JP2024126610A - Processing device, processing program, processing method, and processing system - Google Patents

Abstract

[Problem] To efficiently select medical resources. [Solution] A processing device including at least one processor is provided, the at least one processor being configured to execute a process for acquiring measurement data measuring a state of a human body, acquiring result information on the state of the body based on the measurement data, and transmitting output information in which at least one medical resource selected based on the result information from among a plurality of medical resources used in a medical procedure on the body is specified to another processing device connected via a communication network. [Selected Figure] Figure 4

Description

The present disclosure relates to a processing device, a processing program, a processing method, and a processing system for processing measurement data of the human body.

Processing systems capable of processing measurement data of the human body and outputting the acquired data have been known. Patent Document 1 describes a medical image diagnostic device that includes an ultraviolet light source that irradiates ultraviolet light, an irradiation control unit that moves the position of the ultraviolet light source relative to an object to be irradiated with the ultraviolet light in an examination room and controls the irradiation of the ultraviolet light to the object to be irradiated, and an imaging unit that captures medical images of the subject, and has a display that displays the captured medical images and analysis results of the medical images.

JP 2022-014262 A

In light of the above-mentioned technologies, the present disclosure aims to provide a processing device, processing program, processing method, and processing system capable of efficiently selecting medical resources through various embodiments.

According to one aspect of the present disclosure, there is provided a processing device having at least one processor, the at least one processor being configured to execute a process for acquiring measurement data measuring a physical condition of a human being, acquiring result information on the physical condition based on the measurement data, and transmitting output information in which at least one medical resource selected based on the result information from among multiple medical resources used in a medical procedure on the body is identified to another processing device connected via a communication network.

According to one aspect of the present disclosure, there is provided a processing program for causing a computer having at least one processor to function in such a way that the at least one processor acquires measurement data that measures a physical condition of a human being, acquires result information regarding the physical condition based on the measurement data, and transmits output information in which at least one medical resource selected based on the result information from among multiple medical resources used in a medical procedure on the body is identified to another processing device connected via a communication network.

According to one aspect of the present disclosure, there is provided a processing method executed by at least one processor in a computer having the at least one processor, the processing method including the steps of: acquiring measurement data measuring a state of a human body; acquiring result information on the state of the body based on the measurement data; and transmitting output information in which at least one medical resource selected based on the result information from among multiple medical resources used in a medical procedure on the body is identified to another processing device connected via a communication network.

According to one aspect of the present disclosure, there is provided a processing system including "the processing device described above and another processing device connected to the processing device via a communication network and configured to receive the output information from the processing device."

The present disclosure provides a processing device, processing program, processing method, and processing system that can efficiently select medical resources.

The above effects are merely illustrative for the convenience of explanation and are not limiting. In addition to or instead of the above effects, any effect described in this disclosure or any effect obvious to a person skilled in the art may be achieved.

FIG. 1 is a block diagram showing a configuration of a processing system 1 according to an embodiment of the present disclosure. FIG. 2 is a block diagram showing a configuration of the processing device 100 according to an embodiment of the present disclosure. FIG. 3A is a diagram conceptually illustrating a subject management table stored in the processing device 100 according to an embodiment of the present disclosure. FIG. 3B is a diagram conceptually illustrating a disease information table stored in the processing device 100 according to an embodiment of the present disclosure. FIG. 3C is a diagram conceptually illustrating a resource management table stored in the processing device 100 according to an embodiment of the present disclosure. FIG. 4 is a diagram showing a processing sequence executed in the processing system 1 according to an embodiment of the present disclosure. FIG. 5 is a diagram showing a process flow executed in the model generating device 300 according to an embodiment of the present disclosure. FIG. 6 is a diagram conceptually illustrating an example of measurement data according to an embodiment of the present disclosure. FIG. 7 is a diagram conceptually illustrating an example of a process in which medical information is generated by processing measurement data according to an embodiment of the present disclosure. FIG. 8 is a diagram showing an example of medical information output by the processing device 100 according to an embodiment of the present disclosure. FIG. 9A is a diagram illustrating an example of notification information outputted by the processing device 100 according to an embodiment of the present disclosure. FIG. 9B is a diagram showing an example of medical information output by the processing device 100 according to an embodiment of the present disclosure. FIG. 10A is a diagram illustrating an example of notification information outputted by the processing device 100 according to an embodiment of the present disclosure. FIG. 10B is a diagram showing an example of medical information output by the processing device 100 according to an embodiment of the present disclosure. FIG. 11A is a diagram illustrating an example of notification information outputted by the processing device 100 according to an embodiment of the present disclosure. FIG. 11B is a diagram showing an example of medical information output by the processing device 100 according to an embodiment of the present disclosure.

1. Overview of the Processing System 1 The processing system 1 according to the present disclosure is used to analyze measurement data acquired by a measurement device to acquire result information, and to select necessary medical resources based on the result information. In particular, the processing system 1 is used to process measurement data acquired over multiple parts (e.g., the whole body) of a human subject. Such measurement data is typically obtained by measuring the state of the human body, and CT images (computed tomography images) of the inside of the human body are used. Such CT images are processed by the processing device 100 and used to analyze the state of the human body. The analysis results are then used, for example, to secure medical resources required for medical professionals to perform medical procedures such as treatment, diagnosis, or surgery on humans. This allows the processing system 1 to efficiently select and further secure such medical resources.

The processing system 1 according to the present disclosure is preferably used in medical settings (e.g., medical institutions) where the selection and securing of medical resources is typically required. Among these, it is particularly preferably used in emergency medical settings (however, there is no intention to limit the use to only emergency medical care). In such emergency medical settings, the subject's physical condition is analyzed based on measurement data, and it is necessary to quickly provide the subject with the required medical treatment in accordance with the analysis results. In this case, it is extremely important in terms of speed to quickly select and secure the required medical resources. Therefore, by using the processing system 1 according to the present disclosure, it becomes possible to quickly select and secure appropriate medical resources, particularly in emergency medical settings.

FIG. 1 is a block diagram showing the configuration of a processing system 1 according to an embodiment of the present disclosure. According to FIG. 1, the processing system 1 includes a processing device 100 for processing measurement data acquired by a measurement device 400 and outputting medical information, a management device 500 for managing medical resources, and a terminal device 600 that can be used by medical personnel. The processing system may also include a display device 200 for displaying medical information, a measurement device 400 for acquiring measurement data, a model generation device 300 for generating a trained analysis model used when the processing device 100 outputs medical information, and the like, as necessary. The processing device 100, the display device 200, the model generation device 300, the measurement device 400, the management device 500, and the terminal device 600 are communicatively connected to each other via a communication network configured by at least one of wireless and wired.

In the present disclosure, the "measuring device 400" may be any device used to obtain measurement data, and is not limited to a specific one. A typical example of such a measuring device is a CT device, but other examples include an MRI device, an X-ray device, an electrocardiogram device, an ultrasound diagnostic device, an endoscope device, a blood pressure monitor device, or a combination of these. Such a measuring device 400 may be used not only in a medical institution, but also in the subject's home, school, workplace, or any other place. The CT device is used to obtain tomographic image data across at least multiple sites of a subject who has been brought to the hospital by ambulance, and can provide useful information for identifying the disease site and determining its severity. Therefore, in the processing system 1 of the present disclosure, a CT device is particularly preferable as the measuring device 400. In the following example, a case where a CT device is mainly used as a measuring device will be described, but of course, this is not limited to this.

