JP7434101B2 - Computer system and social participation support method - Google Patents

Description

The present invention relates to a system and method for supporting social participation of users.

In an aging society, it is becoming important for the elderly to take advantage of their knowledge and experience and actively participate in society as members of society. By promoting social participation of the elderly, it is expected to revitalize society, improve the QoL (Quality of Life) of the elderly, and extend healthy life expectancy.

In order to promote social participation, it is necessary to motivate people to participate in society. One possible method is to provide information that motivates people. For example, it is conceivable to provide elderly people with information indicating declines in physical strength and cognitive function, as in the technology described in Patent Document 1.

Patent Document 1 describes that a user's physical strength value and cognitive function value are predicted, and advice regarding the living situation or social participation status is generated according to the prediction result.

International Publication No. 2018/100797

The technique described in Patent Document 1 requires measuring physical strength and cognitive function using a dedicated device. Therefore, the burden on the user to obtain the information necessary for judgment is large. Further, in the technique described in Patent Document 1, advice is generated when a person requires care, but information indicating an objective prediction of the state of need for care is not presented to the user. Therefore, the advice presented has little appeal to users.

The present invention is a system that supports social participation by acquiring data for presenting information that encourages social participation without imposing a burden on the user, and presenting information generated using the data to the user. and a method.

A typical example of the invention disclosed in this application is as follows. That is, a computer system including at least one computer, wherein the at least one computer includes a calculation device, a storage device connected to the calculation device, and an interface connected to the calculation device for transmitting and receiving data. The computer system acquires social participation data, which is data related to the user's activities, from the user, and performs calculations using an analytical model that calculates a risk index for evaluating the risk of needing care. and an output unit that outputs an analysis result including the risk index, and the social participation data includes user identification information, location information of the place where the activity was performed, and the purpose of the activity. , the analysis model is a model that calculates the risk index from the frequency of activities corresponding to each of a plurality of activity purposes, and the analysis unit aggregates the social participation data of the target user for each activity purpose. calculate the frequency of activities corresponding to each of the plurality of activity purposes, and calculate the risk index of the target user by inputting the frequency of activities corresponding to each of the plurality of activity purposes into the analysis model. do.

According to the present invention, it is possible to support a user's social participation. Problems, configurations, and effects other than those described above will be made clear by the description of the following examples.

1 is a diagram illustrating an example of a computer system configuration of Example 1. FIG. 1 is a diagram showing an example of a hardware configuration of a social participation support system according to a first embodiment; FIG. 3 is a diagram showing an example of a data structure of service information according to the first embodiment. FIG. 3 is a diagram showing an example of a data structure of service information according to the first embodiment. FIG. 3 is a diagram showing an example of a data structure of user information according to the first embodiment. FIG. FIG. 3 is a diagram showing an example of a data structure of activity purpose conversion information according to the first embodiment. FIG. 3 is a diagram showing an example of a data structure of recommendation service classification information according to the first embodiment. FIG. 3 is a diagram showing an example of the data structure of template information according to the first embodiment. FIG. 3 is a diagram showing an example of a data structure of activity history information according to the first embodiment. FIG. 3 is a diagram showing an example of a data structure of nursing care certification information according to the first embodiment. 2 is a diagram showing an example of a data structure of nursing care receipt information in Example 1. FIG. FIG. 3 is a diagram showing an example of a data structure of activity analysis information according to the first embodiment. 2 is a diagram showing an example of a data structure of first risk analysis information in Example 1. FIG. 7 is a diagram illustrating an example of a data structure of second risk analysis information in Example 1. FIG. FIG. 3 is a diagram showing an example of a data structure of activity reference value information according to the first embodiment. 3 is a diagram showing an example of a data structure of risk reference value information in Example 1. FIG. FIG. 2 is a sequence diagram illustrating the flow of processing of the computer system according to the first embodiment. FIG. 2 is a sequence diagram illustrating the flow of processing of the computer system according to the first embodiment. 2 is a flowchart illustrating an example of a social participation data registration process executed by the social participation support system of the first embodiment. 2 is a flowchart illustrating an example of an analytical model generation process executed by the social participation support system of the first embodiment. 2 is a flowchart illustrating an example of a nursing care risk analysis process executed by the social participation support system according to the first embodiment. 3 is a flowchart illustrating an example of message generation processing executed by the social participation support system of the first embodiment. 2 is a diagram showing an example of an analysis result display screen presented by the social participation support system of Example 1. FIG. FIG. 3 is a diagram showing an example of an advice display screen presented by the social participation support system according to the first embodiment. 2 is a flowchart illustrating an example of a simulation process executed by the social participation support system according to the first embodiment. 2 is a diagram showing an example of a simulation screen presented by the social participation support system of Example 1. FIG. 2 is a flowchart illustrating an example of a report generation process executed by the social participation support system according to the first embodiment.

Embodiments of the present invention will be described below with reference to the drawings. However, the present invention should not be construed as being limited to the contents described in the Examples shown below. Those skilled in the art will readily understand that the specific configuration can be changed without departing from the spirit or spirit of the present invention.

In the configuration of the invention described below, the same or similar configurations or functions are denoted by the same reference numerals, and redundant explanations will be omitted.

In this specification, etc., expressions such as "first," "second," and "third" are used to identify constituent elements, and do not necessarily limit the number or order.

The position, size, shape, range, etc. of each component shown in the drawings etc. may not represent the actual position, size, shape, range, etc. in order to facilitate understanding of the invention. Therefore, the present invention is not limited to the position, size, shape, range, etc. disclosed in the drawings and the like.

FIG. 1 is a diagram showing an example of a computer system configuration according to the first embodiment. FIG. 2 is a diagram showing an example of the hardware configuration of the social participation support system 100 according to the first embodiment.

The computer system includes a social participation support system 100, multiple client devices 101, and multiple external systems 102. The social participation support system 100, multiple client devices 101, and multiple external systems 102 are connected via a network 103 such as a LAN (Local Area Network) and a WAN (Wide Area Network). The connection method of the network 103 may be either wired or wireless.

The client device 101 is a device that acquires social participation data and is presented with information generated using the social participation data.

Here, social participation data is data related to user activities. The social participation data may be any data that can identify the user who performed the activity, the date and time of the activity, the purpose of the activity, the location of the activity, and the like.

The client device 101 is, for example, a smartphone or a wearable device. The client device 101 has a processor, a memory, and a communication device (not shown) as a hardware configuration. Further, the client device 101 has a transmitting/receiving section 160 and a social participation data acquisition section 161 as software configurations.

The transmission/reception unit 160 controls transmission and reception of data between the client device 101 and an external device or system. The social participation data acquisition unit 161 acquires social participation data.

Note that the social participation data may be input manually by the user. Further, reading results of codes (one-dimensional codes, two-dimensional codes, etc.), communication logs using beacons, NFC (Near Field Communication), etc., logs of payment services, etc. may be used as social participation data.

The external system 102 is a system operated by a company or the like that provides services to users. For example, there are systems operated by the national and local governments, systems operated by hospitals and nursing care facilities, systems operated by general companies, and systems operated by specific communities. The service of this embodiment is a concept that includes administrative services, services that provide products or merchandise, services that provide events, and the like. Note that products and merchandise may be tangible or intangible. In the following explanation, a company that provides a service will be referred to as a provider.

External system 102 includes a computer having a processor, memory, and communication device. The external system 102 has a transmitter/receiver 170 as a software configuration. The transmission/reception unit 170 controls transmission and reception of data between the external system 102 and an external device or system.

The social participation support system 100 acquires social participation data from the client device 101 and presents health-related information to the user by performing processing using the social participation data. In this embodiment, information regarding the risk of requiring nursing care is presented as information regarding health. In the following explanation, information regarding the risk of needing care will be referred to as risk of needing care.

Here, the nursing care risk represents the risk of becoming in a state where nursing care is required. In addition, the condition requiring nursing care means a condition that is certified as one of the categories of nursing care required status (level of nursing care required) in the long-term care insurance system.

When the social participation support system 100 receives a behavior change scenario (activity improvement plan) for reducing the risk of needing care from a user who has referred to the risk of needing care information, the system 100 provides information regarding improvement effects and services that support behavior change. is presented to the user.

The social participation support system 100 includes at least one computer 200, as shown in FIG. The computer 200 has a processor 201, a network interface 202, a main storage device 203, and a sub storage device 204. The computer 200 may have input devices such as a keyboard, mouse, and touch panel, and output devices such as a display and a printer.

Processor 201 executes a program stored in main storage device 203. By executing processing according to a program, the processor 201 operates as a functional unit (module) that implements a specific function. In the following description, when a process is described using a functional unit as a subject, it is indicated that the processor 201 executes a program that implements the functional unit.

Network interface 202 is an interface for communicating with an external device or system via a network.

