KR20230115720A - Remote Patient Management Systems and Methods - Google Patents

Abstract

According to the embodiment, the patient's pre-management reservation information registered online is input to the first learning model unit to output the first medical examination result, and according to the first medical examination result, whether the patient's general medical treatment or remote medical treatment is determined a cooperative medical institution server that transmits reservation confirmation information to a terminal of a user who has registered the patient information; and after-care reservation information of the patient registered online is input into the second learning model unit to output the second medical examination result, and after remotely monitoring the patient's abnormal symptoms according to the second medical examination result and the previous patient's treatment history It provides a remote patient management system including a higher level medical institution server that determines whether or not the patient visits a medical institution.

Description

Remote Patient Management Systems and Methods {Remote Patient Management Systems and Methods}

One embodiment of the present invention relates to a remote patient management system and method.

Social consensus on the necessity of non-face-to-face treatment is being formed due to the periodic occurrence of infectious diseases imported from abroad, and the introduction of a full-fledged business model for telemedicine is accelerating, such as allowing remote medical treatment in areas other than medically vulnerable areas on a limited basis.

The telemedicine system, which had been limitedly introduced in the existing mountainous regions, military bases, and correctional facilities, had great spatial limitations in construction and also had quality problems.

However, with the development of communication technology and mobile devices, it has become possible to transmit/receive various high-definition/high-quality medical images without installing extra bulky equipment. In addition, there are an increasing number of cases in which mobile devices are used as a means of managing chronic diseases, and a platform that integrates and manages non-face-to-face consulting between patients and medical personnel and medical personnel by linking them with a remote medical treatment system is needed.

A technical problem to be achieved by the present invention is to provide a remote patient management system and method capable of on-line cooperation between a medical facility lacking medical equipment and a superior medical institution.

In addition, to provide a remote patient management system and method capable of identifying symptoms before visiting a medical institution, reserving a medical institution visit schedule, and performing anomaly management through an application after visiting.

In addition, to provide a remote patient management system and method capable of performing accurate and rapid non-face-to-face medical treatment between a medical staff and a patient through online high-resolution image transmission and medical measurement information transmission.

According to the embodiment, the patient's pre-management reservation information registered online is input to the first learning model unit to output the first medical examination result, and according to the first medical examination result, whether the patient's general medical treatment or remote medical treatment is determined a cooperative medical institution server that transmits reservation confirmation information to a terminal of a user who has registered the patient information; and after-care reservation information of the patient registered online is input into the second learning model unit to output the second medical examination result, and after remotely monitoring the patient's abnormal symptoms according to the second medical examination result and the previous patient's treatment history It provides a remote patient management system including a higher level medical institution server that determines whether or not the patient visits a medical institution.

The cooperative medical institution server and the higher-level medical institution server may perform remote consultation through remote imaging equipment and medical measurement equipment.

The higher-level medical institution server transmits a medical information request message to the cooperative medical institution server, analyzes the medical information of the patient received in response to the medical information request message, and remotely monitors abnormal symptoms of the patient. .

The cooperating medical institution server may transmit a measurement result of the medical measurement equipment to the higher level medical institution server using the remote imaging device.

According to an embodiment, the step of outputting a first questionnaire result by inputting advance management reservation information of a patient registered online by a cooperative medical institution server to a first learning model unit; transmitting, by the cooperative medical institution server, reservation confirmation information to a terminal of a user who has registered the patient information after determining whether the patient has general medical treatment or remote medical treatment according to a result of the first medical examination; outputting a result of a second questionnaire by inputting follow-up management reservation information of a patient registered online to a second learning model unit by a server of a higher level medical institution; and remotely monitoring, by the higher level medical institution server, abnormal symptoms of the patient according to the result of the second medical examination and the previous patient's treatment history, and then determining whether or not the patient visits the medical institution. .

The remote patient management system and method according to the embodiment may perform online consultation between a medical facility lacking medical equipment and a superior medical institution.

In addition, symptoms can be identified before visiting a medical institution, a medical institution visit schedule can be reserved, and abnormal symptoms can be managed through an application after the visit.

In addition, accurate and rapid non-face-to-face medical treatment can be performed between medical staff and patients through online transmission of high-resolution images and medical measurement information.

