Method, device, and system for automatically triaging and controlling response

Abstract

The present disclosure relates to a method, device, and system for automatically triaging and controlling a response. The method comprises the steps of: acquiring vitals information about a patient in question; extracting data for each item in the acquired vitals information about the patient; mapping the extracted data for each item to the corresponding item; calculating a severity response score corresponding to the risk level of the patient on the basis of the data for each item mapped to the corresponding item; setting a severity response scale based on the severity response score corresponding to the calculated risk level of the patient; and controlling so that the set severity response scale information is transmitted to at least one terminal and output.

Description

Automatic severity classification and response processing control method, device and system

[0001] The present disclosure relates to a method, device and system for automatically classifying severity and processing corresponding thereto.

[0002] Triage refers to the process of categorizing patients by symptoms to determine the priority of emergency treatment and patient transport. Specifically, the Korean Triage and Acuity Scale (KTAS) classifies all patients visiting the emergency room (ER) according to standardized criteria.

[0003] Although the criteria for classifying the severity of emergency patients have been standardized, most medical institutions manually entered information for severity classification, calculated severity scores, and directly transmitted them to medical institutions for sharing.

[0004] This conventional method may be accurate because it allows for direct observation of the patient and judgment, but it has various problems, such as the inability to guarantee accuracy depending on the expertise or experience of the medical institution recording it, the difficulty in sharing related detailed information with the medical institution in advance, which requires the medical institution to make a second judgment, the inability to prepare for a response related to the severity in advance, which may slow down the response in cases where there is a race for time, and the difficulty in conveying information to the relevant medical institution.

[0005] Therefore, there is a need for a system that accurately and quickly assesses the severity of a patient's condition and shares it with medical institutions to enable rapid and accurate response and continuous observation.

[0006] The present disclosure provides a method, device and system for accurately and quickly assessing the severity of a patient and sharing it with a medical institution for quick and accurate response and continuous careful observation.

[0007] A method for automatically classifying severity and controlling corresponding processing in a server according to at least one of various embodiments of the present disclosure may include the steps of: obtaining vital information of a target patient; extracting data for each item in the obtained vital information of the target patient; mapping the extracted data for each item to each corresponding item; calculating a severity response score according to the patient's risk based on the data for each item mapped to each corresponding item; setting a severity response grade based on the calculated severity response score according to the patient's risk; and controlling the set severity response grade information to be transmitted to at least one terminal so as to be output.

[0008] If the set severity response level information according to at least one of the various embodiments of the present disclosure is a high-risk response level, the method may further include a step of controlling an activation signal to be transmitted to at least one terminal and output.

[0009] The activation signal according to at least one of the various embodiments of the present disclosure may be a signal indicating at least one of information indicating a rapid response, response processing attention, intensive care, and intensive management target for the target patient.

[0010] The method may further include a step of controlling the severity response guide information mapped to the set severity response grade information according to at least one of the various embodiments of the present disclosure to be transmitted to at least one terminal and output.

[0011] The step of controlling the severity response guide information mapped to the set severity response grade information according to at least one of the various embodiments of the present disclosure to be transmitted to at least one terminal and output may further include the step of reading out the candidate severity response guide information from a database linked to the server.

[0012] The step of controlling the severity response guide information mapped to the set severity response grade information according to at least one of the various embodiments of the present disclosure to be transmitted to at least one terminal so as to be output may further include the step of determining the optimal severity guide information for the target patient among the read candidate severity response guide information.

[0013] In the step of calculating a severity response score according to the patient's risk based on each item-specific data mapped to each item according to at least one of the various embodiments of the present disclosure, the severity response score may be calculated based on at least one of a MEWS score and a NEWS score based on the item-specific data.

[0014] In the step of calculating a severity response score according to the patient's risk based on each item-specific data mapped to each item according to at least one of the various embodiments of the present disclosure, the severity response score may be calculated as a sum score of the MEWS score and the NEWS score based on the data for each item.

[0015] The step of setting a response grade based on the severity response score according to the calculated patient risk according to at least one of the various embodiments of the present disclosure may further include a step of extracting, from the database, history information of the severity response score and severity response grade of the target patient, when the calculated severity response score corresponds to the boundary of each severity response grade.

[0016] The step of setting a severity response grade based on the severity response score according to the calculated patient's risk according to at least one of the various embodiments of the present disclosure may be controlled to maintain or adjust the severity response grade based on the calculated severity response score according to the patient's risk based on the history information of the severity response score and severity response grade of the extracted target patient.

