system

Abstract

Provide a system. 【Solution means】 Patient information storage means for storing information such as the medical history, symptom data, and medication history of the patient, Patient information analysis means for analyzing the recorded patient data and predicting the patient's health status and the timing of necessary medical treatment and medication, Reminder generation means for generating a reminder to prompt medical treatment and medication based on the analyzed patient information, Reminder notification means for notifying the generated reminder to the medical provider, Reminder presentation means for presenting the notified reminder to the medical provider visually or audibly, Means for updating the medical provider's input information for inputting the actions taken by the medical provider and the results thereof to keep the data on the server up-to-date, Means for presenting the reminder via a visual display device, A system including.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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 as a 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 a medical institution, it is very important for medical providers to effectively manage a large number of patients and provide medical treatment and medication guidance at appropriate times. However, due to the daily complicated operations and busyness, it is difficult to maintain the accuracy of schedule management and medication guidance. As a result, there may be delays and errors in patient care. Therefore, there is a demand for a system that can efficiently and accurately manage patient care.

Means for Solving the Problems

[0005] This invention uses patient information storage and analysis means to analyze past medical history and symptom data, and generates necessary reminders for each patient. Furthermore, by notifying healthcare providers of these reminders on their terminals and prompting them to provide necessary care at the appropriate time, the workload of healthcare professionals can be reduced and the quality of care can be maintained. This makes it possible to streamline patient management and improve patient satisfaction.

[0006] A "patient information storage system" is part of a system that stores and manages information such as each patient's medical history, symptom data, and medication history.

[0007] "Patient information analysis means" refers to technology used to analyze stored patient data and determine the optimal timing for medical treatment and care.

[0008] A "reminder generation means" is a system element that generates reminders to notify medical staff of the timing of patient consultations and medication administration based on analyzed data.

[0009] A "reminder notification method" is a function that sends generated reminders to terminals used by medical staff, providing necessary information at the appropriate time.

[0010] A "means of presenting reminders to healthcare providers" refers to an interface or device that visually or audibly notifies healthcare staff of a given reminder.

[0011] "Means for updating data based on input from healthcare providers" refers to a function that inputs actions performed by medical staff and their results into the system, thereby updating the database to the latest state. [Brief explanation of the drawing]

[0012] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] The 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.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention relates to a method and system for efficiently managing patient care in a healthcare system. The system aims to improve patient management in healthcare facilities by collecting, analyzing, and notifying information between servers, terminals, and users (healthcare providers).

[0034] First, the server accesses the medical institution's database and stores patient information, including each patient's medical history, symptom data, and medication history. This information is stored in detail for each patient and used during analysis. Next, the server uses patient information analysis tools based on this stored data to generate the timing of each patient's next medical consultation and medication administration.

[0035] Based on the analysis results, the server generates personalized reminders for each patient via a reminder generation mechanism. These reminders include specific appointment dates, medication times, and the need for follow-up. The generated reminders are sent to the healthcare provider's terminal via a reminder notification mechanism. The terminal presents the reminder to the healthcare provider visually or audibly.

[0036] Users can check reminders displayed on their devices and take prompt action regarding patients based on them. For example, reminders can be used to schedule the next appointment or provide medication guidance. Furthermore, by entering the actions taken and their results into the device and updating the data, the data on the server is kept up-to-date.

[0037] As a concrete example, consider a patient with diabetes. The server uses the patient's past blood glucose data and related medical history to predict the timing of the next necessary tests and dietary guidance. It then notifies the healthcare provider with a reminder that includes specific instructions. Based on the information received, the user can efficiently carry out the next consultation and guidance, resulting in smoother patient care. This system allows healthcare providers to reduce the burden of patient management while improving the quality of care.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server accesses the healthcare institution's database and collects each patient's medical history, symptom data, and medication history. This information is then organized and prepared for storage for each individual patient.

[0041] Step 2:

[0042] The server analyzes the collected data. Using patient information analysis tools, it analyzes past treatment patterns and symptom progression to predict the next necessary appointment date and medication timing for each patient. Data analysis algorithms and AI models are utilized in this step.

[0043] Step 3:

[0044] Based on the analysis results, the server generates personalized reminders for each patient using a reminder generation mechanism. These reminders include information such as the date of the consultation, medication instructions, and the details of the next examination.

[0045] Step 4:

[0046] The server sends the generated reminder to the terminal used by the healthcare provider via the reminder notification system.

[0047] Step 5:

[0048] The device displays received reminders in a format that is easy for healthcare providers to read. It also uses audio and on-screen notifications to alert healthcare providers.

[0049] Step 6:

[0050] Users can check reminders on their devices and plan actions based on them. For example, they can adjust appointment schedules with patients or provide medication guidance.

[0051] Step 7:

[0052] The user enters information about the care provided and its results into the terminal. The terminal sends this information to the server, which then updates the patient data.

[0053] Step 8:

[0054] The server updates the database based on the information it receives. This update prepares the system to provide more accurate information in the next analysis.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] While efficient management of patient care is crucial in healthcare facilities, traditional methods have presented challenges in providing timely medical care and medication management tailored to individual patient circumstances. In particular, it has been difficult to consolidate diverse patient information and quickly and accurately communicate to healthcare providers when appropriate action is needed.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes information acquisition means for collecting and recording patient data, data analysis means for analyzing the acquired data and determining the timing of the next medical procedure, and notification generation means for creating personalized notifications from the analysis results. This enables efficient management of patient care by creating reminders for each patient at the appropriate time and notifying healthcare providers.

[0060] "Information acquisition means" refers to functions for collecting and recording patient medical history, symptom data, medication information, etc.

[0061] "Data analysis means" refers to a function that analyzes acquired patient data to determine the timing of the next medical consultation or medication.

[0062] The "notification generation means" is a function for creating personalized notifications based on the analysis results.

[0063] A "notification presentation means" is a function for presenting generated notifications to healthcare providers visually or audibly.

[0064] "Action recording means" refers to a function that records the results of actions taken by healthcare providers and updates patient data based on this information.

[0065] This invention is an information system for healthcare providers to efficiently manage patient care. The system centers around a server, terminals, and users (healthcare providers), and by coordinating these, it collects, analyzes, and notifies information.

[0066] The server accesses the medical institution's database and periodically collects patient medical history, symptom data, medication information, and other data. This information is securely recorded using "information acquisition means." Next, the server analyzes the collected data using "data analysis means" and uses a generative AI model to determine when the next medical consultation or medication is needed. The generative AI model incorporates an analysis algorithm based on the patient's past data, providing the basis for the analysis.

[0067] Based on the analysis results, the server uses a "notification generation means" to create a personalized reminder. This reminder includes specific information such as the next appointment date and medication instructions. Next, the server sends the reminder to the terminal using a "notification presentation means."

[0068] The device visually or audibly notifies healthcare providers of received reminders. This allows users to respond to patients in a timely manner. For example, they can schedule appointments according to reminders or provide medication guidance to patients.

[0069] Users record the actions they perform according to reminders through an "action recording device" and update the database by sending the results to the server. Through this process, the most up-to-date patient information is always maintained within the system and can be used for subsequent analysis.

[0070] As a concrete example, consider a case where a server analyzes past blood glucose data for a patient with diabetes and notifies them of the recommended timing for the next random blood glucose test. An example of a prompt message in this case would be, "Analyze the past blood glucose data of the diabetic patient and predict the timing of the next test."

[0071] This system allows healthcare providers to improve the efficiency of patient management and enhance the quality of care.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server accesses the medical institution's database to collect patient medical history, symptom data, and medication information. It uses patient information obtained from the database as input, and organizes and records it using an information retrieval method. Specifically, it queries the database using an API, periodically checking for and retrieving new data.

[0075] Step 2:

[0076] The server processes the collected patient data using data analysis tools. The input is the patient information obtained in step 1, and the output is analysis results including the timing of the next medical consultation and medication. Here, a generated AI model is used to predict the appropriate timing based on past data. Specifically, the patient's past medical patterns and test results are analyzed using statistical analysis and machine learning algorithms.

[0077] Step 3:

[0078] The server uses a notification generation mechanism to create personalized reminders based on the analysis results. The input is the analysis results from step 2, and the output is reminder information sent to healthcare providers. Specifically, the reminders include the date and time of the next appointment and the required medication timing, and are formatted accordingly.

[0079] Step 4:

[0080] The server sends the generated reminder to the terminal via a notification display device. The input is the reminder created in step 3, and the output is the reminder information delivered to the terminal. Specifically, data is sent to the terminal using a secure communication protocol.

[0081] Step 5:

[0082] The device visually and audibly notifies healthcare providers of received reminders. The input is reminder information sent from the server, and the output is a notification to healthcare providers. Specifically, it uses methods such as pop-up displays and alert sounds to enable healthcare providers to respond immediately.

[0083] Step 6:

[0084] The user takes appropriate action for the patient based on the reminder displayed on the device. The input is the information provided as a reminder, and the output is the medical procedure performed and its results. Specific actions include scheduling appointments and providing medication guidance.

[0085] Step 7:

[0086] The user inputs the actions they perform into the terminal using an action recording device and sends the updated information to the server. The input is the actions performed by the user, and the output is the updated patient data. Specifically, the user uses the input form on the terminal to record the action log and upload it to the server.

[0087] This process allows the entire system to circulate, enabling efficient patient management.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In managing patient care, healthcare providers are required to obtain information quickly and efficiently and take appropriate action. In particular, given the limited means of visually confirming information, the importance of proactive reminders is increasing. However, traditional systems do not adequately address this, leading to a heavy burden on healthcare providers and a decline in the quality of care.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for storing patient information, means for analyzing patient information, means for generating reminders, means for notifying reminders, means for presenting reminders to healthcare providers, means for updating data based on input information from healthcare providers, and means for presenting reminders via a visual display device. By presenting reminders visually, healthcare providers can quickly and efficiently take the next action they need to take.

[0093] "Patient information storage means" refers to a database device or system for storing information about a patient, such as medical history, symptom data, and medication history.

[0094] A "patient information analysis means" is a processing device or program that analyzes recorded patient data to predict the patient's health status and the timing of necessary medical treatment and medication.

[0095] A "reminder generation means" is a program or device for generating reminders to encourage medical treatment or medication adherence based on analyzed patient information.

[0096] A "reminder notification means" is a means or system for communicating generated reminders to healthcare providers.

[0097] A "means for presenting reminders to healthcare providers" refers to a device or application for presenting notified reminders to healthcare providers visually or audibly.

[0098] "Means for updating data based on input from healthcare providers" refers to a processing device or software that inputs actions taken by healthcare providers and their results, and keeps the data on the server up to date.

[0099] "Means of presenting reminders via a visual display device" refers to a device or system that uses a visual display device, such as smart glasses, to present reminders to healthcare providers.

[0100] To implement this invention, a server first manages all information about the patient. The server accesses the medical institution's database and stores detailed data, including the patient's medical history, symptom data, and medication history. Based on this information, a patient information analysis means analyzes each patient's health status and predicts the timing of the next necessary medical treatment or medication.

[0101] Next, the server uses a reminder generation mechanism to generate personalized reminders for each patient based on the analysis results. These reminders include specific appointment dates, medication times, and the need for follow-up. The generated reminders are then sent to the terminals used by healthcare providers via a reminder notification mechanism.

[0102] On the terminal, reminders are visually presented to healthcare providers via a visual display device using a reminder presentation mechanism. By using devices such as smart glasses, reminders can be checked at any time, enabling healthcare providers to take the next action quickly and efficiently.

[0103] Based on reminders, healthcare providers take actions such as scheduling the next appointment or providing patient guidance. Furthermore, by inputting these actions and their results into a terminal, a means of updating the data on the server keeps the healthcare provider's input information up to date.

[0104] As a concrete example, consider the case of a diabetic patient. The server predicts the timing of the next necessary test based on the patient's past blood glucose data and displays a reminder with specific instructions to the healthcare provider via smart glasses. By using this system, healthcare providers can efficiently manage patient care and improve the quality of care.

[0105] An example of a prompt using a generative AI model is, "Create Python code that calculates the next appointment date and medication time based on the user's care data and generates a reminder." By using this prompt, the AI ​​can automatically generate the necessary reminders.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server accesses the medical institution's database to retrieve the patient's medical history, symptom data, and medication history. This data serves as input. The server stores this information using patient information storage devices. This stored data is then used in the next analysis step.

[0109] Step 2:

[0110] The server uses patient information analysis tools to analyze the stored data. The input is the patient data obtained in step 1. Based on this data, the server calculates the timing of the next necessary medical treatment and medication, and this is output as the analysis result. The analysis result includes each patient's next appointment date and medication time.

[0111] Step 3:

[0112] The server generates reminders using a reminder generation mechanism based on the analysis results. The input is the analysis results obtained in step 2. In this process, a reminder specific to each patient is created, including the date of the consultation, medication time, and the need for follow-up. The generated reminders are obtained as output.

[0113] Step 4:

[0114] The server delivers the generated reminder to the terminal using a reminder notification mechanism. The input is the reminder generated in step 3. In this process, the reminder is sent to a visual display device. The output is the reminder received by the terminal.

[0115] Step 5:

[0116] The terminal displays reminders to healthcare providers via a visual display device using a reminder presentation mechanism. The input is a reminder sent from the server. This allows healthcare providers to visually confirm necessary information and decide on the next action to take. Presenting information in real time enables healthcare providers to respond quickly.