1 is a conceptual diagram of a remote patient management system according to an embodiment.
2 is a block diagram of a cooperative medical institution server according to an embodiment.
Fig. 3 is a block diagram of a high-level medical institution server according to an embodiment.
4 is a diagram for explaining the operation of a remote patient management system according to an embodiment.
5 is a flowchart of a remote patient management method according to an embodiment.

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

However, the technical idea of the present invention is not limited to some of the described embodiments, but may be implemented in a variety of different forms, and if it is within the scope of the technical idea of the present invention, one or more of the components among the embodiments can be selectively implemented. can be used by combining and substituting.

In addition, terms (including technical and scientific terms) used in the embodiments of the present invention, unless explicitly specifically defined and described, can be generally understood by those of ordinary skill in the art to which the present invention belongs. It can be interpreted as meaning, and commonly used terms, such as terms defined in a dictionary, can be interpreted in consideration of contextual meanings of related technologies.

Also, terms used in the embodiments of the present invention are for describing the embodiments and are not intended to limit the present invention.

In this specification, the singular form may also include the plural form unless otherwise specified in the phrase, and when described as "at least one (or more than one) of A and (and) B and C", A, B, and C are combined. may include one or more of all possible combinations.

Also, terms such as first, second, A, B, (a), and (b) may be used to describe components of an embodiment of the present invention.

These terms are only used to distinguish the component from other components, and the term is not limited to the nature, order, or order of the corresponding component.

In addition, when a component is described as being 'connected', 'coupled' or 'connected' to another component, the component is not only directly connected to, combined with, or connected to the other component, but also with the component. It may also include the case of being 'connected', 'combined', or 'connected' due to another component between the other components.

In addition, when it is described as being formed or disposed on the "top (above) or bottom (bottom)" of each component, the top (top) or bottom (bottom) is not only a case where two components are in direct contact with each other, but also one A case in which another component above is formed or disposed between two components is also included. In addition, when expressed as "up (up) or down (down)", it may include the meaning of not only an upward direction but also a downward direction based on one component.

Hereinafter, the embodiments will be described in detail with reference to the accompanying drawings, but the same or corresponding components regardless of reference numerals are given the same reference numerals, and overlapping descriptions thereof will be omitted.

Fig. 1 is a conceptual diagram of a remote patient management system according to an embodiment, Fig. 2 is a configuration block diagram of a cooperative medical institution server according to an embodiment, and Fig. 3 is a configuration block diagram of a superior medical institution server according to an embodiment.

1 to 3, the remote patient management system 1 according to the embodiment may include a cooperative medical institution server 10 and a superior medical institution server 20 located remotely from the cooperative medical institution server 10. can

The cooperative medical institution server 10 is a server deployed in a specialized medical institution in a remote area such as rural, mountainous, island areas, etc., where access to professional medical services is low. can

The cooperative medical institution server 10 includes a first input unit 11, a first learning model unit 12, a first communication unit 13, a first remote imaging device 14, a first medical measurement device 15, a first It may include one docking station 16 and a first database 17 .

The cooperating medical institution server 10 inputs the patient's pre-management reservation information registered online to the first learning model, outputs the first medical examination result, and determines whether the patient's general medical treatment or remote medical treatment is determined according to the first medical examination result. After the decision is made, reservation confirmation information may be transmitted to the terminal of the user who has registered the patient information.

In addition, the cooperative medical institution server 10 may perform remote collaboration with the higher level medical institution server 20 through the first remote imaging device 14 and the first medical measurement device 15 .

In addition, the cooperative medical institution server 10 may transmit the measurement result of the first medical measurement device 15 to the higher level medical institution server 20 using the first remote imaging device 14 .

In addition, the cooperative medical institution server 10 determines whether remote consultation is performed through the primary care of the visiting patient, transmits a remote consultation request to the higher level medical institution server 20, and after receiving the remote consultation approval message, the higher level medical institution server ( According to the request of 20), medical information of the patient may be transmitted through the first remote imaging device 14 and the first medical measurement device 15 .

The cooperative medical institution server 10 may have a first database 17 that stores electronic medical record information including medical data, and transmits medical result information received through the first communication unit 13 to the electronic medical record. Information can be integrated and managed.

In addition, the cooperative medical institution server 10 further includes, in the first database 17, the unique number for each medical device, the date of production or registration for each unique number of the medical device, and the number of tests performed on the medical device, so that the hospital manager can use the medical device assets. And it can be configured to facilitate maintenance.

