system

Abstract

The system according to this embodiment aims to improve the efficiency of nursing work, detect human errors, and provide patient-centered care. [Solution] The system according to this embodiment comprises a nursing work processing unit, an error detection unit, and a care provision unit. The nursing work processing unit processes nursing tasks. The error detection unit detects human errors based on the tasks processed by the nursing work processing unit. The care provision unit provides patient-centered care based on the errors detected by the error detection unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the efficiency of nursing work and the detection of human errors have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to an embodiment aims to improve the efficiency of nursing work, detect human errors, and provide care close to the patient.

Means for Solving the Problems

[0006] The system according to an embodiment includes a nursing work processing unit, an error detection unit, and a care providing unit. The nursing work processing unit processes nursing work. The error detection unit detects human errors based on the work processed by the nursing work processing unit. The care providing unit provides care close to the patient based on the errors detected by the error detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve the efficiency of nursing work, detect human errors, and provide patient-centered care. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The medical support system according to an embodiment of the present invention is a system that combines the power of AI and humans to create a new future for medicine. This medical support system improves accuracy and efficiency by having AI process a vast amount of nursing work and detect human errors. Next, by utilizing the workload reduced by AI, humans can concentrate on providing personalized care to each patient. This aims to streamline the work of medical professionals such as nurses and provide higher quality medical services. For example, the medical support system has AI quantify the patient's pulse, facial expressions, mood, etc., and quickly detect abnormalities in the patient. For example, the AI ​​can analyze changes in the patient's facial expressions in real time and detect abnormalities. This allows nurses to respond quickly. Next, the AI ​​detects human errors. For example, the AI ​​can monitor the tasks that nurses should perform and detect omissions and errors. This allows nurses to prevent omissions and errors in their work and perform their duties efficiently. Furthermore, by utilizing the workload reduced by AI, humans can concentrate on providing personalized care to each patient. For example, nurses can grasp the patient's health condition in real time and provide appropriate care to the patient. This allows patients to receive higher quality medical services. This system streamlines the work of healthcare professionals such as nurses, enabling them to provide higher quality medical services. For example, nurses can perform their duties efficiently without becoming exhausted. Doctors can minimize medical errors, and even new doctors can provide high-quality medical services. Furthermore, patients can have a smooth and stress-free hospital stay. As a result, the medical support system can efficiently handle nursing tasks, detect human errors, and provide patient-centered care.

[0029] The medical support system according to this embodiment comprises a nursing task processing unit, an error detection unit, and a care provision unit. The nursing task processing unit processes nursing tasks. For example, the nursing task processing unit measures the patient's vital signs, administers medication, and manages records. For example, the nursing task processing unit measures the patient's pulse and issues an alert if there is an abnormality. The nursing task processing unit can also analyze the patient's facial expressions and detect abnormalities. Furthermore, the nursing task processing unit can evaluate the patient's mood and detect abnormalities. For example, the nursing task processing unit measures the patient's pulse with a sensor and collects data in real time. The nursing task processing unit analyzes the patient's facial expressions using facial recognition technology and detects abnormalities. The nursing task processing unit quantifies the patient's mood using a mood evaluation algorithm and detects abnormalities. The error detection unit detects human errors based on the tasks processed by the nursing task processing unit. For example, the error detection unit monitors the tasks that nurses should perform and detects omissions or errors. The error detection unit can, for example, detect medication errors. Furthermore, the error detection unit can also detect recording errors. In addition, the error detection unit can also detect patient mix-ups. For example, the error detection unit monitors medication records and detects medication errors. The error detection unit monitors the record management system and detects recording errors. The error detection unit monitors the patient identification system and detects patient mix-ups. The care delivery unit provides patient-centered care based on the errors detected by the error detection unit. For example, the care delivery unit can grasp the patient's health status in real time and provide appropriate care. For example, the care delivery unit can monitor the patient's vital signs and respond quickly if there are any abnormalities. In addition, the care delivery unit can also respond with consideration for the patient's emotions. Furthermore, the care delivery unit can also provide individualized care plans. For example, the care delivery unit monitors the patient's vital signs and issues an alert if there are any abnormalities. The care delivery unit analyzes the patient's emotions and responds appropriately. The care delivery unit creates and provides care plans that meet the patient's needs. As a result, the medical support system according to this embodiment can efficiently handle nursing tasks, detect human errors, and provide patient-centered care.

[0030] The nursing operations processing unit handles nursing tasks. For example, it measures patients' vital signs, administers medication, and manages records. Specifically, it uses sensors to measure vital signs such as pulse, blood pressure, temperature, and respiratory rate, collecting data in real time. This data is transmitted to a central database for access by medical staff. The nursing operations processing unit can also analyze patients' facial expressions and detect abnormalities. Using facial recognition technology, it analyzes the patient's facial expressions to detect signs of pain, discomfort, stress, and other issues. Furthermore, the nursing operations processing unit can evaluate patients' moods and detect abnormalities. Using a mood evaluation algorithm, it quantifies the patient's mood and detects abnormalities. For example, it analyzes the patient's tone of voice, speaking style, and behavioral patterns to monitor changes in mood. This allows the nursing operations processing unit to comprehensively understand the patient's health status and respond quickly if abnormalities are detected. In addition, the nursing operations processing unit also manages medication. It manages medication schedules and administers medications at the appropriate times. To prevent medication errors, it uses barcode scanning and RFID tags to identify medications and record administration. This allows the nursing operations processing unit to ensure patient safety and improve the efficiency of nursing operations.

[0031] The error detection unit detects human errors based on tasks processed by the nursing task processing unit. Specifically, it monitors tasks that nurses should perform and detects omissions and errors. For example, it can detect medication errors. By monitoring medication records and comparing the medication schedule with the actual medication history, it detects medication errors. The error detection unit can also detect recording errors. By monitoring the record management system and checking the consistency of the entered data, it detects recording errors. Furthermore, the error detection unit can also detect patient mix-ups. By monitoring the patient identification system and comparing the patient's ID with the treatment details, it prevents patient mix-ups. For example, it scans the barcode or RFID tag attached to the patient's wristband to confirm the patient's ID and treatment details. In this way, the error detection unit can ensure the accuracy of nursing tasks and protect patient safety. In addition, the error detection unit can analyze data using AI and detect abnormal patterns and trends. For example, based on past data, it can predict errors that are likely to occur at specific times or in specific situations and issue warnings in advance. This allows the error detection unit to improve the quality of nursing work and enhance the reliability of the medical field.

