Information processing method, information processing program, and information processing device

Abstract

The present invention provides an information processing method, an information processing program, and an information processing device that visualize information related to the tasks performed by care workers and generate information that supports the efficient improvement of care services. [Solution] The information processing method includes the steps of: accumulating work data of care workers; extracting target data from the work data based on extraction conditions; performing predetermined processing on the target data to generate processed data; and outputting the processed data.

Description

Technical Field

[0001] The present disclosure relates to an information processing method, an information processing program, and an information processing apparatus for caregiving.

Background Art

[0002] Conventionally, a method of aggregating work data collected by time study using a device has been known (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a need to visualize information related to the work performed by care workers and generate information that can efficiently support the improvement of care services.

[0005] An object of the present disclosure is to provide an information processing method, an information processing program, and an information processing apparatus that visualize information related to the work performed by care workers and generate information that can efficiently support the improvement of care services.

Means for Solving the Problems

[0006] (1) An information processing method according to an embodiment of the present disclosure is a method executed by an information processing apparatus for visualizing information related to the work performed by care workers, the method including: accumulating work data of the care workers; extracting target data based on extraction conditions from the work data; executing a predetermined process on the target data to generate processed data; and outputting the processed data.

[0007] (2) In the information processing method described in (1) above, the predetermined processing may be a process for calculating an index relating to the target data.

[0008] (3) In the information processing method described in (1) or (2) above, the predetermined processing may be a process of discovering issues from the target data using an issue discovery model generated by performing machine learning on data to which labels indicating whether or not an issue has occurred or the content of the issue have been assigned to at least a portion of the work data.

[0009] (4) In the information processing method described in (3) above, the problem discovery model may be configured to output information about the problems contained in the target data when the target data is input.

[0010] (5) In the information processing method described in any one of (1) to (4) above, the prescribed processing may be the processing of generating notification documents for the addition of care service fees.

[0011] (6) An information processing program according to one embodiment of the present disclosure is a program to be executed by an information processing device for visualizing information relating to work performed by a care worker, and includes the steps of: accumulating work data of the care worker; extracting target data from the work data based on extraction conditions; performing predetermined processing on the target data to generate processed data; and outputting the processed data.

[0012] (7) An information processing device according to one embodiment of the present disclosure is a device for visualizing information relating to work performed by a care worker, comprising: a data storage unit for storing work data of the care worker; a data processing unit for performing predetermined processing on target data extracted from the work data based on extraction conditions to generate processed data; and an output unit for outputting the processed data. [Effects of the Invention]

[0013] According to the information processing method, information processing program, and information processing apparatus according to an embodiment of the present disclosure, information regarding the work performed by care workers is visualized, and information for assisting in efficiently improving care services is generated.

Brief Description of the Drawings

[0014] [Figure 1] It is a block diagram showing a configuration example of an information processing system according to the present disclosure. [Figure 2] It is a block diagram showing a configuration example of a learning unit. [Figure 3] It is a block diagram showing a configuration example of a work classification estimation unit. [Figure 4] It is a block diagram showing a configuration example of an evaluation unit. [Figure 5] It is a flowchart showing an example of a procedure for evaluating work. [Figure 6] It is a diagram showing an example of a hierarchical structure of work classifications. [Figure 7A] It is a diagram representing an independent process. [Figure 7B] It is a diagram representing a parallel process in which some actions are parallel. [Figure 7C] It is a diagram representing a parallel process in which the first action is parallel. [Figure 7D] It is a diagram representing a parallel process in which all actions are parallel. [Figure 7E] It is a diagram representing a parallel process in which the last action is parallel. [Figure 7F] It is a diagram representing an interrupt process. [Figure 8] It is a timeline representing the time period estimated that a care worker has performed the work of each classification. [Figure 9] It is a timeline representing the time period during which a care worker has stayed at each position. [Figure 10] It is a bar graph representing the length of time estimated that a care worker has performed the work of each classification.

Embodiments for Carrying Out the Invention

[0015] In the field of caregiving services, the shortage of caregivers has become a problem. There is a need to improve the productivity of caregiving services so that operations can be efficiently carried out with a small number of personnel. Also, there is a need to improve the quality of caregiving services to satisfy care recipients who receive caregiving services. A common method for improving the productivity and quality of caregiving services is to understand the caregiving service process, which is a series of flows at the caregiving service site, and to execute improvements to caregiving services such as technology introduction or process improvement.

[0016] Here, it is useful to visualize information on the work performed by caregivers and generate information that supports the efficient improvement of caregiving services. Hereinafter, in the present disclosure, an information processing system 1 (see FIG. 1) that visualizes information on the work performed by caregivers and generates information that supports the improvement of caregiving services will be described with reference to the drawings.

[0017] (Overview of Information Processing System 1) As shown in FIG. 1, an information processing system 1 according to an embodiment includes an information processing device 10, a sensor 40, an input device 50, a display device 60, and a database 70.

[0018] The information processing device 10 acquires information about care workers in care facilities and other care settings. The information about care workers may include information about the locations where care workers have moved within the care facilities and other care settings, and information about the time spent at each location. Information about the location of care workers is also referred to as location information. Information about the time spent at each location by care workers is also referred to as time information. Location information may include locations where care workers have moved and stayed, or locations that care workers have passed through while moving. In other words, location information may include a history of care workers moving to multiple locations. In this disclosure, locations in care facilities and other care settings are also referred to as places within the care facilities and other care settings. Time information may include the length of time that care workers have stayed at each location in the past, or the time that care workers have started and ended their stay at each location in the past. The time that care workers started staying at a certain location corresponds to the time that care workers completed their movement to that location. The time that care workers ended their stay at a certain location corresponds to the time that care workers started moving away from that location. Location and time information may be generated based on measurement results from sensor 40, as described later. Information regarding care workers may further include the schedule of tasks related to care or indirect care tasks that care workers will perform at the care site. The schedule of tasks performed by care workers may also be simply referred to as the work schedule.

[0019] The information processing device 10 estimates the tasks performed by the care worker based on information about the care worker. Furthermore, as will be described later, the tasks performed by the care worker may be classified into multiple categories. The tasks performed by the care worker may include tasks performed while the care worker moves to multiple locations. In other words, one task performed by the care worker may be a task that combines the actions performed by the care worker at each of the multiple locations. One task performed by the care worker may be a task that combines the care worker moving between multiple locations and the actions performed by the care worker at each of the multiple locations. The information processing device 10 may generate a task category estimation model, as will be described later, for example by performing machine learning, and use the generated task category estimation model to estimate the categories of tasks performed by the care worker.

[0020] The information processing device 10 may display the tasks in each category, which are estimated to have been performed by the care worker, as a timeline on the display device 60. The information processing device 10 may also aggregate the time or frequency of the tasks in each category, which are estimated to have been performed by the care worker, and display the aggregated results on the display device 60.

[0021] (Example configuration of Information Processing System 1) The following describes an example configuration of Information Processing System 1.

[0022] <Information Processing Device 10> As shown in Figure 1, the information processing device 10 comprises a work category definition unit 12, a location definition unit 14, a schedule management unit 16, a learning unit 20, a work category estimation unit 30, and an evaluation unit 80. The work category definition unit 12 defines the categories of work performed by the care worker. The location definition unit 14 defines the locations where the care worker moved and stayed, or the locations where the care worker passed through while moving, or the locations where the care worker performed work. The schedule management unit 16 manages the care worker's work schedule. The learning unit 20 generates a work category estimation model used to estimate the categories of work performed by the care worker. The work category estimation unit 30 estimates the categories of work performed by the care worker using the work category estimation model. The evaluation unit 80 evaluates the work performed by the care worker based on work data that includes information about the work performed by the care worker.

[0023] Each of the work category definition unit 12, the location definition unit 14, and the schedule management unit 16 may include one or more processors. The processors may include, but are not limited to, general-purpose processors or dedicated processors specialized for specific processing. Each of the work category definition unit 12, the location definition unit 14, and the schedule management unit 16 may include one or more dedicated circuits. The dedicated circuits may include, for example, FPGAs (Field-Programmable Gate Arrays) or ASICs (Application Specific Integrated Circuits). Each of the work category definition unit 12, the location definition unit 14, and the schedule management unit 16 may include a dedicated circuit instead of a processor, or may include a dedicated circuit together with a processor. Each of the work category definition unit 12, the location definition unit 14, and the schedule management unit 16 may be configured as separate components. At least a part of the work category definition unit 12, the location definition unit 14, and the schedule management unit 16 may be configured as a single unit.

