KR102518329B1 - Apparatus, method and system for managing and processing artificial intelligence-based hospital care and prescription information - Google Patents

Abstract

An artificial intelligence-based method, device, and system for managing and processing hospital treatment and prescription information are disclosed. The artificial intelligence-based method for managing and processing hospital treatment and prescription information according to an embodiment of the present invention comprises the steps of: receiving subscription insurance policy information, receive multiple hospital treatment and prescription information; transmitting insurance compensation claim information, including multiple hospital treatment and prescription information for a preset period, to the insurance company server; receiving insurance compensation processing details from the insurance company server, and comparing the subscription insurance policy information and receipt information with the insurance compensation processing details to determine whether compensation has been properly paid according to the subscription insurance policy information; forming a request for modification of insurance compensation that includes treatment and prescription information from the first hospital for which compensation was determined not to have been properly paid as a result of the judgment; transmitting a request to modify insurance compensation to the insurance company server; receiving insurance compensation modification details from the insurance company server; and mapping and storing insurance compensation processing history and insurance compensation modification history. The present invention is to automatically claim insurance compensation and determine whether insurance compensation is normally paid.

Description

Artificial intelligence-based hospital treatment and prescription information management and processing method, device and system

The following embodiments are artificial intelligence-based hospital treatment and prescription information management and processing methods, apparatuses, and artificial intelligence capable of automatically claiming insurance compensation using hospital treatment and prescription information stored in a user terminal and determining whether insurance compensation is normally paid. It's about the system.

As background art related to the embodiments, Korean Patent Laid-Open Publication No. KR 10-2018-0053947 A discloses a hospital system management method and apparatus based on treatment identification information using a portable terminal. Specifically, the prior literature describes a communication unit that provides a hospital application to a portable terminal and, when patient information is registered through the hospital application, generating treatment identification information for managing the treatment information according to input of treatment information corresponding to the patient information. Includes a medical treatment information management unit, a payment information management unit that generates encrypted card information according to a payment method input received from a portable terminal for medical treatment identification information, and a matching management module that matches and stores encrypted card information and medical treatment identification information Initiate a device that

Through this, the prior literature can provide a hospital application that can operate independently for each hospital, and issues and manages virtual card information using consent information and payment method information registered through the hospital application, such as token information. By providing a management system for medical treatment information and payment information of the hospital system based on virtual card information, personal information leakage can be prevented because actual card information is not stored, and payment based on virtual card information is required without a separate IC card supporting terminal or additional authentication. is possible, providing an effect that can also reduce the infrastructure installation cost and certification cost of the hospital management system.

In addition, Korean Patent Publication No. KR 10-2006-0119251 A discloses a medical information management method and a medical information management device. Specifically, the prior literature receives patient information, medical institution information, prescription information, and medical fee information input through a communication terminal of a hospital, and stores the received patient information, medical institution information, prescription information, and medical fee information in a database, Based on the patient information input through the pharmacy's communication terminal, the patient information, medical institution information, and prescription information stored in the database are transmitted to the pharmacy's communication terminal, and the patient information, pharmacy information, and drug cost information input through the pharmacy's communication terminal are transmitted. Initiates a configuration for receiving and storing the received patient information, pharmacy information, and pharmaceutical cost information in a database.

Through this, the prior literature has an effect of systematically and efficiently managing patients, hospitals, and pharmacies as one system.

However, the preceding literatures are artificial intelligence-based hospital treatment and prescription information management and processing methods, devices and devices that can automatically claim insurance compensation using hospital treatment and prescription information stored in a user terminal and determine whether or not insurance compensation is normally paid. Do not start the system.

Korean Patent Publication No. KR 10-2018-0053947 A Republic of Korea Patent Publication KR 10-2006-0119251 A

Embodiments attempt to automatically claim insurance compensation using hospital treatment and prescription information stored in a user terminal and determine whether or not the insurance compensation is normally paid.

In the embodiments, images before and after the procedure are arranged in a time series order, and the appropriateness verification for the time series order is performed using the size of the treatment area.

Embodiments are intended to recommend hospitals and clinics based on medical records and to provide health status information to users.

An artificial intelligence-based hospital treatment and prescription information management and processing method according to an embodiment of the present invention includes the steps of receiving subscription insurance policy information; Receiving a plurality of hospital treatment and prescription information, wherein the hospital treatment and prescription information includes a disease classification code and receipt information; Transmitting insurance compensation claim information including the plurality of hospital treatment and prescription information for a preset period of time to an insurance company server; receiving insurance compensation processing details from the insurance company server; Comparing the subscription insurance policy information and the receipt information with the insurance compensation processing details to determine whether or not compensation has been normally paid according to the subscription insurance policy information; Forming an insurance compensation modification request including treatment and prescription information of the first hospital for which it is determined that compensation has not been normally paid as a result of the determination; transmitting the insurance compensation modification request to the insurance company server; receiving insurance compensation modification details from the insurance company server; and mapping and storing the insurance compensation processing details and the insurance compensation modification details.

As an embodiment, the hospital treatment and prescription information further includes a plurality of images taken before and after treatment according to treatment, and receiving a treatment progress display request; arranging and displaying the plurality of images in time-series order according to the procedure progress display request; extracting consecutive first and second images from among the plurality of images; calculating first and second sizes by extracting the same treatment area from the first and second images, respectively; verifying adequacy of the time-series sequence of the first and second images using the first and second sizes; and modifying the arrangement order of the first and second images based on the suitability verification result.

As an embodiment, the hospital treatment and prescription information further includes information on hospitals and clinics visited by the user and health status information of the user, but calculating treatment subject information with the highest number of visits during a preset period using the hospitals and clinics information step; forming hospital recommendation information including hospitals and clinics with the highest evaluation index among hospitals and clinics in the same medical subject by using the medical subject information; transmitting the hospital recommendation information to a user terminal; comparing the health state information with a first threshold value; extracting a first condition indicator exceeding the first threshold value from among the health condition information; calculating a first rate at which the first condition indicator exceeds the first threshold and a risk of death according to the first rate; forming food recommendation information for improving the first condition indicator; and transmitting the first ratio, the risk of death, and the food recommendation information to the user terminal.

As an embodiment, the hospital treatment and prescription information further includes the user's travel route information, receiving and storing advertisement information of multiple hospitals and clinics; extracting information on a predetermined number of first hospitals and clinics located within a predetermined distance from the user's movement path using the movement path information; extracting a second hospital or clinic matching the treatment subject information from among the first hospitals or clinics; and transmitting advertisement information of the second hospital or clinic to the user terminal.

As an embodiment, obtaining a user stay history of the user through the moving route information; calculating a stay time of each user based on the stay details of the user, and calculating an average stay time of the user; setting a reference value according to the length of average stay time of the user; calculating a minimum time within the allowable user range by using a value obtained by subtracting the reference value from the average stay time of the user; calculating a maximum time within the user permissible range by adding the reference value to the average stay time of the user; and setting the user permissible range based on the minimum time of the user permissible range and the maximum time of the user permissible range.

