example, the server may determine the expenditure of the medical institution within the first preset period based on information related to the corporate card usage details and information related to deposit/withdrawal details for each account.

For example, the server may determine a first difference value by subtracting the expenses of the medical institution from the income of the medical institution. For example, the server may determine a second difference value obtained by subtracting the average expenditure from the average income within the preset first period for a plurality of medical institutions included in the group.

In step S504, the server may determine whether the first difference value is less than the second difference value. In step S505, if the first difference value is smaller than the second difference value, the server may transmit information related to marketing.

For example, the server may determine the amount of tax paid within the first preset period based on the information related to the corporate tax, the information related to the value-added tax, and the information related to the withholding amount. For example, the server may determine the average tax amount paid within the first preset period for the plurality of medical institutions included in the group.

In step S506, the server may determine whether the difference between the tax amount and the average tax amount is greater than a preset value. In step S507, if the difference between the tax amount and the average tax amount is greater than a preset value, the server may transmit tax-related information.

Additionally, for example, the preset value may be set by Equation 3 below.

In Equation 3, V is the preset value, z is a weight related to the medical department of the medical institution, n is the number of medical institutions included in the group, and c _i is the number of medical institutions included in the group. It is the average tax amount of the i-th medical institution within the preset first period, and c _avg may be an average value of the average tax amount of medical institutions included in the group within the preset first period.

For example, since the distribution of tax amount of medical institutions included in the group is different for each group, if the preset value is applied equally to each group, the tax status of the medical institution may be determined somewhat inaccurately. On the other hand, in the present disclosure, the server can more accurately determine the current tax status of medical institutions by setting a standard value that takes into account the distribution of medical institutions within the classified group.

Additionally, for example, the server may receive a request message for documents related to private health insurance from a terminal associated with the patient. Here, private health insurance may be health insurance in which the private sector is the main entity of insurance business operation. For example, the request message for documents related to private health insurance may include the number and type of documents related to private health insurance and the address of a server related to private health insurance. For example, the server may request information about the medical institution's prescription and information about the medical institution's medical record from a computer server related to the medical institution based on the request message for documents related to the private health insurance. For example, when the server receives information about the medical institution's prescription and information about the medical institution's medical record based on a computer server related to the medical institution, the server receives information about the medical institution's prescription and the medical institution's medical record. Based on this, documents related to the private health insurance can be transmitted to the server related to the private health insurance. That is, for example, the server may determine a prescription and medical record that matches the patient based on information about the medical institution's prescription and the medical record of the medical institution, and provide certification documents related to the matched prescription and medical record. You can send documents related to the above private health insurance. For example, the server may receive an ACK message for a document related to the private health insurance from a server related to the private health insurance. For example, the server may transmit a completion message to a terminal associated with the patient based on the ACK message, and may transmit a completion message to a terminal associated with the medical institution.

Figure 6 is a block diagram showing the configuration of a server according to one embodiment. One embodiment of FIG. 6 may be combined with various embodiments of the present disclosure.

As shown in FIG. 6, the server 600 may include a processor 610, a communication unit 620, and a memory 630. However, not all of the components shown in FIG. 6 are essential components of the server 600. The server 600 may be implemented with more components than those shown in FIG. 6, or the server 600 may be implemented with fewer components than those shown in FIG. 6. For example, the server 600 according to some embodiments may further include a user input interface (not shown), an output unit (not shown), etc. in addition to the processor 610, the communication unit 620, and the memory 630. .

The processor 610 typically controls the overall operation of the server 600. The processor 610 may include one or more processors and control other components included in the server 600. For example, the processor 610 can generally control the communication unit 620 and the memory 630 by executing programs stored in the memory 630. Additionally, the processor 610 may perform the functions of the server 600 shown in FIGS. 3 to 5 by executing programs stored in the memory 630.

According to one embodiment, the processor 610 may obtain financial information about the medical institution and tax information about the medical institution based on business information and authentication information entered into the server 600. The processor 610 may receive operator information and authentication information from at least one device connected to the server through the communication unit 620 through a network.

For example, financial information about the medical institution includes information related to tax invoices, information related to invoices, information related to cash receipts, information related to card sales, information related to insurance claims, information related to corporate card usage history, and account-specific information. It may contain information related to deposit and withdrawal details. For example, tax information about the medical institution may include information related to corporate tax, information related to value-added tax, and information related to withholding tax.

The processor 610 can determine a score for the management status of the medical institution through an evaluation model using a neural network based on the business operator information, financial information about the medical institution, and tax information about the medical institution through the memory 630. there is.

