Method for classifying patients according to severity of infectious disease, and non-face-to-face medical treatment system using same

Abstract

A method for classifying patients according to the severity of an infectious disease, according to an embodiment of the present invention, is a method in which a patient management application executed by at least one processor of an expert terminal classifies patients according to the severity of an infectious disease in order to provide a patient management service, the method comprising the steps of: acquiring digital survey data generated by a terminal of a first patient at a first time point; determining the severity of the first patient according to the acquired digital survey data; determining a first disease group of the first patient on the basis of the determined severity; extracting a first disease management protocol according to the determined first disease group, thereby providing a patient management service; acquiring monitoring data for the first patient at a second time point; calculating improvement information on the basis of the acquired monitoring data; changing the first disease group of the first patient to a second disease group on the basis of the calculated improvement information; and changing a second disease management protocol according to the changed second disease group.

Description

A method for classifying patients according to the severity of infectious diseases and a non-face-to-face treatment system using the method.

[0001] The present invention relates to a method for classifying patients based on the severity of an infectious disease and a non-face-to-face medical treatment system utilizing the method. More specifically, the present invention relates to a method for classifying patients based on the severity of an infectious disease and a non-face-to-face medical treatment system utilizing the method, which provides a customized disease management protocol based on the patient's severity and disease type determined through a digital questionnaire completed by the patient.

[0002] The outbreak of coronavirus disease 2019 (COVID-19) in 2019 resulted in a global pandemic. In response, the World Health Organization (WHO) guidelines recommend that all countries prepare for a surge in healthcare facilities, including those requiring ventilator support, and implement triage protocols.

[0003] Unfortunately, WHO guidelines do not provide a one-size-fits-all approach to assessing the scale of outbreaks in each region. Until dramatic improvements in treatment equipment and vaccinations provide immunity against infectious diseases, adaptive triage models with varying threshold levels will be crucial for effectively utilizing limited medical resources.

[0004] This means that various strategic patient triage measures are needed for pandemic diseases with a clinical spectrum that carries a high risk of acute exacerbation and death. Since COVID-19 has been associated with a collapse of healthcare infrastructure in most countries, an adaptable risk stratification model that considers resource availability trajectories and identifies patients who truly require hospitalization and intensive care could reduce the burden on healthcare systems.

[0005] Numerous predictive models have been developed to effectively classify COVID-19 patients. These models have reported moderate predictive accuracy. However, the reliability and generalizability of these models have been questioned due to their reliance on single-center data, their limitation to a single clinical outcome measure, and their reduced discriminatory performance when data are insufficient.

[0006] Furthermore, existing simulation studies that consider resource limitations have not utilized machine learning models or patient inflow scenarios in the context of COVID-19. Therefore, methods and devices are needed to provide optimal severity prediction models for optimally classifying infectious disease patients under limited clinical data.

[0007] The present invention has been devised to solve the problems of the prior art as described above, and its purpose is to provide a method for classifying patients according to the severity of an infectious disease by constructing a scoring system that determines the severity based on the patient's basic information, current symptoms, concomitant diseases, etc., and a non-face-to-face treatment system using the same.

[0008] In addition, the present invention provides a method for classifying patients according to the severity of an infectious disease, which determines the type of disease according to the characteristics of the patient and provides a disease management protocol that sets a treatment cycle, monitoring cycle, etc. suitable for the disease type, and a non-face-to-face treatment system using the same.

[0009] In addition, the present invention aims to provide a method for classifying patients according to the severity of an infectious disease, which calculates improvement information for patients and changes a disease management protocol in a customized manner according to the calculated improvement information, and a non-face-to-face treatment system using the same.

[0010] However, the technical problems to be solved by the present invention and embodiments of the present invention are not limited to the technical problems described above, and other technical problems may exist.

[0011] A method for classifying patients according to the severity of an infectious disease according to an embodiment of the present invention is a method for classifying patients according to the severity of an infectious disease in order to provide a patient management service by a patient management application executed by at least one processor of a professional terminal, the method comprising: acquiring digital survey data generated in a terminal of a first patient at a first time point; determining the severity of the first patient according to the acquired digital survey data; determining a first disease group of the first patient based on the determined severity; extracting a first disease management protocol according to the determined first disease group to provide a patient management service; acquiring monitoring data for the first patient at a second time point; calculating improvement information based on the acquired monitoring data; changing the first disease group of the first patient to a second disease group based on the calculated improvement information; and changing the second disease management protocol according to the changed second disease group.

[0012] In addition, the step of acquiring the digital survey data includes a step of acquiring basic information data entered for at least one of the patient's date of birth, age, sex, height, weight, body mass index, vaccination information, medications being taken, smoking status, drinking status, and pregnancy status, a step of acquiring symptom data entered for at least one of the patient's body temperature, chills, dyspnea, respiratory rate per minute, cough, headache, sore throat, voice change, runny nose, and muscle pain, and a step of acquiring comorbidity data entered for at least one of diabetes, respiratory disease, immune system disease, liver disease, heart disease, immunocompromised disease, mental health disease, chronic kidney disease, pulmonary disease, cerebrovascular disease, dementia, and surgical history.

[0013] In addition, the step of determining the severity of the first patient includes a step of calculating a RISS score based on a scoring table that assigns a score to at least one variable by a predetermined range, and a step of determining the severity as asymptomatic, mild, moderate, and severe based on the calculated RISS score, wherein the variable is one of the pieces of information entered as the digital survey data.

[0014] In addition, the step of determining the first disease group of the first patient includes a step of extracting a first classification type of a first category according to the number of comorbidities included in comorbidity data for the first patient, a step of extracting a second classification type of a second category according to a RISS score calculated for the first patient, a step of extracting a third classification type of a third category according to the presence or absence of diabetes included in the comorbidity data for the first patient, and a step of determining the first disease group by combining the extracted first to third classification types, wherein the first to third categories include Risk, Severity, and Diabetes.

[0015] In addition, the step of extracting the first disease management protocol according to the first disease group includes the step of extracting classification codes for each of the first to fifth protocol categories based on the first disease management table for the first disease group, which is the first determined patient's disease group, the step of extracting the first disease management protocol matching the extracted classification codes for each category, and the step of providing a patient management service according to the extracted first disease management protocol to at least one of a patient terminal, a specialist terminal, and a guardian terminal, wherein the first to fifth protocol categories include a treatment cycle, a message transmission cycle, a PRO (Patient Report Outcome) transmission cycle, a monitoring cycle, and a judgment cycle.

[0016] In addition, the step of providing a patient management service according to the first disease management protocol further includes a step of providing a patient management service according to a disease management cycle matched to each classification code based on a classification code table, wherein the disease management cycle is information that sets at least one of a treatment cycle in which a specialist performs one of non-face-to-face and face-to-face treatment on a patient, a message sending cycle in which a message is sent when a disease group of a patient is changed, a PRO sending cycle in which a patient diagnosis report including the results of a checkup on the patient's condition is sent, a monitoring cycle in which self-measurement is performed through a monitoring device linked to a patient terminal, and a judgment cycle in which the severity of the patient is judged.