[0017] An automatic severity classification and response processing control device according to at least one of various embodiments of the present disclosure may include: a database; a communication module for communicating with at least one first terminal and a second terminal belonging to a network; and a processor for obtaining vital information of a target patient through the communication module, extracting data for each item in the obtained vital information of the target patient, mapping the extracted data for each item to each corresponding item, calculating a severity response score according to the patient's risk based on the data for each item mapped to each corresponding item, setting a severity response grade based on the calculated severity response score according to the patient's risk, and controlling the transmission and output of the set severity response grade information to at least one terminal of the network.

[0018] The processor according to at least one of the various embodiments of the present disclosure can control an activation signal to be transmitted and output to at least one terminal if the set severity response level information is a high-risk response level.

[0019] The activation signal according to at least one of the various embodiments of the present disclosure may be a signal indicating at least one of information indicating a rapid response, response processing attention, intensive care, and intensive management target for the target patient.

[0020] The processor according to at least one of the various embodiments of the present disclosure can control the severity response guide information mapped to the set severity response grade information to be transmitted to at least one terminal and output.

[0021] The processor according to at least one of the various embodiments of the present disclosure can read the candidate severity response guide information from the database.

[0022] The processor according to at least one of the various embodiments of the present disclosure can determine optimal severity guide information for the target patient from among the retrieved candidate severity response guide information.

[0023] The processor according to at least one of the various embodiments of the present disclosure may calculate the severity response score based on at least one of the MEWS score and the NEWS score based on the data for each item.

[0024] The processor according to at least one of the various embodiments of the present disclosure may calculate the severity response score as a sum of the MEWS score and the NEWS score based on the data for each item.

[0025] The processor according to at least one of the various embodiments of the present disclosure may extract, from the database, history information of the severity correspondence score and severity correspondence grade of the target patient, if the calculated severity correspondence score corresponds to the boundary of each severity correspondence grade.

[0026] The processor according to at least one of the various embodiments of the present disclosure can control to maintain or adjust the severity response grade based on the severity response score calculated according to the patient's risk based on the history information of the severity response score and severity response grade of the extracted target patient.

[0027] A system for automatically classifying severity and controlling response processing according to at least one of various embodiments of the present disclosure comprises: a first terminal for obtaining vital information of a target patient; at least one second terminal; and a computing device, wherein the computing device obtains vital information of the target patient from the first terminal, extracts data for each item in the obtained vital information of the target patient, maps the extracted data for each item to each corresponding item, calculates a severity response score according to the patient's risk based on the data for each item mapped to each corresponding item, sets a severity response grade based on the calculated severity response score according to the patient's risk, and controls the transmission of the set severity response grade information to at least one of the first terminal and the second terminal.

[0028] According to at least one of the various embodiments of the present disclosure, a severity response grade based on risk calculation for target patients can be automatically set, and monitoring and response can be performed according to the set grade, thereby reducing the risk of patients and enabling rapid and accurate response from medical institutions.

[0029] According to at least one of the various embodiments of the present disclosure, a severity response grade based on risk calculation for target patients is automatically set, and medical institution guide information according to the set grade is provided, thereby enabling prompt action and preventing accidents caused by mistakes in advance.

[0030] FIG. 1 is a schematic diagram of an automatic severity classification and response processing control system according to one embodiment of the present disclosure.

[0031] Figure 2 is a block diagram of the configuration of the first terminal of Figure 1.

[0032] Figure 3 is a block diagram of the configuration of the server of Figure 1.

[0033] Figures 4 to 7 are flowcharts illustrating a method for automatically classifying severity and responding to a response in a server according to an embodiment of the present disclosure.

[0034] FIGS. 8 and 9 are diagrams illustrating MEWS and NEWS score calculation according to an embodiment of the present disclosure.

[0035] Figures 10 to 12 are drawings illustrating severity response grades according to one embodiment of the present disclosure.

[0036] FIG. 13 is a drawing illustrating an example of guide information according to one embodiment of the present invention.

[0037] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and any content that is common in the technical field to which this disclosure pertains or that overlaps between embodiments is omitted. The terms "part, module, element, block" used in the specification may be implemented in software or hardware, and depending on the embodiments, multiple "parts, modules, elements, blocks" may be implemented as a single component, or a single "part, module, element, block" may include multiple components.

[0038] Throughout the specification, when a part is said to be "connected" to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection via a wireless communication network.

[0039] Additionally, when a part is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise specifically stated.

[0040] Throughout the specification, when we say that an element is "on" another element, this includes not only cases where the element is in contact with the other element, but also cases where another element exists between the two elements.

[0041] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0042] Singular expressions include plural expressions unless the context clearly indicates otherwise.

[0043] The identification codes for each step are used for convenience of explanation and do not describe the order of each step. Each step may be performed in a different order than specified unless the context clearly indicates a specific order.