The first database 17 may accumulate and store patient attribute information and medical measurement data and calculate statistical values. Medical measurement data is classified for each access port and each medical device and accumulated and stored for a certain period of time, and statistics may mean an average value or sum of power parameters for a certain period of time.

The cooperative medical institution server 10 may perform primary care through the first learning model unit 12 that has performed learning using advance management reservation information including patient attribute information. The cooperative medical institution server 10 may automatically select the patient's medical department and reservation schedule using the primary treatment result and transmit the result to the patient's terminal. In addition, the cooperative medical institution server 10 may output the first questionnaire result. The cooperative medical institution server 10 may determine whether the patient needs general medical treatment or medical treatment at a tertiary medical institution according to the result of the first medical examination, and transmit a request for remote consultation to the tertiary medical institution server 20 if necessary.

The first input unit 11 may receive patient attribute information. The patient's attribute information may include personal attributes, clinical attributes, examination attributes, and disease attributes. Personal attributes relate to the patient's personal information, and may include information such as gender, age, height, weight, department, country, smoking, drinking, coffee, exercise, and sleeping hours necessary for clinical decision-making. there is. Clinical attributes are information about tests performed for diagnosis or treatment of patients, and may include hematological information, serological information, biochemical information, physiological information, bacteriological information, urinary fecal information, image information, etc. . Examination properties may include numerical information about various examinations received by the patient. The disease attribute may include information about a disease possessed by a patient.

The first learning model unit 12 may learn a correlation between the patient's attribute information and treatment cases, determine similar treatment cases from the received patient's attribute information, and output the result as a first medical examination result.

The deep learning model may extract possible similar patient cases from input patient attribute information, and through this, a medical treatment result corresponding to the patient attribute information may be output as a first medical examination result.

That is, the deep learning model, when patient attribute information is input, compares attribute information weighted for each attribute with pre-learned patient case information and extracts similar cases. In this case, the pre-learned information may include patient information of other medical institutions that have an agreement in addition to patient information of a specific medical institution, and patient information may also be included in an integrated server operated by a plurality of medical institutions.

4 is a diagram for explaining the operation of a remote patient management system according to an embodiment.

Referring to FIG. 4 together, in the embodiment, the first learning model unit 12 is a technology capable of pre-screening a disease similar to the patient's symptoms before treatment, and the symptoms related to the information input by the patient step by step. By querying and predicting the learning model unit 12, it helps to search for and accept related hospitals. Artificial intelligence-based paperweight technology is an A.I. Before visiting the hospital with the symptom check engine, based on the user's symptoms, the user's medical records are searched for similar diseases and medical records of recommended individuals, and mobile and telecom devices deliver patient information, pre-questionnaire information, and symptom information in connection with the hospital's medical information system. It may be a bullet-based treatment support solution.

When a patient inputs information, the first learning model unit 12 derives symptoms related to artificial intelligence that has learned about 500,000 cases of medical knowledge data such as medical practical tests and about 13 million cases of insurance claim data for the last 3 years. It is recommended to the user, and similar disease information can be provided through semantic inference through a series of processes.

In addition, the first learning model unit 12 may derive result values by applying unsupervised learning (K-means++) that predicts possible diseases based on the patient's symptoms and additional medical examination results.

This artificial intelligence technology can be implemented by closely replicating a decision-making model that treats patients based on medical examinations, and can be designed to guide a person to find a suitable department by providing information about the department dealing with the disease.

The first learning model unit 12 may include preliminary questionnaire content for enhancing the AI questionnaire algorithm, and may include a dictionary questionnaire algorithm reinforced through learning of the symptom check engine.

For example, the first learning model unit 12 outputs the results of the urology pre-interview when the patient's attribute information is input through learning, and the first questionnaire includes physical symptoms, urinary related, and prostatic hyperplasia suspected information as detailed information. You can print the result.

Alternatively, the first learning model unit 12, through learning, outputs the result of the preliminary medical examination for respiratory medicine when the patient's attribute information is input, and the first questionnaire that includes physical symptoms, respiratory tract related information, and COVID-19 suspected information as detailed information. You can print the result.

Alternatively, the first learning model unit 12, through learning, outputs the result of the elderly depression scale preliminary questionnaire when the patient's attribute information is input, and the first learning model unit 12 includes psychological symptoms, depression related information, and Korean version of the elderly depression suspicion information as detailed information. Paperweight results can be output.