[0032] The care delivery department provides patient-centered care based on errors detected by the error detection department. Specifically, it monitors the patient's health status in real time and provides appropriate care. For example, it monitors the patient's vital signs and responds quickly if there are any abnormalities. Vital sign data is updated in real time, allowing medical staff to respond immediately. The care delivery department can also respond with consideration for the patient's emotions. It analyzes the patient's facial expressions, tone of voice, and behavioral patterns to understand changes in emotions. This allows it to respond appropriately if the patient is feeling anxious or stressed, thereby increasing the patient's sense of security. Furthermore, the care delivery department can provide individualized care plans. It creates and provides care plans tailored to the patient's needs and condition. For example, it proposes the optimal treatment and care based on the patient's medical history and current health status. This allows the care delivery department to provide patient-centered care and improve patient satisfaction. In addition, the care delivery department can build trust by emphasizing communication with patients and their families. For example, it incorporates the opinions and requests of patients and their families through regular counseling and feedback and reflects them in the care plan. This allows the care delivery department to realize patient-centered medical care and improve patients' quality of life (QOL).

[0033] The nursing task processing unit can quantify the patient's pulse, facial expression, mood, etc., and quickly detect abnormalities in the patient. For example, the nursing task processing unit measures the patient's pulse with a sensor and collects data in real time. The nursing task processing unit analyzes the patient's facial expression using facial recognition technology and detects abnormalities. The nursing task processing unit can also quantify the patient's mood using a mood evaluation algorithm and detect abnormalities. This enables rapid response by quickly detecting abnormalities in the patient. Some or all of the above processing in the nursing task processing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the nursing task processing unit can input the patient's pulse data into a generative AI and have the generative AI perform abnormality detection.

[0034] The error detection unit can monitor the tasks that nurses are supposed to perform and detect any omissions or errors. For example, the error detection unit can detect medication errors. It can also detect recording errors. Furthermore, the error detection unit can detect patient mix-ups. By detecting omissions and errors, it is possible to prevent nurses from missing or failing to perform their duties. Some or all of the above-described processes in the error detection unit may be performed using AI, for example, or without AI. For example, the error detection unit can input medication records into AI and have the AI ​​perform medication error detection.

[0035] The care delivery unit can monitor the patient's health status in real time and provide appropriate care. For example, the care delivery unit can monitor the patient's vital signs and respond quickly if any abnormalities are detected. The care delivery unit can also respond with consideration for the patient's emotions. Furthermore, the care delivery unit can provide individualized care plans. This allows for the provision of appropriate care by monitoring the patient's health status in real time. Some or all of the above processes in the care delivery unit may be performed using AI, for example, or not. For example, the care delivery unit can input the patient's vital sign data into AI and have the AI ​​detect abnormalities.

[0036] The nursing task processing unit can analyze changes in a patient's facial expression in real time and detect abnormalities. For example, the nursing task processing unit can use facial expression recognition technology to analyze a patient's facial expression and detect abnormalities. The nursing task processing unit can detect abnormalities based on changes in facial expression. This allows for rapid detection of abnormalities by analyzing changes in a patient's facial expression in real time. Some or all of the above-described processes in the nursing task processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the nursing task processing unit can input the patient's facial expression data into a generative AI and have the generative AI perform abnormality detection.

[0037] The error detection unit can monitor nurses to prevent omissions and errors in their duties. For example, the error detection unit monitors the tasks that nurses are supposed to perform and detects omissions and errors. The error detection unit can detect medication errors. It can also detect recording errors. Furthermore, the error detection unit can detect patient mix-ups. This helps to improve work efficiency by preventing omissions and errors in nurses' duties. Some or all of the above-described processes in the error detection unit may be performed using AI, for example, or without AI. For example, the error detection unit can input medication records into AI and have the AI ​​perform medication error detection.

[0038] The nursing task processing unit can analyze a patient's past medical history and select the optimal method for handling nursing tasks. For example, the nursing task processing unit can use AI to analyze a patient's past medical history and propose the optimal nursing method for a specific medical condition. The nursing task processing unit can also use AI to analyze a patient's past treatment history and select an effective nursing method for similar symptoms. Furthermore, the nursing task processing unit can use AI to refer to a patient's past allergy information and select a nursing method to avoid allergic reactions. In this way, the optimal method for handling nursing tasks can be selected by analyzing a patient's past medical history. Some or all of the above-described processes in the nursing task processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the nursing task processing unit can input patient medical history data into a generative AI and have the generative AI select the optimal nursing method.

[0039] The nursing task processing unit can create individualized nursing plans based on the patient's lifestyle and environment during nursing task processing. For example, the nursing task processing unit can use AI to analyze the patient's lifestyle (diet, exercise, etc.) and create an individualized nursing plan based on that analysis. The nursing task processing unit can also use AI to consider the patient's living environment (home environment, social background, etc.) and propose an optimal nursing plan. Furthermore, the nursing task processing unit can use AI to analyze the patient's occupation and daily routine and create a nursing plan tailored to that. By creating individualized nursing plans based on the patient's lifestyle and environment, more appropriate nursing care can be provided. Some or all of the above processing in the nursing task processing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the nursing task processing unit can input patient lifestyle data into a generative AI and have the generative AI create an individualized nursing plan.

[0040] The nursing task processing unit can adjust nursing plans while considering the opinions and wishes of the patient's family. For example, the nursing task processing unit can use AI to collect the nursing methods desired by the patient's family and adjust the nursing plan based on that. The nursing task processing unit can use AI to analyze information provided by the patient's family and propose the optimal nursing plan. The nursing task processing unit can also use AI to consider the specific care needs of the patient's family and adjust the nursing plan based on that. This allows for the provision of more appropriate nursing plans by considering the opinions and wishes of the patient's family. Some or all of the above processes in the nursing task processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the nursing task processing unit can input the opinions and wishes of the patient's family into a generative AI and have the generative AI perform the adjustment of the nursing plan.

[0041] The nursing task processing unit can adjust nursing plans by considering the patient's social and cultural background when processing nursing tasks. For example, the nursing task processing unit can use AI to analyze the patient's social background (occupation, family environment, etc.) and adjust the nursing plan based on that analysis. The nursing task processing unit can also use AI to consider the patient's cultural background (religion, customs, etc.) and propose the most appropriate nursing plan. Furthermore, the nursing task processing unit can use AI to analyze the patient's social network (friends, community, etc.) and adjust the nursing plan based on that analysis. This allows for the provision of more appropriate nursing plans by considering the patient's social and cultural background. Some or all of the above processing in the nursing task processing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the nursing task processing unit can input the patient's social background data into a generative AI and have the generative AI perform the adjustment of the nursing plan.