In addition, in the present disclosure, the "measurement data" may be any data measured by the measuring device 400, and is not limited to a specific one. Typical examples of such measurement data include CT images, but also include MRI images, three-dimensional measurement data, X-ray image data, electrocardiogram data, echocardiogram data, endoscopic image data, blood pressure measurement data, blood sampling data, respiratory function data, other test data, or combinations thereof. CT images are used to evaluate the state of multiple tomographic planes of a subject across a specified range of the subject (e.g., multiple parts or the entire body). Therefore, it is possible to evaluate not only the state of a specific tomographic plane, but also the state of multiple spatially continuous tomographic planes. By analyzing such CT images, it is possible to obtain information on the type of disease, the part where it occurs, and its severity, so CT images are particularly preferable as measurement data for obtaining medical information.

Note that the measurement data that can be used does not necessarily have to be "spatially continuous measurement data" as described above. For example, it is possible to obtain blood pressure measurement data at a certain point in time and blood pressure measurement data measured continuously in time over a certain period of time from blood pressure measurement data. In this way, even "measurement data measured continuously in time" can be suitably used as the measurement data of the present disclosure. In addition, the measurement data is input to the trained analysis model, but the input data does not have to be all of the measurement data, and may be at least a portion of it. In the following example, a case where CT images are mainly used as measurement data will be described, but of course this is not limited to this.

In addition, in the present disclosure, the required medical resources can be selected using measurement data, but this measurement data does not necessarily have to be the CT images, MRI image data, three-dimensional measurement data, X-ray image data, etc., exemplified above, used alone. For example, it is also possible to select the required medical resources by combining multiple measurement data such as CT images, blood sampling data, and X-ray image data, or by combining measurement data such as CT images and blood sampling data with further information (such as the subject's interview information such as body temperature and subjective symptoms, the subject's medical record information, or physical findings information entered by the medical professional who performed the measurement of the subject's physical condition).

In addition, in the present disclosure, it is conceivable that the measurement data is received from each measurement device, but it may also be received from a database device or server device in which measurement data that has already been measured is stored. In other words, measurement data does not only mean data measured in real time. As an example of such a case, even measurement data stored in a database device or server device for electronic medical records or PACS (Picture Archiving and Communication Systems) can be similarly used in the processing system 1 in the present disclosure.

In addition, in the present disclosure, the term "subject" may refer to any person who is the subject of measurement data, and is not limited to only those with a specific attribute. Therefore, such subjects may include any human being, such as a patient, a test subject, a person to be diagnosed, or a healthy person. In addition, in the present disclosure, the term "operator" may refer to any person who at least operates the processing device 100 or refers to medical information output by the processing device 100, and is not limited to only those with a specific attribute. Therefore, such operators are not limited to medical professionals such as doctors, nurses, and laboratory technicians, but may include any human being, such as the subject himself. In addition, in the present disclosure, the term "medical professional" may refer to any person who is engaged in medical care, and is not limited to only those with a specific attribute. Therefore, such medical professionals may include any human being, such as doctors, nurses, laboratory technicians, medical administrators, staff and managers of medical institutions, emergency personnel, and personnel in charge of medical equipment and medical facilities.

In addition, in this disclosure, "medical resources" may include any resources required for medical procedures, such as medical personnel, medical devices, and medical facilities associated with a medical institution. In other words, medical institutions include not only medical institutions where a measuring device is installed, but also external medical institutions or businesses that provide medical services other than the medical institution (for example, nursing care service providers, businesses that perform remote image interpretation, businesses that provide services such as interpretation and sign language, etc.). In addition, while examples of medical personnel are as described above, such medical personnel may include not only those who belong to a specific medical institution, but also those who belong to external medical institutions or businesses that provide medical services. In addition, medical devices typically include machines and instruments that are used to diagnose, treat, or prevent human diseases, or that are intended to affect the structure or function of the human body. Such medical devices include, but are not limited to, enteral nutrition infusion sets, nebulizers, X-ray films, blood gas analyzers, surgical nonwoven fabrics, X-ray machines, electrocardiographs, ultrasound diagnostic devices, injection needles, blood collection needles, vacuum blood collection tubes, infusion sets for infusion pumps, Foley catheters, suction catheters, hearing aids, home massagers, condoms, particle beam therapy devices, artificial dialysis machines, epidural catheters, infusion pumps, automatic peritoneal irrigation devices, artificial bones, artificial heart-lung machines, multi-person dialysis fluid supply devices, blood component collection devices, artificial ventilators, pacemakers, coronary artery stents, artificial blood vessels, PTCA catheters, central venous catheters, absorbable internal fixation bolts, CT machines, MRI machines, endoscope devices, and blood pressure monitor devices. In addition, the medical equipment may be any equipment typically installed in a medical institution. Such medical equipment includes, but is not limited to, ICUs, HCUs, ambulances, beds, hospital beds, beds and hospital beds in other medical institutions, and the like.

In addition, in this disclosure, terms such as "determination" and "diagnosis" are used for a specific disease or symptom, but these do not necessarily mean a definitive determination or diagnosis by a doctor. For example, these terms naturally include the processing system 1 of the present disclosure being used by the subject himself or herself, or by an operator other than a doctor, and being determined or diagnosed by the processing device 100 included in the processing system 1. These terms also include assistance to a doctor or other person in making a definitive determination or diagnosis.

2. Configuration of the Processing Device 100 FIG. 2 is a block diagram showing the configuration of the processing device 100 according to an embodiment of the present disclosure. According to FIG. 2, the processing device 100 includes a processor 111, a memory 112, an input interface 113, an output interface 114, and a communication interface 115. These components are electrically connected to each other via control lines and data lines. Note that the processing device 100 does not need to include all of the components shown in FIG. 2, and may be configured with some of the components omitted, or other components may be added. For example, the processing device 100 may include a battery or the like for driving each component.

Such a processing device 100 can be suitably applied to any device capable of executing the processing related to the present disclosure, such as a laptop computer, a desktop computer, a smartphone, or a tablet terminal. The processing device 100 can also perform at least a portion of the processing on a server device in the cloud. Therefore, the processing device 100 also includes the server device, or a combination of the server device and a device such as a laptop computer operated by an operator.

In the processing device 100, the processor 111 functions as a control unit that controls the processing device 100 or other components of the processing system 1 based on a program stored in the memory 112. Specifically, the processor 111 executes processes such as "acquiring measurement data that measures the state of the human body," "acquiring result information on the state of the body based on the measurement data," and "transmitting output information in which at least one medical resource selected based on the result information from among multiple medical resources used in medical procedures on the body is identified to another processing device connected via a communication network" based on the program stored in the memory 112. The processor 111 is mainly composed of one or more CPUs, but may be appropriately combined with a GPU, FPGA, etc.

The memory 112 is composed of a RAM, a ROM, a non-volatile memory, a HDD, etc., and functions as a storage unit. The memory 112 stores instructions and commands for various controls of the processing system 1 according to this embodiment as a program. Specifically, the memory 112 stores programs for the processor 111 to execute, such as "processing for acquiring measurement data that measures the state of the human body," "processing for acquiring result information on the state of the body based on the measurement data," and "processing for transmitting output information in which at least one medical resource selected based on the result information from among multiple medical resources used in medical procedures on the body is specified to another processing device connected via a communication network." In addition to the programs, the memory 112 may also store various information stored in the subject management table, the disease information table, and the resource management table.