[0017] In addition, the step of acquiring monitoring data at the second point in time includes the step of acquiring monitoring data at the second point in time, which is a predetermined point in time, after performing disease management for a predetermined period of time based on the first disease management protocol, and the monitoring data includes monitoring results up to the second point in time acquired from a monitoring device linked to the terminal of the first patient, and digital survey data acquired from the terminal of the first patient at the second point in time.

[0018] In addition, the step of calculating the improvement information includes the step of determining a second disease group for the first patient at the second time point, the step of comparing the first and second classification types of the first and second categories of the first disease group determined at the first time point and the second disease group determined at the second time point, and the step of calculating the improvement information of the second disease group compared to the first disease group according to the improvement and deterioration of the first and second classification types.

[0019] In addition, the step of changing the second disease management protocol according to the second disease group includes the steps of extracting classification codes for each of the first to fifth protocol categories based on a second disease management table for the second disease group, which is a disease group of a patient determined according to the degree of improvement of the patient after a predetermined period of time has passed since the first time, the step of extracting a second disease management protocol matching the extracted classification code for each category, and the step of providing a patient management service using the extracted second disease management protocol.

[0020] Meanwhile, a method for classifying patients according to the severity of an infectious disease and a non-face-to-face treatment system using the same according to an embodiment of the present invention comprises: a patient terminal; and a guardian terminal; and an expert terminal including at least one memory and at least one processor; at least one application stored in the memory of the expert terminal and executed by the processor to provide a patient management service, wherein the at least one application acquires digital survey data generated in a terminal of a first patient at a first time point, determines the severity of the first patient according to the acquired digital survey data, determines a first disease group of the first patient based on the determined severity, and extracts a first disease management protocol based on the determined first disease group to provide a patient management service; acquires monitoring data for the first patient at a second time point, calculates improvement information based on the acquired monitoring data, changes the first disease group of the first patient to a second disease group based on the calculated improvement information, and changes the second disease management protocol based on the changed second disease group.

[0021] The method for classifying patients according to the severity of an infectious disease and the non-face-to-face medical treatment system using the same according to an embodiment of the present invention have the effect of drastically reducing the probability of a patient's disease worsening by establishing a scoring system that determines the severity based on the patient's basic information, current symptoms, concomitant diseases, etc., thereby making it easy to perform advance allocation of medical resources by predicting patients at high risk of disease worsening at an early stage.

[0022] In addition, the method for classifying patients according to the severity of an infectious disease according to an embodiment of the present invention and the non-face-to-face treatment system using the same determine the type of disease according to the characteristics of the patient and provide a disease management protocol that sets a treatment cycle, monitoring cycle, etc. suitable for the disease type, thereby determining whether the patient requires hospitalization and intensive care or can be managed only through non-face-to-face treatment, thereby increasing the efficiency of patient management and reducing the burden on the medical system.

[0023] In addition, the method for classifying patients according to the severity of an infectious disease according to an embodiment of the present invention and the non-face-to-face treatment system using the same have the effect of preventing human and economic losses that may occur due to unnecessary and excessive patient management despite improvement in the patient's condition by calculating patient improvement information and customizing the disease management protocol according to the calculated improvement information.

[0024] However, the effects that can be obtained from the present invention are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood from the description below.

[0025] Figure 1 is a conceptual diagram of a patient management service provision system according to an embodiment of the present invention.

[0026] Figure 2 is an internal block diagram of a terminal according to an embodiment of the present invention.

[0027] Figure 3 is a flowchart illustrating a method for classifying patients according to the severity of an infectious disease according to an embodiment of the present invention.

[0028] FIG. 4 and FIG. 5 are examples of drawings for explaining digital survey data according to an embodiment of the present invention.

[0029] FIG. 6 is an example illustrating a scoring table for calculating a RISS score according to an embodiment of the present invention.

[0030] FIG. 7 is an example of a grouping table for classifying disease types according to an embodiment of the present invention.

[0031] FIG. 8 and FIG. 9 are examples of drawings for explaining disease management protocols matched by disease group according to an embodiment of the present invention.

[0032] FIG. 10 is an example of a drawing for explaining a disease management protocol matched to each follow-up group according to patient improvement information according to an embodiment of the present invention.

[0033] The present invention is capable of various modifications and embodiments. Therefore, specific embodiments are illustrated in the drawings and described in detail in the detailed description. The effects and features of the present invention, as well as the methods for achieving them, will become clear with reference to the embodiments described in detail below together with the drawings. However, the present invention is not limited to the embodiments disclosed below and can be implemented in various forms. In the following embodiments, the terms "first," "second," etc. are not used in a limiting sense but are used for the purpose of distinguishing one component from another. Furthermore, the singular expression includes the plural expression unless the context clearly indicates otherwise. Furthermore, terms such as "include" or "have" indicate the presence of a feature or component described in the specification, and do not preemptively exclude the possibility that one or more other features or components may be added. Furthermore, in the drawings, the sizes of components may be exaggerated or reduced for convenience of explanation. For example, the size and thickness of each component shown in the drawings are arbitrarily shown for convenience of explanation, and thus the present invention is not necessarily limited to what is shown.

[0034] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. When describing with reference to the drawings, identical or corresponding components are given the same drawing reference numerals, and redundant descriptions thereof will be omitted.

[0035]

[0036] Figure 1 is a conceptual diagram of a patient management service provision system according to an embodiment of the present invention.

[0037] Referring to FIG. 1, a patient management service provision system (hereinafter, “service provision system”) according to an embodiment of the present invention can provide a patient management service (hereinafter, “patient management service”) that provides a customized disease management protocol according to the patient’s severity and disease type determined through a digital questionnaire performed by the patient.

[0038] In an embodiment, a service providing system implementing the above patient management service may be connected through a terminal (100), a central server (200), and a network (10: Network).

[0039] Here, the network (10) according to the embodiment means a connection structure in which information can be exchanged between each node, such as the terminal (100) and / or the central server (200), and examples of such a network (10) include, but are not limited to, a 3GPP (3rd Generation Partnership Project) network, an LTE (Long Term Evolution) network, a WIMAX (World Interoperability for Microwave Access) network, the Internet, a LAN (Local Area Network), a Wireless LAN (Wireless Local Area Network), a WAN (Wide Area Network), a PAN (Personal Area Network), a Bluetooth network, a satellite broadcasting network, an analog broadcasting network, a DMB (Digital Multimedia Broadcasting) network, etc.

[0040] Hereinafter, the terminal (100) and central server (200) that implement the service provision system will be described in detail with reference to the attached drawings.

[0041]

[0042] -Terminal (100: Terminal)

[0043] A terminal (100) according to an embodiment of the present invention may be a predetermined computing device on which a patient management application (hereinafter, “application”) providing patient management services is installed.

