KR102284347B1 - System and method of creating medical emergency environments for emergency patients with cross-ckeck - Google Patents

Abstract

The present invention relates to a system and a method for creating an emergency environment through cross-checking of an emergency patient, which enable medical staffs and devices to double check information, collected at the scene of incident and during transport, in a secure environment to determine diagnostic information and create a highly reliable emergency environment in advance. The system comprises: a diagnostic result prediction unit including a patient condition confirmation module for classifying externally received patient condition information according to setting criteria and extracting key points therefrom, and a schedule setting module for transmitting the patient condition information and the key points to a medical terminal and transmitting predictive diagnosis information, corresponding to opinion information received from the medical terminal, to a medical schedule management unit; and the medical terminal including a collection information execution module for outputting the patient condition information and the key points as images, and a diagnostic information confirmation module for generating opinion information corresponding to an input value and transmitting the same to the diagnostic result prediction unit. Therefore, the chances of recovery of a patient can be increased.

Description

SYSTEM AND METHOD OF CREATING MEDICAL EMERGENCY ENVIRONMENTS FOR EMERGENCY PATIENTS WITH CROSS-CKECK}

The present invention provides an emergency environment creation system through a cross check of an emergency transfer patient that determines diagnostic information by double-checking the information collected at the scene of an incident and during transport by a medical team and a device in a secure environment, and creates a highly reliable emergency environment in advance; It relates to the composition method.

In general, emergency medical services are largely carried out in the order of emergency report, ambulance dispatch, patient status check, hospital designation, and visitation. In this process, the patient's condition is checked by the paramedics and delivered orally to the designated hospital, and the hospital's emergency medical staff prepares emergency treatment based on the patient's condition confirmed by the paramedics.

However, the patient's condition confirmed by the emergency patient and delivered to the hospital medical staff is only the simple physical condition and appearance of the emergency patient, such as body temperature, respiratory status, and the patient's behavior. It was not possible to obtain objective information to determine the patient's condition in advance. In the end, when an emergency patient visits the hospital, the medical staff prepares for an emergency response, such as reconfirming the emergency patient's biological condition, directly determining the patient's condition, calling a specialist, or preparing an examination.

However, in the conventional emergency medical service, the patient's condition is identified only after transfer to the hospital and emergency response is prepared and proceeded.

Therefore, an emergency medical service technology that enables rapid emergency response by identifying the condition of an emergency patient before visiting, creating a response environment for emergency treatment in advance, and starting direct treatment or examination immediately after visiting an emergency patient was required.

Prior art document 1. Patent Publication No. 10-2019-0138112 (published on December 12, 2019)

Accordingly, the present invention is to solve the above problem, and the patient status confirmed by the medical staff is delivered to the designated hospital before the visit so that the emergency patient can receive medical treatment such as examination and treatment immediately after visiting the hospital, and appropriate medical care according to the information It is a task to solve the provision of emergency environment creation system and creation method through cross check of emergency transfer patients who can suggest actions.

In order to achieve the above object, the present invention

A patient status confirmation module that classifies externally received patient status information according to a set criterion and extracts key points, and transmits the patient status information and key points to a medical terminal, and provides predictive diagnostic information corresponding to the observation information received from the medical terminal. a diagnosis result prediction unit configured with a schedule setting module to be transmitted to the schedule management unit;

a medical terminal configured with a collection information execution module for outputting the patient status information and key points as an image, and a diagnostic information confirmation module for generating observation information corresponding to an input value and transmitting it to a diagnosis result prediction unit;

It is an emergency environment creation system according to real-time information collection of emergency patient status, including

According to the present invention, the diagnosis result prediction unit reliably predicts the disease of the emergency patient based on the patient status information collected while the paramedics transport the emergency patient to the designated hospital, and presents and designates the optimal diagnostic information confirmed to the medical staff. The hospital's medical schedule management department creates a medical environment in advance, such as waiting for specialist medical staff, making reservations for examinations, and preparing wards by referring to the diagnosis information, so that emergency patients can perform medical treatment immediately upon their visit. It has the effect of increasing the chances of recovery.

