Long-term care certification support system, long-term care certification support method, and long-term care certification support program

Abstract

To provide a long-term care certification support system, a long-term care certification support method, and a long-term care certification support program that can reduce workload in a long-term care certification survey and increase accuracy of determination.SOLUTION: A long-term care certification support system comprises a terminal for inputting a certification survey form consisting of a "basic survey" and "special notes" and for outputting a notification of a condition of a subject, an artificial intelligence unit for analyzing the condition of the subject, and a server for estimating the condition of the subject. The artificial intelligence unit includes: a morphological analysis unit for performing morphological analysis of an analysis target in the "special notes"; an inferred basis sentence extraction unit for extracting sentences that serve as the basis for estimation from sentences in the "special notes"; a normalization unit for normalizing expressions of the sentences extracted by the inferred basis sentence extraction unit; a classification estimation unit for estimating a classification number based on the sentences normalized by the normalization unit; and an answer generation unit for generating an answer that corresponds to criteria of the certification survey form based on the classification number estimated by the classification estimation unit.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a long-term care certification support system, a long-term care certification support method, and a long-term care certification support program for assisting in the review and determination of long-term care certification.

Background Art

[0002] In order for the elderly to receive long-term care services under the long-term care insurance system, it is necessary for the insured persons of long-term care insurance to undergo an authentication survey by the local government and receive long-term care certification. In recent years, with the progress of the declining birthrate and aging population, the aging rate has been increasing and the working-age population has been decreasing. The number of applicants for long-term care certification has been on the rise, and in recent years, the pace of increase has been accelerating. On the other hand, the number of local government employees conducting long-term care certification surveys has been on the decline.

Prior Art Documents

Patent Documents

[0003]

Non-Patent Document 1

Non-Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] Non-Patent Document 1 describes the application method for the certification of need for long-term care services under the long-term care insurance system for service users. Specifically, the process from application to receiving long-term care services, such as "Application for Certification of Need for Long-Term Care", "Certification Survey and Physician's Opinion Letter", "Review and Judgment", "Creation of a Certification and Long-Term Care (Long-Term Care Prevention) Service Plan", and "Start of Using Long-Term Care Services", is described.

[0005] Non-Patent Document 2 discloses the description methods of "Basic Survey" and "Special Notes" in the "Certification Survey Form" used for the certification of need for long-term care. For the "Basic Survey", the applicant's status is selected and described based on the definitions of each survey item, etc. For the "Special Notes", when the basis for the selection of each survey item, the case where the selected item in each survey item does not well match the definition of the basic survey item, or when considering whether the actual assistance method is appropriate, the details are described and the form is created as the "Certification Survey Form". Note that the "Basic Survey" and "Special Notes" are associated with the survey items by survey numbers respectively.

[0006] FIG. 19 shows the process from when a local government receives an application for the certification of need for long-term care, investigates and verifies the application content, makes a determination of the need for long-term care, and outputs a notice of the certification result of the need for long-term care.

[0007] Referring to FIG. 19, the conventional business of the certification of need for long-term care will be described. First, the subject (long-term care insurance insured who wishes to receive long-term care services, a person who acts on behalf of the insured's procedures, etc.) submits an application for the certification of need for long-term care to the local government (Flow F90 in FIG. 19). Based on the application for the certification of need for long-term care, the local government dispatches an investigator (local government staff, staff of the long-term care certification survey center, etc.) to the location of the subject (home, facility, etc.). The investigator conducts an interview survey on the physical and mental state of the subject and creates a "Certification Survey Form" consisting of "Basic Survey" and "Special Notes" in paper form (Flow F91 in FIG. 19).

[0008] Local government staff perform electronic processing (OCR (Optical Character Recognition / Reader) capture) of the created "Certification Survey Form" (Figure 19, Flow F92). Enter the electronically processed (OCR captured) survey results (Certification Survey Form) and some items of the attending physician's opinion letter received at the time of application or up to that stage into the computer, and the computer performs the determination of the degree of care needed using a national uniform determination method and outputs it as the result of the primary determination (Figure 19, Flow F93). Based on the result of the primary determination and the attending physician's opinion letter, the Care Certification Review Committee determines the degree of care needed and outputs the result of the secondary determination (Figure 19, Flow F94). The local government conducts the determination of the need for care certification based on the result of the secondary determination and notifies the subject of the certification result (Figure 19, Flow F95).

[0009] As described above, in response to the increasing trend in the number of applicants for care certification, the number of local government staff conducting the care certification surveys is on a decreasing trend, increasing the burden on each local government. What particularly requires a lot of manpower in the process of the care certification survey is the confirmation work of the "Certification Survey Form" created in paper form by local government staff shown in Flow F92 of Figure 19. The local government mobilizes not only the staff of the department in charge of care certification but also the staff of non-in charge departments to deal with it. Due to the shortage of manpower causing congestion in normal operations, there is an urgent issue of improving the work of the confirmation work of the "Certification Survey Form".

[0010] In contrast, using artificial intelligence in the work of the confirmation work of the "Certification Survey Form" is being considered to reduce the number of personnel required for the work. However, there are fluctuations in the descriptions of the "Special Notes" by each investigator, and there is a risk that it cannot be accurately analyzed even using artificial intelligence. In such cases, it becomes difficult to guarantee the accuracy of the determination results of care certification.