[0117] Step 6:

[0118] Healthcare providers, as users, take action based on the displayed reminders. The input is visualized reminder information. For example, they might schedule the next appointment or provide patient guidance. The results of these actions are entered into the terminal, improving patient management.

[0119] Step 7:

[0120] The action results entered into the terminal update the data on the server through a means of updating the data based on the input information from the healthcare provider. The input is the data entered by the healthcare provider into the terminal. In this process, the server's database is updated with the latest patient information and used for future analysis and reminder generation. The output is the updated patient database.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention provides a system incorporating an emotion engine to further enhance patient care management in medical institutions. In addition to conventional patient information management functions, this system has the function of recognizing the emotions of healthcare providers and accordingly providing appropriate notifications and suggesting appropriate response strategies.

[0123] First, the server collects patient information from the medical institution's database as usual and performs analysis. When sending reminders to terminals used by healthcare providers, the emotion engine is built in, allowing the terminal to analyze the healthcare provider's emotional state in real time. The emotion engine recognizes the healthcare provider's voice tone and facial expressions to determine whether they are stressed or relaxed.

[0124] When a user (healthcare provider) checks a reminder using their device, the notification method is optimized according to the healthcare provider's emotional state as determined by the emotion engine. For example, if the healthcare provider is feeling stressed, the notification may be softened, or less urgent reminders may be postponed.

[0125] Furthermore, the emotion engine can also suggest patient interaction strategies based on the healthcare provider's current emotions. For example, if a healthcare provider is showing signs of fatigue, the emotion engine will suggest ways of speaking and content to facilitate smoother communication with the patient. In this way, it helps healthcare providers interact with patients in the best possible mental state.

[0126] This system not only manages patient information but also enables the provision of flexible care that takes into account the emotional needs of healthcare providers. As a result, it is expected to improve the quality of services provided to patients, reduce the burden on healthcare providers, and enable more efficient operations.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server collects and stores patient information such as medical history, symptom data, and medication history from the healthcare institution's database. This information is structured and organized for use in individual patient-based analysis.

[0130] Step 2:

[0131] The server analyzes the collected data using patient information analysis tools to predict the next appointment date, medication timing, and symptoms requiring special attention. This analysis is performed using machine learning models and data analysis algorithms.

[0132] Step 3:

[0133] Based on the analysis results, the server generates personalized reminders for each patient. These reminders include specific appointment dates, medication instructions, and even lifestyle advice suggestions.

[0134] Step 4:

[0135] The device receives reminders sent from the server and submits them to the healthcare provider. During this process, a built-in emotion engine activates, capturing the healthcare provider's voice and facial expressions to analyze their emotional state.

[0136] Step 5:

[0137] The emotion engine analyzes the situation in which healthcare providers are using the device, determining, for example, their level of fatigue, stress, or relaxation. Based on this, it designs notification methods (such as volume, notification frequency, and screen display adjustments) that are appropriate for the user's emotional state.

[0138] Step 6:

[0139] Users check reminders displayed on their devices and plan appropriate actions for patients based on them. For example, they might schedule the next appointment or provide timely medication guidance. At this time, they can also refer to communication strategies suggested by the emotion engine.

[0140] Step 7:

[0141] The user inputs the actions they perform and their results into the terminal. The terminal then sends this information to the server, and the patient data is updated to the latest state.

[0142] Step 8:

[0143] The server updates the database based on information sent by the user and prepares for the next analysis. This update enables more personalized patient care.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0146] In patient care within healthcare institutions, traditional systems that manage and notify patients without considering the emotional state of healthcare providers face challenges such as inconsistent service quality and increased workload for healthcare providers. Therefore, there is a need for a system that allows for flexible and efficient patient care while taking into account the emotional state of healthcare providers.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for storing patient information, means for analyzing patient information, means for generating reminders, means for analyzing the emotional state of healthcare providers, means for optimizing reminder notifications according to the emotional state of healthcare providers, and means for proposing patient care policies using a generated AI model. This enables flexible and efficient patient care that takes into account the emotional state of healthcare providers, thereby improving service quality and reducing workload.

[0149] "Patient information storage means" refers to a function in a medical institution that stores information such as a patient's medical history and treatment details.

[0150] "Patient information analysis tools" refer to functions that perform data analysis based on collected patient information and provide insights useful for medical treatment.

[0151] A "reminder generation method" refers to a function that identifies information or tasks that need to be notified to healthcare providers and creates notifications to inform them of that information.

[0152] "Reminder notification means" refers to methods and functions for communicating generated reminders to healthcare providers.

[0153] "Means of presenting reminders to healthcare providers" refers to functions that visually and audibly present notified reminders to healthcare providers.

[0154] "Means for analyzing the emotional state of healthcare providers" refers to a function that analyzes the voice tone and facial expressions of healthcare providers and evaluates their psychological and emotional state.

[0155] "Means for optimizing reminder notifications according to the healthcare provider's emotions" refers to a function that adjusts the timing and method of notifications based on the healthcare provider's emotional state.

[0156] "Methods for proposing patient care policies using generative AI models" refers to a function that uses generative artificial intelligence models to derive and recommend patient care methods based on the emotional state of healthcare providers.

[0157] "Means for updating data based on input from healthcare providers" refers to a function that reflects new information provided by healthcare providers in the system, keeping the database up-to-date.

[0158] "Means for generating prompt sentences based on emotional state" refers to a function that generates appropriate AI dialogue prompts using the results of emotional analysis of healthcare providers.

[0159] This invention incorporates an emotion engine and features an efficient patient information management and notification system to enable healthcare providers to deliver optimal care to patients.

[0160] The server accesses the medical institution's database, collects information such as the patient's medical history, medical record, and treatment details, and uses patient information storage means to organize and store this information. Furthermore, it uses patient information analysis means to analyze this information and prepares to generate notifications to the healthcare provider's terminal.

[0161] The device analyzes the voice tone and facial expressions of healthcare providers through an emotion engine equipped with voice analysis and facial recognition software, and evaluates their psychological state. For this purpose, general voice analysis software, such as a voice recognition engine, and a facial recognition API are used for facial expression analysis. Based on this analysis, reminder notifications are optimized, prioritizing notifications according to the healthcare provider's emotional state, changing the notification sound, or even changing the notification method itself.

[0162] Healthcare providers, as users, can check reminders notified through their devices and adjust their responses to patients based on information optimized by the emotion engine. For example, if they are feeling stressed, notifications will be changed to a softer tone, and less urgent tasks will be postponed. Furthermore, a generative AI model is used to suggest specific response strategies for patients based on the user's current emotional state. An example of a specific prompt might be, "The healthcare provider seems particularly tired today. What kind of communication style and content would be appropriate to facilitate smooth communication with the patient?"

[0163] This technology will not only improve the quality of patient care but also reduce the workload of healthcare providers. Furthermore, it will enable more efficient work processes that take into account the mental health of healthcare providers, ultimately leading to improved productivity and satisfaction across the entire healthcare field.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The server uses patient information storage to retrieve patient medical history, medical records, and treatment details from a medical institution's database. The input is patient information from the database, and the output is well-formed data for analysis. This data is analyzed by patient information analysis tools to extract treatment history and important medical insights.

[0167] Step 2:

[0168] The server uses a reminder generation mechanism to create notifications based on the patient's treatment plan and appointment schedule. The input is the analyzed patient information obtained in the previous step, and the output is reminder data indicating specific tasks that healthcare providers should perform. For example, it might list the next appointment date or items requiring specific follow-up.

[0169] Step 3:

[0170] The device uses voice analysis and facial recognition software to analyze the emotional state of healthcare providers and evaluate their emotional state in real time. Input is the healthcare provider's voice and facial image data, and output is a score or judgment result indicating their emotional state. Specifically, voice analysis measures tone and pitch, and facial recognition captures changes in facial expressions.

[0171] Step 4:

[0172] The device optimizes reminder notifications based on the emotion analysis results. The input is the reminder content and the result of the emotional state assessment, and the output is an optimized notification (e.g., changing the notification sound or adjusting the display time). For example, if a healthcare provider is stressed, the notification sound will be made softer and non-urgent reminders will be displayed later.

[0173] Step 5:

[0174] The server uses a generative AI model to generate patient response strategies tailored to the healthcare provider's current emotional state. The input consists of emotional state data and reminder data, while the output is a specific suggestion for how to respond to the patient. For example, a suggested prompt might be: "The healthcare provider seems particularly tired today. What kind of communication style and content would be appropriate to facilitate smooth communication with the patient?" This output is then presented to the healthcare provider via a terminal.

[0175] Step 6:

[0176] Healthcare providers, as users, review reminders and suggestions displayed on their devices and act accordingly when interacting with patients. Input consists of optimized notifications and suggestions, while output is data on the actions taken and their results. This data is then reflected in the database for future analysis and notifications.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0179] In elderly care settings, the inability to provide optimal care tailored to the emotional state of care staff leads to challenges in the quality of services provided to residents and the efficiency of staff work. In particular, staff emotional states often affect communication and the quality of care, and there is a need for methods to accurately understand and address this.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes means for storing patient information, means for analyzing patient information, means for generating reminders, means for recognizing the emotional state of the provider, means for optimizing notification methods according to the emotional state of the provider, and means for proposing a response plan based on the emotional state of the provider. This makes it possible to provide optimal care that takes into account the emotional state of the care staff.

[0182] "Patient information storage means" refers to a device or system that holds data about a patient and records it in a way that allows it to be retrieved as needed.

[0183] "Patient information analysis means" refers to a device or method for analyzing recorded patient data and extracting meaning and trends.

[0184] A "reminder generation method" is a function that automatically generates notification content for the provider based on pre-set conditions.

[0185] "Means for presenting reminders to providers" refers to a device or system for presenting generated reminders to care staff or medical professionals in an appropriate format.

[0186] "Means for updating data based on provider input" refers to a function that receives new data and feedback from providers to keep the recorded information up-to-date.

[0187] "Means for recognizing the provider's emotional state" refers to technologies that analyze the provider's voice and facial expressions to identify their emotional state.

[0188] "Means for optimizing notification methods according to the provider's emotional state" refers to a function that adjusts notification content and methods based on the provider's emotions and communicates them through the most effective means.

[0189] "Means for proposing response strategies based on the provider's emotional state" refers to a device or system for proposing the optimal response strategy or communication method, taking into account the provider's current emotions.

[0190] To implement this invention, a system is constructed that includes an information processing device, a cloud server, and an emotion recognition engine for use by the provider. The terminal consists of smart glasses or a tablet, and is responsible for displaying information when the provider interacts with the resident. The terminal is equipped with a camera and a microphone, and captures the provider's facial expressions and voice tone in real time.

[0191] The server includes an emotion recognition engine and analyzes data sent from the terminal using platforms such as AWS® Rekognition and Tone Analyzer. The results of the emotional state evaluation are used to optimize notification methods and propose response strategies. Based on the provider's emotional state, the server makes decisions such as prioritizing less urgent notifications.

[0192] Users (providers) can check reminders and suggestions displayed on the device and adjust their interactions and actions with residents. For example, if the emotion engine determines that the provider is fatigued, a message such as "First, take a deep breath and relax. Then, bring up seasonal topics with the resident." will be displayed on the device.

[0193] By using a generative AI model, it is possible to show an example of a prompt statement as follows:

[0194] "Create suggestions to facilitate smoother communication between care staff and residents when they are feeling tired."

[0195] "Please come up with ideas for ways to reduce stress in caregiving settings."

[0196] In this way, we will realize a system that supports providers in providing care in the optimal mental state.

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The server uses patient information storage devices to record past patient data and prepares it for analysis. Inputs include biometric information and past care history related to the patient, while outputs are structured datasets for analysis. Specifically, data is collected from electronic medical records and sensors and stored in a database.

[0200] Step 2:

[0201] The device captures the provider's real-time voice tone and facial expressions using a camera and microphone, and transmits them to the server. The input is the provider's voice and video, and the output is a data stream to the server. Specifically, it detects eye movements and voice pitch and converts them into digital signals.

[0202] Step 3:

[0203] The server uses an emotion recognition engine to analyze the received audio and video data and evaluate the provider's emotional state. The input is audio and video data sent from the terminal, and the output is labels and scores indicating the emotional state. Specifically, it uses AWS Rekognition and Tone Analyzer to calculate stress levels and the percentage of positive emotions.

[0204] Step 4:

[0205] The server generates the optimal notification method based on the emotional state and sends it to the device. Input is an emotional state label or score, and output is the notification content and its display format. Specifically, if stress levels are high, a quiet notification with reduced sound and vibration is selected.

[0206] Step 5:

[0207] The device appropriately presents received notifications to the provider, supporting the provider in providing effective care. Input is notification instructions from the server, and output is visual or auditory feedback received by the provider. Specifically, this involves displaying messages on glasses or providing instructions via voice.

[0208] Step 6:

[0209] The user (provider) refers to the information displayed on the device and takes action for the resident. Input is notifications and suggestions on the device, and output is specific actions for the resident. Specifically, this might involve speaking to the resident in a calm voice or suggesting activities.

[0210] Step 7:

[0211] The user inputs the results of their actions into a terminal and sends feedback to the server. The input is data on the performance of care and the resident's response, and the output is an updated dataset returned to the server. Specifically, it records the details of successful care and changes in the resident's condition.