Alternatively, the first learning model unit 12 outputs the result of the alcoholism preliminary medical examination when the patient's attribute information is input through learning, and outputs the first medical examination result including psychological symptoms and Korean alcoholism suspicion information as detailed information. can do.

Alternatively, through learning, the first learning model unit 12 outputs a fatigue symptom scale preliminary questionnaire result when the patient's attribute information is input, and outputs a first questionnaire result including physical symptoms and fatigue symptom information as detailed information. can

Alternatively, the first learning model unit 12 outputs the result of the dementia scale preliminary questionnaire when the patient's attribute information is input through learning, and the first questionnaire result including psychological symptoms, depression, and elderly depression suspicion information as detailed information. can be printed out.

In addition, the first learning model unit 12 may provide a hospital recommendation and reservation function according to the AI pre-interview technology and the first questionnaire result. The first learning model unit 12 may include voice recognition-based pre-interview technology and self-diagnosis pre-interview technology, and may provide medical service through a chat service when a doctor's consultation on symptoms is required. there is.

In addition, the first learning model unit 12 may be designed to be interlocked with non-face-to-face image monitoring technology through the first remote image equipment 14 through preliminary questionnaire data collection.

The first learning model unit 12 may use hospital general information, pharmacy general information, hospital cost information, doctor information, medical encyclopedia (disease/drug/examination) information, and the like as learning data. In addition, it is possible to learn together with the preliminary questionnaire data composed through professional medical staff and medical institution workers.

The cooperative medical institution server 10 may transmit the measurement result of the first medical measurement device 15 to the higher level medical institution server 20 using the first remote imaging device 14 .

The cooperative medical institution server 10 according to the embodiment may operate in conjunction with the first remote imaging device 14 and the first medical measurement device 15 . The first remote imaging device 14 may be configured to be portable, and when attached to a first docking station disposed in a cooperative medical institution, the cooperative medical institution server 10 and the first medical measurement equipment 15 perform a plug-and-play operation. ) can be linked to.

The first docking station may include a plurality of ports corresponding to the first remote imaging device 14, the plurality of first medical measurement devices 15, and the hospital server. The plurality of ports include a parallel interface unit, a serial interface unit, a USB interface unit, a DICOM interface unit, an external memory interface unit, an analog video interface unit, and a wireless communication module unit, and each interface unit is configured to have at least one port. It can be.

The first remote imaging device 14 may photograph a patient, medical staff, or the first medical measurement device 15, generate image information including video and audio, and transmit the image information to the first communication unit 13 in real time.

The first remote imaging device 14 may include a display capable of displaying image data received from the server 20 of a higher level medical institution and various data. The display receives image data from the advanced medical institution server 20 and displays it in real time, so that the practitioner can conduct real-time consultation with a remote medical expert.

The first medical measuring device 15 is a medical device for measuring the operating state of various organs of the patient, such as, for example, an image medical device that photographs the patient's body and organs, an electrocardiogram measuring device, and a blood pressure measuring device, and a patient's medical device. It may include clinical devices that measure and analyze the levels of various elements of samples taken from the body or obtained from nature.

In addition, the first medical measurement device 15 may further include one of a blood tester, a medical scope, a stress monitor, a psychotherapy device, a blood pressure monitor, a body fat gauge, a blood sugar gauge, and a thermometer. In addition, the medical measurement equipment may mean an electronic device capable of measuring various bio-signals of a user, and may mean a wearable type device that is directly worn on a user's body, such as a built-in type of clothing or a belt.

The first medical measurement device 15 may measure a user's bio-signal using various devices and transmit medical measurement data generated accordingly to the first communication unit 13 .

The first communication unit 13 may perform wireless communication with the higher level medical institution server 20 . The first communication unit 13 is, for example, LTE (long-term evolution), LTE-A (LTE Advance), CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband), or GSM (global system for mobile communications) may be configured to include at least one communication module.

Also, the first communication unit 13 may receive a doctor's opinion and a prescription from the higher level medical institution server 20 . Physician's opinion and prescription may include a variety of official or unofficial content about medical measurement data, such as a medical professional's personal opinion or prescription opinion.