[0011] Therefore, an object of the present invention is to provide a long-term care certification support system, a long-term care certification support method, and a long-term care certification support program that can reduce the workload in the long-term care certification survey and improve the accuracy of determination by unifying the description content by the investigator in the "Special Notes" of the "Certification Survey Form" into a uniform one nationwide through digitization and classification using artificial intelligence, and estimating it to be an accurate and objective expression regardless of the form of the question.

Means for Solving the Problems

[0012] The long-term care certification support system according to the present invention is a long-term care certification support system including a terminal that inputs a certification survey form consisting of a "basic survey" and "special notes" and notifies the state of the subject, an artificial intelligence unit that analyzes the state of the subject, and a server that estimates the state of the subject. The artificial intelligence unit includes a morphological analysis unit that performs morphological analysis on the analysis target in the "special notes", an estimated basis sentence extraction unit that extracts a sentence serving as an estimated basis from the sentences in the "special notes", a normalization unit that normalizes the expression of the sentence extracted by the estimated basis sentence extraction unit, a classification estimation unit that estimates a classification number based on the sentence normalized by the normalization unit, and an answer generation unit that generates an answer corresponding to the standard of the certification survey form based on the classification number estimated by the classification estimation unit.

[0013] The long-term care certification support system according to the present invention has an unnecessary sentence removal unit that removes unnecessary sentences from the analysis result by the morphological analysis unit. The unnecessary sentence removal unit is characterized by splitting the sentence as the analysis result by the morphological analysis unit into sentences and removing a predetermined character string and a predetermined keyword from the sentences.

[0014] In the long-term care certification support system according to the present invention, the morphological analysis unit is characterized by splitting the analysis target into tokens and converting the split tokens into their original forms.

[0015] The method for supporting certification of needing care according to the present invention is a method for supporting certification of needing care using a terminal for inputting a certification survey form consisting of a "basic survey" and "special notes" and notifying the state of the subject person, an artificial intelligence unit for analyzing the state of the subject person, and a server for estimating the state of the subject person, the method including: a step of morphologically analyzing an analysis target in the "special notes"; a step of extracting a sentence serving as a basis for estimation from a sentence in the "special notes"; a step of normalizing the expression of the sentence extracted by the estimated basis sentence extraction unit; a step of estimating a classification number based on the sentence normalized by the normalization unit; and a step of generating an answer corresponding to the criteria of the certification survey form based on the classification number estimated by the classification estimation unit.

[0016] The program for supporting certification of needing care according to the present invention is a program for supporting certification of needing care using a terminal for inputting a certification survey form consisting of a "basic survey" and "special notes" and notifying the state of the subject person, an artificial intelligence unit for analyzing the state of the subject person, and a server for estimating the state of the subject person, wherein the artificial intelligence unit, in cooperation with the terminal and the server, performs: a step of morphologically analyzing an analysis target in the "special notes"; a step of extracting a sentence serving as a basis for estimation from a sentence in the "special notes"; a step of normalizing the expression of the sentence extracted by the estimated basis sentence extraction unit; a step of estimating a classification number based on the sentence normalized by the normalization unit; and a step of generating an answer corresponding to the criteria of the certification survey form based on the classification number estimated by the classification estimation unit.

Effect of the Invention

[0017] According to the present invention, by using artificial intelligence, the description contents by the investigator in the "basic survey" and "special notes" of the "certification survey form" can be unified into a uniform one nationwide and estimated to be an accurate and objective expression regardless of the form of the question, thereby reducing the work burden in the certification survey of needing care and improving the accuracy of the determination.

Brief Description of the Drawings

[0018]

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Embodiments for Carrying Out the Invention

[0019] Hereinafter, a care-need certification support system, a care-need certification support method, and a care-need certification support program according to embodiments of the present invention will be described in detail with reference to the drawings. FIG. 1 is a block diagram showing the configuration of a care-need certification support system 10 according to the present embodiment. The care-need certification support method is executed according to the procedure shown in FIGS. 3 to 6 by the care-need certification support system 10 shown in FIG. 1. The care-need certification support program is also executed according to the procedure shown in FIGS. 3 to 6 by the operations and controls of the respective components included in the care-need certification support system 10 shown in FIG. 1.

[0020] First, the configuration of the care-need certification support system 10 will be described. In the following embodiments, an example will be described in which, among the "certification investigation forms" shown in FIGS. 10 to 18, the "basic investigation" described in FIGS. 11 to 15 and the "special notes" shown in FIGS. 16 to 18 are targeted.

[0021] As partially illustrated in FIG. 16, the "special notes" include questions and options for the questions. Examples of the questions include (Group 1) "Special notes on items related to physical functions and daily living movements", (Group 2) "Special notes on items related to living functions", (Group 3) "Special notes on items related to cognitive functions", (Group 4) "Special notes on items related to mental / behavioral disorders", (Group 5) "Special notes on items related to adaptation to social life", (Group 6) "Special notes on special medical treatment", and (Group 7) "Special notes on items related to the degree of self-care in daily life" can be mentioned.

[0022] The above (Group 1) corresponds to Questions 1-1 to 1-13 of the "Basic Survey", (Group 2) corresponds to Questions 2-1 to 2-12, (Group 3) corresponds to Questions 3-1 to 3-9, (Group 4) corresponds to Questions 4-1 to 4-15, (Group 5) corresponds to Questions 5-1 to 5-6, (Group 6) corresponds to Question 6, and (Group 7) corresponds to Question 7, respectively.