[0212] 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.

[0213] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0219] 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.

[0220] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0221] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0222] 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.

[0223] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0224] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0225] The 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.

[0226] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0227] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0228] This invention relates to a method and system for efficiently managing patient care in a healthcare system. The system aims to improve patient management in healthcare facilities by collecting, analyzing, and notifying information between servers, terminals, and users (healthcare providers).

[0229] First, the server accesses the medical institution's database and stores patient information, including each patient's medical history, symptom data, and medication history. This information is stored in detail for each patient and used during analysis. Next, the server uses patient information analysis tools based on this stored data to generate the timing of each patient's next medical consultation and medication administration.

[0230] Based on the analysis results, the server generates personalized reminders for each patient via a reminder generation mechanism. These reminders include specific appointment dates, medication times, and the need for follow-up. The generated reminders are sent to the healthcare provider's terminal via a reminder notification mechanism. The terminal presents the reminder to the healthcare provider visually or audibly.

[0231] Users can check reminders displayed on their devices and take prompt action regarding patients based on them. For example, reminders can be used to schedule the next appointment or provide medication guidance. Furthermore, by entering the actions taken and their results into the device and updating the data, the data on the server is kept up-to-date.

[0232] As a concrete example, consider a patient with diabetes. The server uses the patient's past blood glucose data and related medical history to predict the timing of the next necessary tests and dietary guidance. It then notifies the healthcare provider with a reminder that includes specific instructions. Based on the information received, the user can efficiently carry out the next consultation and guidance, resulting in smoother patient care. This system allows healthcare providers to reduce the burden of patient management while improving the quality of care.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server accesses the healthcare institution's database and collects each patient's medical history, symptom data, and medication history. This information is then organized and prepared for storage for each individual patient.

[0236] Step 2:

[0237] The server analyzes the collected data. Using patient information analysis tools, it analyzes past treatment patterns and symptom progression to predict the next necessary appointment date and medication timing for each patient. Data analysis algorithms and AI models are utilized in this step.

[0238] Step 3:

[0239] Based on the analysis results, the server generates personalized reminders for each patient using a reminder generation mechanism. These reminders include information such as the date of the consultation, medication instructions, and the details of the next examination.

[0240] Step 4:

[0241] The server sends the generated reminder to the terminal used by the healthcare provider via the reminder notification system.

[0242] Step 5:

[0243] The device displays received reminders in a format that is easy for healthcare providers to read. It also uses audio and on-screen notifications to alert healthcare providers.

[0244] Step 6:

[0245] Users can check reminders on their devices and plan actions based on them. For example, they can adjust appointment schedules with patients or provide medication guidance.

[0246] Step 7:

[0247] The user enters information about the care provided and its results into the terminal. The terminal sends this information to the server, which then updates the patient data.

[0248] Step 8:

[0249] The server updates the database based on the information it receives. This update prepares the system to provide more accurate information in the next analysis.

[0250] (Example 1)

[0251] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0252] While efficient management of patient care is crucial in healthcare facilities, traditional methods have presented challenges in providing timely medical care and medication management tailored to individual patient circumstances. In particular, it has been difficult to consolidate diverse patient information and quickly and accurately communicate to healthcare providers when appropriate action is needed.

[0253] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0254] In this invention, the server includes information acquisition means for collecting and recording patient data, data analysis means for analyzing the acquired data and determining the timing of the next medical procedure, and notification generation means for creating personalized notifications from the analysis results. This enables efficient management of patient care by creating reminders for each patient at the appropriate time and notifying healthcare providers.

[0255] "Information acquisition means" refers to functions for collecting and recording patient medical history, symptom data, medication information, etc.

[0256] "Data analysis means" refers to a function that analyzes acquired patient data to determine the timing of the next medical consultation or medication.

[0257] The "notification generation means" is a function for creating personalized notifications based on the analysis results.

[0258] A "notification presentation means" is a function for presenting generated notifications to healthcare providers visually or audibly.

[0259] "Action recording means" refers to a function that records the results of actions taken by healthcare providers and updates patient data based on this information.

[0260] This invention is an information system for healthcare providers to efficiently manage patient care. The system centers around a server, terminals, and users (healthcare providers), and by coordinating these, it collects, analyzes, and notifies information.

[0261] The server accesses the medical institution's database and periodically collects patient medical history, symptom data, medication information, and other data. This information is securely recorded using "information acquisition means." Next, the server analyzes the collected data using "data analysis means" and uses a generative AI model to determine when the next medical consultation or medication is needed. The generative AI model incorporates an analysis algorithm based on the patient's past data, providing the basis for the analysis.

[0262] Based on the analysis results, the server uses a "notification generation means" to create a personalized reminder. This reminder includes specific information such as the next appointment date and medication instructions. Next, the server sends the reminder to the terminal using a "notification presentation means."

[0263] The device visually or audibly notifies healthcare providers of received reminders. This allows users to respond to patients in a timely manner. For example, they can schedule appointments according to reminders or provide medication guidance to patients.

[0264] Users record the actions they perform according to reminders through an "action recording device" and update the database by sending the results to the server. Through this process, the most up-to-date patient information is always maintained within the system and can be used for subsequent analysis.

[0265] As a concrete example, consider a case where a server analyzes past blood glucose data for a patient with diabetes and notifies them of the recommended timing for the next random blood glucose test. An example of a prompt message in this case would be, "Analyze the past blood glucose data of the diabetic patient and predict the timing of the next test."

[0266] This system allows healthcare providers to improve the efficiency of patient management and enhance the quality of care.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server accesses the medical institution's database to collect patient medical history, symptom data, and medication information. It uses patient information obtained from the database as input, and organizes and records it using an information retrieval method. Specifically, it queries the database using an API, periodically checking for and retrieving new data.

[0270] Step 2:

[0271] The server processes the collected patient data using data analysis tools. The input is the patient information obtained in step 1, and the output is analysis results including the timing of the next medical consultation and medication. Here, a generated AI model is used to predict the appropriate timing based on past data. Specifically, the patient's past medical patterns and test results are analyzed using statistical analysis and machine learning algorithms.

[0272] Step 3:

[0273] The server uses a notification generation mechanism to create personalized reminders based on the analysis results. The input is the analysis results from step 2, and the output is reminder information sent to healthcare providers. Specifically, the reminders include the date and time of the next appointment and the required medication timing, and are formatted accordingly.

[0274] Step 4:

[0275] The server sends the generated reminder to the terminal via a notification display device. The input is the reminder created in step 3, and the output is the reminder information delivered to the terminal. Specifically, data is sent to the terminal using a secure communication protocol.

[0276] Step 5:

[0277] The device visually and audibly notifies healthcare providers of received reminders. The input is reminder information sent from the server, and the output is a notification to healthcare providers. Specifically, it uses methods such as pop-up displays and alert sounds to enable healthcare providers to respond immediately.

[0278] Step 6:

[0279] The user takes appropriate action for the patient based on the reminder displayed on the device. The input is the information provided as a reminder, and the output is the medical procedure performed and its results. Specific actions include scheduling appointments and providing medication guidance.

[0280] Step 7:

[0281] The user inputs the actions performed into the terminal using the action recording means and transmits the updated information to the server. The input is the action taken by the user, and the output is the updated patient data. Specifically, the input form of the terminal is used to record the action log and upload it to the server.

[0282] With this process, the entire system circulates, enabling efficient patient management.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In the management of patient care, it is required that medical providers obtain information quickly and efficiently and take appropriate actions. In particular, with limited means for visually confirming information, the importance of an active reminder to prompt actions is increasing. However, in conventional systems, this has not been sufficiently achieved, resulting in a large burden on medical providers and a problem of declining care quality.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0287] In this invention, the server includes patient information storage means, patient information analysis means, reminder generation means, reminder notification means, reminder presentation means for medical providers, means for updating the input information of medical providers as data, and means for presenting a reminder via a visual display device. Thereby, by presenting a reminder visually, medical providers can quickly and efficiently perform the next actions to be taken.

[0288] "Patient information storage means" refers to a database device or system for storing information about a patient, such as medical history, symptom data, and medication history.

[0289] A "patient information analysis means" is a processing device or program that analyzes recorded patient data to predict the patient's health status and the timing of necessary medical treatment and medication.

[0290] A "reminder generation means" is a program or device for generating reminders to encourage medical treatment or medication adherence based on analyzed patient information.

[0291] A "reminder notification means" is a means or system for communicating generated reminders to healthcare providers.

[0292] A "means for presenting reminders to healthcare providers" refers to a device or application for presenting notified reminders to healthcare providers visually or audibly.

[0293] "Means for updating data based on input from healthcare providers" refers to a processing device or software that inputs actions taken by healthcare providers and their results, and keeps the data on the server up to date.

[0294] "Means of presenting reminders via a visual display device" refers to a device or system that uses a visual display device, such as smart glasses, to present reminders to healthcare providers.

[0295] To implement this invention, a server first manages all information about the patient. The server accesses the medical institution's database and stores detailed data, including the patient's medical history, symptom data, and medication history. Based on this information, a patient information analysis means analyzes each patient's health status and predicts the timing of the next necessary medical treatment or medication.

[0296] Next, the server uses a reminder generation mechanism to generate personalized reminders for each patient based on the analysis results. These reminders include specific appointment dates, medication times, and the need for follow-up. The generated reminders are then sent to the terminals used by healthcare providers via a reminder notification mechanism.

[0297] On the terminal, reminders are visually presented to healthcare providers via a visual display device using a reminder presentation mechanism. By using devices such as smart glasses, reminders can be checked at any time, enabling healthcare providers to take the next action quickly and efficiently.

[0298] Based on reminders, healthcare providers take actions such as scheduling the next appointment or providing patient guidance. Furthermore, by inputting these actions and their results into a terminal, a means of updating the data on the server keeps the healthcare provider's input information up to date.

[0299] As a concrete example, consider the case of a diabetic patient. The server predicts the timing of the next necessary test based on the patient's past blood glucose data and displays a reminder with specific instructions to the healthcare provider via smart glasses. By using this system, healthcare providers can efficiently manage patient care and improve the quality of care.

[0300] An example of a prompt using a generative AI model is, "Create Python code that calculates the next appointment date and medication time based on the user's care data and generates a reminder." By using this prompt, the AI ​​can automatically generate the necessary reminders.

[0301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0302] Step 1:

[0303] The server accesses the database of the medical institution to obtain the patient's medical history, symptom data, and medication history. This data serves as the input. The server stores this information using the patient information storage means. The stored data is utilized in the next analysis step.

[0304] Step 2:

[0305] The server uses the patient information analysis means to analyze the stored data. The input is the patient data obtained in Step 1. Based on this data, the server calculates the timing of the next necessary medical treatment and medication, which is output as the analysis result. The analysis result includes the next medical appointment date and medication time for each patient.

[0306] Step 3:

[0307] The server generates a reminder using the reminder generation means based on the analysis result. The input is the analysis result obtained in Step 2. In this process, a reminder specialized for each patient is created, including the medical appointment date, medication time, and the need for follow-up. The generated reminder is obtained as the output.

[0308] Step 4:

[0309] The server distributes the generated reminder to the terminal using the reminder notification means. The input is the reminder generated in Step 3. In this process, the reminder is sent to the visual display device. The output is the reminder received by the terminal.

[0310] Step 5:

[0311] At the terminal, the reminder is displayed via the visual display device using the reminder presentation means for the medical provider. The input is the reminder sent from the server. This enables the medical provider to visually confirm the necessary information and determine the next action to take. By presenting the information in real time, prompt response from the medical provider can be achieved.

[0312] Step 6:

[0313] Healthcare providers, as users, take action based on the displayed reminders. The input is visualized reminder information. For example, they might schedule the next appointment or provide patient guidance. The results of these actions are entered into the terminal, improving patient management.

[0314] Step 7:

[0315] The action results entered into the terminal update the data on the server through a means of updating the data based on the input information from the healthcare provider. The input is the data entered by the healthcare provider into the terminal. In this process, the server's database is updated with the latest patient information and used for future analysis and reminder generation. The output is the updated patient database.

[0316] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0317] This invention provides a system incorporating an emotion engine to further enhance patient care management in medical institutions. In addition to conventional patient information management functions, this system has the function of recognizing the emotions of healthcare providers and accordingly providing appropriate notifications and suggesting appropriate response strategies.

[0318] First, the server collects patient information from the medical institution's database as usual and performs analysis. When sending reminders to terminals used by healthcare providers, the emotion engine is built in, allowing the terminal to analyze the healthcare provider's emotional state in real time. The emotion engine recognizes the healthcare provider's voice tone and facial expressions to determine whether they are stressed or relaxed.

[0319] When a user (healthcare provider) checks a reminder using their device, the notification method is optimized according to the healthcare provider's emotional state as determined by the emotion engine. For example, if the healthcare provider is feeling stressed, the notification may be softened, or less urgent reminders may be postponed.

[0320] Furthermore, the emotion engine can also suggest patient interaction strategies based on the healthcare provider's current emotions. For example, if a healthcare provider is showing signs of fatigue, the emotion engine will suggest ways of speaking and content to facilitate smoother communication with the patient. In this way, it helps healthcare providers interact with patients in the best possible mental state.