An apparatus according to an embodiment may be combined with hardware and controlled by a computer program stored in a medium to execute any one of the methods described above.

The embodiments automatically claim insurance compensation using hospital treatment and prescription information stored in a user terminal and determine whether or not the insurance compensation is normally paid to prevent omission of insurance compensation claims due to a user's mistake, and insurance compensation It is easy to determine whether or not the payment has been made correctly according to the insurance policy. In addition, it is possible to improve the efficiency of hospital advertisements by providing users with advertisements of hospitals and clinics that are located close to the user's moving route and match the user's medical subject.

1 is a diagram for explaining a system for managing and processing hospital treatment and prescription information according to an embodiment.
2 is a diagram for explaining a server according to an embodiment.
3 is a diagram for explaining learning of a neural network according to an exemplary embodiment.
4 is a flowchart of a method for managing and processing user hospital treatment and prescription information according to an embodiment.
5 is a flowchart for explaining a process of setting a user permissible range according to an embodiment.
6 is an exemplary diagram of a configuration of a device according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, since various changes can be made to the embodiments, the scope of the patent application is not limited or limited by these embodiments. It should be understood that all changes, equivalents or substitutes to the embodiments are included within the scope of rights.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only, and may be modified and implemented in various forms. Therefore, the embodiments are not limited to the specific disclosed form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical spirit.

Although terms such as first or second may be used to describe various components, such terms should only be construed for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element.

It should be understood that when an element is referred to as being “connected” to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle.

Terms used in the examples are used only for descriptive purposes and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "include" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiment belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in the present application, they should not be interpreted in an ideal or excessively formal meaning. don't

In addition, in the description with reference to the accompanying drawings, the same reference numerals are given to the same components regardless of reference numerals, and overlapping descriptions thereof will be omitted. In describing the embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description will be omitted.

The embodiments may be implemented in various types of products such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent vehicles, kiosks, and wearable devices.

An artificial intelligence (AI) system is a computer system that implements human-level intelligence, and unlike existing rule-based smart systems, machines learn and judge on their own. The more AI systems are used, the higher the recognition rate and the more accurate understanding of user preferences. Conventional rule-based smart systems are gradually being replaced by deep learning-based AI systems.

Artificial intelligence technology consists of machine learning and element technologies using machine learning. Machine learning is an algorithm technology that classifies/learns the characteristics of input data by itself, and element technology is a technology that uses machine learning algorithms such as deep learning to mimic functions such as recognition and judgment of the human brain. It consists of technical fields such as understanding, inference/prediction, knowledge expression, and motion control.

The various fields where artificial intelligence technology is applied are as follows. Linguistic understanding is a technology for recognizing and applying/processing human language/characters, including natural language processing, machine translation, dialogue systems, question and answering, voice recognition/synthesis, and the like. Visual understanding is a technology for recognizing and processing objects like human vision, and includes object recognition, object tracking, image search, person recognition, scene understanding, space understanding, image improvement, and the like. Inference prediction is a technique of reasoning and predicting logically by judging information, and includes knowledge/probability-based reasoning, optimization prediction, preference-based planning, and recommendation. Knowledge expression is a technology that automatically processes human experience information into knowledge data, and includes knowledge construction (data creation/classification) and knowledge management (data utilization). Motion control is a technology for controlling the autonomous driving of a vehicle and the movement of a robot, and includes motion control (navigation, collision, driving), manipulation control (action control), and the like.

In general, in order to apply machine learning algorithms to real life, learning is performed in a trial and error method due to the nature of the basic methodology of machine learning. In particular, deep learning requires hundreds of thousands of iterations. It is impossible to execute this in an actual physical external environment, so instead, the actual physical external environment is virtually implemented on a computer and learning is performed through simulation.

1 is a diagram for explaining a system for managing and processing hospital treatment and prescription information according to an embodiment.

As shown in FIG. 1, the hospital treatment and prescription information management and processing system 100 includes a user terminal 110, a server 120, a database 130, a hospital server 140, and an insurance company server 150. can include According to one embodiment, the database 130 is shown as being configured separately from the server 120, but is not limited thereto, and the database 130 may be provided in the server 120. For example, the server 120 may include multiple artificial intelligence for performing machine learning algorithms. According to one embodiment, the user terminal 110, the server 120, the database 130, the hospital server 140 and the insurance company server 150 may be connected to communicate with each other through a network (N).

The server 120 may receive subscription insurance policy information from the user terminal 110 . According to an embodiment, the user terminal 110 may collect the user's subscription insurance policy information under the user's consent, and send the collected subscription insurance policy information to the server 120 through the network (N) periodically or non-periodically. can be sent to

The server 120 may receive a plurality of hospital treatment and prescription information from the user terminal 110 . According to an embodiment, the user terminal 110 may receive hospital treatment and prescription information from the hospital server 140 when the user finishes hospital treatment and calculates the treatment cost, and transmits the received hospital treatment and prescription information to the server ( 120) can be transmitted. For example, hospital treatment and prescription information includes a disease classification code, receipt information, a number of images taken before and after treatment, information on hospitals and clinics visited by the user, information on the user's health condition, information on the user's travel route, etc. You can, but are not limited to this. According to an embodiment, the user terminal 110 may collect the user's location information with the user's consent, and may form movement path information using the user's location information for a preset period of time.

The server 120 may transmit, to the insurance company server 150, insurance compensation claim information including a plurality of hospital treatment and prescription information for a preset period of time. According to one embodiment, the server 120 forms insurance compensation claim information including a plurality of hospital treatment and prescription information for a preset period of time (eg, 1 month, 6 months, 1 year, etc.), and formed Insurance compensation claim information may be transmitted to the insurance company server 150 through the network N.

The insurance company server 150, based on the insurance claim information transmitted from the server 120, pays the insurance compensation to the corresponding user according to the insurance policy, forms insurance compensation processing details including details of the insurance compensation paid, , The formed insurance compensation processing details may be transmitted to the server 120 through the network N.

The server 120 may receive insurance compensation processing details from the insurance company server 150 . According to an embodiment, the server 120 may receive insurance compensation processing details from the insurance company server 150 through the network N.

The server 120 may compare subscription insurance policy information and receipt information with insurance compensation processing details to determine whether the compensation money has been normally paid according to the subscription insurance policy information. According to one embodiment, the server 120 inputs the subscription insurance policy information to the first artificial intelligence to output the self-pay and compensation rate, inputs the receipt information to the second artificial intelligence to output the payment, and the following equation 1 can be used to calculate the compensation.

In addition, the server 120 may compare the calculated compensation amount and insurance compensation processing details to determine whether or not the compensation money has been normally paid according to information on the subscribed insurance policy.

As a result of the determination, the server 120 may form an insurance compensation modification request including treatment and prescription information of the first hospital for which it is determined that the compensation money has not been normally paid, and transmit the formed insurance compensation modification request to the insurance company server 150. . According to an embodiment, the server 120 forms an insurance compensation modification request including the first hospital treatment and prescription information for which it is determined that the compensation money has not been normally paid and the compensation information calculated as not being paid, and the formed insurance compensation. The modification request may be transmitted to the insurance company server 150 through the network N.