[0023] As options for the questions, matters corresponding to the questions described in the "Basic Survey" are listed. For example, for the above Question (1) "Special notes on items related to physical functions and daily living activities", "Presence or absence of paralysis" (Question 1-1), "Presence or absence of contracture" (Question 1-2), "Turning over in bed" (Question 1-3), "Getting up" (Question 1-4), "Seat holding" (Question 1-5), "Standing position on both feet" (Question 1-6), "Walking" (Question 1-7), "Standing up" (Question 1-8), "Standing position on one foot" (Question 1-9), "Taking a bath" (Question 1-10), "Nail trimming" (Question 1-11), "Vision" (Question 1-12), and "Hearing" (Question 1-13), which correspond to each question of the "Basic Survey", are listed.

[0024] As shown in FIG. 1, the care-need certification support system 10 includes a terminal 20, a server 30, and an artificial intelligence unit 40. The terminal 20 and the server 30 are connected via a communication network 11, and the server 30 and the artificial intelligence unit 40 are connected via a closed network 12. The closed network 12 refers to a wide-area communication network within an organization that is not directly connected to the Internet or the like and connects only limited users or bases.

[0025] The terminal 20 is, for example, a smartphone, a mobile phone, a PC (Personal Computer), or a tablet, and any of them may be used as long as it can implement the functions according to this embodiment. The terminal 20 receives an input from the user, sends a request to the server 30, and performs processes such as displaying the result returned from the server 30. The terminal 20 includes a communication control unit 21, a display unit 22, an input unit 23, and a storage unit 24.

[0026] The communication control unit 21 controls communication with the server 30 via the communication network 11. The display unit 22 can display various information in addition to the answer as the determination result, and not only display but also notify by voice or the like. The input unit 23 inputs various information by the operation of the investigator having the terminal 20, and has a keyboard function, a touch panel function, an image operation function, a photograph function, etc. The storage unit 24 stores the information input from the input unit 23, the information received from the server 30, the information regarding the "basic investigation" and "special remarks" of the "certification investigation form", etc.

[0027] Also, the terminal 20 may be composed of one or two or more (also referred to as a plurality) of terminals 20. In this case, it is preferable to provide a terminal 20 for each of one or two or more persons in charge, one or two or more business offices, etc. Further, computer peripheral devices such as a printer and a mouse (not shown) may be provided for these terminals 20 respectively.

[0028] The server 30 is composed of a computer having a server function, and any one may be used as long as it can exhibit the functions according to this embodiment. The server 30 receives a request from the terminal 20, analyzes the "basic investigation" and "special remarks" of the "certification investigation form" via the artificial intelligence unit 40, and performs a process of returning the determination result to the terminal 20. The server 30 has a communication control unit 31, an investigation item registration unit 32, an answer extraction unit 33, and a storage unit 34.

[0029] The communication control unit 31 controls communication with the terminal 20 via the communication network 11 and communication with the artificial intelligence unit 40 via the closed network 12. The investigation item registration unit 32 registers the investigation items such as the "certification investigation form" received from the terminal 20 in the storage unit 34. The investigation item data registered by the investigation item registration unit 32 is transmitted to the artificial intelligence unit 40 and stored in the storage unit 47.

[0030] When a request for displaying an answer is sent to the server 30 by operating the input unit 23 of the terminal 20, the answer extraction unit 33 accesses the storage unit 47 of the artificial intelligence unit 40, calls an answer corresponding to the request, and sends the called answer to the terminal 20. The storage unit 34 stores information related to the extraction by the answer extraction unit 33 and the like.

[0031] The artificial intelligence unit 40 is composed of a computer having an artificial intelligence (AI (Artificial Intelligence)) function, but is not limited to artificial intelligence, and any device can be used as long as it can implement the functions according to the present embodiment. The artificial intelligence unit 40 receives a request from the server 30, analyzes the "basic survey" and "special notes" of the "certification survey form", and performs a process of returning the analysis result to the server 30. The artificial intelligence unit 40 includes a communication control unit 41, a data conversion unit 42, an evidence sentence extraction unit 43, an AI normalization unit 44 as a normalization unit, an AI estimation unit 45 as a classification estimation unit, an answer generation unit 46, and a storage unit 47.

[0032]

[0033] The communication control unit 41 controls communication with the server 30 via the closed network 12. The data conversion unit 42 includes an investigation item extraction unit 421, a morphological analysis unit 422, and an unnecessary sentence removal unit 423. For the sentences in the "special notes" received from the server 30, the investigation item extraction unit 421 divides the "certification survey form" into "basic survey" and "special notes" for extraction by the evidence sentence extraction unit 43. Further, in each of the "basic survey" and "special notes", data is extracted item by item such as questions and options. Further, morphological analysis by the morphological analysis unit 422 and removal of unnecessary sentences by the unnecessary sentence removal unit 423 are performed on the sentences subjected to morphological analysis. The sentences subjected to these processes are output to the evidence sentence extraction unit 43 and stored in the storage unit 47. The extraction conditions by the investigation item extraction unit 421, the conditions for morphological analysis and unnecessary sentence removal, etc. are stored in the storage unit 47, and the deletion keyword and question keyword used for unnecessary sentence removal are also stored in the storage unit 47 in advance.The presumption basis sentence extraction unit 43 extracts sentences containing the above-mentioned query keywords from the data received from the data conversion unit 42 for AI presumption sentence normalization. The extracted sentences are output to the AI normalization unit 44 and stored in the storage unit 47. The control conditions and the like of the presumption basis sentence extraction process are stored in the storage unit 47.