The advanced medical institution server 20 includes a second input unit 21, a second learning model unit 22, a second communication unit 23, a second remote imaging device 24, a second medical measurement device 25, a second It may include 2 docking stations 26 and a second database 27 .

The advanced medical institution server 20 inputs the patient's follow-up management reservation information registered online to the second learning model unit 22 and outputs the second medical examination result, and according to the second medical examination result and the previous patient's treatment history, After remotely monitoring the patient's abnormal symptoms, it is possible to decide whether or not the patient visits a medical institution.

In addition, the higher-level medical institution server 20 transmits a medical information request message to the cooperative medical institution server 10, analyzes the medical information of the patient received in response to the medical information request message, and remotely monitors the patient's abnormal symptoms. can do.

The advanced medical institution server 20 may output the result of the second medical examination through post-treatment of the visiting patient and continuously monitor abnormal symptoms of the patient.

The higher level medical institution server 20 may have a second database 27 that stores electronic medical record information including medical data, and transmits medical result information received through the second communication unit 23 to the electronic medical record. Information can be integrated and managed.

In addition, the higher level medical institution server 20 further includes, in the second database 27, the unique number for each medical device, the date of production or registration for each unique number of the medical device, the number of tests, medical devices, etc., so that the hospital manager can manage medical device assets. And it can be configured to facilitate maintenance.

The second database 27 may accumulate and store patient attribute information and medical measurement data and calculate statistical values. Medical measurement data is classified for each access port and each medical device and accumulated and stored for a certain period of time, and statistics may mean an average value or sum of power parameters for a certain period of time.

The advanced medical institution server 20 may perform secondary medical care through the second learning model unit 22 that has performed learning using follow-up management reservation information including patient attribute information. The advanced medical institution server 20 may automatically select the patient's medical department and reservation schedule using the secondary medical treatment result and transmit the result to the patient's terminal. In addition, the advanced medical institution server 20 may output the second medical examination result. The higher level medical institution server 20 may determine whether the patient needs treatment at a higher level medical institution according to the result of the second medical examination, and transmit a request for remote consultation to the cooperative medical institution server 10 if necessary.

The second input unit 21 may receive input of patient attribute information. The patient's attribute information may include personal attributes, clinical attributes, examination attributes, and disease attributes. Personal attributes relate to the patient's personal information, and may include information such as gender, age, height, weight, department, country, smoking, drinking, coffee, exercise, and sleeping hours necessary for clinical decision-making. there is. Clinical attributes are information about tests performed for diagnosis or treatment of patients, and may include hematological information, serological information, biochemical information, physiological information, bacteriological information, urinary fecal information, image information, etc. . Examination properties may include numerical information about various examinations received by the patient. The disease attribute may include information about a disease possessed by a patient.

The second learning model unit 22 may learn the correlation between the patient's attribute information and treatment cases, determine similar treatment cases from the received patient's attribute information, and output them as a second medical examination result.

The deep learning model may extract possible similar patient cases from input patient attribute information, and through this, a medical treatment result corresponding to the patient attribute information may be output as a second medical examination result.

That is, the deep learning model, when patient attribute information is input, compares attribute information weighted for each attribute with pre-learned patient case information and extracts similar cases. In this case, the pre-learned information may include patient information of other medical institutions that have an agreement in addition to patient information of a specific medical institution, and patient information may also be included in an integrated server operated by a plurality of medical institutions.

In the embodiment, the second learning model unit 22 is a technology capable of pre-screening a disease similar to the abnormal symptoms of a patient after treatment, and the symptoms related to the information input by the patient in stages are transferred to the second learning model unit 22. By querying and predicting, it predicts abnormal signs and diseases, and helps to search for and apply to related hospitals. Artificial intelligence-based paperweight technology is an A.I. Before visiting the hospital with the symptom check engine, based on the user's symptoms, the user's medical records are searched for similar diseases and medical records of recommended individuals, and mobile and telecom devices deliver patient information, pre-questionnaire information, and symptom information in connection with the hospital's medical information system. It may be a bullet-based treatment support solution.

When the patient inputs information, the second learning model unit 22 derives symptoms related to artificial intelligence that has learned about 500,000 cases of medical knowledge data such as medical practical tests and about 13 million cases of insurance claim data for the last 3 years. It is recommended to the user, and similar disease information can be provided through semantic inference through a series of processes.

