JP6951314B2 - Community comprehensive care business system - Google Patents

Description

An embodiment of the present invention relates to a community-based comprehensive care business system that executes each business of a long-term care benefit / preventive benefit business, a comprehensive business including a long-term care prevention / daily life support business, and a medical / nursing care cooperation promotion business.

Currently, the comprehensive community care system being promoted by the national government is centered on municipalities so that elderly people can receive nursing care, medical care, life support support and services in the areas they are accustomed to living in. It is to comprehensively develop the system of "life support / long-term care prevention". To create such a comprehensive community care system, in order to share nursing care and medical care-related information so that the characteristics, issues, and efforts of each region, including residents, can be objectively and easily grasped. A "visualization" system has been built by the country.

The goal of this "visualization" system is to comprehensively support the formulation and execution of long-term care insurance business plans, etc. in the above-mentioned community-based comprehensive care system, identify issues, and consider appropriate measures by referring to examples of efforts. , Sharing information, awareness of issues and examination status of government officials, promoting cooperation, implementing continuous measures, and promoting the construction of a comprehensive community care system by sharing efforts among local stakeholders. It is to be.

The functions include analysis of the current state of long-term care and medical care, support for extracting issues, support for sharing examples of efforts to solve issues, support for studying measures, support for future estimation of estimated amount of long-term care services, and support for execution management of long-term care and medical care-related plans. It has each function. In other words, it is structured so that the nationwide data on these functions can be freely used by the builders of community-based comprehensive care systems in each local government.

Japanese Unexamined Patent Publication No. 2016-51365

The present invention is a community-based comprehensive care business that quantitatively and qualitatively analyzes and executes each of the long-term care benefit / preventive benefit business, the comprehensive business including the long-term care prevention / daily life support business, and the medical / long-term care cooperation promotion business. To provide the system.

The community-based comprehensive care business system according to the embodiment of the present invention is a community-based comprehensive care that executes each business of a care benefit / preventive benefit business, a comprehensive business including a care prevention / daily life support business, and a medical / nursing cooperation promotion business. Business system, basic elderly data including resident address data and regional characteristic data, nursing care insurance data including nursing care certification data and nursing care receipt data, medical insurance data including elderly specific medical examination data and medical receipt data, Quantitative analysis of elderly information and service usage status by business unit including small areas from medical / nursing data for each of the above-mentioned businesses and a database with regional policy data on community facilities, care prevention facilities, and life support supporters. The regional management support function department that outputs the report and the qualitative report based on the index showing the state change of the mental and physical condition items, and the activity evaluation result related to the quantity and quality of the service are output in the individual form for each business unit. The regional information management function department centrally manages the basic information of the elderly, the status of mental and physical condition utilization services, and the medical status, confirms the effect of the care plan by referring to the past history related to these, and examines the policy for reviewing the next plan. The regional management support function department has an elderly information management function department that supports the above, and the regional management support function department provides data corresponding to each other, including the amount received per beneficiary in the nursing care business, for the self-insurer and non-self-insurer. Amount including the service receipt rate by the position grasping unit that compares and visually outputs the comparison position between the self-insurer and the non-self-insurer and finds the index to be examined, and by the business unit and mental and physical condition. The first factor analysis department that analyzes the problem and outputs the quantitative analysis report based on the analysis result, analyzes the qualitative problem related to the change of the mental and physical condition by the business unit and the mental and physical condition, and the above. It is equipped with a second factor analysis department that outputs qualitative reports, and these regional management support function departments, regional information management function departments, and elderly information management function departments are based on a computer system that data-driven the data possessed by the database. It is characterized by being realized.

It is a figure explaining the concept of the community-based comprehensive care system which concerns on one Embodiment of this invention. It is a functional block diagram explaining the basic structure of the community-based comprehensive care system which concerns on the same embodiment. It is a block diagram explaining the data collation method used in the same embodiment. It is a block diagram explaining another example of the data collation method used in the same embodiment. It is a block diagram for demonstrating the data acquisition method from another department used in the same embodiment. It is a block diagram which shows the structure of the area management support function part shown in FIG. It is a figure which shows an example of the image created by the positioning grasping part shown in FIG. It is a figure which shows other example of the image created by the positioning grasping part shown in FIG. It is a figure which shows the image example of a certain quantitative analysis example in the long-term care business created by the 1st factor analysis department shown in FIG. It is a figure which shows the image of another quantitative analysis example in the long-term care business created by the 1st factor analysis department shown in FIG. It is a figure which shows the image of another quantitative analysis example in the long-term care business created by the 1st factor analysis department shown in FIG. It is a figure which shows the image of another quantitative analysis example in the long-term care business created by the 1st factor analysis department shown in FIG. FIG. 12 is a diagram for explaining which area the service user residing in the small area is using the business establishment in which area is shown in FIG. It is a figure which shows the image of another quantitative analysis example in the long-term care business created by the 1st factor analysis department shown in FIG. It is a figure explaining how much the service user who resides in a plurality of small areas including the suburbs is using the business establishment which exists in which small area in the city shown in FIG. It is a figure which shows the image of a certain quantitative analysis example in the comprehensive business created by the 1st factor analysis department shown in FIG. It is a figure which shows the image of another quantitative analysis example in the comprehensive business created by the 1st factor analysis department. It is a figure which shows the image of another quantitative analysis example in the comprehensive business created by the 1st factor analysis department. It is a figure which shows the image of a certain quantitative analysis example in the medical care cooperation, which was created by the 1st factor analysis department. It is a figure which shows the image of another quantitative analysis example in the medical care cooperation, which was created by the 1st factor analysis department. FIG. 20 is a diagram for explaining which area a business establishment (medical institution) exists in which a service user residing in a small area is used, as shown in FIG. It is a figure which shows the image of another quantitative analysis example in the medical care cooperation, which was created by the 1st factor analysis part shown in FIG. FIG. 22 is a diagram for explaining to what extent service users living in a plurality of subregions including the suburbs are using business establishments (medical institutions) in which subregions in the city. be. It is a figure explaining the duration until the change of the mental and physical state used in the 2nd factor analysis part shown in FIG. It is a figure which shows the image of a certain qualitative analysis example in the long-term care business created by the 2nd factor analysis department shown in FIG. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the nursing care business created by the 2nd factor analysis department. The figure shows an image of a certain qualitative analysis example in the comprehensive business created by the second factor analysis department. It is a figure which shows the image of another qualitative analysis example in the comprehensive business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the comprehensive business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the comprehensive business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the comprehensive business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the comprehensive business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the comprehensive business created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the comprehensive business created by the 2nd factor analysis department. It is a figure which shows the image of a certain qualitative analysis example in the medical care cooperation created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the medical care cooperation created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the medical care cooperation created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the medical care cooperation created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the medical care cooperation created by the 2nd factor analysis department. It is a figure which shows the image of another qualitative analysis example in the medical care cooperation created by the 2nd factor analysis department. It is a figure which shows the image created by the area information management function part shown in FIG. The figure shows another image created by the regional information management function department. The figure shows another image created by the regional information management function department. The figure shows another image created by the regional information management function department. The figure shows another image created by the regional information management function department. The figure shows another image created by the regional information management function department. It is a figure which shows the image created by the elderly person information management function part shown in FIG. Similarly, it is a figure which shows the image created by the elderly person information management function department. It is a figure which shows the relationship which grouped the mental and physical condition index 65 into 9 aggregates. It is a figure which compares and explains three methods of evaluating a business unit by using a mental and physical condition index by a 2nd factor analysis department. It is explanatory drawing of the method of evaluating a business unit using the average maintenance period of a mental and physical condition index. It is a figure explaining the deviation value which is one of the evaluation methods of a business unit. It is a figure explaining the N quantile which is one of the evaluation methods of a business unit. It is explanatory drawing of the method of obtaining the deviation value from the value of the mental and physical condition index for each individual. It is a figure which shows the table which summarized the deviation value obtained for each individual by business establishment, the stage of a mental and physical condition item, and the like. It is a figure explaining the method of obtaining the average deviation value by the stage of the mental and physical condition index of the establishment where the user receives a service, by light / severe degree, etc. from the deviation value obtained for each individual. It is a figure explaining an example of the method of evaluating a business establishment by a deviation value. It is explanatory drawing of the method of obtaining the N quantile and the evaluation group based on it from the stage-by-step maintenance period of the mental and physical condition of each individual. It is a figure which shows the table which summarized the evaluation group based on the N quantile obtained for each individual by the establishment, the stage of a mental and physical condition index, and the like. It is a figure explaining the method of obtaining the evaluation point such as the stage, light / severe, etc. of the establishment where the user receives a service from the evaluation group based on the N quantile obtained for each individual. It is a figure. It is a figure which shows the aggregated result of the group ratio by the business establishment deterioration / improvement. It is a figure which compares the case where the average maintenance period for each business establishment is totaled, and the case where the average deviation value for each business establishment is totaled. It is a figure which shows the deterioration improvement situation of the degree of care-requiring of the user A, a, c, d, and o at a certain business establishment A. It is a figure which shows the improvement rate by the mental and physical condition stage by a service type. It is a distribution map that evaluates each business entity by service type and mental and physical condition using the average deviation value of the maintenance period until deterioration and the improvement rate. It is a figure which shows the result of having classified into three groups A, B, and C corresponding to the distribution map of FIG. 82. It is a figure which shows the example which aggregated the number of establishments corresponding to the combination of the group classification of the degree of need for care, the degree of disability independence, and the degree of dementia independence of the main mental and physical condition items. It is a figure which shows the example of the establishment activity evaluation sheet which is output every year for each establishment A, B, C, ... Visualized the balance and excess / deficiency of service demand (utilization rate) and supply (provision rate) by subregion, corresponding to the main elderly state image (mental and physical condition image x long-term care power x economic power, etc.) by service type. It is a distribution map. It is a figure which shows the system configuration example for obtaining the image as shown in FIG. It is a figure which outlines the flow of the service supply and demand analysis which creates the image data shown in FIG. 86 by the system described in FIG. 87. It is a figure which shows the aggregated content of various analysis data which is the basis of the supply and demand analysis of a long-term care service. The chart shows the overall picture of long-term care services according to the type of service to be analyzed. It is a figure which shows the detail of many service types. It is a figure which shows the combination of "the degree of care required", "mental and physical state image", "nursing ability" and "economic ability" which define the elderly state image. It is a figure explaining the definition of each component stage of a mental and physical state image. It is a figure which shows the data example about the elderly household of the care target created from the Juki data and the certification data. Based on the data of the elderly household in FIG. 94, it is a figure explaining the combination of the elderly household composition pattern, the mental and physical condition of the cohabiting subject person, and the mild severity thereof for determining a long-term care ability. It is a figure which shows the data structure which judges the economic power of an elderly person and a spouse based on the elderly household composition pattern. It is a figure which compares the income level set by the local government with the income level set by the Local Tax Law. It is a figure explaining the data structure for analysis processing in the subregion hierarchy which divided the subregion which constitutes the difference of a municipality into a plurality of layers. It is a figure explaining the process of identifying the most suitable subregional hierarchy for supply and demand analysis from the supply and demand analysis state judgment result in each subregional hierarchy. The horizontal axis is the improvement rate of the degree of care required for each business establishment, and the vertical axis is the average maintenance period until the degree of care required for each business establishment deteriorates. A state in which the best practice candidate establishments B1, B2, B3 and the worst practice candidate establishments W1, W2, W3 judged by FIG. 100 are compared with respect to the establishment characteristic items that are expected to have a strong correlation with the quality of service. It is a figure which shows. As an example of the business unit of the comprehensive business service, it is a distribution map that visualizes the quality of the program of multiple places of visits provided in the local government. It is a scatter plot showing the regional characteristics related to the place of commuting from the new certification rate by subregion and the participation rate of the place of commuting in the same area for all the elderly who require support or less. It is a figure which aggregates by subregion, by service type, and service provision rate, and compares the planned value with the measured value for each year.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

FIG. 1 is a diagram showing a schematic function of a community-based comprehensive care business system in this embodiment. In Fig. 1, the elderly welfare business promoted by insurers such as local governments includes long-term care benefit / preventive benefit business (hereinafter abbreviated as long-term care business) 11, comprehensive business 12 including long-term care prevention / daily life support business, and medical care.・ We are promoting each of the 13 long-term care cooperation promotion projects (hereinafter abbreviated as medical care cooperation). Each of these projects is executed by the community-based comprehensive care business system 15 according to the PDCA cycle consisting of A (analysis of regional issues), P (reflection in the business plan), D (execution of the plan), and C (performance evaluation). NS.

