Information processing system, control program, and control method

Abstract

To provide an information processing system that can identify evaluation results that require reevaluation in quantitative evaluation of a subject's condition based on care records and the like.SOLUTION: An information processing system includes: an acquisition unit that acquires first data regarding evaluation of a subject's condition; a storage unit that stores second data regarding evaluation of the subject's condition before the first data is acquired; and a data identification unit that determines a correspondence between the first data and the second data and, based on the determination result, identifies the first data that needs to be re-evaluated.SELECTED DRAWING: Figure 13

Description

[Technical Field]

[0001] The present invention relates to an information processing system, a control program, and a control method. [Background technology]

[0002] Japan has seen a remarkable increase in life expectancy due to improvements in living standards, sanitary conditions, and medical standards that came with the rapid economic growth after the war. This, combined with a declining birth rate, has led to an aging society with a high aging rate. In such an aging society, it is expected that the number of people who require care, such as nursing care, due to illness, injury, and aging, will increase.

[0003] In nursing care facilities, multiple care staff work in shifts to provide care to one patient. Care records, such as nursing records, are created by the care staff so that the care staff can keep track of each patient's condition. Care records may be created in any written format. In some cases, the care records are required to include a quantitative assessment of each patient's condition to demonstrate the effectiveness of the care plan.

[0004] However, quantitative assessment of each subject's condition is often conducted by a collaborative assessment between care staff and a care manager, or by care staff alone, which can lead to variation and reduce the reliability of the assessment results.

[0005] The following prior art is disclosed in the following patent document: Fixed phrase information that sets fixed phrases to be used for entering care information is stored in a storage unit. Fixed phrases are displayed in the care record field of the care record input screen so that they can be selected using a pull-down menu. The fixed phrase selected by the caregiver is displayed in the care record field. Input is accepted into variable input fields included in the fixed phrase. Then, the care record in which input has been made into the variable input fields is stored in the care record information. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Publication No. 2022-103155 Summary of the Invention [Problem to be solved by the invention]

[0007] However, the care records may not include information necessary for quantitatively assessing the condition of each subject, which may reduce the accuracy of the quantitative assessment of the condition of each subject based on the care records. Furthermore, when the condition of a subject changes, the accuracy of the quantitative assessment of the condition of each subject based on the care records may also reduce. The above-mentioned patent documents cannot address these problems.

[0008] The present invention has been made to solve such problems, and aims to provide an information processing system, a control program, and a control method that can identify evaluation results that require reevaluation in quantitative evaluation of a subject's condition based on care records, etc. [Means for solving the problem]

[0009] The above-mentioned problems of the present invention are solved by the following means.

[0010] (1) An information processing system having an acquisition unit that acquires first data regarding an evaluation of a subject's condition, a memory unit that stores second data regarding an evaluation of the subject's condition before the first data is acquired, and a data identification unit that determines the correspondence between the first data and the second data and, based on the determination result, identifies the first data that needs to be reevaluated.

[0011] (2) The information processing system described in (1) above, wherein the first data is a care record, sensor analysis data obtained by analyzing data obtained from a sensor monitoring the condition of the subject, or care record analysis data obtained by analyzing the care record.

[0012] (3) The information processing system described in (1) above, wherein the second data is data regarding past life or data regarding assessment, and the data identification unit compares the trend of the first data with the trend of the second data, and if the trend of the first data differs from the trend of the second data, identifies the first data as data that requires reevaluation.

[0013] (4) The information processing system described in (1) above, wherein the second data is data of the same type as the first data for the subject, and the data identification unit compares the second data with the first data, and if there is a difference between the second data and the first data, identifies the first data as data that requires reevaluation.

[0014] (5) The information processing system according to (1) above, further comprising an alert notification unit that issues an alert for the first data determined by the data identification unit to require reevaluation.

[0015] (6) The information processing system described in (4) above, further comprising a subject identification unit that identifies the subject from whom the first data was obtained as a subject to reevaluation when the difference between the first data and the second data is greater than or equal to a predetermined threshold.

[0016] (7) The information processing system according to (6) above, further comprising a reevaluation target notification unit that notifies the reevaluation target identified by the target identification unit.

[0017] (8) (a) acquiring first data relating to an evaluation of the subject's condition; (b) storing second data relating to an evaluation of the subject's condition before acquiring the first data; and (c) determining a correspondence between the first data and the second data and, based on the determination result, identifying the first data that needs to be reevaluated. A control program for causing a computer to execute a process having the above.

[0018] (9) The control program described in (8) above, wherein the first data is a care record, sensor analysis data obtained by analyzing data obtained from a sensor monitoring the condition of the subject, or care record analysis data obtained by analyzing the care record.

[0019] (10) The control program described in (8) above, wherein the second data is data regarding past life or assessment, and in step (c), the trend of the first data is compared with the trend of the second data, and if the trend of the first data differs from the trend of the second data, the first data is identified as data that requires reevaluation.

[0020] (11) The control program described in (8) above, wherein the second data is the same type of data as the first data for the subject, and in step (c), the second data is compared with the first data, and if there is a difference between the second data and the first data, the first data is identified as data that requires reevaluation.

[0021] (12) The control program according to (8) above, wherein the process further comprises a step (d) of issuing an alert for the first data determined to require reevaluation in the step (c).

[0022] (13) The control program described in (11) above, wherein the processing further includes a step (e) of identifying the subject from whom the first data was obtained as a subject to reevaluation if the difference between the first data and the second data is greater than or equal to a predetermined threshold.

[0023] (14) The control program according to (13) above, wherein the process further comprises a step (f) of notifying the person to be reevaluated identified in the step (e).

[0024] (15) A control method executed by an information processing system, comprising: a step (a) of acquiring first data relating to an evaluation of a subject's condition; a step (b) of storing second data relating to an evaluation of the subject's condition prior to acquiring the first data; and a step (c) of determining a correspondence between the first data and the second data and, based on the determination result, identifying the first data that needs to be reevaluated.

[0025] (16) The control method described in (15) above, wherein the first data is a care record, sensor analysis data obtained by analyzing data obtained from a sensor monitoring the condition of the subject, or care record analysis data obtained by analyzing the care record.