The insurance company server 140 checks the received insurance compensation modification request, and if it is determined that the insurance compensation modification request is justified, additionally pays the compensation calculated as not paid, and forms an insurance compensation modification history including additionally paid compensation information It can be transmitted to the server 120 through the network (N).

The server 120 may receive insurance compensation modification details, map the insurance compensation processing details and insurance compensation modification details, and store them in the database 130 . According to an embodiment, the server 120 may receive insurance compensation modification details from the insurance company server 150, map insurance compensation processing details corresponding to the received insurance compensation modification details, and store them in the database 130. there is. For example, the server 120 forms insurance compensation processing details and insurance compensation modification details as images, maps the formed images, and stores them to confirm the insurance compensation processing details and insurance compensation modification details at a later time upon a user's request. can make this easier.

The server 120 may receive a procedure progress display request from the user terminal 110 . According to an embodiment, the user terminal 110 may transmit a procedure progress display request formed by receiving a user request to the server 120 through the network N.

The server 120 may arrange and display a plurality of images in time series order according to a procedure progress display request. According to an embodiment, the plurality of images taken before and after the procedure may include photographing time information, and the server 120 arranges the plurality of images in time series order using the photographing time information to display the information to the user terminal 110. can be displayed.

The server 120 may extract consecutive first and second images from among a plurality of images. According to an embodiment, the server 120 may extract first and second images, which are two random consecutive images, among a plurality of images arranged in time series order.

The server 120 may calculate the first and second sizes by extracting the same treatment area from the first and second images, respectively. According to an embodiment, the server 120 may input the first image to the third artificial intelligence to extract a treatment area and calculate a first size of the treatment area. In addition, the server 120 may input the second image to the fourth artificial intelligence, extract the same treatment area as in the first image, and calculate a second size of the treatment area.

The server 120 may use the calculated first and second sizes to verify the adequacy of the time series order arrangement of the first and second images. According to an embodiment, since the first image is an image ahead of the second image in time series, the server 120 determines that the time series sequence is properly arranged when the first size is greater than or equal to the second size, When the first size is smaller than the second size, it is confirmed that the time series sequence is not appropriately configured, and thus, the validity of the time series sequence is verified.

The server 120 may correct the arrangement order of the first and second images based on the suitability verification result. According to an embodiment, if the first size is smaller than the second size based on the suitability verification result, the server 120 corrects the arrangement order of the first and second images so that the second image is in a time series order higher than the first image. If the arrangement order is corrected so that is preceded, and the first size is greater than or equal to the second size based on the suitability verification result, the arrangement order of the first and second images may not be modified.

The server 120 may calculate treatment subject information with the highest number of visits during a preset period by using hospital and clinic information. According to an embodiment, the hospital and clinic information visited by the user includes information on medical subjects (eg, internal medicine, orthopedics, otolaryngology, dermatology, dentistry, etc.), and the user's health condition information includes the user's height, weight, body fat percentage, It may include various status indicators such as blood pressure and blood sugar.

The server 120 may form hospital recommendation information including hospitals and clinics with the highest evaluation index among hospitals and clinics in the same medical subject by using the calculated medical subject information. According to an embodiment, the server 120 may calculate the evaluation index of the hospital or clinic using Equation 2 below.

In Equation 2, the user evaluation score may be formed by scoring the user evaluation star rating for the corresponding hospital or clinic in SNS (Social Network Service), and the number of medical accidents may be formed by extracting information recorded in a public institution server. . Also, the first weight may include a positive weight, and the second weight may include a negative weight.

The server 120 may transmit the formed hospital recommendation information to the user terminal. According to one embodiment, the server 120 may transmit the formed hospital recommendation information to the user terminal 110 through the network (N).

The server 120 may perform a comparison between health state information and a first threshold value. According to an embodiment, the server 120 includes various health status indicators (eg, height, weight, body fat percentage, blood pressure, blood sugar, etc.) included in the health status information and a first threshold indicating a recommended range of the various status indicators, and comparison can be performed.

The server 120 may extract a first condition indicator exceeding a first threshold value from among health condition information. According to an embodiment, the server 120 may extract a corresponding condition indicator (eg, blood pressure) as a first condition indicator when a specific condition indicator among various condition indicators exceeds a first threshold value, which is a recommended range. .

The server 120 may calculate a first rate in which the first condition indicator exceeds a first threshold value and a risk rate of death according to the first rate. According to an embodiment, the server 120 sets the first ratio to 25% {(150-120)*100/ 120}. In addition, the server 120 may calculate the risk of death according to the first ratio by checking a mapping table (for example, searchable from a public institution server such as the Ministry of Food and Drug Safety) indicating a correlation between blood pressure and risk of death. there is.

The server 120 may form food recommendation information for improving the first condition indicator. According to an embodiment, the server 120 searches public institution servers such as the Ministry of Food and Drug Safety to form food recommendation information (eg, banana, milk, potato, etc.) for improving blood pressure, which is the first condition indicator. can do.

The server 120 may transmit the first ratio, mortality risk rate, and food recommendation information to the user terminal. According to an embodiment, the server 120 transmits food recommendation information together with the calculated first ratio and mortality risk rate to the user terminal 110 to provide information so that the user is alert to health and pays attention to health management. can

The server 120 may receive and store advertisement information of multiple hospitals and clinics. According to one embodiment, the server 120 may receive advertisement information from the user terminal 110 managed by a plurality of hospitals and clinics through the network N and store it in the database 130 .

The server 120 may extract information on a predetermined number of first hospitals and clinics located within a predetermined distance from the user's movement path by using the movement path information. According to an embodiment, the server 120 may determine the user's movement line using the user's movement path information included in the hospital treatment and prescription information, and may determine the user's movement line at a predetermined distance (eg, 500 m, 1 km, Information on first hospitals and clinics of a predetermined number (eg, 10, 20, 50, etc.) located within 1.5 km may be extracted.

The server 120 may extract a second hospital or clinic that matches the treatment subject information from among the first hospitals and clinics. According to an embodiment, the server 120 may extract a second hospital and clinic that matches the treatment subject information (eg, internal medicine) extracted from the hospital treatment and prescription information from among the extracted first hospitals and clinics.

The server 120 may transmit the extracted advertisement information of the second hospital or clinic to the user terminal. According to an embodiment, the server 120 may retrieve the extracted advertisement information of the second hospital or clinic from the database 130 and transmit it to the user terminal 110 .

The server 120 may receive a request for access to a web site from the user terminal 110 .

The server 120 processes the access request to the website, provides the home page of the website to the user terminal 110, and displays the home page on the screen of the user terminal 110, Operations of the user terminal 110 may be controlled.

The server 120 may obtain first user information by checking the first user account. At this time, the server 120 checks the first user account logged in to the user terminal 110, searches the user information stored in the database 130 through the first user account, and retrieves the first user information from the database 130. information can be obtained. Here, the first user information is user information about the first user, and may include hospital treatment and prescription information, profile information, and behavioral information of the first user.