[0042] The error detection unit can analyze past error data, identify error patterns, and propose preventive measures. For example, the error detection unit can use AI to analyze past error data and identify errors that are more likely to occur during specific time periods. The error detection unit can use AI to analyze past error data and identify error patterns in specific tasks. Furthermore, the error detection unit can use AI to refer to past error data and identify error tendencies in specific nurses. In this way, by analyzing past error data, error patterns can be identified and preventive measures can be proposed. Some or all of the above processing in the error detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the error detection unit can input past error data into a generative AI and have the generative AI identify error patterns.

[0043] The error detection unit can assess the risk of error based on the nurse's experience and skill level when an error is detected. For example, the error detection unit may use AI to refer to the nurse's years of experience and assess the risk of error. The error detection unit may also use AI to analyze the nurse's skill level and assess the risk of error. Furthermore, the error detection unit can also use AI to analyze the nurse's past work history and assess the risk of error. This allows for a more accurate risk assessment by evaluating the risk of error based on the nurse's experience and skill level. Some or all of the above processing in the error detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the error detection unit can input the nurse's experience data into a generative AI and have the generative AI perform the risk assessment.

[0044] The error detection unit can assess the risk of error by considering the nurse's work status and fatigue level when an error is detected. For example, the error detection unit can use AI to refer to the nurse's work hours and assess the risk of error based on the fatigue level. The error detection unit can also use AI to analyze the nurse's rest status and assess the risk of error based on the fatigue level. Furthermore, the error detection unit can use AI to analyze the nurse's work shifts and assess the risk of error based on the fatigue level. This allows for a more accurate assessment of the risk of error by considering the nurse's work status and fatigue level. Some or all of the above processing in the error detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the error detection unit can input the nurse's work data into a generating AI and have the generating AI perform the risk assessment.

[0045] The error detection unit can analyze the nurse's communication history to assess the risk of error when an error is detected. For example, the error detection unit can use AI to analyze the nurse's communication history and assess the risk of error. The error detection unit can also use AI to analyze the frequency of communication within the nurse's team and assess the risk of error. Furthermore, the error detection unit can use AI to refer to the content of the nurse's communication and assess the risk of error. This allows for a more accurate assessment of the risk of error by analyzing the nurse's communication history. Some or all of the above processing in the error detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the error detection unit can input the nurse's communication data into a generative AI and have the generative AI perform the risk assessment.

[0046] The care delivery unit can analyze a patient's past care history and select the optimal care method. For example, the care delivery unit can use AI to analyze a patient's past care history and propose the optimal care method for a specific medical condition. The care delivery unit can use AI to analyze a patient's past treatment history and select an effective care method for similar symptoms. The care delivery unit can also use AI to refer to a patient's past allergy information and select a care method to avoid allergic reactions. In this way, the optimal care method can be selected by analyzing a patient's past care history. Some or all of the above processes in the care delivery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the care delivery unit can input patient care history data into a generative AI and have the generative AI select the optimal care method.

[0047] The care delivery unit can create a care plan based on the patient's living environment and family support situation when providing care. For example, the care delivery unit can use AI to consider the patient's living environment (home environment, social background, etc.) and propose an optimal care plan. The care delivery unit can use AI to analyze the patient's family support situation and create a care plan based on that analysis. The care delivery unit can also use AI to consider the patient's living environment (home environment, social background, etc.) and adjust the care plan accordingly. By creating a care plan based on the patient's living environment and family support situation, more appropriate care can be provided. Some or all of the above processes in the care delivery unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the care delivery unit can input patient living environment data into a generative AI and have the generative AI create a care plan.

[0048] The care delivery unit can adjust the content of care while considering the patient's hobbies and interests. For example, the care delivery unit can use AI to analyze the patient's hobbies (music, reading, etc.) and adjust the care content based on that. The care delivery unit can also use AI to consider the patient's interests (sports, art, etc.) and suggest the most appropriate care method. Furthermore, the care delivery unit can use AI to refer to the patient's past hobbies and interests and adjust the care content based on that. This allows for the provision of more appropriate care by considering the patient's hobbies and interests. Some or all of the above processes in the care delivery unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the care delivery unit can input data on the patient's hobbies and interests into a generative AI and have the generative AI perform the adjustment of the care content.

[0049] The care delivery department can enhance care support by leveraging the patient's social network during care delivery. For example, the care delivery department can use AI to analyze the patient's communication history with friends and family and enhance care support based on that analysis. The care delivery department can also use AI to analyze the patient's social network (friends, community, etc.) and adjust the care plan accordingly. Furthermore, the care delivery department can use AI to consider the patient's social background (occupation, family environment, etc.) and propose optimal care support. In this way, care support is enhanced by leveraging the patient's social network. Some or all of the above processes in the care delivery department may be performed using, for example, generative AI, or not. For example, the care delivery department can input the patient's social network data into generative AI and have the generative AI perform the enhancement of care support.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] Medical support systems can also be equipped with functions to monitor patients' lifestyles and support health management. For example, the nursing operations processing unit can record the patient's diet and exercise levels using sensors and evaluate their health status. Furthermore, the error detection unit can analyze the patient's lifestyle data and predict health risks. In addition, the care delivery unit can provide personalized health advice based on the patient's lifestyle. This enables comprehensive health management that takes the patient's lifestyle into consideration.

[0052] Medical support systems can provide care that takes into account the patient's cultural background. For example, the nursing operations processing unit can record the patient's religion and cultural customs and adjust care accordingly. Furthermore, the error detection unit can detect specific care needs based on cultural background. In addition, the care delivery unit can provide care plans that respect the patient's cultural background. This enables the provision of individualized care that takes the patient's cultural background into consideration.

[0053] The medical support system can analyze a patient's past medical history and propose the optimal care method. For example, the nursing task processing unit uses AI to analyze a patient's past medical history and propose the optimal care method for a specific medical condition. The error detection unit uses AI to analyze past treatment history and select effective care methods for similar symptoms. Furthermore, the care provision unit uses AI to refer to the patient's past allergy information and select care methods to avoid allergic reactions. In this way, by analyzing a patient's past medical history, the system can provide the optimal care method.

[0054] Medical support systems can adjust care content by taking into account the patient's hobbies and interests. For example, the nursing task processing unit uses AI to analyze the patient's hobbies (music, reading, etc.) and adjust care content accordingly. The error detection unit also uses AI to consider the patient's interests (sports, art, etc.) and suggest the most appropriate care method. Furthermore, the care delivery unit uses AI to refer to the patient's past hobbies and interests and adjust care content accordingly. This allows for the provision of more appropriate care by considering the patient's hobbies and interests.