[0026] (17) The control method described in (15) above, wherein the second data is data regarding past LIFE or data regarding assessment, and in step (c), the trend of the first data is compared with the trend of the second data, and if the trend of the first data differs from the trend of the second data, the first data is identified as data that requires reevaluation.

[0027] (18) The control method described in (15) above, wherein the second data is data of the same type as the first data for the subject, and in step (c), the second data is compared with the first data, and if there is a difference between the second data and the first data, the first data is identified as data that requires reevaluation.

[0028] (19) The control method according to (15) above, further comprising the step (d) of issuing an alert for the first data determined to require reevaluation in the step (c).

[0029] (20) The control method described in (18) above, further comprising a step (e) of identifying the subject from whom the first data was obtained as a subject to reevaluation if the difference between the first data and the second data is greater than or equal to a predetermined threshold.

[0030] (21) The control method described in (20) above, further comprising a step (f) of notifying the reevaluation target person identified in step (e). [Effects of the Invention]

[0031] First data relating to an evaluation of the subject's condition is acquired, and a correspondence relationship between the first data and stored second data relating to the evaluation of the subject's condition before the acquisition of the first data is determined. Then, based on the determination result, the first data that requires reevaluation is identified. This makes it possible to identify evaluation results that require reevaluation for quantitative evaluation of the subject's condition based on nursing records, etc. [Brief explanation of the drawings]

[0032] [Figure 1] FIG. 1 is a diagram illustrating an overall configuration of an information processing system. [Figure 2] FIG. 2 is a block diagram showing a schematic configuration of a server. [Figure 3] FIG. 2 is a block diagram showing the functions of a control unit. [Figure 4] FIG. 2 is an explanatory diagram showing information processing of some functions of a control unit. [Figure 5] FIG. 1 is a diagram showing the 13 types of items in DBD13. [Figure 6] FIG. 10 is an explanatory diagram showing an example of a determination of applicability of items in DBD13 for each care record. [Figure 7] FIG. 10 is a diagram showing an example of a graph showing the relationship between the number of hits for one item of DBD13 and the known condition of a subject. [Figure 8] 10 is an explanatory diagram for explaining an evaluation of a subject's condition based on a corresponding determination result using an approximate formula. FIG. [Figure 9] FIG. 10 is a diagram showing ADL items included in data related to LIFE. [Figure 10] FIG. 2 is a block diagram showing a hardware configuration of a detection unit. [Figure 11] FIG. 2 is a block diagram showing a schematic configuration of a mobile terminal. [Figure 12]10 is a flowchart illustrating an example of the operation of the server. [Figure 13] 10 is a flowchart illustrating another example of the operation of the server. [Figure 14] 10 is a flowchart showing yet another example of the operation of the server. DETAILED DESCRIPTION OF THE INVENTION

[0033] A condition assessment device, a condition assessment method, and a condition assessment program according to an embodiment of the present invention will be described below with reference to the drawings. In the drawings, identical elements are designated by the same reference numerals, and duplicate explanations will be omitted. Also, the dimensional proportions in the drawings are exaggerated for the sake of explanation, and may differ from the actual proportions.

[0034] In this specification, the subject includes a care recipient who receives care such as nursing care or caregiving. The subject includes, for example, a person requiring nursing care, a person requiring support, and a patient. For simplicity of explanation, the following description will be given taking the case where the subject is a person requiring nursing care as an example.

[0035] FIG. 1 is a diagram showing the overall configuration of an information processing system 1. As shown in FIG. 1, the information processing system 1 includes a server 10, a fixed terminal 20, one or more mobile terminals 30, and one or more detection units 40. These are connected to each other so as to be able to communicate with each other via a network 70, such as a LAN (Local Area Network), a telephone network, or a data communication network, either wired or wirelessly. The network 70 may include a repeater that relays communication signals. In the example shown in FIG. 1, the server 10, the fixed terminal 20, the mobile terminal 30, and the detection unit 40 are connected to each other so as to be able to communicate with each other via a network 70, such as a wireless LAN (for example, a LAN conforming to the IEEE 802.11 standard) that includes an access point 71.

[0036] The mobile terminal 30 may be carried by each of the care staff 50 who care for the subject 80.

[0037] The detection unit 40 may be provided in each subject 80's room, for example.

[0038] The information processing system 1 may be configured by the server 10 alone.

[0039] The server 10 may be configured as a cloud server or an on-premise server installed in a building such as a nursing home or hospital.

[0040] The fixed terminal 20 is installed in a building such as a welfare facility for the elderly or a hospital.

[0041] (Server 10) 2 is a block diagram showing a schematic configuration of the server 10. The server 10 includes a control unit 11, a communication unit 12, and a storage unit 13. These components are interconnected by a bus.

[0042] The control unit 11 is configured with a CPU (Central Processing Unit) and memories such as RAM (Random Access Memory) and ROM (Read Only Memory), and controls each part of the server 10 and performs arithmetic processing according to a program.

[0043] The communication unit 12 is an interface for communicating with other devices including, for example, the fixed terminal 20 and the mobile terminal 30 via the network 70, and may be, for example, a LAN card.

[0044] The storage unit 13 is configured by a hard disk drive (HDD), a solid state drive (SSD), etc. The storage unit 13 stores various programs and various data.

[0045] Fig. 3 is a block diagram showing the functions of the control unit 11. Fig. 4 is an explanatory diagram showing information processing of some functions of the control unit 11.

[0046] By executing a program, the control unit 11 functions as an acquisition unit 111, a classification unit 112, an evaluation unit 113, a display control unit 114, a learning unit 115, an approximate formula creation unit 116, an identification unit 117, and a notification unit 118. The identification unit 117 constitutes a data identification unit and a subject identification unit. The notification unit 118 constitutes an alert notification unit and a reevaluation subject notification unit.