[0321] This system not only manages patient information but also enables the provision of flexible care that takes into account the emotional needs of healthcare providers. As a result, it is expected to improve the quality of services provided to patients, reduce the burden on healthcare providers, and enable more efficient operations.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The server collects and stores patient information such as medical history, symptom data, and medication history from the healthcare institution's database. This information is structured and organized for use in individual patient-based analysis.

[0325] Step 2:

[0326] The server analyzes the collected data using patient information analysis tools to predict the next appointment date, medication timing, and symptoms requiring special attention. This analysis is performed using machine learning models and data analysis algorithms.

[0327] Step 3:

[0328] Based on the analysis results, the server generates personalized reminders for each patient. These reminders include specific appointment dates, medication instructions, and even lifestyle advice suggestions.

[0329] Step 4:

[0330] The device receives reminders sent from the server and submits them to the healthcare provider. During this process, a built-in emotion engine activates, capturing the healthcare provider's voice and facial expressions to analyze their emotional state.

[0331] Step 5:

[0332] The emotion engine analyzes the situation in which healthcare providers are using the device, determining, for example, their level of fatigue, stress, or relaxation. Based on this, it designs notification methods (such as volume, notification frequency, and screen display adjustments) that are appropriate for the user's emotional state.

[0333] Step 6:

[0334] Users check reminders displayed on their devices and plan appropriate actions for patients based on them. For example, they might schedule the next appointment or provide timely medication guidance. At this time, they can also refer to communication strategies suggested by the emotion engine.

[0335] Step 7:

[0336] The user inputs the actions they perform and their results into the terminal. The terminal then sends this information to the server, and the patient data is updated to the latest state.

[0337] Step 8:

[0338] The server updates the database based on information sent by the user and prepares for the next analysis. This update enables more personalized patient care.

[0339] (Example 2)

[0340] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0341] In patient care within healthcare institutions, traditional systems that manage and notify patients without considering the emotional state of healthcare providers face challenges such as inconsistent service quality and increased workload for healthcare providers. Therefore, there is a need for a system that allows for flexible and efficient patient care while taking into account the emotional state of healthcare providers.

[0342] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0343] In this invention, the server includes means for storing patient information, means for analyzing patient information, means for generating reminders, means for analyzing the emotional state of healthcare providers, means for optimizing reminder notifications according to the emotional state of healthcare providers, and means for proposing patient care policies using a generated AI model. This enables flexible and efficient patient care that takes into account the emotional state of healthcare providers, thereby improving service quality and reducing workload.

[0344] "Patient information storage means" refers to a function in a medical institution that stores information such as a patient's medical history and treatment details.

[0345] "Patient information analysis tools" refer to functions that perform data analysis based on collected patient information and provide insights useful for medical treatment.

[0346] A "reminder generation method" refers to a function that identifies information or tasks that need to be notified to healthcare providers and creates notifications to inform them of that information.

[0347] "Reminder notification means" refers to methods and functions for communicating generated reminders to healthcare providers.

[0348] "Means of presenting reminders to healthcare providers" refers to functions that visually and audibly present notified reminders to healthcare providers.

[0349] "Means for analyzing the emotional state of healthcare providers" refers to a function that analyzes the voice tone and facial expressions of healthcare providers and evaluates their psychological and emotional state.

[0350] "Means for optimizing reminder notifications according to the healthcare provider's emotions" refers to a function that adjusts the timing and method of notifications based on the healthcare provider's emotional state.

[0351] "Methods for proposing patient care policies using generative AI models" refers to a function that uses generative artificial intelligence models to derive and recommend patient care methods based on the emotional state of healthcare providers.

[0352] "Means for updating data based on input from healthcare providers" refers to a function that reflects new information provided by healthcare providers in the system, keeping the database up-to-date.

[0353] "Means for generating prompt sentences based on emotional state" refers to a function that generates appropriate AI dialogue prompts using the results of emotional analysis of healthcare providers.

[0354] This invention incorporates an emotion engine and features an efficient patient information management and notification system to enable healthcare providers to deliver optimal care to patients.

[0355] The server accesses the medical institution's database, collects information such as the patient's medical history, medical record, and treatment details, and uses patient information storage means to organize and store this information. Furthermore, it uses patient information analysis means to analyze this information and prepares to generate notifications to the healthcare provider's terminal.

[0356] The device analyzes the voice tone and facial expressions of healthcare providers through an emotion engine equipped with voice analysis and facial recognition software, and evaluates their psychological state. For this purpose, general voice analysis software, such as a voice recognition engine, and a facial recognition API are used for facial expression analysis. Based on this analysis, reminder notifications are optimized, prioritizing notifications according to the healthcare provider's emotional state, changing the notification sound, or even changing the notification method itself.

[0357] Healthcare providers, as users, can check reminders notified through their devices and adjust their responses to patients based on information optimized by the emotion engine. For example, if they are feeling stressed, notifications will be changed to a softer tone, and less urgent tasks will be postponed. Furthermore, a generative AI model is used to suggest specific response strategies for patients based on the user's current emotional state. An example of a specific prompt might be, "The healthcare provider seems particularly tired today. What kind of communication style and content would be appropriate to facilitate smooth communication with the patient?"

[0358] This technology will not only improve the quality of patient care but also reduce the workload of healthcare providers. Furthermore, it will enable more efficient work processes that take into account the mental health of healthcare providers, ultimately leading to improved productivity and satisfaction across the entire healthcare field.

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1:

[0361] The server uses patient information storage to retrieve patient medical history, medical records, and treatment details from a medical institution's database. The input is patient information from the database, and the output is well-formed data for analysis. This data is analyzed by patient information analysis tools to extract treatment history and important medical insights.

[0362] Step 2:

[0363] The server uses a reminder generation mechanism to create notifications based on the patient's treatment plan and appointment schedule. The input is the analyzed patient information obtained in the previous step, and the output is reminder data indicating specific tasks that healthcare providers should perform. For example, it might list the next appointment date or items requiring specific follow-up.

[0364] Step 3:

[0365] The device uses voice analysis and facial recognition software to analyze the emotional state of healthcare providers and evaluate their emotional state in real time. Input is the healthcare provider's voice and facial image data, and output is a score or judgment result indicating their emotional state. Specifically, voice analysis measures tone and pitch, and facial recognition captures changes in facial expressions.

[0366] Step 4:

[0367] The device optimizes reminder notifications based on the emotion analysis results. The input is the reminder content and the result of the emotional state assessment, and the output is an optimized notification (e.g., changing the notification sound or adjusting the display time). For example, if a healthcare provider is stressed, the notification sound will be made softer and non-urgent reminders will be displayed later.

[0368] Step 5:

[0369] The server uses a generative AI model to generate patient response strategies tailored to the healthcare provider's current emotional state. The input consists of emotional state data and reminder data, while the output is a specific suggestion for how to respond to the patient. For example, a suggested prompt might be: "The healthcare provider seems particularly tired today. What kind of communication style and content would be appropriate to facilitate smooth communication with the patient?" This output is then presented to the healthcare provider via a terminal.

[0370] Step 6:

[0371] Healthcare providers, as users, review reminders and suggestions displayed on their devices and act accordingly when interacting with patients. Input consists of optimized notifications and suggestions, while output is data on the actions taken and their results. This data is then reflected in the database for future analysis and notifications.

[0372] (Application Example 2)

[0373] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0374] In elderly care settings, the inability to provide optimal care tailored to the emotional state of care staff leads to challenges in the quality of services provided to residents and the efficiency of staff work. In particular, staff emotional states often affect communication and the quality of care, and there is a need for methods to accurately understand and address this.

[0375] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0376] In this invention, the server includes means for storing patient information, means for analyzing patient information, means for generating reminders, means for recognizing the emotional state of the provider, means for optimizing notification methods according to the emotional state of the provider, and means for proposing a response plan based on the emotional state of the provider. This makes it possible to provide optimal care that takes into account the emotional state of the care staff.

[0377] "Patient information storage means" refers to a device or system that holds data about a patient and records it in a way that allows it to be retrieved as needed.

[0378] "Patient information analysis means" refers to a device or method for analyzing recorded patient data and extracting meaning and trends.

[0379] A "reminder generation method" is a function that automatically generates notification content for the provider based on pre-set conditions.

[0380] "Means for presenting reminders to providers" refers to a device or system for presenting generated reminders to care staff or medical professionals in an appropriate format.

[0381] "Means for updating data based on provider input" refers to a function that receives new data and feedback from providers to keep the recorded information up-to-date.

[0382] "Means for recognizing the provider's emotional state" refers to technologies that analyze the provider's voice and facial expressions to identify their emotional state.

[0383] "Means for optimizing notification methods according to the provider's emotional state" refers to a function that adjusts notification content and methods based on the provider's emotions and communicates them through the most effective means.

[0384] "Means for proposing response strategies based on the provider's emotional state" refers to a device or system for proposing the optimal response strategy or communication method, taking into account the provider's current emotions.

[0385] To implement this invention, a system is constructed that includes an information processing device, a cloud server, and an emotion recognition engine for use by the provider. The terminal consists of smart glasses or a tablet, and is responsible for displaying information when the provider interacts with the resident. The terminal is equipped with a camera and a microphone, and captures the provider's facial expressions and voice tone in real time.

[0386] The server includes an emotion recognition engine and uses platforms such as AWS Rekognition and Tone Analyzer to analyze data sent from the device. The results of the emotional state evaluation are used to optimize notification methods and suggest response strategies. Based on the provider's emotional state, the server makes decisions such as prioritizing less urgent notifications.

[0387] Users (providers) can check reminders and suggestions displayed on the device and adjust their interactions and actions with residents. For example, if the emotion engine determines that the provider is fatigued, a message such as "First, take a deep breath and relax. Then, bring up seasonal topics with the resident." will be displayed on the device.

[0388] By using a generative AI model, it is possible to show an example of a prompt statement as follows:

[0389] "Create suggestions to facilitate smoother communication between care staff and residents when they are feeling tired."

[0390] "Please come up with ideas for ways to reduce stress in caregiving settings."

[0391] In this way, we will realize a system that supports providers in providing care in the optimal mental state.

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The server uses patient information storage devices to record past patient data and prepares it for analysis. Inputs include biometric information and past care history related to the patient, while outputs are structured datasets for analysis. Specifically, data is collected from electronic medical records and sensors and stored in a database.

[0395] Step 2:

[0396] The device captures the provider's real-time voice tone and facial expressions using a camera and microphone, and transmits them to the server. The input is the provider's voice and video, and the output is a data stream to the server. Specifically, it detects eye movements and voice pitch and converts them into digital signals.

[0397] Step 3:

[0398] The server uses an emotion recognition engine to analyze the received audio and video data and evaluate the provider's emotional state. The input is audio and video data sent from the terminal, and the output is labels and scores indicating the emotional state. Specifically, it uses AWS Rekognition and Tone Analyzer to calculate stress levels and the percentage of positive emotions.

[0399] Step 4:

[0400] The server generates the optimal notification method based on the emotional state and sends it to the device. Input is an emotional state label or score, and output is the notification content and its display format. Specifically, if stress levels are high, a quiet notification with reduced sound and vibration is selected.

[0401] Step 5:

[0402] The device appropriately presents received notifications to the provider, supporting the provider in providing effective care. Input is notification instructions from the server, and output is visual or auditory feedback received by the provider. Specifically, this involves displaying messages on glasses or providing instructions via voice.

[0403] Step 6:

[0404] The user (provider) refers to the information displayed on the device and takes action for the resident. Input is notifications and suggestions on the device, and output is specific actions for the resident. Specifically, this might involve speaking to the resident in a calm voice or suggesting activities.

[0405] Step 7:

[0406] The user inputs the results of their actions into a terminal and sends feedback to the server. The input is data on the performance of care and the resident's response, and the output is an updated dataset returned to the server. Specifically, it records the details of successful care and changes in the resident's condition.

[0407] 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.

[0408] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0409] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0414] 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.

[0415] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0416] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0417] 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.

[0418] 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.

[0419] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0420] The 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.

[0421] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0422] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0423] This invention relates to a method and system for efficiently managing patient care in a healthcare system. The system aims to improve patient management in healthcare facilities by collecting, analyzing, and notifying information between servers, terminals, and users (healthcare providers).

[0424] First, the server accesses the medical institution's database and stores patient information, including each patient's medical history, symptom data, and medication history. This information is stored in detail for each patient and used during analysis. Next, the server uses patient information analysis tools based on this stored data to generate the timing of each patient's next medical consultation and medication administration.

[0425] Based on the analysis results, the server generates personalized reminders for each patient via a reminder generation mechanism. These reminders include specific appointment dates, medication times, and the need for follow-up. The generated reminders are sent to the healthcare provider's terminal via a reminder notification mechanism. The terminal presents the reminder to the healthcare provider visually or audibly.

[0426] Users can check reminders displayed on their devices and take prompt action regarding patients based on them. For example, reminders can be used to schedule the next appointment or provide medication guidance. Furthermore, by entering the actions taken and their results into the device and updating the data, the data on the server is kept up-to-date.

[0427] As a concrete example, consider a patient with diabetes. The server uses the patient's past blood glucose data and related medical history to predict the timing of the next necessary tests and dietary guidance. It then notifies the healthcare provider with a reminder that includes specific instructions. Based on the information received, the user can efficiently carry out the next consultation and guidance, resulting in smoother patient care. This system allows healthcare providers to reduce the burden of patient management while improving the quality of care.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The server accesses the healthcare institution's database and collects each patient's medical history, symptom data, and medication history. This information is then organized and prepared for storage for each individual patient.