[0034] The AI normalization unit 44 as a normalization unit normalizes Chinese numeral notations, Japanese fraction notations, range notations, unnecessary modifiers, and frequency notations for the sentences extracted by the presumption basis sentence extraction unit 43 for classification by the AI estimation unit 45. The normalized sentences are output to the AI estimation unit 45 and stored in the storage unit 47. The normalization conditions and the like are stored in the storage unit 47 in advance.

[0035] The AI estimation unit 45 as a classification estimation unit is composed of, for example, a language model and estimates the classification corresponding to the query in the "special notes". The estimation result is output to the answer generation unit 46 and stored in the storage unit 47.

[0036] The answer generation unit 46 determines an AI estimation option, that is, a care-required determination result based on a national uniform standard, based on the estimation result by the AI estimation unit 45, and stores this determination result as an answer in the storage unit 47.

[0037] The communication network 11 and the closed network 12 are each configured as a communication network connecting the server 30 and the terminal 20, and the server 30 and the artificial intelligence unit 40. For example, they are a public switched telephone network (PSTN), a mobile phone network, an IP phone network, a closed network, a wireless LAN (WiFi), and may be any network or other communication network that functions as such.

[0038] Note that although the artificial intelligence unit 40 and the server 30 are shown as separate configurations in FIG. 1, they may also be an integrated configuration.

[0039] Next, with reference to FIGS. 2 and 3, the process from when the local government receives an application for long-term care certification, analyzes and examines the application content using the long-term care certification support system 10 based on the content of the investigated application, determines long-term care needs, and outputs a notification of the certification result for long-term care certification will be described.

[0040] As shown in FIG. 2, first, the subject submits an application for long-term care certification to the local government (flow F10 in FIG. 2). Based on the application for long-term care certification, the local government dispatches an investigator to the location of the subject. The investigator conducts an interview survey on the physical and mental state of the subject, inputs items of the "certification survey form" consisting of "basic survey" and "special notes" from the input unit 23 of the terminal 20, creates the "certification survey form" as electronic data, and stores it in the storage unit 24 of the terminal 20 (flow F11 in FIG. 2, step S1 in FIG. 3).

[0041] As an example, the "certification survey form" as the above-mentioned electronic data is created by the investigator bringing a portable terminal 20 such as a tablet to the location of the subject when going there, and inputting it into the tablet terminal at any time while conducting the interview survey. Also, the investigator may create the "certification survey form" on paper during the interview survey, and input the "certification survey form" (electronic data) from the input unit 23 of the terminal 20 installed in the local government based on the paper "certification survey form". Furthermore, the paper "certification survey form" written by the investigator may be captured from the input unit 23 of the terminal 20 by OCR (Optical Character Recognition) or the like. These are just examples, and it is sufficient that the "certification survey form" consisting of "basic survey" and "special notes" can be captured as electronic data via the terminal 20.

[0042] Next, the content confirmation process by the long-term care certification support system 10 shown in flow F12 of FIG. 2 will be described below with reference to FIG. 3. Note that since the processes of flows F13 to F15 in FIG. 2 are the same as those of flows F93 to F95 in FIG. 19, the description thereof will be omitted.

[0043] First, the terminal 20 transmits the electronic data of the created "certification survey form" (step S1 in FIG. 3) to the server 30 via its communication control unit 21 (step S2 in FIG. 3). The server 30 receives the electronic data of the "certification survey form" via its communication control unit 31 and registers each survey item in the storage unit 34 (step S3 in FIG. 3). Regarding the electronic data of the "certification survey form", the survey item extraction unit 421 divides it into "basic survey" and "special notes", and extracts it item by item in each of the "basic survey" and "special notes", and registers it in the storage unit 47 (step S4 in FIG. 3).

[0044] The artificial intelligence unit 40 receives, via its communication control unit 41, the electronic data of the "certification survey form" extracted item by item by the survey item extraction unit 421, and the data conversion unit 42 performs conversion of the data to be analyzed. Here, the data to be analyzed in this specification refers to the item-by-item data in the "basic survey" and "special notes" extracted item by item by the survey item extraction unit 421.

[0045] The data conversion unit 42 includes a morphological analysis unit 422 and an unnecessary sentence removal unit 423. The morphological analysis unit 422 performs morphological analysis on each item of the "special notes" in the received data to be analyzed (step S5 in FIG. 3), and the unnecessary sentence removal unit 423 removes unnecessary sentences from the analysis result of the morphological analysis (step S6 in FIG. 3). The morphological analysis and unnecessary part removal will be described in detail later.

[0046] For the data to be analyzed from which unnecessary sentences have been removed by the unnecessary sentence removal unit 423, the presumption basis sentence extraction unit 43 extracts the presumption basis sentence (step S7 in FIG. 3), the AI normalization unit 44 performs predetermined normalization (step S8 in FIG. 3), and further, classification is presumed by the AI presumption unit 45 (step S9 in FIG. 3).

[0047] The AI estimation unit 45 sends the estimated classification to the answer generation unit 46, and the answer generation unit 46 generates an answer as an option (estimated option) selected in "basic investigation" and "special matters" based on the received data (step S10 in FIG. 3). The generated answer is stored in the storage unit 47 of the server 30. These steps (steps S7 to S10 in FIG. 3) will be described in detail later.