The server 120 may receive a search request for the first content from the user terminal 110 .

Specifically, when the representative image of the first content is clicked on the homepage displayed on the screen of the user terminal 110, the server 120 may receive a search request for the first content from the user terminal 110. For example, when the first content is advertising content of a first hospital, the representative image of the first content is the name of the hospital, and the inquiry request for the first content is a request for inquiring detailed information of the first hospital. can

The server 120 may obtain first content information by checking the first content. At this time, the server 120 may obtain the first content information from the database 130 by checking the first content through a search request for the first content, and searching content information stored in the database 130 . Here, the first content information is content information about the first content, and may include information about the treatment course of the first hospital, information on the number of doctors on duty, and the like.

The server 120 may predict a stay time of the first user for the first content based on the first user information and the first content information. Here, the stay time of the first user may mean the time required for the first user to receive treatment while staying at the first hospital.

The server 120 may check whether the sojourn time of the first user is within the user-allowed range. Here, the user tolerance range may be set differently according to embodiments, and a detailed description related thereto will be described later with reference to FIG. 5 .

When the server 120 determines that the stay time of the first user is within the user-allowed range, the server 120 provides the first content to the user terminal 110 and displays the first content on the screen of the user terminal 110. The operation of the user terminal 110 may be controlled so that a web page for one content is displayed. Here, the web page for the first content is a web page linked by clicking on the representative image of the first content, and may include detailed information on the first content. For example, when the representative image of the first content is the name of a hospital, the web page for the first content may be a page displaying detailed information about the hospital.

That is, when the server 120 receives a search request for the first hospital from the user terminal 110, as a result of predicting the first user's stay time for the first content, the first user's stay time is determined by the user. If it is confirmed that the content is within the range, the first content may be determined as content suitable for the first user, and the first content may be provided to the user terminal 110 .

If the server 120 determines that the stay time of the first user does not fall within the user tolerance range and is out of the user tolerance range, the server 120 determines whether the stay time of the first user is shorter than the minimum time within the user tolerance range. can be checked.

When the server 120 determines that the first user's stay time is shorter than the minimum time allowed in the user range, the server 120 provides recommendation information for the second content to the user terminal 110, and the user terminal 110 The operation of the user terminal 110 may be controlled so that recommendation information on the second content is displayed on the screen of ). Here, the second content is content of a hospital with the same medical subject as the first content and fewer doctors on duty than the first content, and recommendation information for the second content may include a representative image of the second content.

For example, the first content is advertisement content for a hospital where 10 doctors work and treats the first department (plastic surgery) as advertisement content for the first hospital, and the second content is advertisement content for the second hospital. In the case of advertisement content of a hospital where two doctors work and treats the first medical department (plastic surgery), the server 120 determines that the first user's stay time is shorter than the minimum time allowed by the user, the database 130 ), it is possible to check the second content of the second hospital, which has fewer doctors on duty than the first hospital, and recommend information on the second content to the user terminal 110.

That is, when the server 120 receives a search request for the first content from the user terminal 110, as a result of predicting the first user's stay time for the first content, the first user's stay time is determined by the user. If it is confirmed that it is outside the range, the first hospital determines that the hospital is not suitable for the first user, and if the first user's stay time is found to be shorter than the minimum time allowed by the user, the first user's stay time is increased. In order to do so, the second content of the second hospital where fewer doctors than the first hospital work may be determined as content suitable for the first user, and recommendation information on the second content may be provided to the user terminal 110 . At this time, the server 120 may provide the user terminal 110 with recommendation information on the first content and the second content.

If the server 120 determines that the first user's stay time is longer than the minimum time allowed by the user, the server 120 determines that the first user's stay time is out of the user's allowable range, so that the first user's stay time is within the user's allowable range. It can be confirmed that it is longer than the maximum time of , and if it is confirmed that the first user's stay time is longer than the maximum time allowed by the user, the server 120 provides recommendation information on the third content to the user terminal 110. and control the operation of the user terminal 110 so that recommendation information on the third content is displayed on the screen of the user terminal 110 . Here, the third content is content of a hospital having the same medical subject as the first content and having more doctors on duty than the first content, and recommendation information on the third content may include a representative image of the third content.

For example, the first content is advertisement content for a hospital where 10 doctors work and treats the first department (plastic surgery) as advertisement content for a first hospital, and the third content is advertisement content for a third hospital. In the case of advertising content of a hospital where two doctors work and treats the first medical department (plastic surgery), the server 120 determines that the first user's stay time is longer than the maximum time allowed by the user, the database 130 ), it is possible to check the third content of the third hospital, where the number of doctors on duty is greater than that of the first hospital, and recommend information on the third content to the user terminal 110.

That is, when the server 120 receives a search request for the first content from the user terminal 110, as a result of predicting the first user's stay time for the first content, the first user's stay time is determined by the user. If it is determined that the first content is out of the range, it is determined that the first content is unsuitable for the first user, and if the first user's staying time is longer than the maximum time allowed by the user, the first user's staying time is reduced. In order to do so, it is determined that the third content of the third hospital where more doctors than the first hospital work is content suitable for the first user, and recommendation information on the third content may be provided to the user terminal 110 . At this time, the server 120 may provide the user terminal 110 with recommendation information for the first content and the third content.

The server 120 obtains user stay details of the user through moving route information included in hospital treatment and prescription information, and based on the user stay details, checks the stay time for each user, and calculates the user's average stay time The standard value is set according to the length of the user's average stay time, the minimum time in the user's allowable range is calculated by subtracting the reference value from the user's average stay time, and the standard value is added to the user's average stay time. As a value, the maximum time of the user permissible range can be calculated, and the user permissible range can be set based on the minimum time of the user permissible range and the maximum time of the user permissible range.

The server 120 may obtain details of the user's stay from movement route information. According to an embodiment, the server 120 may extract user sojourn details from movement path information using a machine learning algorithm such as deep learning. For example, the server 120 may extract places in which the latitude and longitude of the user do not change for a predetermined time or longer from movement route information as user sojourn details.

The network N may perform wireless or wired communication between the user terminal 110, the server 120, the database 130, the hospital server 140, the insurance company server 150, and the like. For example, the network may include long-term evolution (LTE), LTE Advanced (LTE-A), code division multiple access (CDMA), wideband CDMA (WCDMA), wireless broadband (WiBro), wireless fidelity (WiFi), Bluetooth ( Bluetooth), near field communication (NFC), global positioning system (GPS), or global navigation satellite system (GNSS). For example, the network may perform wired communication according to a method such as universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), or plain old telephone service (POTS). .

The database 130 may store various data. Data stored in the database 130 is obtained, processed, or used by at least one component of the user terminal 110, the server 120, the hospital server 140, and the insurance company server 150. , software (eg: program). Database 130 may include volatile and/or non-volatile memory. As an embodiment, the database 130 includes subscription insurance policy information received from the user terminal 110, hospital treatment and prescription information, insurance compensation processing details received from the insurance company server 150, insurance compensation modification details, and the server ( Insurance compensation claim information formed in 120), insurance compensation modification request, etc. may be stored.