[0431] Step 2:

[0432] The server analyzes the collected data. Using patient information analysis tools, it analyzes past treatment patterns and symptom progression to predict the next necessary appointment date and medication timing for each patient. Data analysis algorithms and AI models are utilized in this step.

[0433] Step 3:

[0434] Based on the analysis results, the server generates personalized reminders for each patient using a reminder generation mechanism. These reminders include information such as the date of the consultation, medication instructions, and the details of the next examination.

[0435] Step 4:

[0436] The server sends the generated reminder to the terminal used by the healthcare provider via the reminder notification system.

[0437] Step 5:

[0438] The device displays received reminders in a format that is easy for healthcare providers to read. It also uses audio and on-screen notifications to alert healthcare providers.

[0439] Step 6:

[0440] Users can check reminders on their devices and plan actions based on them. For example, they can adjust appointment schedules with patients or provide medication guidance.

[0441] Step 7:

[0442] The user enters information about the care provided and its results into the terminal. The terminal sends this information to the server, which then updates the patient data.

[0443] Step 8:

[0444] The server updates the database based on the information it receives. This update prepares the system to provide more accurate information in the next analysis.

[0445] (Example 1)

[0446] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0447] While efficient management of patient care is crucial in healthcare facilities, traditional methods have presented challenges in providing timely medical care and medication management tailored to individual patient circumstances. In particular, it has been difficult to consolidate diverse patient information and quickly and accurately communicate to healthcare providers when appropriate action is needed.

[0448] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0449] In this invention, the server includes information acquisition means for collecting and recording patient data, data analysis means for analyzing the acquired data and determining the timing of the next medical procedure, and notification generation means for creating personalized notifications from the analysis results. This enables efficient management of patient care by creating reminders for each patient at the appropriate time and notifying healthcare providers.

[0450] "Information acquisition means" refers to functions for collecting and recording patient medical history, symptom data, medication information, etc.

[0451] "Data analysis means" refers to a function that analyzes acquired patient data to determine the timing of the next medical consultation or medication.

[0452] The "notification generation means" is a function for creating personalized notifications based on the analysis results.

[0453] A "notification presentation means" is a function for presenting generated notifications to healthcare providers visually or audibly.

[0454] "Action recording means" refers to a function that records the results of actions taken by healthcare providers and updates patient data based on this information.

[0455] This invention is an information system for healthcare providers to efficiently manage patient care. The system centers around a server, terminals, and users (healthcare providers), and by coordinating these, it collects, analyzes, and notifies information.

[0456] The server accesses the medical institution's database and periodically collects patient medical history, symptom data, medication information, and other data. This information is securely recorded using "information acquisition means." Next, the server analyzes the collected data using "data analysis means" and uses a generative AI model to determine when the next medical consultation or medication is needed. The generative AI model incorporates an analysis algorithm based on the patient's past data, providing the basis for the analysis.

[0457] Based on the analysis results, the server uses a "notification generation means" to create a personalized reminder. This reminder includes specific information such as the next appointment date and medication instructions. Next, the server sends the reminder to the terminal using a "notification presentation means."

[0458] The device visually or audibly notifies healthcare providers of received reminders. This allows users to respond to patients in a timely manner. For example, they can schedule appointments according to reminders or provide medication guidance to patients.

[0459] Users record the actions they perform according to reminders through an "action recording device" and update the database by sending the results to the server. Through this process, the most up-to-date patient information is always maintained within the system and can be used for subsequent analysis.

[0460] As a concrete example, consider a case where a server analyzes past blood glucose data for a patient with diabetes and notifies them of the recommended timing for the next random blood glucose test. An example of a prompt message in this case would be, "Analyze the past blood glucose data of the diabetic patient and predict the timing of the next test."

[0461] This system allows healthcare providers to improve the efficiency of patient management and enhance the quality of care.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] The server accesses the medical institution's database to collect patient medical history, symptom data, and medication information. It uses patient information obtained from the database as input, and organizes and records it using an information retrieval method. Specifically, it queries the database using an API, periodically checking for and retrieving new data.

[0465] Step 2:

[0466] The server processes the collected patient data using data analysis tools. The input is the patient information obtained in step 1, and the output is analysis results including the timing of the next medical consultation and medication. Here, a generated AI model is used to predict the appropriate timing based on past data. Specifically, the patient's past medical patterns and test results are analyzed using statistical analysis and machine learning algorithms.

[0467] Step 3:

[0468] The server uses a notification generation mechanism to create personalized reminders based on the analysis results. The input is the analysis results from step 2, and the output is reminder information sent to healthcare providers. Specifically, the reminders include the date and time of the next appointment and the required medication timing, and are formatted accordingly.

[0469] Step 4:

[0470] The server sends the generated reminder to the terminal via a notification display device. The input is the reminder created in step 3, and the output is the reminder information delivered to the terminal. Specifically, data is sent to the terminal using a secure communication protocol.

[0471] Step 5:

[0472] The device visually and audibly notifies healthcare providers of received reminders. The input is reminder information sent from the server, and the output is a notification to healthcare providers. Specifically, it uses methods such as pop-up displays and alert sounds to enable healthcare providers to respond immediately.

[0473] Step 6:

[0474] The user takes appropriate action for the patient based on the reminder displayed on the device. The input is the information provided as a reminder, and the output is the medical procedure performed and its results. Specific actions include scheduling appointments and providing medication guidance.

[0475] Step 7:

[0476] The user inputs the actions they perform into the terminal using an action recording device and sends the updated information to the server. The input is the actions performed by the user, and the output is the updated patient data. Specifically, the user uses the input form on the terminal to record the action log and upload it to the server.

[0477] This process allows the entire system to circulate, enabling efficient patient management.

[0478] (Application Example 1)

[0479] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0480] In managing patient care, healthcare providers are required to obtain information quickly and efficiently and take appropriate action. In particular, given the limited means of visually confirming information, the importance of proactive reminders is increasing. However, traditional systems do not adequately address this, leading to a heavy burden on healthcare providers and a decline in the quality of care.

[0481] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0482] In this invention, the server includes means for storing patient information, means for analyzing patient information, means for generating reminders, means for notifying reminders, means for presenting reminders to healthcare providers, means for updating data based on input information from healthcare providers, and means for presenting reminders via a visual display device. By presenting reminders visually, healthcare providers can quickly and efficiently take the next action they need to take.

[0483] "Patient information storage means" refers to a database device or system for storing information about a patient, such as medical history, symptom data, and medication history.

[0484] A "patient information analysis means" is a processing device or program that analyzes recorded patient data to predict the patient's health status and the timing of necessary medical treatment and medication.

[0485] A "reminder generation means" is a program or device for generating reminders to encourage medical treatment or medication adherence based on analyzed patient information.

[0486] A "reminder notification means" is a means or system for communicating generated reminders to healthcare providers.

[0487] A "means for presenting reminders to healthcare providers" refers to a device or application for presenting notified reminders to healthcare providers visually or audibly.

[0488] "Means for updating data based on input from healthcare providers" refers to a processing device or software that inputs actions taken by healthcare providers and their results, and keeps the data on the server up to date.

[0489] "Means of presenting reminders via a visual display device" refers to a device or system that uses a visual display device, such as smart glasses, to present reminders to healthcare providers.

[0490] To implement this invention, a server first manages all information about the patient. The server accesses the medical institution's database and stores detailed data, including the patient's medical history, symptom data, and medication history. Based on this information, a patient information analysis means analyzes each patient's health status and predicts the timing of the next necessary medical treatment or medication.

[0491] Next, the server uses a reminder generation mechanism to generate personalized reminders for each patient based on the analysis results. These reminders include specific appointment dates, medication times, and the need for follow-up. The generated reminders are then sent to the terminals used by healthcare providers via a reminder notification mechanism.

[0492] On the terminal, reminders are visually presented to healthcare providers via a visual display device using a reminder presentation mechanism. By using devices such as smart glasses, reminders can be checked at any time, enabling healthcare providers to take the next action quickly and efficiently.

[0493] Based on reminders, healthcare providers take actions such as scheduling the next appointment or providing patient guidance. Furthermore, by inputting these actions and their results into a terminal, a means of updating the data on the server keeps the healthcare provider's input information up to date.

[0494] As a concrete example, consider the case of a diabetic patient. The server predicts the timing of the next necessary test based on the patient's past blood glucose data and displays a reminder with specific instructions to the healthcare provider via smart glasses. By using this system, healthcare providers can efficiently manage patient care and improve the quality of care.

[0495] An example of a prompt using a generative AI model is, "Create Python code that calculates the next appointment date and medication time based on the user's care data and generates a reminder." By using this prompt, the AI ​​can automatically generate the necessary reminders.

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The server accesses the medical institution's database to retrieve the patient's medical history, symptom data, and medication history. This data serves as input. The server stores this information using patient information storage devices. This stored data is then used in the next analysis step.

[0499] Step 2:

[0500] The server uses patient information analysis tools to analyze the stored data. The input is the patient data obtained in step 1. Based on this data, the server calculates the timing of the next necessary medical treatment and medication, and this is output as the analysis result. The analysis result includes each patient's next appointment date and medication time.

[0501] Step 3:

[0502] The server generates reminders using a reminder generation mechanism based on the analysis results. The input is the analysis results obtained in step 2. In this process, a reminder specific to each patient is created, including the date of the consultation, medication time, and the need for follow-up. The generated reminders are obtained as output.

[0503] Step 4:

[0504] The server delivers the generated reminder to the terminal using a reminder notification mechanism. The input is the reminder generated in step 3. In this process, the reminder is sent to a visual display device. The output is the reminder received by the terminal.

[0505] Step 5:

[0506] The terminal displays reminders to healthcare providers via a visual display device using a reminder presentation mechanism. The input is a reminder sent from the server. This allows healthcare providers to visually confirm necessary information and decide on the next action to take. Presenting information in real time enables healthcare providers to respond quickly.

[0507] Step 6:

[0508] Healthcare providers, as users, take action based on the displayed reminders. The input is visualized reminder information. For example, they might schedule the next appointment or provide patient guidance. The results of these actions are entered into the terminal, improving patient management.

[0509] Step 7:

[0510] The action results entered into the terminal update the data on the server through a means of updating the data based on the input information from the healthcare provider. The input is the data entered by the healthcare provider into the terminal. In this process, the server's database is updated with the latest patient information and used for future analysis and reminder generation. The output is the updated patient database.

[0511] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0512] This invention provides a system incorporating an emotion engine to further enhance patient care management in medical institutions. In addition to conventional patient information management functions, this system has the function of recognizing the emotions of healthcare providers and accordingly providing appropriate notifications and suggesting appropriate response strategies.

[0513] First, the server collects patient information from the medical institution's database as usual and performs analysis. When sending reminders to terminals used by healthcare providers, the emotion engine is built in, allowing the terminal to analyze the healthcare provider's emotional state in real time. The emotion engine recognizes the healthcare provider's voice tone and facial expressions to determine whether they are stressed or relaxed.

[0514] When a user (healthcare provider) checks a reminder using their device, the notification method is optimized according to the healthcare provider's emotional state as determined by the emotion engine. For example, if the healthcare provider is feeling stressed, the notification may be softened, or less urgent reminders may be postponed.

[0515] Furthermore, the emotion engine can also suggest patient interaction strategies based on the healthcare provider's current emotions. For example, if a healthcare provider is showing signs of fatigue, the emotion engine will suggest ways of speaking and content to facilitate smoother communication with the patient. In this way, it helps healthcare providers interact with patients in the best possible mental state.

[0516] This system not only manages patient information but also enables the provision of flexible care that takes into account the emotional needs of healthcare providers. As a result, it is expected to improve the quality of services provided to patients, reduce the burden on healthcare providers, and enable more efficient operations.

[0517] The following describes the processing flow.

[0518] Step 1:

[0519] The server collects and stores patient information such as medical history, symptom data, and medication history from the healthcare institution's database. This information is structured and organized for use in individual patient-based analysis.

[0520] Step 2:

[0521] The server analyzes the collected data using patient information analysis tools to predict the next appointment date, medication timing, and symptoms requiring special attention. This analysis is performed using machine learning models and data analysis algorithms.

[0522] Step 3:

[0523] Based on the analysis results, the server generates personalized reminders for each patient. These reminders include specific appointment dates, medication instructions, and even lifestyle advice suggestions.

[0524] Step 4:

[0525] The device receives reminders sent from the server and submits them to the healthcare provider. During this process, a built-in emotion engine activates, capturing the healthcare provider's voice and facial expressions to analyze their emotional state.

[0526] Step 5:

[0527] The emotion engine analyzes the situation in which healthcare providers are using the device, determining, for example, their level of fatigue, stress, or relaxation. Based on this, it designs notification methods (such as volume, notification frequency, and screen display adjustments) that are appropriate for the user's emotional state.

[0528] Step 6:

[0529] Users check reminders displayed on their devices and plan appropriate actions for patients based on them. For example, they might schedule the next appointment or provide timely medication guidance. At this time, they can also refer to communication strategies suggested by the emotion engine.

[0530] Step 7:

[0531] The user inputs the actions they perform and their results into the terminal. The terminal then sends this information to the server, and the patient data is updated to the latest state.