[0024] Each of the work division definition unit 12, the location definition unit 14, and the schedule management unit 16 may include a storage unit. The storage unit may store programs executed by each of the work division definition unit 12, the location definition unit 14, and the schedule management unit 16, or information or data used in the processing of each of the work division definition unit 12, the location definition unit 14, and the schedule management unit 16. The storage unit may include, for example, semiconductor memory, magnetic memory, or optical memory. The storage unit may include an electromagnetic storage medium such as a magnetic disk. The storage unit may include a non-temporary computer-readable medium. The storage unit may function as, for example, main memory, auxiliary memory, or cache memory. The storage unit may function as the work memory of each of the work division definition unit 12, the location definition unit 14, and the schedule management unit 16. The memory unit may be configured integrally with the processors of the work category definition unit 12, the location definition unit 14, and the schedule management unit 16, or it may be configured separately from the processors of the work category definition unit 12, the location definition unit 14, and the schedule management unit 16.

[0025] <<Learning Section 20>> As shown in Figure 2, the learning unit 20 includes an action measurement unit 22, a position measurement unit 24, a time measurement unit 25, and a model generation unit 26.

[0026] Each component of the learning unit 20 may include one or more processors or one or more dedicated circuits, similar to the work category definition unit 12, the location definition unit 14, and the schedule management unit 16. The learning unit 20 may also include a storage unit, similar to the work category definition unit 12, the location definition unit 14, and the schedule management unit 16.

[0027] <<Work classification estimation section 30>> As shown in Figure 3, the work section estimation unit 30 includes a position measurement unit 32, a position recognition unit 33, a time measurement unit 34, an estimation unit 35, and an output unit 36.

[0028] Each component of the work category estimation unit 30 may include one or more processors or one or more dedicated circuits, similar to each of the work category definition unit 12, the location definition unit 14, and the schedule management unit 16, or each of the components of the learning unit 20. The work category estimation unit 30 may also include a storage unit, similar to each of the work category definition unit 12, the location definition unit 14, and the schedule management unit 16, or the learning unit 20.

[0029] <<Evaluation Section 80>> As shown in Figure 4, the evaluation unit 80 includes a data storage unit 81, a data processing unit 82, an output unit 83, and a problem estimation unit 84.

[0030] Each component of the evaluation unit 80 may include one or more processors or one or more dedicated circuits, similar to each of the work category definition unit 12, the location definition unit 14, and the schedule management unit 16, or to each of the components of the learning unit 20 or the work category estimation unit 30. The evaluation unit 80 may also include a storage unit, similar to each of the work category definition unit 12, the location definition unit 14, and the schedule management unit 16, or to the learning unit 20 or the work category estimation unit 30.

[0031] The work category definition unit 12, the location definition unit 14, the schedule management unit 16, the learning unit 20, the work category estimation unit 30, and the evaluation unit 80 may each be configured as separate units. At least a part of the work category definition unit 12, the location definition unit 14, the schedule management unit 16, the learning unit 20, the work category estimation unit 30, and the evaluation unit 80 may be configured as an integrated unit.

[0032] <<Other configurations of the information processing device 10>> The information processing device 10 may include a communication module configured to communicate with other devices such as a sensor 40, an input device 50, a display device 60, or a database 70. The communication module may be configured to communicate with other devices via a network, or it may be configured to communicate with other devices via P2P (Peer to Peer) without a network. The communication module may be configured to communicate with other devices via wired or wireless connections. The communication module may support mobile communication standards such as 4G (4th Generation) or 5G (5th Generation). The communication module may support communication standards such as LAN (Local Area Network). The communication module may support wired or wireless communication standards. The communication module is not limited to these and may support various communication standards.

[0033] The information processing device 10 may be configured to include at least one server. The information processing device 10 may be implemented as a computer such as a desktop PC (Personal Computer), notebook PC, or tablet PC. The information processing device 10 may be implemented as a smartphone or tablet. The information processing device 10 is not limited to these examples and may include various devices.

[0034] The information processing device 10 may be configured as a device equipped with a learning unit 20 and a device equipped with a work category estimation unit 30. The information processing device 10 equipped with the learning unit 20 is also called a learning device. The information processing device 10 equipped with the work category estimation unit 30 is also called a work category estimation device. The information processing device 10 may not be equipped with the learning unit 20 and may acquire a work category estimation model generated by an external device.

[0035] <Sensor 40> Sensor 40 may be configured to detect the location of care workers in care facilities or other care settings, as information relating to care workers. Sensor 40 outputs the detection result of the care worker's location to the information processing device 10. Sensor 40 may include, for example, a location measurement device that uses radio waves from wireless communication installed at the care setting, and detect the location information of care workers. Sensor 40 may include a human presence sensor or camera installed at a location to be targeted for detecting the location of care workers, so as to detect the location of care workers in the care setting. Sensor 40 may include a location measurement device that uses a satellite positioning system such as GPS (Global Positioning System). Sensor 40 may include a location measurement device that uses an Autonomous Activity Measurement System (PDR). Sensor 40 is not limited to these examples and may include various other devices.

[0036] Sensor 40 may be configured to detect the body movements or posture of a care worker as information about the care worker. Sensor 40 may include, for example, an accelerometer or an angular velocity sensor worn by the care worker. Sensor 40 may include, for example, a depth camera that photographs the care worker and generates point cloud data of the care worker. Sensor 40 is not limited to these examples and may include various other devices such as a magnetic sensor, a barometric pressure sensor, a BLE (Bluetooth Low Energy) sensor, or an UWB (Ultra-Wide Band) sensor.

[0037] The sensor 40 may be configured to measure time. The sensor 40 may output to the information processing device 10 the detection result of information about the care worker and the time when the information was detected, in association with each other.

[0038] <Input device 50> The input device 50 accepts information input. The input device 50 may include, for example, a pointing device such as a touch sensor or a mouse. The input device 50 may include physical keys. The input device 50 may include an audio input device such as a microphone. The input device 50 may include a display device that displays the content of the input. The input device 50 may be configured as a touch display in which the display device and the touch panel are integrated.

[0039] The input device 50 may be implemented as, for example, a computer such as a desktop PC, notebook PC, or tablet PC, or a terminal such as a smartphone or tablet. The input device 50 is not limited to these examples and may be implemented as various devices.

[0040] <Display device 60> The display device 60 displays information generated by the information processing device 10. The display device 60 may be configured to include various displays, such as a liquid crystal display, an organic EL (Electro-Luminescence) or inorganic EL display, or an LED (Light Emitting Diode) display, so that the information can be displayed as characters, symbols, or images.

[0041] The display device 60 may be implemented as, for example, a computer such as a desktop PC, notebook PC, or tablet PC, or as a terminal such as a smartphone or tablet. The display device 60 is not limited to these examples and may be implemented as various devices.

[0042] <Database 70> The database 70 may store information about care workers collected by the information processing device 10. The database 70 may also store the estimated results of the care worker's work category estimated by the information processing device 10.

[0043] The database 70 may include at least one server or storage device. The database 70 may include, for example, semiconductor memory, magnetic memory, or optical memory. The database 70 may include an electromagnetic storage medium such as a magnetic disk. The database 70 may include a communication module configured to communicate with other devices such as the information processing device 10.

[0044] The information processing system 1, information processing device 10, or database 70 described above may be implemented using cloud services or in an on-premise environment.

[0045] (Example of an action to estimate the division of work) The following describes an example of how the information processing device 10 estimates the category of work performed by a care worker in the information processing system 1, with reference to Figures 1 to 3.

[0046] Referring to Figure 1, in the information processing device 10, the work category definition unit 12 defines multiple categories for classifying the tasks performed by care workers. The tasks performed by care workers may be classified, for example, by the type of care service. Examples of categories for classifying the tasks performed by care workers will be described later. The information processing device 10 may also obtain definitions of categories for classifying the tasks performed by care workers from an external device, without having a work category definition unit 12.

[0047] As described later, the information processing device 10 uses a work category estimation unit 30 to estimate the category of work performed by the care worker based on information about the care worker. The work category estimation unit 30 may select and output at least one category as the estimated category of work performed by the care worker from among a plurality of categories defined by the work category definition unit 12. The plurality of categories defined by the work category definition unit 12 are candidate categories to be selected as the estimated category of work performed by the care worker, and are also called candidate categories.