[0047] The acquisition unit 111 acquires data related to the evaluation of the condition of the subject 80. Hereinafter, the data related to the evaluation of the condition of the subject 80 is also referred to as "evaluation-related data." The evaluation-related data constitutes first data. The evaluation-related data includes at least one of a care record, data obtained by analyzing data obtained from the detection unit 40, and data obtained by analyzing the care record. Hereinafter, data obtained by analyzing data obtained from the sensor of the detection unit 40 is also referred to as "sensor analysis data." Data obtained by analyzing the care record is also referred to as "care record analysis data."

[0048] The acquisition unit 111 can acquire care records created in fixed sentences or non-fixed sentences. The care records can be input by the care staff 50 into each mobile terminal 30 and received by the communication unit 12. The care records are input by the care staff 50 into the mobile terminal 30 for each subject 80 at least once a day. That is, the acquisition unit 111 accepts one or more care records for one subject.

[0049] The acquisition unit 111 may acquire sensor analysis data from the detection unit 40. When data obtained from the sensors of the detection unit 40 is analyzed by the evaluation unit 113, the acquisition unit 111 may acquire the sensor analysis data from the evaluation unit 113. As described below, the sensors of the detection unit 40 may include a camera 44 and a body movement sensor 45 (see FIG. 11 ). The sensor analysis data includes, for example, the life rhythm, wandering, and repetitive behavior of the subject 80. The life rhythm includes, for example, daytime sleeping hours, nighttime sleeping hours, and time spent confined to one's room.

[0050] The acquisition unit 111 may acquire the care record analysis data from the evaluation unit 113. The care record analysis data includes the condition of the subject. The condition of the subject is, for example, a score for each item of the Dementia Behavior Disturbance Scale (DBD) 13 described later.

[0051] As shown in FIG. 4, the classification unit 112 may determine whether a predetermined label applies to each acquired care record and output the result. That is, the classification unit 112 classifies the care record into the corresponding label by determining whether the care record applies to the predetermined label. One care record may be classified into one or more labels. The classification unit 112 includes a machine-learned language model. Any language model may be used as long as it determines whether a predetermined label applies to each input care record and outputs the result. For example, BERT (Bidirectional Encoder Representations from Transformers) is used as the language model. The predetermined labels include any labels corresponding to the evaluations of the subject 80. For simplicity, the following description will be given taking the case where the predetermined labels are the 13 items of the DBD 13 as an example. The DBD 13, which is the Dementia Behavioral Disorder Scale, is an evaluation index that can concisely detect peripheral symptoms (behavioral and psychological symptoms) of dementia. As described below, the language model is machine-learned by the learning unit 115. Hereinafter, the machine-learned language model will also be referred to as a "classification model."

[0052] Figure 5 shows the 13 types of items in the DBD13. Examples of DBD13 items include 2. "Frequently loses, misplaces, or hides things" (Loss, etc.) and 3. "Shows no interest in everyday things" (Indifference). Other DBD13 items include 4. "Wakes up in the middle of the night for no particular reason" (Night-time waking) and 7. "Walks around a lot" (Wandering around). Other DBD13 items include 8. "Repeated the same actions over and over" (Repetitive behavior) and 9. "Uses abusive language" (Abusive language). Other DBD13 items include 10. "Wears inappropriate clothing that is out of place or out of season" (Inappropriate clothing). Other DBD13 items include 11. "Refuses to be cared for" (Rejective behavior).

[0053] The classification unit 112 determines whether or not "asking the same thing over and over again" applies to item 1 of the DBD 13 for each care record and outputs the result. For example, if "asking the same thing over and over again" applies, it outputs "1", and if not, it outputs "0". The classification unit 112 also determines whether or not items 2 to 13 of the DBD 13 apply in the same way and outputs the result.

[0054] FIG. 6 is an explanatory diagram showing an example of the applicability determination of the items of the DBD 13 for each care record.

[0055] In the example shown in Figure 6, the care record includes the time the care record was entered, the record type, and the record text. The care record in the upper row of Figure 6 includes the following non-standard text: "The patient rested well except for going to the bathroom until 11:00 PM, but from 11:00 PM, he wandered around the hallway and peeked out from his room door for about 30 minutes." The care record also includes the following non-standard text: "I tried to encourage him to fall asleep repeatedly, but he refused to listen." Based on these texts, the care record was classified as falling under items 4, 7, and 8 of DBD13. The classification model is thought to have classified the care record into items 4, 7, and 8 of DBD13 by specifically responding to the bolded parts of the text shown in Figure 6. The care record in the lower row of Figure 6 includes the following non-standard text: "The patient tried to remove his jacket and pants while on the floor." The care record also includes the following non-standard text: "I watched over him to make sure he didn't remove his clothes while on the floor." From these records, the care record has been classified as falling under item 10 of DBD13. It is believed that the classification model reacted particularly to the parts written in bold in the records shown in Figure 6, and classified the care record as item 10 of DBD13.

[0056] The classification unit 112 determines whether each item in the DBD 13 applies to one or more care records for a predetermined period. The predetermined period is a period of fixed length, such as one year, but may also be an indefinite period of time. For simplicity of explanation, the following description will be given taking the case where the predetermined period is one year as an example.

[0057] The classification unit 112 can calculate and output the number of hits (number of occurrences) of each item in the DBD 13 for each subject 80 over a predetermined period of one year. That is, the classification unit 112 calculates, for example, the number of hits of each item in the DBD 13 for subject A over one year. The classification unit 112 can output the number of hits of each item in the DBD 13 for each subject 80 over one year as a hit / miss determination result.

[0058] The evaluation unit 113 evaluates the condition of the subject 80 based on the result of the suitability determination. The evaluation unit 113 can evaluate the condition of the subject 80 based on the result of the suitability determination using an approximation formula. The condition of the subject 80 is, for example, a score for each item in the DBD 13. The score for each item in the DBD 13 can be any value between 0 and 4. In this case, a score of "0" corresponds to an evaluation of "never." A score of "1" corresponds to an evaluation of "almost never." A score of "2" corresponds to an evaluation of "occasionally." A score of "3" corresponds to an evaluation of "often." A score of "4" corresponds to an evaluation of "always." The approximation formula can be a formula that specifies the relationship between the number of times an item in the DBD 13 applies in a year (the suitability determination result) and the score for that item (the condition of the subject 80). As will be described later, the approximation formula is created by the approximation formula creation unit 116. The approximation formula is created from the relationship between the number of hits of a predetermined label for a subject 80 whose status is known, over a predetermined period of one year, and the status of the known subject 80. In this embodiment, the approximation formula is created from the relationship between the number of hits of one item in the DBD13 for a subject 80 whose status is known, over a period of one year, and the status of the known subject 80. The approximation formula is created for each item in the DBD13. Note that the calculation of the number of hits of each item in the DBD13 for each subject 80 over a period of one year by the classifying unit 112 based on the output of the classification model, as described above, may be performed by the evaluating unit 113.