1 is a diagram schematically showing an embodiment of an emergency environment creation system according to the present invention;
2 is a block diagram showing an embodiment of an emergency environment creation system according to the present invention;
3 is a block diagram showing another embodiment of an emergency environment creation system according to the present invention;
4 is a diagram schematically illustrating an emergency environment creation system according to the present invention in which diagnostic information is output to a terminal of a medical staff;
5 is a flowchart illustrating an embodiment of a method for creating an emergency environment according to the present invention;
6 is a block diagram illustrating a medical schedule management unit of an emergency environment creation system according to the present invention;
7 is a block diagram showing another embodiment of an emergency environment creation system according to the present invention;
8 is a diagram schematically illustrating an embodiment in which a query sentence is displayed through the terminal of the emergency information collection unit.

The features and effects of the present invention described above will become apparent through the following detailed description in relation to the accompanying drawings, and accordingly, those of ordinary skill in the art to which the present invention pertains can easily implement the technical idea of the present invention. There will be. Since the present invention can have various changes and can have various forms, specific embodiments are illustrated in the drawings and described in detail in the text. However, this is not intended to limit the present invention to a specific disclosed form, it should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the present invention. The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram schematically illustrating an embodiment of an emergency environment creation system according to the present invention, and FIG. 2 is a block diagram illustrating an embodiment of an emergency environment creation system according to the present invention.

1 and 2, the emergency environment creation system according to the present invention includes a patient status confirmation module 130 for classifying externally received patient status information according to a setting criterion and extracting key points, and the patient status information and A diagnosis result prediction unit 100 configured with a schedule setting module 160 that transmits a key point to the medical terminal 500 and transmits predictive diagnosis information corresponding to the observation information received from the medical terminal 500 to the medical schedule management unit 400 . ); A medical terminal comprising a collection information execution module 520 that outputs the patient status information and key points as an image, and a diagnostic information confirmation module 530 that generates observation information corresponding to an input value and transmits it to the diagnosis result prediction unit 100 . (500);

The patient status check module 130 classifies the patient status information according to the setting criteria and extracts key points. The patient status information is collected by the paramedic's inquiry and judgment about the emergency patient, the emergency patient's biological status through the medical device 211, etc., and the body shape and behavior of the emergency patient through the imaging of the camera 231 Therefore, it is possible to classify the collection channel of the patient's condition information (e.g., paramedic's oral, medical device, camera, etc.) as a setting standard. In addition, the setting criteria may be exemplified by various conditions such as the age, gender, and characteristics of the emergency scene of the emergency patient.

Subsequently, the patient status confirmation module 130 extracts major key points by analyzing the patient status information. The key point is the main information that can determine the diagnostic information. In the image information collected by the emergency information collection unit 200, a characteristic section different from the standard image, a word belonging to a designated keyword in the text of the voice information, a measurement value above the standard value in the measurement information of the medical device 211, etc. will be exemplified as key points. can

The schedule setting module 160 transmits the patient status information and key points to the medical terminal 500 , and transmits predictive diagnostic information corresponding to the observation information received from the medical terminal 500 to the medical schedule management unit 400 . Since the confirmation state confirmation module 130 extracts key points for diagnosing an emergency patient's disease by analyzing the patient state information, the medical staff can reveal the findings through only the key points even without direct contact with the emergency patient. By applying this, the schedule setting module 160 transmits the patient status information and key points to the medical terminal 500 so that the medical staff can directly check them. In addition, the medical staff who has confirmed the medical terminal 500 inputs and transmits patient status information and findings corresponding to key points, and the schedule setting module 160 provides predictive diagnosis information corresponding to the observation information received from the medical terminal 500 . It is transmitted to the medical schedule management unit (400). The observation information may be the name of the disease of the emergency patient, and may be a department or examination equipment required for treatment.

The medical terminal 500 is designated to be automatically connected to the diagnosis result prediction unit 100 and the emergency information collection unit 200 in an emergency, and the collection information execution module 520 receives the information received from the diagnosis result prediction unit 100. The patient status information and key points are displayed as images. The patient status information of the emergency information collection unit 200 is an image, text, voice, etc., and the collection information execution module 520 of the medical terminal 500 outputs the data in real time through a monitor or a speaker to be checked by the medical staff. make it possible In addition, the key point extracted from the diagnosis result prediction unit 100 is information determined as main information for diagnosis, and the collection information execution module 520 outputs the key point alone or the patient status information and the key point.