[0044] Here, the application according to the embodiment can be divided into an application for patients, an application for professionals, and / or an application for caregivers.

[0045] At this time, the applications for patients, experts, and guardians may be designed to perform different functional operations by distinguishing a single application or user entity.

[0046] That is, the above patient, expert and guardian applications may be designed to grant different permissions and operate differently depending on the accounts of a single application or expert (e.g., a doctor), patient and guardian.

[0047] Additionally, the application for patients, experts, and guardians may be a medication management platform that provides an Internet environment so that multiple patients, experts, and guardians can use a customized exercise prescription service.

[0048] Returning to the above, in the embodiment, the terminal (100) having the above healthcare application installed may include a patient terminal (100-1) used by a patient, a specialist terminal (100-2) used by a specialist (in the embodiment, a doctor), and a guardian terminal (100-3) used by a guardian.

[0049] In an embodiment, the patient terminal (100-1) may have the patient health management application installed, the expert terminal (100-2) may have the expert health management application installed, and the guardian terminal (100-3) may have the guardian health management application installed.

[0050] Here, the patient terminal (100-1), expert terminal (100-2), and guardian terminal (100-3) are intended to distinguish the user in the embodiment, and their components and functional operations may be the same.

[0051] Additionally, in the embodiment, the patient terminal (100-1) can be linked with a predetermined self-monitoring device used by the patient.

[0052] In detail, from a hardware perspective, the terminal (100) may include a mobile type computing device (101) and / or a desktop type computing device (102) on which an application is installed.

[0053] Here, the mobile type computing device (101) may be a mobile device such as a smart phone or tablet PC on which an application is installed.

[0054] For example, mobile type computing devices (101) may include smart phones, mobile phones, digital broadcasting devices, personal digital assistants (PDAs), portable multimedia players (PMPs), tablet PCs, etc.

[0055] In addition, the desktop type computing device (102) may include a device installed with a program for executing a customized exercise prescription service based on wired / wireless communication, such as a personal computer such as a fixed desktop PC, laptop computer, or ultrabook with an application installed.

[0056] Additionally, depending on the embodiment, the terminal (100) may further include a server computing device that provides a customized exercise prescription service environment.

[0057] Figure 2 is an internal block diagram of a terminal according to an embodiment of the present invention.

[0058] Referring to FIG. 2, from a functional perspective, the terminal (100) may include a memory (110), a processor assembly (120), a communication processor (130), an interface module (140), an input system (150), a sensor system (160), and a display system (170). These components may be configured to be included within the housing of the terminal (100).

[0059] In detail, in the memory (110), an application (111) is stored, and the application (111) can store one or more of various application programs, data, and commands for providing a patient management service environment.

[0060] That is, the memory (110) can store commands and data that can be used to create a patient management service environment.

[0061] Additionally, the memory (110) may include a program area and a data area.

[0062] Here, the program area according to the embodiment can be linked between the operating system (OS) that boots the terminal (100) and functional elements, and the data area can store data generated according to the use of the terminal (100).

[0063] Additionally, the memory (110) may include at least one non-transitory computer-readable storage medium and one or more temporary computer-readable storage medium.

[0064] For example, the memory (110) may be a variety of storage devices such as a ROM, EPROM, flash drive, hard drive, etc., and may include web storage that performs the storage function of the memory (110) on the Internet.

[0065] The processor assembly (120) may include at least one processor capable of executing instructions of an application (111) stored in the memory (110) to perform various tasks for creating a patient management service environment.

[0066] In an embodiment, the processor assembly (120) can control the overall operation of the components through an application (111) of the memory (110) to provide patient management services.

[0067] This processor assembly (120) may be a system on chip (SOC) suitable for a terminal (100) including a central processing unit (CPU) and / or a graphics processing unit (GPU), and may execute an operating system (OS) and / or application programs stored in a memory (110) and control each component mounted on the terminal (100).

[0068] Additionally, the processor assembly (120) can communicate with each component internally via a system bus and can include one or more predetermined bus structures including a local bus.

[0069] Additionally, the processor assembly (120) may be implemented by including at least one of application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, micro-controllers, microprocessors, and other electrical units for performing functions.

[0070] The communication processor (130) may include one or more devices for communicating with external devices. The communication processor (130) may communicate via a wireless network.

[0071] In detail, the communication processor (130) can communicate with a terminal (100) that stores a content source for implementing a patient management service environment, and can communicate with various user input components, such as a controller that receives user input.

[0072] In an embodiment, the communication processor (130) can transmit and receive various data related to patient management services to and from another terminal (100) and / or an external server.

[0073] This communication processor (130) can wirelessly transmit and receive data with at least one of a base station, an external terminal, and an arbitrary server on a mobile communication network constructed through a communication device capable of performing technical standards or communication methods for mobile communication (e.g., LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), 5G NR (New Radio), WIFI) or short-range communication methods.

[0074] The interface module (140) can connect the terminal (100) to one or more other devices so that they can communicate with each other. In detail, the interface module (140) can include wired and / or wireless communication devices compatible with one or more different communication protocols.

[0075] Through this interface module (140), the terminal (100) can be connected to multiple input / output devices.

[0076] For example, the interface module (140) can be connected to an audio output device such as a headset port or speaker to output audio.

[0077] As an example, the audio output device is described as being connected via an interface module (140), but an embodiment in which it is installed inside the terminal (100) may also be included.

[0078] Additionally, for example, the interface module (140) may be connected to an input device such as a keyboard and / or mouse to obtain user input.

[0079] Such an interface module (140) may be configured to include at least one of a wired / wireless headset port, an external charger port, a wired / wireless data port, a memory card port, a port for connecting a device equipped with an identification module, an audio I / O (Input / Output) port, a video I / O (Input / Output) port, an earphone port, a power amplifier, an RF circuit, a transceiver, and other communication circuits.

[0080] The input system (150) can detect user input (e.g., gestures, voice commands, button operations, or other types of input) related to patient care services.

[0081] In detail, the input system (150) may include a predetermined button, a touch sensor, and / or an image sensor (161) that receives user motion input.

[0082] Additionally, the input system (150) can be connected to an external controller through an interface module (140) to receive user input.

[0083] The sensor system (160) may include various sensors such as an image sensor (161), a position sensor (IMU, 163), an audio sensor (165), a distance sensor, a proximity sensor, and a contact sensor.

[0084] Here, the image sensor (161) can capture images and / or videos of the physical space around the terminal (100).

[0085] In an embodiment, the image sensor (161) can capture and acquire various images and / or videos related to patient management services.

[0086] In addition, the image sensor (161) can capture an image by photographing the direction in which it is positioned on the front or / and rear of the terminal (100), and can capture a physical space through a camera positioned toward the outside of the terminal (100).