[0048] Information regarding care workers includes the locations where care workers move. In the information processing device 10, the location definition unit 14 defines the locations where care workers move. The locations where care workers move may include locations where care workers stay or locations where care workers pass through. The locations where care workers move may be defined as places classified by the function of providing care services within the care facility. The locations where care workers move may be defined as metadata such as, for example, the rooms of care service users, dining rooms, bathrooms, toilets, recreation rooms, or corridors. The locations where care workers move may also be defined as coordinate information that can be associated with the coordinates of a floor plan within the care facility. The information processing device 10 may obtain the definition of the locations where care workers move from an external device without having a location definition unit 14.

[0049] Information regarding care workers includes their work schedules. In the information processing device 10, the schedule management unit 16 manages the care workers' work schedules. The care workers' work schedules may include information regarding the care workers' shifts. Care workers' shifts may be defined as early shifts or late shifts, or day shifts or night shifts, etc. The care workers' work schedules may include information that associates the category of the scheduled work with the time or place where the work is scheduled to be performed. The schedule management unit 16 may set work schedules based on input from the care workers themselves or from the managers or supervisors of the care site. The schedule management unit 16 may also set work schedules based on the work plan of the care site. The schedule management unit 16 may also set work schedules based on the actual work performed.

[0050] <Example of operation of learning unit 20> The following describes an example of how the learning unit 20 of the information processing device 10 generates a work category estimation model, with reference to Figure 2. The work category estimation model is a model used by the work category estimation unit 30 to estimate the categories of work performed by the care worker.

[0051] The behavior measurement unit 22 acquires information regarding the care worker's behavior. The care worker's behavior may be measured by the sensor 40. The sensor 40 may measure the care worker's behavior by detecting the movement of various parts of the care worker's body. When the care worker's behavior is measured by the sensor 40, the behavior measurement unit 22 may acquire information regarding the care worker's behavior from the sensor 40.

[0052] The behavior measurement unit 22 obtains the definition of the work category performed by the care worker from the work category definition unit 12. The behavior measurement unit 22 associates the information regarding the care worker's behavior with at least one of several work categories performed by the care worker. The work category performed by the care worker may be associated with the information regarding the care worker's behavior by inputting it into the input device 50 by the care worker themselves or another worker.

[0053] The behavior measurement unit 22 outputs the classification of tasks performed by the care worker, which is associated with information about the care worker's actions, to the model generation unit 26 as the correct behavior.

[0054] The position measurement unit 24 obtains the definition of the location to which the care worker moves from the position definition unit 14. The position measurement unit 24 obtains the care worker's location information. The position measurement unit 24 may obtain metadata defining the location to which the care worker moves as the care worker's location information. The position measurement unit 24 may obtain the care worker's coordinate information, convert the coordinate information into metadata, and obtain it as the care worker's location information. In other words, the care worker's location information may include metadata defining the location to which the care worker moves. The care worker's location information may include the care worker's coordinate information. The care worker's location information may include categories of tasks that can be performed at each location to which the care worker can move. The care worker's location information may include information on equipment installed at each location to which the care worker can move.

[0055] The position of the care worker may be measured by the sensor 40. The sensor 40 may measure the position of the care worker, for example, by detecting the position of the care worker as coordinates. The sensor 40 may also measure the position of the care worker by detecting the presence of the care worker at a specific location in a care facility or other care setting, and the location where the presence of the care worker is detected is taken as the position of the care worker. When the position of the care worker is measured by the sensor 40, the position measurement unit 24 may acquire the position information of the care worker from the sensor 40.

[0056] The position of the care worker may be measured by the care worker themselves or by another worker inputting the position into the input device 50. When the care worker's position is input into the input device 50, the position measurement unit 24 may acquire the care worker's position information from the input device 50.

[0057] The position measurement unit 24 outputs the locations where the care worker has moved or stayed as location information of the care worker to the model generation unit 26.

[0058] Care workers may move to multiple locations. The position measurement unit 24 may output information about the multiple locations to which the care worker has moved as care worker position information to the model generation unit 26. The behavior measurement unit 22 acquires information about the care worker's behavior at at least one of the multiple locations to which the care worker has moved. When the behavior measurement unit 22 acquires information about the care worker's behavior at one location, it associates the information about the care worker's behavior at that location with the category of work performed by the care worker at that location. When the behavior measurement unit 22 acquires information about the care worker's behavior at two or more locations, it associates the information about the care worker's behavior at each location with the category of work performed by the care worker at each location. The behavior measurement unit 22 outputs information to the model generation unit 26 that associates the information about the care worker's behavior at each location with the category of work performed by the care worker.

[0059] The care worker's location information may include information about the location where the care worker was located before the location where the behavior measurement unit 22 acquired information about the care worker's behavior. The care worker's location information may include information about the location to which the care worker moved after the location where the behavior measurement unit 22 acquired information about the care worker's behavior. The care worker's location information may include categories of work that can be performed at at least one of the multiple locations to which the care worker moved. The care worker's location information may include information about equipment installed at at least one of the multiple locations to which the care worker moved.

[0060] The time measurement unit 25 acquires time information of the care worker. The time information includes the time the care worker stayed at a single location if the care worker moved to that location. The time information includes the time the care worker stayed at each of the multiple locations if the care worker moved to multiple locations. The time information may include the time the care worker stayed at each location. The time the care worker stayed at a particular location may be expressed as the length of time the care worker stayed at that location. The time the care worker stayed at a particular location may be expressed as at least one of the time the care worker started staying at that location or the time the care worker ended staying at that location, or as a combination of these.

[0061] The time a care worker spends at a particular location may be measured by the sensor 40, as described later. When the time a care worker spends at a particular location is measured by the sensor 40, the time measurement unit 25 may acquire the care worker's time information from the sensor 40. Thus, the sensor 40 may be equipped with a time measurement function. The time measurement unit 25 may acquire the time a care worker spends at one location or at each of multiple locations using the sensor 40.

[0062] The time a care worker spends in a particular location may be measured by the care worker themselves or another care worker inputting the information into the input device 50. When the time a care worker spends in a particular location is input into the input device 50, the time measurement unit 25 may acquire the care worker's time information from the input device 50.

[0063] The time measurement unit 25 outputs to the model generation unit 26 the time spent at each of the one or more locations the care worker moved to, i.e., the time spent at each location, as time information for the care worker.

[0064] When a care worker moves to multiple locations, the time measurement unit 25 may output information regarding the time the care worker spent at each of the multiple locations as time information to the model generation unit 26. The care worker's time information may include information regarding the time the care worker spent at the location where the care worker was before the location where the behavior measurement unit 22 acquired information regarding the care worker's behavior. The care worker's time information may also include information regarding the time the care worker spent at the location to which the care worker moved after the location where the behavior measurement unit 22 acquired information regarding the care worker's behavior.

[0065] The schedule management unit 16 outputs the work schedule of the care workers to the model generation unit 26.

[0066] The information used for model generation may include information recording the actual duration or number of times a task was performed for each defined location in the care setting where the categories of tasks performed by care workers are estimated. The information used for model generation may include information recording the duration or number of times a planned task was performed for each defined location in the care setting. The information used for model generation may include information defining tasks that are expected to occur for each defined location in the care setting. The location-specific information used for model generation may include information recorded by time studies or information estimated by machine learning models. The locations defined in the care setting may include the target locations for time studies or indoor positioning, or the locations where the task is planned to be performed.

[0067] The information used for model generation may include information recording the actual duration or number of tasks performed for each time slot defined in the caregiving setting. The information used for model generation may include information recording the duration or number of tasks planned for each time slot defined in the caregiving setting. The information used for model generation may include information defining tasks expected to occur for each time slot defined in the caregiving setting. The information for each time slot used for model generation may include information recorded by time studies or information estimated by machine learning models. The time slots defined in the caregiving setting may include the time slots targeted by time studies or indoor positioning, or the time slots in which tasks are planned to be performed.

[0068] The information used to generate the model may include information recording the actual duration or number of tasks performed for each role category defined in the caregiving setting. The information used to generate the model may include information recording the duration or number of tasks planned for each role category defined in the caregiving setting. The information used to generate the model may include information defining tasks expected to occur for each role category defined in the caregiving setting. The information for each role category used to generate the model may include information recorded by time studies or information estimated by machine learning models. The role categories defined in the caregiving setting may include, for example, the scope of work that a caregiver is responsible for, or the job title of a caregiver.

[0069] <<Model Generation>> The following describes an example of how to generate a work classification estimation model based on information about care workers.

[0070] The model generation unit 26 obtains the classification of tasks performed by care workers as correct actions from the behavior measurement unit 22.