The diagnosis information confirmation module 530 generates observation information corresponding to the input value and transmits it to the diagnosis result prediction unit 100 . The medical staff who check the medical terminal 500 check the patient status information and key points output on the monitor 501 of the medical terminal 500 (refer to FIG. 4), You can enter a name, etc. directly or select it. This input value is transmitted to the diagnosis result prediction unit 100 by the communication unit 510 .

The emergency information collection unit 200 includes a measurement information check module 210 that converts the measurement value of the medical device 211 into measurement information, and a voice information check that recognizes the collection value of the microphone 221 and converts it into voice information module 220 and at least one of an image information check module 230 that analyzes the image image of the camera 231 and converts it into image information, and transmits one or more of the measurement information, audio information, and image information to an emergency patient and a data processing module 240 that processes to be transmitted to the diagnosis result prediction unit 100 by setting the individual patient status information. In addition, the emergency information collection unit 200 connects to the navigation device N mounted on the ambulance 200a to receive the distance from the designated hospital or the expected arrival time, and the received location or time information is linked to the patient status information. Thus, it further includes a navigation communication module 270 that transmits the diagnosis result prediction unit 100 . For reference, the input/output unit 150 of the diagnosis result prediction unit 100 outputs position or time information and uses it as a time limit for generating prediction diagnosis information.

Since the emergency information collection unit 200 is a means for collecting various patient status information from emergency patients, it may be installed in the ambulance 200a or configured in the terminal 200b carried by the emergency responder. The measurement information check module 210 is connected to various medical devices 211 such as a heart rate monitor or ventilator provided in the ambulance 200a, etc. to receive the measurement values measured by the medical device 211, and a diagnosis result prediction unit ( 100) and converted into measurement information in the data format processed in

The voice information check module 220 receives the collected value, which is the voice signal of the microphone 221 carried by the paramedic, and converts it into voice information in a data format processed by the diagnosis result prediction unit 100 . In addition, the voice information check module 220 generates text information by recording the voice signal as text.

The image information check module 230 receives the captured image image of the camera 231 carried by the ambulance 200a or the paramedic and converts it into image information in a data format processed by the diagnosis result prediction unit 100 . The camera 231 automatically adjusts the lighting according to the illuminance of the scene to take pictures. For reference, the photographed image includes images such as the patient's pupils and skin color.

The data processing module 240 sets the measurement information, audio information, image information, and text information as patient status information for each emergency patient and processes it to be transmitted to the diagnosis result prediction unit 100 . Since the change of symptoms by time is very important for emergency response to emergency patients, the measurement information, audio information, and image information about the emergency patient are merged by time unit and converted into data format patient status information, and the diagnosis result prediction unit 100 is transmitted in real time in a streaming method through the communication unit 260 . However, the communication method of the data processing module 240 is not limited to the streaming method, and is performed through various methods such as digital communication. For reference, the data processing module 240 secures and transmits patient status information with various security solutions such as a block chain to protect personal information.

The emergency information collection unit 200 operates in conjunction with one or more selected collection devices such as the medical device 211, the microphone 221, and the camera 231, but various collection devices may be additionally configured.

3 is a block diagram illustrating another embodiment of an emergency environment creation system according to the present invention, and FIG. 4 is a diagram schematically illustrating a state in which the emergency environment creation system according to the present invention outputs diagnostic information to a terminal of a medical staff am.

1 and 3 to 4 , the emergency environment creation system according to the present invention includes a data storage unit 110 that stores patient status information linked to diagnostic information, and a key point in the data storage unit 110 . The corresponding primary diagnosis information is searched, and the secondary diagnosis information is selected based on the probability value of each primary diagnosis information according to the selection condition and transmitted to the medical terminal 500. In response to the fertilization of the selected secondary diagnosis information, Updating the data storage unit further includes a data learning module (140). In addition, the collection information execution module 520 outputs the secondary diagnostic information as an image. In addition, the schedule setting module 160 configures the secondary diagnosis information approved in the observation information as predictive diagnosis information.

The diagnosis result prediction unit 100 may be installed in a designated hospital and may manage the emergency schedule of the designated hospital corresponding to the patient status information received from the emergency information collection unit 200, and may be installed in the emergency situation management center (M), etc. You can also suggest an emergency schedule for a designated hospital.