[0532] Step 8:

[0533] The server updates the database based on information sent by the user and prepares for the next analysis. This update enables more personalized patient care.

[0534] (Example 2)

[0535] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0536] In patient care within healthcare institutions, traditional systems that manage and notify patients without considering the emotional state of healthcare providers face challenges such as inconsistent service quality and increased workload for healthcare providers. Therefore, there is a need for a system that allows for flexible and efficient patient care while taking into account the emotional state of healthcare providers.

[0537] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0538] In this invention, the server includes means for storing patient information, means for analyzing patient information, means for generating reminders, means for analyzing the emotional state of healthcare providers, means for optimizing reminder notifications according to the emotional state of healthcare providers, and means for proposing patient care policies using a generated AI model. This enables flexible and efficient patient care that takes into account the emotional state of healthcare providers, thereby improving service quality and reducing workload.

[0539] "Patient information storage means" refers to a function in a medical institution that stores information such as a patient's medical history and treatment details.

[0540] "Patient information analysis tools" refer to functions that perform data analysis based on collected patient information and provide insights useful for medical treatment.

[0541] A "reminder generation method" refers to a function that identifies information or tasks that need to be notified to healthcare providers and creates notifications to inform them of that information.

[0542] "Reminder notification means" refers to methods and functions for communicating generated reminders to healthcare providers.

[0543] "Means of presenting reminders to healthcare providers" refers to functions that visually and audibly present notified reminders to healthcare providers.

[0544] "Means for analyzing the emotional state of healthcare providers" refers to a function that analyzes the voice tone and facial expressions of healthcare providers and evaluates their psychological and emotional state.

[0545] "Means for optimizing reminder notifications according to the healthcare provider's emotions" refers to a function that adjusts the timing and method of notifications based on the healthcare provider's emotional state.

[0546] "Methods for proposing patient care policies using generative AI models" refers to a function that uses generative artificial intelligence models to derive and recommend patient care methods based on the emotional state of healthcare providers.

[0547] "Means for updating data based on input from healthcare providers" refers to a function that reflects new information provided by healthcare providers in the system, keeping the database up-to-date.

[0548] "Means for generating prompt sentences based on emotional state" refers to a function that generates appropriate AI dialogue prompts using the results of emotional analysis of healthcare providers.

[0549] This invention incorporates an emotion engine and features an efficient patient information management and notification system to enable healthcare providers to deliver optimal care to patients.

[0550] The server accesses the medical institution's database, collects information such as the patient's medical history, medical record, and treatment details, and uses patient information storage means to organize and store this information. Furthermore, it uses patient information analysis means to analyze this information and prepares to generate notifications to the healthcare provider's terminal.

[0551] The device analyzes the voice tone and facial expressions of healthcare providers through an emotion engine equipped with voice analysis and facial recognition software, and evaluates their psychological state. For this purpose, general voice analysis software, such as a voice recognition engine, and a facial recognition API are used for facial expression analysis. Based on this analysis, reminder notifications are optimized, prioritizing notifications according to the healthcare provider's emotional state, changing the notification sound, or even changing the notification method itself.

[0552] Healthcare providers, as users, can check reminders notified through their devices and adjust their responses to patients based on information optimized by the emotion engine. For example, if they are feeling stressed, notifications will be changed to a softer tone, and less urgent tasks will be postponed. Furthermore, a generative AI model is used to suggest specific response strategies for patients based on the user's current emotional state. An example of a specific prompt might be, "The healthcare provider seems particularly tired today. What kind of communication style and content would be appropriate to facilitate smooth communication with the patient?"

[0553] This technology will not only improve the quality of patient care but also reduce the workload of healthcare providers. Furthermore, it will enable more efficient work processes that take into account the mental health of healthcare providers, ultimately leading to improved productivity and satisfaction across the entire healthcare field.

[0554] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0555] Step 1:

[0556] The server uses patient information storage to retrieve patient medical history, medical records, and treatment details from a medical institution's database. The input is patient information from the database, and the output is well-formed data for analysis. This data is analyzed by patient information analysis tools to extract treatment history and important medical insights.

[0557] Step 2:

[0558] The server uses a reminder generation mechanism to create notifications based on the patient's treatment plan and appointment schedule. The input is the analyzed patient information obtained in the previous step, and the output is reminder data indicating specific tasks that healthcare providers should perform. For example, it might list the next appointment date or items requiring specific follow-up.

[0559] Step 3:

[0560] The device uses voice analysis and facial recognition software to analyze the emotional state of healthcare providers and evaluate their emotional state in real time. Input is the healthcare provider's voice and facial image data, and output is a score or judgment result indicating their emotional state. Specifically, voice analysis measures tone and pitch, and facial recognition captures changes in facial expressions.

[0561] Step 4:

[0562] The device optimizes reminder notifications based on the emotion analysis results. The input is the reminder content and the result of the emotional state assessment, and the output is an optimized notification (e.g., changing the notification sound or adjusting the display time). For example, if a healthcare provider is stressed, the notification sound will be made softer and non-urgent reminders will be displayed later.

[0563] Step 5:

[0564] The server uses a generative AI model to generate patient response strategies tailored to the healthcare provider's current emotional state. The input consists of emotional state data and reminder data, while the output is a specific suggestion for how to respond to the patient. For example, a suggested prompt might be: "The healthcare provider seems particularly tired today. What kind of communication style and content would be appropriate to facilitate smooth communication with the patient?" This output is then presented to the healthcare provider via a terminal.

[0565] Step 6:

[0566] Healthcare providers, as users, review reminders and suggestions displayed on their devices and act accordingly when interacting with patients. Input consists of optimized notifications and suggestions, while output is data on the actions taken and their results. This data is then reflected in the database for future analysis and notifications.

[0567] (Application Example 2)

[0568] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0569] In elderly care settings, the inability to provide optimal care tailored to the emotional state of care staff leads to challenges in the quality of services provided to residents and the efficiency of staff work. In particular, staff emotional states often affect communication and the quality of care, and there is a need for methods to accurately understand and address this.

[0570] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0571] In this invention, the server includes means for storing patient information, means for analyzing patient information, means for generating reminders, means for recognizing the emotional state of the provider, means for optimizing notification methods according to the emotional state of the provider, and means for proposing a response plan based on the emotional state of the provider. This makes it possible to provide optimal care that takes into account the emotional state of the care staff.

[0572] "Patient information storage means" refers to a device or system that holds data about a patient and records it in a way that allows it to be retrieved as needed.

[0573] "Patient information analysis means" refers to a device or method for analyzing recorded patient data and extracting meaning and trends.

[0574] A "reminder generation method" is a function that automatically generates notification content for the provider based on pre-set conditions.

[0575] "Means for presenting reminders to providers" refers to a device or system for presenting generated reminders to care staff or medical professionals in an appropriate format.

[0576] "Means for updating data based on provider input" refers to a function that receives new data and feedback from providers to keep the recorded information up-to-date.

[0577] "Means for recognizing the provider's emotional state" refers to technologies that analyze the provider's voice and facial expressions to identify their emotional state.

[0578] "Means for optimizing notification methods according to the provider's emotional state" refers to a function that adjusts notification content and methods based on the provider's emotions and communicates them through the most effective means.

[0579] "Means for proposing response strategies based on the provider's emotional state" refers to a device or system for proposing the optimal response strategy or communication method, taking into account the provider's current emotions.

[0580] To implement this invention, a system is constructed that includes an information processing device, a cloud server, and an emotion recognition engine for use by the provider. The terminal consists of smart glasses or a tablet, and is responsible for displaying information when the provider interacts with the resident. The terminal is equipped with a camera and a microphone, and captures the provider's facial expressions and voice tone in real time.

[0581] The server includes an emotion recognition engine and uses platforms such as AWS Rekognition and Tone Analyzer to analyze data sent from the device. The results of the emotional state evaluation are used to optimize notification methods and suggest response strategies. Based on the provider's emotional state, the server makes decisions such as prioritizing less urgent notifications.

[0582] Users (providers) can check reminders and suggestions displayed on the device and adjust their interactions and actions with residents. For example, if the emotion engine determines that the provider is fatigued, a message such as "First, take a deep breath and relax. Then, bring up seasonal topics with the resident." will be displayed on the device.

[0583] By using a generative AI model, it is possible to show an example of a prompt statement as follows:

[0584] "Create suggestions to facilitate smoother communication between care staff and residents when they are feeling tired."

[0585] "Please come up with ideas for ways to reduce stress in caregiving settings."

[0586] In this way, we will realize a system that supports providers in providing care in the optimal mental state.

[0587] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0588] Step 1:

[0589] The server uses patient information storage devices to record past patient data and prepares it for analysis. Inputs include biometric information and past care history related to the patient, while outputs are structured datasets for analysis. Specifically, data is collected from electronic medical records and sensors and stored in a database.

[0590] Step 2:

[0591] The device captures the provider's real-time voice tone and facial expressions using a camera and microphone, and transmits them to the server. The input is the provider's voice and video, and the output is a data stream to the server. Specifically, it detects eye movements and voice pitch and converts them into digital signals.

[0592] Step 3:

[0593] The server uses an emotion recognition engine to analyze the received audio and video data and evaluate the provider's emotional state. The input is audio and video data sent from the terminal, and the output is labels and scores indicating the emotional state. Specifically, it uses AWS Rekognition and Tone Analyzer to calculate stress levels and the percentage of positive emotions.

[0594] Step 4:

[0595] The server generates the optimal notification method based on the emotional state and sends it to the device. Input is an emotional state label or score, and output is the notification content and its display format. Specifically, if stress levels are high, a quiet notification with reduced sound and vibration is selected.

[0596] Step 5:

[0597] The device appropriately presents received notifications to the provider, supporting the provider in providing effective care. Input is notification instructions from the server, and output is visual or auditory feedback received by the provider. Specifically, this involves displaying messages on glasses or providing instructions via voice.

[0598] Step 6:

[0599] The user (provider) refers to the information displayed on the device and takes action for the resident. Input is notifications and suggestions on the device, and output is specific actions for the resident. Specifically, this might involve speaking to the resident in a calm voice or suggesting activities.

[0600] Step 7:

[0601] The user inputs the results of their actions into a terminal and sends feedback to the server. The input is data on the performance of care and the resident's response, and the output is an updated dataset returned to the server. Specifically, it records the details of successful care and changes in the resident's condition.

[0602] 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.

[0603] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0604] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0605] [Fourth Embodiment]

[0606] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0607] 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.

[0608] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0609] 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.

[0610] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0611] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0612] 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.

[0613] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0614] 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.

[0615] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0616] The 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.

[0617] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0618] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0619] This invention relates to a method and system for efficiently managing patient care in a healthcare system. The system aims to improve patient management in healthcare facilities by collecting, analyzing, and notifying information between servers, terminals, and users (healthcare providers).

[0620] First, the server accesses the medical institution's database and stores patient information, including each patient's medical history, symptom data, and medication history. This information is stored in detail for each patient and used during analysis. Next, the server uses patient information analysis tools based on this stored data to generate the timing of each patient's next medical consultation and medication administration.

[0621] Based on the analysis results, the server generates personalized reminders for each patient via a reminder generation mechanism. These reminders include specific appointment dates, medication times, and the need for follow-up. The generated reminders are sent to the healthcare provider's terminal via a reminder notification mechanism. The terminal presents the reminder to the healthcare provider visually or audibly.

[0622] Users can check reminders displayed on their devices and take prompt action regarding patients based on them. For example, reminders can be used to schedule the next appointment or provide medication guidance. Furthermore, by entering the actions taken and their results into the device and updating the data, the data on the server is kept up-to-date.

[0623] As a concrete example, consider a patient with diabetes. The server uses the patient's past blood glucose data and related medical history to predict the timing of the next necessary tests and dietary guidance. It then notifies the healthcare provider with a reminder that includes specific instructions. Based on the information received, the user can efficiently carry out the next consultation and guidance, resulting in smoother patient care. This system allows healthcare providers to reduce the burden of patient management while improving the quality of care.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The server accesses the healthcare institution's database and collects each patient's medical history, symptom data, and medication history. This information is then organized and prepared for storage for each individual patient.

[0627] Step 2:

[0628] The server analyzes the collected data. Using patient information analysis tools, it analyzes past treatment patterns and symptom progression to predict the next necessary appointment date and medication timing for each patient. Data analysis algorithms and AI models are utilized in this step.

[0629] Step 3:

[0630] Based on the analysis results, the server generates personalized reminders for each patient using a reminder generation mechanism. These reminders include information such as the date of the consultation, medication instructions, and the details of the next examination.

[0631] Step 4:

[0632] The server sends the generated reminder to the terminal used by the healthcare provider via the reminder notification system.

[0633] Step 5:

[0634] The device displays received reminders in a format that is easy for healthcare providers to read. It also uses audio and on-screen notifications to alert healthcare providers.

[0635] Step 6:

[0636] Users can check reminders on their devices and plan actions based on them. For example, they can adjust appointment schedules with patients or provide medication guidance.

[0637] Step 7:

[0638] The user enters information about the care provided and its results into the terminal. The terminal sends this information to the server, which then updates the patient data.

[0639] Step 8:

[0640] The server updates the database based on the information it receives. This update prepares the system to provide more accurate information in the next analysis.