[0044] The operating principle and embodiments of the present disclosure are described below with reference to the attached drawings.

[0045] As used herein, the term "device according to the present disclosure" encompasses a variety of devices capable of performing computational processing and providing results to a user. For example, the device according to the present disclosure may include a computer, a server device, and a portable terminal, or may be any one of them.

[0046] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0047] The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, a web server, etc.

[0048] The above portable terminal may include, for example, a wireless communication device that ensures portability and mobility, and may include all kinds of handheld-based wireless communication devices such as a PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminal, smart phone, etc., and wearable devices such as a watch, ring, bracelet, anklet, necklace, glasses, contact lens, or head-mounted device (HMD).

[0049] The artificial intelligence-related functions according to the present disclosure are operated via a processor and memory. The processor may be comprised of one or more processors. In this case, one or more processors may be a general-purpose processor such as a CPU, an AP, a Digital Signal Processor (DSP), a graphics-only processor such as a GPU or a Vision Processing Unit (VPU), or an artificial intelligence-only processor such as an NPU. One or more processors control the processing of input data according to predefined operating rules or artificial intelligence models stored in memory. Alternatively, if one or more processors are artificial intelligence-only processors, the artificial intelligence-only processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0050] The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that the basic artificial intelligence model is learned by a learning algorithm using a plurality of learning data, thereby creating a predefined operation rules or artificial intelligence model set to perform a desired characteristic (or purpose). This learning may be performed on the device itself on which the artificial intelligence according to the present disclosure is performed, or may be performed through a separate server and / or system. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0051] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values, and performs neural network operations through operations between the operation results of the previous layer and the multiple weights. The multiple weights of the multiple neural network layers may be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model is reduced or minimized during the learning process. The artificial neural network may include a deep neural network (DNN), and examples thereof include, but are not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks.

[0052] According to an exemplary embodiment of the present disclosure, a processor can implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that imitates human neurons (biological neurons) to enable machines to learn. Artificial intelligence methodologies can be categorized into supervised learning, in which input data and output data are provided together as training data depending on the learning method, so that the solution (output data) to the problem (input data) is determined; unsupervised learning, in which only input data is provided without output data, so that the solution (output data) to the problem (input data) is not determined; and reinforcement learning, in which a reward (Reward) is provided from an external environment whenever an action (Action) is taken in the current state (State), and learning is performed in a direction to maximize this reward. In addition, artificial intelligence methodologies can be categorized according to the architecture of the learning model. The architectures of widely used deep learning technologies can be categorized into convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs).

[0053] The present device and system may include an artificial intelligence model. The artificial intelligence model may be a single artificial intelligence model or may be implemented as multiple artificial intelligence models. The artificial intelligence model may be composed of a neural network (or artificial neural network) and may include statistical learning algorithms that mimic biological neurons in machine learning and cognitive science. A neural network may refer to a model in general that has problem-solving capabilities by changing the binding strength of synapses through learning, formed by artificial neurons (nodes) that form a network by combining synapses. The neurons of the neural network may include a combination of weights or biases. The neural network may include one or more layers composed of one or more neurons or nodes. For example, the device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can infer a desired result (output) from an arbitrary input (input) by changing the weights of neurons through learning.

[0054] The processor can create a neural network, train (or learn) a neural network, perform a calculation based on received input data, generate an information signal based on the calculation result, or retrain the neural network. The models of the neural network can include various types of models such as CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), RNN (Recurrent Neural Network), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, etc., but are not limited thereto. The processor can include one or more processors for performing calculations according to the models of the neural network. For example, the neural network can be a deep neural network. It may include a deep neural network.

[0055] Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto) Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Depp Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), Generative Adversarial Network (GAN), Liquid State Machine (LSM), Extreme Learning Machine (ELM), It will be understood by those skilled in the art that any neural network may be included, including but not limited to ESN (Echo State Network), DRN (Deep Residual Network), DNC (Differentiable Neural Computer), NTM (Neural Turning Machine), CN (Capsule Network), KN (Kohonen Network), and AN (Attention Network).

[0056] According to an exemplary embodiment of the present disclosure, the processor may be configured to perform a process for generating a CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, Region with Convolution Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restrcted Boltzman Machine (RBM), Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA for natural language processing, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics for vision processing, Visual Understanding, Video Synthesis, ResNet for data intelligence, Anomaly Detection, Prediction, Time-Series Forecasting, Various artificial intelligence structures and algorithms, including optimization, recommendation, and data creation, can be utilized, but are not limited thereto. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0057] FIG. 1 is a schematic diagram of an automatic severity classification and response processing control system (1) according to one embodiment of the present disclosure.