The patient state information stored in the data storage unit 110 includes not only the body temperature, pulse, blood pressure, respiration, complexion, etc., but also the patient's behavioral state, voice, breath sound, and tone. The behavior is confirmed by paying attention to the main parts of the patient's hands, feet, neck, head, and the like. In addition, the patient status information may include information on various chemical components scattered in the air. The chemical composition information is collected through a known olfactory sensor (not shown). Here, the patient status information is linked to general diagnostic information mainly diagnosed by medical staff according to the patient status. For example, cough in the patient state is stored in the data storage unit 110 by being linked to diagnostic information such as a cold, bronchitis, and lung disease. Accordingly, various diagnostic information may be overlapped and linked to the patient state stored in the data storage unit 110 , and the same diagnostic information may also be linked to various patient states. For reference, since the patient status information is collected by the paramedics at the emergency site and during transport of the emergency patient, it is confirmed by the paramedics in an ambulance, etc. In addition, the data storage unit 110 stores text information in which a voice signal is recorded. In the text information, diagnosis information or patient status information of a corresponding event is linked and stored in a word embedded method or the like. Therefore, in case of emergency, the same or similar text information can be retrieved from the data storage unit 110 through a recurrent neural network (RNN) technology by using the text information collected in the field as a keyword, and the linked text information and the linked text information can be retrieved. You can check diagnostic information or patient status information. Therefore, the on-site emergency responder recommends and supports the existing patient condition information or diagnostic information to the on-site emergency responder, thereby preventing the emergency responder's on-site mistakes.

The data learning module 140 searches for diagnostic information corresponding to a key point in the data storage unit 110, selects the corresponding diagnostic information based on a probability value for each diagnostic information according to the selection condition, and fertilizes the selected diagnostic information. In response, the data storage unit 110 is updated. As described above, a lot of diagnostic information is linked to the patient status information, and the diagnostic result prediction unit 100 needs to select optimal diagnostic information suitable for the current emergency patient from among a lot of diagnostic information. To this end, common diagnostic information linked with a plurality of key points extracted from the patient status information is searched. However, the emergency information collection unit 200 has a limit in collecting sufficient patient condition information for the diagnosis result prediction unit 100 to accurately diagnose an emergency patient, and collects enough and accurately key points for emergency patient diagnosis in the patient condition information. There are limits to what you can do. Therefore, the data learning module 140 collects selection conditions such as the emergency environment, age, medical history, etc., such as accidents and symptoms that occurred in the emergency patient, and collects the selection conditions by the emergency patient and the emergency responder, and based on the selection conditions It is detected by calculating the probability value of diagnostic information. As an example, the patient status check module 130 collects the emergency patient's 'head', 'bleed' and 'fracture' from image information and conversations, which are patient status information, and extracts them as key points, and data learning When 'road' is checked as a selection condition while the module 140 searches for a plurality of diagnostic information linked to the key point, the data learning module 140 links 'head', 'bleeding' and 'fracture' in common. Among the diagnostic information obtained, 'cerebral hemorrhage', a disease with the highest probability value, is selected as the diagnosis information of the emergency patient through selection conditions such as 'road'. As a result, the data learning module 140 can select diagnostic information with high reliability as the number of collections such as key points and selection conditions increases. For reference, the data learning module 140 of this embodiment cross-checks diagnostic algorithms of various techniques in parallel in order to prevent bias of diagnostic information, and diseases identified in the calculation process of the diagnostic algorithms are based on probability values according to selection conditions. Accordingly, a plurality of pieces may be selected as diagnostic information.

The data learning module 140 of this embodiment selects diagnostic information according to key points and selection conditions based on machine learning technology, and stores the probability values of diagnostic information corresponding to key points and selection conditions according to the fertilization of the selected diagnostic information. It is changed and updated in the unit 110 .

In addition, the data learning module 140 of this embodiment continuously links the diagnosis information of the corresponding event and the patient status information to the text information generated by the voice information check module 220 . The link of diagnostic information or patient status information to text information may be implemented in a word embedded method or the like.

For reference, the probability value is the probability of being diagnosed with the corresponding diagnostic information in the case of a specific key point and selection condition. The diagnostic information having the maximum probability value is confirmed as the final diagnostic information. However, the data learning module 140 does not present only the diagnostic information of the maximum probability value, and may be selected as the final diagnostic information in consideration of recently popular diseases and viruses.