[0055] The medical support system can adjust care plans to take into account the opinions and wishes of the patient's family. For example, the nursing operations processing unit uses AI to collect information on the care methods desired by the patient's family and adjust the care plan accordingly. The error detection unit uses AI to analyze information provided by the patient's family and propose the optimal care plan. Furthermore, the care delivery unit uses AI to consider the specific care needs of the patient's family and adjust the care plan accordingly. This allows for the provision of more appropriate care plans by considering the opinions and wishes of the patient's family.

[0056] The medical support system can assess the risk of errors by considering nurses' work status and fatigue levels. For example, the nursing work processing unit uses AI to reference nurses' work hours and assess the risk of errors based on their fatigue levels. The error detection unit also uses AI to analyze nurses' rest periods and assess the risk of errors based on their fatigue levels. Furthermore, the care delivery unit uses AI to analyze nurses' work shifts and assess the risk of errors based on their fatigue levels. This allows for a more accurate assessment of the risk of errors by considering nurses' work status and fatigue levels.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The nursing task processing unit handles nursing tasks. Specifically, it measures patients' vital signs, administers medication, and manages records. For example, it measures the patient's pulse rate with a sensor and collects data in real time. It also analyzes the patient's facial expressions using facial recognition technology and detects abnormalities. Furthermore, it can quantify the patient's mood using a mood evaluation algorithm and detect abnormalities. Step 2: The error detection unit detects human errors based on the tasks processed by the nursing task processing unit. Specifically, it monitors the tasks that nurses should perform and detects omissions and errors. For example, it can detect medication errors, record-keeping errors, and patient mix-ups. It monitors medication records to detect medication errors, monitors the record management system to detect record-keeping errors, and monitors the patient identification system to detect patient mix-ups. Step 3: The care delivery unit provides patient-centered care based on errors detected by the error detection unit. Specifically, it monitors the patient's health status in real time and provides appropriate care. For example, it monitors the patient's vital signs and responds quickly if there are any abnormalities. It also provides care that takes the patient's emotions into consideration and provides an individualized care plan. It analyzes the patient's emotions, responds appropriately, and creates and provides a care plan that meets the patient's needs.

[0059] (Example of form 2) The medical support system according to an embodiment of the present invention is a system that combines the power of AI and humans to create a new future for medicine. This medical support system improves accuracy and efficiency by having AI process a vast amount of nursing work and detect human errors. Next, by utilizing the workload reduced by AI, humans can concentrate on providing personalized care to each patient. This aims to streamline the work of medical professionals such as nurses and provide higher quality medical services. For example, the medical support system has AI quantify the patient's pulse, facial expressions, mood, etc., and quickly detect abnormalities in the patient. For example, the AI ​​can analyze changes in the patient's facial expressions in real time and detect abnormalities. This allows nurses to respond quickly. Next, the AI ​​detects human errors. For example, the AI ​​can monitor the tasks that nurses should perform and detect omissions and errors. This allows nurses to prevent omissions and errors in their work and perform their duties efficiently. Furthermore, by utilizing the workload reduced by AI, humans can concentrate on providing personalized care to each patient. For example, nurses can grasp the patient's health condition in real time and provide appropriate care to the patient. This allows patients to receive higher quality medical services. This system streamlines the work of healthcare professionals such as nurses, enabling them to provide higher quality medical services. For example, nurses can perform their duties efficiently without becoming exhausted. Doctors can minimize medical errors, and even new doctors can provide high-quality medical services. Furthermore, patients can have a smooth and stress-free hospital stay. As a result, the medical support system can efficiently handle nursing tasks, detect human errors, and provide patient-centered care.

[0060] The medical support system according to this embodiment comprises a nursing task processing unit, an error detection unit, and a care provision unit. The nursing task processing unit processes nursing tasks. For example, the nursing task processing unit measures the patient's vital signs, administers medication, and manages records. For example, the nursing task processing unit measures the patient's pulse and issues an alert if there is an abnormality. The nursing task processing unit can also analyze the patient's facial expressions and detect abnormalities. Furthermore, the nursing task processing unit can evaluate the patient's mood and detect abnormalities. For example, the nursing task processing unit measures the patient's pulse with a sensor and collects data in real time. The nursing task processing unit analyzes the patient's facial expressions using facial recognition technology and detects abnormalities. The nursing task processing unit quantifies the patient's mood using a mood evaluation algorithm and detects abnormalities. The error detection unit detects human errors based on the tasks processed by the nursing task processing unit. For example, the error detection unit monitors the tasks that nurses should perform and detects omissions or errors. The error detection unit can, for example, detect medication errors. Furthermore, the error detection unit can also detect recording errors. In addition, the error detection unit can also detect patient mix-ups. For example, the error detection unit monitors medication records and detects medication errors. The error detection unit monitors the record management system and detects recording errors. The error detection unit monitors the patient identification system and detects patient mix-ups. The care delivery unit provides patient-centered care based on the errors detected by the error detection unit. For example, the care delivery unit can grasp the patient's health status in real time and provide appropriate care. For example, the care delivery unit can monitor the patient's vital signs and respond quickly if there are any abnormalities. In addition, the care delivery unit can also respond with consideration for the patient's emotions. Furthermore, the care delivery unit can also provide individualized care plans. For example, the care delivery unit monitors the patient's vital signs and issues an alert if there are any abnormalities. The care delivery unit analyzes the patient's emotions and responds appropriately. The care delivery unit creates and provides care plans that meet the patient's needs. As a result, the medical support system according to this embodiment can efficiently handle nursing tasks, detect human errors, and provide patient-centered care.

[0061] The nursing operations processing unit handles nursing tasks. For example, it measures patients' vital signs, administers medication, and manages records. Specifically, it uses sensors to measure vital signs such as pulse, blood pressure, temperature, and respiratory rate, collecting data in real time. This data is transmitted to a central database for access by medical staff. The nursing operations processing unit can also analyze patients' facial expressions and detect abnormalities. Using facial recognition technology, it analyzes the patient's facial expressions to detect signs of pain, discomfort, stress, and other issues. Furthermore, the nursing operations processing unit can evaluate patients' moods and detect abnormalities. Using a mood evaluation algorithm, it quantifies the patient's mood and detects abnormalities. For example, it analyzes the patient's tone of voice, speaking style, and behavioral patterns to monitor changes in mood. This allows the nursing operations processing unit to comprehensively understand the patient's health status and respond quickly if abnormalities are detected. In addition, the nursing operations processing unit also manages medication. It manages medication schedules and administers medications at the appropriate times. To prevent medication errors, it uses barcode scanning and RFID tags to identify medications and record administration. This allows the nursing operations processing unit to ensure patient safety and improve the efficiency of nursing operations.