[0058] Figure 2 is a block diagram of the configuration of the first terminal (100) of Figure 1.

[0059] Figure 3 is a block diagram of the configuration of the server (200) of Figure 1.

[0060] An automatic severity classification and response processing control system (1) according to at least one of the various embodiments of the present disclosure may be configured to include a first terminal (100), a server (200), and a second terminal (300).

[0061] The first terminal (100) may be a terminal device that obtains vital information of the target patient.

[0062] The first terminal (100) can transmit the vital information of the target patient to the server (200). At this time, the first terminal (100) can transmit the vital information of the target patient to the server (200) as raw data without processing it, or transmit it to the server (200) after processing it.

[0063] According to an embodiment, the first terminal (100) may be a device that receives vital information of a target patient from a vital information acquisition device of the target patient and provides an input interface for input.

[0064] The server (200) obtains vital information of the target patient from the first terminal (100), automatically calculates a severity response score according to the risk of the target patient, sets a severity response grade, and then transmits a control signal according to the set severity response grade to at least one of the first terminal (100) and the second terminal (200).

[0065] The server (200) may be located remotely and may be called by various names, such as a computing device or a controller, depending on the embodiment.

[0066] The second terminal (300) can receive and output various information or signals from the first terminal (100) or server (200).

[0067] Although the components constituting the automatic severity classification and response processing control system (1) are illustrated as a single component in FIG. 1 for convenience of explanation, there may be multiple components.

[0068] At least one of the first terminal (100) and the second terminal (300) may be a fixed terminal such as a monitor, signage, or digital TV, or a mobile terminal such as a smartphone, mobile phone, tablet PC, or laptop.

[0069] According to one embodiment, at least one of the first terminal (100) and the second terminal (300) may be in the form of a smart watch, smart glasses, or the like.

[0070] According to another embodiment, at least one of the first terminal (100) and the second terminal (300) can provide an extended reality service such as virtual reality, augmented reality, or mixed reality.

[0071] According to another embodiment, at least one of the first terminal (100) and the second terminal (300) may be a dedicated terminal for operation in the automatic severity classification and response processing control system (1).

[0072] According to one embodiment, at least one of the first terminal (100), the second terminal (300) and the server (200) may include an artificial intelligence engine in relation to the automatic severity classification and response processing control system (1) according to the present disclosure.

[0073] For convenience of explanation, the following description is given as an example of including an artificial intelligence engine for automatic severity classification, judgment, and response processing control in the server (200), but is not limited thereto.

[0074] Meanwhile, at least one of the terminals of FIG. 1, i.e., the first terminal (100) and the second terminal (300), can output relevant information through an output unit. At this time, the output unit may include a display panel, a speaker, etc. However, the display, speaker, etc., which are output units, do not necessarily need to be built-in, and it is sufficient if they can be linked with at least one of the first terminal (100) and the second terminal (300) to output data.

[0075] Additionally, at least one of the first terminal (100) and the second terminal (300) may utilize an application pre-downloaded and installed for information output. At least one of the first terminal (100) and the second terminal (300) may execute an application based on communication with the server (200) to provide or receive related data.

[0076] Connection and communication between the first terminal (100), the server (200), and the second terminal (300) may be referred to as wired / wireless. For convenience of explanation, the present disclosure uses wireless communication methods such as Bluetooth or Wi-Fi as an example, but is not limited thereto.

[0077] Accordingly, the communication module in FIGS. 2 and 3 described below may be a module that supports such wired / wireless communication methods.

[0078] As described above, the server (300) performs operations according to data and requests obtained from at least one of the first terminal (100) and the second terminal (300), and can return the results of the operations thus performed to the corresponding configuration.

[0079] Referring to FIG. 2, the components of the first terminal (100) are described in more detail as follows.

[0080] The first terminal (100) may be configured to include a memory (160) and a processor.

[0081] The processor may include a communication module, a band unit, a coupling means configured to couple one end and the other end of the band unit to each other, at least one sensor arranged in one area of ​​the band unit to recognize Braille, and a control unit that processes or controls Braille data recognized by the at least one sensor to be processed.

[0082] Referring to FIG. 2, the processor may include a communication module (110), a data measurement unit (120), a data processing unit (130), a control unit (140), a memory (150), etc.

[0083] The communication module (110) can provide a wireless communication interface environment to support the first terminal (100) to exchange data with the server (200) (or the second terminal (300)).

[0084] The communication module (110) can provide a communication interface environment as needed to support the first terminal (100) to exchange data with the second terminal (300).

[0085] The communication module (110) may be an LTE, LTE-A, 5G, Wi-Fi communication module, etc.