The schedule setting module 160 transmits predictive diagnosis information corresponding to the selected diagnosis information to the medical schedule management unit 400 . The predictive diagnosis information is made by linking a medical action such as a corresponding treatment or treatment to the diagnosis information selected by the data learning module 140 . After all, the schedule setting module 160 transmits predictive diagnostic information including medical actions such as medical treatment or treatment to the medical schedule management unit 400, which is the medical management system of the designated hospital, so that the emergency patient at the designated hospital can promptly take emergency actions. make it happen A specific configuration of the medical schedule management unit 400 will be described below. For reference, the schedule setting module 160 transmits the predictive diagnosis information after secure processing with various security solutions such as a block chain to protect personal information.

The 'communication unit 120', which has not been described in the diagnosis result prediction unit 100, is an assembly for communicating with each of the emergency information collection unit 200 and the medical schedule management unit 400, and the 'input/output unit 150' is a diagnosis result These are a 'monitor' that outputs the execution status of the prediction unit 100 and a 'keyboard' operated by a user to control the operation of the diagnosis result prediction unit 100 .

On the other hand, the patient status confirmation module 130 of the diagnosis result prediction unit 100 extracts the body image of the emergency patient from the image information of the image information check module 230 received from the emergency information collection unit 200, and Analyze the image to extract a feature section and designate it as a key point. As described above, since the camera 231 is configured in the paramedic or ambulance 200a, the camera 231 captures the face or the whole body of the emergency patient in real time during emergency response. The patient status check module 130 analyzes the image information received from the emergency information collection unit 200 and extracts the body image of the emergency patient in a CNN (Convolution Neural Network) method. The body image may be a face image and a whole body image. Subsequently, the patient status check module 130 compares the body image with the standard image to check the image difference. That is, by comparing the standard image defined as a healthy body shape with the body image of the current emergency patient, the presence or absence of differences in skin color, wounds, bleeding, boils, etc. Physical deformation, such as fractures, can be checked only with images. The patient status check module 130 classifies the corresponding part of the body image showing the image difference from the standard image into a feature section and designates it as a key point.

In addition, the patient status confirmation module 130 of the diagnosis result prediction unit 100 recognizes the voice information of the voice information check module 230 received from the emergency information collection unit 200, converts it into text, and designates one of the texts. Extract keywords and designate them as keypoints. The emergency information collection unit 200 collects the voice of the emergency responder and the voice of the emergency patient and converts it into voice information, and the patient status check module 130 receives the voice signal frequency of the emergency responder from the voice information and the voice signal frequency of the emergency patient. classify by comparison. Therefore, the voice information converted into text is classified by first responders and emergency patients. Meanwhile, the designated keyword may be a noun expressing pain, type of accident, date, medical history, and physical condition in phrases uttered by emergency patients or paramedics. Therefore, if the emergency patient says, 'Yesterday I collided with a bicycle and hit my head on the ground', the patient status check module 130 converts the text into "yesterday", "bicycle", "collision", " Extracts "head", "ground", and "crash" as designated keywords. Here, all of the designated keywords may be designated as key points, but "collision", "head", "collision", etc. which are directly related to the body and emergency situations may be designated as key points.

As shown in FIG. 4 , the collection information execution module 520 of the medical terminal 500 displays an image 502 of an emergency patient, which is patient status information, and a guide 503 , which is secondary diagnostic information, through the monitor 501 . output In the end, the medical staff can check the appearance of the emergency patient as in the field through the scene output on the image image 502, and the condition of the emergency patient and the secondary diagnosis information confirmed by machine learning through the guide mat 503 can be checked As a result, the medical staff who has checked the image image 502 and the guide message 503 can input whether to agree to the secondary diagnosis information with a quick judgment, and the diagnosis information confirmation module 530 generates observation information according to the input value. Thus, the diagnosis result is transmitted to the prediction unit 100 . The observation information is consent to the secondary diagnosis information, and if the observation information is consent, the data learning module 140 confirms the secondary diagnosis information, and in the case of disagreement, the data learning module 140 determines the secondary diagnosis Except for the information, other diagnostic information is extracted.