Based on the fact that the score for the management status of the medical institution is lower than the preset score, the processor 610 sends information about the prescription of the medical institution and the medical record of the medical institution to the computer server related to the medical institution through the communication unit 620. You can request information about

The processor 610 compares the information related to the insurance claim with the information about the medical institution's prescription received from a computer server related to the medical institution and the information about the medical record of the medical institution through the memory 630, thereby making the first claim. Details and second claim details can be determined.

For example, the first claim details may be insurance claim details omitted from the information related to the insurance claim. For example, the second claim details may be insurance claim details added to the information related to the insurance claim.

The processor 610 includes financial information about the medical institution, tax information about the medical institution, a score on the management status of the medical institution, the first claim details, and the second claim details through the communication unit 620. Information on the management status of can be transmitted to the terminal.

The processor 610 uses the memory 630 to preset a system based on information related to the tax invoice, information related to the invoice, information related to the cash receipt, information related to the card sales, and information related to the insurance claim. The income of the above-mentioned medical institution can be determined within 1 period. The processor 610 may determine the expenditure of the medical institution within the first preset period based on information related to the corporate card usage details and information related to deposit/withdrawal details for each account through the memory 630. The processor 610 may determine a first difference value obtained by subtracting the expenditures of the medical institution from the income of the medical institution. The processor 610 may determine a group in which the medical institution is included among a plurality of groups set based on information on the management status of the plurality of medical institutions. The processor 610 may determine a second difference value obtained by subtracting the average expenditure from the average income within the first preset period for a plurality of medical institutions included in the group. The processor 610 may transmit marketing-related information to a terminal related to the medical institution through the communication unit 620 based on the fact that the first difference value is smaller than the second difference value.

The processor 610 can determine the score for the management status of the medical institution using the above-described equation 1 through the memory 630.

The processor 610 may determine the amount of tax paid within the first preset period based on the information related to the corporate tax, the information related to the value-added tax, and the information related to the withholding amount through the memory 630. The processor 610 may determine the average tax amount paid within the first preset period for the plurality of medical institutions included in the group. The processor 610 may transmit tax-related information to a terminal related to the medical institution through the communication unit 620 based on the difference between the tax amount and the average tax amount being greater than a preset value.

For example, a neural network may include an input layer, one or more hidden layers, and an output layer.

The processor 610, through the memory 630, sends each learning data consisting of plural business information, financial information for a plurality of medical institutions, tax information for a plurality of medical institutions, correct answer predicted financial information, and predicted tax information to the neural network. It can be input to the input layer, pass through the one or more hidden layers and the output layer, and output as an output vector. The processor 610 inputs the output vector into a loss function layer connected to the output layer through the memory 630, and uses a loss function to compare the output vector with the correct answer vector for each training data through the loss function layer. The loss value can be output using , and the evaluation model can be generated based on the parameters of the neural network being learned in a direction in which the loss value decreases.

The communication unit 620 may include one or more components that allow the server 600 to communicate with other devices (not shown) and servers (not shown). The other device (not shown) may be a computing device such as the server 600 or a sensing device, but is not limited thereto. The communication unit 620 may receive a user input from another electronic device or receive data stored in an external device from an external device through a network.

For example, the communication unit 620 may transmit and receive a message to establish a connection with at least one device. The communication unit 620 may transmit information generated by the processor 610 to at least one device connected to the server. The communication unit 620 may receive information from at least one device connected to the server. The communication unit 620 may transmit information related to the received information in response to information received from at least one device.

The memory 630 may store programs for processing and control of the processor 610. For example, the memory 630 may store information input to a server or information received from another device through a network. Additionally, the memory 630 may store data generated by the processor 610. For example, the memory 630 may include the score for the management status generated by the processor 610, the first claim details, the second claim details, the income of the medical institution, the expenditures of the medical institution, the tax amount of the medical institution, the first difference value, the second claim details, 2 The difference value and average tax amount can be stored. The memory 630 may store information input to or output from the server 600. For example, the memory 630 may store a preset number of medical subjects, values related to the preset number of medical subjects, a preset range, a preset first period, a preset score, and a preset value.

The memory 630 is a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, SD or XD memory, etc.), and RAM. (RAM, Random Access Memory) SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk , and may include at least one type of storage medium among optical disks.

The embodiments described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, and a field programmable gate (FPGA). It may be implemented using one or more general-purpose or special-purpose computers, such as an array, programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may execute an operating system (OS) and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the embodiments have been described with limited drawings as described above, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the claims described below.