[0071] The model generation unit 26 acquires information about the care worker. The model generation unit 26 may acquire the location where the care worker moved from the location measurement unit 24. The model generation unit 26 may acquire the time the care worker stayed at the location where they moved from the time measurement unit 25. In other words, the model generation unit 26 may acquire the location where the care worker moved and the time the care worker stayed at that location as information about the care worker. The model generation unit 26 may acquire the care worker's work schedule from the schedule management unit 16. The model generation unit 26 may acquire all or some of the above-mentioned information as information about the care worker.

[0072] The model generation unit 26 generates a work category estimation model by using information about care workers as training data and performing learning using the tasks performed by care workers, i.e., correct actions, as training data. For example, the model generation unit 26 may use data combining the location where the care worker moved and the time the care worker stayed at that location as training data. The data used as training data is not limited to this example and may be single data or various other combinations. The model generation unit 26 may generate a work category estimation model by performing learning using data that associates location information and time information included in the training data with the work category performed by the care worker, i.e., correct actions, included in the training data. The model generation unit 26 outputs the generated work category estimation model to the work category estimation unit 30.

[0073] When a care worker moves to multiple locations, the model generation unit 26 may obtain location information from the location measurement unit 24, which includes information about the multiple locations the care worker has moved to, and time information from the time measurement unit 25, which includes information about the time the care worker spent at each of the multiple locations the care worker has moved to. The combined information of location information, which includes information about the multiple locations the care worker has moved to, and time information, which includes information about the time the care worker spent at each of the multiple locations the care worker has moved to, is also referred to as the care worker's movement history information. In other words, when a care worker moves to multiple locations, the model generation unit 26 can obtain the care worker's movement history information and the categories of work performed by the care worker at each location included in the movement history information.

[0074] The model generation unit 26 may perform learning using data that combines the care worker's movement history information and information regarding the time the care worker stayed at each location, i.e., time information, with the categories of work performed by the care worker at each location included in the movement history information, and generate a work category estimation model. In this case, the generated work category estimation model may be configured to accept the care worker's movement history information as input and output the estimation result of the category of work performed by the care worker at at least one of the multiple locations to which the care worker moved included in the care worker's movement history information. In other words, the work category estimation model is configured to accept location information regarding the multiple locations to which the care worker moved and time information regarding the time the care worker stayed at each location as input, and to output the result of what the care worker performed at at least one of the multiple locations. Thus, an information processing device that estimates the category of work performed by a care worker with high probability from the care worker's location information and time information is based on the inventor's unique knowledge.

[0075] <Example of operation of the work category estimation unit 30> The following describes an example of how the work category estimation unit 30 of the information processing device 10 estimates the category of work performed by a care worker using a work category estimation model, with reference to Figure 3.

[0076] The position measurement unit 32 obtains the definition of the location where the care worker moves or stays from the position definition unit 14. The position measurement unit 32 measures the location where the care worker moves or stays and outputs the measurement result of the location where the care worker moved or stayed to the position recognition unit 33. The position measurement unit 32 obtains the coordinate information of the care worker as the measurement result of the location where the care worker moved or stayed. Based on the coordinate information of the care worker obtained by the position measurement unit 32, the position recognition unit 33 recognizes the position of the care worker as metadata defined in the position definition unit 14. In other words, the position recognition unit 33 may generate metadata of the location where the care worker moved as the result of recognizing the care worker's position. The position recognition unit 33 outputs the recognition result of the care worker's position to the estimation unit 35. The position recognition unit 33 may output the recognition result of each of the multiple locations included in the care worker's movement history to the estimation unit 35. The position measurement unit 32 and the position recognition unit 33 may be configured as a single unit.

[0077] The time measurement unit 34 measures the time spent by the care worker at one location or multiple locations, i.e., the time spent at each location. The time measurement unit 34 outputs the measurement results of the time spent by the care worker at one location or multiple locations, i.e., the time spent at each location, to the estimation unit 35. The time measurement unit 34 may be configured identically to or similarly to the time measurement unit 25 of the learning unit 20.

[0078] The estimation unit 35 operates the work category estimation model generated by the learning unit 20. As described above, the work category estimation model is configured to accept location information and time information of the care worker as input and to output the estimation result of the work category performed by the care worker at at least one location. The estimation unit 35 inputs the location recognition result of the care worker obtained from the location recognition unit 33 as the care worker's location information to the work category estimation model. The estimation unit 35 inputs the measurement result of the time the care worker stayed at the location to which they moved, obtained from the time measurement unit 34, as the care worker's time information to the work category estimation model. The estimation unit 35 outputs the estimation result of the care worker's work category output from the work category estimation model to the output unit 36.

[0079] The estimation unit 35 may input information obtained from the position recognition unit 33, which recognizes multiple locations to which the care worker has moved, and the time measurement results obtained from the time measurement unit 34, which measure the time the care worker spent at each of the multiple locations, as the care worker's movement history information into the work category estimation model. The estimation unit 35 may output to the output unit 36 ​​the estimated result of the work category performed by the care worker at at least one location included in the care worker's movement history information output from the work category estimation model.

[0080] When a care worker moves between multiple locations, the care worker's location information included in the movement history information may include information about the locations where the care worker was located before the location where the care worker performed the task for which classification is to be estimated. Furthermore, the care worker's location information included in the movement history information may include information about the locations where the care worker was located after the location where the care worker performed the task for which classification is to be estimated. The care worker's time information included in the movement history information may include information about the time the care worker spent at the location where the care worker was located before the location where the care worker performed the task for which classification is to be estimated. Furthermore, the care worker's time information included in the movement history information may include information about the time the care worker spent at the locations where the care worker moved after the location where the care worker performed the task for which classification is to be estimated.

[0081] The output unit 36 ​​outputs the estimated results of the care worker's work category obtained from the estimation unit 35, and either displays them on the display device 60 or stores them in the database 70.

[0082] <Summary> As described above, the information processing system 1 can estimate the categories of work performed by a care worker. The work category estimation results may be used as work data in the work evaluation by the evaluation unit 80 described later. Furthermore, in order to perform the means of estimating the categories of work performed by a care worker from the care worker's location information and time information of this embodiment, location information may be measured for each of the locations within the care facility where the care worker whose work category is to be estimated moved or stayed. In addition, information regarding the time spent at each of the locations within the care facility where the care worker whose work category is to be estimated stayed, i.e., time information, may be measured. For example, as illustrated as a timeline in Figure 9 described later, information regarding the time spent at each of the locations within the care facility where the care worker moved or stayed may be measured for the total working hours of a care worker, i.e., the period from 8:30 to 17:15 which is the subject of measurement in the timeline of Figure 9. Note that the start time or end time of working hours is just an example and is not limited to this. As described above, by measuring location and time information for care workers who are the target of work category estimation, it becomes possible to estimate all work categories performed by the care worker throughout their entire working hours. For example, as illustrated in Figure 8 below, the time spent by a care worker performing each work category during their entire working hours, i.e., the period from 8:30 to 17:15 measured in the timeline in Figure 9, can be estimated. By estimating the time spent by a care worker performing each work category throughout their entire working hours, it becomes possible to evaluate which work category is consumed the most time.

[0083] (Example of actions to evaluate the work performed by care workers) The following describes an example of how the evaluation unit 80 of the information processing device 10 evaluates the work performed by the care worker using work data, with reference to Figure 4.

[0084] <Accumulation of work data> The data storage unit 81 acquires and stores work data related to the tasks performed by the care worker. The data storage unit 81 may store the work data in the storage unit of the information processing device 10 or in the database 70. The data storage unit 81 may acquire the work category estimation results from the work category estimation unit 30 and store them as work data. The data storage unit 81 may acquire the aggregated results of work categories collected by the time study and store them as work data. The aggregated results of work categories by the time study may be input from the input device 50. The data storage unit 81 may acquire information about the care worker measured by the sensor 40 and store it as work data.

[0085] <Processing of work data> The data processing unit 82 extracts target data from the accumulated work data based on extraction conditions, performs predetermined processing on the target data, and generates processed data.

[0086] Extraction criteria may be set by users such as care workers and care recipients, as well as administrators or managers of care facilities. Extraction criteria may include, for example, conditions that specify data related to work performed by a specific care worker, conditions that specify data related to work performed for a specific care recipient, or conditions that specify data related to work performed on a specific day or time. Extraction criteria may include conditions that specify multiple care workers or multiple care recipients together. Extraction criteria may include conditions that specify multiple days or time periods together. Extraction criteria may include conditions that combine conditions that specify a care worker or care recipient with conditions that specify the day or time period on which the work was performed.

[0087] The prescribed processing may include, for example, the processing of calculating an index related to the target data. The index related to the target data may be calculated in a way that represents the characteristics of the target data.