[0086] The data measurement unit (120) can sense and measure information about the target patient depending on the type or kind of the device.

[0087] Here, information about the target patient may be, for example, vital information required for calculating the Modified Early Warning Score (MEWS) shown in FIG. 8 or the New Early Warning Score (NEWS) shown in FIG. 9, but is not limited thereto.

[0088] Referring to Figure 8, MEWS items may include heart rate, systolic blood pressure, respiratory rate, temperature, AVPU score (A: Alert, V: response to Voice, P: reacting to Pain, U: Un-Responsive, score), oxygen saturation (SpO2), etc.

[0089] Meanwhile, MEWS items may further include urine production (below 75 ml during the previous 4 hours) and patient's condition (worried about patient's condition).

[0090] Referring to Figure 9, NEWS (National Early Warning Score) may include six physiological parameters: systolic blood pressure, pulse rate, respiratory rate, body temperature, oxygen saturation SpO2, level of consciousness + oxygen therapy.

[0091] The data processing unit (130) can generate a signal processed in a format necessary for transmitting data sensed through the data measurement unit (120) to an external device, i.e., a server (200) and / or a second terminal (300).

[0092] The control unit (140) can control the operation of each component of the first terminal (100). The specific operation of the control unit (140) will be described later.

[0093] The memory (150) can pre-store various information or update the stored information. The memory (150) can temporarily store data acquired from the first terminal (100) or data received from the server (200) or the second terminal (300).

[0094] The memory (150) can store information regarding software, such as applications and programs, or related processing engines, necessary for processing or validating data sensed through the data measurement unit (120), as needed. The processing engine may include, for example, an artificial intelligence learning engine. Such an engine may be implemented within the control unit (140).

[0095] Referring to FIG. 3, the server (200) may be configured to include a memory (260) and a processor.

[0096] The processor may be configured to include a communication module (210), a data extraction unit (220), a severity score calculation unit (230), a risk group classification unit (240), a control unit (250), a memory (260), etc.

[0097] The communication module (210) provides a communication interface environment, so that the server (200) can exchange data with the first terminal (100) and / or the second terminal (300).

[0098] The communication module (210) may be any one of LTE, LTE-A, 5G, and Wi-Fi communication modules.

[0099] The data extraction unit (220) can extract necessary data from a signal obtained from the first terminal (100) through the communication module (210).

[0100] The severity score calculation unit (230) can calculate the severity score based on information corresponding to various items included in MEWS or NEWS of FIGS. 8 to 9 extracted through the data extraction unit (220).

[0101] The severity score calculation unit (230) can use the MEWS of FIG. 8 and NEWS information of FIG. 9 to calculate the severity score.

[0102] For example, the severity score calculation unit (230) can calculate the severity score using only one of the MEWS information of FIG. 8 and the NEWS information of FIG. 9.

[0103] Alternatively, the severity score calculation unit (230) may calculate a severity score using the sum of the MEWS information of FIG. 8 and the NEWS information of FIG. 9. At this time, if any of the items of each piece of information are identical, similar, or overlapping, a value indicating a worse condition of the target patient may be used, or vice versa.

[0104] Alternatively, if the values ​​for duplicate, identical, or similar items among the MEWS items of FIG. 8 and the NEWS information items of FIG. 9 differ by a threshold value or more, a request may be made to re-sensing the target patient's information for the corresponding item.

[0105] Figure 11 illustrates information for entering MEWS and NEWS scores implemented in table form. In this case, when a corresponding EWS item is selected, at least one of the MEWS and NEWS scores, or their sum, can be output.

[0106] Despite the above re-sensing, if the difference between the two values ​​is still greater than the threshold or greater than the threshold compared to the previous measurement, re-sensing of at least one of the entire MEWS and NEWS for the target patient may be requested.

[0107] The above-mentioned re-sensing or re-sensing can be performed, for example, by the control unit (250) or by the severity score calculation unit (230) controlled by the control unit (250).

[0108] The risk group classification unit (240) can classify the risk group of the target patient according to the severity score calculated by the severity score calculation unit (230) described above, as illustrated in FIG. 10. At this time, the risk group classification unit (240) can set a severity response grade.

[0109] The risk group and severity response grade of these target patients can be divided into high-risk group, medium-risk group, and low-risk group.

[0110] Referring to Figure 10, this is an example where a calculated severity score of 0-6 is classified as a low-risk group, 8-10 as a medium-risk group, and 12 or higher as a high-risk group.

[0111] The control unit (250) can control to guide ward observation if the patient is in the low-risk group, guide to proceed with patient rounding if the patient is in the medium-risk group, and guide to proceed with high-risk patient rounding if the patient is in the high-risk group.