[0059] The display control unit 114 transmits the status (evaluation result) of the subject 80 evaluated by the evaluation unit 113 to the mobile terminal 30 in association with information identifying the subject 80. The information identifying the subject 80 may include, for example, the name of the subject 80, the room number, etc. The display control unit 114 thereby causes the mobile terminal 30 to display the status of the subject 80 for each subject 80. The display control unit 114 may also transmit the number of hits for each item of the DBD 13 for a predetermined period of one year to the mobile terminal 30 along with the status of the subject 80 evaluated by the evaluation unit 113. The display control unit 114 thereby causes the mobile terminal 30 to display the status of the subject 80 for each subject 80 along with the number of hits for each item of the DBD 13 for a predetermined period of one year.

[0060] The learning unit 115 performs machine learning on a language model to generate a classification model for the classification unit 112. The learning unit 115 can machine learn a language model using a combination of care records for a predetermined period of one year of multiple subjects 80 and the applicability information for each item in the DBD 13 of each care record corresponding to the care record.

[0061] The approximate formula creation unit 116 creates the above-mentioned approximate formula. The approximate formula creation unit 116 creates the approximate formula from the relationship between the number of hits of one item in the DBD 13 over a one-year period for multiple subjects 80 whose conditions are known and the conditions of the known subjects 80. The approximate formula creation unit 116 can create an approximate formula for each item in the DBD 13. The approximate formula creation unit 116 can create an approximate formula by linear approximation.

[0062] FIG. 7 is a diagram showing an example of a graph showing the relationship between the number of hits for one item of DBD13 and the state of a known subject 80. In the example of FIG. 7, a graph showing the relationship between the number of hits (number of hits) for item 9 (abusive language) of DBD13 over a one-year period and the evaluation results for item 9 (abusive language) of DBD13 is shown. Multiple plots (dots) in the graph show the relationship between the number of hits for item 9 of DBD13 over a one-year period for multiple subjects 80 and the state of the subjects 80. Based on the relationship (plots), an approximate equation can be created by linear approximation. The approximate equation is shown by a solid line in the graph.

[0063] FIG. 8 is an explanatory diagram for explaining the evaluation of the state of the subject 80 based on the result of the judgment using an approximate formula.

[0064] Using the approximation formula, the score for item 9 of DBD13 of the subject 80, which corresponds to the number of times that item 9 of DBD13 was hit in one year, can be calculated as the evaluation result. As shown in Figure 9, if the number of times that item 9 of DBD13 was hit in one year is 38, the score for item 9 of DBD13 of the subject 80 is calculated to be 3.9.

[0065] The identification unit 117 determines the correspondence between the evaluation-related data acquired by the acquisition unit 111 and data related to the evaluation of the subject 80's condition before the evaluation-related data was acquired. Hereinafter, data related to the evaluation of the subject 80's condition before the evaluation-related data was acquired will also be referred to as "past data." The past data constitutes "second data." The identification unit 117 identifies evaluation-related data that needs to be re-evaluated based on the determination result of the correspondence between the evaluation-related data and the past data.

[0066] Past data includes, for example, data related to past LIFE (Long-term care Information system For Evidence) and data related to assessments. LIFE is an information system in which information such as the care plans and content provided by nursing care facilities and businesses and the condition of nursing care service users is sent in a specific format to the Ministry of Health, Labor and Welfare, where the data is analyzed and fed back. Assessment data is an evaluation of the condition of the subject 80, conducted when the subject 80 moves into a facility, etc.

[0067] FIG. 9 is a diagram showing ADL (Activities of Daily Living) items included in the data related to LIFE.

[0068] An example of an ADL item is "walking on level ground." Each ADL item is evaluated as "independent," "partial assistance," or "complete assistance."

[0069] The past data can be stored in the storage unit 13.

[0070] The identification unit 117 compares the trend of the evaluation-related data with the trend of the past data, and if the trend of the evaluation-related data differs from the trend of the past data, identifies the evaluation-related data as data that requires reevaluation. The identification unit 117 may identify the evaluation-related data that requires reevaluation based on a rule. The rule may be stored in advance in the storage unit 13 as a relationship between the evaluation-related data and the past data. The identification unit 117 may identify the evaluation-related data that requires reevaluation from the evaluation-related data and the past data using a trained model of a neural network. Specifically, if the evaluation-related data is a score for each item of the DBD 13 included in the care record analysis data and the past data is an evaluation for each ADL item, the identification unit 117 identifies the evaluation-related data that requires reevaluation, for example, as follows.

[0071] The subject 80 was evaluated as anything other than "independent" in the ADL item "walking on flat ground." The subject 80 was evaluated as anything other than "0" in the item 7 "walking around excessively" in DBD13. In this case, the identification unit 117 identifies the item "walking around excessively" in DBD13 as evaluation-related data that requires reevaluation. This is because the subject 80 whose "walking on flat ground" is not "independent" is unlikely to walk around excessively.