The input interface 113 functions as an input unit that accepts instructions input by the operator to the processing device 100. Examples of the input interface 113 include various hard keys such as a keyboard and a mouse, and a touch panel that is superimposed on the display of the display device 200 and has an input coordinate system corresponding to the display coordinate system of the display. In the case of a touch panel, icons corresponding to the command to be input are displayed on the display, and the operator inputs instructions via the touch panel to select each icon. The method of detecting the instruction input by the subject using the touch panel may be any method, such as a capacitive type or a resistive film type. The input interface 113 does not always need to be physically provided in the processing device 100, and may be connected as necessary via a wired or wireless network.

The output interface 114 functions as an output unit for outputting information such as result information and medical information obtained as a result of analysis by the processing device 100. One example of the output interface 114 is an interface for connecting to an external device or equipment such as a display device 200 composed of a liquid crystal panel, an organic EL display, a plasma display, or the like. However, if the processing device 100 itself has a display, the display can function as the output interface. Also, if it is connected to a display device 200 or the like via a communication interface 115, the communication interface 115 can also function as the output interface 114.

The communication interface 115 functions as a communication unit for transmitting and receiving measurement data, medical information, output information, etc. between the measurement device 400, the display device 200, the model generating device 300, the management device 500, the terminal device 600, other processing devices, server devices, etc. via a wired or wireless communication network. Examples of the communication interface 115 include various types of connectors for wired communication such as USB and SCSI, wireless communication transmitting and receiving devices such as wireless LAN, Bluetooth (registered trademark), and infrared, and various connection terminals for printed circuit boards and flexible circuit boards.

The configuration of the display device 200 will not be described in detail, but as described above, it includes a display configured as a liquid crystal panel, an organic EL display, a plasma display, or the like, at least one of an input interface and a communication interface that accepts various types of information input from the processing device 100, and a processor that performs processing to display the various types of information accepted on the display.

Furthermore, the configuration of the model generation device 300 for generating a trained analysis model will not be described in detail. However, like the processing device 100, it includes a processor, memory, an input interface, an output interface, and a communication interface. Such devices may include a laptop computer, a desktop computer, a smartphone, a tablet terminal, a server device, or a combination of these.

Furthermore, the configuration of the management device 500, which manages the operating status of medical resources such as reservation status and work status, and functions as one of the other processing devices, will not be described in detail. However, like the processing device 100, it includes a processor, memory, an input interface, an output interface, and a communication interface. Examples of such devices include laptop computers, desktop computers, smartphones, tablet terminals, server devices, or combinations of these.

Furthermore, the configuration of the terminal device 600 that functions as one of the other processing devices, which is carried by a medical professional for use, or is installed in correspondence with each medical device or medical equipment for use by a medical professional to operate and control each medical device or medical equipment, will not be described in detail. However, like the processing device 100, it includes a processor, memory, input interface, output interface, and communication interface. Examples of such devices that can be used include a laptop computer, a desktop computer, a smartphone, a tablet terminal, a server device, or a combination of these.

In the present disclosure, the processing device 100, the model generating device 300, the management device 500, and the terminal device 600 are provided as separate entities. However, this is not limited to the above, and for example, the processing device 100 may function as at least one of the model generating device 300, the management device 500, and the terminal device 600.

3A is a diagram conceptually illustrating a subject management table stored in the processing device 100 according to an embodiment of the present disclosure. The information stored in the subject management table is updated and stored as necessary in accordance with the progress of processing by the processor 111 of the processing device 100.

According to FIG. 3A, the subject management table stores subject information, measurement data, input information, result information, medical information, etc., in association with subject ID information. "Subject ID information" is information specific to each subject for identifying each subject. Subject ID information is generated each time a new subject is registered by the operator or the subject himself/herself. Subject information is information related to the attributes of each subject. Examples of such attribute information include subject name information, date of birth information, gender information, address information, other measurement data, medical record information, findings information, interview information, disease-related information (for example, causes of disease such as traffic accidents, falls, fires, etc. provided by emergency personnel, accident occurrence circumstances, witness information, physical findings, etc.), etc.

"Measurement data" is data indicating the state of the human body measured by the measuring device 400, and is information received from the measuring device 400 via the communication interface 115. When CT images are used as the measurement data, it means each of multiple CT images taken over a predetermined range (e.g., multiple parts of the body or the entire body), or the entire multiple CT images. Such CT images may be either still images or videos, and may be in any image format, such as black and white or color images. Specific examples of such measurement data will be described later in FIG. 6, etc.

"Input information" is information that is input to the trained analysis model to obtain result information. When multiple CT images are used as measurement data, the input information typically uses one or more specific CT images from the multiple CT images. Such multiple CT images are acquired at regular spatial intervals. In other words, each CT image included in the multiple CT images has spatial continuity or relevance with the CT images before and after it. Specific examples of such input information will be described later in FIG. 7, etc.

"Result information" is information obtained by inputting input information into a trained analysis model. When multiple CT images are used as measurement data, the result information is typically information indicating the type of disease detected in each CT image, the location of the disease, and its severity. Examples of the severity of such diseases include not only specific numerical values, but also classifications based on numerical values (for example, "Level 1", "Level 2", "Level 3", or "Level 4", or "Mild", "Moderate", or "Severe", and the number of classifications can be any). Specific examples of such result information will be described later in FIG. 7, etc.

"Medical information" is information generated based on the result information. The medical information is output to the display device 200 via the output interface 114 and displayed on the display device 200, or transmitted to a terminal device 600 selected based on the result information. When multiple CT images are used as measurement data, the medical information typically includes medical information shown in Figures 8 to 11B. That is, in addition to result information such as the name of the detected disease, the location where it is present, and its severity, the medical information may include the measurement data used for the detection, papers or websites showing how to deal with a specific disease, information about medicines useful for a specific disease, information about medical institutions specializing in a specific disease, information about devices and equipment for treating a specific disease, etc.

The information stored in the subject management table may be stored in the memory 112 of the processing device 100, but may also be stored in other devices, such as other processing devices or database devices.

FIG. 3B is a diagram conceptually illustrating a disease information table stored in the processing device 100 according to an embodiment of the present disclosure. The information stored in the disease information table is updated and stored as needed in accordance with the progress of processing by the processor 111 of the processing device 100.

According to FIG. 3B, the disease information table stores disease information, site information, severity information, required resource information, etc., in association with disease ID information. The "disease ID" information is information for identifying each disease with information specific to each disease identified by analyzing measurement data. Examples of such diseases include bleeding, vascular disorders, impaired consciousness, fractures, pneumonia, lung cancer, pleural effusion, tumors, stones, sinusitis, etc. The "disease information" is information on the disease name, symptoms, or pathology of each disease. In addition to the disease name, the disease information may also include papers or websites showing how to deal with each disease, information on medicines useful for each disease, information on medical institutions specializing in each disease, information on devices and equipment for treating each disease, etc.

"Location information" is information for identifying the location of each disease identified by analyzing the measurement data. Examples of such locations include body parts such as the head, chest, abdomen, and pelvis, as well as parts such as the liver, spleen, pancreas, bones, and blood vessels. "Severity information" is information for identifying the severity of each disease identified by analyzing the measurement data. Examples of such severity include not only specific numerical values or numerical ranges, but also classifications based on numerical values (for example, "Level 1," "Level 2," "Level 3," or "Level 4," or "mild," "moderate," or "severe," and the number of classifications can be any number).