In Fig. 1, dementia measure 14 is shown as one of the welfare projects for the elderly, but the dementia measure is included in all of the above-mentioned projects 11, 12, and 13, and is comprehensive to the region. The care business system 15 is intended for the above-mentioned businesses 11, 12, and 13.

The community-based comprehensive care business system 15 quantitatively and qualitatively analyzes various data independently held in the database 16 by insurers such as local governments and data obtained from the above-mentioned national “visualization” system 17. We will support each project 11, 12, and 13 to realize the PDCA cycle described above. This community-based comprehensive care business system 15 is constructed by a computer system, and various data independently owned by the insurer such as the local government mentioned above in the database 16 and data obtained from the national "visualization" system 17 can be obtained. By data-driven, various functions described later accompanied by quantitative analysis and qualitative analysis are realized.

FIG. 2 shows a community management support function section 21, a community information management function section 22, an elderly information management function section 23, and a database 16 that realize each function described later in the community comprehensive care business system 15 constructed by a computer system. The data structure of is shown.

The database 16 aggregates and shows various data shown in FIG. 1 by field. That is, the database 16 contains basic elderly data 160 including resident address data and regional characteristic data, nursing care insurance data 161 including nursing care certification data and nursing care receipt data, and medical insurance including elderly specific medical examination data and medical receipt data. It has data 162, regional policy data 163 regarding community facilities, care prevention facilities, and life support supporters.

The regional management support function department 21 is aging for each business of the above-mentioned nursing care business 11, general business 12, and medical care cooperation 13 by business unit (by small area or business establishment, etc.) from medical / nursing data. Create and output a quantitative analysis report on personal information and service usage status, and a qualitative report showing changes in the state of mental and physical condition items.

That is, for the long-term care business 11, a quantitative analysis such as supply and demand analysis of long-term care services is performed, and a qualitative analysis is performed using an improvement rate indicating a change in mental and physical condition, a maintenance period until deterioration, and the like. Output the result.

In addition, for the comprehensive project 12, regional preventive measures supply and demand analysis will be performed as a quantitative analysis, and analysis using the average independence period by mental and physical condition will be performed as a qualitative analysis, and the results will be output.

Regarding the medical-assistance cooperation 13, a medical care cooperation analysis or the like is performed as a quantitative analysis, and an analysis using the mental and physical condition before and after hospitalization, the length of stay at home, etc. is performed as a qualitative analysis, and the results are output.

The regional information management function unit 22 outputs the activity evaluation results related to the quantity and quality of services and their own history in individual form for each person in charge of the business unit (subregional person in charge or long-term care provider).

That is, for the long-term care business 11, an analysis is performed using the average maintenance period for each mental and physical condition of the long-term care business establishment (home system, facility system, etc.), and the results are output.

In addition, regarding the comprehensive business 12, an analysis is performed using the independence period of the general business operator (resident-led organization, etc.) by disease and mental and physical condition, and the results are output.

Regarding the medical care cooperation 13, an analysis is performed using the readmission rate by disease of a multidisciplinary team (home care support office, etc.), and the results are output.

The elderly information management function unit 23 centrally manages the basic information of the elderly, the mental and physical condition utilization service status, and the medical status, and provides the past history related to these. By referring to this, it is possible to support the confirmation of the effect of the care plan and the review policy of the next plan.

That is, for the long-term care business 11, the degree of long-term care required, the maintenance period of the mental and physical condition, the long-term care ability, the economic ability, and the like are analyzed, and the results are output.

In addition, for the comprehensive business 12, the usage status of home-visit long-term care / outpatient long-term care, the number of informal services, etc. are analyzed, and the results are output.

Regarding the medical-assistance cooperation 13, home medical information, hospitalization / discharge, disease name, medication to be taken, etc. are analyzed, and the results are output.

Each function by the regional management support function unit 21, the regional information management function unit 22, and the elderly information management function unit 23 is the data of the database 16 and the national "visualization" system 17 (not shown in FIG. 2). It is realized by data-driven the data obtained from.

Here, looking at the long-term care certification data and the long-term care benefit record data that compose the long-term care insurance data 161 of the database 16, the long-term care certification data is required to be renewed and certified at that time in a cycle of once every two years. Mental and physical condition items including the degree of long-term care and the degree of independence of disability are output for each individual user. On the other hand, in the long-term care benefit record data, the degree of long-term care required, the type of service provided, and the number of units thereof are output as monthly data in accordance with the monthly billing for each user.

Since these data have different output timings and output items, if they are managed individually, they will be difficult to use for later analysis processing. Therefore, it is necessary to match these data with each other and integrate them. The matching method will be described below.

In other words, in order to aggregate the outcome index (index for evaluating the quality of service) that contributes to independence support and prevention of the severity of long-term care, the long-term care certification data, which is mental and physical information, and the benefit performance data, which is service usage information, are collected. It is necessary to match each user individually. As shown in FIG. 3, the long-term care certification data and the long-term care benefit record data retain the system management number and the year / month information obtained by hash-converting the insured person number. The year and month information held by the long-term care certification data is information on the physical and mental condition within the certification period (period data from the start month to the end month), and the long-term care benefit record data is service usage information for each service provision month. To hold.

Therefore, in order to be able to carry out monthly aggregation and analysis, we created a matching relationship table that decomposes the certification period of the long-term care certification data on a monthly basis, and care that the system management number and service provision date match in the matching relationship table. Match the benefit record data.

FIG. 3 shows an example of a matching relationship table 161c that links the long-term care certification data 161a and the long-term care benefit actual data 161b. In FIG. 3, the long-term care certification data 161a of the user requiring long-term care 2 whose certification validity period is from April 2015 to March 2016, and the long-term care benefit record data 161b having the service record generated in each month during that period. Is shown on a monthly basis to create a matching relationship table 161c.

In the matching relationship table 161c, the system management number and the service provision date match, and the mental and physical condition items including the degree of long-term care required and the degree of independence of disability, which are individually set in the long-term care certification data 161a and the long-term care benefit record data 161b, , The services provided and the number of units thereof are integrated on a monthly basis, and the outcome indicators that contribute to the above-mentioned independence support and prevention of the severity of long-term care can be aggregated and analyzed on a monthly basis.

It should be noted that the medical insurance data 162 can be matched with the elderly specific medical examination data and the medical receipt data by the same method.

Next, the method of collating the data requiring nursing care certification for which there is a classification change application will be described with reference to FIG. If the insured person's certification for long-term care is changed during the certification period due to the application for changing the classification of the certification for long-term care, there is data for certification for long-term care that overlaps the certification period. When the duplicated certification data for long-term care is decomposed on a monthly basis, a matching relationship table in which a plurality of data in which the system management number and the year / month information match exist is generated, which makes subsequent handling difficult. Therefore, the duplicated part of the matching relationship table is deleted and updated with the changed certification data for long-term care.

FIG. 4 shows an example of updating the matching relationship table when a classification change application is made to the long-term care 3 during the certification period of the long-term care 2. In FIG. 4, the long-term care certification data 161a1 for the renewal application of the long-term care 2 shown in FIG. And the long-term care certification data 161a2 of the change application for the long-term care 3 shown in FIG. 4 (b) are generated. Therefore, as shown in FIG. 4C, it overlaps from July 2015 to March 2016. When these long-term care certification data 161a1 and 161a2 are collated with the long-term care benefit actual data as described with reference to FIG. 3, a duplicate matching relationship table 161c1 is generated as shown in FIG. 4 (d). Therefore, by performing the cleaning process giving priority after the classification change, the non-overlapping matching relationship table 161c2 shown in FIG. 4 (e) is created.

Next, a case where data is imported from the national “visualization” system to the community-based comprehensive care business system 15 of a local government or the like will be described with reference to FIG.

As the data of the "visualization" system, the long-term care insurance qualification data (including the user's address code, etc.), the long-term care certification data, and the long-term care benefit record data in the model local government will be targeted. To anonymize these data, the insured number is transformed using an irreversible hash function.

However, the standardized hash function is open to the public, and by creating a comprehensive conversion table for the "10-digit numerical value" (insured number), it is possible to estimate the data before conversion. In order to make this guess impossible, anonymity is ensured by adding a private value unique to the model municipality to the insured number and performing hash conversion, making it impossible to create a conversion table. Furthermore, the date of birth of the user is converted to the date of birth, which is the minimum necessary information for linking with monthly benefit performance data.

The data encrypted in this way is taken into, for example, a portable PC (personal computer), the PC is brought back and decrypted, and the decrypted data is stored in the corresponding database.

In this way, arbitrary data can be freely imported from the "visualization" system 17 to the community-based comprehensive care business system 15 of local governments and the like offline.

Data encryption and hard disk encryption significantly reduce the risk of information leakage, including the route for moving data from the model municipality to an external aggregation / analysis environment.

Here, each function of the regional management support function unit 21, the regional information management function unit 22, and the elderly information management function unit 23 shown in FIG. 2 will be made to correspond to the PDCA cycle described above.

As will be described later, the regional management support function department 21 performs various analyzes, extracts issues for each business unit (subregions and business establishments), determines the priority of problem solving, and so on. Analysis). In addition, since the content and goals of the efforts are set, that is, the future estimation of the local government using the subregion future estimation (quantitative planning), cost-effectiveness simulation (qualitative planning), and "visualization system" is performed, P ( Reflection in business plan). Furthermore, C (performance evaluation) is also supported because each of the above-mentioned projects is evaluated for performance, and based on the results, business guidance, guidance for subregion staff, support for the elderly, etc. are provided.

The function in the regional information management function department 22 is to perform subregion / business guidance, analysis of guidance target issues, guidance target plan formulation, guidance target plan execution, and guidance target performance evaluation for the project being implemented. Corresponds to (execution of plan).

In addition, for each function by the elderly information management function unit 23, personal information search (elderly information search), personal information confirmation (elderly history information display), personal information batch output (elderly history information batch output), Since the materials for the community care meeting are output in a batch, it also corresponds to D (execution of the plan).

In this way, each function by the regional management support function unit 21 corresponds to A, P, C, and each function by the regional information management function unit 22 and the elderly information management function unit 23 corresponds to D. The PDCA cycle is executed by each functional unit.

As mentioned above, the regional management support function department 21 describes each business of the nursing care business 11, the comprehensive business 12, and the medical care cooperation 13 by business unit (by subregion or by business establishment, etc.) from the medical / nursing data. In addition, a quantitative analysis report on elderly information and service usage status, and a qualitative report showing changes in the state of mental and physical condition items are created. It has a factor analysis unit 52 and a second factor analysis unit 53.