In the present invention, artificial intelligence (AI) means a technology that imitates human learning ability, reasoning ability, perception ability, etc., and implements it with a computer, and concepts such as machine learning and symbolic logic can include Machine learning (ML) is an algorithm technology that classifies or learns the characteristics of input data by itself. Artificial intelligence technology is a machine learning algorithm that analyzes input data, learns the result of the analysis, and can make judgments or predictions based on the result of the learning. In addition, technologies that mimic the functions of the human brain, such as recognition and judgment, using machine learning algorithms, can also be understood as artificial intelligence. For example, technical fields such as linguistic understanding, visual understanding, inference/prediction, knowledge expression, and motion control may be included.

Machine learning may refer to processing that trains a neural network model using experience of processing data. Through machine learning, computer software could mean improving its own data processing capabilities. A neural network model is constructed by modeling a correlation between data, and the correlation may be expressed by a plurality of parameters. The neural network model derives a correlation between data by extracting and analyzing features from given data, and optimizing the parameters of the neural network model by repeating this process can be referred to as machine learning. For example, a neural network model may learn a mapping (correlation) between an input and an output with respect to data given as an input/output pair. Alternatively, even when only input data is given, the neural network model may learn the relationship by deriving a regularity between given data.

An artificial intelligence learning model or a neural network model may be designed to implement a human brain structure on a computer, and may include a plurality of network nodes having weights while simulating neurons of a human neural network. A plurality of network nodes may have a connection relationship between them by simulating synaptic activities of neurons that transmit and receive signals through synapses. In the artificial intelligence learning model, a plurality of network nodes can send and receive data according to a convolutional connection relationship while being located in layers of different depths. The artificial intelligence learning model may be, for example, an artificial neural network model, a convolutional neural network model (CNN), and the like. As an embodiment, the artificial intelligence learning model may be machine-learned according to methods such as supervised learning, unsupervised learning, and reinforcement learning. Machine learning algorithms for performing machine learning include Decision Tree, Bayesian Network, Support Vector Machine, Artificial Neural Network, Ada-boost , Perceptron, Genetic Programming, Clustering, etc. may be used.

Of these, CNNs are a type of multilayer perceptrons designed to use minimal preprocessing. A CNN consists of one or several convolution layers and general artificial neural network layers on top, and additionally utilizes weights and pooling layers. Thanks to this structure, CNN can fully utilize the input data with a two-dimensional structure. Compared to other deep learning structures, CNN shows good performance in both video and audio fields. CNNs can also be trained via standard back-propagation. CNNs are easier to train than other feedforward artificial neural network techniques and have the advantage of using fewer parameters.

Convolutional networks are neural networks that contain sets of nodes with bound parameters. The increasing size of available training data and the availability of computational power, combined with algorithmic advances such as piecewise linear unit and dropout training, have greatly improved many computer vision tasks. With huge data sets, such as those available for many tasks today, overfitting is not critical, and increasing the size of the network improves test accuracy. Optimal use of computing resources becomes a limiting factor. To this end, a distributed, scalable implementation of deep neural networks can be used.

2 is a diagram for explaining a server according to an embodiment.

As shown in FIG. 2 , server 120 may include one or more processors 122 , one or more memories 124 and/or transceivers 126 . As an example, at least one of these components of the server 120 may be omitted or other components may be added to the server 120 . Additionally or alternatively, some of the components may be integrated and implemented, or implemented as a singular or plural entity. At least some of the internal and external components of the server 120 are connected to each other through a bus, general purpose input/output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI). Data and/or signals can be sent and received.

One or more processors 122 may control at least one component of the server 120 connected to the processor 122 by driving software (eg, instructions, programs, etc.). In addition, the processor 122 may perform operations such as various calculations, processing, data generation, and processing related to the present invention. In addition, the processor 122 may load data or the like from one or more memories 124 or store data in one or more memories 124 .

One or more processors 122 may receive subscription insurance policy information from the user terminal 110 through the transceiver 126 . According to one embodiment, the user terminal 110 may collect the user's subscription insurance policy information under the user's consent, and send the collected subscription insurance policy information periodically or non-periodically through the transceiver 126 to one or more processors ( 122).

One or more processors 122 may receive a plurality of hospital treatment and prescription information from the user terminal 110 through the transceiver 126 . According to an embodiment, the user terminal 110 may receive hospital treatment and prescription information from the hospital server 140 when the user finishes hospital treatment and calculates the treatment cost, and transmits the received hospital treatment and prescription information to the server ( 120) can be transmitted.

The one or more processors 122 may transmit insurance compensation claim information including a plurality of hospital treatment and prescription information for a preset period of time to the insurance company server 150 through the transceiver 126 . According to one embodiment, the processor 122 forms insurance compensation claim information including a plurality of hospital treatment and prescription information for a preset period of time (eg, 1 month, 6 months, 1 year, etc.), and formed Insurance compensation claim information may be transmitted to the insurance company server 150 through the transceiver 126 .

One or more processors 122 may receive insurance compensation processing details from the insurance company server 150 through the transceiver 126 .

One or more processors 122 may determine whether or not the compensation has been normally paid according to the subscription insurance policy information by comparing subscription insurance policy information and receipt information with insurance compensation processing details. According to one embodiment, the processor 122 inputs the insurance policy information to the first artificial intelligence to output the self-pay and compensation rate, inputs the receipt information to the second artificial intelligence to output the payment, and calculates Equation 1 Compensation can be calculated using In addition, the one or more processors 122 may compare the calculated compensation amount with insurance compensation processing details to determine whether or not the compensation money has been normally paid according to the information on the subscription insurance policy.

The one or more processors 122 form an insurance compensation modification request including the treatment and prescription information of the first hospital for which it is determined that the compensation money was not normally paid as a result of the determination, and transmit the formed insurance compensation modification request to the insurance company server 150. can According to one embodiment, the processor 122 forms an insurance compensation modification request including the first hospital treatment and prescription information for which it is determined that the compensation money has not been normally paid and the compensation information calculated as not being paid, and the formed insurance compensation. The modification request may be transmitted to the insurance company server 150 through the transceiver 126 .

One or more processors 122 may receive insurance compensation revision details through the transceiver 126 , map the insurance compensation processing details and insurance compensation revision details, and store them in one or more memories 124 . According to an embodiment, the processor 122 receives insurance compensation modification details from the insurance company server 150, maps insurance compensation processing details corresponding to the received insurance compensation modification details, and stores them in one or more memories 124. can do. For example, the processor 122 forms each image of insurance compensation processing details and insurance compensation modification details, maps the formed images, and stores them to confirm the insurance compensation processing details and insurance compensation modification details at a later time upon a user's request. can make this easier.

The one or more processors 122 may receive hospital information from the database 130 through the transceiver 126 and provide the user with information about the desired hospital location, time, and desired medical department.