[0088] The indicator may include the amount of work performed by the care worker. The amount of work may be expressed by the amount of time spent working or the number of times worked. The indicator may also include the amount of time the care worker spent in the care recipient's room or a specific location. A graph of the amount of work or the amount of time spent may be generated as processed data.

[0089] The indicator may include the amount of services received by the care recipient. The amount of services received by the care recipient may include the amount of work performed for the care recipient. The amount of services received by the care recipient may include the time spent by care workers in the care recipient's room, or the time spent by care workers accompanying the care recipient. Processed data may include graphs of work volume, time spent, or time spent accompanying.

[0090] The indicator may include the workflow performed by the care worker. The workflow may include the transitions in the care worker's actions or locations. A timeline representing the transitions in the care worker's actions or locations may be generated as processed data.

[0091] The indicator may include the ratio of direct tasks to indirect tasks. Direct tasks are tasks performed directly on the person receiving care. Indirect tasks may include preparatory work for direct tasks, or cleaning, etc. A graph showing the ratio of direct tasks to indirect tasks may be generated as processed data.

[0092] The indicator may include the workload of care workers. For example, a workload may be set for each category of work. The workload of care workers may be calculated as the sum of the product of the amount of work performed by the care worker and the workload set for the category of work performed. A graph of the workload of care workers may be generated as processed data.

[0093] The indicators are not limited to those described above. The indicators may include cross-tabulated items obtained by combining the above-mentioned items or other items. The results of the cross-tabulation may be generated as processed data.

[0094] The prescribed processing may include the process of generating forms. The forms may include, but are not limited to, notification documents for additional care service fees. The additional care service fees may include, but are not limited to, additional fees for productivity improvement promotion systems.

[0095] The notification documents for the productivity improvement promotion system allowance include a time study questionnaire. The prescribed processing includes generating a conversion table that maps the work categories included in the work data to the designated work to be recorded in the time study questionnaire, in order to record the designated work in the time study questionnaire. The prescribed processing also includes calculating, in minutes, the time spent on the designated work for each care worker every 10 minutes, and generating a table with the calculation results. The prescribed processing may also include printing the table.

[0096] The prescribed processing may include, for example, a process for discovering issues related to the work performed by care workers from the target data. Issues may include, for example, the occurrence of unnecessary work, the occurrence of unreasonable work, or the occurrence of uneven work, or so-called inconsistent work. Issues may also include the occurrence of accidents during work, or the potential for accidents, or so-called near misses.

[0097] The prescribed processing may include the process of receiving and recording input of issues discovered by the user. Users may discover issues based on indicators calculated for the target data.

[0098] The prescribed process may include a process for recording the time or place where the problem occurred. The prescribed process may include a process for associating and storing information about the care worker, including measurement data, etc., from when the care worker was performing the task in which the problem occurred. The prescribed process may include a process for assigning a label to the information about the care worker at the time the care worker was performing the task, indicating whether or not a problem occurred in the task performed by the care worker, or a label indicating the content of the problem. The label indicating whether or not a problem occurred in the task performed by the care worker, or a label indicating the content of the problem, is also collectively referred to as a problem-related label. The prescribed process may include a process for generating a problem discovery model by performing machine learning using data to which problem-related labels have been assigned to at least a portion of the information about the care worker, i.e., the work data. The problem discovery model may be used in the problem estimation unit 84 described later.

[0099] The output unit 83 outputs processed data such as indicators, reports, or tasks generated by the data processing unit 82. The output of processed data makes the work data visible.

[0100] <Identifying problems> The problem estimation unit 84 estimates the problems included in the target data using the problem discovery model described above. The problem discovery model may be configured to output information about problems included in the target data in response to input of the target data or indicators. The problem discovery model may be configured to extract and output problems included in the target data from among candidate problems as information about problems. Candidate problems are multiple candidate problems that can be considered to be included in the target data. The problem discovery model may be configured to output a likelihood that each of the candidate problems included in the target data is likely to be included. The problem discovery model may be configured to output the probability that problems are included in the target data.

[0101] The output unit 83 may output the information regarding the issues output from the issue estimation unit 84 as processed data. The output unit 83 may display the likelihood of the target data containing an issue using color, based on the likelihood of the candidate issues output from the issue discovery model.

[0102] The problem discovery model may be generated by learning in the data processing unit 82 as described above, but it may also be generated by an external device. The problem estimation unit 84 may acquire and use the problem discovery model from an external device.

[0103] <Example of evaluation 1> In the evaluation unit 80, the data storage unit 81 may store the results of a time study that measures work data, including the categories of work performed by each care worker, when multiple care workers work the same shift on different days and perform tasks. The data processing unit 82 may generate data that arranges the hourly work content of multiple care workers in a timeline and display it so that differences in the work procedures of each care worker can be compared. The data processing unit 82 may also generate data that calculates and arranges the time spent by multiple care workers for each task, the number of times each care worker performed a task, or the ratio of direct tasks to indirect tasks, and display it so that differences in the work content of each care worker can be compared. The data processing unit 82 may also generate data that calculates and arranges the time that multiple care workers spent in a specific location, such as the care recipient's room or a common space, and display it so that differences in the work locations of each care worker can be compared.

[0104] <Example of evaluation 2> The data storage unit 81 may periodically store the work data of care workers. The data processing unit 82 may calculate the time spent by care workers on each task within a single shift, the number of tasks performed by care workers, the ratio of direct to indirect tasks, or the average amount of time spent by care workers in a specific location. The data processing unit 82 may calculate the average values ​​for work data within a single shift for multiple shifts and display them so that differences in workload in each shift can be compared.

[0105] The administrator or operator of a nursing facility may quantitatively evaluate the work of nursing care workers by referring to the visualized data as exemplified above. Also, the administrator or operator of a nursing facility may review the allocation of nursing care workers or the nursing care process in the nursing facility by referring to the visualized data. Further, the administrator or operator of a nursing facility may introduce nursing care equipment by referring to the visualized data. That is, the administrator or operator of a nursing facility may improve the operations in the nursing facility by referring to the visualized data.

[0106] The data storage unit 81 may store the work data before and after the improvement of the operations in a nursing facility. The data processing unit 82 may visualize the work data before and after the improvement of the operations. The administrator or operator in a nursing facility may check whether the expected effects of the improvement have occurred by referring to the visualized data before and after the improvement of the operations, and may consider new improvements.

[0107] <Example of Evaluation 3> In order to obtain the Productivity Improvement Promotion System Addition (II) in nursing care services, it is necessary to conduct a time study survey for 5 days. Also, it is necessary to record in 10-minute intervals how many minutes of work corresponding to the specified items were performed within those 10 minutes. The data storage unit 81 may store, as the work data of nursing care workers, the results of indoor positioning by the sensor 40 or the work category estimation results by the work category estimation unit 30 by automatically recording them.

[0108] The data processing unit 82 may generate a table that converts the names used to distinguish tasks performed by care workers in the work data into the names of specified items to be recorded in the time study questionnaire for obtaining the Productivity Improvement Promotion System Addition (II). The data processing unit 82 may convert the names of tasks included in the care worker's work data into the names of specified items to be recorded in the time study questionnaire based on the conversion table, and calculate, in 10-minute increments, how many minutes within each 10-minute period the care worker performed the task corresponding to the specified item. The data processing unit 82 may output the calculation results in a format that conforms to the time study questionnaire for obtaining the Productivity Improvement Promotion System Addition (II). The data processing unit 82 may generate the time study questionnaire itself.

[0109] <Example of evaluation 4> The data storage unit 81 may automatically record the indoor positioning results from the sensor 40 or the work category estimation results from the work category estimation unit 30, and store them as work data of the care worker. The data storage unit 81 may also store records of accidents or near misses that occurred during work performed by the care worker as work data.

[0110] The data processing unit 82 may assign labels to the work data, which has been divided into fixed time intervals, to identify whether or not an accident or near miss occurred during the divided period. The data processing unit 82 may perform machine learning using the data to which the work data has been labeled, and generate a machine learning model such as an LSTM (Long Short Term Memory) or a transformer. The machine learning model thus generated may be configured to output a determination result when newly acquired work data is input, indicating whether the work data contains data representing work or work flow that could lead to an accident or near miss. The machine learning model may be configured to extract and output data representing work or work flow that could lead to an accident or near miss from the work data.

[0111] If the manager or operator of a nursing care facility becomes aware of the existence of data representing a task or workflow that could lead to an accident or near miss, they may investigate or implement countermeasures for the task corresponding to that data as a business issue and improve the business operations.