[0087] This image sensor (161) may include an image sensor device and an image processing module. In detail, the image sensor (161) may process still images or moving images obtained by an image sensor device (e.g., CMOS or CCD).

[0088] In addition, the image sensor (161) can process still images or moving images acquired through the image sensor device using an image recognition process (e.g., OCR, etc.) and / or an image processing module to extract necessary information and transmit the extracted information to the processor.

[0089] Such an image sensor (161) may be a camera assembly including at least one camera. The camera assembly may include a general camera that captures images in the visible light band, and may further include special cameras such as an infrared camera or a stereo camera.

[0090] In addition, the image sensor (161) as described above may be included in the terminal (100) and operated according to an embodiment, or may be included in an external device (e.g., an external server, etc.) and operated through linkage based on the communication processor (130) and / or interface module (140) described above.

[0091] The position sensor (IMU, 163) can detect at least one of the movement and acceleration of the terminal (100). For example, it can be formed by a combination of various position sensors such as an accelerometer, a gyroscope, and a magnetometer.

[0092] Additionally, the position sensor (IMU, 163) can recognize spatial information about the physical space around the terminal (100) by working in conjunction with a position communication processor (130), such as the GPS of the communication processor (130).

[0093] The audio sensor (165) can recognize sounds around the terminal (100).

[0094] In detail, the audio sensor (165) may include a microphone capable of detecting voice input from a user using the terminal (100).

[0095] In an embodiment, the audio sensor (165) can receive voice data required for patient management services from the user.

[0096] The display system (170) can output various information related to patient management services as graphic images.

[0097] As an example, the display system (170) can display various user interfaces for patient management services.

[0098] Such displays may include at least one of a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), a flexible display, a 3D display, and an e-ink display.

[0099] The above components may be arranged within the housing of such a terminal (100), and the user interface may include a touch sensor (173) on a display (171) configured to receive user touch input.

[0100] In detail, the display system (170) may include a display (171) that outputs an image and a touch sensor (173) that detects a user's touch input.

[0101] For example, the display (171) may be implemented as a touch screen by forming a mutual layer structure with the touch sensor (173) or forming an integral structure. Such a touch screen may function as a user input unit that provides an input interface between the terminal (100) and the user, and at the same time, provide an output interface between the terminal (100) and the user.

[0102] The terminal (100) including the above-described components may store at least one digital survey data, basic information data, symptom data, concomitant disease data, severity, RISS score, scoring table, disease group, grouping table, disease management protocol, disease management table, classification code table, monitoring data and / or improvement information in the memory (110) according to an embodiment.

[0103] Meanwhile, depending on the embodiment, the terminal (100) may further perform at least some of the functional operations performed by the central server (200) described below.

[0104]

[0105] -Central Server (200: Server)

[0106] Meanwhile, the central server (200) according to the embodiment of the present invention can perform a series of processes for providing patient management services.

[0107] In detail, in an embodiment, the central server (200) can provide the patient management service by exchanging data necessary to enable the patient management service process to be driven by an external device, such as a terminal (100), with the external device.

[0108] In more detail, in an embodiment, the central server (200) may provide an environment in which an application (111) can operate on an external device (in an embodiment, a mobile type computing device (100-1) and / or a desktop type computing device (100-2)).

[0109] To this end, the central server (200) may include application programs, data and / or commands for the application (111) to operate, and may transmit and receive various data based thereon with the external device.

[0110] Additionally, in the embodiment, the central server (200) can perform various deep learning for patient management services in conjunction with a deep learning neural network.

[0111] Here, the deep learning neural network according to the embodiment may include a convolutional neural network (CNN), an R-CNN (Regions with CNN features), a Fast R-CNN, a Faster R-CNN, a Mask R-CNN, etc., and may include any deep learning neural network that includes an algorithm capable of performing the embodiment described below, and the embodiment of the present invention does not limit or restrict such deep learning neural network itself.

[0112] At this time, depending on the embodiment, the deep learning neural network may be installed directly on the central server (200) or may operate as a device separate from the central server (200) to perform deep learning for the patient management service.

[0113] In the following examples, an example is described in which a deep learning neural network is directly installed on a central server (200) to perform deep learning.

[0114] In addition, in the embodiment, the central server (200) can read out a predetermined deep learning neural network driving program constructed to perform the deep learning from memory and perform the deep learning described below according to the read out predetermined deep learning neural network system.

[0115] Additionally, in the embodiment, the central server (200) can store and manage various application programs, commands and / or data for implementing patient management services.

[0116] In an embodiment, the central server (200) can store and manage digital survey data, basic information data, symptom data, concomitant disease data, severity, RISS score, scoring table, disease group, grouping table, disease management protocol, disease management table, classification code table, monitoring data and / or improvement information, etc.

[0117] However, in the embodiment of the present invention, the functional operations that the central server (200) can perform are not limited to those described above, and other functional operations can be performed.

[0118] Meanwhile, referring further to FIG. 1, in the embodiment, the central server (200) as described above may be implemented as a computing device including at least one processor module (210: Processor Module) for data processing, at least one communication module (220: Communication Module) for data exchange with an external device, and at least one memory module (230: Memory Module) for storing various application programs, data, and / or commands for providing patient management services.

[0119] Here, the memory module (230) can store one or more of an operating system (OS), various application programs, data, and commands for providing patient management services.

[0120] Additionally, the memory module (230) may include a program area and a data area.

[0121] Here, the program area according to the embodiment may be linked between the operating system (OS) that boots the server and functional elements, and the data area may store data generated according to the use of the server.

[0122] In an embodiment, such a memory module (230) may be a variety of storage devices such as ROM, RAM, EPROM, flash drive, hard drive, etc., and may also be a web storage that performs the storage function of the memory module (230) on the Internet.

[0123] Additionally, the memory module (230) may be a removable recording medium on the server.

[0124] Meanwhile, the processor module (210) can control the overall operation of each unit described above to implement patient management service.

[0125] This processor module (210) may be a system on chip (SOC) suitable for a server including a central processing unit (CPU) and / or a graphics processing unit (GPU), and may execute an operating system (OS) and / or application programs stored in a memory module (230) and control each component mounted on the server.

[0126] In addition, the processor module (210) can communicate with each component internally via a system bus and can include one or more predetermined bus structures including a local bus.

[0127] Additionally, the processor module (210) may be implemented using at least one of application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, micro-controllers, microprocessors, and other electrical units for performing functions.

[0128] In the above description, it has been described that the central server (200) according to the embodiment of the present invention performs the functional operations as described above, but depending on the embodiment, at least a part of the functional operations performed by the central server (200) may be performed by an external device (e.g., terminal (100), etc.), and at least a part of the functional operations performed by the external device may be further performed by the central server (200), and various other embodiments may be possible.