The "required resource information" is information for identifying medical resources to be provided to each subject according to the disease, its location, and its severity identified by analyzing the measurement data. Such required resource information may include information identifying each medical worker, each medical device, and each piece of medical equipment, their number, their duration, etc. For example, the required resource information may include:
Each row stores information such as: - Surgeon / 1 person / 4:00 AM to 6:00 AM, November 27th, 2022 - Nurses / 3:00 AM to 8:00 AM, November 27th, 2022 - Syringe / 1 person / 4:00 AM to TBD, November 27th, 2022 - Ultrasound diagnostic machine / 1 person / 4:00 AM to 5:00 AM, November 27th, 2022 - ICU / 1 room / 3:00 AM to 8:00 AM, November 27th, 2022 - General bed / 1 bed / 8:00 AM, November 27th, 2022 to 5:00 PM, November 30th, 2022.

That is, by referring to the disease information table shown in Fig. 3B, the name of the disease, the part where the disease is present, and the severity of the disease are analyzed from the measurement data, and it is possible to specify the medical resources required to provide the medical treatment, as well as the number and duration of the medical resources, from the analyzed information. For example, disease information G1 = bleeding, part information J1 = chest, and severity information = moderate are stored in association with disease ID information F1, and furthermore, as required resource information L1,
・Surgeon / 1 person / November 27, 2022 4:00 AM to 6:00 AM・Nurse / 3 people / November 27, 2022 3:00 AM to 8:00 AM・Syringe / 1 piece / November 27, 2022 4:00 AM to undecided・Ultrasound diagnostic device / 1 piece / November 27, 2022 4:00 AM to 5:00 AM・ICU / 1 room / November 27, 2022 3:00 AM to 8:00 AM・General bed / 1 bed / November 27, 2022 8:00 AM to November 30, 2022 5:00 PM are stored. At this time, by analyzing the measurement data, when the result information is acquired that the disease name is bleeding, the part is chest, and the severity information is moderate, the required resource information L1 is selected by referring to the disease ID information F1 and the associated disease information G1, part information J1, and severity information K1.

The information stored in the disease information table may be stored in the memory 112 of the processing device 100, but may also be stored in other devices, such as other processing devices or database devices.

FIG. 3C is a diagram conceptually illustrating a resource management table stored in a processing device 100 according to an embodiment of the present disclosure. Information stored in the resource management table is received from the management device 500 and stored according to the progress of processing by the processor 111 of the processing device 100. In addition, when output information identifying the medical resource selected by the processing device 100 is transmitted to the management device 500, the resource management table is updated and stored in the management device 500 based on the output information.

According to FIG. 3C, the resource management table stores resource information, attribute information, operation information, transmission destination information, etc. in association with resource ID information. "Resource ID information" is information for identifying each medical resource with information unique to each medical resource. "Resource information" is information for identifying the name of each medical resource. Specifically, the names of medical personnel such as doctors and nurses, the names of each medical device (e.g., electrocardiogram A, electrocardiogram B, syringe manufactured by X company (model number Y), etc.), and the names of each medical facility (e.g., room T of S hospital, etc.) are stored. "Attribute information" is information indicating the attributes of each medical resource. Specifically, when the medical resource is a medical personnel, information such as a surgeon, internist, anesthesiologist, nurse, and laboratory technician is stored. Also, when the medical resource is a medical device, information such as a blood gas analyzer, a syringe needle, an electrocardiogram, and an ultrasound diagnostic device is stored. Additionally, if the medical resource is medical equipment, information such as ICU, HCU, ambulance, hospital bed, etc. is stored.

"Operation information" is information indicating the operating status of each medical resource. In other words, by referring to the operation information, it is possible to identify the period during which each medical resource is available. Examples of such operation information include information indicating the working status of medical personnel (typically, information indicating the period during which they are available), information indicating the reservation status of medical devices and medical equipment, and information indicating the inventory status. "Destination information" is information that specifies the destination of request information and notification information generated as output information by the processing device 100. Various types of destination information can be used, such as the email address, telephone number, SNS, and chat tool account of each medical personnel, information identifying the location on the network of the terminal device for controlling each medical device, and information identifying the terminal device of the administrator who manages the reservation status of each medical equipment.

The information stored in the resource management table may be stored in the memory 112 of the processing device 100, but may also be stored in other devices, such as other processing devices or database devices.

4. Processing sequence executed in the processing system 1 FIG. 4 is a diagram showing a processing sequence executed in the processing system 1 according to an embodiment of the present disclosure. Specifically, FIG. 4 is a diagram showing a processing sequence executed in the processing device 100, the management device 500, the terminal device 600, and the like, from when the processing device 100 receives measurement data from the measurement device 400 until output information based on the measurement data is output. Each process in each device is performed by the processor of each device reading and executing a program stored in the memory of each device. In the following example, the management device 500 is similarly installed in the medical institution where the processing device 100 is installed, and medical resources are also similarly present in the medical institution. However, this is of course not limited to this, and it is also possible to use a management device installed in an external medical institution or a business providing a medical service instead, or to use the management devices in combination. In addition, the medical resources managed by these management devices including the management device 500 can include medical resources belonging not only to the medical institution but also to external medical institutions and medical services.

According to FIG. 4, although not specifically shown, for example, when a subject is rushed to a medical institution, the processor 111 of the processing device 100 controls the communication interface 115 to receive subject information (T11) together with the subject's subject ID information from another processing device (e.g., a medical interview terminal device, an electronic medical record terminal device, a PACS, etc.), and stores the subject information in the subject information table in association with the received subject ID information. Note that the subject information may be obtained by accepting instruction input from an operator via the input interface 113 of the processing device 100.

Next, although not specifically shown, a medical professional operates the measuring device 400 (e.g., a CT device) to take a CT image of the subject's entire body. The taken CT image of the entire body is then acquired by the measuring device 400 as measurement data, and is transmitted to the processing device 100 via the communication interface of the measuring device 400 together with subject ID information for identifying the measured subject. When the processor 111 of the processing device 100 receives the measurement data (T12) from the measuring device 400 via the communication interface 115, it stores the received measurement data in the subject management table in association with the subject ID information.

Next, the processor 111 of the processing device 100 accepts an operation input by an operator via the input interface 113 in order to select the desired subject ID information for which measurement data is to be analyzed from among the multiple subjects stored in the subject management table. Then, upon accepting the operation input, the processor 111 reads out the measurement data associated with the subject ID information from the subject management table and executes an analysis process (S11). As an example, the analysis process is performed by inputting the read-out measurement data into a trained analysis model.

Here, FIG. 5 is a diagram showing a process flow executed in the model generating device 300 according to an embodiment of the present disclosure. Specifically, FIG. 5 is a diagram showing a process flow related to the generation of a trained analytical model used in the analysis process of S11 in FIG. 4. This process flow is mainly performed by the processor of the model generating device 300 reading and executing a program stored in memory.

According to FIG. 5, for example, a step of acquiring measurement data measured by the measuring device 400 or another measuring device as learning data (S111) may be executed. For example, when a CT device is used as the measuring device 400, multiple CT images of the subject's entire body are used as the measurement data. Note that, although CT images actually taken using a CT device as described above may be used as the learning data, simulated images acquired by a simulation using image processing may also be used. Below, a case where multiple CT images are used as learning data will be described.

Next, a process is performed in which information about the disease of the body part seen in each of the acquired CT images (e.g., the disease name, the part where the disease exists, and its severity) is labeled as disease information (S112). The labeling is performed by a medical professional such as a doctor inputting the type and degree (severity) of surgical damage, the type and degree (e.g., severity) of internal lesion or inflammation, and the part where these are seen as findings (e.g., body parts such as the head, chest, abdomen, pelvis, liver, spleen, pancreas, bones, blood vessels, etc.). Note that the above degree (severity) is not limited to a specific numerical value, and examples include classifications such as "Level 1", "Level 2", "Level 3", or "Level 4", or "Mild", "Moderate", or "Severe". Alternatively or in addition, CT images showing surgical damage of different severity at a specific site that has been prepared in advance, or CT images showing medical lesions or inflammation of different severity at a specific site, may be prepared, the degree of agreement with these images may be calculated by image analysis processing, and labeling may be performed based on this degree of agreement.