[0072] Subject 80 was rated "total assistance" for a number of ADL items above the specified threshold. Subject 80 received a rating other than "0" for DBD13 items 2, 3, 7, 8, 10, 12, and 13. DBD13 item 2 is "Frequently loses, misplaces, or hides things." DBD13 item 3 is "Shows no interest in everyday things." DBD13 item 7 is "Panders around excessively." DBD13 item 8 is "Performs the same actions over and over again." DBD13 item 10 is "Wears inappropriate clothing that is out of place or out of season." DBD13 item 12 is "Hoards things for no apparent reason." DBD13 item 13 is "Empties the contents of drawers and dressers." In this case, the identifying unit 117 identifies items 2, 3, 7, 8, 10, 12, and 13 of the DBD 13 as evaluation-related data that require reevaluation. This is for the following reason: the subject 80 in this example requires "full assistance" for most ADL items, and his / her personal care is almost entirely left to the care staff 50. For such a subject 80, it is considered appropriate to assign an evaluation of "0" to items 2, 3, 7, 8, 10, 12, and 13 of the DBD 13. The predetermined threshold can be appropriately set through experiments in terms of the detection accuracy of evaluation-related data that requires reevaluation.

[0073] The subject 80 was evaluated as requiring "full assistance" or "partial assistance" for a number of ADL items exceeding a predetermined threshold. In the DBD13 evaluation for the subject 80, there was an item for which the score improved by two or more points from the previous evaluation. In this case, the identification unit 117 identifies the item for which the score improved by two or more points from the previous evaluation as evaluation-related data requiring reevaluation. This is for the following reason: the subject 80 in this example is evaluated as requiring "full assistance" or "partial assistance" for most ADL items, and most of his or her personal care is left to the care staff 50. Such a subject 80 typically deteriorates gradually in the DBD13 evaluation, so it is highly likely that a significant improvement would be a mistake.

[0074] When the evaluation-related data is sensor analysis data, the identifying unit 117 identifies the evaluation-related data that needs to be re-evaluated, for example, as follows.

[0075] The subject 80 is evaluated as requiring "full assistance" or "partial assistance" for a number of ADL items equal to or greater than a predetermined threshold. Wandering is detected in the sensor analysis data for the subject 80. In this case, the identification unit 117 identifies the sensor analysis data of wandering as evaluation-related data that requires reevaluation. This is for the following reason. In this example, the subject 80 is evaluated as requiring "full assistance" or "partial assistance" for most ADL items, and most of his or her personal care is left to the care staff 50. It is considered unlikely that such a subject 80 will wander.

[0076] When the evaluation-related data is a care record, the identifying unit 117 identifies the evaluation-related data that needs to be reevaluated, for example, as follows.

[0077] The subject 80 was evaluated as "fully assisted" for a number of ADL items equal to or greater than a predetermined threshold. The care record includes a manual evaluation of the DBD 13 by the care staff 50, and a rating other than "0" was given for item 7 of the DBD 13, "walking around excessively." In this case, the identification unit 117 identifies item 7 of the DBD 13, "walking around excessively," as evaluation-related data that requires reevaluation. This is for the following reason. In this example, the subject 80 is evaluated as "fully assisted" for most ADL items, and his / her personal care is almost entirely left to the care staff 50. For such a subject 80, it is considered appropriate to evaluate item 7 of the DBD 13, "walking around excessively," as "0."

[0078] When both the evaluation-related data and the past data are scores for each item of DBD13 included in the care record analysis data, the identification unit 117 identifies the evaluation-related data that needs to be reevaluated, for example, as follows: This case corresponds to the case where the first data and the second data are the same type of data.

[0079] A comparison of the evaluation in DBD13, which is evaluation-related data, with the evaluation in DBD13, which is past data, reveals a difference. In this case, the identifying unit 117 identifies the item with the difference as evaluation-related data that requires re-evaluation. This is because if there is a difference from the past evaluation result, there is a possibility that the evaluation result in DBD13, which is evaluation-related data, is incorrect.

[0080] The identification unit 117 compares the evaluation-related data with past evaluation-related data. If the difference between the evaluation-related data and the past evaluation-related data is equal to or greater than a predetermined threshold, the identification unit 117 can identify the subject 80 from whom the evaluation-related data was acquired as a subject to be re-evaluated. The past evaluation-related data is preferably evaluation-related data acquired most recently before the evaluation-related data was acquired. The predetermined threshold can be appropriately set through experiments from the perspective of the accuracy of detecting subjects 80 who require re-evaluation.

[0081] The notification unit 118 issues an alert for the evaluation-related data identified by the identification unit 117. Specifically, the notification unit 118 transmits, as an alert, notification information for notifying the mobile terminal 30 or the fixed terminal 20 that the evaluation-related data identified by the identification unit 117 is data that requires reevaluation. The alert may be transmitted simultaneously with the state (evaluation result) of the subject 80 transmitted by the display control unit 114.

[0082] The notification unit 118 notifies the reevaluation target person identified by the identification unit 117. Specifically, the notification unit 118 transmits contact information to the mobile terminal 30 or the fixed terminal 20 for contacting the reevaluation target person identified by the identification unit 117 as a target person 80 who is recommended to reevaluate the target person 80's condition. The contact information may be transmitted simultaneously with the condition (evaluation result) of the target person 80 transmitted by the display control unit 114.

[0083] (Detection unit 40) FIG. 10 is a block diagram showing the hardware configuration of the detection unit 40. As shown in FIG.

[0084] As shown in Fig. 10, the detection unit 40 includes a control unit 41, a communication unit 42, a memory unit 43, a camera 44, and a body motion sensor 45, and these components are interconnected by a bus. Each component may be mounted in a single housing or in a separate housing. The camera 44 and the body motion sensor 45 may be disposed on the ceiling of a room, for example. The camera 44 and the body motion sensor 45 may also be disposed on the upper part of a wall or attached to a bed.

[0085] The control unit 41 includes a CPU, RAM, ROM, etc. The control unit 41 controls and performs calculations on each part of the detection unit 40 according to a program. The functions of the control unit 41 will be described in detail later.

[0086] The communication unit 42 is an interface circuit (for example, a LAN card, a wireless communication circuit, etc.) for communicating with, for example, the server 10 via a LAN.

[0087] The storage unit 43 includes an HDD and / or an SSD, etc. The storage unit 43 stores various programs and various data.

[0088] The camera 44 may capture an image of a photographing area including the bed in the room of the subject 80, for example, and output a photographed image (image data). The camera 44 may be configured to include an imaging optical system and a two-dimensional image sensor. The photographed image includes a still image and a video. The camera 44 may be, for example, a visible light camera or a near-infrared camera. Hereinafter, the photographed image captured by the camera 44 may also be simply referred to as a "photographed image."