[0112] According to one embodiment of the present disclosure, the control unit (250) can transmit an activation signal to at least one of the first terminal (100) and the second terminal (300) when the risk group classification unit (240) classifies the target patient as a medium-risk or high-risk group.

[0113] At this time, the activation signal may indicate the implementation of patient rounding or high-risk patient rounding, as illustrated in FIG. 10.

[0114] Meanwhile, the activation signal may be a signal including guide information indicating at least one of information indicating a rapid response to the target patient, response processing attention, intensive care, and intensive management target.

[0115] Referring to Fig. 10, in particular, when the severity response level is set to the high-risk group, high-risk patient rounding is performed, a severity assessment is performed, and necessary tests such as ABG and Trop I tests are performed, and then other interventions such as V / SQ () hrs check and I / O and multi-monitoring are applied, and a report is made to the second terminal (300) (i.e., the attending physician), and activation may be performed when necessary. The above-described contents may not be mandatory procedures and may be performed when necessary.

[0116] Figure 12 shows the response list and termination list for each target patient.

[0117] Looking at the response list, information such as target patient, call method, risk level (i.e. severity response level (high risk group, medium risk group, low risk group, etc.), patient status, patient location (ward), patient registration number, patient name, patient gender, patient age, and other notes can be input or output.

[0118] Meanwhile, each item in Figure 12 is selectable, and in particular, when the Assess item is selected, information such as height / weight, severity, pressure ulcer severity, and fall severity can be provided. In other words, if the severity grade is, for example, Group 2, specific information can be provided regarding why the severity grade is Group 2 and which disease or target item influenced the severity grade.

[0119] The control unit (250) can control the operation of each component of the processor.

[0120] The control unit (250) may further include an artificial intelligence engine (not shown) necessary for processing data obtained from the first terminal (100).

[0121] The control unit (250) may further include at least one of a STT (Speech to Text), TTS (Text to Speech), or NLP (Natural Language Processing) engine or module necessary for processing data obtained from the first terminal (100) or for transmitting a control signal to the second terminal (300) in relation to the data included in the signal.

[0122] When a severity response score is calculated for a target patient and a severity response grade is finally determined, the control unit (250) provides guide information for specific treatment preparation or treatment execution according to the determined severity response grade, thereby enabling adaptive response according to the patient's severity response grade.

[0123] Through this, the control unit (250) can control the system to respond in a three-dimensional manner rather than judging and responding to all target patients based on the same criteria, that is, responding to dangerous patients more quickly and accurately, and monitoring relatively less dangerous patients while delaying treatment, thereby enabling efficient patient management and treatment.

[0124] FIG. 13 illustrates an example of guide information according to one embodiment of the present invention.

[0125] Referring to Figure 13, treatment guidance information for the first 5 minutes is illustrated. If necessary, move to the treatment room, prepare an emergency cart, secure an IV route (20G or more), measure respiratory rate for 1 minute, auscultate, check for use of the sternocleidomastoid and abdominal muscles, measure blood pressure and pulse, refill capillaries, observe skin motling, assess level of consciousness, measure BST, and check pupillary light reflexes. These can be sequentially checked and treated.

[0126] However, the guide information in Fig. 13 may be standard guide information for the corresponding severity response level.

[0127] This standard guidance information can be applied primarily to cases where the severity response level is low-risk or medium-risk.

[0128] Alternatively, the standard guidance information may also apply when the severity response level is initially determined to be high-risk or when a healthcare facility is providing initial treatment.

[0129] Alternatively, standard guidance information may be given priority in cases where a severe response level has been established but is not related to a specific disease.

[0130] When a severity response level is set, the control unit (250) primarily provides standard guide information for the severity response level, but when feedback is input for the standard guide information provided initially, new guide information can be searched and extracted accordingly and provided again for a second time.

[0131] Alternatively, if a moderate response grade is set, the control unit (250) can check the detailed information of the set severe response grade and, if it is related to a specific disease, provide grade-specific guide information for the disease.

[0132] The aforementioned guide information may be initially provided to the second terminal (300) and may be provided only upon confirmation by the second terminal (300). In this case, if the guide information initially provided by the second terminal (300) is modified, modified guide information reflecting the modified information may be provided.

[0133] When the severity response level is set to a predetermined level or higher (e.g., high-risk group), the control unit (250) can extract all information related to medical procedures performed or performed at medical institutions for the patient, such as the patient's visit history, treatment history, treatment history, treatment or post-treatment reaction history, and allergy information history.

[0134] The information of the target patient extracted in this way can be collected by the control unit (250) and transmitted as is to the second terminal (300).