Claims (3)

In a method where a server provides information about the management status of a medical institution to a terminal related to the medical institution using a neural network,
Obtaining financial information about the medical institution and tax information about the medical institution based on business information and authentication information entered into the server;
Financial information for the above medical institutions includes information related to tax invoices, information related to invoices, information related to cash receipts, information related to card sales, information related to insurance claims, information related to corporate card use details, and deposit/withdrawal details by account. contains information,
The tax information for the medical institution includes information related to corporate tax, information related to value-added tax, and information related to withholding tax,
Determining a score for the management status of the medical institution based on the business operator information, financial information predicted for the medical institution, and tax information predicted for the medical institution - financial information predicted for the medical institution and predictions for the medical institution The tax information is generated through an evaluation model using a neural network based on the business information, financial information about the medical institution, and tax information about the medical institution;
Requesting information on prescriptions of the medical institution and information on the medical record of the medical institution from a computer server associated with the medical institution based on the fact that the score for the management status of the medical institution is lower than a preset score;
determining first claim details and second claim details by comparing information related to the insurance claim with information about the medical institution's prescription received from a computer server related to the medical institution and information about the medical record of the medical institution;
The first claim details are insurance claim details omitted from the information related to the insurance claim,
The second claim details are insurance claim details added to the information related to the insurance claim,
Determining the income of the medical institution within a first preset period based on information related to the tax invoice, information related to the invoice, information related to the cash receipt, information related to the card sales, and information related to the insurance claim. ;
determining expenditures at the medical institution within the first preset period based on information related to the corporate card usage details and information related to deposit/withdrawal details for each account; and
Information on the management status of the medical institution, including financial information on the medical institution, tax information on the medical institution, a score on the management status of the medical institution, the first claim details, and the second claim details, is related to the medical institution. Including the step of transmitting to the terminal,
A first difference value is determined by subtracting the expenses of the medical institution from the income of the medical institution,
The group in which the medical institution is included is determined among a plurality of groups established based on information on the management status of the plurality of medical institutions,
A second difference value is determined by subtracting average expenses from average income within the first preset period for a plurality of medical institutions included in the group,
Based on the fact that the first difference value is smaller than the second difference value, information related to marketing is transmitted to a terminal related to the medical institution,
The score for the management status of the medical institution is determined by the following equation,

In the above equation, p is a score for the management status of the medical institution, z is a weight related to the medical department of the medical institution, n is the number of medical institutions included in the group, and a is the evaluation. is the revenue of the medical institution predicted for a second period preset by the model, where b is the expenditure of the medical institution predicted for the second period preset by the evaluation model, and c is the is the tax amount of the medical institution predicted for the set second period, where a _i is the average income of the ith medical institution within the preset first period, and b _i is the average expenditure of the ith medical institution within the preset first period; , where c _i is the average tax amount of the ith medical institution within the first preset period,
The amount of tax paid within the first preset period is determined based on the information related to the corporate tax, the information related to the value-added tax, and the information related to the withholding tax,
The average tax amount paid within the first preset period for the plurality of medical institutions included in the group is determined,
Based on the difference between the tax amount and the average tax amount being greater than a preset value, tax-related information is transmitted to a terminal related to the medical institution,
method.
According to clause 1,
Receiving a request message for documents related to private health insurance from a terminal related to the patient;
The request message for documents related to the private health insurance includes the number and type of documents related to the private health insurance and the address of the server related to the private health insurance,
requesting information on prescriptions from the medical institution and information on the medical record of the medical institution from a computer server associated with the medical institution based on the request message for documents related to the private health insurance; and
It further includes receiving information about the medical institution's prescription and information about the medical record of the medical institution from a computer server related to the medical institution,
A prescription and medical record matching the patient are determined based on information about the medical institution's prescription and the medical record of the medical institution,
Documents related to the private health insurance, including certification documents related to the matched prescription and medical record, are transmitted to a computer server associated with the medical institution,
method.
According to clause 1,
The neural network includes an input layer, one or more hidden layers, and an output layer,
Each learning data consisting of multiple business information, financial information about multiple medical institutions, tax information about multiple medical institutions, correct answer predicted financial information, and predicted tax information is input to the input layer of the neural network, and the one or more An output vector is output through a hidden layer and an output layer, and the output vector is input to a loss function layer connected to the output layer. The loss function layer compares the output vector with the correct answer vector for each training data. A loss value is output using a loss function, and the evaluation model is generated based on the parameters of the neural network being learned in a direction in which the loss value decreases.
method.

Images