The main storage device 203 stores programs and data executed by the processor 201. The main storage device 203 is, for example, a memory. The main storage device 203 is also used as a work area.

The secondary storage device 204 permanently stores data. The secondary storage device 204 is, for example, an HDD (Hard Disk Drive) or an SSD (Solid State Drive).

Note that the program stored in the main storage device 203 may be stored in the secondary storage device 204. In this case, the processor 201 reads the program from the secondary storage device 204, loads it into the main storage device 203, and executes the program loaded into the main storage device 203.

The social participation support system 100 includes a transmitting/receiving section 110, an analytical model generating section 111, an analyzing section 112, a message generating section 113, a recommending section 114, and a report generating section 115 as a software configuration. The main storage device 203 stores programs that implement the above-mentioned functional units.

The social participation support system 100 includes service information 120, user information 121, activity purpose conversion information 122, analysis model information 123, template information 124, recommendation service classification information 125, activity history information 130, nursing care certification information 131, and nursing care receipt information. 132, activity analysis information 140, first risk analysis information 141, second risk analysis information 142, activity reference value information 150, and risk reference value information 151 are held. The above information is stored in the secondary storage device 204. Note that the above information may be stored in the main storage device 203.

Service information 120 is information for managing services provided to users. Details of the data structure of the service information 120 will be explained using FIGS. 3A and 3B.

User information 121 is information for managing users who use social participation support system 100. The details of the data structure of the user information 121 will be explained using FIG. 4.

Activity purpose conversion information 122 is information for specifying the activity purpose of the activity corresponding to the social participation data. The details of the data structure of the activity purpose conversion information 122 will be explained using FIG. 5.

In this embodiment, it is assumed that there are five activity purposes: "athletic hobbies", "cultural hobbies", "work", "volunteer", and "other". That is, the activities of this embodiment are classified into one of "athletic hobbies," "cultural hobbies," "work," "volunteer," and "others."

The analysis model information 123 is definition information of an analysis model for calculating a risk index for evaluating the risk of requiring nursing care. The analysis model information 123 stores, for example, a neural network for calculating a risk index from a feature amount calculated using social participation data and definition information of a mathematical formula using the feature amount as a variable.

The template information 124 is information for managing templates used to generate advice for reducing the risk of requiring nursing care. In this embodiment, an activity improvement plan is generated as advice for reducing the risk of requiring nursing care. The details of the data structure of the template information 124 will be explained using FIG. 6.

The recommendation service category information 125 is information for managing categories of services that support behavior change. The details of the data structure of the recommendation service classification information 125 will be explained using FIG. 7.

Activity history information 130 is information for managing social participation data as a user's activity history. The details of the data structure of the activity history information 130 will be explained using FIG. 8.

The nursing care certification information 131 is information for managing data related to nursing care certification. The details of the data structure of the nursing care certification information 131 will be explained using FIG. 9.

Nursing care receipt information 132 is information for managing nursing care receipts. The details of the data structure of the nursing care receipt information 132 will be explained using FIG. 10.

The activity analysis information 140 is information for managing activity data composed of feature amounts calculated from social participation data. As described later, the activity data includes the frequency of activities for each activity purpose. The details of the data structure of the activity analysis information 140 will be explained using FIG. 11.

The first risk analysis information 141 is information for managing analysis results (analysis results) regarding the risk of requiring nursing care. The details of the data structure of the first risk analysis information 141 will be explained using FIG. 12.

The second risk analysis information 142 is information for managing simulation results (simulation results) regarding nursing care risk. The details of the data structure of the second risk analysis information 142 will be explained using FIG. 13.

The activity reference value information 150 is information for managing the reference activity frequency of each activity purpose in groups classified based on user attributes. The details of the data structure of the activity reference value information 150 will be explained using FIG. 14.

Risk reference value information 151 is information for managing reference risk indicators in groups classified based on user attributes. The details of the data structure of the risk reference value information 151 will be explained using FIG. 15.

The transmission/reception unit 110 controls transmission and reception of data between the social participation support system 100 and an external device or system.

The analytical model generation unit 111 generates an analytical model for calculating a risk index using activity data, data regarding nursing care certification, and nursing care receipts.

The analysis unit 112 calculates a risk index using the activity data and the analysis model. Furthermore, the analysis unit 112 calculates relative evaluation information regarding the risk of requiring nursing care based on the risk index.

The message generation unit 113 generates a message including advice for reducing the risk of needing care, using the analysis result and the template.

The recommendation unit 114 uses the service information 120 and the recommended service category information 125 to identify a service that supports behavioral change.

The report generation unit 115 generates a report regarding the user's risk of requiring care, and transmits it to the client device 101. Further, the report generation unit 115 generates a report regarding the risk of requiring nursing care of the user who uses the service provided by the external system 102, and transmits it to the external system 102.

Regarding each functional unit that the social participation support system 100 has, a plurality of functional units may be combined into one functional unit, or one functional unit may be divided into a plurality of functional units for each function. For example, the analysis function of the analysis unit 112 and the output function of outputting analysis results and the like may be separated.

Note that the social participation support system 100 may be configured from a plurality of computers 200. In this case, the functional units may be distributed and arranged in each computer 200. Further, information may be distributed and arranged in each computer 200.

3A and 3B are diagrams showing an example of the data structure of the service information 120 according to the first embodiment. For convenience of the drawing format, the service information 120 will be divided into two parts for explanation.

The service information 120 stores entries including a service ID 301, service 302, provider 303, preferential treatment 304, preferential treatment content 305, destination 306, and user 307. One entry corresponds to one service.

Service ID 301 is a field that stores identification information (service ID) for uniquely identifying a service.

Service 302 is a field group that stores information regarding the service. The service 302 includes a service classification 311 and a name 312. The service category 311 is a field that stores the service category. For example, a set of major categories and minor categories of services is stored. Name 312 is a field that stores the name of the service. Note that the service 302 may include a field for storing specific contents of the service.

Provider 303 is a field group that stores information regarding a service provider. The provider 303 includes a provider classification 321 and a provider name 322. The provider classification 321 is a field that stores the provider classification. Provider name 322 is a field that stores the name of the provider.

Preferential treatment 304 is a field that stores details of preferential treatment for the analysis result of the risk of needing care. If there is no preferential treatment, "N/A" is stored in the preferential treatment 304. The preferential treatment content 305 is a field that stores specific details of preferential treatment.

The destination 306 is a field that stores the address of the destination of reports and the like.

User 307 is a field that stores information about users who are using the service. The user 307 stores user identification information, name, etc.

FIG. 4 is a diagram showing an example of the data structure of the user information 121 according to the first embodiment.

Entries including a user ID 401, name 402, date of birth 403, gender 404, address 405, first destination 406, and second destination 407 are stored. One entry corresponds to one user.

User ID 401 is a field that stores identification information (user ID) for uniquely identifying a user.

Name 402 is a field that stores the user's name. The date of birth 403 is a field that stores the user's date of birth. Gender 404 is a field that stores the user's gender. Address 405 is a field that stores the user's address.

A first destination 406 and a second destination 407 are fields that store addresses of report destinations. In this embodiment, the first destination 406 stores the address of the user himself/herself, and the second destination 407 stores the addresses of people related to the user, such as family members.

FIG. 5 is a diagram showing an example of the data structure of the activity purpose conversion information 122 according to the first embodiment.

The activity purpose conversion information 122 stores an entry including an activity purpose 501 and a keyword 502. One entry corresponds to one activity purpose.

Activity purpose 501 is a field that stores the activity purpose. The keyword 502 is a field that stores keywords related to the purpose of the activity.

FIG. 6 is a diagram showing an example of the data structure of the template information 124 according to the first embodiment.

The template information 124 stores entries including an activity purpose 601 and a template 602. One entry corresponds to one activity purpose.

Activity purpose 601 is a field that stores the activity purpose of an activity recommended for improvement. The activity purpose 601 stores "total" in addition to the five activity purposes. "Total" means that at least one of the activity objectives is targeted for improvement. A template for an entry in which "total" is set in the activity purpose 601 is always used regardless of the type of activity purpose.

The template 602 is a field that stores a template for generating a message including advice (suggestions for improving activities) for reducing the risk of requiring care.

FIG. 7 is a diagram showing an example of the data structure of the recommendation service classification information 125 according to the first embodiment.

The recommendation service category information 125 stores entries including an activity purpose 701 and a service category 702. One entry corresponds to one activity purpose.

Activity purpose 701 is a field that stores the activity purpose of the activity that is the target of behavior modification. The activity purpose 701 stores "TOTAL" and "GENERAL" in addition to the five activity purposes. "Total" means that at least one of the activity objectives is subject to behavioral change. "GENERAL" means that the purpose of the activity does not matter. Services for entries in which "TOTAL" and "GENERAL" are set in the activity purpose 701 are always provided regardless of the type of activity purpose.