[0048] In response to a request from the terminal 20, the answer extraction unit 33 of the server 30 extracts the answer generated by the answer generation unit 46 and stored in the storage unit 47 (step S11 in FIG. 3), and transmits it to the terminal 20 via the communication control unit 31 for return (step S12 in FIG. 3). The terminal 20 receives the transmitted answer via the communication control unit 21 (step S13 in FIG. 3), and displays the content corresponding to the instruction information input from the input unit 23 on the display unit 22 (step S14 in FIG. 3).

[0049] <Unnecessary sentence removal> (step S6 in FIG. 3, FIG. 4) Referring to FIG. 4, the process flow of the unnecessary sentence removal shown in step S6 of FIG. 3 will be described. FIG. 4 also shows the morphological analysis before the unnecessary sentence removal process and the estimated basis sentence extraction process after the unnecessary sentence removal process. Here, an example of question 1-1 "presence or absence of paralysis etc." will be given for explanation. Assume that the sentence of the special matter extracted in step S4 of FIG. 3 is "Both lower limbs cannot be lifted up to the horizontal. An operation on the right femur was performed 5 years ago and an artificial bone is implanted. 'Both lower limbs' was selected." In the morphological analysis at the stage before text removal (step S5 in FIGS. 3 and 4), first, the morphological analysis unit 422 reads each sentence described in the "Special Note" among the data retrieved by the survey item retrieval unit 421 and stored in the storage unit 47. The morphological analysis unit 422 divides the read sentence into tokens, determines the part of speech of each token, and normalizes each token. A token is a sequence of characters representing one unit of meaning in a sentence or text, and includes punctuation marks, numbers, and other symbols that convey meaning. As normalization, for example, the conversion from hiragana notation of a verb to kanji notation and the conversion to the base form of the verb are performed. Examples of the conversion to the base form of the verb include the conversion of "not able to be lifted" to "able to be lifted not" and the conversion of "is in" to "is in". For the former, it is converted to the base form, "lift" + "able to be" + "not", and for the latter, it is converted to the base form "enter" + "te" + "is in". In the example of Question 1-1, the sentence "Both lower limbs cannot be lifted up to the horizontal. Surgery was performed on the right femur 5 years ago. An artificial bone is in it. "Both lower limbs" is selected for this." is output after morphological analysis. The tokens normalized in this way and the sentence obtained by applying the normalized tokens to the sentence before division are stored in the storage unit 47.

[0050] Next, the unnecessary sentence removal unit 423 divides the sentences of the "Special Notes" after the morphological analysis by the morphological analysis unit 422 is completed (step S61 in FIG. 4), and for each of the divided sentences, removes the character strings of the options of the "Basic Survey" (step S62 in FIG. 4). Since the "Special Notes" are sentences for the current situation survey, when the options are directly described, the option character strings are removed. The character strings to be removed are the character strings shown as the options set for each question of the "Basic Survey" (FIGS. 11 to 15), and the symbols in parentheses are also removed. For example, as the options for question 1-1 "Presence or absence of paralysis, etc.", "None", "Left upper limb", "Right upper limb", "Left lower limb", "Right lower limb", "Others (limb deficiency)" are set. In the example of question 1-1, by sentence division in step S61 of FIG. 4, "Both lower limbs cannot be lifted up to the horizontal. Surgery on the right femur was performed 5 years ago. An artificial bone is implanted. For selecting "both lower limbs"." is output, and by removing the option character strings in step S62 of FIG. 4, "Both lower limbs cannot be lifted up to the horizontal. Surgery on the right femur was performed 5 years ago. An artificial bone is implanted. For selecting." is output.

[0051] Subsequently, for the sentence for which the option character string removal (step S62 in FIG. 4) has been executed, if it contains a preset "deletion keyword", the unnecessary sentence removal unit 423 removes this (step S63 in FIG. 4). The "deletion keyword" is stored in the storage unit 47 of the server 30. Examples of the deletion keyword include words that are assumed to be described in the special notes of question 1-1, such as "surgery", "interview", etc., and verbs are described in the original form of the token. In the example of question 1-1, "Both lower limbs cannot be lifted up to the horizontal. Surgery on the right femur was performed 5 years ago. An artificial bone is implanted. For selecting." is output.

[0052] Next, the unnecessary sentence removal unit 423 determines whether a preset "question keyword" is included in the sentence for which the deletion keyword removal (step S63 in FIG. 4) has been executed (step S64 in FIG. 4). In this determination, for each sentence, if the question keyword is included, it is determined as "True" as the state expression, and if not, it is determined as "False". The "question keyword" is a word included in the questions of the "basic survey" (FIGS. 11 to 15) and the options set for each question, which has a certain meaning and is stored in the storage unit 47 in advance. As the "question keyword" of question 1-1, for example, "limb", "leg", "hand", "finger", "paralysis", "contracture", etc. can be mentioned, and verbs are described in the original form of the token. In the example of question 1-1, "Both lower limbs cannot be lifted to the horizontal." is determined as "True", "Performed on the right femur 5 years ago." is determined as "False", "There is an artificial bone in it." is determined as "False", "For selection." is determined as "False".