When multiple CT images and corresponding label information are obtained as training data, a step of performing machine learning of disease patterns (type of disease, its location, and its severity) using the CT images is executed by the processor (S113). As an example, the machine learning is performed by providing a set of training data and label information to a neural network composed of a combination of neurons, and repeating learning while adjusting the parameters of each neuron so that the output of the neural network is the same as the label information. Then, a step of acquiring a trained analysis model (for example, a neural network and parameters) is executed (S114). This causes the processor to finish generating the trained analysis model. The acquired trained analysis model may be transmitted to the processing device 100 via a communication interface and stored in the memory 112 of the processing device 100. It may also be stored in the memory of the model generation device 300, another processing device, a server device, etc.

Returning to FIG. 4, the processor 111 of the processing device 100 executes an analysis process by inputting the acquired measurement data into the trained analysis model generated in this manner. Then, by inputting the measurement data into the trained analysis model, the processor 111 of the processing device 100 acquires result information indicating the disease name, the location of the disease, and the severity of the disease contained in each measurement data (S12). When the processor 111 of the processing device 100 acquires the result information, the processor 111 stores the result information in the subject management table in association with the subject ID information.

Next, when the result information is acquired, the processor 111 of the processing device 100 generates medical information based on the result information (S13). Examples of medical information may include, in addition to the name of the disease, its location, and its severity information contained in the result information, measurement data used for detection, papers or websites showing how to deal with a specific disease, information on medicines useful for a specific disease, information on medical institutions specializing in a specific disease, information on devices and equipment for treating a specific disease, etc.

The processor 111 of the processing device 100 may generate medical information by referring to other information on the subject (e.g., interview information on the subject, medical record information on the subject, or physical findings information input by a medical professional who performed the measurement of the subject's physical condition) in addition to the result information. The processor 111 may also combine multiple measurement data when acquiring result information or generating medical information. For example, the processor 111 acquires result information derived from CT image data by inputting a CT image into a trained analysis model for processing CT image data, acquires result information derived from blood sampling data by inputting blood sampling data into a trained analysis model for processing blood sampling data, acquires result information derived from X-ray images by inputting X-ray image data into a trained analysis model for processing X-ray image data, and acquires result information as a comprehensive evaluation by combining each piece of result information (e.g., performing processing such as addition, weighting, and ensemble). By combining multiple measurement data in this way, more diverse evaluations are possible.

Next, when the medical information is generated, the processor 111 of the processing device 100 stores the generated medical information in the subject management table in association with the subject ID information. The processor 111 then outputs the generated medical information (T13) to the display device 200 or the like via the output interface 114. Details of the medical information output at this time will be described later in FIG. 8.

Next, when the analysis process is performed and the result information is obtained, the processor 111 of the processing device 100 transmits a transmission request (T14) for the latest resource management table information stored in the management device 500 to the management device 500 via the communication interface 115. This is transmitted to check the current operating status of each medical resource in order to select and secure the medical resources required to provide medical treatment based on the result information. When the processor of the management device 500 receives the transmission request via the communication interface, it reads out the resource management table stored in memory and the various information managed therein (S14) and transmits it to the processing device 100 as resource information (T15). The transmitted resource information is as shown in FIG. 3C.

Next, when the processor 111 of the processing device 100 receives the resource information via the communication interface 115, it stores the received resource information in the memory 112. Then, the processor 111 refers to the disease information table based on the analyzed result information (i.e., the disease name, the part where it exists, and its severity). Then, the processor 111 identifies information that identifies the medical resources required to provide the medical procedure, their number, and their period from the necessary resource information in the disease information table. Based on this identified information, the processor 111 refers to the attribute information and operation information of each medical resource stored in the resource management table stored in the memory 112, and selects the medical resource to be secured. Then, the processor 111 generates request information including subject ID information that identifies the subject, resource information and resource ID information of the selected medical resource, their number, and information on their period (S15), and transmits the generated request information (T16) to the management device 500 via the communication interface 115. The processing device 100 processor 111 may select medical resources and generate request information by referring to other information about the subject (e.g., the subject's medical record information, or physical findings entered by a medical professional who performed measurements of the subject's physical condition) in addition to the result information.

Next, when the processor of the management device 500 receives the request information from the processing device 100, it refers to the resource management table stored in the memory and judges whether or not the medical resources can be secured based on the resource ID information, the number of resources, and the period included in the request information. If the medical resources can be secured, the processor of the management device 500 approves the request for medical resources (S16). Then, the processor of the management device 500 updates and stores the operation information associated with the resource ID information included in the request information in the resource management table stored in the memory based on the number and period included in the request information (S17). If it is difficult to secure the medical resources, it transmits a notice to that effect to the processing device 100 and requests that the selection of the medical resources be readjusted. If readjustment is necessary, the processor 111 of the processing device 100 notifies the operator of the effect, accepts an instruction input from the operator, reselects the medical resources, and transmits the request information again. The processes related to S16 and S17 are repeated below.

In the above, the request information is sent to the management device 500, and the selected medical resources are secured in the management device 500. However, the request information may be sent directly to the selected medical device, for example.

Next, when the medical resource is secured, the processor of the management device 500 transmits securing information (T17) including the subject ID information, the resource ID information, and a notice that the resource has been secured to the processing device 100 via the communication interface. Then, when the processor 111 of the processing device 100 receives the securing information via the communication interface 115, it generates medical information including notification information (S18). Specifically, the processor 111 refers to the resource management table and identifies the destination information associated with the received resource ID information as the destination of the notification information. The processor 111 also generates medical information including notification information using the measurement data, the result information obtained by analyzing the measurement data, and the subject information, and stores the generated medical information in the subject management table in association with the subject ID information. Details of the generated notification information and medical information will be described later in FIG. 9 and the like.

Next, the processor 111 of the processing device 100 reads out the medical information (T18) including the generated notification information from the memory 112, and transmits the information to each terminal device 600 via the communication interface 115. In this case, the terminal device 600 to which the information is to be transmitted is selected based on the destination specified as the destination information. For example, if the destination is a medical worker's email address, telephone number, SNS, chat tool account, etc., a terminal device that the medical worker possesses and can use is selected and the information is transmitted. Also, if information specifying the location on the network of a terminal device for controlling each medical device is specified as the destination, the terminal device specified by the location is selected and the information is transmitted. Also, if information specifying the terminal device of the administrator who manages the reservation status of each medical facility, etc. is specified, the terminal device specified by the location is selected and the information is transmitted.

When the processor of each terminal device 600 receives the medical information including the notification information via the communication interface, it outputs to the display of each terminal device 600 a message indicating that the notification information has been received (S19). The output may be audio via a speaker instead of a display. The processor of the terminal device 600 also outputs the received medical information to the display (S20). By referring to the medical information, it becomes possible to immediately understand the contents of the notification. Details of the output notification information and medical information will be described later in FIG. 10 etc. This concludes this processing sequence.

In FIG. 4, the medical information including the notification information is transmitted from the processing device 100 to each selected terminal device 600. However, this is not limited to the above, and for example, the medical information including the notification information and its destination information may be transmitted from the processing device 100 to the management device 500, and then transmitted from the management device 500 to each terminal device 600.