[0062] The error detection unit detects human errors based on tasks processed by the nursing task processing unit. Specifically, it monitors tasks that nurses should perform and detects omissions and errors. For example, it can detect medication errors. By monitoring medication records and comparing the medication schedule with the actual medication history, it detects medication errors. The error detection unit can also detect recording errors. By monitoring the record management system and checking the consistency of the entered data, it detects recording errors. Furthermore, the error detection unit can also detect patient mix-ups. By monitoring the patient identification system and comparing the patient's ID with the treatment details, it prevents patient mix-ups. For example, it scans the barcode or RFID tag attached to the patient's wristband to confirm the patient's ID and treatment details. In this way, the error detection unit can ensure the accuracy of nursing tasks and protect patient safety. In addition, the error detection unit can analyze data using AI and detect abnormal patterns and trends. For example, based on past data, it can predict errors that are likely to occur at specific times or in specific situations and issue warnings in advance. This allows the error detection unit to improve the quality of nursing work and enhance the reliability of the medical field.

[0063] The care delivery department provides patient-centered care based on errors detected by the error detection department. Specifically, it monitors the patient's health status in real time and provides appropriate care. For example, it monitors the patient's vital signs and responds quickly if there are any abnormalities. Vital sign data is updated in real time, allowing medical staff to respond immediately. The care delivery department can also respond with consideration for the patient's emotions. It analyzes the patient's facial expressions, tone of voice, and behavioral patterns to understand changes in emotions. This allows it to respond appropriately if the patient is feeling anxious or stressed, thereby increasing the patient's sense of security. Furthermore, the care delivery department can provide individualized care plans. It creates and provides care plans tailored to the patient's needs and condition. For example, it proposes the optimal treatment and care based on the patient's medical history and current health status. This allows the care delivery department to provide patient-centered care and improve patient satisfaction. In addition, the care delivery department can build trust by emphasizing communication with patients and their families. For example, it incorporates the opinions and requests of patients and their families through regular counseling and feedback and reflects them in the care plan. This allows the care delivery department to realize patient-centered medical care and improve patients' quality of life (QOL).

[0064] The nursing task processing unit can quantify the patient's pulse, facial expression, mood, etc., and quickly detect abnormalities in the patient. For example, the nursing task processing unit measures the patient's pulse with a sensor and collects data in real time. The nursing task processing unit analyzes the patient's facial expression using facial recognition technology and detects abnormalities. The nursing task processing unit can also quantify the patient's mood using a mood evaluation algorithm and detect abnormalities. This enables rapid response by quickly detecting abnormalities in the patient. Some or all of the above processing in the nursing task processing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the nursing task processing unit can input the patient's pulse data into a generative AI and have the generative AI perform abnormality detection.

[0065] The error detection unit can monitor the tasks that nurses are supposed to perform and detect any omissions or errors. For example, the error detection unit can detect medication errors. It can also detect recording errors. Furthermore, the error detection unit can detect patient mix-ups. By detecting omissions and errors, it is possible to prevent nurses from missing or failing to perform their duties. Some or all of the above-described processes in the error detection unit may be performed using AI, for example, or without AI. For example, the error detection unit can input medication records into AI and have the AI ​​perform medication error detection.

[0066] The care delivery unit can monitor the patient's health status in real time and provide appropriate care. For example, the care delivery unit can monitor the patient's vital signs and respond quickly if any abnormalities are detected. The care delivery unit can also respond with consideration for the patient's emotions. Furthermore, the care delivery unit can provide individualized care plans. This allows for the provision of appropriate care by monitoring the patient's health status in real time. Some or all of the above processes in the care delivery unit may be performed using AI, for example, or not. For example, the care delivery unit can input the patient's vital sign data into AI and have the AI ​​detect abnormalities.

[0067] The nursing task processing unit can analyze changes in a patient's facial expression in real time and detect abnormalities. For example, the nursing task processing unit can use facial expression recognition technology to analyze a patient's facial expression and detect abnormalities. The nursing task processing unit can detect abnormalities based on changes in facial expression. This allows for rapid detection of abnormalities by analyzing changes in a patient's facial expression in real time. Some or all of the above-described processes in the nursing task processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the nursing task processing unit can input the patient's facial expression data into a generative AI and have the generative AI perform abnormality detection.

[0068] The error detection unit can monitor nurses to prevent omissions and errors in their duties. For example, the error detection unit monitors the tasks that nurses are supposed to perform and detects omissions and errors. The error detection unit can detect medication errors. It can also detect recording errors. Furthermore, the error detection unit can detect patient mix-ups. This helps to improve work efficiency by preventing omissions and errors in nurses' duties. Some or all of the above-described processes in the error detection unit may be performed using AI, for example, or without AI. For example, the error detection unit can input medication records into AI and have the AI ​​perform medication error detection.

[0069] The nursing task processing unit can estimate a patient's emotions and adjust the priority of nursing tasks based on the estimated emotions. For example, if a patient is feeling anxious, the AI ​​in the nursing task processing unit can detect this emotion and instruct the nurse to prioritize it. If a patient is relaxed, the AI ​​in the nursing task processing unit can detect this emotion and adjust the tasks to prioritize other high-priority tasks. The nursing task processing unit can also detect if a patient is in pain and adjust nursing tasks to prioritize pain relief. By adjusting the priority of nursing tasks based on the patient's emotions, more appropriate nursing care can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the nursing task processing unit may be performed using AI or not using AI. For example, the nursing task processing unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The nursing task processing unit can analyze a patient's past medical history and select the optimal method for handling nursing tasks. For example, the nursing task processing unit can use AI to analyze a patient's past medical history and propose the optimal nursing method for a specific medical condition. The nursing task processing unit can also use AI to analyze a patient's past treatment history and select an effective nursing method for similar symptoms. Furthermore, the nursing task processing unit can use AI to refer to a patient's past allergy information and select a nursing method to avoid allergic reactions. In this way, the optimal method for handling nursing tasks can be selected by analyzing a patient's past medical history. Some or all of the above-described processes in the nursing task processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the nursing task processing unit can input patient medical history data into a generative AI and have the generative AI select the optimal nursing method.

[0071] The nursing task processing unit can create individualized nursing plans based on the patient's lifestyle and environment during nursing task processing. For example, the nursing task processing unit can use AI to analyze the patient's lifestyle (diet, exercise, etc.) and create an individualized nursing plan based on that analysis. The nursing task processing unit can also use AI to consider the patient's living environment (home environment, social background, etc.) and propose an optimal nursing plan. Furthermore, the nursing task processing unit can use AI to analyze the patient's occupation and daily routine and create a nursing plan tailored to that. By creating individualized nursing plans based on the patient's lifestyle and environment, more appropriate nursing care can be provided. Some or all of the above processing in the nursing task processing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the nursing task processing unit can input patient lifestyle data into a generative AI and have the generative AI create an individualized nursing plan.