When the insurer is a local government, the positioning grasping unit 51 uses the data of the "visualization" system to compare the corresponding data between the local government and other local governments, for example, the national average or the average of other local government groups. , Clarify the position of the local government and find an index that needs to be examined.

The first factor analysis unit 52 analyzes quantitative problems including the service receipt rate by business unit such as a subregion or business establishment, and by mental and physical condition, and makes it possible to identify a problem solution based on the analysis result. It is a thing.

The second factor analysis unit 53 analyzes qualitative problems related to changes in mental and physical conditions by business unit such as a subregion or business establishment, and by mental and physical condition, and makes it possible to identify problem-solving solutions based on the analysis results. It is a thing.

When the insurer is a local government (here, a city) as described above, the positioning grasping unit 51 uses the data of the "visualization" system to, for example, as shown in FIG. 7, per beneficiary in the long-term care business. Generate an image comparing the monthly benefit amount (home / facility / residential system) with your own city, the national average other than your own city, and other cities, etc., clarify the position of your own city, and use the indicators that need to be examined. I'm finding out.

In FIG. 7, a coordinate plane is used which is divided into four regions of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant by the x-axis and the y-axis that intersect each other. One of the x-axis and the y-axis (here, the y-axis) is the numerical axis of the monthly benefit per home-based service recipient, and the other (here, the x-axis) is the facility / residential service recipient 1. It is used as the numerical axis of monthly benefits per person. Then, assuming that the intersection (center) of the x-axis and the y-axis is the coordinates 70 of the national average monthly benefit per beneficiary, the coordinates 72 of the monthly benefit per beneficiary of the city and the average receipt of the prefecture to which the recipient belongs. The image shows in which quadrant the coordinates 73 of the monthly benefit per person are located.

According to FIG. 7, the coordinates 71 of the own city are located in the first quadrant, the coordinates 70 of the national average located in the center, the coordinates 72 of the other city group to be compared located in the second quadrant, and the coordinates of the third quadrant. Compared with the coordinates 73 of the average of the prefectures to which they belong, it is found that the average monthly benefit per beneficiary is higher in both the home service (x-axis) and the facility / residential service (y-axis). Therefore, the target value to be aimed at becomes clear.

For example, if the target value is the national average coordinate 70 or the coordinate 73 of the prefecture average located in the third quadrant, the beneficiaries in both the home-based service (x-axis) and the facility / residential service (y-axis). Measures must be taken to reduce the monthly benefit per person. In addition, if the target value is the coordinates 72 of the other city group to be compared, which is located in the second quadrant, the target value is to take measures to reduce the monthly benefit per person who receives the home service (x-axis). You can find the value and the measures to achieve it.

In the example shown in FIG. 8, the positioning grasping unit 51 compares the monthly benefit per beneficiary (home / facility / residential system) in the long-term care business with the national average of the city and other than the city ( Radar chart) is generated, the position of the city is clarified, and indicators that need to be examined are found.

That is, in FIG. 8, the monthly benefits per beneficiary of a plurality of types of facility / residential services, which have a plurality of numerical axes extending radially and are different from each other for the national average of the city and the comparison target, are shown above. Allocation is made for each of the plurality of numerical axes according to multiple types of facilities / residential services. In the example of FIG. 8, the multiple types of facilities / residential services that are different from each other are “special care”, “elderly health”, “medical care bed”, “specific facility”, “group home”, and “small-scale multifunctional”, and they receive the benefits. Set the monthly benefit value per person to the length from the center point of the corresponding numerical axis. Then, a polygonal graph formed by connecting the tips of these set lengths by the own city and the national average of the comparison target is formed, and an image of a radar chart is generated.

According to FIG. 8, the value of monthly benefits per beneficiary in each facility / residential service is not so different from the national average except for group homes, but it is outstanding for group homes compared to the national average. It turns out that it is expensive. Therefore, if the value to be aimed at is the national average, it becomes clear that it is necessary to take measures to reduce the monthly benefit per beneficiary, especially for group homes.

Next, the function of the first factor analysis unit 52 that performs the quantitative problem analysis will be described. The first factor analysis unit 52 performs a quantitative analysis on the long-term care business 11, the comprehensive business 12, and the medical care cooperation 13, but first, an embodiment of the quantitative analysis in the long-term care business 11 will be described. ..

In quantitative analysis, as mentioned above, it is possible to analyze quantitative problems including service receipt rates by business unit such as subregions or business establishments, and by mental and physical condition, and to identify problem solutions based on the analysis results. Hereinafter, various examples for that purpose will be described.

FIG. 9 shows an image created by factor analysis by the first factor analysis unit 52. In this example, the insurer is a local government (here, the city), and the monthly benefit per beneficiary in the home service of the nursing care business is the average of the city and the city that provides the home service as a business unit. It is compared with a plurality of subregions (subregions 1 to 8). That is, the difference in the amount of benefits for each subregion 1 to 8 with respect to the average value of the city is calculated, an image showing these relationships is generated, the position of each subregion 1 to 8 in the city is clarified, and an index to be examined is required. Is found.

In FIG. 9, a coordinate plane is used which is divided into four regions of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant by the x-axis and the y-axis that intersect each other. Either one of the x-axis and the y-axis (here, the y-axis) is used as the numerical axis of the monthly benefit per beneficiary of a person with mild physical and mental condition (for example, the degree of care required is 2 or less). The other (here, the x-axis) is used as the numerical axis of the monthly benefit per beneficiary of a person with severe physical and mental condition (for example, the degree of care required is 3 or higher).

Then, assuming that the intersection (center) of the x-axis and the y-axis is the coordinates 90 of the monthly benefit per beneficiary of the city average, each beneficiary 1 of a plurality of small areas 1 to 7 providing home-based services. By arranging the coordinates 91 to 98 of the monthly benefit per person in the quadrant corresponding to the numerical value, the difference in the benefit amount with respect to the own city average value becomes clear depending on which quadrant these coordinates 91 to 98 are located in.

According to FIG. 9, the coordinates 91 of the subregion 1 and the coordinates 97 of the subregion 7 are located in the first quadrant, and the monthly benefit per person is higher than the average of the city for both the mild and severe needing care. It becomes clear. In this way, the difference in the amount of benefits for people with severe and mild needs for nursing care in each subregion becomes clear. Therefore, it is possible to take measures based on the results of this analysis.

In the example of FIG. 10, the insurer is a local government (here, the city), and the monthly benefit per beneficiary in the facility / residential service of the long-term care business (here, the group home) is the average of the city. Comparisons are made with multiple business establishments (business establishments 1 to 8) in the city that carry out group homes (hereinafter abbreviated as GH), which are business units. That is, the difference in the amount of benefits for each business establishment 1 to 8 with respect to the average value of the city is calculated, an image showing these relationships is generated, the position of the business establishments 1 to 8 in the city is clarified, and the index to be examined is used. I'm finding out.

In FIG. 10, a coordinate plane is used which is divided into four regions of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant by the x-axis and the y-axis that intersect each other. Either one of the x-axis and the y-axis (here, the y-axis) is used as the numerical axis of the monthly benefit per beneficiary of a person with mild physical and mental condition (for example, dementia independence degree II or less). The other (here, the x-axis) is used as the numerical axis of the monthly benefit per beneficiary of a person with severe physical and mental condition (for example, dementia independence degree III or higher).

Then, assuming that the intersection (center) of the x-axis and the y-axis is the coordinate 100 of the monthly benefit per beneficiary of the city's GH average, each beneficiary of a plurality of business establishments 1 to 8 providing home-based services. The coordinates 101 to 108 of the monthly benefit per person are arranged in the quadrant corresponding to the numerical value. As a result, the difference in the amount of benefits with respect to the average value of the GH of the own city becomes clear depending on which quadrant these coordinates 101 to 108 are located in.

According to FIG. 10, the coordinates 102 of the establishment 2 and the coordinates 106 of the establishment 6 are located in the first quadrant, and the monthly benefit per person is higher than the average of the GH in the city for both mild and severe dementia independence. It becomes clear that it is high. In this way, the difference in the amount of benefits for persons with severe and mild dementia independence for each business establishment becomes clear. Therefore, it is possible to take measures based on the results of this analysis.

In the above embodiment, the amount to be analyzed is the monthly benefit per beneficiary, and this data uses the data accumulated in the long-term care insurance data 162 shown in FIG. In addition, various amounts other than the monthly benefit per beneficiary can be analyzed as described later.

In the example of FIG. 11, the results of analyzing the mental and physical condition x subregion x service type x user ratio in the long-term care business are shown in the band graph of (a) and the table of (b). The X mark does not represent multiplication, but means a combination (hereinafter, the same applies).

In this embodiment, home-based services 11a, group home services 11b, and groups other than group homes, which are provided in multiple subregions of the local government, are provided according to mental and physical conditions (here, dementia independence degree IIIa or higher is targeted). For other facilities / residential services 11c, the user ratio for each of these services is displayed in a graph for each subregion. Further, the average value of the user ratio of each subregion for each service is displayed as a graph as the average value of the local government, and the first factor analysis unit 52 shown in FIG. 6 generates the image of the graph described above.

As described above, the purpose of this embodiment is to aggregate the number of users of each service according to the physical and mental condition and to visualize the ratio of service users in each subregion.

For that purpose, the user ratio of service types (home service, group home, other facilities / residential services) according to the physical and mental condition in each subregion is aggregated and made into a band graph.

As a result, as a comparison between subregion 1 to subregion 4 when the degree of independence of dementia is limited to elderly people of IIIa or higher, subregion 1 and subregion 3 have low group home utilization rates and subregions. It can be seen that the utilization rate of group homes is high in 2 and subregion 4.

The following are possible reasons why the utilization rate of group homes in subregion 1 and subregion 3 is low.
・ There are few establishments that provide group home services.
・. There is a strong tendency toward family care in the relevant area.
・ There are many households in the area who cannot afford to receive services.

In response to these reasons, it is possible to confirm the amount of service provided by group homes in subregion 1 and subregion 3, grasp service problems, and utilize them in future measures.

In the example shown in FIG. 12, the graph is displayed by service type × user residence area × business location area × user ratio in the long-term care business. That is, as shown in FIG. 13, it captures which area the service user who lives in a small area in the city is using the business establishment.

In this embodiment, as the purpose, the number of users by service type (here, outpatient long-term care service) and the location area of the business establishment that provides the service are totaled, and the service user ratio by user residence area is calculated. Is visualized.

Therefore, as shown in FIG. 12, for the service users in each subregion, the location office 12a in the subregion, the location office 12b in another subregion (city, etc.), and the location office outside the city The number of users of 12c is totaled to obtain the user ratio, which is summarized in the band graph of (a) and the table of (b).

As a result, outpatient care service users often use business establishments in the subregion in subregion 1 and businesses in the subregion 2 as a comparison between subregion 1 and subregion 4. It can be seen that the use of the place is small. The reason for this is that the outpatient long-term care service users in the subregion 1 have a high service utilization rate at the business establishments in the subregion, which is considered to be due to the sufficient amount of services provided in the subregion. On the other hand, the reason why the outpatient care service users in the subregion 2 have a low service utilization rate of the business establishments in the subregion is considered to be that the amount of services provided in the subregion is insufficient.

As a future response, we will investigate the ratio of users by area of residence of outpatient care service establishments located in subregion 1, and if the ratio of users in the subregion is low, the amount of service supplied is excessive. Therefore, it is possible to consider measures such as not increasing the number of business establishments any more.