[0129]

[0130] - How to classify patients based on the severity of the infectious disease

[0131] Hereinafter, a method for classifying patients according to the severity of an infectious disease in order to provide a patient management service by an application (111) executed by at least one processor of a terminal (100) (more specifically, a professional terminal (100-2)) according to an embodiment of the present invention is described in detail with reference to the attached FIGS. 3 to 10.

[0132] In an embodiment of the present invention, at least one processor of the terminal (100) can execute at least one application (111) stored in at least one memory (110) or operate in a background state.

[0133] Hereinafter, the method of classifying patients according to the severity of the infectious disease described above is briefly described as being performed by the application (111) by the at least one processor operating to execute the command of the application (111).

[0134] Figure 3 is a flowchart illustrating a method for classifying patients according to the severity of an infectious disease according to an embodiment of the present invention.

[0135] Referring to FIG. 3, in the embodiment, the application (111) can obtain digital survey data. (S101)

[0136] In detail, in the embodiment, the application (111) can obtain digital survey data generated according to a digital survey performed on a predetermined patient terminal.

[0137] Here, the digital survey data according to the embodiment may be data on a questionnaire written by the patient to determine the patient's symptoms and / or signs regarding the patient's current condition.

[0138] FIG. 4 and FIG. 5 are examples of drawings for explaining digital survey data according to an embodiment of the present invention.

[0139] Referring to FIGS. 4 and 5, in the embodiment, digital survey data (DS) may include at least one of basic information data (301), symptom data (302), and / or concomitant disease data (303).

[0140] Basic information data (301) may be information entered on at least one of the patient's date of birth, age, gender, height, weight, body mass index, vaccination information, medications being taken, smoking status, drinking status, and / or pregnancy status.

[0141] Symptom data (302) may be information entered about at least one of the patient's body temperature, chills, dyspnea, respiratory rate per minute, cough, headache, sore throat, voice change, runny nose, and / or muscle pain.

[0142] The accompanying disease data (303) may be information entered for at least one of diabetes, respiratory disease, immune system disease, liver disease, heart disease, immunocompromised disease, mental health disease, chronic kidney disease, pulmonary disease, cerebrovascular disease, dementia, and surgical history.

[0143] In an embodiment, a patient and a specialist can determine the condition of the patient and write / check the contents included in the basic information data (301), symptom data (302), and / or concomitant disease data (303).

[0144] To increase the accuracy of information entered in the above digital survey data (DS), in the embodiment, the application (111) may obtain the patient's medical history by linking with the central server (200). At this time, the patient may be a patient who has performed a predetermined examination using a device recorded in the central server (200), or a returning patient with a previous medical history.

[0145] That is, in the embodiment, the application (111) can automatically input data requiring a certain examination among the basic information data (301), symptom data (302), and concomitant disease data (303) based on the acquired medical history.

[0146] For example, height, weight, body mass index, body temperature, and respiratory rate can be automatically linked to hospital-measured data and filled in the appropriate fields. If a patient's medical history includes an underlying medical condition, that information can also be automatically linked and checked in the appropriate fields.

[0147] Additionally, in the embodiment, the application (111) can determine the severity of the first patient based on the acquired digital survey data. (S103)

[0148] In detail, in the embodiment, the application (111) can determine the severity of the first patient by calculating the RISS score based on the acquired digital survey data.

[0149] The RISS (Respiratory Infection Severity Score) score according to the embodiment may be a score calculated to determine whether the patient's condition is severe.

[0150] In an embodiment, the application (111) can calculate a RISS score based on a scoring table.

[0151] FIG. 6 is an example illustrating a scoring table for calculating a RISS score according to an embodiment of the present invention.

[0152] Referring to FIG. 6, in the embodiment, the application (111) can calculate the RISS score based on a scoring table (ST) that assigns a score (403-1, 403-2, 403-3) for each variable (401) within a predetermined range (402-1, 402-2, 402-3).

[0153] The above variable (401), range (402) and score (403) for each variable can be preset by an expert. For example, the variable called 'body temperature' has a first range of 'less than 37 degrees', a second range of '37-38 degrees', and a third range of '38 degrees or more', and 0 points can be assigned to the first range, 1 point to the second range, and 5 points to the third range.

[0154] That is, there can be at least two ranges for each variable, and the scores matched to each range can be larger as they go toward the range that affects the severity (e.g., the third range).

[0155] In another embodiment, the application (111) may also set a predetermined score (403) for a predetermined record included in the basic information data (301), symptom data (302) and / or comorbidity data (303) of the digital survey data (DS).

[0156] For example, if a patient checks that he or she has asthma among respiratory diseases in the accompanying disease data (303) and the preset score for asthma is 3 points, 3 points may be additionally calculated when calculating the patient's RISS score.

[0157] In an embodiment, the application (111) can determine the severity of a patient based on the calculated RISS score.

[0158] In detail, in the embodiment, the application (111) can determine the severity as asymptomatic (S0) if the RISS score is 0 to 1, mild (S1) if the RISS score is 1 to 8, moderate (S2) if the RISS score is 8 to 14, and severe and critical if the RISS score is 15 or more.

[0159] At this time, if the RISS score is 15 points or higher and the severity is determined as severe or critical, the application (111) in the embodiment can execute a severe patient emergency process.

[0160] Specifically, the critically ill patient emergency process may be a process that guides the patient through a prescribed procedure for transporting the patient to an associated critical care center (e.g., a university hospital). For example, the application (111) may provide information on hospitals / ward status, hospital contact information, and ambulance contact information to at least one of the patient terminal (100-1), specialist terminal (100-2), and / or guardian terminal (100-3).

[0161] Additionally, in the embodiment, the application (111) can determine the first disease group of the first patient based on the determined severity. (S105)

[0162] In detail, in the embodiment, the application (111) can determine the first disease group of the first patient based on the RISS score calculated when determining the severity and the comorbidity data (303) included in the digital survey data.

[0163] Here, the disease groups according to the embodiment may be disease types classified according to predetermined criteria to manage each patient differently based on their characteristics. Among these, the first disease group refers to the disease group initially determined for the patient. Meanwhile, the second disease group refers to the disease group re-determined after the patient has been managed for a predetermined period of time by the patient management service since the initial diagnosis.

[0164] In an embodiment, the application (111) can extract types for each category based on comorbidity data (303) and RISS scores, and determine disease groups by combining the extracted types.

[0165] To determine the above disease group, in the embodiment, the application (111) can use a grouping table.

[0166] FIG. 7 is an example of a grouping table for classifying disease types according to an embodiment of the present invention.

[0167] Referring to FIG. 7, in the embodiment, the application (111) can determine the disease group of the first patient based on a grouping table (GT) in which predetermined parameters (511, 512, 513) are set for each category (501, 502, 503).

[0168] To this end, in the embodiment, the application (111) can extract classification types for each category (501, 502, 503). At this time, the extracted classification types may have higher risk levels as they go from the first parameter (511) to the third parameter (513).