Thereafter, when the secondary diagnosis information is confirmed by the observation information through the designated process of the data learning module 140 , the schedule setting module 160 configures the secondary diagnosis information approved in the observation information as predictive diagnosis information.

The 'communication unit 260', which is not described in the emergency information collection unit 200, is an assembly for communicating with the diagnosis result prediction unit 100, and the 'input/output unit 250' is the execution status of the emergency information collection unit 200 It is a 'monitor' that outputs , and a 'keyboard' operated by the user to control the operation of the emergency information collection unit 200 .

5 is a flowchart illustrating an embodiment of a method for creating an emergency environment according to the present invention, and FIG. 6 is a block diagram illustrating a medical schedule management unit of the emergency environment creation system according to the present invention.

1 to 6, the emergency environment creation method based on the emergency environment creation system according to the present invention proceeds as follows.

S10; Emergency information collection stage

The emergency responder takes care of the emergency patient and collects patient status information using the emergency information collection unit 200 while being transported to a designated hospital.

In general, an emergency responder checks and collects the current state of an emergency patient through a medical device 211 , a microphone 221 , a camera 231 , and the like. As a result, the measurement value of the medical device 211 is converted into measurement information by the measurement information check module 210 , and the collected value of the microphone 221 is converted into voice information by the voice information check module 220 , and the camera The captured video image of 231 is converted into video information by the video information check module 230 .

S20; Patient status information transmission step

The data processing module 240 sets the measurement information, audio information, and image information as patient status information for each emergency patient, and transmits it to the diagnosis result prediction unit 100 through the communication unit 260 . As described above, measurement information, audio information, and image information are merged in units of time and converted into a data format, and the converted patient status information includes identification information of an emergency patient.

The patient status information is transmitted to the diagnosis result prediction unit 100 in real time, and the diagnosis result prediction unit 100 automatically checks the received patient status information in real time to prepare for a subsequent procedure.

S30; Patient status check step

The patient condition confirmation module 130 of the diagnosis result prediction unit 100 classifies the patient condition information received from the emergency information collection unit 200 according to a setting criterion and extracts key points.

Since the patient status information includes measurement information, voice information, and image information collected by the emergency information collection unit 200, the patient status confirmation module 130 integrates the transmitted measurement information and voice information into the patient status information. Classify video information. At this time, the setting criterion is a collection channel, that is, a type of terminal that collects information such as the medical device 211 , the microphone 221 , and the camera 231 .

However, the setting criterion is not limited to the type of terminal, and may be a place where an emergency patient occurred, age, gender, etc., and patient status information may be classified according to the setting criterion.

S40; Keypoint Extraction Steps

Subsequently, the patient status confirmation module 130 extracts key points by analyzing the measurement information, audio information, and image information classified from the patient status information. Since the key point is a major criterion for diagnosis, it is extracted according to a set algorithm.

There is a limit to the patient status information that paramedics can collect while transporting emergency patients. In other words, it is difficult to make an accurate diagnosis only with the patient status information collected by the paramedics. Furthermore, since a plurality of pieces of diagnostic information are linked to the patient status information in the data storage unit 110 , when an emergency responder collects only one patient status in an emergency environment, a large number of diagnostic information linked to the corresponding patient status information is provided to the medical staff. As a result, the medical staff cannot determine any diagnostic information for an emergency patient from one patient condition and numerous diagnostic information.

Accordingly, the diagnosis result prediction unit 100 collects as many types of patient status information as possible and extracts diagnostic information commonly linked to the corresponding patient status information. However, even in this case, since the patient status information collected by the emergency information collection unit 200 is practically limited, the number of diagnostic information commonly linked to the plurality of patient status information collected by the emergency information collection unit 200 is bound to be large. there was no Of course, the medical staff could not predict reliable diagnostic information only with the patient status information collected by the emergency information collection unit 200 and the diagnostic information stored in the data storage unit 110 .

The data learning module 140 according to the present invention extracts key points by analyzing measurement information, voice information, and image information configured in the patient status information, and searches the data storage unit 110 for diagnostic information corresponding to the key points.

Since the extraction of the key point has been described above, a detailed description thereof will be omitted.

S50; Diagnostic information selection stage

The data learning module 140 searches for diagnostic information corresponding to a key point in the data storage unit 110 and selects the corresponding diagnostic information based on a probability value for each diagnostic information according to the selection condition.