The service category 702 is a field that stores the category of a service that supports behavior change. The service category 702 stores the value set in the service category 311.

FIG. 8 is a diagram showing an example of the data structure of the activity history information 130 according to the first embodiment.

The activity history information 130 stores entries including user ID 801, time stamp 802, points 803, spots 804, activity purpose 805, stay/movement 806, and means of movement 807. One entry corresponds to one piece of social participation data.

User ID 801 is the same field as user ID 401.

The timestamp 802 is a field that stores the date and time when social participation data was acquired. Note that when the social participation data is acquired from the client device 101 in real time, the time stamp 802 may store the date and time when the social participation data was transmitted to the social participation support system 100.

Point 803 is a field that stores the user's location information. In the point 803 of this embodiment, the latitude and longitude measured using the GPS (Global Positioning System) function installed in the client device 101 are stored.

A spot 804 is a field that stores information on a location specified from the user's location information. The spot 804 stores the names of facilities and roads.

Activity purpose 805 is a field that stores the activity purpose of the activity corresponding to the social participation data.

Stay/move 806 is a field that stores information indicating whether the user is moving or staying at a certain location. The stay/move field 806 stores either "move" indicating that the user is moving or "stay" indicating that the user is staying at a certain location.

The transportation means 807 is a field that stores information indicating the user's transportation means. Note that if "stay" is set in the stay/move field 806, "N/A" is stored in the travel means 807.

FIG. 9 is a diagram showing an example of the data structure of the nursing care certification information 131 according to the first embodiment.

The nursing care certification information 131 stores entries including an insured number 901, a name 902, a date of birth 903, a gender 904, an application date 905, a nursing care status classification 906, and a valid period 907. One entry corresponds to one user's nursing care certification result.

The insured number 901 is a field that stores the insured number of the user who has been certified as requiring nursing care.

The name 902, date of birth 903, and gender 904 are the same fields as the name 402, date of birth 403, and gender 404.

The application date 905 is a field that stores the date and time when the application for nursing care certification was made.

The care need state classification 906 is a field that stores the care need state classification certified by the care need certification. Validity period 907 is a field that stores the validity period of certification.

FIG. 10 is a diagram showing an example of the data structure of the nursing care receipt information 132 according to the first embodiment.

Nursing care receipt information 132 stores entries including insured number 1001, name 1002, nursing care status category 1003, validity period 1004, service details 1005, insurance claim amount 1006, and self-pay amount 1007. One entry corresponds to one nursing care receipt.

The insured number 1001 is a field that stores the insured number of the user who used the nursing care service.

The name 1002 is the same field as the name 402. The care-requiring state classification 1003 is the same field as the care-requiring state classification 906. The validity period 1004 is the same field as the validity period 907.

Service content 1005 is a field that stores information indicating the content of the nursing care service used. The service content 1005 stores, for example, the name of the service.

The insurance claim amount 1006 is a field that stores the insurance claim amount for the nursing care service used. The self-pay amount 1007 is a field that stores the self-pay amount for the nursing care service used.

FIG. 11 is a diagram showing an example of the data structure of the activity analysis information 140 according to the first embodiment.

Activity analysis information 140 stores entries including activity data ID 1101, user ID 1102, and activity frequency 1103. One entry corresponds to one piece of activity data.

The activity data ID 1101 is a field that stores identification information (activity data ID) for uniquely identifying activity data.

User ID 1102 is the same field as user ID 401.

The activity frequency 1103 is a field group that stores the frequency of activities for each activity purpose. The activity frequency 1103 includes a total of 1111, athletic hobbies 1112, cultural hobbies 1113, employment 1114, volunteering 1115, and others 1116.

The athletic hobby 1112 is a field that stores the frequency of activities with the activity purpose "athletic hobby." The cultural hobbies 1113 is a field that stores the frequency of activities with the activity purpose "cultural hobbies." Work field 1114 is a field that stores the frequency of activities with the activity purpose of "work." Volunteer field 1115 is a field that stores the frequency of activities for which the activity purpose is "volunteer." Others 1116 is a field that stores the frequency of activities with an activity purpose of “others.” The total 1111 is a field that stores the total frequency of activities for each activity purpose.

Note that activity data may be managed for each user. In this case, the entry does not include the activity data ID 1101. The value is overwritten in the user's entry.

FIG. 12 is a diagram showing an example of the data structure of the first risk analysis information 141 according to the first embodiment.

The first risk analysis information 141 stores entries including an analysis result ID 1201, a user ID 1202, an activity data ID 1203, and a risk index 1204. One entry corresponds to one analysis result.

Analysis result ID 1201 is a field that stores identification information (analysis result ID) for uniquely identifying an analysis result.

User ID 1202 is the same field as user ID 401. The activity data ID 1203 is the same field as the activity data ID 1101. Note that the activity data ID 1203 stores identification information of activity data used to calculate the risk index.

Risk index 1204 is a field group that stores risk indexes. The risk index 1204 includes a nursing care required certification rate 1211, a nursing care required state period 1212, and a self-payment amount 1213.

The nursing care certification rate 1211 is a field that stores the probability of being certified as needing nursing care within three years. Note that the period for prediction can be set arbitrarily. The nursing care required state period 1212 is a field that stores the period in which nursing care is required. The self-pay amount 1213 is a field that stores the self-pay amount required for nursing care.

FIG. 13 is a diagram showing an example of the data structure of the second risk analysis information 142 according to the first embodiment.

The second risk analysis information 142 stores entries including a simulation ID 1301, a user ID 1302, an analysis result ID 1303, a behavior change scenario 1304, and a risk index 1305. One entry corresponds to one simulation result.

The simulation ID 1301 is a field that stores identification information (simulation ID) for uniquely identifying a simulation result.

User ID 1302 is the same field as user ID 401. Analysis result ID 1303 is the same field as analysis result ID 1201. The analysis result ID 1303 stores identification information of the analysis result used in the simulation.

Behavior change scenario 1304 is a field that stores a behavior change scenario. The behavior change scenario 1304 stores one or more pairs of activity purpose and frequency.

Risk index 1305 is the same field group as risk index 1204. The risk index 1305 includes a nursing care required certification rate 1311, a nursing care required state period 1312, and a self-pay amount 1313. Values calculated by simulation are stored in the nursing care required certification rate 1311, the nursing care required state period 1312, and the self-payment amount 1313.

FIG. 14 is a diagram showing an example of the data structure of the activity reference value information 150 according to the first embodiment.

The activity reference value information 150 stores entries including a group ID 1401, gender 1402, age 1403, and activity frequency 1404. One entry exists for one group grouped by a combination of gender and age.

Group ID 1401 is a field that stores group identification information (group ID).

Gender 1402 and age 1403 are a field group that stores group attributes. Gender 1402 is a field that stores gender. The year 1403 is a field that stores the year.

The activity frequency 1404 is a field group that stores the reference activity frequency of each activity purpose of the group. The activity frequency 1404 includes a total of 1411, athletic hobbies 1412, cultural hobbies 1413, employment 1414, volunteering 1415, and others 1416.

The athletic hobby 1412 is a field that stores the reference activity frequency of the activity purpose "athletic hobby." The cultural hobby 1413 is a field that stores the reference activity frequency of the activity purpose "cultural hobby." Work 1414 is a field that stores the reference activity frequency of the activity purpose "work". Volunteer 1415 is a field that stores the reference activity frequency of the activity purpose "volunteer". Others 1416 is a field that stores the reference activity frequency of the activity purpose “Others”. The total 1411 is a field that stores the total reference activity frequency for each activity purpose.

The reference activity frequency stored in each field is, for example, a statistical value such as an average value, a median value, and a mode value. It may be a threshold that includes 95% of users who have not been certified as requiring nursing care.

Note that each field may store a plurality of reference values of different types.

FIG. 15 is a diagram showing an example of the data structure of the risk reference value information 151 according to the first embodiment.

The risk reference value information 151 stores entries including a group ID 1501, gender 1502, age 1503, nursing care certification rate 1504, nursing care required state period 1505, and self-payment amount 1506. There is one entry for each group grouped by gender and age.

Group ID 1501, gender 1502, and age 1503 are the same fields as group ID 1401, gender 1402, and age 1403. In this embodiment, the groups of activity frequencies and risk indicators are the same, but they may be different.

The nursing care recognition rate 1504 is a field group that stores a reference value of the nursing care recognition rate of users belonging to the group. The nursing care certification rate 1504 includes an average value 1511 and a reference value 1512.

Average value 1511 is a field that stores the average value of the nursing care certification rate. The reference value 1512 is a field that stores statistical values other than the average value of the nursing care certification rate. For example, the median value and the mode value. It may be a threshold that includes 95% of users who have not been certified as requiring nursing care.