In addition, the second learning model unit 22 may derive result values by applying unsupervised learning (K-means++) that predicts possible diseases based on the patient's symptoms and additional medical examination results.

This artificial intelligence technology can be implemented by closely replicating a decision-making model that treats patients based on medical examinations, and can be designed to guide a person to find a suitable department by providing information about the department dealing with the disease.

The second learning model unit 22 may include pre-interview contents for upgrading the AI questionnaire algorithm, and may include a post-document algorithm reinforced through learning of the symptom check engine.

For example, the second learning model unit 22 outputs the result of the urology pre-interview when the patient's attribute information is input through learning, and the second questionnaire includes physical symptoms, urinary related, and prostatic hypertrophy suspected information as detailed information. You can print the result.

Alternatively, the second learning model unit 22, through learning, outputs the result of the respiratory internal medicine post-interview when the patient's attribute information is input, and the second questionnaire includes physical symptoms, respiratory related information, and suspected COVID-19 as detailed information. You can print the result.

Alternatively, the second learning model unit 22, through learning, outputs the result of the elderly depression scale prior questionnaire when the patient's attribute information is input, and the second learning model unit 22 includes psychological symptoms, depression related information, and Korean version of the elderly depression suspicion information as detailed information. Paperweight results can be output.

Alternatively, through learning, the second learning model unit 22 outputs the result of the alcoholism pre-diagnosis when the patient's attribute information is input, and outputs the result of the second medical examination including psychological symptoms and Korean alcoholism suspicion information as detailed information. can do.

Alternatively, through learning, the second learning model unit 22 outputs a fatigue symptom scale preliminary questionnaire result when the patient's attribute information is input, and outputs a second questionnaire result including physical symptoms and fatigue symptom information as detailed information. can

Alternatively, the second learning model unit 22 outputs the result of the dementia scale preliminary questionnaire when the patient's attribute information is input through learning, and the second questionnaire result including psychological symptoms, depression, and elderly depression suspicion information as detailed information. can be printed out.

In addition, the second learning model unit 22 may provide a hospital recommendation and reservation function according to the AI pre-interview technology and the second questionnaire result. The second learning model unit 22 may include voice recognition-based pre-interview technology and self-diagnosis pre-interview technology, and may provide medical service through a chat service when a doctor's consultation on symptoms is required. there is.

In addition, the second learning model unit 22 may be designed to be interlocked with non-face-to-face image monitoring technology through the second remote image equipment 24 through pre-interview data collection.

The second learning model unit 22 may use hospital general information, pharmacy general information, hospital cost information, doctor information, medical encyclopedia (disease/drug/examination) information, and the like as learning data. In addition, it is possible to learn together with the preliminary questionnaire data composed through professional medical staff and medical institution workers.

The higher level medical institution server 20 may transmit the measurement result of the second medical measurement device 25 to the cooperative medical institution server 10 using the second remote imaging device 24 .

The advanced medical institution server 20 according to the embodiment may operate in conjunction with the second remote imaging device 24 and the second medical measurement device 25 . The second remote imaging device 24 can be configured to be portable, and when mounted on the second docking station 26 disposed in a higher level medical institution, the higher level medical institution server 20 and the second medical measurement can be performed through a plug-and-play operation. It can be interlocked with equipment 25 .

The second docking station 26 may include a plurality of ports corresponding to the second remote imaging device 24, a plurality of second medical measurement devices 25, and a hospital server. The plurality of ports include a parallel interface unit, a serial interface unit, a USB interface unit, a DICOM interface unit, an external memory interface unit, an analog video interface unit, and a wireless communication module unit, and each interface unit is configured to have at least one port. It can be.

The second remote imaging device 24 may photograph a patient, medical staff, or the second medical measurement device 25, generate image information including video and audio, and transmit the image information to the second communication unit 23 in real time.

The second remote imaging device 24 may include a display capable of displaying image data received from the server 10 of a cooperative medical institution and various data. The display receives image data from the cooperative medical institution server 10 and displays it in real time, so that the practitioner can conduct real-time consultation with a remote medical expert or patient.

The second medical measuring device 25 is a medical device for measuring the operating state of various organs of the patient, such as, for example, an imaging medical device that photographs the patient's body and organs, an electrocardiogram measuring device, and a blood pressure measuring device, and a patient's medical device. It may include clinical devices that measure and analyze the levels of various elements of samples taken from the body or obtained from nature.