[0053] Subsequently, the estimated basis sentence extraction unit 43 extracts the estimated basis sentence from the original text based on the sentence in which the determination of "True" that includes the question keyword is made in the keyword inclusion determination (step S64 in FIG. 4) among the sentences for which the deletion keyword removal (step S63 in FIG. 4) process has been executed (step S7 in FIGS. 3 and 4). In the example of question 1-1, the original text "Both lower limbs cannot be lifted to the horizontal." for which the keyword inclusion determination result in step S64 of FIG. 4 is "True" is extracted as the estimated basis sentence.

[0054] <AI Estimated Sentence Normalization> (step S8 in FIG. 3, FIG. 5) The estimated basis sentence extracted by the estimated basis sentence extraction unit 43 is output to the AI normalization unit 44 as the normalization unit. The AI normalization unit 44 executes the AI estimated sentence normalization process (see S8 in FIG. 3) shown in FIG. 5 described below to create an AI estimated sentence for the subsequent AI estimation (step S9 in FIG. 3) for the sentence input from the estimated basis sentence extraction unit 43.

[0055] In the AI ​​estimated sentence normalization process, first, as described above, the estimated basis sentence extracted by the estimated basis sentence extraction unit 43 is input to the AI ​​normalization unit 44 (step S81 in FIG. 5). The AI ​​normalization unit 44 performs morphological analysis on each sentence of the input sentence, and converts the parts of speech and original forms into sequences on a token-by-token basis (step S82 in FIG. 5).

[0056] Next, the AI ​​normalization unit 44 normalizes the Chinese numerals for each token into Arabic numerals (step S83 in FIG. 5). For example, normalization is performed to convert the description "一二三四五六七八九0" into Arabic numerals "1234567890".

[0057] Next, the AI ​​normalization unit 44 normalizes the Japanese fraction notation containing a mixture of kanji and hiragana for each token into a notation containing only Arabic numerals and symbols (step S84 in FIG. 5). For example, normalization is performed to convert the notation "two-thirds" into the Arabic numeral notation "2 / 3."

[0058] Furthermore, the AI ​​normalization unit 44 performs a normalization process for each token to unify the range notation into a predetermined notation (step S85 in FIG. 5). For example, normalization is performed to convert notations such as "1, 2" or "1·2" into notation such as "1-2," which means the range from 1 to 2.

[0059] Next, the AI ​​normalization unit 44 performs a normalization process to remove predetermined unnecessary modifiers from each token (step S86 in FIG. 5). The modifiers to be removed here are modifiers that may cause estimation errors during later AI estimation (step S9 in FIG. 3), and are stored in advance in the form of a table in the storage unit 47. The AI ​​normalization unit 44 refers to this table to remove unnecessary modifiers from tokens. For example, the modifier "here" is removed from expressions such as "past week" and "past month", which are words with ambiguous meanings or words that may have multiple meanings, and normalizes the expressions to "1 week" and "1 month".

[0060] Subsequently, the AI normalization unit 44 performs normalization processing to unify the frequency expressions for each token into a predetermined format (step S87 in FIG. 5). The normalization of the frequency expressions is a unification process into the expressions defined in the "basic survey", for example, "once a week", "once a month", or expressions similar thereto used in "going out frequency" of 2-12, which are preset and stored in the storage unit 47. When there are multiple such stored expressions, rules for which expression to apply according to the frequency are also preset and stored in the storage unit 47, and based on these rules, the expressions are unified into the optimal frequency expression. For example, expressions such as "1 to 9 times a week", "2 to 9 times every two weeks", "3 to 9 times every three weeks", "4 to 9 times every four weeks", "1 to 9 times / week", "4 to 9 times / month", "2 to 9 times every 10 days", "4 to 9 times a month", "once every 1 to 7 days" are all unified to "once a week", and expressions such as "1 to 3 times every 2 to 4 weeks", "1 to 3 times / month", "once every 10 days", "1 to 3 times a month", "once every 2 to 4 weeks", "once a month", "once a month" are all unified to "once a month", and expressions such as "1 to 9 times a day", "1 to 9 times / day" are all unified to "once a day".

[0061] Note that in the above description, steps S83 to S87 were executed in order, but the order of these processes is not limited to this example.

[0062] <AI Estimation, Answer Generation> (steps S9 and S10 in FIG. 3, FIGS. 6 to 8) The text of the "Special Notes" for which the processing of AI Estimation Unit Normalization (step S8 in FIG. 3) has ended is input to the AI estimation unit 45. The data of the "basic survey" extracted in the process of extracting the survey items is also sent to the AI estimation unit 45. The AI estimation unit 45 performs AI estimation processing on the "basic survey" and the "Special Notes" that have been processed from morphological analysis (step S5 in FIG. 3) to AI Estimation Sentence Normalization (step S8 in FIG. 3), and based on this estimation result, the answer generation unit 46 generates an answer as an estimation option.

[0063] Figure 6 shows an example of the flow of AI estimation processing (steps S91 to S92) for the sentences described in the "Special Notes" and the subsequent answer generation processing (steps S93 to S97). In this example, first, the sentence normalized by the AI estimation sentence normalization processing (step S8 in FIG. 3) is input to the AI estimation unit 45 (step S91 in FIG. 6). The AI estimation unit 45 executes AI estimation processing for each sentence of the input sentence (step S92 in FIG. 6). This AI estimation processing is a natural language processing model pre-trained by associating sentences with classification numbers for each question. For example, it is performed using a known model such as BERT (Bidirectional Encoder Representations from Transformers). From the target sentence, it is estimated which option among the options for the "basic survey" question is applicable, and based on this result, a classification number for the corresponding question is generated.