Also, in FIG. 4, a case has been described in which result information is obtained by inputting input information into a trained analysis model. However, using a trained analysis model is merely one example, and other methods may of course be used. For example, when CT images are used as input information, other image analysis processes may be used, such as a method of comparing the degree of agreement with an image in which the disease state is analyzed.

5. Measurement Data As described above, the measurement data is data indicating the state of the human body measured by the measurement device 400, and is information received from the measurement device 400 via the communication interface 115. Fig. 6 is a diagram conceptually illustrating an example of measurement data according to an embodiment of the present disclosure. Fig. 6 illustrates a case in which a plurality of CT images taken by the measurement device 400 (e.g., a CT device) are used as the measurement data.

Such CT images are typically obtained by capturing cross sections in the thickness direction from the head to the legs of the body at a spatially constant interval (e.g., slice interval = 5 mm) over a predetermined range of the human body (at least multiple sites (e.g., head, chest, abdomen, pelvis, etc.) or the entire body. In FIG. 6, multiple CT images including at least CT images Dm-7 to Dm and CT images Dn-7 to Dn as measurement data are captured at a spatially constant interval (e.g., slice interval = 5 mm) across multiple sites A to D (i.e., the entire body). Note that each site shown in FIG. 6 is an example for convenience of explanation and does not necessarily accurately represent a specific site of the human body.

Since the multiple CT images thus obtained are taken at regular intervals as described above, for example, by referring to multiple consecutive CT images such as CT image Dm-7 to CT image Dm, it is possible to confirm the state of the disease appearing in the area included in each CT image. That is, by focusing on each CT image, it is possible to detect the name of the disease included in each CT image, its area on the cross section, and its severity. Furthermore, by focusing on multiple consecutive CT images, it is possible to grasp the name of the disease, its area on the cross section, and the transition of the severity. In the present disclosure, by performing an analysis process using a specific one or multiple (preferably one) CT images selected from the multiple CT images as input information, it is possible to obtain the name of the disease included in each CT image, its area, and its severity as result information. Furthermore, by performing a similar process on multiple CT images that have spatial continuity or association, it is possible to obtain spatial transition information of the name of the disease, its area, and its severity. Note that "having spatial continuity or correlation" does not only mean that each CT image is taken at a fixed interval (e.g., slice interval = 5 mm), but also means that the images are taken by a series of measurement processes performed on the same subject using the measurement device 400.

Here, CT images are often used in emergency medical care to determine the presence or absence of disease. However, in such cases, CT images are taken at least across multiple body parts (and in some cases the entire body) at regular intervals, so medical personnel must check a considerable number of CT images one by one to determine the presence or absence of disease. In the processing system 1 according to the present disclosure, multiple CT images, which are the acquired measurement data, are used as input information, and information indicating the results of the above-mentioned determination or information that assists in the above-mentioned determination can be output as medical information. Therefore, the processing system 1 according to the present disclosure can be particularly useful in determining the presence or absence of disease in emergency medical care. However, naturally, its use is not limited to such determinations in emergency medical care.

6. Processing Related to Generation of Medical Information As described in Fig. 6, measurement data (e.g., CT images) captured by the measurement device 400 is transmitted to the processing device 100 and processed by the processor 111 of the processing device 100 to generate medical information. Fig. 7 is a diagram conceptually illustrating an example of a process in which medical information is generated by processing measurement data according to an embodiment of the present disclosure.

Figure 7 shows that, as shown in Figure 6, multiple CT images (CT images D1-1 to D1-n) taken by the measuring device 400 across a predetermined range of the body (e.g., multiple parts of the body or the entire body) are acquired as measurement data by the processing device 100.

Next, according to FIG. 7, these multiple CT images are selected for each individual CT image (CT image E1-1, CT image E1-2 to CT image E1-n) by processing of the processor 111, and input as input information to the trained analysis model (analysis processing). Note that all CT images D1-1 to D1-n (measurement data) taken by the measurement device 400 and acquired by the processing device 100 may be acquired as individual CT images (CT image E1-1, CT image E1-2 to CT image E1-n) that are input information. Also, CT images selected at regular intervals (for example, every 5 images) from all CT images D1-1 to CT image D1-n (measurement data) may be acquired as individual CT images (CT image E1-1, CT image E1-2 to CT image E1-n) that are input information. In addition, a CT image selected by the operator from all CT images D1-1 to D1-n (measurement data) may be acquired as individual CT images (CT image E1-1, CT image E1-2 to CT image E1-n) that are input information.

As described above, CT image E1-1, CT image E1-2 to CT image E1-n are acquired as input information and input to the trained analysis model to generate result information. That is, when CT image E1-1 is input, the name of the disease contained in the image, the part where it exists, and its severity are acquired as result information. When CT image E1-2 is input, the name of the disease contained in the image, the part where it exists, and its severity are acquired as another result information. When CT image E1-n is input, the name of the disease contained in the image, the part where it exists, and its severity are acquired as yet another result information. In this way, in the analysis process, the name of the disease, the part where it exists, and its severity are acquired as result information for each CT image acquired as input information. Then, the acquired information indicating the name of the disease, the part where it exists, and its severity is stored as result information for each CT image in the subject management table.

In the above, a case has been described in which each CT image is input as input information to the trained analysis model, but CT images may be prepared in groups of several images, for example three images, and input to the trained analysis model for each group. In this case, the name of the disease obtained for each CT image, the location of the disease, and the average, maximum, and minimum severity of the disease may be calculated for each group, and these may be used as result information.

Next, as shown in FIG. 7, medical information is generated by the processor 111 based on the result information obtained.

7. Examples of medical information and notification information to be output <Examples of medical information to be output at T13 in FIG. 4>
7, when the result information is acquired from the input information, medical information is generated by processing of the processor 111 (T13 in FIG. 4). Then, the generated medical information is stored in the subject management table of the memory 112 and is output via the output interface 114 to, for example, the display device 200.

Figure 8 is a diagram showing an example of medical information output by a processing device 100 according to an embodiment of the present disclosure. Of these, Figure 8 shows an example of medical information that includes, in addition to the output result information, a CT image (measurement data) that was used to detect the currently output result information.

8, the medical information includes a display area 16 displaying the disease name (bleeding), the site where it occurred (site A), and a bar graph showing its severity obtained as the result information, and a display area 17 displaying the disease name (fracture), the site where it occurred (site B), and a bar graph showing its severity obtained as the result information. The medical information also includes a CT image (measurement data) 15 used to obtain the result information shown in the display area 16 and the display area 17. The CT image (measurement data) 15 is switched and displayed by accepting an operation input to the switching button 22. The result information displayed in the display area 16 and the display area 17 is also switched in response to the switched and displayed CT image 15.

9, the medical information further includes a curve 19 showing the spatial transition of the severity of the disease at site A, a curve 20 showing the spatial transition of the severity of the disease at site B, and a curve 21 showing the spatial transition of the severity of the disease at site C. Here, for each CT image, which is the input information, the result information of the specific disease, the site where it exists, and the severity is obtained. Therefore, by processing the processor 111, the specific disease, the site where it exists, and the severity are arranged in the order in which the individual CT images were taken, that is, in the order of the sections taken, so that the spatial transition of the severity can be shown as a curve. In the example of FIG. 9, the horizontal axis shows the position of the section taken, and the vertical axis shows the severity of the disease. This allows the operator to more intuitively grasp the position of the section to be confirmed.