On the other hand, the ratio of users by area of residence of outpatient care service establishments located in subregion 2 is investigated, and if the ratio of users in the subregion is high, it is considered that the amount of service supply is insufficient. Therefore, measures such as increasing the number of business establishments can be considered.

In the example shown in FIG. 14, the graph is displayed by service type × business location area × user residence area × user ratio in the long-term care business. That is, as shown in FIG. 15, it captures how much a service user residing in a plurality of small areas including the suburbs is using a business establishment existing in which small area in the city.

In this embodiment, for the purpose, the number of users in the service user's residential area that provides the service is totaled for each service type (here, the outpatient long-term care service), and the ratio of service users by business location is visualized. doing.

Therefore, as shown in FIG. 14, for the service users in each subregion, the user 14a residing in the subregion, the user 14b residing in another subregion (city, etc.), and the use of the suburban residence. Persons 14c are totaled to obtain the user ratio, which is summarized in the band graph of (a) and the table of (b).

As a result, as for outpatient care service users, as a comparison between subregion 1 to subregion 4, the ratio of users residing in the subregion 1 is low in the establishments in subregion 1, and the ratio of users residing in the subregion 2 is low. It can be seen that the ratio of users residing in the subregion is high in business establishments. The reason is that the ratio of users who live in the subregion 1 is low, which is considered to be because the service demand in the subregion is low. On the other hand, it is considered that the reason why the business establishments in the subregion 2 have a high ratio of users living in the subregion is that there is a large demand for services in the subregion.

As a future response, we will investigate the ratio of outpatient care service users living in subregion 1 by business location, and if the ratio of users in the subregion is high, the amount of service supplied is excessive. Therefore, consider measures such as not increasing the number of business establishments any more.

On the other hand, the ratio of outpatient care service users residing in subregion 2 by business location is examined, and if the ratio of users in the subregion is low, it is possible that the amount of service supplied is insufficient. Therefore, consider measures such as increasing the number of business establishments.

The explanation so far has been the explanation of the example regarding the quantitative analysis in the long-term care business 11 by the first factor analysis unit 52 of the regional management support function unit 21 shown in FIG. An example of quantitative analysis in the comprehensive project 12 including long-term care prevention and daily life support by the factor analysis unit 52 of 1 will be described.

In the example of FIG. 16, the purpose is to visualize the utilization rate of services such as comprehensive projects in each subregion in the comprehensive project 12, and compare it with the average value of local governments to grasp the implementation status of measures in each subregion. There is. In FIG. 16, for example, the results of analyzing the elderly gender / age group × subregion × service such as comprehensive business × utilization rate, etc. in the comprehensive business 12 are shown in (a) line graph and (b) table. Shown.

Here, as an example of measures in the comprehensive project 12, the health examination consultation rate 16a, the energy-building station participation rate 16b, the monitoring service usage rate 16c, and the food distribution service usage rate 16d are aggregated into a line graph. .. Each utilization rate, etc. is calculated using the number of elderly people by gender and age group in the subregion.

In the example of FIG. 16, women and the elderly are targeted, and it is shown that the participation rate of the energy-building station 16b is higher in the subregion 1 than the local government average and lower in the subregion 4 than the local government average.

The following can be considered as the reasons why the participation rate 16b of the energy-building station in the subregion 4 is low.
・ There are few places to supply local services for measures.
・ The service is not well known in the area.
・ There are many households in the area who do not have time to receive services.
・. The relevant area flows to other service types other than the Genki-zukuri station.

For these reasons, as a future response, we will check the participation status of the energy-building station in subregion 1 and introduce the measures as best practices to other regions. In addition, it is possible to set activity goals such as confirming the situation of local service users in subregions 4 and the like, grasping problems and utilizing them in measures.

In the example shown in FIG. 17, the graph of the elderly gender / age group × user residence area × general business service location area × user ratio in the comprehensive business is displayed. That is, as explained in FIG. 13, a service user (targeted to a female late-stage elderly person) residing in a small area in the city uses a business establishment (comprehensive service business) in which area. It captures what is happening.

In this embodiment, the purpose is to aggregate the number of users in the comprehensive business service location area and visualize the service user ratio by user residence area.

Therefore, as shown in FIG. 17, for service users in each subregion, the business establishments 17a in the subregion, the business establishments 17b in other subregions (city, etc.), and the business establishments outside the city The number of users of 17c is totaled, the user ratio is calculated, and summarized in the band graph of (a) and the table of (b).

As a result, as a comparison between the subregion 1 to the subregion 4, the users of the comprehensive business service often use the business establishments in the subregion 1 and the subregion 2 is the business establishment in the subregion. It can be seen that the use of is small. The reason is that the general business service users in the subregion 1 have a high utilization rate of the comprehensive business service location area in the subregion, and it is considered that the amount of services provided in the subregion is sufficient. On the other hand, the reason why the general business service users in the subregion 2 have a low utilization rate of the comprehensive business service location area in the subregion is considered to be that the amount of services provided in the subregion is insufficient.

As a future response, we will investigate the user ratio by user residence area of the comprehensive business service establishment located in subregion 1, and if the user ratio in the subregion is low, the amount of service supply is excessive. Therefore, it is possible to consider measures such as not increasing the number of business establishments any more. On the other hand, the user ratio by user residence area of the comprehensive business service establishment located in the subregion 2 is investigated, and if the user ratio in the subregion is high, it is considered that the service supply amount is insufficient. Therefore, measures such as increasing the number of business establishments can be considered.

In the example shown in FIG. 18, the graph of the elderly gender / age group × the comprehensive business service location area × the user residence area × the user ratio in the comprehensive business is displayed. That is, as explained in FIG. 15, the business of the comprehensive business service in which the comprehensive business service users (targeted to the female late-stage elderly) who live in a plurality of subregions including the suburbs exist in which subregion. It captures how much you are using the place.

In this embodiment, the purpose is to aggregate the number of users in the service user residential area for each service type of the comprehensive business and to visualize the service user ratio for each business location.

Therefore, as shown in FIG. 18, for the service users in each subregion, the users 18a residing in the subregion, the users 18b residing in other subregions (city, etc.), and the use of the suburban residence. Persons 18c are totaled, the user ratio is calculated, and summarized in the band graph of (a) and the table of (b).

As a result, in the example of FIG. 18, as a comparison between the subregion 1 to the subregion 4, the ratio of the users who live in the subregion 1 is low in the business establishment of the subregion 1. , It can be seen that the ratio of users residing in the subregion 2 is high in the business establishments in the subregion 2. The reason is that the ratio of users who live in the subregion 1 is low, which is considered to be because the service demand in the subregion is low. On the other hand, it is considered that the reason why the business establishments in the subregion 2 have a high ratio of users living in the subregion is that there is a large demand for services in the subregion.

As a future measure, check the ratio of users of general business service users residing in subregion 1 by business location, and if the ratio of users in the subregion is high, the amount of service supplied is excessive. Therefore, it is possible to consider measures such as not increasing the number of business establishments any more. On the other hand, the ratio of users of general business service users residing in subregion 2 by business location is examined, and if the ratio of users in the subregion is low, it is possible that the amount of service supply is insufficient. Therefore, measures such as increasing the number of business establishments can be considered.

Next, an example relating to the quantitative analysis in the medical-assistance cooperation 13 by the first factor analysis unit 52 will be described.

The purpose of the embodiment of FIG. 19 is to aggregate the users of home medical services in each subregion by disease classification or degree of long-term care in the medical care cooperation 13, and to visualize the user ratio by subregion. be. In FIG. 19, for example, the results of analyzing the disease classification / degree of care required × subregion × home medical service × user ratio in the medical care cooperation 13 are shown in (a) a line graph and (b) a table. It is shown by.

Here, as an example of home medical service for the elderly suffering from stroke, the number of users of home-visit medical care 19a, home-visit nursing care 19b, home-visit home visit 19c, and other 19d is totaled, and the user ratio is shown in a line graph.

In the example of FIG. 19, the elderly suffering from stroke are targeted, and the service utilization rate of the home-visit medical care 19a is high in the subregion 2 and low in the subregion 1 as a comparison among the subregions 1 to 4. You can see that. Further, although the service utilization rate of the home-visit home visit 19b is proportional to the utilization rate of the home-visit nursing care 19c, it can be seen that the service utilization rate of the home-visit home visit 19c and the like is low only in the subregion 2.

In this way, since the usage rate of home-visit medical services is high and the usage rate of home-visit services is low in subregion 2, it is considered that home medical services are well-developed and home medical services are sufficiently supplied in subregion 2. .. For this reason, it is recommended to interview home medical users in subregion 2 and widely disseminate information as a best practice.

In the embodiment shown in FIG. 20, the graph of home medical service, user's residential area, home medical institution's location, and user ratio is displayed in a graph. That is, as shown in FIG. 21, a business establishment (home medical institution) in which a small area in the city has a home medical service user (target is a female late-stage elderly) exists in any small area including the suburbs. ) Is used.

In this embodiment, the purpose is to aggregate the number of users of the home medical institution and visualize the service user ratio by the user's residential area.

Therefore, as shown in FIG. 20, for service users in each subregion, the location business of the subregion 20a, the location business of another subregion (city, etc.) 20b, and the location business of the subregion 20c. The number of users of the place is totaled, the user ratio is calculated, and summarized in the band graph of (a) and the table of (b).

As a result, in the example of FIG. 20, as a comparison among the subregions 1 to 4, the users of the subregion 1 often use the home medical services in the subregion, and the users of the subregion 2 are the same. It can be seen that the use of home medical services in small areas is low. The reason for this is that the home medical service users in the subregion 1 have a high utilization rate of the home medical institution location area in the subregion, which is considered to be due to the sufficient amount of services provided in the subregion. On the other hand, the reason why the home medical service users in the subregion 2 have a low utilization rate of the home medical institution location area in the subregion is considered to be that the amount of services provided in the subregion is insufficient.

As a future response, we will investigate the ratio of users of home medical service establishments located in subregion 2 by area of residence, and if the ratio of users in the subregion is high, the amount of service supply may be insufficient. Therefore, it is possible to consider measures such as increasing the number of home medical institutions.

In the embodiment shown in FIG. 22, the graph is displayed by home medical service, by home medical institution location area, by user's residential area, and by user ratio in medical-care cooperation. That is, as shown in FIG. 23, home medical service users (targeted to the elderly women) who live in a plurality of subregions including the suburbs exist in any subregion of the city. It captures how much you are using your business office (home medical institution).

In this embodiment, for the purpose, the number of users in the service user's residential area is totaled for each type of home medical service, and the ratio of service users for each home medical institution location area is visualized.

Therefore, as shown in FIG. 22, for the service users in each subregion, the user 22a residing in the subregion, the user 22b residing in another subregion (city, etc.), and the use of the suburban residence. Persons 22c are totaled, the user ratio is calculated, and summarized in the band graph of (a) and the table of (b).

As a result, in the example of FIG. 22, as for the home medical service users, the ratio of the users who live in the subregion 1 is low in the business establishments in the subregion 1 as a comparison in the subregion 1 to the subregion 4. , It can be seen that the ratio of users residing in the subregion 2 is high in the business establishments in the subregion 2. Here, it is considered that the reason why the ratio of users who live in the subregion 1 is low is that the service demand in the subregion is small. On the other hand, it is considered that the reason why the business establishments in the subregion 2 have a high ratio of users living in the subregion is that there is a large demand for services in the subregion.

In this way, the ratio of home medical service users residing in subregion 2 by business location is examined, and if the ratio of users in the subregion is low, it is possible that the amount of service supplied is insufficient. Therefore, it is possible to consider measures such as increasing the number of home medical institutions.