Since the key points extracted from the measurement information, voice information, and image information are information that allows to understand the specific biological state of the emergency patient as well as the cause of the emergency and the type of emergency treatment, the data learning module 140 is based on the information. By accurately grasping the patient's condition, it predicts the disease that has occurred in an emergency patient and determines the corresponding diagnostic information. Since the key point includes a probability value for each related disease, the disease of the emergency patient is predicted based on the probability value.

Since the method of predicting disease and determining diagnostic information based on the probability value has been described above, a detailed description thereof will be omitted.

S60; Schedule setting steps

The schedule setting module 160 transmits predictive diagnosis information corresponding to the selected diagnosis information to the medical schedule management unit 400 . The predictive diagnosis information is made by linking a medical action such as a corresponding treatment or treatment to the diagnosis information selected by the data learning module 140 . Accordingly, when the predictive diagnosis information is transmitted to the medical schedule management unit 400 , the medical department and examination equipment corresponding to the corresponding diagnosis information are automatically presented in the schedule.

S70; Medical Appointment Steps

The medical schedule management unit 400 built as a system in a designated hospital stores the call information of the medical staff for each department, and the medical staff schedule module 410 that checks and calls the call information corresponding to the call signal, and stores the schedule information for each medical device and at least one of a medical device schedule module 420 for updating schedule information for each medical device according to a reservation signal, and a ward schedule module 430 for storing schedule information for each ward and updating schedule information for each ward according to a hospitalization signal do. In addition, the medical schedule management unit 400 includes a management module 440 that confirms the predictive diagnosis information of the schedule setting module 160 and transmits at least one of a corresponding call signal, a reservation signal, and a hospitalization signal.

Accordingly, one or more of a corresponding call signal, a reservation signal, and a hospitalization signal are transmitted according to the diagnostic information included in the predictive diagnostic information, and the medical staff schedule module 410 that has received the call signal from the management module 440 responds to the call signal. It automatically calls the medical staff of the designated hospital by sending the corresponding call information. In addition, the medical device schedule module 420 that has received the reservation signal from the management module 440 transmits schedule information for each medical device corresponding to the reservation signal to update the reservation schedule of the designated hospital medical device. In addition, the hospital room schedule module 430 that has received the hospitalization signal from the management module 440 updates the reservation schedule of the designated hospital by transmitting schedule information for each room corresponding to the hospitalization signal.

Eventually, the patient status information collected in the emergency patient transport process is analyzed by the diagnosis result prediction unit 100 to generate diagnosis information, and the predictive diagnosis information including the diagnosis information is transmitted to the medical schedule management unit 400 of a designated hospital. Therefore, emergency patients can receive immediate examination and treatment along with specialist treatment upon arrival at the designated hospital.

7 is a block diagram illustrating another embodiment of an emergency environment creation system according to the present invention, and FIG. 8 is a diagram schematically illustrating an embodiment in which a query is displayed through the terminal of the emergency information collection unit.

1, 7, and 8, in the emergency environment creation system according to the present invention, the data storage unit 110 stores the query process information for each diagnosis information; The data learning module 140 retrieves the query process information for each primary diagnosis information from the data storage unit 110 ; Transmits the query items included in the query process information received from the data learning module 140 as query data to the emergency information collection unit 200 , and responds to the questions in the patient status information received from the emergency information collection unit 200 . It further includes a query selection module 170 that checks whether the information is one piece of information and filters the corresponding query process information to continue a subsequent query.

In the previous embodiment, the emergency information collection unit 200 queries the emergency patient based on the knowledge and experience of the emergency responder, manipulates the medical device 211, or uses the camera 231 to photograph a specific part of the emergency patient. Gathered status information. However, the patient status information collected in this way is different from the treatment method of a specialist, and therefore there is a limit in determining an accurate diagnosis based on the patient status of an emergency patient.

The emergency environment creation system according to the present embodiment stores query process information for each diagnosis information in the data storage unit 110 . To describe the query process information in more detail, a specific disease causes a special change in the body of an emergency patient. Therefore, it is desirable to track the changes and symptoms of the body from the emergency patient to determine whether the disease is in question. The query process information includes query content for checking physical changes and symptoms according to a specific disease.