[0169] In the embodiment, the above categories can be divided into Risk (risk group, 501), Severity (severity, 502), and Diabetes (diabetes, 503). In the following, the first category is Risk, the second category is Severity, and the third category is Diabetes.

[0170] At this time, in the embodiment, the application (111) can determine Risk and Diabetes based on the accompanying disease data (303).

[0171] In detail, in the embodiment, the application (111) can extract the classification type of Risk as R0 (low risk group) if the number of comorbidities included in the comorbidity data (303) is 0, as R1 (medium risk group) if the number is 2 or less, and as R2 (high risk group) if the number is 3 or more. In addition, if the comorbidity data (303) is input as no diabetes, the classification type of Diabetes can be extracted as D0 (no diabetes), and if it is input as present, the classification type of Diabetes can be extracted as D1 (diabetes present).

[0172] Additionally, in the embodiment, the application (111) can determine the Severity based on the RISS score calculated when determining the severity.

[0173] In detail, in the embodiment, the application (111) can extract the classification type of Severity as S0 (asymptomatic) if the RISS score is 0 to 2 points, S1 (mild) if the RISS score is 2 to 10 points, and S2 (moderate) if the RISS score is 10 points or more.

[0174] In other words, in the embodiment, the application (111) can extract the first to third classification types (C1, C2, C3) from the first to third categories (501, 502, 503) respectively based on the severity and comorbidity data.

[0175] Additionally, in the embodiment, the application (111) can determine a disease group (GRP) by combining the extracted first to third classification types (C1, C2, C3).

[0176] For example, the application (111) can extract the first classification type (C1) as 'R0', the second classification type (C2) as 'S1', and the third classification type (C3) as 'D0' for a first patient who has no comorbidities, a RISS score of 1, and no history of diabetes diagnosis, and determine the disease group (GRP) of the patient as 'R0 S1 D0'.

[0177] Additionally, in the embodiment, the application (111) can extract a disease management protocol according to the determined disease group. (S107)

[0178] Here, the disease management protocol according to the embodiment may mean a predetermined communication protocol that provides guidelines for managing a disease by matching different management cycles by category according to the determined disease type.

[0179] These disease management protocols may match different classification codes to each of at least one category of treatment cycle, message sending cycle, patient report outcome (PRO) sending cycle, monitoring cycle, and judgment cycle.

[0180] FIGS. 8 and 9 are exemplary diagrams illustrating disease management protocols matched to disease groups according to an embodiment of the present invention. Specifically, FIG. 8 illustrates a first disease management table (600) for the base group, and FIG. 9 illustrates a classification code table (CT) of the disease management protocol.

[0181] Referring to FIG. 8, in the embodiment, the application (111) can extract a disease management protocol based on the first disease management table (600) for the base group, which is the initially determined disease group of the patient.

[0182] In an embodiment, the application (111) can extract a classification code for each protocol category matching the base group of the first patient.

[0183] For example, referring to the fourth row (610) of the first disease management table (600), if the base group of the first patient includes 'R0S2', 'in2' can be extracted for the treatment cycle category, 'ms3' for the message sending cycle category, 'pro3' for the PRO sending cycle category, 'mo2' for the monitoring cycle category, and 'd1' for the judgment cycle category.

[0184] At this time, in the embodiment, the application (111) can provide patient management services according to the disease management cycle matched to each classification code based on the classification code table (CT) illustrated in FIG. 10.

[0185] Here, the classification code table (CT) according to the embodiment may be a table that sets and displays the disease management cycle for each category that each classification code signifies.

[0186] To take the above example again, if the base group of the first patient includes 'R0S2', the patient management service can be provided with a treatment cycle of every 48 hours corresponding to 'in2', a message sending cycle of every 12 hours corresponding to 'ms3', a PRO sending cycle of every 8 hours corresponding to 'pro3', a monitoring cycle of every 12 hours corresponding to 'mo2', and a judgment cycle of every 24 hours corresponding to 'd1'.

[0187] That is, in the embodiment, the application (111) can provide patient management services according to a disease management cycle matched to the extracted category classification code based on the classification code table (CT).

[0188] In other words, in the embodiment, the application (111) can provide patient management services according to the provided disease management protocol. (S109)

[0189] In an embodiment, the patient management service may be a service provided to a patient, a professional, and / or a guardian to manage the patient's disease according to a management cycle set by a plurality of categories set in a disease management protocol.

[0190] The treatment cycle may be a set period in which a specialist provides non-face-to-face / face-to-face treatment to a patient.

[0191] The message sending cycle may be set to the cycle of messages sent when the patient's severity classification changes.

[0192] The PRO sending cycle may be a cycle for sending reports containing examination results on the patient's condition.

[0193] The monitoring cycle may be a set cycle for monitoring performed by the individual patient using a self-measurement monitoring device, etc.

[0194] The judgment cycle may be a cycle that sets the cycle for judging the patient's severity.

[0195] In an embodiment, the application (111) may provide a patient management service that transmits predetermined information to at least one of a patient terminal (100-1), a specialist terminal (100-2), and / or a guardian terminal (100-3) through such a disease management protocol.

[0196] At this time, the PRO transmission cycle among the disease management protocols can only be transmitted to the expert terminal (100-2) as it is a report on patient diagnosis.

[0197] Additionally, in the embodiment, the application (111) of the patient terminal (100-1) can determine a non-face-to-face treatment schedule according to the treatment cycle among the disease management protocols.

[0198] At this time, in the embodiment, the application (111) of the patient terminal (100-1) can obtain schedule information of the doctor to be treated by linking with the expert terminal (100-2).

[0199] Additionally, in the embodiment, the application (111) of the patient terminal (100-1) can filter and display schedules for which non-face-to-face treatment is possible, reflecting the acquired schedule information. Accordingly, a non-face-to-face treatment schedule can be determined according to the provided treatment cycle.

[0200] Additionally, in the embodiment, the application (111) can obtain monitoring data at a second point in time. (S111)

[0201] In detail, in the embodiment, the application (111) can obtain monitoring data at a second point in time, which is a predetermined point in time, after performing disease management for a predetermined period of time based on a disease management protocol according to a first disease group of a patient that is initially determined.

[0202] At this time, the predetermined period for performing the above disease management can be determined manually by a specialist or determined by a disease management protocol.

[0203] In an embodiment, monitoring data may include self-measurement (monitoring) results and / or digital survey data at a second time point. The self-measurement results may be monitoring result data including a predetermined numerical value related to a patient's disease measured using a self-measurement monitoring device or the like.

[0204] Additionally, the self-measurement results may include manual data directly entered by the patient based on measurements made using a monitoring device, or automatic data automatically entered and transmitted from the monitoring device. To this end, the patient terminal (100-1) may be linked to a specific monitoring device to transmit and receive data.