Next, in the regional management support function unit 21 shown in FIGS. 2 and 6, the function of the second factor analysis unit 53 that analyzes qualitative problems for each of the long-term care business 11, the comprehensive business 12, and the medical care cooperation 13. Will be explained. As described above, the second factor analysis unit 53 analyzes qualitative problems related to changes in mental and physical conditions by business unit such as a subregion or business establishment, and by mental and physical condition, and solves problems based on the analysis results. It makes it possible to identify.

Here, in the long-term care business 11, the maintenance period until a change (deterioration or improvement) in the mental and physical condition is used as an important index when performing a qualitative analysis. This maintenance period will be described with reference to FIG.

The mental and physical condition is defined to that extent, and it is said that the mental and physical condition deteriorated when the stage increased, and the mental and physical condition improved when the stage decreased. In FIG. 24, the degree of need for nursing care is used as the mental and physical condition, and the state of change (deterioration or improvement) is shown by dividing it into four patterns.

Pattern 1 shows a case where the maintenance period from the need for nursing care 3 to the need for nursing care 4 is shorter than the average maintenance period until the deterioration, that is, the deterioration is early.

Pattern 2 shows a case where the maintenance period from the need for nursing care 3 to the need for nursing care 4 is longer than the average maintenance period until the deterioration, that is, the deterioration is slow.

Pattern 3 shows a case where the maintenance period from the improvement from the need for care 3 to the need for care 2 is shorter than the average maintenance period until the improvement, that is, the improvement is quick.

Pattern 4 shows a case where the maintenance period from the improvement from the need for care 3 to the need for care 2 is longer than the average maintenance period until the improvement, that is, the improvement is slow.

These maintenance periods are applied not only to the above-mentioned gradual changes in the degree of need for care, but also to various changes in the mental and physical condition including the degree of independence of dementia. Further, the maintenance period until the above-mentioned change can be easily calculated by using the matching relationship table 161c of the long-term care insurance data 161 shown in FIG. Hereinafter, each example of performing a qualitative analysis of the long-term care business 11 will be described.

In the example of FIG. 25, the second factor analysis unit 53 uses the insurer as a local government (here, the city), and sets the average maintenance period until deterioration of the physical and mental condition of the home-based service of the long-term care business as the city average. , A comparison is made with multiple subregions (subregions 1 to 8) in the city that provide home-based services, which is a business unit. That is, the difference in the average maintenance period of each subregion 1 to 8 until deterioration with respect to the average value of the own city is obtained, an image showing these relationships is generated, and the position of each subregion 1 to 8 of the own city is clarified. We have found an index that needs to be examined.

In FIG. 25, a coordinate plane is used which is divided into four regions of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant by the x-axis and the y-axis that intersect each other. Either one of the x-axis and the y-axis (here, the y-axis) is used as the numerical axis of the average maintenance period until the deterioration of the person with mild mental and physical condition (for example, the degree of care required is 2 or less). The other (here, the x-axis) is used as the numerical axis of the average maintenance period until the deterioration of the person with severe physical and mental condition (for example, the degree of care required is 3 or more).

Then, when the intersection (center) of the x-axis and the y-axis is set to the coordinates 250 of the average maintenance period until the deterioration of the local city average, each of the multiple subregions 1 to 8 providing the home-based service up to each deterioration. Coordinates 251 to 258 of the average maintenance period are arranged in the quadrant corresponding to the numerical value. From this, the difference in the average maintenance period until deterioration with respect to the own city average value becomes clear depending on which quadrant these coordinates 251 to 258 are located in.

According to FIG. 25, the coordinates 253 of the subregion 3 and the coordinates 255 of the subregion 5 are located in the first quadrant, and the average maintenance period until deterioration is longer than the own city average for both the mild and severe needing care. It becomes clear (qualitatively preferable). On the other hand, the coordinates 251 of the subregion 1 and the coordinates 257 of the subregion 7 are located in the third quadrant, and the average maintenance period until deterioration is shorter than the own city average for both the mild and severe needing care ( It becomes clear that it is qualitatively unfavorable).

In this way, the difference in the average maintenance period until deterioration between severe and mild people requiring nursing care is clarified for each subregion, and it is possible to take measures based on this analysis result.

In the example of FIG. 26, the insurer is a local government (here, the city), and the average maintenance period until deterioration in the facilities / residential services (here, the group home) of the long-term care business is set as the city average and the business. Comparisons are made with multiple business establishments (business establishments 1 to 8) in the city that implement group homes (hereinafter sometimes abbreviated as GH), which is a unit. That is, the difference in the average maintenance period of each business establishment 1 to 8 until deterioration with respect to the average value of the own city is obtained, an image showing these relationships is generated, and the position of the business establishments 1 to 8 of the own city is clarified. We are finding indicators that need to be examined.

In FIG. 26, a coordinate plane is used which is divided into four regions of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant by the x-axis and the y-axis that intersect each other. Either one of the x-axis and the y-axis (here, the y-axis) is used as the numerical axis of the average maintenance period until deterioration of a person with mild mental and physical condition (for example, dementia independence degree II or less). The other (here, the x-axis) is used as the numerical axis of the average maintenance period until deterioration of a person with severe mental and physical condition (for example, dementia independence degree III or higher).

Then, when the intersection (center) of the x-axis and the y-axis is set to the coordinates 260 of the average maintenance period until the deterioration of the GH average of the own city, each GH of the plurality of business establishments 1 to 8 providing the GH service Coordinates 261 to 268 are arranged in the quadrant corresponding to the numerical value. From this, the difference in the average maintenance period until deterioration with respect to the own city GH average value becomes clear depending on which quadrant these coordinates 261 to 268 are located in.

According to FIG. 26, the coordinates 263 of the establishment 3 and the coordinates 267 of the subregion 7 are located in the first quadrant, and both the mild and severe dementia independence are maintained on average until worse than the GH average in their own city. It becomes clear that the period is long (qualitatively preferable). On the other hand, the coordinates 252 of subregion 2 and the coordinates 256 of subregion 6 are located in the third quadrant, and the average maintenance period until deterioration from the city average is longer for both mild and severe dementia independence. It becomes clear that it is short (quality unfavorable).

In this way, the difference in the average maintenance period until deterioration of dementia independence between severe and mild dementia in each subregion becomes clear, and it is possible to take measures based on this analysis result.

In the embodiment of FIG. 27, the purpose is to visualize the average value of the maintenance period of each mental and physical condition aggregated at each business establishment in the long-term care business 11, and to determine the speed at which the physical and mental condition of the user at each business establishment deteriorates. It is to visualize. That is, in FIG. 27, the results of analyzing, for example, by service type × by establishment × by aggregated mental and physical condition item × average maintenance period (business plan unit) until deterioration in the nursing care business 11 are shown in (a) a line graph and It is shown in the table of (b).

Here, each business establishment is GH, and the items of intensive mental and physical condition are the degree of long-term care required 27a, the degree of independence of dementia 27b, the degree of independence of disability 27c, and the physical function / wake-up movement (Group 1: Items selected from the physical function / wake-up movement). However, long-term care services are not expected to lead to improvement / maintenance of mental and physical condition (excluding items of paralysis / contracture, sight / hearing) 27d, life function (group 2: items selected from life function) 27e, cognition. There are 7 items: functional / social life adaptation (3rd group: cognitive function, 5th group: items selected from social life adaptation) 27f, and mental / behavioral disorders (4th group: items selected from mental / behavioral disorders) 27g. The average value of the maintenance period of these mental and physical condition items at the business establishment is aggregated and made into a line graph.

In the example of FIG. 27, as a comparison among the group home establishments 1 to 6, the establishments 4 and 6 have an average maintenance period including mental / behavioral disorders 27g and cognitive function / social life adaptation 27f. Is long overall, and it can be seen that the average maintenance period including the average maintenance period 27d of the physical function / occupancy movement 27d and the living function of the establishment 2 and the establishment 5 is short overall.

From these facts, it is considered that the quality of the long-term care service is high in the business establishment 4 and the business establishment 6, and the long-term care service of the business establishment 2 and the business establishment 5 is low. Therefore, as a future action, we will hear about business establishments 4 and 6 and introduce the good points to other business establishments as best practices. In addition, we will hear about business establishments 2 and 5 to understand their problems and give guidance.

In the example of FIG. 28, the average value of the maintenance period of the mental and physical condition (detailed item: hereinafter referred to as detailed mental and physical condition item) at each business establishment (referred to as GH) in the nursing care business 11 is aggregated, and each business establishment Visualize the speed at which the user's physical and mental condition deteriorates. In other words, the average maintenance period (business plan unit) until deterioration is analyzed and displayed as a line graph by service type x detailed mental and physical condition item x business establishment x deterioration.

Detailed mental and physical condition items are 65 items in total, including the degree of long-term care, dementia independence, disability independence, and 62 certified survey items. Make a graph.

Here, the relationship between the above-mentioned detailed certification survey items and the aggregated mental and physical condition items summarized by field is shown below.

Physical function / moving movement: Items selected from 1 group Living function: Items selected from 2 groups Cognitive function: Items selected from 3 groups Mental / behavioral disorders: Items selected from 4 groups Social life adaptation: Items selected from 5 groups Items Excluded from the above: Items of paralysis / contracture and eyesight / hearing that are not expected to lead to improvement / maintenance of mental and physical condition by long-term care services

In the example of FIG. 28, the average maintenance period of the establishment 1 (folded line 28a) is longer than that of the average of all the establishments (folded line 28d), and the establishment 2 (folded line 28b) is the physical system (physical function).・ It can be seen that the average maintenance period of (movement, living function) is short, and the average maintenance period of the mental system (cognitive function, mental / behavioral disorder, social life adaptation) is short at the establishment 3 (line 28c).

From these facts, it is considered that the quality of the long-term care service of the business establishment 1 is high, the quality of the physical care service of the business establishment 2 is low, and the quality of the mental care service of the business establishment 3 is low. For this reason, as future actions, the physical system will hear about business establishments 1 and 3, and the mental system will hear about business establishments 1 and 2, and the good points of each will be used as best practices for other business establishments. It is recommended to introduce. In addition, regarding problems, the physical system will hear about the establishment 2 and the mental system will hear about the establishment 3, and each problem will be grasped and instructed.

The purpose of the embodiment of FIG. 29 is to visualize the improvement rate of each mental and physical condition aggregated at each business establishment in the long-term care business 11. That is, in FIG. 29, the results of analyzing, for example, by service type × by establishment × by aggregated mental and physical condition item × improvement rate (business plan unit) in the nursing care business 11, are shown in the line graph of (a) and (b). It is shown in the table of.

The improvement rate of the business establishment refers to the ratio of the users of the business establishment where the improvement described in FIG. 24 has occurred.

In FIG. 29, the service type is GH, and the items of intensive mental and physical condition are the degree of care required 29a, the degree of independence of dementia 29b, the degree of independence of disability 29c, and the physical function / wake-up movement (Group 1: Items selected from the physical function / wake-up movement). However, care services are not expected to improve / maintain the physical and mental condition (excluding items of paralysis / contracture, sight / hearing) 29d, life function (group 2: items selected from life function) 29e, cognition. There are 7 items: functional / social life adaptation (3rd group: cognitive function, 5th group: items selected from social life adaptation) 29f, and mental / behavioral disorders (4th group: items selected from mental / behavioral disorders) 29g. The improvement rates of these mental and physical condition items at business establishments (GH) are tabulated and made into a line graph.