[0641] (Example 1)

[0642] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] While efficient management of patient care is crucial in healthcare facilities, traditional methods have presented challenges in providing timely medical care and medication management tailored to individual patient circumstances. In particular, it has been difficult to consolidate diverse patient information and quickly and accurately communicate to healthcare providers when appropriate action is needed.

[0644] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0645] In this invention, the server includes information acquisition means for collecting and recording patient data, data analysis means for analyzing the acquired data and determining the timing of the next medical procedure, and notification generation means for creating personalized notifications from the analysis results. This enables efficient management of patient care by creating reminders for each patient at the appropriate time and notifying healthcare providers.

[0646] "Information acquisition means" refers to functions for collecting and recording patient medical history, symptom data, medication information, etc.

[0647] "Data analysis means" refers to a function that analyzes acquired patient data to determine the timing of the next medical consultation or medication.

[0648] The "notification generation means" is a function for creating personalized notifications based on the analysis results.

[0649] A "notification presentation means" is a function for presenting generated notifications to healthcare providers visually or audibly.

[0650] "Action recording means" refers to a function that records the results of actions taken by healthcare providers and updates patient data based on this information.

[0651] This invention is an information system for healthcare providers to efficiently manage patient care. The system centers around a server, terminals, and users (healthcare providers), and by coordinating these, it collects, analyzes, and notifies information.

[0652] The server accesses the medical institution's database and periodically collects patient medical history, symptom data, medication information, and other data. This information is securely recorded using "information acquisition means." Next, the server analyzes the collected data using "data analysis means" and uses a generative AI model to determine when the next medical consultation or medication is needed. The generative AI model incorporates an analysis algorithm based on the patient's past data, providing the basis for the analysis.

[0653] Based on the analysis results, the server uses a "notification generation means" to create a personalized reminder. This reminder includes specific information such as the next appointment date and medication instructions. Next, the server sends the reminder to the terminal using a "notification presentation means."

[0654] The device visually or audibly notifies healthcare providers of received reminders. This allows users to respond to patients in a timely manner. For example, they can schedule appointments according to reminders or provide medication guidance to patients.

[0655] Users record the actions they perform according to reminders through an "action recording device" and update the database by sending the results to the server. Through this process, the most up-to-date patient information is always maintained within the system and can be used for subsequent analysis.

[0656] As a concrete example, consider a case where a server analyzes past blood glucose data for a patient with diabetes and notifies them of the recommended timing for the next random blood glucose test. An example of a prompt message in this case would be, "Analyze the past blood glucose data of the diabetic patient and predict the timing of the next test."

[0657] This system allows healthcare providers to improve the efficiency of patient management and enhance the quality of care.

[0658] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0659] Step 1:

[0660] The server accesses the medical institution's database to collect patient medical history, symptom data, and medication information. It uses patient information obtained from the database as input, and organizes and records it using an information retrieval method. Specifically, it queries the database using an API, periodically checking for and retrieving new data.

[0661] Step 2:

[0662] The server processes the collected patient data using data analysis tools. The input is the patient information obtained in step 1, and the output is analysis results including the timing of the next medical consultation and medication. Here, a generated AI model is used to predict the appropriate timing based on past data. Specifically, the patient's past medical patterns and test results are analyzed using statistical analysis and machine learning algorithms.

[0663] Step 3:

[0664] The server uses a notification generation mechanism to create personalized reminders based on the analysis results. The input is the analysis results from step 2, and the output is reminder information sent to healthcare providers. Specifically, the reminders include the date and time of the next appointment and the required medication timing, and are formatted accordingly.

[0665] Step 4:

[0666] The server sends the generated reminder to the terminal via a notification display device. The input is the reminder created in step 3, and the output is the reminder information delivered to the terminal. Specifically, data is sent to the terminal using a secure communication protocol.

[0667] Step 5:

[0668] The device visually and audibly notifies healthcare providers of received reminders. The input is reminder information sent from the server, and the output is a notification to healthcare providers. Specifically, it uses methods such as pop-up displays and alert sounds to enable healthcare providers to respond immediately.

[0669] Step 6:

[0670] The user takes appropriate action for the patient based on the reminder displayed on the device. The input is the information provided as a reminder, and the output is the medical procedure performed and its results. Specific actions include scheduling appointments and providing medication guidance.

[0671] Step 7:

[0672] The user inputs the actions they perform into the terminal using an action recording device and sends the updated information to the server. The input is the actions performed by the user, and the output is the updated patient data. Specifically, the user uses the input form on the terminal to record the action log and upload it to the server.

[0673] This process allows the entire system to circulate, enabling efficient patient management.

[0674] (Application Example 1)

[0675] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0676] In managing patient care, healthcare providers are required to obtain information quickly and efficiently and take appropriate action. In particular, given the limited means of visually confirming information, the importance of proactive reminders is increasing. However, traditional systems do not adequately address this, leading to a heavy burden on healthcare providers and a decline in the quality of care.

[0677] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0678] In this invention, the server includes means for storing patient information, means for analyzing patient information, means for generating reminders, means for notifying reminders, means for presenting reminders to healthcare providers, means for updating data based on input information from healthcare providers, and means for presenting reminders via a visual display device. By presenting reminders visually, healthcare providers can quickly and efficiently take the next action they need to take.

[0679] "Patient information storage means" refers to a database device or system for storing information about a patient, such as medical history, symptom data, and medication history.

[0680] A "patient information analysis means" is a processing device or program that analyzes recorded patient data to predict the patient's health status and the timing of necessary medical treatment and medication.

[0681] A "reminder generation means" is a program or device for generating reminders to encourage medical treatment or medication adherence based on analyzed patient information.

[0682] A "reminder notification means" is a means or system for communicating generated reminders to healthcare providers.

[0683] A "means for presenting reminders to healthcare providers" refers to a device or application for presenting notified reminders to healthcare providers visually or audibly.

[0684] "Means for updating data based on input from healthcare providers" refers to a processing device or software that inputs actions taken by healthcare providers and their results, and keeps the data on the server up to date.

[0685] "Means of presenting reminders via a visual display device" refers to a device or system that uses a visual display device, such as smart glasses, to present reminders to healthcare providers.

[0686] To implement this invention, a server first manages all information about the patient. The server accesses the medical institution's database and stores detailed data, including the patient's medical history, symptom data, and medication history. Based on this information, a patient information analysis means analyzes each patient's health status and predicts the timing of the next necessary medical treatment or medication.

[0687] Next, the server uses a reminder generation mechanism to generate personalized reminders for each patient based on the analysis results. These reminders include specific appointment dates, medication times, and the need for follow-up. The generated reminders are then sent to the terminals used by healthcare providers via a reminder notification mechanism.

[0688] On the terminal, reminders are visually presented to healthcare providers via a visual display device using a reminder presentation mechanism. By using devices such as smart glasses, reminders can be checked at any time, enabling healthcare providers to take the next action quickly and efficiently.

[0689] Based on reminders, healthcare providers take actions such as scheduling the next appointment or providing patient guidance. Furthermore, by inputting these actions and their results into a terminal, a means of updating the data on the server keeps the healthcare provider's input information up to date.

[0690] As a concrete example, consider the case of a diabetic patient. The server predicts the timing of the next necessary test based on the patient's past blood glucose data and displays a reminder with specific instructions to the healthcare provider via smart glasses. By using this system, healthcare providers can efficiently manage patient care and improve the quality of care.

[0691] An example of a prompt using a generative AI model is, "Create Python code that calculates the next appointment date and medication time based on the user's care data and generates a reminder." By using this prompt, the AI ​​can automatically generate the necessary reminders.

[0692] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0693] Step 1:

[0694] The server accesses the medical institution's database to retrieve the patient's medical history, symptom data, and medication history. This data serves as input. The server stores this information using patient information storage devices. This stored data is then used in the next analysis step.

[0695] Step 2:

[0696] The server uses patient information analysis tools to analyze the stored data. The input is the patient data obtained in step 1. Based on this data, the server calculates the timing of the next necessary medical treatment and medication, and this is output as the analysis result. The analysis result includes each patient's next appointment date and medication time.

[0697] Step 3:

[0698] The server generates reminders using a reminder generation mechanism based on the analysis results. The input is the analysis results obtained in step 2. In this process, a reminder specific to each patient is created, including the date of the consultation, medication time, and the need for follow-up. The generated reminders are obtained as output.

[0699] Step 4:

[0700] The server delivers the generated reminder to the terminal using a reminder notification mechanism. The input is the reminder generated in step 3. In this process, the reminder is sent to a visual display device. The output is the reminder received by the terminal.

[0701] Step 5:

[0702] The terminal displays reminders to healthcare providers via a visual display device using a reminder presentation mechanism. The input is a reminder sent from the server. This allows healthcare providers to visually confirm necessary information and decide on the next action to take. Presenting information in real time enables healthcare providers to respond quickly.

[0703] Step 6:

[0704] Healthcare providers, as users, take action based on the displayed reminders. The input is visualized reminder information. For example, they might schedule the next appointment or provide patient guidance. The results of these actions are entered into the terminal, improving patient management.

[0705] Step 7:

[0706] The action results entered into the terminal update the data on the server through a means of updating the data based on the input information from the healthcare provider. The input is the data entered by the healthcare provider into the terminal. In this process, the server's database is updated with the latest patient information and used for future analysis and reminder generation. The output is the updated patient database.

[0707] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0708] This invention provides a system incorporating an emotion engine to further enhance patient care management in medical institutions. In addition to conventional patient information management functions, this system has the function of recognizing the emotions of healthcare providers and accordingly providing appropriate notifications and suggesting appropriate response strategies.

[0709] First, the server collects patient information from the medical institution's database as usual and performs analysis. When sending reminders to terminals used by healthcare providers, the emotion engine is built in, allowing the terminal to analyze the healthcare provider's emotional state in real time. The emotion engine recognizes the healthcare provider's voice tone and facial expressions to determine whether they are stressed or relaxed.

[0710] When a user (healthcare provider) checks a reminder using their device, the notification method is optimized according to the healthcare provider's emotional state as determined by the emotion engine. For example, if the healthcare provider is feeling stressed, the notification may be softened, or less urgent reminders may be postponed.

[0711] Furthermore, the emotion engine can also suggest patient interaction strategies based on the healthcare provider's current emotions. For example, if a healthcare provider is showing signs of fatigue, the emotion engine will suggest ways of speaking and content to facilitate smoother communication with the patient. In this way, it helps healthcare providers interact with patients in the best possible mental state.

[0712] This system not only manages patient information but also enables the provision of flexible care that takes into account the emotional needs of healthcare providers. As a result, it is expected to improve the quality of services provided to patients, reduce the burden on healthcare providers, and enable more efficient operations.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] The server collects and stores patient information such as medical history, symptom data, and medication history from the healthcare institution's database. This information is structured and organized for use in individual patient-based analysis.

[0716] Step 2:

[0717] The server analyzes the collected data using patient information analysis tools to predict the next appointment date, medication timing, and symptoms requiring special attention. This analysis is performed using machine learning models and data analysis algorithms.

[0718] Step 3:

[0719] Based on the analysis results, the server generates personalized reminders for each patient. These reminders include specific appointment dates, medication instructions, and even lifestyle advice suggestions.

[0720] Step 4:

[0721] The device receives reminders sent from the server and submits them to the healthcare provider. During this process, a built-in emotion engine activates, capturing the healthcare provider's voice and facial expressions to analyze their emotional state.

[0722] Step 5:

[0723] The emotion engine analyzes the situation in which healthcare providers are using the device, determining, for example, their level of fatigue, stress, or relaxation. Based on this, it designs notification methods (such as volume, notification frequency, and screen display adjustments) that are appropriate for the user's emotional state.

[0724] Step 6:

[0725] Users check reminders displayed on their devices and plan appropriate actions for patients based on them. For example, they might schedule the next appointment or provide timely medication guidance. At this time, they can also refer to communication strategies suggested by the emotion engine.

[0726] Step 7:

[0727] The user inputs the actions they perform and their results into the terminal. The terminal then sends this information to the server, and the patient data is updated to the latest state.

[0728] Step 8:

[0729] The server updates the database based on information sent by the user and prepares for the next analysis. This update enables more personalized patient care.

[0730] (Example 2)

[0731] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0732] In patient care within healthcare institutions, traditional systems that manage and notify patients without considering the emotional state of healthcare providers face challenges such as inconsistent service quality and increased workload for healthcare providers. Therefore, there is a need for a system that allows for flexible and efficient patient care while taking into account the emotional state of healthcare providers.

[0733] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0734] In this invention, the server includes means for storing patient information, means for analyzing patient information, means for generating reminders, means for analyzing the emotional state of healthcare providers, means for optimizing reminder notifications according to the emotional state of healthcare providers, and means for proposing patient care policies using a generated AI model. This enables flexible and efficient patient care that takes into account the emotional state of healthcare providers, thereby improving service quality and reducing workload.

[0735] "Patient information storage means" refers to a function in a medical institution that stores information such as a patient's medical history and treatment details.

[0736] "Patient information analysis tools" refer to functions that perform data analysis based on collected patient information and provide insights useful for medical treatment.

[0737] A "reminder generation method" refers to a function that identifies information or tasks that need to be notified to healthcare providers and creates notifications to inform them of that information.

[0738] "Reminder notification means" refers to methods and functions for communicating generated reminders to healthcare providers.

[0739] "Means of presenting reminders to healthcare providers" refers to functions that visually and audibly present notified reminders to healthcare providers.