[0135] Alternatively, the information of the target patient extracted in this way can be collected and processed by the control unit (250) and then transmitted to the second terminal (300).

[0136] Alternatively, the information of the target patient extracted in this way may be collected and processed by the control unit (250), and then predetermined guide information may be generated and provided to the second terminal (300).

[0137] In FIGS. 2 and 3, the memory (150, 260) may include at least one type of storage medium among a flash memory type, a hard disk type, an SSD (Solid State Disk type), an SDD (Silicon Disk Drive type), a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk.

[0138] In FIGS. 2 and 3, only one memory (150, 260) is illustrated for convenience, but it is not limited thereto and there may be multiple memory units.

[0139] In FIGS. 2 and 3, the memory (150, 260) is illustrated and described as being built into the first terminal (100) or server (200) for convenience, but is not limited thereto. For example, in FIGS. 2 and 3, the memory (150, 260) may also be implemented in the form of a DB server located externally or remotely.

[0140] In FIGS. 2 and 3, the memory and processor may be implemented as separate chips. Alternatively, the memory and processor may be implemented as a single chip.

[0141] At least one component may be added or deleted to correspond to the performance of the components illustrated in FIGS. 2 and 3. Furthermore, it will be readily apparent to those skilled in the art that the relative positions of the components may be altered to correspond to the performance or structure of the system.

[0142] Meanwhile, each component illustrated in FIGS. 2 and 3 refers to software and / or hardware components such as Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC).

[0143] Figures 4 to 7 are flowcharts illustrating a method for automatically classifying severity and responding to a response in a server according to an embodiment of the present disclosure.

[0144] A method for automatically classifying severity and controlling corresponding processing in a server according to at least one of the various embodiments of the present disclosure may be as follows.

[0145] First, referring to FIG. 4, at step S110, the processor can obtain vital information of the target patient.

[0146] At step S120, the processor can extract data for each item in the vital information of the target patient obtained above.

[0147] At step S130, the processor can map the extracted data for each item to each item.

[0148] At step S140, the processor can calculate a severity response score according to the patient's risk based on each item-specific data mapped to each of the corresponding items.

[0149] At step S150, the processor can set a severity response grade based on the severity response score according to the patient's risk level calculated above.

[0150] At step S160, the processor can control the above-set severity response level information to be transmitted to at least one terminal and output.

[0151] Next, Fig. 5 may be an example of a processing method according to the severity response level information set above in Fig. 4.

[0152] Referring to FIG. 5, at step S210, the processor can determine whether the set response level is a high-risk response level.

[0153] At step S220, the processor can generate an activation signal if the response level set as a result of the judgment at step S210 is a high-risk response level.

[0154] At step S230, the processor may transmit the generated activation signal to the terminal. The activation signal may also include a control command that controls the output of information contained in the signal through the output interface of the terminal.

[0155] Meanwhile, as described above, the activation signal may be a signal indicating at least one of information indicating a rapid response, response processing attention, intensive care, and intensive management target for the target patient.

[0156] Figure 6 may be an example of a processing method related to providing response guide information.

[0157] The processor can control the severity response guide information mapped to the above-described severity response level information to be transmitted to at least one terminal and output.

[0158] On the other hand, at step S310, the processor can read candidate severity response guide information from a database linked to the server.

[0159] At step S320, the processor can determine optimal severity guide information for the target patient from among the retrieved candidate severity response guide information.

[0160] Figure 7 may be an example related to calculating severity response grade scores and setting response grades.

[0161] As described above, the processor calculates a severity response score according to the patient's risk based on each item-specific data mapped to each item. At this time, the severity response score can be calculated based on at least one of the MEWS score and NEWS score based on each item-specific data, or the sum thereof.

[0162] At step S410, the processor can determine whether the calculated severity response score corresponds to the boundary of each severity response grade.

[0163] At step S420, if the calculated severity response score falls within the boundary of the severity response grade as a result of the judgment at step S410, the processor can extract the history information of the severity response score and severity response grade of the target patient from the database.

[0164] In step S430, the processor can control to maintain or adjust the severity response grade based on the severity response score calculated according to the patient's risk, based on the extracted severity response score and the history information of the severity response grade of the target patient.

[0165] The present disclosure may also provide guidance in the form of a combination of two or more of the above-described embodiments.

[0166] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing computer-executable instructions. The instructions may be stored in the form of program code, and when executed by a processor, may generate program modules to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0167] Computer-readable storage media include all types of storage media that store instructions that can be deciphered by a computer. Examples include read-only memory (ROM), random access memory (RAM), magnetic tape, magnetic disks, flash memory, and optical data storage devices.