In the example of FIG. 29, as a comparison between the group home establishments 1 to 6, the establishments 4 and 6 have an improvement rate including mental / behavioral disorders 27g and cognitive function / social life adaptation 27f. It can be seen that the overall improvement rate is high, including the physical function / wake-up movement 27d and the average maintenance period 27e of the living function of the establishment 2 and the establishment 5.

From these facts, it is considered that the long-term care services at the business establishments 4 and 6 are suitable for the physical and mental condition of the user, and conversely, the long-term care services at the business establishments 2 and 5 are not suitable for the physical and mental condition of the user. Be done. Therefore, as a future action, we will interview the care managers of business establishments 2 and 5 to understand the problems and give guidance.

In the embodiment of FIG. 30, the improvement rate of the detailed mental and physical condition at each business establishment (referred to as GH) in the long-term care business 11 is aggregated and visualized. In other words, we analyze each service type x detailed mental and physical condition item x business establishment x improvement rate (business plan unit) and display it as a line graph.

Detailed mental and physical condition items are 65 items in total, including the degree of long-term care, dementia independence, disability independence, and 62 certified survey items. Make a graph.

Here, the relationship between the detailed certification survey items and the aggregated mental and physical condition items summarized by field is shown below.

Physical function / moving movement: Items selected from 1 group Living function: Items selected from 2 groups Cognitive function: Items selected from 3 groups Mental / behavioral disorders: Items selected from 4 groups Social life adaptation: Items selected from 5 groups Items Excluded from the above: Items of paralysis / contracture and eyesight / hearing that are not expected to lead to improvement / maintenance of mental and physical condition by long-term care services

In the example of FIG. 30, compared with the average of all business establishments (folded line 30d), the business establishment 1 (folded line 30a) has a higher overall improvement rate, and the business establishment 2 (folded line 30b) has a physical system (physical function / occupancy). It can be seen that the improvement rate of the movement and living function) is low, and the improvement rate of the mental system (cognitive function, mental / behavioral disorder, social life adaptation) is low at the business establishment 3 (line 30c).

From these facts, it is considered that the quality of the long-term care service of the business establishment 1 is high, the quality of the physical care service of the business establishment 2 is low, and the quality of the mental care service of the business establishment 3 is low. For this reason, as future actions, the physical system will hear about business establishments 1 and 3, and the mental system will hear about business establishments 1 and 2, and the good points of each will be used as best practices for other business establishments. It is recommended to introduce. In addition, regarding problems, the physical system will hear about the establishment 2 and the mental system will hear about the establishment 3, and each problem will be grasped and instructed.

In the embodiment of FIG. 31, the purpose is the user whose purpose is slower than the average for each of the aggregated mental and physical conditions at each business establishment as described in FIG. 24 (user of pattern 2 in FIG. 24). Visualize the ratio. That is, in FIG. 31, for example, in the nursing care business 11, the result of analyzing the service type × business establishment × aggregated mental and physical condition item × user ratio (business plan unit) whose deterioration is slower than the average is shown in the line graph of (a). , And (b) are shown in the table.

The ratio of users of business establishments whose deterioration is slower than the average refers to the ratio of users of the business establishment in pattern 2 of FIG. 24.

In FIG. 31, the service type is GH, and the intensive mental and physical condition items are the degree of care required 31a, the degree of independence of dementia 31b, the degree of independence of disability 31c, physical function / wake-up movement 31d, and living function (31e, cognitive function / social life adaptation). There are 7 items, 31f and 31g of mental and behavioral disorders, and the ratio of users whose mental and physical condition items at each business establishment (GH) deteriorates more slowly than the average is tabulated and made into a line graph.

In the example of FIG. 31, as a comparison among the group home establishments 1 to 6, the establishments 4 and 6 have a mental / behavioral disorder 31g and cognitive function / social life adaptation 31f, which are worse than the average. It can be seen that the ratio of slow users is high, and the ratio of users whose physical function / wake-up movement 31d and living function of business establishments 2 and 5 are slower than average is low.

From these facts, it is considered that the long-term care services at the business establishments 4 and 6 are suitable for the physical and mental condition of the user, and conversely, the long-term care services at the business establishments 2 and 5 are not suitable for the physical and mental condition of the user. Be done. Therefore, as a future action, we will interview the care managers of business establishments 2 and 5 to understand the problems and give guidance.

In the embodiment of FIG. 32, the ratio of users whose detailed mental and physical conditions at each business establishment (referred to as GH) in the long-term care business 11 deteriorates more slowly than the average is aggregated and visualized. In other words, the ratio of users by service type x detailed mental and physical condition item x business establishment x slower deterioration than average (business plan unit) is analyzed and displayed as a line graph.

Detailed mental and physical condition items are 65 items in total, including the degree of long-term care, dementia independence, disability independence, and 62 certified survey items. Make a graph. The relationship between the detailed certification survey items and the aggregated mental and physical condition items summarized by field is the same as described above, and the description is omitted.

In the example of FIG. 32, as compared with the average of all business establishments (line chart 32d), the ratio of users whose deterioration is slower than the average of business establishment 1 (line chart 32a) is higher than that of business establishment 2 (line chart 32b). The percentage of users whose deterioration is slower than the average of the physical system (physical function / moving movement, living function) is low, and the average of the mental system (cognitive function, mental / behavioral disorder, social life adaptation) at business establishment 3 (line 32c). It can be seen that the ratio of users whose deterioration is slower is low.

From these facts, it is considered that the quality of the long-term care service of the business establishment 1 is high, the quality of the physical care service of the business establishment 2 is low, and the quality of the mental care service of the business establishment 3 is low. For this reason, as future actions, the physical system will hear about business establishments 1 and 3, and the mental system will hear about business establishments 1 and 2, and the good points of each will be used as best practices for other business establishments. It is recommended to introduce. In addition, regarding problems, the physical system will hear about the establishment 2 and the mental system will hear about the establishment 3, and each problem will be grasped and instructed.

In the example of FIG. 33, the purpose is to visualize the average maintenance period until the deterioration of each mental and physical condition aggregated in each subregion where the home service is provided in the long-term care business 11, and the user in each subregion. The purpose is to visualize the speed at which the physical and mental condition of the patient deteriorates. That is, in FIG. 33, the results of analyzing, for example, by service type × by subregion × by intensive mental and physical condition item × average maintenance period (business plan unit) until deterioration in the nursing care business 11 are shown in (a) a line graph and It is shown in the table of (b).

Here, the service type is a home service, and the intensive mental and physical condition items are the degree of care required 33a, the degree of independence of dementia 33b, the degree of independence of disability 33c, physical function / wake-up movement 33d, living function 33e, cognitive function / social life adaptation 33f. , Mental and behavioral disorders 33g, 7 items, and the average value of the maintenance period of these mental and physical condition items at each business establishment is totaled and made into a line graph.

In the example of FIG. 33, as a comparison among subregions 1 to 6, subregions 3 and 5 have an overall average maintenance period including 33 g of mental / behavioral disorders and 33f of cognitive function / social life adaptation. It can be seen that the average maintenance period including the physical function / wake-up movement 33d and the average maintenance period 33e of the living function in the subregion 1 and the subregion 4 is short as a whole.

From these facts, the care plans of home care support providers (care managers) in subregions 3 and 5 grasp the needs of users, and the care plans of care managers in subregions 1 and 4 are It is considered that it does not meet the needs of users. Therefore, as a future action, we will interview the care managers in subregion 3 and subregion 5 and introduce the good points to other business establishments as best practices. In addition, we will interview the care managers in subregion 1 and subregion 4 to understand their problems and give guidance.

In the example of FIG. 34, the average value of the maintenance period of the mental and physical condition (detailed item: hereinafter referred to as detailed mental and physical condition item) in each subregion in which the home service is provided in the long-term care business 11 is aggregated and each is calculated. Visualize the speed at which the physical and mental condition of users deteriorates in small areas. In other words, the average maintenance period (business plan unit) until deterioration is analyzed and displayed as a line graph by service type x detailed mental and physical condition item x subregion x deterioration.

Detailed mental and physical condition items are 65 items in total, including the degree of long-term care, dementia independence, disability independence, and 62 certified survey items. Make a graph. The relationship between the above-mentioned detailed certification survey items and the aggregated mental and physical condition items summarized by field is the same as described above, and the description thereof will be omitted.

In the example of FIG. 34, the average maintenance period of the subregion 1 (complex line 34a) is longer overall than that of the local government average (complex line 34d), and the subregion 2 (fold line 34b) is the physical system (physical function / occupancy movement). , Living function) has a short average maintenance period, and subregion 3 (line 34c) has a short average maintenance period of the mental system (cognitive function, mental / behavioral disorders, social life adaptation).

From these facts, the care plan of the home care support provider (care manager) in the subregion 1 grasps the needs of the user, and the physical care plan of the care manager in the subregion 2 is the needs of the user. It does not match, and it is considered that the care manager's mental care plan in subregion 3 does not meet the needs of the user. Therefore, as future actions, the physical system will hear the care managers in subregion 1 and subregion 3, and the mental system will hear the care managers in subregion 1 and subregion 2, and the good points of each will be the best practice. , It is recommended to refer to other subregions. Regarding problems, the physical system hears the care manager in the subregion 2, and the mental system hears the care manager in the subregion 3, and each problem is grasped and instructed.

The purpose of the embodiment of FIG. 35 is to visualize the improvement rate of each mental and physical condition aggregated in each subregion where the home service is provided in the long-term care business 11. That is, in FIG. 35, the results of analyzing, for example, by service type × by subregion × by aggregated mental and physical condition item × improvement rate (business plan unit) in the nursing care business 11, are shown in the line graph of (a) and (b). It is shown in the table of.

The improvement rate of a subregion refers to the percentage of users in the subregion who have made the improvement described in FIG. 24.

In FIG. 35, the service type is a home service, and the intensive mental and physical condition items are the degree of care required 35a, the degree of independence of dementia 35b, the degree of independence of disability 35c, the physical function / wake-up movement 35d, the living function 35e, the cognitive function / social life adaptation. There are 7 items, 35f and 35g of mental and behavioral disorders, and the improvement rates of these mental and physical condition items in each subregion are tabulated and made into a line graph.

In the example of FIG. 35, as a comparison between subregions 1 to 6, subregions 4 and 6 have an overall improvement rate including mental / behavioral disorders 35 g and cognitive function / social life adaptation 35f. It is high, and it can be seen that the improvement rate including the physical function / wake-up movement 35d and the average maintenance period 35e of the living function in the subregion 2 and the subregion 5 is short as a whole.

From these facts, the care plans of home care support providers (care managers) in subregions 4 and 6 grasp the needs of users, and conversely, the spirit of care managers in subregions 2 and 5. It is considered that the care plan of the system does not meet the needs of the user. Therefore, as a future action, we will interview the care managers in subregion 2 and subregion 5, grasp the problems, and give guidance.

In the example of FIG. 36, the improvement rate of the detailed mental and physical condition in each subregion where the home service is provided in the long-term care business 11 is aggregated and visualized. In other words, the service type x detailed mental and physical condition item x subregion x improvement rate (business plan unit) is analyzed and displayed as a line graph.

Detailed mental and physical condition items are 65 items in total, including the degree of long-term care, dementia independence, disability independence, and 62 certified survey items. Make a graph. The relationship between the detailed certification survey items and the aggregated mental and physical condition items summarized by field is the same as described above, and the description is omitted.