[0740] "Means for analyzing the emotional state of healthcare providers" refers to a function that analyzes the voice tone and facial expressions of healthcare providers and evaluates their psychological and emotional state.

[0741] "Means for optimizing reminder notifications according to the healthcare provider's emotions" refers to a function that adjusts the timing and method of notifications based on the healthcare provider's emotional state.

[0742] "Methods for proposing patient care policies using generative AI models" refers to a function that uses generative artificial intelligence models to derive and recommend patient care methods based on the emotional state of healthcare providers.

[0743] "Means for updating data based on input from healthcare providers" refers to a function that reflects new information provided by healthcare providers in the system, keeping the database up-to-date.

[0744] "Means for generating prompt sentences based on emotional state" refers to a function that generates appropriate AI dialogue prompts using the results of emotional analysis of healthcare providers.

[0745] This invention incorporates an emotion engine and features an efficient patient information management and notification system to enable healthcare providers to deliver optimal care to patients.

[0746] The server accesses the medical institution's database, collects information such as the patient's medical history, medical record, and treatment details, and uses patient information storage means to organize and store this information. Furthermore, it uses patient information analysis means to analyze this information and prepares to generate notifications to the healthcare provider's terminal.

[0747] The device analyzes the voice tone and facial expressions of healthcare providers through an emotion engine equipped with voice analysis and facial recognition software, and evaluates their psychological state. For this purpose, general voice analysis software, such as a voice recognition engine, and a facial recognition API are used for facial expression analysis. Based on this analysis, reminder notifications are optimized, prioritizing notifications according to the healthcare provider's emotional state, changing the notification sound, or even changing the notification method itself.

[0748] Healthcare providers, as users, can check reminders notified through their devices and adjust their responses to patients based on information optimized by the emotion engine. For example, if they are feeling stressed, notifications will be changed to a softer tone, and less urgent tasks will be postponed. Furthermore, a generative AI model is used to suggest specific response strategies for patients based on the user's current emotional state. An example of a specific prompt might be, "The healthcare provider seems particularly tired today. What kind of communication style and content would be appropriate to facilitate smooth communication with the patient?"

[0749] This technology will not only improve the quality of patient care but also reduce the workload of healthcare providers. Furthermore, it will enable more efficient work processes that take into account the mental health of healthcare providers, ultimately leading to improved productivity and satisfaction across the entire healthcare field.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] The server uses patient information storage to retrieve patient medical history, medical records, and treatment details from a medical institution's database. The input is patient information from the database, and the output is well-formed data for analysis. This data is analyzed by patient information analysis tools to extract treatment history and important medical insights.

[0753] Step 2:

[0754] The server uses a reminder generation mechanism to create notifications based on the patient's treatment plan and appointment schedule. The input is the analyzed patient information obtained in the previous step, and the output is reminder data indicating specific tasks that healthcare providers should perform. For example, it might list the next appointment date or items requiring specific follow-up.

[0755] Step 3:

[0756] The device uses voice analysis and facial recognition software to analyze the emotional state of healthcare providers and evaluate their emotional state in real time. Input is the healthcare provider's voice and facial image data, and output is a score or judgment result indicating their emotional state. Specifically, voice analysis measures tone and pitch, and facial recognition captures changes in facial expressions.

[0757] Step 4:

[0758] The device optimizes reminder notifications based on the emotion analysis results. The input is the reminder content and the result of the emotional state assessment, and the output is an optimized notification (e.g., changing the notification sound or adjusting the display time). For example, if a healthcare provider is stressed, the notification sound will be made softer and non-urgent reminders will be displayed later.

[0759] Step 5:

[0760] The server uses a generative AI model to generate patient response strategies tailored to the healthcare provider's current emotional state. The input consists of emotional state data and reminder data, while the output is a specific suggestion for how to respond to the patient. For example, a suggested prompt might be: "The healthcare provider seems particularly tired today. What kind of communication style and content would be appropriate to facilitate smooth communication with the patient?" This output is then presented to the healthcare provider via a terminal.

[0761] Step 6:

[0762] Healthcare providers, as users, review reminders and suggestions displayed on their devices and act accordingly when interacting with patients. Input consists of optimized notifications and suggestions, while output is data on the actions taken and their results. This data is then reflected in the database for future analysis and notifications.

[0763] (Application Example 2)

[0764] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0765] In elderly care settings, the inability to provide optimal care tailored to the emotional state of care staff leads to challenges in the quality of services provided to residents and the efficiency of staff work. In particular, staff emotional states often affect communication and the quality of care, and there is a need for methods to accurately understand and address this.

[0766] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0767] In this invention, the server includes means for storing patient information, means for analyzing patient information, means for generating reminders, means for recognizing the emotional state of the provider, means for optimizing notification methods according to the emotional state of the provider, and means for proposing a response plan based on the emotional state of the provider. This makes it possible to provide optimal care that takes into account the emotional state of the care staff.

[0768] "Patient information storage means" refers to a device or system that holds data about a patient and records it in a way that allows it to be retrieved as needed.

[0769] "Patient information analysis means" refers to a device or method for analyzing recorded patient data and extracting meaning and trends.

[0770] A "reminder generation method" is a function that automatically generates notification content for the provider based on pre-set conditions.

[0771] "Means for presenting reminders to providers" refers to a device or system for presenting generated reminders to care staff or medical professionals in an appropriate format.

[0772] "Means for updating data based on provider input" refers to a function that receives new data and feedback from providers to keep the recorded information up-to-date.

[0773] "Means for recognizing the provider's emotional state" refers to technologies that analyze the provider's voice and facial expressions to identify their emotional state.

[0774] "Means for optimizing notification methods according to the provider's emotional state" refers to a function that adjusts notification content and methods based on the provider's emotions and communicates them through the most effective means.

[0775] "Means for proposing response strategies based on the provider's emotional state" refers to a device or system for proposing the optimal response strategy or communication method, taking into account the provider's current emotions.

[0776] To implement this invention, a system is constructed that includes an information processing device, a cloud server, and an emotion recognition engine for use by the provider. The terminal consists of smart glasses or a tablet, and is responsible for displaying information when the provider interacts with the resident. The terminal is equipped with a camera and a microphone, and captures the provider's facial expressions and voice tone in real time.

[0777] The server includes an emotion recognition engine and uses platforms such as AWS Rekognition and Tone Analyzer to analyze data sent from the device. The results of the emotional state evaluation are used to optimize notification methods and suggest response strategies. Based on the provider's emotional state, the server makes decisions such as prioritizing less urgent notifications.

[0778] Users (providers) can check reminders and suggestions displayed on the device and adjust their interactions and actions with residents. For example, if the emotion engine determines that the provider is fatigued, a message such as "First, take a deep breath and relax. Then, bring up seasonal topics with the resident." will be displayed on the device.

[0779] By using a generative AI model, it is possible to show an example of a prompt statement as follows:

[0780] "Create suggestions to facilitate smoother communication between care staff and residents when they are feeling tired."

[0781] "Please come up with ideas for ways to reduce stress in caregiving settings."

[0782] In this way, we will realize a system that supports providers in providing care in the optimal mental state.

[0783] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0784] Step 1:

[0785] The server uses patient information storage devices to record past patient data and prepares it for analysis. Inputs include biometric information and past care history related to the patient, while outputs are structured datasets for analysis. Specifically, data is collected from electronic medical records and sensors and stored in a database.

[0786] Step 2:

[0787] The device captures the provider's real-time voice tone and facial expressions using a camera and microphone, and transmits them to the server. The input is the provider's voice and video, and the output is a data stream to the server. Specifically, it detects eye movements and voice pitch and converts them into digital signals.

[0788] Step 3:

[0789] The server uses an emotion recognition engine to analyze the received audio and video data and evaluate the provider's emotional state. The input is audio and video data sent from the terminal, and the output is labels and scores indicating the emotional state. Specifically, it uses AWS Rekognition and Tone Analyzer to calculate stress levels and the percentage of positive emotions.

[0790] Step 4:

[0791] The server generates the optimal notification method based on the emotional state and sends it to the device. Input is an emotional state label or score, and output is the notification content and its display format. Specifically, if stress levels are high, a quiet notification with reduced sound and vibration is selected.

[0792] Step 5:

[0793] The device appropriately presents received notifications to the provider, supporting the provider in providing effective care. Input is notification instructions from the server, and output is visual or auditory feedback received by the provider. Specifically, this involves displaying messages on glasses or providing instructions via voice.

[0794] Step 6:

[0795] The user (provider) refers to the information displayed on the device and takes action for the resident. Input is notifications and suggestions on the device, and output is specific actions for the resident. Specifically, this might involve speaking to the resident in a calm voice or suggesting activities.

[0796] Step 7:

[0797] The user inputs the results of their actions into a terminal and sends feedback to the server. The input is data on the performance of care and the resident's response, and the output is an updated dataset returned to the server. Specifically, it records the details of successful care and changes in the resident's condition.

[0798] 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.

[0799] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0800] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0801] 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.

[0802] Figure 9 shows an 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.

[0803] 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.

[0804] 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.

[0805] 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, motorcycles, etc., 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, for example, based 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.

[0806] 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."

[0807] 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.

[0808] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0809] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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 the like 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.

[0818] 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.

[0819] The following is further disclosed regarding the embodiments described above.

[0820] (Claim 1)

[0821] means for storing patient information,

[0822] Patient information analysis method,

[0823] Reminder generation means,

[0824] Reminder notification methods,

[0825] A means of providing reminders to healthcare providers,

[0826] A means of updating data based on input information from healthcare providers,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, further comprising means for distributing the generated reminders to terminals used by each healthcare provider.

[0830] (Claim 3)

[0831] The system according to claim 1, comprising means for a healthcare provider to take action based on a reminder displayed on a terminal and to input the result of the action.

[0832] "Example 1"

[0833] (Claim 1)

[0834] Information acquisition means for collecting and recording patient data,

[0835] A data analysis method that analyzes acquired data to determine the timing of the next medical procedure,

[0836] A notification generation means that creates personalized notifications from the analysis results,

[0837] A notification presentation means for visually or audibly presenting the generated notification,

[0838] A means of recording the results of healthcare providers' actions and updating the data,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, having a function to distribute generated notifications to devices used by each healthcare professional.

[0842] (Claim 3)

[0843] The system according to claim 1, which has a function for a medical professional to take action based on a notification displayed on the device and to record the result.

[0844] "Application Example 1"

[0845] (Claim 1)

[0846] means for storing patient information,

[0847] Patient information analysis method,

[0848] Reminder generation means,

[0849] Reminder notification methods,

[0850] A means of providing reminders to healthcare providers,

[0851] A means of updating data based on input information from healthcare providers,

[0852] A means for presenting a reminder via a visual display device,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, further comprising means for distributing the generated reminders to visual display devices used by each healthcare provider.

[0856] (Claim 3)

[0857] The system according to claim 1, comprising means for a healthcare provider to take action based on a reminder displayed on a visual display device and to input the result of the action.

[0858] "Example 2 of combining an emotion engine"

[0859] (Claim 1)

[0860] means for storing patient information,

[0861] Patient information analysis method,

[0862] Reminder generation means,

[0863] Reminder notification methods,

[0864] A means of providing reminders to healthcare providers,

[0865] A means of analyzing the emotional state of healthcare providers,

[0866] A means to optimize reminder notifications according to the emotions of healthcare providers,

[0867] A method for proposing patient care strategies using generative AI models,

[0868] A means of updating data based on input information from healthcare providers,

[0869] A means for generating prompt sentences based on emotional state,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, further comprising means for distributing the generated reminders to terminals used by each healthcare provider.

[0873] (Claim 3)

[0874] The system according to claim 1, comprising means for a healthcare provider to take action based on a reminder displayed on a terminal and to input the result of the action.

[0875] "Application example 2 when combining with an emotional engine"

[0876] (Claim 1)

[0877] means for storing patient information,

[0878] Patient information analysis method,

[0879] Reminder generation means,

[0880] Reminder notification methods,

[0881] A means of providing reminders to providers,

[0882] A means of updating the data based on the provider's input information,

[0883] Means for recognizing the provider's emotional state,

[0884] A means to optimize the notification method according to the provider's emotional state,

[0885] A means of proposing a response strategy based on the provider's emotional state,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, further comprising means for distributing the generated reminders to information processing devices used by each provider.

[0889] (Claim 3)

[0890] The system according to claim 1, comprising means for the provider to take action based on a reminder displayed on an information processing device and to input the result of the action. [Explanation of Symbols]

[0891] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A patient information storage means for storing information such as the patient's medical history, symptom data, and medication history, A patient information analysis method that analyzes recorded patient data to predict the patient's health status and the timing of necessary medical treatment and medication, A reminder generation means that generates reminders to encourage medical treatment and medication based on analyzed patient information, A reminder notification method that informs healthcare providers of the generated reminders, A means of presenting a notified reminder to a healthcare provider visually or audibly, A means of updating data by inputting information from healthcare providers, which allows them to input actions taken by healthcare providers and their results, and keeps the data on the server up to date. A means for presenting a reminder via a visual display device, A system that includes this.

2. The system according to claim 1, further comprising means for distributing the generated reminders to visual display devices used by each healthcare provider.

3. The system according to claim 1, further comprising means for a healthcare provider to take action based on a reminder displayed on a visual display device and to input the result of the action.

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