[0168] The disclosed embodiments have been described with reference to the attached drawings as described above. Those skilled in the art will understand that the present disclosure can be implemented in forms other than the disclosed embodiments without altering the technical spirit or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be construed as limiting.

Claims

1. In the method of automatically classifying severity and controlling response processing on the server, A step of obtaining vital information of a target patient; A step of extracting data for each item in the vital information of the target patient obtained above; A step of mapping the extracted data for each item to each item; A step of calculating a severity response score according to the patient's risk based on each item's data mapped to each of the above items; A step for setting a severity response grade based on the severity response score calculated above; and Including a step of controlling the above-set severity response level information to be transmitted to at least one terminal and output. Automatic severity classification and response processing control method.

2. In claim 1, If the above set severity response level information is a high-risk response level, the step of controlling an activation signal to be transmitted to at least one terminal and output is further included. The above activation signal is a signal indicating at least one of information indicating a rapid response, response processing attention, intensive care, and intensive management target for the target patient. Automatic severity classification and response processing control method.

3. In claim 2, Further comprising a step of controlling the response guide information mapped to the above-described set severity response grade information to be transmitted to at least one terminal so as to be output. Automatic severity classification and response processing control method.

4. In claim 3, The step of controlling the above response guide information to be transmitted to at least one terminal and outputted is: Further comprising a step of reading out the candidate severity response guide information from a database linked to the above server. Automatic severity classification and response processing control method.

5. In claim 4, The step of controlling the above response guide information to be transmitted to at least one terminal and outputted is: A method further comprising: determining optimal severity guide information for the target patient among the candidate severity response guide information extracted above; Automatic severity classification and response processing control method.

6. In claim 1, The above severity response scores are: The score is calculated by adding the MEWS score and NEWS score based on the data for each item above. Automatic severity classification and response processing control method.

7. In claim 6, The step of setting the severity response grade based on the severity response score calculated above is: If the calculated severity response score corresponds to the boundary of each severity response grade, a step of extracting the severity response score and severity response grade history information of the target patient from the database; and Further comprising a step of controlling to maintain or adjust the severity response grade based on the severity response score and severity response grade history information of the extracted target patient. Automatic severity classification and response processing control method.

8. In the automatic severity classification and response processing control device, database; A communication module for communicating with at least one first terminal and a second terminal belonging to the network; and Obtain vital information of the target patient through the above communication module, Extract data for each item in the vital information of the target patient obtained above, By mapping the data for each item extracted above to each item, a severity response score according to the patient's risk is calculated based on the data for each item mapped to each item. Set the severity response grade based on the severity response score calculated above, A processor including a processor that controls the transmission and output of the set response severity level information to at least one terminal of the network. Automatic severity classification and response processing control device.

9. In claim 8, The above processor, If the above set severity response level information is a high-risk response level, control is provided to transmit and output an activation signal to at least one terminal. The above activation signal is a signal indicating at least one of information indicating a rapid response, response processing attention, intensive care, and intensive management target for the target patient. Automatic severity classification and response processing control device.

10. In claim 9, The above processor, Controlling the severity response guide information mapped to the above-mentioned severity response level information to be transmitted to at least one terminal and outputted. Automatic severity classification and response processing control device.

11. In claim 10, The above processor, Retrieving the candidate severity response guide information from the above database, Automatic severity classification and response processing control device.

12. In claim 11, The above processor, Among the candidate severity response guide information read out above, determining the optimal severity guide information for the target patient. Automatic severity classification and response processing control device.

13. In claim 8, The above processor, The severity response score is calculated by adding the MEWS score and NEWS score based on the data for each item above. Automatic severity classification and response processing control device.

14. In claim 16, The above processor, If the severity response score calculated above corresponds to the boundary of each severity response grade above, the history information of the severity response score and severity response grade of the target patient is extracted from the database, Controlling to maintain or adjust the severity response grade based on the severity response score and severity response grade history information of the extracted target patient, Automatic severity classification and response processing control device.

15. In the automatic severity classification and response processing control system, A first terminal for obtaining vital information of a target patient; at least one second terminal; and Including computing devices, The above computing device, Obtain vital information of the target patient from the first terminal, Extract data for each item in the vital information of the target patient obtained above, By mapping the data for each item extracted above to each item, a severity response score according to the patient's risk is calculated based on the data for each item mapped to each item. Set the severity response grade based on the severity response score according to the risk of the patient calculated above, A computing device that controls the transmission of the set response severity level information to at least one of the first terminal and the second terminal, and a processor that controls the transmission to at least one terminal of the network, Automatic severity classification and response processing control system.

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