[0072] The nursing task processing unit can estimate a patient's emotions and adjust the content of nursing tasks based on the estimated emotions. For example, if a patient is feeling anxious, the AI ​​in the nursing task processing unit can detect this emotion and prioritize nursing tasks to help the patient relax. If a patient is relaxed, the AI ​​in the nursing task processing unit can detect this emotion and adjust the tasks to prioritize other high-priority tasks. The nursing task processing unit can also detect if a patient is in pain and adjust the nursing tasks to prioritize pain relief. By adjusting the content of nursing tasks based on the patient's emotions, more appropriate nursing care can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the nursing task processing unit may be performed using AI, for example, or not using AI. For example, the nursing task processing unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The nursing task processing unit can adjust nursing plans while considering the opinions and wishes of the patient's family. For example, the nursing task processing unit can use AI to collect the nursing methods desired by the patient's family and adjust the nursing plan based on that. The nursing task processing unit can use AI to analyze information provided by the patient's family and propose the optimal nursing plan. The nursing task processing unit can also use AI to consider the specific care needs of the patient's family and adjust the nursing plan based on that. This allows for the provision of more appropriate nursing plans by considering the opinions and wishes of the patient's family. Some or all of the above processes in the nursing task processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the nursing task processing unit can input the opinions and wishes of the patient's family into a generative AI and have the generative AI perform the adjustment of the nursing plan.

[0074] The nursing task processing unit can adjust nursing plans by considering the patient's social and cultural background when processing nursing tasks. For example, the nursing task processing unit can use AI to analyze the patient's social background (occupation, family environment, etc.) and adjust the nursing plan based on that analysis. The nursing task processing unit can also use AI to consider the patient's cultural background (religion, customs, etc.) and propose the most appropriate nursing plan. Furthermore, the nursing task processing unit can use AI to analyze the patient's social network (friends, community, etc.) and adjust the nursing plan based on that analysis. This allows for the provision of more appropriate nursing plans by considering the patient's social and cultural background. Some or all of the above processing in the nursing task processing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the nursing task processing unit can input the patient's social background data into a generative AI and have the generative AI perform the adjustment of the nursing plan.

[0075] The error detection unit can estimate the nurse's emotions and adjust the accuracy of error detection based on the estimated emotions. For example, if the nurse is tired, the AI ​​in the error detection unit can detect that emotion and improve the accuracy of error detection. If the nurse is stressed, the AI ​​in the error detection unit can detect that emotion and adjust the accuracy of error detection. The error detection unit can also maintain normal error detection accuracy if the nurse is relaxed. By adjusting the accuracy of error detection based on the nurse's emotions, more accurate error detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the error detection unit may be performed using AI, for example, or not using AI. For example, the error detection unit can input the nurse's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The error detection unit can analyze past error data, identify error patterns, and propose preventive measures. For example, the error detection unit can use AI to analyze past error data and identify errors that are more likely to occur during specific time periods. The error detection unit can use AI to analyze past error data and identify error patterns in specific tasks. Furthermore, the error detection unit can use AI to refer to past error data and identify error tendencies in specific nurses. In this way, by analyzing past error data, error patterns can be identified and preventive measures can be proposed. Some or all of the above processing in the error detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the error detection unit can input past error data into a generative AI and have the generative AI identify error patterns.

[0077] The error detection unit can assess the risk of error based on the nurse's experience and skill level when an error is detected. For example, the error detection unit may use AI to refer to the nurse's years of experience and assess the risk of error. The error detection unit may also use AI to analyze the nurse's skill level and assess the risk of error. Furthermore, the error detection unit can also use AI to analyze the nurse's past work history and assess the risk of error. This allows for a more accurate risk assessment by evaluating the risk of error based on the nurse's experience and skill level. Some or all of the above processing in the error detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the error detection unit can input the nurse's experience data into a generative AI and have the generative AI perform the risk assessment.

[0078] The error detection unit can estimate the nurse's emotions and adjust the timing of error detection based on the estimated emotions. For example, if the nurse is tired, the AI ​​in the error detection unit can detect that emotion and increase the frequency of error detection. If the nurse is stressed, the AI ​​in the error detection unit can detect that emotion and adjust the timing of error detection. The error detection unit can also maintain the normal timing of error detection if the nurse is relaxed. By adjusting the timing of error detection based on the nurse's emotions, it becomes possible to detect errors at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the error detection unit may be performed using AI, for example, or not using AI. For example, the error detection unit can input the nurse's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The error detection unit can assess the risk of error by considering the nurse's work status and fatigue level when an error is detected. For example, the error detection unit can use AI to refer to the nurse's work hours and assess the risk of error based on the fatigue level. The error detection unit can also use AI to analyze the nurse's rest status and assess the risk of error based on the fatigue level. Furthermore, the error detection unit can use AI to analyze the nurse's work shifts and assess the risk of error based on the fatigue level. This allows for a more accurate assessment of the risk of error by considering the nurse's work status and fatigue level. Some or all of the above processing in the error detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the error detection unit can input the nurse's work data into a generating AI and have the generating AI perform the risk assessment.

[0080] The error detection unit can analyze the nurse's communication history to assess the risk of error when an error is detected. For example, the error detection unit can use AI to analyze the nurse's communication history and assess the risk of error. The error detection unit can also use AI to analyze the frequency of communication within the nurse's team and assess the risk of error. Furthermore, the error detection unit can use AI to refer to the content of the nurse's communication and assess the risk of error. This allows for a more accurate assessment of the risk of error by analyzing the nurse's communication history. Some or all of the above processing in the error detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the error detection unit can input the nurse's communication data into a generative AI and have the generative AI perform the risk assessment.

[0081] The care delivery unit can estimate the patient's emotions and adjust the content of care based on the estimated emotions. For example, if the patient is feeling anxious, the AI ​​can detect that emotion and provide care to help them relax. If the patient is relaxed, the AI ​​can detect that emotion and prioritize other more urgent care needs. The care delivery unit can also detect if the patient is in pain and provide care that prioritizes pain relief. By adjusting the content of care based on the patient's emotions, more appropriate care can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the care delivery unit may be performed using AI or not using AI. For example, the care delivery unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The care delivery unit can analyze a patient's past care history and select the optimal care method. For example, the care delivery unit can use AI to analyze a patient's past care history and propose the optimal care method for a specific medical condition. The care delivery unit can use AI to analyze a patient's past treatment history and select an effective care method for similar symptoms. The care delivery unit can also use AI to refer to a patient's past allergy information and select a care method to avoid allergic reactions. In this way, the optimal care method can be selected by analyzing a patient's past care history. Some or all of the above processes in the care delivery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the care delivery unit can input patient care history data into a generative AI and have the generative AI select the optimal care method.