[0205] Additionally, in the embodiment, the application (111) can calculate improvement information based on the acquired monitoring data. (S113)

[0206] Here, the improvement information according to the embodiment may be information determining the degree of improvement of the second disease group (in the example, the follow-up group) of patients determined at the second time point (in the example, after a predetermined period of time after the first time point) compared to the first disease group (in the example, the base group) of patients determined at the first time point (in the example, the first time point).

[0207] To derive the above-mentioned improvement information, in the embodiment, the application (111) can determine the severity and disease group for the first patient at a second time point based on monitoring data. The method for determining the severity and disease group is identical to the process described above, and is therefore omitted.

[0208] Additionally, in the embodiment, the application (111) can compare the first disease group and the second disease group to produce improvement information.

[0209] At this time, referring again to FIG. 7, in the embodiment, the application (111) can compare the classification types of the first and second categories (501, 502) of the first disease group and the classification types of the first and second categories (501, 502) of the second disease group.

[0210] The classification type of the third category (503) is information on the presence or absence of diabetes, which has a very low possibility of being cured within a short period of time from the first point to the second point, and therefore may be excluded from the reference when calculating improvement information.

[0211] At this time, in the embodiment, the application (111) can determine the improvement information as P0 if the classification type remains the same, P+1 if it has improved by one level, and P+2 if it has improved by two levels. Conversely, it can determine P-1 if it has worsened by one level, and P-2 if it has worsened by two levels.

[0212] Here, an improvement of one or two steps means that the classification type has changed from the third parameter (513) toward the first parameter (511), and a deterioration of one or two steps means that the classification type has changed from the first parameter (511) toward the third parameter (513).

[0213] In other words, in the embodiment, the application (111) can determine and compare the disease status of the patient at the second time point with the disease status of the patient at the first time point based on the acquired monitoring data through the first disease group and the second disease group, and calculate improvement information.

[0214] Additionally, in the embodiment, the application (111) can change the first disease group (in the embodiment, the base group) to the second disease group (in the embodiment, the follow-up group) based on the generated improvement information. (S115)

[0215] The follow-up group according to the embodiment may refer to a disease group determined based on improvement information determined according to the degree of improvement of the patient after a certain period of time has passed since the initial stage.

[0216] Additionally, in the embodiment, the application (111) may determine to provide a disease management protocol that matches the changed second disease group. (S117)

[0217] At this time, in the embodiment, the application (111) can change and provide patient management services based on a second disease management table in which disease management protocols matched to each follow-up group according to the generated improvement information are displayed.

[0218] FIG. 10 is an example of a drawing for explaining a disease management protocol matched to each follow-up group according to patient improvement information according to an embodiment of the present invention.

[0219] Referring to FIG. 10, in the embodiment, the application (111) can change the disease management protocol based on the second disease management table (700) for the follow-up group, which is a disease group according to the patient's improvement information.

[0220] That is, in the embodiment, the application (111) can extract a category-specific classification code matching the improvement information of the first patient.

[0221] For example, if the base group of the first patient included 'R0S2' and the follow-up group included 'R0S1' and the improvement information was determined as 'P-1', the application (111) can extract 'in1, ms1, pro1, mo1, d1' that match the improvement information 'P-1' of the 8th row (710).

[0222] Accordingly, in the embodiment, the application (111) can change the disease management protocol based on the second disease management table (700) according to the patient's improvement information.

[0223] Accordingly, the application (111) has the effect of efficiently managing the patient's disease by changing the frequency of services provided in the future according to the degree to which the patient's condition has worsened or improved since receiving the patient management service, and increasing the patient's compliance with the provided services, thereby increasing the rate of disease improvement through rehabilitation or treatment.

[0224] Meanwhile, in the embodiment, the application (111) can predict the time of complete recovery by converting the severity and improvement information of patients who already suffered from the corresponding concomitant disease into big data based on the concomitant disease data included in the digital survey data.

[0225] To this end, in the embodiment, the application (111) can match the comorbidity data of the nth patient with the determined severity and improvement information and store them as a data set.

[0226] Additionally, in the embodiment, the application (111) can convert a stored data set into big data. At this time, in the embodiment, the application (111) can extract a first concomitant disease whose severity and improvement information is above a preset value from among the data sets of multiple types of concomitant disease data converted into big data.

[0227] Additionally, in the embodiment, the application (111) can set a predetermined weight to the extracted first comorbidity.

[0228] Accordingly, in the embodiment, the application (111) can reflect the set weight when calculating the RISS score.

[0229] Meanwhile, in the embodiment, the application (111) of the expert terminal (100-2) may also calculate the RISS score based on the Delphi method.

[0230] To this end, in the embodiment, the application (111) of the expert terminal (100-2) can perform a scoring survey in which weights for each variable (401) are manually entered. The scoring survey may be a survey in which an expert enters weights for each of multiple variables included in the patient's digital survey data.

[0231] Accordingly, in the embodiment, the application (111) of the expert terminal (100-2) can determine the weight for each variable (401) by synthesizing the results of multiple scoring surveys based on the Delphi method.

[0232] That is, in the embodiment, the application (111) of the expert terminal (100-2) can change the range of each variable and the score assigned to the range by reflecting the weight of each determined variable (401) in the scoring table.

[0233] Additionally, in the embodiment, the application (111) can also predict the time of recovery of a patient based on a deep learning model for predicting the time of recovery that uses digital survey data as input data and recovery time data as output data.

[0234]

[0235] Above, the method for classifying patients according to the severity of an infectious disease according to an embodiment of the present invention and the non-face-to-face treatment system using the same have the effect of drastically reducing the probability of a patient's disease worsening by establishing a scoring system that determines the severity based on the patient's basic information, current symptoms, concomitant diseases, etc., thereby making it easy to perform advance allocation of medical resources by predicting patients with a high risk of disease worsening at an early stage.

[0236] In addition, the method for classifying patients according to the severity of an infectious disease according to an embodiment of the present invention and the non-face-to-face treatment system using the same determine the type of disease according to the characteristics of the patient and provide a disease management protocol that sets a treatment cycle, monitoring cycle, etc. suitable for the disease type, thereby determining whether the patient requires hospitalization and intensive care or can be managed only through non-face-to-face treatment, thereby increasing the efficiency of patient management and reducing the burden on the medical system.

[0237] In addition, the method for classifying patients according to the severity of an infectious disease according to an embodiment of the present invention and the non-face-to-face treatment system using the same have the effect of preventing human and economic losses that may occur due to unnecessary and excessive patient management despite improvement in the patient's condition by calculating patient improvement information and customizing the disease management protocol according to the calculated improvement information.

[0238] The embodiments of the present invention described above may be implemented in the form of program commands that can be executed through various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program commands, data files, data structures, etc., either singly or in combination. The program commands recorded on the computer-readable recording medium may be specially designed and configured for the present invention 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 recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program commands, such as ROMs, RAMs, and flash memories. Examples of program commands include not only machine language codes generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc. Hardware devices may be changed into one or more software modules to perform processing according to the present invention, and vice versa.