The care-requiring state period 1505 is a field group that stores a reference value of the care-requiring state period of a user belonging to a group. The nursing care state period 1505 includes an average value 1521 and a reference value 1522.

The average value 1521 is a field that stores the average value of the care-requiring state period. The reference value 1522 is a field that stores statistical values other than the average value of the care-requiring state period. For example, the median value and the mode value. It may be a threshold that includes 95% of users who have not been certified as requiring nursing care.

The self-pay amount 1506 is a field group that stores the reference value of the self-pay amount of the users belonging to the group. The self-payment amount 1506 includes an average value 1531 and a reference value 1532.

The average value 1531 is a field that stores the average value of the self-payment amount. The reference value 1532 is a field that stores statistical values other than the average value of the self-payment amount. For example, the median value and the mode value. It may be a threshold that includes 95% of users who have not been certified as requiring nursing care.

16A and 16B are sequence diagrams illustrating the processing flow of the computer system according to the first embodiment.

The client device 101 transmits a user registration request including account data to the social participation support system 100 (step S101). Account data includes name, date of birth, gender, address, destination address, etc.

When the social participation support system 100 receives a user registration request, it adds an entry to the user information 121. The social participation support system 100 generates a user ID and stores the user ID in the user ID 401 of the added entry. Furthermore, the social participation support system 100 stores values in each field of the added entry based on the account data. Note that the social participation support system 100 may transmit the user ID to the client device 101.

The external system 102 operated by the service provider transmits a service registration request including service registration data to the social participation support system 100 (step S102). The service registration data includes service content, provider, preferential treatment content, destination address, customer's personal information, etc. The customer's personal information includes, for example, name, date of birth, gender, and address. Note that the service provider is not limited to a general company, but may also be a government agency.

When the social participation support system 100 receives a service registration request, it adds an entry to the service information 120. The social participation support system 100 generates a service ID and stores the service ID in the service ID 301 of the added entry. The social participation support system 100 stores values in the service 302, provider 303, preferential treatment 304, preferential treatment content 305, and destination 306 of the added entry based on the service registration data. The social participation support system 100 refers to the user information 121 based on the user's personal information included in the service registration data and acquires the user ID. The social participation support system 100 stores the user ID in the user 307 of the added entry. Note that for users who are not registered in the user information 121, personal information (for example, name) is stored as is.

The social participation support system 100 acquires care requirement certification data and a care receipt by making an inquiry to the external system 102 operated by an administrative agency based on the user information 121 (step S103).

The social participation support system 100 adds an entry to the nursing care certification information 131, and stores a value in each field of the added entry based on the nursing care certification data. Furthermore, the social participation support system 100 adds an entry to the nursing care receipt information 132, and stores values in each field of the added entry based on the nursing care receipt.

When social participation support system 100 receives social participation data from client device 101 (step S104), it executes social participation data registration processing (step S105). Details of the social participation data registration process will be explained using FIG. 17.

The social participation support system 100 executes an analytical model generation process at an arbitrary timing (step S106). For example, when a certain period of time has elapsed or when the number of received social participation data is greater than a threshold value, the analytical model generation process is executed. Details of the analytical model generation process will be explained using FIG. 18.

The client device 101 transmits an analysis request including the user ID to the social participation support system 100 (step S107).

When the social participation support system 100 receives the analysis request, it executes a nursing care risk analysis process and a message generation process (step S108, step S109). Details of the nursing care risk analysis process will be explained using FIG. 19. Details of the message generation process will be explained using FIG. 20. Note that the nursing care risk analysis process and the message generation process may be executed as batch processing. For example, the nursing care risk analysis process and the message generation process are executed for each user on a daily, weekly, or monthly basis.

The social participation support system 100 transmits the analysis result calculated by the nursing care risk analysis process and the message generated by the message generation process to the client device 101 (step S110).

The client device 101 transmits, to the social participation support system 100, a simulation request including the analysis result and the behavior change scenario input by the user who has referred to the message (step S111).

When the social participation support system 100 receives the simulation request, it executes the simulation process (step S112). Details of the simulation process will be explained using FIG. 23.

The social participation support system 100 transmits the simulation results and recommendation service information to the client device 101 (step S113). Here, the recommendation service represents a service that supports behavior change.

The social participation support system 100 executes report generation processing at an arbitrary timing (step S114). For example, the report generation process is executed when a certain period of time has passed or when an execution request has been received. Details of the report generation process will be explained using FIG. 25.

The social participation support system 100 transmits a report to the client device 101 and the external system 102 (step S115, step S116, step S117).

Furthermore, the social participation support system 100 executes an analytical model generation process at an arbitrary timing (step S118). This updates the analytical model.

FIG. 17 is a flowchart illustrating an example of a social participation data registration process executed by the social participation support system 100 of the first embodiment.

The transmitter/receiver 110 adds an entry to the activity history information 130 (step S201).

Specifically, the transmitter/receiver 110 stores the user ID, time stamp, and location information included in the social participation data in the user ID 801, time stamp 802, and point 803 of the added entry.

Next, the transmitting/receiving unit 110 calculates the user's stay time at the location indicated by the location information included in the social participation data (step S202). Specifically, the following processing is executed.

(S202-1) The transmitting/receiving unit 110 refers to the activity history information 130 and searches for an entry in which the user ID included in the social participation data is stored in the user ID 801.

(S202-2) The transmitting/receiving unit 110 refers to the location information stored in the point 803 of the searched entry, and when the error with the location information stored in the point 803 of the added entry is less than or equal to the threshold value. Identify a group of entries with consecutive series. In other words, a group of entries indicating that the user stayed at a certain point is identified.

Note that the transmitting/receiving unit 110 may calculate the user's moving speed, acceleration, etc. using the points 803 of a plurality of entries, and may specify the entry group based on the moving speed, acceleration, etc.

(S202-3) The transmitting/receiving unit 110 acquires the oldest time stamp from the identified entry group. The transmitter/receiver 110 calculates the difference between the time stamp included in the social participation data and the acquired time stamp as the stay time. The above is the explanation of the process in step S202.

Next, the transmitting/receiving unit 110 determines whether the residence time is greater than the threshold α (step S203). It is assumed that the threshold value α is set in advance. For example, the threshold value α is 30 minutes. However, the threshold value α can be set to any value and can be updated at any timing.

If the stay time is greater than the threshold α, the transmitter/receiver 110 stores “stay” in the stay/move 806 of the added entry (step S204). At this time, the transmitting/receiving unit 110 stores "N/A" in the moving means 807 of the added entry.

Next, the transmitting/receiving unit 110 identifies the place of stay based on the location information stored in the point 803 of the added entry (step S205).

Specifically, the transmitting/receiving unit 110 acquires GIS (Geographic Information System) information corresponding to location information, and specifies the place of stay based on the GIS information. The transmitter/receiver 110 also acquires information regarding the place of stay. The GIS information may be held by the social participation support system 100 or may be obtained from an external system.

Next, the transmitting/receiving unit 110 specifies the activity purpose based on the information regarding the place of stay, and stores the specified activity purpose in the activity purpose 805 of the added entry (step S206). Thereafter, the transmitter/receiver 110 ends the social participation data registration process. Specifically, the following processing is executed.

(S206-1) The transmitting/receiving unit 110 extracts keywords from the information regarding the place of stay. For example, keywords are extracted from the name of the place of stay, the descriptive text of the place of stay, etc.

(S206-2) The transmitting/receiving unit 110 refers to the activity purpose conversion information 122 based on the keyword and searches for an entry in which the keyword extracted as the keyword 502 is stored. The transmitting/receiving unit 110 obtains the value of the activity purpose 501 of the searched entry.

(S206-3) The transmitting/receiving unit 110 stores the obtained value in the activity purpose 805 of the added entry. The above is the explanation of the process of step S206.

In step S203, if the stay time is equal to or less than the threshold α, the transmitting/receiving unit 110 stores “move” in the stay/move 806 of the added entry (step S204). At this time, the transmitting/receiving unit 110 stores "N/A" in the spot 804 and activity purpose 805 of the added entry.

Next, the transmitting/receiving unit 110 specifies the transportation means, and stores the specified transportation means in the transportation means 807 of the added entry (step S205). Thereafter, the transmitter/receiver 110 ends the social participation data registration process. The moving means can be specified based on, for example, moving speed and acceleration.

FIG. 18 is a flowchart illustrating an example of an analytical model generation process executed by the social participation support system 100 of the first embodiment.

The analytical model generation unit 111 starts the user's loop processing (step S301).

Specifically, the analysis model generation unit 111 selects one entry from the user information 121. In the following explanation, the user corresponding to the selected entry will be referred to as a target user.

Next, the analytical model generation unit 111 generates activity data of the target user (step S302). Specifically, the following processing is executed.