[0112] <Example of an evaluation flowchart> The evaluation unit 80 of the information processing device 10 may evaluate the work of care workers by executing an information processing method that includes the steps of the flowchart illustrated in Figure 5. The information processing method may be implemented as an information processing program to be executed by the processor constituting the evaluation unit 80. The information processing program may be stored on a non-temporary computer-readable medium.

[0113] The data storage unit 81 of the evaluation unit 80 stores the work data (step S1). The data processing unit 82 of the evaluation unit 80 extracts the target data from the work data (step S2). The data processing unit 82 processes the target data (step S3). The output unit 83 of the evaluation unit 80 outputs the processed data generated by processing the target data (step S4). After executing the procedure in step S4, the evaluation unit 80 finishes executing the procedure in the flowchart of Figure 5.

[0114] <Summary> As described above, the information processing device 10 relating to this disclosure outputs evaluation results of tasks performed by care workers. Care facility managers or operators, or care workers, can easily identify problems in their work by referring to the evaluation results. The information processing device 10 may also output results that estimate the problems in the work as part of the evaluation results. Care facility managers or operators, or care workers, can easily understand the problems in their work. By discovering or understanding the problems in their work, care facility managers or operators, or care workers, can efficiently improve care services. In other words, the evaluation results of the work are useful as information that supports the efficient improvement of care services.

[0115] (Other examples) Other embodiments are described below.

[0116] <Example of estimating work divisions> An embodiment for estimating the work categories of care workers will be described by applying the operation example of the work category estimation unit 30 of the information processing device 10 described above to the following environment.

[0117] First, a time study was conducted to measure the work performed by a total of 15 day and night shift care workers at a nursing care facility. Specifically, the start time, end time, work location, and actions of each care worker were measured.

[0118] Definition data was generated to classify the work area into 10 categories. The work area categories were defined as: mobile area, user's room, dining room, living room, bathroom, toilet, office, staff room, corridor, and other areas.

[0119] Definition data was generated to classify the tasks performed by care workers into 35 categories. The categories of tasks were determined to be: getting up and going to bed, changing positions, dressing and grooming, assisting the user's movement, toileting, meals, hygiene, environmental maintenance, laundry, going out, recreation, transportation, medical procedures, medication management, vital sign measurement, medical examinations, direct interventions on physical function, physical therapy, occupational therapy, speech therapy, care plans, nutritional management, meal service, hygiene management, care worker movement, meetings, office cleaning, and other tasks; understanding the user, information sharing, record keeping, verification, handling visitors, and other services; and breaks.

[0120] Based on the information measured by the time study, the learning unit 20 generated training data that included the location and time at which the care worker performed the task, plus the locations and duration of the 10 tasks performed immediately preceding the task. The learning unit 20 also generated data on the task categories performed by the care worker as training data to generate a task category estimation model. However, in this embodiment, the training data generated based on the measurement results from the time study included 25 out of 35 task categories.

[0121] As described above, the work category estimation model may accept information about the care worker's movement history as input and output an estimated result of the work category performed by the care worker at at least one location to which the care worker moved, as included in the movement history information. In this embodiment, the work category estimation model estimates the work category of the care worker based on location information about the location to which the care worker moved before the location where the action to be estimated was performed, and time information about the time the care worker stayed at that location.

[0122] The learning unit 20 performed machine learning on the training data and target data using a random forest, support vector machine (SVM), or a three-layer convolutional neural network to generate a work portion estimation model.

[0123] The work category estimation unit 30 estimated the work categories of care workers using the work category estimation model generated as described above. Here, the recognition rate of each model was verified by 5-fold cross-validation. The recognition rate is the percentage of the estimated work category that matches the correct category. In the verification, the work category estimation unit 30 input location information and time information when performing a work for which the correct category was known into the work category estimation model. The work category estimation unit 30 obtained the estimated work category from the work category estimation model and calculated the recognition rate by comparing it with the correct category. As a result, the recognition rate using the model generated by machine learning with random forest was 41.7%. The recognition rate using the model generated by machine learning with SVM was 52.2%. The recognition rate using the model generated by machine learning with neural network was 52.5%.

[0124] <Examples of categories of caregiving tasks performed by care workers> The tasks related to caregiving performed by care workers may be classified into multiple hierarchical levels, as illustrated in Figure 6. In the example in Figure 6, the tasks related to caregiving performed by care workers are divided into three levels called major, medium, and minor classifications. A major classification includes at least one medium classification. That is, the number of classifications belonging to a major classification is less than or equal to the number of classifications belonging to a medium classification. A medium classification includes at least one minor classification. That is, the number of classifications belonging to a medium classification is less than or equal to the number of classifications belonging to a minor classification. A major classification is also called the first level. A medium classification is also called the second level. A minor classification is also called the third level. The second level is included in the first level. That is, the second level is a lower level than the first level. The third level is included in the second level. That is, the third level is a lower level than the second level. The number of levels is not limited to three; there may be two or four or more levels.

[0125] In this embodiment, the major classification, or first level, is a classification that categorizes the work performed by care workers by the type of service. The major classification may include, for example, caregiving, nursing, rehabilitation, care support, meals and nutrition, indirect work, non-work-related work, common work, or other work. The major classification may also include measurement errors as a classification for when the work performed by care workers cannot be classified into any of the above categories. The major classification may include various categories, but is not limited to these examples.

[0126] In this embodiment, the intermediate classification, or second level, is a classification that categorizes the tasks performed by care workers according to the purpose or circumstances of the task. The intermediate classification corresponds to the classification that categorizes actions into 35 types in the above-described embodiment of classification estimation.

[0127] The intermediate classification may include, as a category belonging to the major classification of care, for example, getting up and going to bed, changing positions, dressing and grooming, user mobility, excretion, meals, hygiene, environmental maintenance, laundry, going out, recreation, or transportation. The intermediate classification may also include, as a category belonging to the major classification of nursing, for example, medical procedures, medication management, vital sign measurement, physical examination, or direct intervention on physical function.

[0128] The subcategory may include, for example, physical therapy, occupational therapy, or speech-language therapy as a division belonging to the major category of rehabilitation. The subcategory may include, for example, care plans as a division belonging to the major category of care support. The subcategory may include, for example, nutritional management, meal service, or hygiene management as a division belonging to the major category of food and nutrition.

[0129] The subcategory may include, for example, the movement of care workers, meetings, or office cleaning, as it is a category belonging to the major category of indirect work. The subcategory may include, for example, breaks, as it is a category belonging to the major category of non-work-related work. The subcategory may include, for example, understanding users, information sharing, record keeping, verification, handling visitors, or other services, as it is a category belonging to the major category of common work.

[0130] The subcategory, or third level, is defined as a classification of tasks performed by care workers based on their content.

[0131] A subcategory may include, for example, assistance with getting up or assistance with going to bed, as a category belonging to the intermediate categories of getting up and going to bed. A subcategory may include, for example, assistance with changing body position or reclining adjustment, as a category belonging to the intermediate categories of dressing and grooming. A subcategory may include, for example, assistance with dressing or grooming, as a category belonging to the intermediate categories of user mobility. A subcategory may include, for example, wheelchair guidance, walking assistance, transfer assistance, or standing assistance, as a category belonging to the intermediate categories of excretion. A subcategory may include, for example, assistance with excretion, diaper changing, or handwashing assistance, as a category belonging to the intermediate categories of meals. The subcategory may include, for example, assistance with bathing, foot bathing, face washing, wiping, perineal cleansing, oral care, earwax removal, nail trimming, shaving, hair washing, hand washing assistance, or gargling assistance, as a subcategory of the hygienic category. The subcategory may include, for example, changing sheets, garbage collection, room cleaning, checking user's belongings, adjusting room temperature, humidity control, lighting control, or ventilation, as a subcategory of the laundry category, for example, washing or collecting laundry. The subcategory may include, for example, assistance with shopping or accompanying users on shopping trips, as a subcategory of the outing category. The subcategory may include, for example, exercise, oral exercises, music, picture storytelling, or cooking, as a subcategory of the recreation category. The subcategory may include, for example, assistance with transportation or getting on and off vehicles, as a subcategory of the transportation category.