Also, according to FIG. 9, the medical information includes a selection bar 41 that is superimposed on at least one of curves 19, 20, and 21. By receiving an operation input by the operator on the selection bar 41 via the input interface 113, the processor 111 can switch the result information to be output as medical information. That is, when the selection bar 41 is moved in a desired direction by the operator, the result information corresponding to the position to which it is moved is also switched in the same way, and the CT image (measurement data) 15 corresponding to the result information is also switched in the same way. Therefore, the operator can more intuitively switch the CT image and result information to be output as medical information while referring to each curve.

<Example of medical information output in T18 of FIG. 4>
7, a medical resource (e.g., a surgeon) is selected, and medical information including notification information is output to a terminal device 600 that is held and available by the surgeon selected as the destination. The notification information included in the output medical information is then received by the terminal device 600 and output to a display of the terminal device 600.

Fig. 9A is a diagram showing an example of notification information output by a processing device 100 according to an embodiment of the present disclosure. Specifically, Fig. 9A shows an example of notification information output from the processing device 100 and displayed on the display of a terminal device 600 carried by a surgeon and available for use. According to Fig. 9A, upon receipt of the notification information, a notification information display area 23 is popped up on the display following processing by the processor. The subject ID, subject information, result information (disease name, its location, and its severity), and the selected period are displayed in the notification information display area 23.

Here, the medical resources selected based on the result information include not only surgeons but also other medical personnel, medical equipment, and medical facilities. Information on the other medical resources selected together (for example, the name of the operating room used for the subject's surgery and the type of equipment used) may be displayed on the terminal device 600 of the surgeon selected as a medical resource. Regarding the period of the notification information, only information on the period associated with the surgeon to whom it is sent is selected and output. In addition, the disease name, its location, and its severity are not necessarily one, since information is generated for each disease identified by the analysis process. However, the notification information is intended to quickly notify the surgeon that he or she has been selected as a medical resource, and is displayed superimposed on other displays. Therefore, only the most severe disease among multiple diseases may be included in the notification information.

Furthermore, by accepting operational input to the notification information display area 23 by a surgeon who can use the terminal device 600, medical information is output to the display of the terminal device 600.

9B is a diagram showing an example of medical information output by the processing device 100 according to an embodiment of the present disclosure. Specifically, FIG. 9B shows an example of medical information output from the processing device 100 and displayed on the display of the terminal device 600 carried by a surgeon so that it can be used. According to FIG. 9B, the medical information is displayed on the display after being processed by the processor in response to an operation input for the notification information. The medical information includes a display area 26 in which a bar graph showing the disease name (bleeding), the site (site A) where the bleeding occurred, and the severity thereof obtained as the result information is displayed, and a display area 27 in which a bar graph showing the disease name (fracture), the site (site B) where the bleeding occurred, and the severity thereof obtained as the result information is displayed. The medical information also includes a CT image (measurement data) 24 used to obtain the result information shown in the display area 26 and the display area 27. The CT image 24 is switched and displayed by receiving an operation input to the switch button 25. The result information displayed in the display area 26 and the display area 27 is also switched in response to the switched and displayed CT image 24.

According to FIG. 9B, in addition to these result information, the medical information includes information necessary for providing medical treatment, such as medical record information 28, findings information 29, and disease-related information 30 associated with the subject ID information, or information useful for selecting subjects to whom medical treatment should be provided preferentially by triage. Note that in the example of FIG. 9B, medical record information, findings information, and disease-related information are given as examples, but other information such as interview information, subject name information, date of birth information, gender information, address information, other measurement data, period, papers and websites showing how to deal with diseases, information on useful medicines, and the selection status of other medical resources can be included. In addition, the example shown in FIG. 9B shows a case where a surgeon is selected as a medical resource. Therefore, even if the medical worker is the same, the information included as medical information may be changed depending on the attributes, such as when a laboratory technician is selected as a medical resource. Specifically, the medical information output to the terminal device 600 of the laboratory technician may be preferentially displayed with information on the medical equipment the laboratory technician is in charge of.

Next, as described in FIG. 7, a medical resource (e.g., an ultrasound diagnostic device, which is a medical device) is selected, and medical information including notification information is output to the terminal device 600 used to control the ultrasound diagnostic device selected as the destination. The notification information included in the output medical information is then received by the terminal device 600 and output to the display of the terminal device 600.

Fig. 10A is a diagram showing an example of notification information output by a processing device 100 according to an embodiment of the present disclosure. Specifically, Fig. 10A shows an example of notification information output from the processing device 100 and displayed on a display of a terminal device 600 that controls an ultrasound diagnostic device. According to Fig. 10A, upon receipt of the notification information, a notification information display area 31 is popped up on the display after processing by the processor. The subject ID, subject information, result information (disease name, its location, and its severity), and the selected period are displayed in the notification information display area 31.

Here, the medical resources selected based on the result information include not only the ultrasound diagnostic device but also other medical personnel, medical equipment, and medical facilities. The terminal device 600 that controls the ultrasound diagnostic device selected as the medical resource may display information on the other medical resources selected together (for example, the name of the medical personnel who will perform treatment using the ultrasound diagnostic device). Of the notification information, only the information on the period associated with the ultrasound diagnostic device is selected and output. In addition, the disease name, its location, and its severity are not necessarily one, since information is generated for each disease identified by the analysis process. However, the notification information may be configured to preferentially display information such as the disease name of the disease identified by the disease ID associated with the ultrasound diagnostic device as required resource information in the disease information table.

Furthermore, by accepting operational input to the notification information display area 31 by a medical professional who can use the terminal device 600, medical information is output to the display of the terminal device 600.

10B is a diagram showing an example of medical information output by the processing device 100 according to an embodiment of the present disclosure. Specifically, FIG. 10B shows an example of medical information output from the processing device 100 and displayed on the display of a terminal device 600 for controlling an ultrasound diagnostic device. According to FIG. 10B, the medical information is displayed on the display after being processed by the processor in response to an operation input for the notification information. As in FIG. 10B, the medical information includes a display area 34 in which a bar graph showing the disease name (bleeding), the site (site A) where the bleeding occurred, and the severity thereof obtained as the result information is displayed, a display area 35 in which a bar graph showing the disease name (fracture), the site (site B) where the bleeding occurred, and the severity thereof obtained as the result information is displayed, a CT image (measurement data) 32 used to obtain the result information shown in the display area 34 and the display area 35, and a switching button 33.

According to FIG. 10B, the medical information further includes information useful for setting medical equipment, such as medical record information 36, findings information 39, and interview information 37 associated with the subject ID information. In addition to these, the medical information also includes information useful for operating and controlling medical equipment, such as information 38 related to the control of the ultrasound diagnostic device. Note that in the example of FIG. 10B, the medical record information, findings information, interview information, and information related to the medical equipment are given as examples, but it is possible to include other information such as subject name information, date of birth information, gender information, address information, other measurement data, period, papers and websites showing how to deal with diseases, information on useful medicines, and the selection status of other medical resources. In addition, the example shown in FIG. 10B shows a case where an ultrasound diagnostic device is selected as a medical resource. Therefore, even if the same medical equipment is used, for example, when an electrocardiograph is selected as a medical resource, the information included as medical information may be changed depending on the attributes. Specifically, the medical information output to the terminal device 600 for controlling the electrocardiograph may be preferentially displayed with regard to the electrocardiograph.

Next, as explained in FIG. 7, medical resources (e.g., an ICU and general hospital beds, which are medical equipment) are selected, and medical information including notification information is output to a terminal device 600 that can be used by the administrator of the ICU and general hospital bed selected as the destination. The notification information included in the output medical information is then received by the terminal device 600 and output to the display of the terminal device 600.