The one or more processors 122 determine the location of a plurality of hospitals desired by the user, the time desired to receive service, and the type of desired service based on the user selection, transmits the determined information to the server, and receives the service. You may request payment of a deposit for

The one or more processors 122 determine whether a service can be provided at a time desired by the user among a plurality of hospitals desired by the user, whether a service desired by the user can be provided, and the location of hospitals from the user's location at a specified level. It is possible to receive information about whether or not it is within the range and the price condition and display it to the user.

The one or more processors 122 display the locations of a plurality of hospitals on a map based on the user's location, and when the user selects at least one of the hospitals mapped and displayed on the navigation map, at a time desired by the user. Whether the service can be provided, whether the user can provide the desired service, provide information about the distance from the user's current location to the hospital, transportation information and pricing conditions, and whether to make a reservation at the selected hospital. You can display a guide asking questions.

One or more processors 122 determine a hospital to make a reservation based on the user's selection, receive a cost for a service desired by the user from a network (N) interlocked with the determined hospital, receive a deposit deposited by the user, and Information asking whether or not to confirm the reservation may be transmitted.

One or more processors 122 may confirm the reservation and display reservation information to the user based on receiving a signal instructing the user to receive a deposit and confirm the reservation on the network N interlocked with the hospital. .

One or more processors 122, based on receiving a signal indicating that the deposit deposited by the user is insufficient on the network (N) interlocked with the hospital, provides a guide for selecting whether to confirm the reservation by adding the deposit amount. Based on the user's input that selects to confirm the reservation by adding the deposit, information on the added deposit is transmitted to the hospital-linked network (N), and the hospital-linked network (N) transmits the information to the user. Reservation may be confirmed and reservation information may be displayed to the user based on receiving a signal instructing to receive an additional deposit and to confirm the reservation.

One or more processors 122 request an additional amount if the size of the service cost exceeds the user's deposit amount, and if the size of the service cost is smaller than the user's deposit amount, the difference is paid to the designated user's account. In addition, evaluation of the provided service may be requested, and the user's evaluation may be registered on the server 120 .

One or more memories 124 may store various data. Data stored in the memory 124 is data acquired, processed, or used by at least one component of the server 120, and may include software (eg, commands, programs, etc.). Memory 124 may include volatile and/or non-volatile memory. In the present invention, a command or a program is software stored in the memory 124, and is an operating system for controlling the resources of the server 120, an application, and/or an application that provides various functions so that the application can utilize the resources of the server 120. It may include middleware provided to .

The one or more memories 124 include subscription insurance policy information, hospital treatment and prescription information received from the above-described user terminal 110, insurance compensation processing details received from the insurance company server 150, insurance compensation modification details, and the server 120 It is possible to store insurance compensation claim information, insurance compensation modification request, etc. formed in Additionally, the one or more memories 124 may store instructions that, when executed by the one or more processors 122, cause the one or more processors 122 to perform operations.

As an example, server 120 may further include a transceiver 126 . The transceiver 126 may perform wireless or wired communication between the user terminal 110, the server 120, the database 130, the hospital server 140, the insurance company server 150, and/or other devices. For example, the transceiver 126 may transmit eMBB (enhanced mobile broadband), URLLC (Ultra Reliable Low-Latency Communications), MMTC (Massive Machine Type Communications), LTE (long-term evolution), LTE-A (LTE Advance), UMTS (Universal Mobile Telecommunications System), GSM (Global System for Mobile communications), CDMA (code division multiple access), WCDMA (wideband CDMA), WiBro (Wireless Broadband), WiFi (wireless fidelity), Bluetooth (Bluetooth), NFC ( Wireless communication may be performed according to a method such as near field communication (GPS), global positioning system (GPS), or global navigation satellite system (GNSS). For example, the transceiver 126 may perform wired communication according to a method such as universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), or plain old telephone service (POTS). there is.

As an embodiment, one or more processors 122 control the transceiver 126 to obtain information from the user terminal 110, the server 120, the database 130, the hospital server 140, and the insurance company server 150. can do. Information obtained from the user terminal 110, the server 120, the database 130, the hospital server 140, and the insurance company server 150 may be stored in one or more memories 124.

As an example, server 120 may be a device of various types. For example, server 120 may be a portable communication device, a computer device, or a device according to a combination of one or more of the foregoing devices. The server 120 of the present invention is not limited to the above devices.

Various embodiments of the server 120 according to the present invention may be combined with each other. Each embodiment may be combined according to the number of cases, and the embodiment of the server 120 made in combination also belongs to the scope of the present invention. In addition, internal/external components of the server 120 according to the present invention described above may be added, changed, replaced, or deleted according to embodiments. In addition, the aforementioned internal/external components of the server 120 may be implemented as hardware components.

3 is a diagram for explaining learning of a neural network according to an exemplary embodiment.

As shown in FIG. 3, the learning device uses a neural network ( 123) can be learned. In addition, the learning device may train the neural network 123 to calculate the details of the user's stay from the moving route information. According to one embodiment, the learning device may be a separate subject different from the server 120, but is not limited thereto.

The neural network 123 includes an input layer 121 into which training samples are input and an output layer 125 which outputs training outputs, and can be learned based on differences between the training outputs and labels. Here, the labels may be defined based on copayments and compensation rates corresponding to subscription insurance policy information, payments corresponding to receipt information, treatment areas corresponding to the first and second images, and user sojourn details corresponding to movement route information. there is. The neural network 123 is connected to a group of a plurality of nodes, and is defined by weights between connected nodes and an activation function that activates the nodes.

The learning device may train the neural network 123 using a gradient descent (GD) technique or a stochastic gradient descent (SGD) technique. The learning device may use a loss function designed by outputs and labels of the neural network.

The learning device may calculate a training error using a predefined loss function. The loss function may be predefined with labels, outputs and parameters as input variables, where the parameters may be set by weights in the neural network 123. For example, the loss function may be designed in a mean square error (MSE) form, an entropy form, and the like, and various techniques or methods may be employed in an embodiment in which the loss function is designed.

The learning device may find weights affecting the training error using a backpropagation technique. Here, weights are relationships between nodes in the neural network 123 . The learning device may use the SGD technique using labels and outputs to optimize the weights found through the backpropagation technique. For example, the learning device may update weights of a loss function defined based on labels, outputs, and weights using the SGD technique.

According to an embodiment, the learning device extracts first text objects from subscription insurance policy information, obtains first labels corresponding to the first text objects, which are deductibles and compensation rates, and sets the first text objects to a first neural network. When applied to the network, first training outputs corresponding to the first text objects may be generated, and the first neural network may be trained based on the first training outputs and the first labels.

The learning device extracts second text objects from the receipt information, obtains second labels that are payment information corresponding to the second text objects, applies the second text objects to a second neural network, and obtains second text objects. Second training outputs corresponding to are generated, and the second neural network may be trained based on the second training outputs and the second labels.

The learning device extracts first objects from the first image, obtains third labels that are treatment area objects corresponding to the first objects, applies the first objects to a third neural network, and corresponds to the first objects. and generate third training outputs, and learn a third neural network based on the third training outputs and the third labels.