In addition, the second medical measurement device 25 may further include one of a blood tester, a medical scope, a stress monitor, a psychotherapy device, a blood pressure monitor, a body fat gauge, a blood sugar gauge, and a thermometer. In addition, the medical measurement equipment may mean an electronic device capable of measuring various bio-signals of a user, and may mean a wearable type device that is directly worn on a user's body, such as a built-in type of clothing or a belt.

The second medical measurement device 25 may measure the user's bio-signal using various devices and transmit medical measurement data generated accordingly to the second communication unit 23 .

The second communication unit 23 may perform wireless communication with the cooperative medical institution server 10 . The second communication unit 23 is, for example, LTE (long-term evolution), LTE-A (LTE Advance), CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband), or GSM (global system for mobile communications) may be configured to include at least one communication module.

In addition, the second communication unit 23 may receive advance management reservation information, a first medical examination result, and the like from the cooperative medical institution server 10 .

The remote patient management system according to the embodiment is a high-quality non-face-to-face in medically vulnerable areas or infection-risk areas in order to eliminate medical blind spots, reduce the burden of medical expenses, and improve the convenience of patients and guardians, such as screening clinics, etc. It can be designed to utilize medical services. The remote patient management system can provide on-site video communication-based non-face-to-face medical support medical services by converging commercially available tablet terminals and V2oIP (Voice and Video over Internet Protocol) software.

The remote patient management system according to the embodiment is a non-face-to-face treatment system that can be moved in various types of medically vulnerable areas such as screening clinics and public health centers, and can be used throughout the entire cycle of treatment (Pre-Hospital, On-Hospital, After-Hospital). Tact can support non-face-to-face treatment.

In the embodiment, Pre-Hospital includes a processor that pre-confirms a patient's medical history through an AI-based medical examination system, On-Hospital includes a non-face-to-face remote consultation process using a high-resolution video docking station, and After-Hospital may include a follow-up management and monitoring process through a non-face-to-face treatment support system.

In addition, through the docking system for interlocking IoMT and high-resolution images, it is possible to select the exact position of the patient's affected part in a remote high-level medical institution, and it is possible to provide high-resolution images that can be delivered without screen enlargement and distortion of the patient's image.

In addition, it is possible to support non-face-to-face treatment by linking various medical images to a tablet PC through a docking station for linking IoMT (Internet of Medical Things)-based medical measurement equipment.

The remote patient management system according to the embodiment is applied with AI questionnaire-based medical diagnosis assistance guidance technology, so that the health insurance review and assessment service's big data-based AI questionnaire and IoMT linkage technology are used to link medical devices to quickly determine the presence or absence of a patient's health condition. Diagnosis-assisted guidance service can be provided to help medical staff in decision-making.

In addition, through the smartphone application, patients can receive non-face-to-face post-management services even after treatment, and continuously receive services such as AI questionnaires, medical appointments, and non-face-to-face medical treatment.

First, the cooperative medical institution server may output a first questionnaire result by inputting advance management reservation information of a patient registered online to a first learning model unit (S501).

Next, the cooperative medical institution server may transmit reservation confirmation information to the terminal of the user who has registered the patient information after determining whether the patient will receive general medical treatment or remote medical treatment according to the result of the first medical examination. The cooperative medical institution server may automatically select the patient's medical department and reservation schedule using the first medical examination result, and transmit the result to the patient's terminal. In addition, the cooperative medical institution server may use the first medical examination result to determine whether treatment is required at a higher level medical institution, and transmit a request for remote consultation to the higher level medical institution server if necessary (S502).

Next, the higher-level medical institution server may transmit a remote collaboration approval message to the cooperative medical institution server after confirming the request for remote consultation received from the cooperative medical institution server. After determining whether remote consultation is possible in response to the request for remote consultation, the higher-level medical institution server may transmit a message to the cooperative medical institution server (S503).

The cooperative medical institution server may receive the remote collaboration approval message.

Next, the higher-level medical institution server may transmit a medical information request message to a cooperative medical institution server approved for remote collaboration. The medical information request message may be a message including a request for patient image information, medical measurement information, and the like (S504).

Next, the cooperative medical institution server may transmit medical information of the patient through the remote imaging device and the medical measurement device according to the request of the higher level medical institution server (S505).