In the example of FIG. 36, the improvement rate is higher in the small area 1 (line 36a) as a whole, and the improvement rate in the subregion 2 (line 36b) is higher than that of the average of all business establishments (line 36d). It can be seen that the improvement rate of movement and living function) is low, and the improvement rate of the mental system (cognitive function, mental / behavioral disorder, social life adaptation) is low in subregion 3 (line 36c).

From these facts, the care plan of the home care support provider (care manager) in the subregion 1 grasps the needs of the user, and the physical care plan of the care manager in the subregion 2 is the needs of the user. It does not match, and it is considered that the care manager's mental care plan in subregion 3 does not meet the needs of the user. Therefore, as future actions, the physical system will hear the care managers in subregion 1 and subregion 3, and the mental system will hear the care managers in subregion 1 and subregion 2, and the good points of each will be the best practice. , It is recommended to refer to other subregions. Regarding problems, the physical system hears the care manager in the subregion 2, and the mental system hears the care manager in the subregion 3, and each problem is grasped and instructed.

In the embodiment of FIG. 37, the purpose is the user whose purpose is slower than the average, which is described in FIG. 24, for each of the aggregated mental and physical conditions in each subregion where the home service is provided (FIG. 24). (Users of pattern 2) ratio is visualized. That is, in FIG. 37, for example, in the long-term care business 11, the result of analyzing the service type × subregion × aggregated mental and physical condition item × user ratio (business plan unit) whose deterioration is slower than the average is shown in the line graph of (a). , And (b) are shown in the table.

The ratio of users in a subregion whose deterioration is slower than the average refers to the ratio of users in the pattern 2 of FIG. 24 among the users in the subregion.

In FIG. 37, the service type is a home service, and the intensive mental and physical condition items are the degree of care required 37a, the degree of independence of dementia 37b, the degree of independence of disability 37c, physical function / wake-up movement 37d, living function 37e, cognitive function / social life adaptation. There are 7 items, 37f and 37g of mental and behavioral disorders, and the ratio of users whose deterioration is slower than the average of these mental and physical condition items in each subregion is tabulated and made into a line graph.

In the example of FIG. 37, as a comparison among the subregions 1 to 6, the subregions 4 and 6 are the users whose mental / behavioral disorders 37 g and cognitive function / social life adaptation 37f are slower than the average. It can be seen that the ratio is high, and the ratio of users whose physical function / wake-up movement 37d and living function 37e in subregion 2 and subregion 5 deteriorates more slowly than the average is low.

From these facts, the care plans of home care support providers (care managers) in subregions 4 and 6 grasp the needs of users, and conversely, the care of care managers in subregions 2 and 5 It is considered that the plan does not meet the needs of the user. Therefore, as a future action, we will interview the care managers in subregion 2 and subregion 5, grasp the problems, and give guidance.

In the example of FIG. 38, the ratio of users whose detailed mental and physical conditions in each subregion where the home service is provided in the long-term care business 11 deteriorates more slowly than the average is aggregated and visualized. In other words, the ratio of users by service type x detailed mental and physical condition item x subregion x user ratio (business plan unit) whose deterioration is slower than the average is analyzed and displayed as a line graph.

Detailed mental and physical condition items are 65 items in total, including the degree of long-term care, dementia independence, disability independence, and 62 certified survey items. Make a graph. The relationship between the detailed certification survey items and the aggregated mental and physical condition items summarized by field is the same as described above, and the description is omitted.

In the example of FIG. 38, as compared with the local government average (line 38d), the subregion 1 (line 38a) has a higher proportion of users whose deterioration is slower than the average, and the subregion 2 (line 38b) is a physical system. The percentage of users whose deterioration is slower than average in (physical function / moving movement, living function) is low, and the improvement rate of the mental system (cognitive function, mental / behavioral disorder, social life adaptation) is low in subregion 3 (line 38c). It turns out to be low.

From these facts, the care plan of the home care support company (care manager) in the subregion 1 grasps the needs of the user, and the physical care plan of the care manager in the subregion 2 meets the needs of the user. It does not match, and it is considered that the care manager's mental care plan in Subregion 3 does not meet the needs of the user. Therefore, as future actions, the physical system will hear the care managers in subregion 1 and subregion 3, and the mental system will hear the care managers in subregion 1 and subregion 2, and the good points of each will be the best practice. , It is recommended to refer to other subregions. Regarding problems, the physical system hears the care manager in the subregion 2 and the mental system hears the care manager in the subregion 3 to understand the problems and give guidance.

In the embodiment of FIG. 39, a business is carried out by setting a selection threshold for long-term care effort in the mental and physical condition of the user (initial user) at the start of the service and resident information such as households, and totaling the long-term care effort according to the size. The purpose is to visualize the amount of time and effort required to care for the initial user. That is, the results of analyzing the service type × business establishment × initial user selection condition × user ratio are shown in the line graph in (a) and the table in (b).

Here, the reason for analyzing the initial user selection ratio is to make the evaluation fair for each business establishment. That is, when a business establishment that provides a long-term care service accepts a user (initial user) who uses the long-term care service for the first time, it is one of the evaluation scales that the acceptance (selection) of the initial user is biased. The length of the average duration of physical and mental health may not accurately represent the quality of service at the establishment.

Therefore, as shown in FIG. 39, the initial user selection condition items are long-term care required care 2 or less 39a, disability independence degree A1 or less 39b, dementia independence degree IIa or less 39c, non-living 39d, household income 39e, and business. By setting a distance of 39f from the place, totaling the ratio of the number of users at each place of business, and making a line graph, the amount of time and effort required for long-term care is visualized.

In the example of FIG. 39, as a comparison among the establishments 1 to 7, it can be seen that the establishment 1 has a high ratio of dementia independence degree IIa or less. From this, it is highly probable that the establishment 1 intentionally selects elderly people with a low degree of independence of dementia and provides services. As a future action, it will be possible to hear about business establishment 1, grasp problems, and give guidance.

In the example shown in FIG. 40, the maintenance period until deterioration of the degree of long-term care required, the degree of disability independence, and the degree of dementia independence of all service users and the average monthly amount of long-term care benefit expenses up to the average life expectancy are totaled, and all service users The purpose is to visualize the speed at which the physical and mental condition deteriorates and the cost of long-term care benefits. In other words, all service users x the degree of disability independence, the maintenance period until the degree of dementia independence deteriorates, and the average monthly long-term care benefit cost up to the average life expectancy x the distribution of users is analyzed and visualized.

For this purpose, the mental and physical condition items are set as three items: the degree of care required, the degree of disability independence, and the degree of dementia independence. , The average monthly long-term care benefit cost up to the average life expectancy was plotted on the bubble size to create a bubble chart. Here, the value (= Z2) of the average monthly long-term care benefit cost up to the average lifespan for the bubble chart is the original value (= Z1), the minimum value (= MIN), the emphasis power index n of the bubble size, and the emphasis coefficient. Using a, Z2 = Z1 + (Z1-MIN) ⁿ × a By converting to, the difference in bubble size is emphasized and displayed.

As a result, in the example of FIG. 40, the user group (region 1) in which the maintenance period until the deterioration of the degree of disability independence and the maintenance period until the deterioration of the dementia independence both are long (region 1), and the user group in which both are short (region 3). There is a group of users (areas 2 and 4) in which only one is long. In addition, it can be seen that the average monthly long-term care benefit cost up to the average life expectancy tends to be smaller as the maintenance period until deterioration tends to be longer.

From these, there is a moderate positive correlation (correlation coefficient = 0.651) between the maintenance period until the deterioration of disability independence and the maintenance period until the deterioration of dementia independence, and only one of them. The long-term user group (areas 2 and 4) is different from the overall tendency, and it is considered that there are individual causes.

As a future action, users of a group of users (area 1) who have a long maintenance period until the degree of disability independence deteriorates and a long maintenance period until the degree of dementia independence deteriorates. And widely disseminate information as a best practice. In particular, guidance for users in the group of users (area 3) who have a short maintenance period until the degree of disability independence deteriorates and the maintenance period until the degree of dementia independence deteriorates, and the establishments that provide long-term care services to those users. Use for.

In addition, we interviewed users in a long user group (areas 2 and 4) and business establishments that provide long-term care services to those users, grasped the causes and problems of each, and gave advice. , Can provide measures such as providing guidance as needed.

In the example shown in FIG. 41, the maintenance period until deterioration of the degree of long-term care required, the degree of disability independence, and the degree of dementia independence of home-based service users and the average monthly long-term care benefit cost up to the average life expectancy are tabulated by subregion and small. The purpose is to visualize the speed at which mental and physical conditions deteriorate and the cost of long-term care benefits by region. In other words, home-based services x average maintenance period until deterioration of disability independence and dementia independence, average monthly long-term care benefit costs up to average life span x subregional distribution are analyzed and visualized.

For this purpose, the mental and physical condition items are defined as the degree of long-term care, the degree of disability independence, and the degree of dementia independence. The average monthly long-term care benefit cost up to the average life expectancy was plotted on the maintenance period and bubble size to create a bubble chart. Here, the value (= Z2) of the average monthly long-term care benefit cost up to the average lifespan for the bubble chart is the original value (= Z1), the minimum value (= MIN), the emphasis power index n of the bubble size, and the emphasis coefficient. Using a, Z2 = Z1 + (Z1-MIN) ⁿ × a By converting to, the difference in bubble size is emphasized and displayed.

As a result, in the example of FIG. 41, the user group (region 1) in which the maintenance period until the deterioration of the degree of disability independence and the maintenance period until the deterioration of the dementia independence both are long (region 1), and the user group in which both are short (region 3). There is a group of users (areas 2 and 4) in which only one is long. In addition, it can be seen that the average monthly long-term care benefit cost up to the average life expectancy tends to be smaller as the maintenance period until deterioration tends to be longer.

Subregions where the correlation (moderate positive correlation) and tendency of the maintenance period until the deterioration of disability independence of all users and the maintenance period until deterioration of dementia independence are different, only one of them is long Subregions of the group (Region 2 and) are thought to have individual causes.

As a future action, we will hear about the subregions of the subregion group (Region 1), which have a long maintenance period until the degree of disability independence and dementia independence deteriorate, and widely disseminate information as a best practice. In particular, it is used for guidance in subregions of subregions (region 3) where the maintenance period until the degree of disability independence and dementia independence deteriorates is both short.

In addition, it is possible to provide measures such as hearing small areas of a small area group (areas 2 and 4) in which only one of them is long, grasping the causes and problems of each, giving advice, and giving guidance as necessary.

In the example shown in FIG. 42, the maintenance period until deterioration of the degree of long-term care required, the degree of disability independence, and the degree of dementia independence of facility / residential service users and the average monthly amount of long-term care benefit expenses up to the average life expectancy are tabulated for each business establishment. The purpose is to visualize the speed at which the physical and mental condition deteriorates and the long-term care benefit costs for each business establishment. In other words, facility services x average maintenance period until deterioration of disability independence and dementia independence, average monthly long-term care benefit costs up to average life span x establishment distribution are analyzed and visualized.

For this purpose, the mental and physical condition items are defined as the degree of long-term care, the degree of disability independence, and the degree of dementia independence. Plot the average monthly long-term care benefit cost up to the average life expectancy on the maintenance period and bubble size, and make a bubble chart. For the average monthly long-term care benefit cost value (= Z2) up to the average lifespan for the bubble chart, use the original value (= Z1), the minimum value (= MIN), the emphasis power index n of the bubble size, and the emphasis coefficient a. Z2 = Z1 + (Z1-MIN) ⁿ × a By converting to, the difference in bubble size is emphasized and displayed.