The learning device extracts second objects from the second image, obtains fourth labels that are treatment area objects corresponding to the second objects, applies the second objects to a fourth neural network, and corresponds to the second objects. generating fourth training outputs, and learning a fourth neural network based on the fourth training outputs and the fourth labels.

The learning device extracts first location information from movement path information, obtains fifth labels corresponding to the first location information, which are user stay details objects, applies the first location information to a fifth neural network, and obtains first Fifth training outputs corresponding to the location information may be generated, and a fifth neural network may be trained based on the fifth training outputs and the fifth labels.

According to an embodiment, the learning device may generate training feature vectors based on configuration features, length features, and pattern features of subscription insurance policy information. Various methods may be employed to extract features.

According to an embodiment, the learning device may generate training feature vectors based on configuration features, location features, and pattern features of alphanumeric information. Various methods may be employed to extract features.

According to an embodiment, the learning device may generate training feature vectors based on configuration features, color features, location features, and pattern features of the first and second images. Various methods may be employed to extract features.

According to an embodiment, the learning device may generate training feature vectors based on configuration features, length features, and pattern features of movement path information. Various methods may be employed to extract features.

According to an embodiment, the learning device may obtain training outputs by applying training feature vectors to the neural network 123 . The learning device may learn the self-pay and reward ratio calculation algorithm of the neural network 123 based on the training outputs and the first labels. The learning device calculates training errors corresponding to the training outputs, and optimizes the connection relationship of nodes in the neural network 123 to minimize the training errors to train the self-pay and compensation rate calculation algorithm of the neural network 123. can The server 120 may calculate the self-payment and the compensation rate from information on the subscription insurance policy using the first neural network for which learning has been completed.

According to an embodiment, the learning device may obtain training outputs by applying training feature vectors to the neural network 123 . The learning device may learn the payment calculation algorithm of the neural network 123 based on the training outputs and the second labels. The learning device may train the payment calculation algorithm of the neural network 123 by calculating training errors corresponding to training outputs and optimizing a connection relationship of nodes in the neural network 123 to minimize the training errors. The server 120 may calculate the payment amount from the receipt information using the second neural network for which learning has been completed.

According to an embodiment, the learning device may obtain training outputs by applying training feature vectors to the neural network 123 . The learning device may learn the treatment region extraction algorithm of the neural network 123 based on the training outputs and the third labels. The learning device calculates training errors corresponding to the training outputs, and optimizes a connection relationship between nodes in the neural network 123 to minimize the training errors so as to train the treatment region extraction algorithm of the neural network 123. . The server 120 may extract the treatment area from the first image by using the third neural network for which learning has been completed.

According to an embodiment, the learning device may obtain training outputs by applying training feature vectors to the neural network 123 . The learning device may learn the treatment region extraction algorithm of the neural network 123 based on the training outputs and the fourth labels. The learning device calculates training errors corresponding to the training outputs, and optimizes a connection relationship between nodes in the neural network 123 to minimize the training errors so as to train the treatment region extraction algorithm of the neural network 123. . The server 120 may extract the treatment area from the second image using the fourth neural network for which learning has been completed.

4 is a flowchart of a method for managing and processing user hospital treatment and prescription information according to an embodiment.

Although process steps, method steps, algorithms, etc. are described in a sequential order in the flowchart of FIG. 4, such processes, methods and algorithms may be configured to operate in any suitable order. In other words, the steps of the processes, methods and algorithms described in the various embodiments of the invention need not be performed in the order described herein. Also, although some steps are described as being performed asynchronously, in other embodiments some of these steps may be performed concurrently. Further, illustration of a process by depiction in the drawings does not mean that the illustrated process is exclusive of other changes and modifications thereto, and that any of the illustrated process or steps thereof may be one of various embodiments of the present invention. It is not meant to be essential to one or more, and it does not imply that the illustrated process is preferred.

As shown in FIG. 4, in step S410, subscription insurance policy information is received. For example, referring to FIGS. 1 to 3 , the server 120 of the system 100 for managing and processing hospital treatment and prescription information may receive subscription insurance policy information from the user terminal 110 . According to an embodiment, the user terminal 110 may collect the user's subscription insurance policy information under the user's consent, and send the collected subscription insurance policy information to the server 120 through the network (N) periodically or non-periodically. can be sent to

In step S420, hospital treatment and prescription information is received. For example, referring to FIGS. 1 to 3 , the server 120 of the system 100 for managing and processing hospital treatment and prescription information is provided from a user terminal 110 to a number of hospitals. Medical treatment and prescription information may be received. According to an embodiment, the user terminal 110 may receive hospital treatment and prescription information from the hospital server 140 when the user finishes hospital treatment and calculates the treatment cost, and transmits the received hospital treatment and prescription information to the server ( 120) can be transmitted.

In step S430, insurance compensation claim information is transmitted. For example, referring to FIGS. 1 to 3 , the server 120 of the system 100 for managing and processing hospital treatment and prescription information includes a number of hospital treatments for a preset period of time. And insurance compensation claim information including prescription information may be transmitted to the insurance company server 150 . According to one embodiment, the server 120 forms insurance compensation claim information including a plurality of hospital treatment and prescription information for a preset period of time (eg, 1 month, 6 months, 1 year, etc.), and formed Insurance compensation claim information may be transmitted to the insurance company server 150 through the network N.

In step S440, insurance compensation processing details are received. For example, referring to FIGS. 1 to 3, the server 120 of the management and processing system 100 of hospital treatment and prescription information processes insurance compensation from the insurance company server 150. history can be received. According to an embodiment, the server 120 may receive the insurance compensation processing details formed by the insurance company server 150 from the insurance company server 150 through the network N in response to the insurance claim information transmitted in step S430. there is.

In step S450, it is determined whether compensation is normally paid. For example, referring to FIGS. 1 to 3, the server 120 of the management and processing system 100 of hospital treatment and prescription information is the subscription insurance policy information received in step S410. And it is possible to determine whether or not the compensation money has been normally paid according to the subscription insurance policy information by comparing the receipt information with the insurance compensation processing details received in step S440. According to one embodiment, the server 120 inputs the subscription insurance policy information to the first artificial intelligence to output the self-pay and compensation rate, inputs the receipt information to the second artificial intelligence to output the payment, and calculates Equation 1 Compensation can be calculated using In addition, the server 120 may compare the calculated compensation amount and insurance compensation processing details to determine whether or not the compensation money has been normally paid according to information on the subscribed insurance policy.

In step S460, an insurance compensation modification request is formed. For example, referring to FIGS. 1 to 3, referring to FIGS. 1 to 3, the server 120 of the system 100 for managing and processing hospital treatment and prescription information determines that the reward is normal as a result of the determination in step S450. An insurance compensation modification request including first hospital treatment and prescription information determined to be non-paid may be formed.

In step S470, an insurance compensation modification request is transmitted. For example, referring to FIGS. 1 to 3 , the server 120 of the system 100 for managing and processing hospital treatment and prescription information receives an insurance compensation modification request formed in step S460. It can be transmitted to the insurance company server 150 through the network N.