Fig. 11A is a diagram showing an example of notification information output by a processing device 100 according to an embodiment of the present disclosure. Specifically, Fig. 11A shows an example of notification information output from the processing device 100 and displayed on the display of a terminal device 600 that can be used by an administrator who manages medical facilities such as an ICU and general hospital beds. According to Fig. 11A, upon receiving the notification information, a notification information display area 42 is popped up on the display after processing by the processor. The content displayed in the notification information display area 42 is the same as that in Fig. 10A.

Here, the medical resources selected based on the result information include multiple medical resources such as ICU and general hospital beds. In such a case, the notification information may be configured to give priority to the period of the medical equipment that will be used first.

Furthermore, by accepting operational input to the notification information display area 42 by an administrator who has access to the terminal device 600, medical information is output to the display of the terminal device 600.

FIG. 11B is a diagram showing an example of medical information output by a processing device 100 according to an embodiment of the present disclosure. Specifically, FIG. 11B shows an example of medical information output from the processing device 100 and displayed on the display of a terminal device 600 available to an administrator. According to FIG. 11B, display area 45, display area 46, CT image (measurement data) 43, and switching button 44 are similar to display area 34, display area 35, CT image (measurement data) 32, and switching button 33 in FIG. 10B.

According to FIG. 11B, the medical information further includes information on the number of ICUs and general hospital beds required and the period for which they are selected as medical resources. By including this information, the administrator can immediately grasp the required medical equipment and can quickly begin preparing the equipment or give instructions. Naturally, the medical information can also include information such as medical records, findings, interview information, subject name, date of birth, gender, address, other measurement data, periods, papers and websites showing how to deal with diseases, information on useful medicines, and the selection status of other medical resources.

As described above, in this embodiment, it is possible to provide a processing device, processing program, processing method, and processing system that can efficiently select medical resources.

In the above embodiment, the severity of a specific disease is shown as medical information using a bar graph or a curve. However, these are merely examples of ways to express the severity, and the severity may be shown as specific numerical values or classification information such as "applicable" or "not applicable", "Level 1", "Level 2", "Level 3" or "Level 4", "mild", "moderate", or "severe".

In the above embodiment, a case has been described in which a CT image measured by the measuring device 400 is used as input information. However, the CT image measured by the measuring device 400 may be subjected to various pre-processing such as filtering using a band pass filter including a high pass filter or a low pass filter, an averaging filter, a Gaussian filter, a Gabor filter, a Canny filter, a Sobel filter, a Laplacian filter, a median filter, a bilateral filter, etc., blood vessel extraction processing using a Hessian matrix, etc., segmentation processing of a specific region (e.g., a disease region) using machine learning, trimming processing of the segmented region, haze removal processing, super-resolution processing, and combinations thereof, to thereby obtain high definition, region extraction, noise removal, edge enhancement, image correction, image conversion, etc., and the like, and may be used as input information.

In the above embodiment, a case where a CT device is used as the measuring device 400 has been described. However, the present invention is not limited to these, and an MRI device, an X-ray device, an electrocardiograph device, an ultrasound diagnostic device, an endoscope device, a blood pressure monitor device, or a combination of these may be used as the measuring device 400, and MRI images, three-dimensional measurement data, X-ray image data, electrocardiogram data, echo examination data, endoscopic image data, blood pressure measurement data, blood sampling data, respiratory function data, other examination data, or a combination of these may be used as input information. For example, when blood pressure measurement data is used, local blood pressure measurement data at a certain point in time or blood pressure measurement data that changes over time in a specified period of time can be used as input information.

In the above embodiment, the presence or absence of a specific disease, its location, and its accuracy are obtained based on the measurement data measured by the measuring device 400. However, in addition to the measurement data measured by the measuring device 400, interview information, findings information, other measurement data (MRI images, three-dimensional measurement data, X-ray image data, electrocardiogram data, echocardiography data, endoscopic image data, blood pressure measurement data, blood sampling data, respiratory function data, other test data, or a combination of these), etc. may also be used to obtain the information.

The trained analysis model described in the above embodiment was generated using a neural network or a convolutional neural network. However, it is not limited to these, and it is also possible to generate it using machine learning such as the nearest neighbor method, decision tree, regression tree, random forest, etc.

The processes and procedures described in this specification can be realized not only by those explicitly described in the embodiments, but also by software, hardware, or a combination of these. Specifically, the processes and procedures described in this specification are realized by implementing logic corresponding to the processes in a medium such as an integrated circuit, volatile memory, non-volatile memory, magnetic disk, optical storage, etc. In addition, the processes and procedures described in this specification can be implemented as computer programs and executed by various computers including processing devices and server devices.

Even if the processes and procedures described herein are described as being executed by a single device, software, component, or module, such processes or procedures may be executed by multiple devices, multiple software, multiple components, and/or multiple modules. Furthermore, even if the various information described herein is described as being stored in a single memory or storage unit, such information may be stored in multiple memories provided in a single device or multiple memories distributed across multiple devices. Furthermore, the software and hardware elements described herein may be realized by integrating them into fewer components or breaking them down into more components.

1 Processing system 100 Processing device 200 Display device 300 Model generating device 400 Measuring device 500 Management device (processing device)
600 Terminal device (processing device)

Claims (13)

A processing device comprising at least one processor,
The at least one processor:
Acquire measurement data that measures the state of a person's body,
obtaining result information regarding the physical condition based on the measurement data;
transmitting output information in which at least one medical resource selected based on the result information from among a plurality of medical resources used in the medical procedure on the body is identified to another processing device connected via a communication network;
a processing unit configured to perform processing for: The processing device of claim 1, wherein the at least one medical resource is a medical professional. The processing device according to claim 2, wherein the output information includes medical resource identification information for identifying the medical worker and medical information generated based on the result information. The processing device according to claim 2, wherein the other processing device is a medical staff terminal device available to the medical staff. The processing device according to claim 1, wherein the at least one medical resource is one of a medical device and a medical facility. The processing device according to claim 5, wherein the output information includes medical resource identification information for identifying at least one of the medical devices and the medical facilities, and request information requesting the use of one of the medical devices and the medical facilities identified by the medical resource identification information. The processing device according to claim 5, wherein the other processing device is a management device for managing the medical device and the use of the medical device or the medical facility. the other processing devices include a plurality of other processing devices associated with each of the plurality of medical resources;
the output information is transmitted to at least one other processing device selected based on the result information.
The processing device of claim 1 . The result information includes information regarding the severity of the physical condition;
The at least one medical resource is selected from a plurality of medical resources according to the severity.
The processing device of claim 1 . The processing device according to claim 1, wherein the result information is obtained by inputting the measurement data into a trained analysis model. In a computer having at least one processor, the at least one processor is
Acquire measurement data that measures the state of a person's body,
obtaining result information regarding the physical condition based on the measurement data;
transmitting output information in which at least one medical resource selected based on the result information from among a plurality of medical resources used in the medical procedure on the body is identified to another processing device connected via a communication network;
A processing program that makes it function as follows. A processing method executed by at least one processor in a computer having the at least one processor, comprising:
Obtaining measurement data that measures a physical condition of a human being;
obtaining result information regarding the physical condition based on the measurement data;
transmitting output information in which at least one medical resource selected based on the result information from among a plurality of medical resources used in the medical procedure on the body is specified to another processing device connected via a communication network;
A processing method comprising: A processing device according to claim 1 ;
another processing device coupled to the processing device via a communication network and configured to receive the output information from the processing device;
13. A processing system comprising:

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