[0089] The body movement sensor 45 transmits microwaves to a predetermined irradiation area and receives reflected waves, thereby detecting the Doppler shift of the microwaves caused by body movement (e.g., breathing) of the subject 80 as body movement. The predetermined irradiation area is an area where microwaves can be irradiated onto the subject 80, and may be, for example, an area including all or part of the bed. The body movement sensor 45 detects chest movement (up and down movement of the chest) associated with breathing of the subject 80. The body movement sensor 45 can then detect abnormal slight body movement by detecting a disruption in the period of the chest body movement or an amplitude of the chest body movement that is below a preset threshold.

[0090] The function of the control unit 41 will now be described.

[0091] The control unit 41 recognizes the behavior of the subject 80 from the captured image. The recognized behaviors include, for example, getting up, getting out of bed, getting out of a seat, falling, dropping, walking, and going out. The recognized behaviors may further include wandering and repetitive behaviors.

[0092] The control unit 41 detects image silhouettes (hereinafter referred to as "human silhouettes") from multiple captured images (e.g., video images). Human silhouettes can be detected, for example, by extracting a range of pixels with relatively large differences using time subtraction, which subtracts images captured at different times. Human silhouettes may also be detected using background subtraction, which subtracts a background image from a captured image. Human silhouettes may also be replaced by joint points detected from captured images using a trained neural network model.

[0093] The control unit 41 can detect the behavior and state of the subject 80 from temporal changes in the posture (e.g., standing, sitting, and lying down) of the subject 80 recognized based on the human silhouette. The control unit 41 may also detect the behavior and state of the subject 80 from the relative positional relationship between the human silhouette and objects in the room, such as the bed 90. For example, "waking up" can be detected when the human silhouette crosses the area of ​​the bed 90, which is preset as a coordinate area in the captured image. "Sleeping" can be detected when the human silhouette is included in the area of ​​the bed 90. "Sleeping" can also be detected based on body movement detected by the body movement sensor 45. "Wandering" within the room can be detected when the human silhouette continues to move for a predetermined period of time. "Wandering" outside the room can be detected when the human silhouette is not detected for a predetermined period of time and by visual confirmation by the care staff 50. "Solitary self-isolation" can be detected when the human silhouette continues to be detected for more than a predetermined period of time. "Repeated behavior" can be detected when a human silhouette is repeatedly detected and not detected a predetermined number of times or more in any of a plurality of pre-set partition ranges.

[0094] When the control unit 41 detects a predetermined behavior that has been set in advance, it transmits an event notification including the detected predetermined behavior, a unique ID (e.g., MAC address or IP address) that identifies the detection unit 40, and the time of detection to the server 10 via the communication unit 42. The event notification is a notification including information such as the behavior of the subject 80 that should be notified to the care staff 50 or the like. The predetermined behavior includes, for example, the subject 80 getting up, getting out of bed, getting out of a seat, falling, slipping, walking, and going out. The recognized behavior may further include wandering and repetitive behavior.

[0095] The event notification may include the name of the subject 80 instead of the unique ID that identifies the detection unit 40. In this case, the control unit 41 may identify the name of the subject 80 based on the ID, using a table that is stored in advance in the storage unit 43 and that registers the correspondence between the unique ID that identifies the detection unit 40 and the name of the subject 80.

[0096] The predetermined behavior and state of the subject 80 may be detected by the server 10. In this case, the control unit 41 transmits the captured image and a signal of the body movement detected by the body movement sensor 45 to the server 10.

[0097] (Fixed terminal 20) The fixed terminal 20 is a PC (Personal Computer) and includes a control unit, a storage unit, a display unit, a communication unit, an input unit, etc. The functions of the control unit, storage unit, and communication unit are similar to those of the corresponding components of the server 10, and therefore description thereof will be omitted.

[0098] The input unit accepts input of various instructions and information from the administrator. The control unit performs various registrations in the server 10 based on the instructions and information accepted by the input unit. The control unit can register the subject in the server 10 in association with the care staff member 50 in charge of the subject.

[0099] The control unit may cause the display unit to display alerts, contact information, and the status of the subject 80 received from the server 10.

[0100] (Mobile terminal 30) 11 is a block diagram showing a schematic configuration of the mobile terminal 30. The mobile terminal 30 includes a control unit 31, a wireless communication unit 32, a memory unit 33, a display unit 34, an input unit 35, and an audio input / output unit 36. These components are connected to each other via a bus. The mobile terminal 30 may be configured by a communication terminal device that can be carried by the care staff 50, such as a tablet computer, a smartphone, or a mobile phone. The mobile terminal 30 may be replaced by a fixed terminal.

[0101] The control unit 31 has the same basic components as the control unit 11 of the server 10, such as a CPU, RAM, and ROM.

[0102] The wireless communication unit 32 has a function of performing wireless communication according to standards such as Wi-Fi and Bluetooth (registered trademark), and performs wireless communication with each device directly or via an access point 71.

[0103] The storage unit 33 stores various data and programs and is configured by, for example, a flash memory.

[0104] The display unit 34 and the input unit 35 are touch panels, and a touch sensor as the input unit 35 is provided on the display surface of the display unit 34 which is made up of a liquid crystal or the like.

[0105] The voice input / output unit 36 ​​is configured with, for example, a speaker and a microphone. The voice input / output unit 36 ​​enables voice communication between the care staff 50 and other mobile terminals 30 via the wireless communication unit 32.

[0106] The control unit 31 accepts the care records of each subject 80 input to the input unit 35. The control unit 31 transmits the care records to the server 10 together with information that identifies the subject 80 via the wireless communication unit 32.

[0107] The control unit 31 may cause the display unit 34 to display the alerts, contact information, and status of the subject 80 received from the server 10.

[0108] 12 is a flowchart showing an example of the operation of the server 10. This flowchart can be executed by the control unit 11 of the server 10 in accordance with a program.