In step S480, insurance compensation correction details are received. For example, referring to FIGS. 1 to 3, the server 120 of the management and processing system 100 of hospital treatment and prescription information is transferred to the insurance company server 150 in step S470. Insurance compensation modification details may be received from the insurance company server 150 in response to the transmitted insurance compensation modification request.

In step S490, insurance compensation processing/correction details are stored. For example, referring to FIGS. 1 to 3, the server 120 of the management and processing system 100 of hospital treatment and prescription information is provided from the insurance company server 150 in step S480. The received insurance compensation modification details and insurance compensation processing details may be mapped and stored in the database 130 . According to an embodiment, the server 120 forms insurance compensation processing details and insurance compensation modification details as images, maps the formed images, and stores them to provide insurance compensation processing details and insurance compensation modification details at a later time upon a user's request. can be made easy to check.

5 is a flowchart for explaining a process of setting a user permissible range according to an embodiment.

Referring to FIG. 5 , first, in step S401, the server 120 may obtain details of a user's stay through hospital treatment and prescription information. Here, the hospital treatment and prescription information may include stay details of a user who has visited the first hospital, and the user stay details may include details of which user visited the first hospital for how long.

For example, when a user carrying the user terminal 110 visits the first hospital, the time when the user terminal 110 is located within 5 m from the first hospital and the time when the user terminal 110 is 30 m or more away from the first hospital Until , the user and the stay time may be added to the user's stay details as measured by the stay time of the first hospital.

In step S402, the server 120 may check the stay time for each user based on the user's stay history at the first hospital.

For example, if the first user, the second user, and the third user are registered as visiting users in the user visit details of the first hospital, the server 120 determines the first user's stay time and the second user's stay time. and the sojourn time of the third user, respectively.

In step S403, the server 120 may calculate the average stay time of the first hospital through the average value of the confirmed stay times for each user.

For example, when the first user stays at the first hospital for 30 minutes, the second user stays at the first hospital for 60 minutes, and the third user stays at the first hospital for 45 minutes, The server 120 may calculate the average stay time of the first hospital as 45 minutes.

In step S404, the server 120 may set a reference value according to the length of average length of stay in the first hospital.

For example, the server 120 may set the reference value to 4 minutes and 30 seconds when the average stay time at the first hospital is 45 minutes, and set the reference value to 3 minutes when the average stay time at the first hospital is 30 minutes. . A criterion for setting a reference value may be set differently according to embodiments.

In step S405, the server 120 may calculate the minimum time within the allowable user range as a value obtained by subtracting the reference value from the average length of stay at the first hospital.

For example, when the average length of stay at the first hospital is 45 minutes and the reference value is 4 minutes and 30 seconds, the server 120 may calculate the minimum time allowed by the user as 40 minutes and 30 seconds.

In step S406, the server 120 may calculate the maximum time within the allowable user range as a value obtained by adding a reference value to the average stay time of the first hospital.

For example, when the average length of stay at the first hospital is 45 minutes and the reference value is 4 minutes and 30 seconds, the server 120 may calculate the maximum time allowed by the user as 49 minutes and 30 seconds.

In step S407, the server 120 may set the user-allowed range based on the minimum time of the user-allowed range and the maximum time of the user-allowed range.

For example, when the average length of stay at the first hospital is 45 minutes, the server 120 may set the allowable user range to a range of 40 minutes 30 seconds to 49 minutes 30 seconds.

6 is an exemplary diagram of a configuration of a device according to an embodiment.

Device 501 according to one embodiment includes a processor 502 and a memory 503 . The device 501 according to an embodiment may be the above-described server or terminal. The processor may include at least one device described above with reference to FIGS. 1 to 4 or may perform at least one method described above with reference to FIGS. 1 to 4 . The memory 503 may store information related to the above method or store a program in which the above method is implemented. Memory 503 may be volatile memory or non-volatile memory.

The processor 502 may execute a program and control the device 501 . Program codes executed by the processor 502 may be stored in the memory 503 . The device 501 may be connected to an external device (eg, a personal computer or network) through an input/output device (not shown) and exchange data.

The embodiments described above may be implemented as 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, a field programmable gate (FPGA). array), programmable logic units (PLUs), microprocessors, or any other device capable of executing and responding to instructions. A processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

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. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in 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 hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler. 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.

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

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

Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

Claims (3)

In the management and processing method of artificial intelligence-based hospital treatment and prescription information performed by at least one of a server, a hospital server, and an insurance company server,
Receiving subscription insurance policy information;
Receiving a plurality of hospital treatment and prescription information, wherein the hospital treatment and prescription information includes a disease classification code and receipt information;
Transmitting insurance compensation claim information including the plurality of hospital treatment and prescription information for a preset period of time to an insurance company server;
receiving insurance compensation processing details from the insurance company server;
Comparing the subscription insurance policy information and the receipt information with the insurance compensation processing details to determine whether or not compensation has been normally paid according to the subscription insurance policy information;
Forming an insurance compensation modification request including treatment and prescription information of the first hospital for which it is determined that compensation has not been normally paid as a result of the determination;
transmitting the insurance compensation modification request to the insurance company server;
receiving insurance compensation modification details from the insurance company server; and
Mapping and storing the insurance compensation processing details and the insurance compensation modification details,
The hospital treatment and prescription information,
It further includes information about hospitals and clinics visited by the user and information on the health status of the user,
calculating treatment subject information with the highest number of visits during a preset period of time using the hospital and clinic information;
forming hospital recommendation information including hospitals and clinics with the highest evaluation index among hospitals and clinics in the same medical subject by using the medical subject information;
transmitting the hospital recommendation information to a user terminal;
comparing the health state information with a first threshold value;
extracting a first condition indicator exceeding the first threshold value from among the health condition information;
calculating a first rate at which the first condition indicator exceeds the first threshold and a risk of death according to the first rate;
forming food recommendation information for improving the first condition indicator; and
Further comprising transmitting the first ratio, the risk of death, and the food recommendation information to the user terminal,
The hospital treatment and prescription information,
Further including the user's movement path information,
Receiving and storing advertisement information of multiple hospitals and clinics;
extracting information on a predetermined number of first hospitals and clinics located within a predetermined distance from the user's movement path using the movement path information;
extracting a second hospital or clinic matching the treatment subject information from among the first hospitals or clinics; and
Further comprising transmitting advertisement information of the second hospital or clinic to the user terminal,
AI-based hospital treatment and prescription information management and processing method. According to claim 1,
The hospital treatment and prescription information,
Further including a plurality of images taken before and after the procedure according to the treatment,
Receiving a procedure progress display request;
arranging and displaying the plurality of images in time-series order according to the procedure progress display request;
extracting consecutive first and second images from among the plurality of images;
calculating first and second sizes by extracting the same treatment area from the first and second images, respectively;
verifying adequacy of the time-series sequence of the first and second images using the first and second sizes; and
Modifying the arrangement order of the first and second images based on the suitability verification result
Including more,
AI-based hospital treatment and prescription information management and processing method. delete

Images