Meanwhile, the data learning module 140 searches the data storage unit 110 for query process information for each primary diagnosis information, and the query selection module 170 searches the query process information for the data learning module 140 . is transmitted as query data to the emergency information collection unit 200 so that the corresponding body changes and symptoms are confirmed. In this embodiment, the query data is transmitted to the terminal 200b carried by the paramedic, and the query content of the query data is output as a message as shown in FIG. 8 . The emergency responder answers the query of the query selection module 170 exemplified as a treatment assistant by measuring or visually confirming the condition of the emergency patient or talking to him/her in order to confirm the contents of the query. As a result of the confirmation, if the symptom for which confirmation is requested in the inquiry process information to the emergency patient is not confirmed, the information is transmitted to the diagnosis result prediction unit 100, and the inquiry selection module 170 filters the inquiry process information. That is, if a symptom is not confirmed in an emergency patient among a plurality of query process information, the query selection module 170 excludes the query process information of the diagnostic information about the disease that should have the corresponding symptom from the query target, and the query process information of other diseases The query content is detected and transmitted to the emergency information collection unit 200 .

Using the collected patient status information, the patient status check module 130 generates a key point, and the data learning module 140 finally selects secondary diagnostic information.

For reference, the answer of the paramedic to the query may be a direct text input of the paramedic through the terminal 200b as in the present embodiment. ) and a collection device such as the camera 231 may be used to answer.

In the detailed description of the present invention described above, although it has been described with reference to preferred embodiments of the present invention, those skilled in the art or those having ordinary knowledge in the art will have the spirit of the present invention described in the claims to be described later. And it will be understood that the present invention can be variously modified and changed without departing from the technical scope.

100; diagnosis result prediction unit 200a; ambulance 200b; terminal
300; Diagnosis result

Claims (5)

A patient status confirmation module that classifies externally received patient status information according to a set criterion and extracts key points, and transmits the patient status information and key points to a medical terminal, and provides predictive diagnostic information corresponding to the observation information received from the medical terminal. a diagnosis result prediction unit configured with a schedule setting module to be transmitted to the schedule management unit; A medical terminal configured with a collection information execution module for outputting the patient status information and key points as an image, and a diagnostic information confirmation module for generating observation information corresponding to an input value and transmitting it to a diagnosis result prediction unit; in,
The diagnosis result prediction unit includes: a data storage unit for storing patient state information linked to the diagnosis information; a data learning module that selects the corresponding secondary diagnosis information and transmits it to the medical terminal, and updates the data storage unit in response to the application of the selected secondary diagnosis information;
the collection information execution module outputs secondary diagnostic information as an image;
The schedule setting module configures the secondary diagnosis information approved in the observation information as predictive diagnosis information; an emergency environment creation system according to real-time information collection of emergency patient status. delete The method of claim 1,
A measurement information check module that converts a measurement value of a medical device into measurement information, an audio information check module that recognizes the collected value of a microphone and converts it into audio information, and a video information that analyzes the captured video image of the camera and converts it into video information Emergency information collection unit including at least one of the check modules, and including a data processing module that sets at least one of the measurement information, audio information, and image information as patient status information for each emergency patient to be transmitted to the diagnosis result prediction unit Emergency environment creation system according to real-time information collection of emergency patient status, characterized in that it further comprises. According to claim 1, wherein the medical schedule management unit,
A medical device schedule module that stores call information of medical staff by department and checks and calls information in response to a call signal, a medical device schedule module that stores schedule information for each medical device and updates schedule information for each medical device according to a reservation signal; storing schedule information for each ward and including at least one of a ward schedule module for updating schedule information for each ward according to a hospitalization signal;
Emergency environment creation system according to real-time information collection of emergency patient status, characterized in that it includes; a management module that confirms the predictive diagnosis information of the schedule setting module and transmits at least one of a call signal, a reservation signal, and a hospitalization signal. . 4. The method of claim 3,
the data storage unit stores query process information for each diagnostic information;
the data learning module searches the data storage unit for query process information for each primary diagnosis information;
Transmits the query items included in the query process information received from the data learning module as query data to the emergency information collection unit, and checks whether the information corresponding to the query items in the patient status information received from the emergency information collection unit is Further comprising a query selection module for filtering the query process information to continue the subsequent query;
Emergency environment creation system according to real-time information collection of emergency patient status, characterized in that.

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