[0109] The control unit 11 acquires evaluation-related data (S101). Specifically, the control unit 11 acquires care records, sensor analysis data, or care record analysis data as the evaluation-related data.

[0110] The control unit 11 determines whether the difference between the acquired review-related data and the past review-related data is equal to or greater than a predetermined threshold (S102). Specifically, for example, the control unit 11 determines whether the difference between the review of DBD13, which is the acquired review-related data, and the review of DBD13 in the past is equal to or greater than a predetermined threshold.

[0111] If the control unit 11 determines that the difference between the acquired review-related data and the past review-related data is not equal to or greater than the predetermined threshold (S102: NO), the control unit 11 ends the process. The control unit 11 uses the acquired review-related data as is.

[0112] When the control unit 11 determines that the difference between the acquired evaluation-related data and the past evaluation-related data is equal to or greater than a predetermined threshold (S102: YES), it determines the correspondence between the acquired evaluation-related data and the past data (S103). Specifically, for example, the control unit 11 determines whether the evaluation trend of DBD13, which is the acquired evaluation-related data, is the same as or different from the evaluation trend of ADL, which is the past data.

[0113] If the control unit 11 determines that the correspondence between the acquired review-related data and the past data is valid (S104: YES), the control unit 11 ends the process. The control unit 11 uses the acquired review-related data as is.

[0114] If the control unit 11 determines that the correspondence between the acquired evaluation-related data and the past data is inappropriate (S104: NO), it issues an alert for the acquired evaluation-related data (S105). Then, the control unit 11 accepts a correction to the evaluation value of the evaluation-related data (S106). Specifically, the control unit 11 accepts a correction to the evaluation-related data whose correspondence with the past data was determined to be inappropriate in step S104, and uses the evaluation-related data that reflects the accepted correction. The correction to the evaluation-related data can be input by the care staff 50 to the mobile terminal 30.

[0115] 13 is a flowchart showing another example of the operation of the server 10. This flowchart can be executed by the control unit 11 of the server 10 in accordance with a program.

[0116] The control unit 11 acquires evaluation-related data (S201). Specifically, the control unit 11 acquires care records, sensor analysis data, or care record analysis data as the evaluation-related data.

[0117] The control unit 11 determines whether the difference between the acquired review-related data and the past review-related data is equal to or greater than a predetermined threshold (S202). Specifically, for example, the control unit 11 determines whether the difference between the review of the DBD13, which is the acquired review-related data, and the review of the past DBD13 is equal to or greater than a predetermined threshold.

[0118] If the control unit 11 determines that the difference between the acquired review-related data and the past review-related data is not equal to or greater than the predetermined threshold (S202: NO), the control unit 11 ends the process. The control unit 11 uses the acquired review-related data as is.

[0119] When the control unit 11 determines that the difference between the acquired evaluation-related data and the past evaluation-related data is equal to or greater than a predetermined threshold (S202: YES), it notifies the person to be reevaluated (S203). Then, the control unit 11 accepts a correction to the evaluation value of the evaluation-related data of the person to be reevaluated (S204). The control unit 11 uses the evaluation-related data that reflects the accepted correction. The correction to the evaluation-related data can be input by the care staff 50 at the mobile terminal 30.

[0120] 14 is a flowchart showing yet another example of the operation of the server 10. This flowchart can be executed by the control unit 11 of the server 10 in accordance with a program.

[0121] The control unit 11 acquires evaluation-related data (S301). Specifically, the control unit 11 acquires care records, sensor analysis data, or care record analysis data as evaluation-related data.

[0122] The control unit 11 determines the correspondence between the acquired evaluation-related data and past data (S302). Specifically, for example, the control unit 11 determines whether the evaluation trend of DBD13, which is the acquired evaluation-related data, is the same as or different from the evaluation trend of ADL, which is past data.

[0123] If the control unit 11 determines that the correspondence between the acquired review-related data and the past data is valid (S303: YES), the control unit 11 ends the process. The control unit 11 uses the acquired review-related data as is.

[0124] If the control unit 11 determines that the correspondence between the acquired evaluation-related data and the past data is inappropriate (S303: NO), it issues an alert for the acquired evaluation-related data (S304). Then, the control unit 11 accepts a correction to the evaluation value of the evaluation-related data (S305). Specifically, the control unit 11 accepts a correction to the evaluation-related data whose correspondence with the past data was determined to be inappropriate in step S303, and uses the evaluation-related data that reflects the accepted correction. The correction to the evaluation-related data can be input by the care staff 50 to the mobile terminal 30.

[0125] The embodiment has the following advantages.

[0126] First data relating to an evaluation of the subject's condition is acquired, and a correspondence relationship between the first data and stored second data relating to the evaluation of the subject's condition before the acquisition of the first data is determined. Then, based on the determination result, the first data that requires reevaluation is identified. This makes it possible to identify evaluation results that require reevaluation for quantitative evaluation of the subject's condition based on nursing records, etc.

[0127] The first data may be care records, sensor analysis data obtained by analyzing data obtained from sensors that monitor the condition of the subject, or care record analysis data obtained by analyzing the care records, thereby improving the accuracy of identifying evaluation results that require reevaluation.

[0128] The second data is data related to past LIFE or assessments. The trends of the first data and the second data are compared. If the trends of the first data and the second data differ, the first data is identified as data that requires reevaluation. This improves the accuracy of identifying evaluation results that require reevaluation.

[0129] The second data is the same type of data as the first data for the subject. The second data is compared with the first data. If there is a difference between the second data and the first data, the first data is identified as data that requires reevaluation. This improves the accuracy of identifying evaluation results that require reevaluation.

[0130] In addition, an alert is sent for the first data that is determined to require reevaluation, making it possible to notify the user of the evaluation result that requires reevaluation.

[0131] Furthermore, if the difference between the first data and the second data is equal to or greater than a predetermined threshold, the subject from whom the first data was obtained is identified as a subject for reassessment, thereby making it possible to identify subjects who require reassessment.

[0132] In addition, the identified persons who are to be reevaluated will be notified, thereby making it possible to publicize the persons who need to be reevaluated.