[0132] The subcategory may include, as a division within the medium category of medical procedures, for example, tracheal suctioning, wound care, ointment application, disinfection, drug application, intravascular pressure monitoring, intravenous infusion, gastrostomy management, urinary management, enemas, or oxygen therapy. The subcategory may include, as a division within the medium category of drug management, for example, oral administration, enteral administration, dispensing, infusion, injection, suppository administration, or inventory checks. The subcategory may include, as a division within the medium category of vital sign measurement, for example, temperature measurement, SpO2 measurement, pulse measurement, blood pressure measurement, weight measurement, or vital sign measurement. The subcategory may include, as a division within the medium category of medical examinations, for example, attending medical examinations or accompanying patients to hospitals. The subcategory may include, as a division within the medium category of direct interventions on bodily functions, for example, massage or functional recovery.

[0133] The subcategory may include, for example, range of motion exercises, thermotherapy, gait training, standing training, stair climbing, exercise training, or physical therapy assistance, as it belongs to the intermediate category of physical therapy. The subcategory may include, for example, occupational tasks, calculation tasks, cognitive tasks, memory tasks, artistic tasks, play tasks, or occupational therapy assistance, as it belongs to the intermediate category of rehabilitation. The subcategory may include, for example, expression training, auditory comprehension training, swallowing function assessment, indirect swallowing training, or direct swallowing training.

[0134] A subcategory may include, for example, care plan creation, conferences, monitoring, or assessment, as a category belonging to the intermediate category of care plans. A subcategory may include, for example, nutrition care plan creation, conferences, monitoring, nutrition assessment, nutrition screening, or nutritional counseling, as a category belonging to the intermediate category of nutrition management. A subcategory may include, for example, menu planning, cooking, meal count management, or ingredient procurement, as a category belonging to the intermediate category of hygiene management, as a category belonging to the intermediate category of hygiene management, as a category belonging to hygiene management, ingredient management, or equipment management.

[0135] A subcategory may include, for example, movement, waiting, or transportation, as a category belonging to the intermediate category of movement of care workers. A subcategory may include, for example, morning meetings, evening meetings, or conferences, as a category belonging to the intermediate category of meetings. A subcategory may include, for example, cleaning of common areas or tidying up, as a category belonging to the intermediate category of office cleaning.

[0136] The subcategory may include, for example, verbal communication, active listening, monitoring, checking the user's condition, or responding to nurse calls, as it belongs to the intermediate category of understanding the user. The subcategory may include, for example, communication, handover, telephone calls, or sending and receiving faxes, as it belongs to the intermediate category of information sharing. The subcategory may include, for example, record making, calculations, printing, and copying, as it belongs to the intermediate category of record making. The subcategory may include, for example, record checking, work checking, or equipment checking, as it belongs to the intermediate category of confirmation. The subcategory may include, for example, dealing with family members or dealing with visitors, as it belongs to the intermediate category of dealing with visitors.

[0137] Subcategories may include common tasks such as preparation, cleanup, or handwashing.

[0138] The learning unit 20 may identify the categories of work performed by care workers and generate training data based on the categories belonging to each of the hierarchical classifications described above. As mentioned above, the number of categories belonging to major classifications is less than the number of categories belonging to intermediate classifications. Also, the number of categories belonging to intermediate classifications is less than the number of categories belonging to minor classifications.

[0139] Here, we assume that the number of pieces of information about the care worker's behavior that the learning unit 20 can collect from the behavior measurement unit 22 is constant. When associating the collected information about the care worker's behavior with work categories, the number of pieces of information about the care worker's behavior that can be associated with each work category is inversely proportional to the number of work categories. In other words, the fewer the number of work categories classified by the work category estimation model, the larger the amount of training data corresponding to that work category. The larger the amount of training data, the higher the estimation accuracy of that work category. Therefore, the learning unit 20 may generate multiple sets of training data and training data that combine different numbers of work categories associated with the collected information about the care worker's behavior, and generate a work category estimation model trained using each set of data.

[0140] For example, the learning unit 20 may generate training data by classifying the collected information on the behavior of care workers into categories belonging to major classifications, and then perform learning using that training data and the corresponding training data to generate a work category estimation model that can estimate the work category of care workers within the major classifications. The learning unit 20 may generate training data by classifying the collected information on the behavior of care workers into categories belonging to medium classifications, and then perform learning using that training data and the corresponding training data to generate a work category estimation model that can estimate the work category of care workers within the medium classifications. The learning unit 20 may generate training data by classifying the collected information on the behavior of care workers into categories belonging to minor classifications, and then perform learning using that training data and the corresponding training data to generate a work category estimation model that can estimate the work category of care workers within the minor classifications.

[0141] The learning unit 20 may generate a work classification estimation model that can estimate the classification of a care worker's work in various other classification groups, not limited to major, medium, or minor classifications. For example, the learning unit 20 may generate a work classification estimation model that estimates the work performed by a care worker as either a work involving movement or a work that does not involve movement. In the above embodiment, when classifying into classifications limited to work involving movement and work that does not involve movement, the recognition rate was increased to 82.8% using the above method with a neural network.

[0142] The learning unit 20 may generate a work classification estimation model that can estimate the classification of a care worker's work using one of the major, medium, or minor classifications. The learning unit 20 may generate a work classification estimation model that can estimate the classification of a care worker's work using two or more of the major, medium, or minor classifications. For example, the learning unit 20 may generate a work classification estimation model that can estimate the classification of a care worker's work using both the major and medium classifications. In this case, the work classification estimation model outputs both the estimation results for the classifications included in the major classification and the estimation results for the classifications included in the medium classification.

[0143] <Estimated using subsequent movement history> In the embodiment described above, the work classification estimation model was configured to estimate the classification of a caregiver's work based on location information relating to the locations the caregiver moved to before the location where the work to be classified was performed, and time information relating to the time the caregiver stayed at those locations. In other words, the classification of a work performed by a caregiver at a certain location was estimated based on the locations the caregiver had moved to in the past.

[0144] The work classification estimation model may be configured to estimate the classification of a caregiver's work based on location information regarding the location the caregiver moved to after the location where the work to be classified was performed, and time information regarding the time the caregiver stayed at that location. In other words, the classification of a work performed by a caregiver at a certain location may be estimated based on the caregiver's future locations.

[0145] In this example, the learning unit 20 generated training data by combining location information representing the work location where the work to be estimated as a work category was performed and time information representing the time the work was performed, based on the information measured by the time study, with location information representing the location where the five tasks immediately preceding the work to be estimated as a work category were performed and time information representing the time the tasks were performed, and location information representing the location where the five tasks immediately following the work to be estimated as a work category were performed and time information representing the time the tasks were performed. The learning unit 20 generated training data to be used as training data for generating a work category estimation model, which included the work categories performed by care workers at the locations identified by the location information in the training data. The learning unit 20 generated a work category estimation model by performing training using the training data and training data.

[0146] The work classification estimation unit 30 estimated the work classification of the care worker using the work classification estimation model generated as described above. Here, the recognition rate of each model was verified by 5-fold cross-validation. The recognition rate using the model generated by machine learning with a neural network was 55.6%. The recognition rate using the model generated by machine learning with a neural network based on the locations the care worker has moved to in the past was 52.5%. Therefore, the recognition rate can be improved by using a model generated based not only on the locations the care worker has moved to in the past but also on the locations they will move to in the future.

[0147] On the other hand, if the categories of tasks performed by a care worker are estimated by a model generated based on the care worker's past locations, they can be estimated in real time or near real time by measuring the locations the care worker moves to and the time the care worker spends at each location.

[0148] <Generating training data through data assimilation> In the above-described embodiment, the training data generated based on the measurement results from the time study contained 25 out of 35 types of tasks. In other words, there were 10 types of tasks that were not included in the training data. When there are tasks that are not included in the training data, the model generated by performing training using that training data is unlikely to be configured to output tasks that are not included in the training data as estimated results. Furthermore, the model generated by performing training using that training data may not output tasks that are not included in the training data as estimated results.

[0149] Tasks not included in the measurement results from time studies are considered to be tasks that rarely occur. These rarely occurring tasks may include, for example, tasks performed in the event of a disaster or accident. It is difficult to collect data on these rarely occurring tasks through time studies.

[0150] Therefore, the learning unit 20 may use a method called data assimilation to generate training data that corresponds to the training data, which includes work categories not included in the measurement results from the time study. Data assimilation is a method for generating training data or training data through simulation. The model generated by performing learning using the training data or training data thus generated can be configured to output as estimation results even work categories that rarely occur and are not included in the measurement results from the time study.

[0151] <Examples of other items included in the training data> The training data may include other items. For example, the training data may include nurse call information, monitoring sensor information, care equipment logs, or work manual information. The training data may include seasonal variation or calendar information. The training data may include climate data such as weather or temperature. The training data may include information about care workers that can be obtained using IoT (Internet of Things) devices, other than the location and time information of care workers.