In this case, the remote video equipment of the cooperative medical institution's server and the tertiary medical institution's server can each photograph a patient, medical staff or medical measurement equipment, generate video information including video and audio, and transmit it to the other party in real time. In addition, the medical measurement equipment of the cooperative medical institution server may measure the bio-signal of the user using various devices and transmit the medical measurement data generated accordingly to the higher-level medical institution server.

Next, the higher-level medical institution server may transmit the doctor's opinion and prescription, which have analyzed the patient's medical information, to the cooperative medical institution server (S506).

Next, the higher level medical institution server may output the result of the second medical examination by inputting the registered patient's follow-up management reservation information to the second learning model unit through online (S507).

Next, the higher-level medical institution server may remotely monitor abnormal symptoms of the patient according to the result of the second medical examination and the previous patient's treatment history, and then determine whether the patient visits the medical institution. The higher-level medical institution server may determine abnormal symptoms of the patient by using the result of the second medical examination and the previous patient's treatment history, and may automatically select a department and reservation schedule and transmit the result to the patient's terminal. In addition, the higher-level medical institution server may determine whether consultation with the cooperative medical institution is necessary using the second medical examination result, and transmit a request for remote consultation to the cooperative medical institution server if necessary. Alternatively, when there are no abnormal symptoms or abnormal symptoms exist but a visit to a medical institution is not required, monitoring findings may be transmitted to the user terminal (S508).

The term '~unit' used in this embodiment means software or a hardware component such as a field-programmable gate array (FPGA) or ASIC, and '~unit' performs certain roles. However, '~ part' is not limited to software or hardware. '~bu' may be configured to be in an addressable storage medium and may be configured to reproduce one or more processors. Therefore, as an example, '~unit' refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, and procedures. , subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Functions provided within components and '~units' may be combined into smaller numbers of components and '~units' or further separated into additional components and '~units'. In addition, components and '~units' may be implemented to play one or more CPUs in a device or a secure multimedia card.

Although the above has been described with reference to preferred embodiments of the present invention, those skilled in the art will variously modify and change the present invention within the scope not departing from the spirit and scope of the present invention described in the claims below. You will understand that it can be done.

10: Partner medical institution server
11: first input unit
12: first learning model unit
13: first communication unit
14: First remote video equipment
15: first medical measuring equipment
16 First docking station
17: first database
20: Senior Medical Institution Server
21: second input unit
22: second learning model unit
23: second communication unit
24: Second remote video equipment
25: second medical measuring equipment
26: second docking station
27: second database

Claims (5)

The preliminary management reservation information of the patient registered online is input to the first learning model unit to output the first questionnaire result, and after determining whether the patient is receiving general medical treatment or remote medical treatment according to the first medical examination result, the patient information is stored. A cooperative medical institution server that transmits reservation confirmation information to a terminal of a registered user; and
Post-management reservation information of patients registered online is input to the second learning model unit to output the second medical examination result, and after remotely monitoring the patient's abnormal symptoms according to the second medical examination result and the previous patient's treatment history, A remote patient management system that includes a higher level medical institution server that determines whether or not a patient visits a medical institution.
According to claim 1,
The remote patient management system of claim 1 , wherein the cooperative medical institution server and the advanced medical institution server perform remote consultation through remote imaging equipment and medical measurement equipment.
According to claim 2,
The higher-level medical institution server transmits a medical information request message to the cooperative medical institution server, analyzes the medical information of the patient received in response to the medical information request message, and remotely monitors abnormal symptoms of the patient. management system.
According to claim 3,
The remote patient management system of claim 1 , wherein the cooperative medical institution server transmits a measurement result of the medical measurement equipment to the superior medical institution server using the remote video equipment.
outputting a first questionnaire result by inputting advance management reservation information of a patient registered online through a cooperative medical institution server to a first learning model unit;
transmitting, by the cooperative medical institution server, reservation confirmation information to a terminal of a user who has registered the patient information after determining whether the patient has general medical treatment or remote medical treatment according to a result of the first medical examination;
outputting a result of a second questionnaire by inputting follow-up management reservation information of a patient registered online to a second learning model unit by a server of a higher level medical institution; and
Remotely monitoring abnormal symptoms of the patient according to the result of the second medical examination and the previous patient's treatment history, and then determining whether the patient visits the medical institution by the higher level medical institution server.

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