[0064] Specifically, as illustrated in FIGS. 7 and 8, the AI estimation unit 45 assigns a classification number to the target sentence. FIG. 7 shows the case where the answer to the question is single, and FIG. 8 shows the case where the answer to the question is multiple.

[0065] In the example shown in FIG. 7, for question 2-1 "Transfer" of the "basic survey", it is estimated which option the sentence resulting from the AI estimation sentence normalization processing (step S8 in FIG. 3) corresponds to. For example, for the AI estimation sentence "Transferring by oneself while holding on to the surroundings.", it is estimated that the option "Not assisted" is applicable, and the number "1" corresponding to this option becomes the classification number. Also, for the sentence "The nurse watches over during wheelchair transfer.", it is estimated that the option "Watching over, etc." is applicable, and the number "2" corresponding to this option becomes the classification number. For question 2-1 "Transfer", "Not assisted", "Watching over, etc.", "Partial assistance", and "Full assistance" are the options, and a single answer of choosing one of them is required. Classification numbers 1 to 4 corresponding one-to-one to the selected option are assigned. As a result, it becomes clear which option of the "basic survey" the sentence resulting from the AI estimation sentence normalization processing corresponds to.

[0066] In the example shown in FIG. 8, for question 1-1, “Presence or absence of paralysis, etc.” in the “Basic Survey”, it is estimated which option the sentence in the result of the AI estimation sentence normalization process (step S8 in FIG. 3) corresponds to. Multiple answers are allowed for question 1-1.

[0067] For example, for the AI estimation sentence “Both upper and lower limbs can perform the specified movement”, it is estimated that the option “None” applies, and the number “1” corresponding to this option becomes the classification number. Also, for the sentence “Both upper limbs cannot perform the specified movement”, it is estimated that the two options “Left upper limb” and “Right upper limb” apply, and the number “2” corresponding to “Left upper limb” and the number “4” corresponding to “Right upper limb” are added together, and “6” becomes the classification number.

[0068] For question 1-1, “Presence or absence of paralysis, etc.”, for the six options, they are assigned to the bits of a 6-bit binary array of 1, 2, 4, 8, 16, 32, and the sum of the coefficients of each bit is set as the classification number. Even when there are two or more multiple answers, non-overlapping classification numbers can be assigned. This makes it clear which option in the “Basic Survey” the sentence in the result of the AI estimation sentence normalization process corresponds to even in the case of multiple answers. Note that in this embodiment, an example of a 6-bit array is given, but the bit array can be arbitrarily set according to the number of options.

[0069] After classification numbers are assigned to each sentence of the text that has undergone the AI estimation sentence normalization process as described above, the answer generation unit 46 executes an answer generation process (steps S93 to S97 in FIG. 6) to generate an answer. In this answer generation process, first, it is determined whether the question for the classified sentence is a single answer or a multiple answer (step S93 in FIG. 6). If it is a single answer, the classification number is determined as the AI estimated option (step S94 in FIG. 6).

[0070] On the other hand, in the case of multiple answers, the assigned classification numbers are converted into a set of numbers corresponding to each of the multiple selected options, and the AI estimated options are determined to these numbers (step S95 in FIG. 6). For example, in the example shown in FIG. 8, when the classification number is 30, since "left upper limb", "right upper limb", "left lower limb", and "right lower limb" are options, they are converted into a set of numbers "2", "4", "8", "16" corresponding to these options.

[0071] The AI estimated options respectively determined in steps S94 and S95 above are generated as answers to each sentence of "Special Notes" (step S94 in FIG. 6). This answer is stored in the storage unit 47 and transmitted to the server 30 via the communication control units 31 and 41 and stored in the storage unit 34 (step S97 in FIG. 6).

[0072] Here, for the "basic survey", in addition to being input from the input unit 23 of the terminal 20, the answer content is recognized as characters from the PDF data of the basic survey (see FIGS. 11 to 15) and stored in the storage unit 24. The AI estimation unit 45 stores in the storage unit 47, as a table, the answers generated for "Special Notes" corresponding to each question of the "basic survey". The stored table can be displayed on the display unit 22 of the terminal 20 via the communication network 11 and the closed area network 12, and since the answers to each question of the "basic survey" and the answers in "Special Notes" can be viewed in comparison, it becomes possible to easily confirm the inconsistencies in the corresponding questions, easily recognize and correct the inconsistencies before the secondary determination in the flow F14 of FIG. 2, and thus contribute to improving the efficiency of the review by the review committee.

[0073] <Multi - process processing> The care - need certification support system 10 is configured to be capable of multi - process processing. The multi - process processing is controlled, for example, by a control function of the server 30, and can perform batch processing for the same question of "Special Notes" in a plurality of "certification survey forms". Thereby, it becomes possible to efficiently perform the series of processes shown in FIG. 3 for a plurality of "certification survey forms".

[0074] Figure 9 shows an overview of the batch processing flow for the same question, such as Question 1-1, in the "Special Notes" of six "Accreditation Survey Forms". In Figure 9, the "Accreditation Survey Form" is abbreviated as the "Survey Form". Also, Figure 9 gives an example of the processing for Question 1-1, but multi-process processing is also possible for other questions. Furthermore, the number of processes that can be batch processed is not limited to the six illustrated in Figure 9, and can be 1 to 5, or 7 or more.