[0083] The care delivery unit can create a care plan based on the patient's living environment and family support situation when providing care. For example, the care delivery unit can use AI to consider the patient's living environment (home environment, social background, etc.) and propose an optimal care plan. The care delivery unit can use AI to analyze the patient's family support situation and create a care plan based on that analysis. The care delivery unit can also use AI to consider the patient's living environment (home environment, social background, etc.) and adjust the care plan accordingly. By creating a care plan based on the patient's living environment and family support situation, more appropriate care can be provided. Some or all of the above processes in the care delivery unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the care delivery unit can input patient living environment data into a generative AI and have the generative AI create a care plan.

[0084] The care delivery unit can estimate the patient's emotions and adjust the timing of care based on those emotions. For example, if the patient is feeling anxious, the AI ​​can detect that emotion and prioritize providing care to help the patient relax. If the patient is relaxed, the AI ​​can detect that emotion and prioritize other, more urgent care. The care delivery unit can also detect if the patient is in pain and prioritize pain relief. By adjusting the timing of care based on the patient's emotions, care can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the care delivery unit may be performed using AI or not using AI. For example, the care delivery unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The care delivery unit can adjust the content of care while considering the patient's hobbies and interests. For example, the care delivery unit can use AI to analyze the patient's hobbies (music, reading, etc.) and adjust the care content based on that. The care delivery unit can also use AI to consider the patient's interests (sports, art, etc.) and suggest the most appropriate care method. Furthermore, the care delivery unit can use AI to refer to the patient's past hobbies and interests and adjust the care content based on that. This allows for the provision of more appropriate care by considering the patient's hobbies and interests. Some or all of the above processes in the care delivery unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the care delivery unit can input data on the patient's hobbies and interests into a generative AI and have the generative AI perform the adjustment of the care content.

[0086] The care delivery department can enhance care support by leveraging the patient's social network during care delivery. For example, the care delivery department can use AI to analyze the patient's communication history with friends and family and enhance care support based on that analysis. The care delivery department can also use AI to analyze the patient's social network (friends, community, etc.) and adjust the care plan accordingly. Furthermore, the care delivery department can use AI to consider the patient's social background (occupation, family environment, etc.) and propose optimal care support. In this way, care support is enhanced by leveraging the patient's social network. Some or all of the above processes in the care delivery department may be performed using, for example, generative AI, or not. For example, the care delivery department can input the patient's social network data into generative AI and have the generative AI perform the enhancement of care support.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] Medical support systems can also be equipped with functions to monitor patients' lifestyles and support health management. For example, the nursing operations processing unit can record the patient's diet and exercise levels using sensors and evaluate their health status. Furthermore, the error detection unit can analyze the patient's lifestyle data and predict health risks. In addition, the care delivery unit can provide personalized health advice based on the patient's lifestyle. This enables comprehensive health management that takes the patient's lifestyle into consideration.

[0089] Medical support systems can leverage patients' social networks to alleviate feelings of isolation. For example, the nursing operations processing unit records the patient's communication history with friends and family to maintain social connections. The error detection unit monitors changes in the patient's social network to detect the risk of isolation. Furthermore, the care delivery unit can utilize the patient's social network to provide support to reduce feelings of isolation. This strengthens the patient's social connections and supports their mental health.

[0090] Medical support systems can provide care that takes into account the patient's cultural background. For example, the nursing operations processing unit can record the patient's religion and cultural customs and adjust care accordingly. Furthermore, the error detection unit can detect specific care needs based on cultural background. In addition, the care delivery unit can provide care plans that respect the patient's cultural background. This enables the provision of individualized care that takes the patient's cultural background into consideration.

[0091] The medical support system can estimate a patient's emotions and adjust the timing of care based on those emotions. For example, the nursing task processing unit prioritizes relaxation care if the patient is feeling anxious. Similarly, the error detection unit can adjust the timing of care if the patient is stressed, as this increases the risk of errors. Furthermore, the care delivery unit can prioritize other, more urgent care if the patient is relaxed. This allows for the provision of optimal care timing based on the patient's emotions.

[0092] The medical support system can analyze a patient's past medical history and propose the optimal care method. For example, the nursing task processing unit uses AI to analyze a patient's past medical history and propose the optimal care method for a specific medical condition. The error detection unit uses AI to analyze past treatment history and select effective care methods for similar symptoms. Furthermore, the care provision unit uses AI to refer to the patient's past allergy information and select care methods to avoid allergic reactions. In this way, by analyzing a patient's past medical history, the system can provide the optimal care method.

[0093] The medical support system can estimate a nurse's emotions and adjust their workload based on those emotions. For example, the nursing workload processing unit reduces the workload if the nurse is tired. The error detection unit can also adjust the workload to increase the risk of errors if the nurse is stressed. Furthermore, the care delivery unit can maintain a normal workload if the nurse is relaxed. This allows for optimal workload adjustment based on the nurse's emotions.

[0094] Medical support systems can adjust care content by taking into account the patient's hobbies and interests. For example, the nursing task processing unit uses AI to analyze the patient's hobbies (music, reading, etc.) and adjust care content accordingly. The error detection unit also uses AI to consider the patient's interests (sports, art, etc.) and suggest the most appropriate care method. Furthermore, the care delivery unit uses AI to refer to the patient's past hobbies and interests and adjust care content accordingly. This allows for the provision of more appropriate care by considering the patient's hobbies and interests.

[0095] The medical support system can adjust care plans to take into account the opinions and wishes of the patient's family. For example, the nursing operations processing unit uses AI to collect information on the care methods desired by the patient's family and adjust the care plan accordingly. The error detection unit uses AI to analyze information provided by the patient's family and propose the optimal care plan. Furthermore, the care delivery unit uses AI to consider the specific care needs of the patient's family and adjust the care plan accordingly. This allows for the provision of more appropriate care plans by considering the opinions and wishes of the patient's family.

[0096] The medical support system can assess the risk of errors by considering nurses' work status and fatigue levels. For example, the nursing work processing unit uses AI to reference nurses' work hours and assess the risk of errors based on their fatigue levels. The error detection unit also uses AI to analyze nurses' rest periods and assess the risk of errors based on their fatigue levels. Furthermore, the care delivery unit uses AI to analyze nurses' work shifts and assess the risk of errors based on their fatigue levels. This allows for a more accurate assessment of the risk of errors by considering nurses' work status and fatigue levels.