(S302-1) The analytical model generation unit 111 acquires social participation data that satisfies the following conditions from the activity history information 130.
(Condition 1) Identification information of the target user is stored in the user ID 801.
(Condition 2) “N/A” is not stored in the activity purpose 805.
(Condition 3) The timestamp stored in the timestamp 802 is included within the analysis period.

Note that the analysis period is set in advance. For example, a period from the current time to one year ago, a period from the current time to six months ago, or a period from the current time to two years ago can be set as the analysis period. However, the analysis period can be set arbitrarily, and it can be updated at any timing.

(S302-2) The analytical model generation unit 111 compiles the acquired social participation data into blocks of data. Specifically, the analytical model generation unit 111 collects social participation data that are continuous in time and have the same activity purpose into one block of data (block data).

(S302-3) The analysis model generation unit 111 groups the block data in units of analysis time. For example, block data is grouped on a monthly basis.

(S302-4) The analytical model generation unit 111 calculates the number of block data for each activity purpose for each group. Furthermore, the analytical model generation unit 111 generates data including the average value of the number of block data for each activity purpose and the average value of the total number of block data for all activity purposes as activity data. The above is the explanation of the process of step S302.

Next, the analytical model generation unit 111 acquires the target user's nursing care authorization data from the nursing care authorization information 131, and acquires the target user's nursing care receipt from the nursing care receipt information 132 (step S303).

Note that if the target user's data does not exist in the nursing care certification information 131 and the nursing care receipt information 132, the analytical model generation unit 111 generates additional data indicating that the target user is not certified as requiring nursing care.

Next, the analytical model generation unit 111 generates analytical data in which the activity data is associated with the nursing care certification data and the nursing care receipt (step S304).

Note that the analytical model generation unit 111 may convert the nursing care certification data and the nursing care receipt into data in an arbitrary format, and generate analytical data in which the data and activity data are associated.

Note that if the target user's data does not exist in the nursing care certification information 131 and the nursing care receipt information 132, the analytical model generation unit 111 generates additional data indicating that the data does not exist, and associates the activity data with the additional data. generate analyzed data.

In step S305, it is determined whether the processing has been completed for all users registered in the user information 121 (step S305).

If the processing has not been completed for all users registered in the user information 121, the analytical model generation unit 111 returns to step S301 and executes the same processing.

When the processing is completed for all users registered in the user information 121, the analytical model generation unit 111 uses the analytical data to generate a first analytical model that receives the activity data as input and calculates the nursing care certification rate. Then, the definition information of the first analysis model is stored in the analysis model information 123 (step S306). Since the analytical model generation method is well known, detailed explanation will be omitted. The first analysis model outputs 0% or a very small value when activity data associated with additional data is input.

Next, the analytical model generation unit 111 uses the analytical data to generate a second analytical model that accepts the activity data as input and calculates the care-requiring state period (step S307), and adds the second analytical model to the analytical model information 123. Stores model definition information. Since the analytical model generation method is well known, detailed explanation will be omitted. The second analysis model outputs 0 years or a very small value when activity data associated with additional data is input.

Next, the analytical model generation unit 111 uses the analytical data to generate a third analytical model that accepts the activity data as input and calculates the self-pay amount for nursing care costs (step S308), and adds the third analytical model to the analytical model information 123. Stores the definition information of the analysis model. After that, the analytical model generation unit 111 ends the analytical model generation process. Since the analytical model generation method is well known, detailed explanation will be omitted. The third analysis model outputs 0 yen or a very small value when activity data associated with additional data is input.

Note that the processing in steps S306, S307, and S308 may be executed in different order or may be executed in parallel.

Note that the activity data may include parameters such as gender and age that are risk factors other than the frequency of activity.

Note that an analysis model may be generated in which the average length of stay for each purpose of going out and the frequency of use of transportation means are used as explanatory variables. Furthermore, in addition to activity data, an analysis model may be generated using health checkup results, medical receipts, and activity amounts such as number of steps, walking time, and calorie consumption as explanatory variables.

FIG. 19 is a flowchart illustrating an example of the nursing care risk analysis process executed by the social participation support system 100 of the first embodiment.

Here, an example will be described in which analysis requests are received from multiple client devices 101 during the same time period. In this case, the analysis unit 112 generates a user list including user IDs, and then executes the process described below.

The analysis unit 112 starts user loop processing (step S401).

Specifically, the analysis unit 112 selects one user ID from the user list. In the following description, the user corresponding to the selected user ID will be referred to as a target user.

Next, the analysis unit 112 generates activity data of the target user (step S402).

The method of generating activity data is the same as that described in step S302. In step S402, the analysis unit 112 registers the activity data in the activity analysis information 140. Specifically, the analysis unit 112 adds an entry to the activity analysis information 140, generates an activity data ID, and stores the activity data ID in the activity data ID 1101 of the added entry. The analysis unit 112 stores the user ID in the user ID 1102 of the added entry, and also stores the activity data in the activity frequency 1103.

Next, the analysis unit 112 calculates the nursing care certification rate using the definition information of the first analysis model stored in the analysis model information 123 and the activity data (step S403).

Next, the analysis unit 112 calculates the care-requiring state period using the definition information of the second analysis model stored in the analysis model information 123 and the activity data (step S404).

Next, the analysis unit 112 calculates the nursing care cost self-payment amount using the definition information of the third analysis model stored in the analysis model information 123 and the activity data (step S405).

Next, the analysis unit 112 stores the analysis result in the first risk analysis information 141 (step S406).

Specifically, the analysis unit 112 adds an entry to the first risk analysis information 141, generates an analysis result ID, and stores the analysis result ID in the analysis result ID 1201 of the added entry. The analysis unit 112 stores the user ID in the user ID 1202 of the added entry, and stores the activity data ID in the activity data ID 1203. In addition, the analysis unit 112 adds the calculated nursing care certification rate, nursing care needing state period, and nursing care cost copayment amount to the added entry's nursing care certification rate 1211, nursing care needing state period 1212, and copayment amount 1213. Store.

Next, the analysis unit 112 obtains the reference activity frequency and reference risk index of the group to which the target user belongs (step S407). Specifically, the following processing is executed.

(S407-1) The analysis unit 112 refers to the user information 121 and searches for an entry in which the user ID of the target user is stored in the user ID 401. The analysis unit 112 obtains the values of the date of birth 403 and gender 404 of the searched entry. The analysis unit 112 calculates the age of the target user based on the date of birth.

(S407-2) The analysis unit 112 refers to the activity reference value information 150, and stores an age range in which the gender 1402 matches the gender of the target user and the age 1403 includes the age of the target user. Search for entries. That is, the entry of the group to which the target user belongs is searched. The analysis unit 112 obtains the group ID 1401 and activity frequency 1404 values of the searched entry.

(S407-3) The analysis unit 112 refers to the risk reference value information 151 and searches for an entry in which the group ID obtained from the group ID 1401 is stored in the group ID 1501. That is, the entry of the group to which the target user belongs is searched. The analysis unit 112 acquires the values of the nursing care required certification rate 1504, the nursing care required state period 1505, and the self-payment amount 1506 of the searched entry. The above is the explanation of the process of step S407.

Next, the analysis unit 112 registers the analysis information in the analysis result list (step S408).

Specifically, the analysis unit 112 registers analysis information including a user ID, group ID, analysis result, reference activity frequency, and reference risk index in the analysis result list.

Next, the analysis unit 112 determines whether processing has been completed for all users registered in the user list (step S409).

If the processing for all users registered in the user list has not been completed, the analysis unit 112 returns to step S401 and executes the same processing.

When the processing for all users registered in the user list is completed, the analysis unit 112 generates a distribution of risk indicators for the group (step S410). Specifically, the following processing is executed.

(S410-1) The analysis unit 112 refers to the risk reference value information 151 and obtains the gender 1502 and age 1503 of each entry. That is, a combination of attributes that define a group is obtained.

(S410-2) The analysis unit 112 generates a data set by grouping the analysis results stored in the first risk analysis information 141 based on the combination of attributes that define the group. Group identification information is assigned to the dataset.

(S410-3) The analysis unit 112 selects one group. The analysis unit 112 generates a distribution of each risk index based on the selected group of data sets. The analysis unit 112 stores the group identification information and the risk index distribution in a work area in association with each other.

The analysis unit 112 executes (S410-3) for all groups. The above is the explanation of the process of step S410.

Next, the analysis unit 112 updates the activity reference value information 150 (step S411). Specifically, the following processing is executed.

(S411-1) The analysis unit 112 selects one piece of analysis information registered in the analysis result list and obtains the group ID included in the analysis information.

(S411-2) The analysis unit 112 searches for an entry in which the acquired group ID is stored in the group ID 1401. The analysis unit 112 updates the activity frequency 1404 of the identified group based on the activity frequency 1404 of the searched entry and the activity data included in the analysis information.