[0075] In the example shown in Figure 9, when the texts for the same Question 1-1 of Survey Forms 1 to 6 are batch-input from the input unit 23 or batch-read from the storage unit 24 (step S101 in Figure 9), a series of processes shown in Figure 3 are processed in multi-process in the server 30 and the artificial intelligence unit 40. For each survey form, AI estimation (step S9 in Figure 3) is performed and an answer is generated (steps S102 and S103 in Figure 9). At the terminal 20, the answers generated for each survey form are returned all at once, and the determination result is displayed on the display unit 22 (step S104 in Figure 9).

[0076] According to the care-need certification support system, care-need certification support method, and care-need certification support program according to this embodiment, by the processing shown in Figure 3, an answer that is a uniform and objective expression nationwide corresponding to the questions and options of the "Basic Survey" can be generated from the text described in the "Special Notes". Therefore, it is possible to improve the efficiency while ensuring the quality of the work of checking the "Accreditation Survey Form", and to enhance the care-need certification system.

[0077] Regarding the consistency between the "basic survey" and the "special notes", a method of decomposing and analyzing the response sentences described in the "special notes" through artificial intelligence and comparing them with the responses of the "basic survey" can be considered. However, since there are still fluctuations in the descriptions by the investigators in the analysis results of the response sentences in the "special notes", it is difficult to lead to the efficiency improvement of the comparison work with the "basic survey" in the review meeting. In contrast, in this embodiment, since the content of the "special notes" is digitized and classified according to the "basic survey", the comparison with the responses of the "basic survey" can be accurately performed in a short time, so the efficiency of the examiner's work can be improved.

[0078] This invention can be embodied in many forms without departing from its essential characteristics. Therefore, it goes without saying that the above-described embodiments are for illustrative purposes only and do not limit the present invention.

Explanation of Signs

[0079] 10 Care Need Certification Support System 11 Communication Network 12 Closed Domain Network 20 Terminal 21 Communication Control Unit 22 Display Unit 23 Input Unit 24 Storage Unit 30 Server 31 Communication Control Unit 32 Survey Item Registration Unit 33 Answer Extraction Unit 34 Storage Unit 40 Artificial Intelligence Unit 41 Communication Control Unit 42 Data Conversion Unit 421 Survey Item Extraction Unit 422 Morphological Analysis Unit 423 Unnecessary Sentence Removal Unit 43 Basis for Estimation Sentence Extraction Unit 44 AI Normalization Unit (Normalization Unit) 45 AI Estimation Unit (Estimation Unit) 46 Answer Generation Unit 47 Storage Unit

Claims

1. A terminal for inputting a certification survey form consisting of a "basic survey" and "special notes" and notifying the status of the subject, An artificial intelligence unit for analyzing the status of the subject, A server for estimating the status of the subject, provided with a support system for certified nursing care determination, The artificial intelligence unit, A morphological analysis unit for morphological analysis of the analysis target in the "special notes", An estimated basis sentence extraction unit for extracting a sentence serving as an estimated basis from the sentences in the "special notes", A normalization unit for normalizing the expression of the sentence extracted by the estimated basis sentence extraction unit, A classification estimation unit for estimating a classification number based on the sentence normalized by the normalization unit, An answer generation unit for generating an answer corresponding to the criteria of the certification survey form based on the classification number estimated by the classification estimation unit, characterized by having A support system for certified nursing care determination.

2. It has an unnecessary sentence removal unit for removing unnecessary sentences from the analysis result by the morphological analysis unit, The unnecessary sentence removal unit divides the sentence as the analysis result by the morphological analysis unit into sentences, and removes a predetermined character string and a predetermined keyword from the sentences, The support system for certified nursing care determination according to claim 1, characterized by this.

3. The morphological analysis unit divides the analysis target into tokens and converts the divided tokens into their original forms, The support system for certified nursing care determination according to claim 2, characterized by this.

4. A method for supporting certified nursing care determination using a terminal for inputting a certification survey form consisting of a "basic survey" and "special notes" and notifying the status of the subject, an artificial intelligence unit for analyzing the status of the subject, and a server for estimating the status of the subject, A step of performing morphological analysis on the analysis target in the "special notes", A step of extracting a sentence serving as an estimated basis from the sentences in the "special notes", A step of normalizing the expression of the sentence extracted by the estimated basis sentence extraction unit, A step of estimating a classification number based on the sentence normalized by the normalization unit, A step of generating an answer corresponding to the criteria of the certification survey form based on the classification number estimated by the classification estimation unit, characterized by having A method for supporting certified nursing care determination.

5. A program for supporting certified nursing care determination using a terminal for inputting a certification survey form consisting of a "basic survey" and "special notes" and notifying the status of the subject, an artificial intelligence unit for analyzing the status of the subject, and a server for estimating the status of the subject, The artificial intelligence unit collaborates with the terminal and the server, A step of performing morphological analysis on the analysis target in the "Special Notes", A step of extracting a sentence serving as an estimation basis from the sentences in the "Special Notes", A step of normalizing the expression of the sentence extracted by the estimated basis sentence extraction unit, A step of estimating a classification number based on the sentence normalized by the normalization unit, A step of generating an answer corresponding to the criteria of the certification survey form based on the classification number estimated by the classification estimation unit, and executing the above steps A support program for certified nursing care qualification characterized by the above.

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