[0097] The medical support system can estimate a patient's emotions and adjust the content of care based on those emotions. For example, if the nursing task processing unit is feeling anxious, it will prioritize care to help the patient relax. Similarly, if the error detection unit is experiencing pain, it can prioritize pain relief. Furthermore, if the patient is relaxed, the care delivery unit can prioritize other, more urgent care needs. This allows for the provision of optimal care based on the patient's emotions.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The nursing task processing unit handles nursing tasks. Specifically, it measures patients' vital signs, administers medication, and manages records. For example, it measures the patient's pulse rate with a sensor and collects data in real time. It also analyzes the patient's facial expressions using facial recognition technology and detects abnormalities. Furthermore, it can quantify the patient's mood using a mood evaluation algorithm and detect abnormalities. Step 2: The error detection unit detects human errors based on the tasks processed by the nursing task processing unit. Specifically, it monitors the tasks that nurses should perform and detects omissions and errors. For example, it can detect medication errors, record-keeping errors, and patient mix-ups. It monitors medication records to detect medication errors, monitors the record management system to detect record-keeping errors, and monitors the patient identification system to detect patient mix-ups. Step 3: The care delivery unit provides patient-centered care based on errors detected by the error detection unit. Specifically, it monitors the patient's health status in real time and provides appropriate care. For example, it monitors the patient's vital signs and responds quickly if there are any abnormalities. It also provides care that takes the patient's emotions into consideration and provides an individualized care plan. It analyzes the patient's emotions, responds appropriately, and creates and provides a care plan that meets the patient's needs.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the nursing task processing unit, error detection unit, and care provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the nursing task processing unit measures the patient's vital signs using the sensors and camera 42 of the smart device 14 and analyzes them by the identification processing unit 290 of the data processing unit 12. The error detection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 to monitor the nurse's work and detect any omissions or errors. The care provision unit is implemented by, for example, the control unit 46A of the smart device 14 to grasp the patient's health status in real time and provide appropriate care. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the nursing task processing unit, error detection unit, and care provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the nursing task processing unit measures the patient's vital signs using the camera 42 and microphone 238 of the smart glasses 214, and these are analyzed by the identification processing unit 290 of the data processing unit 12. The error detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and monitors the nurse's work to detect omissions and errors. The care provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and grasps the patient's health status in real time and provides appropriate care. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the nursing task processing unit, error detection unit, and care provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the nursing task processing unit measures the patient's vital signs using the camera 42 and microphone 238 of the headset terminal 314, and these are analyzed by the identification processing unit 290 of the data processing unit 12. The error detection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and monitors the nurse's work to detect omissions and errors. The care provision unit is implemented by, for example, the control unit 46A of the headset terminal 314, and grasps the patient's health status in real time and provides appropriate care. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the nursing task processing unit, error detection unit, and care provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the nursing task processing unit measures the patient's vital signs using the camera 42 and microphone 238 of the robot 414, and these are analyzed by the identification processing unit 290 of the data processing unit 12. The error detection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and monitors the nurse's work to detect omissions and errors. The care provision unit is implemented by, for example, the control unit 46A of the robot 414, and grasps the patient's health status in real time and provides appropriate care. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) Nursing task processing unit that handles nursing tasks, An error detection unit that detects human errors based on the tasks processed by the aforementioned nursing task processing unit, The system includes a care provision unit that provides patient-centered care based on errors detected by the error detection unit. A system characterized by the following features. (Note 2) The aforementioned nursing operations processing unit is The system quantifies the patient's pulse, facial expression, and mood to quickly detect any abnormalities in the patient. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned error detection unit, It monitors the tasks that nurses are supposed to perform and detects any omissions or errors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned care provision unit is To monitor patients' health status in real time and provide them with appropriate care. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned nursing operations processing unit is The system analyzes changes in the patient's facial expressions in real time to detect abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned error detection unit, Monitoring to prevent omissions and errors in nurses' duties. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned nursing operations processing unit is Estimate the patient's emotions and adjust nursing priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned nursing operations processing unit is Analyze the patient's past medical history and select the most appropriate method for handling nursing tasks. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned nursing operations processing unit is When performing nursing duties, create individualized nursing plans based on the patient's lifestyle and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned nursing operations processing unit is Estimate the patient's emotions and adjust nursing care based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned nursing operations processing unit is When performing nursing duties, adjust the nursing plan while taking into account the opinions and wishes of the patient's family. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned nursing operations processing unit is When performing nursing tasks, adjust the nursing plan considering the patient's social and cultural background. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned error detection unit, The system estimates the nurse's emotions and adjusts the accuracy of error detection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned error detection unit, We analyze past error data to identify error patterns and propose preventative measures. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned error detection unit, When an error is detected, the risk of the error is assessed based on the nurse's experience and skill level. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned error detection unit, The system estimates the nurse's emotions and adjusts the timing of error detection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned error detection unit, When an error is detected, the risk of the error is assessed by considering the nurse's work status and fatigue level. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned error detection unit, When an error is detected, the nurse's communication history is analyzed to assess the risk of the error. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned care provision unit is Estimate the patient's emotions and adjust the care based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned care provision unit is Analyze the patient's past care history and select the optimal care method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned care provision unit is When providing care, a care plan is created based on the patient's living environment and family support situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned care provision unit is The system estimates the patient's emotions and adjusts the timing of care based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned care provision unit is When providing care, we adjust the content of care to take into account the patient's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned care provision unit is When providing care, leverage the patient's social network to enhance support for that care. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. Nursing task processing unit that handles nursing tasks, An error detection unit that detects human errors based on the tasks processed by the aforementioned nursing task processing unit, The system includes a care provision unit that provides patient-centered care based on errors detected by the error detection unit. A system characterized by the following features.

2. The aforementioned nursing operations processing unit is The system quantifies the patient's pulse, facial expression, and mood to quickly detect any abnormalities in the patient. The system according to feature 1.

3. The aforementioned error detection unit, It monitors the tasks that nurses are supposed to perform and detects any omissions or errors. The system according to feature 1.

4. The aforementioned care provision unit is To monitor patients' health status in real time and provide them with appropriate care. The system according to feature 1.

5. The aforementioned nursing operations processing unit is The system analyzes changes in the patient's facial expressions in real time to detect abnormalities. The system according to feature 1.

6. The aforementioned error detection unit, Monitoring to prevent omissions and errors in nurses' duties. The system according to feature 1.

7. The aforementioned nursing operations processing unit is Estimate the patient's emotions and adjust nursing priorities based on those estimated emotions. The system according to feature 1.

8. The aforementioned nursing operations processing unit is Analyze the patient's past medical history and select the most appropriate method for handling nursing tasks. The system according to feature 1.

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