The analysis unit 112 executes (S411-1) and (S411-2) for all analysis information in the analysis list. The above is the explanation of the process of step S411.

Next, the analysis unit 112 updates the risk reference value information 151 (step S412). After that, the analysis unit 112 ends the nursing care risk analysis process. Specifically, the following processing is executed.

(S412-1) The analysis unit 112 selects one piece of analysis information registered in the analysis result list and obtains the group ID included in the analysis information.

(S412-2) The analysis unit 112 searches for an entry in which the acquired group ID is stored in the group ID 1401. The analysis unit 112 determines the nursing care certification rate of the searched entry based on the analysis result included in the analysis information, the nursing care certification rate 1504, the nursing care need state period 1505, and the self-payment amount 1506 of the searched entry. 1504, nursing care required state period 1505, and self-payment amount 1506 are updated.

The analysis unit 112 executes (S412-1) and (S412-2) for all analysis information in the analysis result list. The above is the explanation of the process of step S412.

Note that the processing in steps S403, S404, and S405 may be executed in a different order or may be executed in parallel.

FIG. 20 is a flowchart illustrating an example of message generation processing executed by the social participation support system 100 of the first embodiment. FIG. 21 is a diagram showing an example of an analysis result display screen presented by the social participation support system 100 of the first embodiment. FIG. 22 is a diagram showing an example of an advice display screen presented by the social participation support system 100 of the first embodiment.

The analysis unit 112 starts loop processing of the analysis results (step S501).

Specifically, the analysis unit 112 selects one piece of analysis information from the analysis result list.

The analysis unit 112 calculates the amount of deviation in activity frequency for each activity purpose of users in the group (step S502). Specifically, the following processing is executed.

(S502-1) The analysis unit 112 obtains the activity data ID from the selected analysis information. The analysis unit 112 refers to the activity analysis information 140 and searches the activity data ID 1101 for an entry in which the acquired activity data ID is stored. The analysis unit 112 obtains the value of the activity frequency 1103 of the searched entry.

(S502-2) The analysis unit 112 calculates the difference between the activity frequency of each activity purpose of the user and the reference activity frequency of each activity purpose included in the analysis information. The calculated difference becomes the amount of deviation in the activity frequency of each activity purpose of the users in the group. The above is the explanation of the process of step S502.

Next, the analysis unit 112 generates a message encouraging the activity of the activity objective to be improved based on the amount of deviation in the activity frequency of each activity objective (step S503). Specifically, the following processing is executed.

(S503-1) The analysis unit 112 selects a predetermined number of activity objectives in descending order of the amount of deviation. For example, the analysis unit 112 selects three activity objectives in descending order of the amount of deviation.

(S503-2) The analysis unit 112 outputs a message generation instruction including the selected activity purpose to the message generation unit 113. Thereafter, it waits for a response from the message generation unit 113.

(S503-3) When receiving the message generation instruction, the message generation unit 113 searches the activity purpose 601 for an entry in which the selected activity purpose is stored, and stores the template in the template 602 of the searched entry. get. The message generation unit 113 generates a message based on the template. Message generation section 113 outputs the generated message to analysis section 112.

Note that the message generation unit 113 may generate a message using the analysis information as well as the template.

(S503-4) If the analysis unit 112 receives the message, it ends the process of step S503. The above is the explanation of the process in step S503.

Next, the analysis unit 112 generates risk analysis result information for presenting an analysis result and advice information for presenting a message (step S504). Specifically, the following processing is executed.

(S504-1) The analysis unit 112 obtains the distribution of risk indicators corresponding to the group ID included in the analysis information from the work area. The analysis unit 112 generates risk analysis result information based on the user ID and analysis result included in the analysis information, information on the group to which the user belongs, and the distribution of risk indicators.

(S504-2) The analysis unit 112 generates advice information based on the activity frequency of each activity purpose of the user, the reference activity frequency of each activity purpose included in the analysis information, and the message. The above is the explanation of the process in step S504.

The analysis unit 112 transmits risk analysis result information and advice information to the client device 101 (step S505).

Specifically, the analysis unit 112 refers to the user information 121 and searches for an entry in which the user ID included in the analysis information is stored in the user ID 401. The analysis unit 112 obtains the addresses stored in the first destination 406 and second destination 407 of the searched entry. The analysis unit 112 transmits risk analysis result information and advice information to the acquired address. In this way, analysis results and messages are sent to the user and the user's associates.

Next, the analysis unit 112 determines whether processing of all analysis information (analysis results) registered in the analysis result list has been completed (step S506).

If the processing of all analysis information (analysis results) registered in the analysis result list has not been completed, the analysis unit 112 returns to step S501 and executes the same processing.

When processing of all analysis information (analysis results) registered in the analysis result list is completed, the analysis unit 112 ends the message generation process. At this time, the analysis unit 112 initializes the analysis result list.

Here, a screen displayed on the client device 101 that receives risk analysis result information and advice information will be described using FIGS. 21 and 22.

An analysis result display screen 2100 shown in FIG. 21 is a screen displayed based on risk analysis result information. The analysis result display screen 2100 includes a risk index display field 2101, a distribution display field 2102, and a selection button 2103.

The risk index display column 2101 is a column for displaying risk indicators (analysis results). In this embodiment, the nursing care certification rate, the nursing care required state period, and the nursing care cost self-payment amount are displayed.

The selection button 2103 is an operation button for selecting the distribution of risk indicators to be displayed.

The distribution display field 2102 is a field that displays the distribution of the risk index selected by the user operating the selection button 2103. The distribution display column 2102 displays the user's risk index along with the distribution of the risk index. Furthermore, information on the group to which the user belongs is also displayed in the distribution display field 2102.

Advice display screen 2200 shown in FIG. 22 is a screen displayed based on advice information. The advice display screen 2200 includes an activity frequency display field 2201, a ranking display field 2202, and an advice display field 2203.

The activity frequency display column 2201 is a column that displays the activity frequency of each activity purpose of the user. In FIG. 22, the user's activity frequency and the reference activity frequency of each activity purpose of the group to which the user belongs are displayed as a radar chart. Furthermore, in the activity frequency display column 2201, operation buttons for selecting values to be compared are displayed. The display of the radar chart is switched by the user operating the operation button.

The ranking display field 2202 is a field that displays rankings of activity purposes with large discrepancies. The advice display column 2203 is a column for displaying improvement suggestions (advice) for the activity of the activity purpose displayed in the ranking display column 2202. You can switch the advice displayed by selecting the tab corresponding to the ranking.

As shown in the analysis result display screen 2100, by displaying the distribution of risk indicators for groups of the same gender and age as the relative evaluation of the users in the group, the user can understand that the risk of needing care is approaching. can be easily understood as a realistic problem. Thereby, it is possible to encourage the user to change his or her behavior in order to reduce the risk of needing care.

As shown in the advice display screen 2200, by presenting advice that reflects the user's activity trends, the user can accurately and easily grasp guidelines for behavioral change. This allows users to be encouraged to take action to change their behavior. In other words, social participation can be supported.

Further, by presenting similar information to the user's related parties, it is possible to encourage the user to change his or her behavior through the user's related parties.

FIG. 23 is a flowchart illustrating an example of a simulation process executed by the social participation support system 100 of the first embodiment. FIG. 24 is a diagram showing an example of a simulation screen presented by the social participation support system 100 of the first embodiment.

When the analysis unit 112 receives a simulation request from the client device 101, it presents a screen to the client device 101 in order to receive input of a behavior change scenario (step S601). Here, the screen presented to the client device 101 will be described using FIG. 23.

The simulation screen 2400 includes a behavior change scenario input field 2401, a simulation result field 2402, and a recommendation service field 2403.

The behavior change scenario input field 2401 is a field for inputting a behavior change scenario. The behavior change scenario input field 2401 includes an input column 2410 consisting of an activity purpose field 2411 and a frequency field 2412, an add button 2420, and a simulation button 2430.

The activity purpose column 2411 is a column for inputting the activity purpose of the activity that is the target of behavior modification. For example, in the activity purpose column 2411, the activity objectives displayed in the ranking display column 2202 are displayed in a pull-down format. The frequency column 2412 is a column for inputting the frequency of the activity corresponding to the input purpose of the activity. For example, the frequency column 2412 displays recommended frequencies in a pull-down format.

Add button 2420 is an operation button for adding input column 2410.

The simulation button 2430 is an operation button for transmitting a simulation execution request to the social participation support system 100. When the user operates the simulation button 2430, the client device 101 includes the value set in the input column 2310 as a behavior change scenario and transmits a simulation execution request including the user ID to the social participation support system 100.

The simulation result column 2402 is a column for displaying simulation results. The simulation result column 2402 displays the current risk index and the risk index (simulation value) when the activity is performed according to the behavior change scenario. Further, in the simulation result column 2402, a value indicating the improvement effect is also displayed.