[0239] The specific implementations described in the present invention are exemplary embodiments and do not limit the scope of the present invention in any way. For the sake of brevity, descriptions of conventional electronic components, control systems, software, and other functional aspects of the systems may be omitted. In addition, the lines connecting or connecting members between components illustrated in the drawings are merely representative of functional connections and / or physical or circuit connections, and may be replaced or represented as various additional functional connections, physical connections, or circuit connections in an actual device. In addition, unless specifically mentioned as “essential,” “important,” etc., a component may not be absolutely necessary for the application of the present invention.

[0240] Although the detailed description of the present invention has been described with reference to preferred embodiments of the present invention, it will be understood by those skilled in the art or having ordinary knowledge in the art that various modifications and changes can be made to the present invention without departing from the spirit and technical scope of the present invention as set forth in the claims below. Accordingly, the technical scope of the present invention should not be limited to the contents described in the detailed description of the specification, but should be defined by the claims.

[0241] The form for carrying out the invention is the same as the best form for carrying out the invention described above.

[0242] It has industrial applicability in that it can dramatically reduce the probability of a patient's disease worsening by predicting patients at high risk of disease worsening early and facilitating advance allocation of medical resources.

Claims

1. A method for classifying patients according to the severity of an infectious disease to provide patient management services, wherein a patient management application executed by at least one processor of a professional terminal, A step of acquiring digital survey data generated from a terminal of a first patient at a first point in time; A step of determining the severity of the first patient based on the acquired digital survey data; A step of determining a first disease group of the first patient based on the determined severity; A step of providing patient management services by extracting a first disease management protocol according to the first disease group determined above; A step of acquiring monitoring data for the first patient at a second time point; A step of calculating improvement information based on the acquired monitoring data; A step of changing the first disease group of the first patient to a second disease group based on the above-mentioned improvement information; and a step of changing the second disease management protocol according to the changed second disease group; Method of classifying patients according to the severity of infectious disease.

2. In paragraph 1, The step of obtaining the above digital survey data is as follows: A step of obtaining basic information data entered for at least one of the patient's date of birth, age, sex, height, weight, body mass index, vaccination information, medications being taken, smoking status, drinking status, and pregnancy status; A step of obtaining input symptom data for at least one of the patient's body temperature, chills, dyspnea, respiratory rate per minute, cough, headache, sore throat, voice change, runny nose, and muscle pain; and A step of obtaining data on comorbidities entered for at least one of diabetes, respiratory disease, immune system disease, liver disease, heart disease, immunocompromised disease, mental health disease, chronic kidney disease, lung disease, cerebrovascular disease, dementia, and surgical history. Method of classifying patients according to the severity of infectious disease.

3. In paragraph 1, The step of determining the severity of the first patient is: A step of calculating a RISS score based on a scoring table that assigns a score to at least one variable by a predetermined range, It includes a step of determining the severity as asymptomatic, mild, moderate and severe based on the RISS score calculated above. The above variable is one of the pieces of information entered as the digital survey data. Method of classifying patients according to the severity of infectious disease.

4. In paragraph 3, The step of determining the first disease group of the first patient is: A step of extracting a first classification type of a first category based on the number of comorbidities included in the comorbidity data for the first patient; A step of extracting a second classification type of a second category based on the RISS score calculated for the first patient, A step of extracting a third classification type of a third category based on the presence or absence of diabetes included in the comorbidity data for the first patient above, and A step of determining a first disease group by combining the first to third classification types extracted above, The above first to third categories include Risk, Severity and Diabetes. Method of classifying patients according to the severity of infectious disease.

5. In paragraph 1, The step of extracting the first disease management protocol according to the first disease group above is: A step of extracting classification codes for the first to fifth protocol categories based on the first disease management table for the first disease group, which is the first disease group of the patient determined; A step of extracting a first disease management protocol matching the above extracted category-specific classification code, A step of providing a patient management service according to the first disease management protocol extracted above to at least one of a patient terminal, a specialist terminal, and a guardian terminal, The above first to fifth protocol categories include a treatment cycle, a message sending cycle, a PRO (Patient Report Outcome) sending cycle, a monitoring cycle, and a judgment cycle. Method of classifying patients according to the severity of infectious disease.

6. In paragraph 5, The step of providing patient management services according to the above first disease management protocol is: Further comprising a step of providing patient management services according to a disease management cycle matched to each classification code based on the classification code table, The above disease management cycle is: A treatment cycle in which a specialist performs either non-face-to-face or face-to-face treatment on a patient, Message sending cycle for sending messages when the patient's disease group changes, PRO sending cycle for sending patient diagnosis reports containing examination results on the patient's condition, A monitoring cycle that performs self-measurement through a monitoring device linked to the patient terminal, Information that sets at least one of the judgment cycles for judging the patient's severity Method of classifying patients according to the severity of infectious disease.

7. In paragraph 1, The step of acquiring monitoring data at the second point in time is: A step of acquiring monitoring data at a second point in time, which is a predetermined point in time, after performing disease management for a predetermined period of time based on the first disease management protocol, The above monitoring data is, The monitoring results up to the second time point obtained from the monitoring device linked to the terminal of the first patient, Including digital survey data acquired from the terminal of the first patient at the second time point Method of classifying patients according to the severity of infectious disease.

8. In paragraph 1, The step of calculating the above improvement information is: A step of determining a second disease group for the first patient at the second time point; A step of comparing the first and second classification types of the first and second categories of the first disease group determined at the first time point and the second disease group determined at the second time point, A step of calculating improvement information of the second disease group compared to the first disease group according to the improvement and deterioration of the first and second classification types. Method of classifying patients according to the severity of infectious disease.

9. In paragraph 1, The step of changing the second disease management protocol according to the second disease group is: A step of extracting classification codes for the first to fifth protocol categories based on the second disease management table for the second disease group, which is a disease group of the patient determined according to the degree of improvement of the patient after a predetermined period of time has passed since the first, A step of extracting a second disease management protocol matching the above extracted category-specific classification code, A step of providing patient management services using the second disease management protocol extracted above. Method of classifying patients according to the severity of infectious disease.

10. It is linked with the patient terminal and the guardian terminal. A professional terminal comprising at least one memory and at least one processor; at least one application stored in the memory and executed by the processor to provide a patient management service, wherein the at least one application is: Obtain digital survey data generated from the terminal of the first patient at the first time point, Determine the severity of the first patient based on the digital survey data obtained above, Based on the severity determined above, the first disease group of the first patient is determined, Provide patient management services by extracting the first disease management protocol according to the first disease group determined above, Obtain monitoring data for the first patient at the second time point, Based on the monitoring data obtained above, improvement information is calculated, Based on the improvement information produced above, the first disease group of the first patient is changed to the second disease group, Change the second disease management protocol according to the above changed second disease group. A method for classifying patients according to the severity of infectious diseases and a non-face-to-face treatment system using the method.

Images