[0133] The configuration of the information processing system 1 described above is a main configuration for explaining the features of the above embodiment, but is not limited to the above configuration and can be modified in various ways within the scope of the claims. Furthermore, configurations that are included in general information processing systems are not excluded.

[0134] For example, the functions of the server 10 may be provided in the fixed terminal 20, the mobile terminal 30, or the detection unit 40.

[0135] Furthermore, the server 10, the fixed terminal 20, the mobile terminal 30, and the detection unit 40 may each be configured by a plurality of devices, or any of a plurality of devices may be configured as a single device.

[0136] In addition, some steps may be omitted from the above-described flowcharts, other steps may be added, some of the steps may be executed simultaneously, or one step may be divided into multiple steps and executed.

[0137] The means and methods for performing various processes in the information processing system 1 described above can be realized by either a dedicated hardware circuit or a programmed computer. The programs may be provided by a computer-readable recording medium such as a USB memory or a DVD (Digital Versatile Disc)-ROM, or may be provided online via a network such as the Internet. In this case, the programs recorded on the computer-readable recording medium are typically transferred to and stored in a storage unit such as a hard disk. The programs may be provided as standalone application software or may be incorporated as a function into the software of a device such as a detection unit. [Explanation of symbols]

[0138] 1 Information processing system, 10 servers, 11 control section, 111 Acquisition Department; 112 Classification Department, 113 Evaluation Department, 114 display control unit, 115 Learning Department, 116 Approximation formula creation section, 117 Specific Department; 118 Notification Department; 12 Communications Department, 13 storage section, 20 fixed terminals, 30 mobile devices, 31 control section, 32 Radio Communication Department, 33 Memory section, 34 Display section, 35 input section, 36 Audio input / output unit, 40 detection unit, 70 networks, 50 care staff, 80 Target Audience.

Claims

1. an acquisition unit that acquires first data related to an evaluation of a subject's condition; a storage unit that stores second data related to an evaluation of the subject's condition before acquiring the first data; a data specifying unit that determines a correspondence relationship between the first data and the second data and specifies the first data that needs to be reevaluated based on a result of the determination; An information processing system having the above.

2. The information processing system of claim 1 , wherein the first data is a care record, sensor analysis data obtained by analyzing data obtained from a sensor monitoring the subject's condition, or care record analysis data obtained by analyzing the care record.

3. the second data is data relating to a past LIFE or data relating to an assessment; 2. The information processing system according to claim 1, wherein the data identification unit compares a trend of the first data with a trend of the second data, and if the trend of the first data differs from the trend of the second data, identifies the first data as data that requires reevaluation.

4. the second data is data of the same type as the first data of the subject; 2. The information processing system according to claim 1, wherein the data identification unit compares the second data with the first data, and if there is a difference between the second data and the first data, identifies the first data as data that needs to be reevaluated.

5. The information processing system according to claim 1 , further comprising an alert notification unit that issues an alert for the first data determined by the data specifying unit to require reevaluation.

6. The information processing system according to claim 4, further comprising a subject identification unit that identifies the subject from whom the first data was obtained as a subject to reevaluation if the difference between the first data and the second data is equal to or greater than a predetermined threshold.

7. The information processing system according to claim 6 , further comprising a reevaluation target notifying unit that notifies the reevaluation target identified by the target identifying unit.

8. (a) acquiring first data relating to an assessment of a subject's condition; (b) storing second data relating to an assessment of the subject's condition prior to acquiring the first data; (c) determining a correspondence relationship between the first data and the second data, and identifying the first data that needs to be reevaluated based on the determination result; A control program for causing a computer to execute a process having the above.

9. The control program of claim 8, wherein the first data is a care record, sensor analysis data obtained by analyzing data obtained from a sensor monitoring the subject's condition, or care record analysis data obtained by analyzing the care record.

10. the second data is data relating to a past LIFE or data relating to an assessment; 9. The control program according to claim 8, wherein in step (c), the trend of the first data is compared with the trend of the second data, and if the trend of the first data differs from the trend of the second data, the first data is identified as data that requires reevaluation.

11. the second data is data of the same type as the first data of the subject; 9. The control program according to claim 8, wherein in the step (c), the second data is compared with the first data, and if there is a difference between the second data and the first data, the first data is identified as data that needs to be reevaluated.

12. The process comprises:

9. The control program according to claim 8, further comprising the step (d) of issuing an alert for the first data determined to require reevaluation in the step (c).

13. The process comprises: The control program according to claim 11, further comprising a step (e) of identifying the subject from whom the first data was obtained as a subject to reevaluation if the difference between the first data and the second data is equal to or greater than a predetermined threshold.

14. The process comprises: The control program according to claim 13 , further comprising a step (f) of notifying the person to be reevaluated identified in said step (e).

15. A control method executed by an information processing system, comprising: (a) acquiring first data relating to an assessment of a subject's condition; (b) storing second data relating to an assessment of the subject's condition prior to acquiring the first data; (c) determining a correspondence relationship between the first data and the second data, and identifying the first data that needs to be reevaluated based on the determination result; A control method comprising:

16. The control method of claim 15, wherein the first data is a care record, sensor analysis data obtained by analyzing data obtained from a sensor monitoring the condition of the subject, or care record analysis data obtained by analyzing the care record.

17. the second data is data relating to a past LIFE or data relating to an assessment; 16. The control method according to claim 15, wherein in step (c), the trend of the first data is compared with the trend of the second data, and if the trend of the first data differs from the trend of the second data, the first data is identified as data that requires reevaluation.

18. the second data is data of the same type as the first data of the subject; 16. The control method according to claim 15, wherein in step (c), the second data is compared with the first data, and if there is a difference between the second data and the first data, the first data is identified as data that needs to be reevaluated.

19. The control method according to claim 15, further comprising the step (d) of issuing an alert for the first data determined to require reevaluation in the step (c).

20. The control method described in claim 18, further comprising a step (e) of identifying the subject from whom the first data was obtained as a reevaluation subject if the difference between the first data and the second data is equal to or greater than a predetermined threshold.

21. The control method according to claim 20, further comprising the step (f) of notifying the person to be reevaluated identified in the step (e).

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