The recommendation service column 2403 is a column that displays services that support behavior change.

The above is the explanation of the simulation screen 2400. Returning to the explanation of FIG. 23.

The analysis unit 112 determines whether a simulation execution request has been received from the client device 101 (step S602).

If a simulation execution request has not been received from the client device 101, the analysis unit 112 returns to step S602 after a certain period of time and executes the same process.

When receiving a simulation execution request from the client device 101, the analysis unit 112 generates activity data for simulation (step S603). Specifically, the following processing is executed.

(S603-1) The analysis unit 112 refers to the first risk analysis information 141 and searches for an entry (analysis result) in which the user ID included in the simulation execution request is stored in the user ID 1202. The analysis unit 112 selects the latest entry from among the searched entries.

(S603-2) The analysis unit 112 refers to the activity analysis information 140 and obtains an entry (activity data) in which the value of the activity data ID 1203 of the selected entry is stored in the activity data ID 1101.

(S603-3) The analysis unit 112 generates activity data for simulation by changing the value of the activity frequency 1103 of the acquired entry based on the behavior change scenario. The above is the explanation of the process in step S603.

Next, the analysis unit 112 calculates the nursing care certification rate using the definition information of the first analysis model stored in the analysis model information 123 and the activity data for simulation (step S604). The process in step S604 is the same as the process in step S403.

Next, the analysis unit 112 calculates the care-requiring state period using the definition information of the second analysis model stored in the analysis model information 123 and the activity data for simulation (step S605). The process in step S605 is the same as the process in step S404.

Next, the analysis unit 112 uses the definition information of the third analysis model stored in the analysis model information 123 and the activity data for simulation to calculate the nursing care cost self-payment amount (step S606). The process in step S606 is the same as the process in step S405.

Next, the analysis unit 112 stores the simulation results in the second risk analysis information 142 (step S607).

Specifically, the analysis unit 112 adds an entry to the second risk analysis information 142, generates a simulation ID, and stores the simulation ID in the simulation ID 1301 of the added entry. The analysis unit 112 stores the user ID included in the simulation execution request in the user ID 1302 of the added entry, and stores the value of the analysis result ID 1201 of the entry searched in step S603 in the analysis result ID 1303. The analysis unit 112 stores the behavior change scenario included in the simulation execution request in the behavior change scenario 1304 of the added entry. In addition, the analysis unit 112 stores the calculated nursing care certification rate, the nursing care needing state period, and the nursing care cost self-payment amount in the nursing care certification rate 1311, the nursing care needing state period 1312, and the self-pay amount 1313.

Next, the analysis unit 112 calculates the amount of change in each risk index (step S608).

Specifically, the analysis unit 112 calculates the difference between each risk index included in the analysis result obtained in step S603 and each risk index included in the simulation result.

Next, the analysis unit 112 identifies a recommendation service based on the behavior change scenario (step S609). Specifically, the following processing is executed.

(S609-1) The analysis unit 112 extracts the activity purpose from the behavior change scenario, and outputs a search request including the extracted activity purpose to the recommendation unit 114. The analysis unit 112 waits for a response from the recommendation unit 114.

(S609-2) The recommendation unit 114 refers to the recommendation service category information 125 and searches the activity purpose 701 for an entry in which the extracted activity purpose is stored.

(S609-3) The recommendation unit 114 selects one searched entry.

(S609-4) The recommendation unit 114 refers to the service information 120 and searches for an entry in which the value of the service category 702 of the entry searched for in the service category 311 is stored. The recommendation unit 114 registers the searched entry in the recommendation service list.

The recommendation unit 114 executes the processes (S609-3) and (S609-4) for all entries searched from the recommendation service category information 125.

(S609-5) The recommendation unit 114 outputs a recommended service list to the analysis unit 112 as a response. The above is the explanation of the process in step S609.

Next, the analysis unit 112 generates simulation information and recommendation service information (step S610).

Specifically, the analysis unit 112 generates simulation information based on the analysis result, the simulation result, and the amount of change in each risk index. The analysis unit 112 also generates recommendation service information based on the recommendation service list.

Note that the analysis unit 112 may refer to the preferential treatment 304 and the preferential treatment details 305 based on the amount of change in the risk index, and generate recommendation service information so that the corresponding preferential contents can be displayed.

Next, the analysis unit 112 transmits simulation information and recommendation service information to the client device 101 (step S611). After that, the analysis unit 112 ends the simulation process.

Specifically, the analysis unit 112 refers to the user information 121 and searches for an entry in which the user ID included in the simulation execution request is stored in the user ID 401. The analysis unit 112 obtains the addresses of the first destination 406 and second destination 407 of the searched entry. The analysis unit 112 transmits simulation information and recommendation service information to the acquired address.

In this embodiment, the social participation support system 100 identifies the recommendation service and presents the service, but the external system 102 may also execute the process. For example, the social participation support system 100 transmits analysis results, simulation results, behavior change scenarios, and amounts of change in each risk index to the external system 102. The external system 102 identifies a service that can be provided based on each piece of information, and transmits information on the service to the social participation support system 100.

As shown in the simulation screen 2400, by presenting the recommendation service together with the simulation results, it is possible to motivate and encourage the user to change his or her behavior. Further, by presenting similar information to the user's related parties, it is possible to encourage the user to change his or her behavior through the user's related parties. In other words, it is possible to support the user's social participation. For service providers, this provides an opportunity to acquire service users.

FIG. 25 is a flowchart illustrating an example of a report generation process executed by the social participation support system 100 of the first embodiment. Here, report generation processing that is periodically executed will be described.

The report generation unit 115 starts user loop processing (step S701).

Specifically, the report generation unit 115 selects one entry from the user information 121. In the following explanation, the user corresponding to the selected entry will be referred to as a target user.

Next, the report generation unit 115 acquires the analysis result of the target user from the first risk analysis information 141 (step S702).

Specifically, the report generation unit 115 obtains an entry in which the user ID of the target user is stored in the user ID 1102. Note that only the latest entry may be acquired.

Next, the report generation unit 115 generates a report based on the target user's analysis results (step S703).

For example, the report generation unit 115 generates a report showing changes in risk indicators over time. Note that this embodiment is not limited to the content of the generated report.

Next, the report generation unit 115 transmits the report to the client device 101 (step S704).

Specifically, the report generation unit 115 refers to the user information 121 and searches for an entry in which the user ID of the target user is stored in the user ID 401. The report generation unit 115 obtains the addresses of the first destination 406 and second destination 407 of the searched entry. The report generation unit 115 transmits the report to the acquired address.

Next, the report generation unit 115 determines whether the processing has been completed for all users (step S705).

If the process has not been completed for all users, the report generation unit 115 returns to step S701 and executes the same process.

When the processing is completed for all users, the report generation unit 115 starts loop processing of the service (step S706).

Specifically, the report generation unit 115 selects one entry from the service information 120. In the following description, the service corresponding to the selected entry will be referred to as a target service.

Next, the report generation unit 115 identifies users who are using the target service (step S707).

Specifically, the report generation unit 115 obtains the user ID from the user 307 of the selected entry.

Next, the report generation unit 115 obtains the analysis results for each user from the first risk analysis information 141 (step S708).

Specifically, the report generation unit 115 obtains an entry in which the obtained user ID is stored in the user ID 1102. Note that only the latest entry may be acquired.

Next, the report generation unit 115 generates a report based on the analysis results of each user (step S709).

For example, the report generation unit 115 generates a report showing statistical values, time changes, etc. of risk indicators. This embodiment is not limited to the content of the generated report.

Next, the report generation unit 115 transmits the report to the external system 102 (step S710).

Specifically, the report generation unit 115 obtains the address of the destination 306 of the entry selected in step S706. The report generation unit 115 transmits the report to the acquired address.

Next, the report generation unit 115 determines whether processing has been completed for all services (step S711).

If the processing has not been completed for all services, the report generation unit 115 returns to step S706 and executes the same processing.

When the processing is completed for all services, the report generation unit 115 ends the report generation process.

Note that the user loop processing and the service loop processing may be executed in parallel.

Note that when a report generation request is received from a specific user, only the processes from step S702 to step S704 are executed. Furthermore, when a report generation request is received from a specific external system, only the processes from step S707 to step S710 are executed.

By transmitting a report to the user and the user's related parties, it is possible to understand the current status and time trends of the user's risk of needing care. By transmitting a report to the external system 102, it is possible to grasp the current status and time trends of the user's risk of needing care. Furthermore, the service provider can improve service quality, develop new services, strengthen competitiveness, etc. based on the report. In addition, providers can contribute to society by providing services that reduce the risk of users requiring nursing care.