[0152] <Description of the care process> In care services, the tasks performed by care workers can be described as a care process that combines multiple actions. A care process can be described as an independent process in which a care worker performs multiple actions one by one in sequence, as shown in Figure 7A, for example. In Figure 7A, the care worker proceeds from (1) "Action 1" to "Action 2," and then from (2) "Action 2" to "Action 3."

[0153] The care process can be described not only as an independent process, but also as a parallel process in which a caregiver performs multiple actions simultaneously. A parallel process can be described as one in which some actions are performed in parallel, as shown in Figure 7B, for example. In Figure 7B, the caregiver (1) proceeds from "Action 1" to "Action 2", (2) and (3) performs "Action 2" and "Action 3" in parallel, and (4) proceeds from "Action 2" or "Action 3" to "Action 4".

[0154] A parallel process can be described as having the first actions performed in parallel, as shown in Figure 7C, for example. In Figure 7C, the caregiver (1) proceeds from "Action 1" to "Action 2" and "Action 3" in parallel, (2) performs "Action 2" and "Action 3" in parallel, and (3) proceeds from "Action 2" to "Action 4".

[0155] A parallel process can be described as all actions occurring in parallel, as shown in Figure 7D, for example. In Figure 7D, the care worker (1) proceeds from "Action 1" to "Action 2" and "Action 3" in parallel, performing "Action 2" and "Action 3" in parallel, and (2) proceeds from "Action 2" and "Action 3" to "Action 4".

[0156] A parallel process can be described as having parallel final actions, as shown in Figure 7E, for example. In Figure 7E, the caregiver (1) proceeds from "Action 1" to "Action 2", (2) performs "Action 2" and "Action 3" in parallel, and (3) proceeds from "Action 2" and "Action 3" to "Action 4" in parallel.

[0157] The care process can also be described as an interruption process, as illustrated in Figure 7F, in which a caregiver temporarily suspends a specific action to perform another action and then returns to the original action. In Figure 7F, the caregiver (1) proceeds from "Action 1" to "Action 2", (2) interrupts "Action 2" to perform "Action 3", (3) after completing "Action 3", returns to the interrupted "Action 2", and (4) proceeds from "Action 2" to "Action 4".

[0158] The learning unit 20 may generate movement history information of care workers that takes into account the flow of actions in these care processes as learning data, generate work categories corresponding to the learning data as training data, and generate a work category estimation model by performing learning using the training data. The work category estimation model may be configured to output estimation results of work categories that include some of the actions included in the care process. The work category estimation model may be configured to treat the entire care process as a single task and output estimation results of the work categories for that task.

[0159] The learning unit 20 may generate movement history information, including the order of movement to the locations where each action in the care process is performed, as learning data when each action in the care process is performed at a different location, generate work categories corresponding to that learning data as training data, and generate a work category estimation model by performing learning using that training data. In this case, the work category estimation model is configured to treat the entire care process as a single task and output the estimation result of the work category for that task.

[0160] <Example of displaying estimation results> As described above, the output unit 36 ​​of the work category estimation unit 30 may display the estimated results of the work categories performed by the care worker on the display device 60. The display device 60 may display the estimation results in the form of a timeline corresponding to the time period in which the care worker is estimated to have performed each work category, as illustrated in Figure 8. The horizontal axis in Figure 8 represents the passage of time from left to right. The time period in which each work category was performed is shown as a black rectangle. Displaying it in this way makes it easier to understand the estimated results of the care worker's work categories.

[0161] The display device 60 may display the time periods spent by care workers at each location in the form of a timeline, as illustrated in Figure 9. The horizontal axis in Figure 9 represents the passage of time from left to right. The time periods spent by care workers at each location are shown as black rectangles.

[0162] The display device 60 may display the total estimated time spent by the care worker on each task category as a bar graph, as illustrated in Figure 10. In the graph in Figure 10, the horizontal axis corresponds to the task category. The vertical axis represents the estimated time spent by the care worker on each task category. If a task category is performed in multiple sessions, the estimated time spent on that task category is the sum of the time spent on each session. The display device 60 may also display the frequency of each task category performed by the care worker as a histogram. In other words, the display device 60 may display statistical data on the task categories performed by the care worker. Displaying the data in this way makes it easier to understand the estimation results of the care worker's task categories.

[0163] When the work category estimation unit 30 performs estimation in real time using the estimation unit 35, the display device 60 may change the displayed content in real time according to the real-time estimation results. The display device 60 may display the estimation results of work categories performed by multiple care workers in a comparable format. The display device 60 may display in a comparable format the work categories performed by care workers estimated based on location and time information acquired before the procedure of the work performed by the care worker was changed, and the work categories performed by care workers estimated based on location and time information acquired after the procedure of the work performed by the care worker was changed. The display device 60 is not limited to these examples and may display the estimation results of work categories performed by care workers in various other forms. The output unit 36 ​​of the work category estimation unit 30 may output the content to be displayed on the display device 60 as data or information to another device such as a database 70.

[0164] (summary) As described above, the information processing device 10 according to this embodiment can visualize and evaluate work using work data from care workers. The evaluation results of the work are useful information that helps in the discovery or understanding of problems in the work. By evaluating the work, the information processing device 10 can generate information that helps in the efficient improvement of care services.

[0165] Furthermore, the information processing device 10 can estimate the categories of care-related tasks performed by care workers in care facilities and other care settings by inputting the care worker's location and time information into a work category estimation model and obtaining the estimation results of the work category from the work category estimation model. By using the work category estimation model, the information processing device 10 can collect work data from care workers without requiring human intervention such as observers. As a result, work data from care workers is collected efficiently. By efficiently collecting work data from care workers, the improvement of care services using the work data from care workers is efficiently supported.

[0166] While embodiments relating to this disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art can make various modifications or alterations based on this disclosure. Therefore, it should be noted that these modifications or alterations are included within the scope of this disclosure. For example, the functions included in each component or step can be rearranged in a logically consistent manner, and multiple components or steps can be combined into one or divided. While embodiments relating to this disclosure have been described primarily in terms of apparatus, embodiments relating to this disclosure can also be realized as methods including steps performed by each component of the apparatus. Embodiments relating to this disclosure can also be realized as methods, programs, or storage media recording programs executed by a processor in the apparatus. These should also be understood to be included within the scope of this disclosure. [Explanation of Symbols]

[0167] 1. Information Processing System 10. Information processing device (12: Work category definition unit, 14: Location definition unit, 16: Schedule management unit) 20. Learning Unit (22: Behavior Measurement Unit, 24: Location Measurement Unit, 25: Time Measurement Unit, 26: Model Generation Unit) 30. Work Classification Estimation Unit (32: Position Measurement Unit, 33: Position Recognition Unit, 34: Time Measurement Unit, 35: Estimation Unit, 36: Output Unit) 40 sensors 50 Input devices 60 Display device 70 Databases 80 Evaluation Unit (81: Data Storage Unit, 82: Data Processing Unit, 83: Output Unit, 84: Problem Estimation Unit)

Claims

1. An information processing method performed by an information processing device for visualizing information related to tasks performed by care workers, The steps include: accumulating work data of the aforementioned care worker, The steps include: extracting target data from the aforementioned work data based on extraction conditions, The steps include: performing a predetermined process on the target data to generate processed data; The step of outputting the processed data Information processing methods, including those mentioned above.

2. The information processing method according to claim 1, wherein the predetermined processing is a process for calculating an index relating to the target data.

3. The information processing method according to claim 1 or 2, wherein the predetermined processing is a process of discovering issues from the target data using a problem discovery model generated by performing machine learning on data to which labels indicating whether or not an issue has occurred or the content of the issue have been assigned to at least a portion of the work data.

4. The information processing method according to claim 3, wherein the problem discovery model is configured to output information about problems contained in the target data when the target data is input.

5. The information processing method according to claim 1 or 2, wherein the prescribed processing is the process of generating notification documents for the addition of care service fees.

6. An information processing program to be executed by an information processing device for visualizing information related to the tasks performed by care workers, The steps include: accumulating work data of the aforementioned care worker, The steps include: extracting target data from the aforementioned work data based on extraction conditions, The steps include: performing a predetermined process on the target data to generate processed data; The step of outputting the processed data Includes information processing programs.

7. An information processing device for visualizing information related to tasks performed by care workers, A data storage unit for storing the work data of the aforementioned care workers, A data processing unit that performs predetermined processing on target data extracted from the aforementioned work data based on extraction conditions to generate processed data, The output unit outputs the processed data mentioned above. An information processing device equipped with the following features.

Images