Note that the present invention is not limited to the above-described embodiments, and includes various modifications. Further, for example, the configurations of the embodiments described above are explained in detail in order to explain the present invention in an easy-to-understand manner, and the present invention is not necessarily limited to having all the configurations described. Further, a part of the configuration of each embodiment can be added to, deleted from, or replaced with other configurations.

Further, each of the above-mentioned configurations, functions, processing units, processing means, etc. may be partially or entirely realized in hardware by designing, for example, an integrated circuit. Further, the present invention can also be realized by software program codes that realize the functions of the embodiments. In this case, a storage medium on which a program code is recorded is provided to a computer, and a processor included in the computer reads the program code stored on the storage medium. In this case, the program code itself read from the storage medium realizes the functions of the embodiments described above, and the program code itself and the storage medium storing it constitute the present invention. Examples of storage media for supplying such program codes include flexible disks, CD-ROMs, DVD-ROMs, hard disks, SSDs (Solid State Drives), optical disks, magneto-optical disks, CD-Rs, magnetic tapes, A non-volatile memory card, ROM, etc. are used.

Further, the program code for realizing the functions described in this embodiment can be implemented in a wide range of program or script languages such as assembler, C/C++, Perl, Shell, PHP, Python, and Java (registered trademark).

Furthermore, by distributing the software program code that realizes the functions of the embodiment via a network, it can be stored in a storage means such as a computer's hard disk or memory, or a storage medium such as a CD-RW or CD-R. Alternatively, a processor included in the computer may read and execute the program code stored in the storage means or the storage medium.

In the above-described embodiments, the control lines and information lines are those considered necessary for explanation, and not all control lines and information lines are necessarily shown in the product. All configurations may be interconnected.

100 Social participation support system 101 Client device 102 External system 103 Networks 110, 160, 170 Transmission/reception section 111 Analysis model generation section 112 Analysis section 113 Message generation section 114 Recommendation section 115 Report generation section 120 Service information 121 User information 122 Activity purpose conversion information 123 Analysis model information 124 Template information 125 Recommendation service classification information 130 Activity history information 131 Nursing care certification information 132 Nursing care receipt information 140 Activity analysis information 141 First risk analysis information 142 Second risk analysis information 150 Activity reference value information 151 Risk reference value Information 161 Social participation data acquisition unit 200 Computer 201 Processor 202 Network interface 203 Main storage device 204 Secondary storage device 2100 Analysis result display screen 2200 Advice display screen 2400 Simulation screen

Claims (12)

A computer system including at least one computer,
The at least one computer has a calculation device, a storage device connected to the calculation device, and an interface connected to the calculation device for transmitting and receiving data,
The computer system is
an analysis unit that acquires social participation data, which is data related to the user's activities, from the user and executes calculations using an analytical model that calculates a risk index for evaluating the risk of requiring care;
an output unit that outputs an analysis result including the risk index,
The social participation data includes user identification information, location information of the place where the activity was performed, and the purpose of the activity,
The analysis model is a model that calculates the risk index from the frequency of activities corresponding to each of a plurality of activity objectives,
The analysis department is
calculating the frequency of activities corresponding to each of the plurality of activity purposes by aggregating the social participation data of the target user for each activity purpose;
A computer system characterized in that the risk index of the target user is calculated by inputting the frequency of activities corresponding to each of the plurality of activity purposes into the analysis model. The computer system according to claim 1,
comprising a message generation unit that generates a message including advice for reducing the risk of requiring nursing care;
managing reference value information storing reference values of frequency of activities corresponding to each of the plurality of activity purposes of a group defined by a combination of gender and age;
The message generation unit includes:
Reference to the frequency of activity corresponding to each of the plurality of activity purposes of the target user and the frequency of activity corresponding to each of the plurality of activity purposes of the group to which the target user belongs, obtained from the reference value information. Calculate the difference between the values and
identifying the activity purpose for which the difference is large, and generating the message including, as the advice, an improvement plan for the activity corresponding to the identified activity purpose;
A computer system, wherein the output unit transmits the message to the target user. The computer system according to claim 1,
comprising a recommendation section that outputs recommendation service information indicating services that support behavior change to reduce the risk of requiring nursing care;
When the analysis unit receives a behavior change scenario that includes a target activity purpose to be subjected to behavior change and a target frequency of the activity corresponding to the target activity purpose, the analysis unit includes a target frequency of the activity corresponding to the target activity purpose, and a target frequency of the activity corresponding to the target activity purpose. calculating an estimated risk index by inputting into the analysis model the frequency of activities corresponding to activity purposes other than the target activity purpose;
The recommendation unit identifies the service that supports behavior change corresponding to the behavior change scenario,
A computer system, wherein the output unit transmits a simulation result including the estimated risk index and the recommendation service information to the target user. The computer system according to claim 1,
comprising a report generation unit that generates a report using the analysis results,
Manages service information that stores data relating service content, service providers, and users who use the service;
The report generation unit includes:
generating a first report using the analysis results of the user;
generating a second report using the analysis result of the user who uses the service with reference to the service information;
The output section is
transmitting the first report to at least one of the user and a person related to the user;
A computer system characterized in that the second report is transmitted to the service provider. The computer system according to claim 1,
The computer system is characterized in that the analysis unit calculates at least one of the probability of being certified as requiring nursing care, the period during which the state of requiring nursing care continues, and the self-pay amount of nursing care costs as the risk index. The computer system according to claim 1,
The analysis unit calculates the analysis model using the frequency of activities corresponding to each of the plurality of activity purposes calculated from the social participation data, information regarding the user's certification of need for care, and the user's care receipt. A computer system characterized by executing processing for generating data. A social participation support method executed by a computer system including at least one computer, the method comprising:
The at least one computer has a calculation device, a storage device connected to the calculation device, and an interface connected to the calculation device for transmitting and receiving data,
The social participation support method is
a first step in which the at least one computer acquires social participation data, which is data related to the user's activities, from the user;
a second step in which the at least one computer executes an operation using an analytical model to calculate a risk index for evaluating the risk of requiring nursing care, and outputs an analysis result including the risk index;
The social participation data includes user identification information, location information of the place where the activity was performed, and the purpose of the activity,
The analysis model is a model that calculates the risk index from the frequency of activities corresponding to each of a plurality of activity objectives,
The second step is
the at least one computer calculating the frequency of activities corresponding to each of the plurality of activity purposes by aggregating the social participation data of the target user for each activity purpose;
A society characterized in that the at least one calculator calculates the risk index of the target user by inputting frequency of activities corresponding to each of the plurality of activity purposes into the analysis model. How to support participation. The social participation support method according to claim 7,
The computer system manages reference value information that stores reference values of frequency of activities corresponding to each of the plurality of activity purposes of groups defined by a combination of gender and age,
The social participation support method includes a third step in which the at least one computer generates a message including advice for reducing the risk of needing care;
The third step is
The at least one calculator calculates the activity frequency corresponding to each of the plurality of activity purposes of the target user and each of the plurality of activity purposes of a group to which the target user belongs, which is obtained from the reference value information. calculating the difference between the frequency of the corresponding activity and a reference value;
the step of the at least one computer identifying the activity purpose for which the difference is large, and generating the message including, as the advice, an improvement plan for the activity corresponding to the identified activity purpose;
A method for supporting social participation, comprising the step of: the at least one computer transmitting the message to the target user. The social participation support method according to claim 7,
When the at least one computer receives a behavior change scenario that includes a target activity purpose to be subject to behavior change and a target frequency of the activity corresponding to the target activity purpose, , a frequency of activities corresponding to an activity purpose other than the target activity purpose, and calculating an estimated risk index by inputting into the analysis model;
the at least one computer identifying a service that supports behavior change corresponding to the behavior change scenario;
A method for supporting social participation, comprising the step of: the at least one computer transmitting simulation results including the estimated risk index and recommendation service information indicating the identified service to the target user. The social participation support method according to claim 7,
The computer system manages service information that stores data that associates service contents, service providers, and users who use the service,
The social participation support method is
the at least one computer generating a first report using the analysis results of the user;
The at least one computer refers to the service information and generates a second report using the analysis result of the user who uses the service;
the at least one computer transmitting the first report to at least one of the user and a person related to the user;
A method for supporting social participation, comprising the step of: the at least one computer transmitting the second report to the service provider. The social participation support method according to claim 7,
Social participation support characterized in that the at least one calculator calculates at least one of the probability of being certified as requiring nursing care, the period during which the state of requiring nursing care continues, and the self-pay amount of nursing care costs as the risk index. Method. The social participation support method according to claim 7,
The at least one computer performs the analysis using the frequency of activities corresponding to each of the plurality of activity purposes calculated from the social participation data, information regarding the user's nursing care requirement certification, and the user's nursing care receipt. A method for supporting social participation, comprising the step of executing processing for generating a model.

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