As a result, in the example of FIG. 42, only one of the establishment group (area 1) having a long average maintenance period until the degree of independence of disability and the degree of independence of dementia deteriorates and the establishment group (area 3) having a short average maintenance period is only one of them. There is a long group of establishments (areas 2 and 4). In addition, it can be seen that the average monthly long-term care benefit cost up to the average life expectancy tends to decrease as the maintenance period until deterioration tends to decrease.

Business establishments where the correlation (moderate positive correlation) and the tendency of the maintenance period until the deterioration of the degree of disability independence of all users and the maintenance period until the deterioration of the degree of dementia independence are different, only one of them is long. It is considered that there are individual causes in the establishments of the group (area 2 and).

As a future action, we will hear about the establishments of the establishment group (area 1) that have a long maintenance period until the degree of independence of disability and the degree of independence of dementia worsen, and widely disseminate information as a best practice. In particular, it is used for guidance of establishments in the group of establishments (area 3) that have a short maintenance period until the degree of independence of disability and the degree of independence of dementia deteriorate.

It is possible to provide measures such as hearing the establishments of the establishment group (areas 2 and 4) where only one of them is long, grasping the cause and problem of each, giving advice, and giving guidance as necessary.

In the embodiment shown in FIG. 43, the maintenance period until the degree of long-term care required for home-based service users deteriorates, the improvement rate of the degree of long-term care, and the per capita long-term care benefit cost are tabulated by subregion, and the mental and physical condition is in each subregion. The purpose is to visualize the relationship between changes and long-term care benefit costs. In other words, home-based services x average maintenance period until the degree of need for long-term care deteriorates, improvement rate, long-term care benefit cost per person x subregional distribution are analyzed and visualized.

For that purpose, the maintenance period until the degree of long-term care needs to deteriorate for home-based service users, the improvement rate of the degree of long-term care, and the per capita long-term care benefit cost are tabulated by subregion, and the average maintenance until the degree of long-term care deteriorates on the horizontal axis. Plot the period, the improvement rate of the degree of long-term care required on the vertical axis, and the long-term care benefit cost per person on the size of the bubble, and make a bubble chart. Here, the value of the per capita long-term care benefit cost (= Z2) for the bubble chart uses the original value (= Z1), the minimum value (= MIN), the emphasis power index n of the bubble size, and the emphasis coefficient a. , Z2 = Z1 + (Z1-MIN) ⁿ × a, so that the difference in bubble size was emphasized and displayed.

As a result, in the example of FIG. 43, the average maintenance period until the degree of care required deteriorates and the improvement rate of the degree of care required are both subregional groups (areas in the figure) that are equal to or higher than the average value of the local government, and the degree of care required also deteriorates. Subregional groups whose average maintenance period and improvement rate of the degree of long-term care are both below the local government average (area 3 in the figure), and only one of them is above the local government average (areas 2 and 4 in the figure). be.

Per capita long-term care benefit costs for subregions (areas 1 and 2 in the figure) where the improvement rate of the degree of long-term care is equal to or higher than the average value of local governments It is generally larger than the per capita long-term care benefit costs in the subregions 3 and 4) in the figure, and it is presumed that there is a correlation between the improvement rate of the degree of care required and the per capita long-term care benefit costs.

As a future action, we will hear about the small areas of the subregion group (area 1 in the figure) where the average maintenance period until the degree of care required deteriorates and the improvement rate of the degree of care required are both above the average value of the local government, and widely information as a best practice. Expand. In particular, it is used for guidance in small areas of a small area group (area 3 in the figure) where both the average maintenance period until the degree of care required deteriorates and the improvement rate of the degree of care required is less than the average value of the local government. In addition, we will verify whether there is a correlation between the improvement rate of the degree of long-term care required and the per capita long-term care benefit cost.

In the embodiment shown in FIG. 44, the maintenance period until the degree of long-term care required for group home users deteriorates, the improvement rate of the degree of long-term care, and the per capita long-term care benefit cost are totaled for each business establishment, and the physical and mental condition changes for each business establishment. The purpose is to visualize the relationship between the situation and the long-term care benefit costs. In other words, facility services x average maintenance period until the degree of long-term care needs to deteriorate, the rate of improvement in the degree of long-term care required, per capita long-term care benefit costs x distribution of establishments are analyzed and visualized.

For that purpose, the maintenance period until the degree of long-term care needs to deteriorate for group home users, the improvement rate of the degree of long-term care, and the per capita long-term care benefit cost are totaled for each business establishment, and the horizontal axis is the average maintenance period until the degree of long-term care deteriorates. , Plot the improvement rate of the degree of long-term care required on the vertical axis, and the long-term care benefit cost per person on the size of the bubble, and make a bubble chart. The value of per capita long-term care benefit cost (= Z2) for the bubble chart is Z2 = using its original value (= Z1), minimum value (= MIN), bubble size emphasis power index n and emphasis coefficient a. Z1 + (Z1-MIN) ⁿ × a By converting to, the difference in bubble size is emphasized and displayed.

As a result, in the example of the group home shown in FIG. 44, the establishment group (area 1) in which the average maintenance period until the deterioration of the degree of care required and the improvement rate of the degree of care required are both equal to or higher than the average value of the local government, and the deterioration of the degree of care required also deteriorates. There is a group of establishments (areas 3) in which both the average maintenance period up to and the improvement rate of the degree of long-term care are less than the local government average value, and a group of business establishments (areas 2 and 4) in which only one of them is equal to or higher than the local government average value.

The per capita long-term care benefit costs for establishments in the group of establishments (areas 1 and 2) whose improvement rate of the degree of long-term care is equal to or higher than the average value of the local government are the group of establishments (area 3) in which the improvement rate of the degree of long-term care is less than the average value of the local government. It is estimated that there is a correlation between the improvement rate of the degree of care required and the per capita long-term care benefit cost, which is larger than the per capita long-term care benefit cost of the establishments in 4).

As a future action, we will hear about the establishments of the establishment group (area 1) where the average maintenance period until the degree of care required deteriorates and the improvement rate of the degree of care required are both above the average value of the local government, and widely disseminate information as a best practice. do. In particular, it is used for guidance of establishments in the group of establishments (area 3) where the average maintenance period until the degree of need for nursing care deteriorates and the improvement rate of the degree of need for nursing care are both less than the average value of the local government. In addition, it is possible to provide measures such as verifying whether there is a correlation between the improvement rate of the degree of long-term care required and the per capita long-term care benefit cost.

The examples so far relate to the qualitative analysis in the long-term care business 11, but next, the examples of the qualitative analysis in the comprehensive business 12 described with reference to FIG. 1 will be described.

FIG. 45 shows an image created by the second factor analysis unit 53 shown in FIG. In this example, the new certification rate by the elderly gender / age group × subregion × nursing care status category in the comprehensive project 12 is displayed as a graph. That is, the degree of long-term care required for the elderly is newly certified, and the new certification rate is aggregated, analyzed, and visualized by subregion and by the degree of long-term care required.

For this purpose, as shown in FIG. 45, seven levels of care-requiring levels are set from support 1 to care-requiring 5 for female late-stage elderly, and the new certification rate for each care-requiring level is set for each subregion. It is calculated and summarized in the band graph of (a) and the table of (b).

As a result, as a comparison among the subregions 1 to 7, the new accreditation rate of the subregion 5 is high, and the new accreditation rate of the subregion 1 is low. In addition, it can be seen that the lower the degree of long-term care required, the higher the new certification rate for the newly certified long-term care status category.

From these facts, it is considered that the subregion 1 has the effect of measures for independence support or services such as comprehensive projects. In addition, it is considered that the subregion 5 has no effect of independence support measures or services such as comprehensive projects. For this reason, we will hear about services such as comprehensive businesses in subregion 1 and introduce measures to other businesses as best practices. In addition, it is possible to provide measures such as hearing about services such as comprehensive businesses in subregion 5, grasping problems, and providing guidance.

In the example of FIG. 46, the purpose is to visualize the average value of the independence period in each subregion in the comprehensive project 12, and to determine the speed at which the elderly in each subregion deteriorate to the support-requiring state or the long-term care-requiring state. It is to visualize. That is, in FIG. 46, for example, the results of analyzing the elderly gender / age group x subregion x aggregated mental and physical condition item x average independence period (business plan unit) in the comprehensive project 12 are shown in (a) line graph. And (b) are shown in the table.

In FIG. 46, the analysis target is a female elderly person, and the items of intensive mental and physical condition are the degree of care required 46a, the degree of independence of dementia 46b, the degree of independence of disability 46c, the physical function / wake-up movement 46d, the living function 46e, and the cognitive function /. There are 7 items, social life adaptation 46f and mental / behavioral disorder 46g, and the period from independence to deterioration of the elderly (65 years old or older) to the state requiring support or the degree of long-term care (65 years old birth month). (From) is aggregated to make a fold line graph. It is a line graph.

In the example of FIG. 46, as a comparison among the subregions 1 to 8, the average independence period of the subregions 4 and 6 is long, and the average independence period of the subregions 2 and 5 is short. From these facts, the measures of the comprehensive business in the subregions 4 and 6 are effective for the elderly in the region, and conversely, the measures of the comprehensive business in the subregions 2 and 5 are not effective for the elderly in the region. Or it is considered that effective measures have not been taken. Therefore, as a future action, we will investigate the measures in subregions 2 and 5 and their usage status and effects, grasp the problems, and reexamine the measures.

In the example of FIG. 47, the average value of the independence period according to the physical and mental condition in each subregion in the comprehensive project 12 is aggregated, and the speed at which the elderly in each subregion deteriorate to the support-requiring state or the long-term care-requiring state is visualized. do. In other words, the elderly gender / age group x detailed mental and physical condition items x subregion x average independence period (business plan unit) are analyzed and displayed in a line graph.

The detailed mental and physical condition items are a total of 65 items, including the degree of long-term care, dementia independence, disability independence, and 62 certified survey items. Aggregate the average values (from the month of birth of 65 years old) and make a line graph. The relationship between the detailed certification survey items and the aggregated mental and physical condition items summarized by field is the same as described above, and the description is omitted.

In the example of FIG. 47, the elderly women in the latter stages are analyzed and others, and the average independence period of subregion 1 (fold line 47a) is generally longer than that of the local government average (fold line 47d), and subregion 2 (fold line 47a) The average independence period of the physical system (physical function / moving movement, living function) is short in the broken line 47b), and the average independence period of the mental system (cognitive function, mental / behavioral disorder, social life adaptation) in the subregion 3 (folding line 47c). It turns out that is short.

From these facts, the measures of the comprehensive business of Subregion 1 are effective for the elderly in the region. The measures of the comprehensive business of subregion 2 are not effective for the physical system of the elderly in the area, or the measures that are effective for the physical system are not implemented. It can be said that the measures of the comprehensive business of the subregion 3 are not effective for the mental system of the elderly in the area, or that the measures that are effective for the mental system are not implemented.

Therefore, as future actions, the physical system investigates the measures and their usage status and effects in subregion 1 and subregion 3, and the mental system investigates the measures and their usage status and effects in subregion 1 and subregion 2. Then, introduce each good measure as a best practice to other regions. In addition, the physical system investigates the measures of subregion 2 and its usage status and effects, and the mental system investigates the measures of subregion 3 and its usage status and effects, grasps the problems, and re-does those measures. Can be considered.

FIG. 48 shows an image example created by the second factor analysis unit 53 shown in FIG. 6, and this example is a measure in the comprehensive project 12 during the mental and physical condition item independence period in each subregion. The usage status is aggregated and visualized. In other words, the average utilization rate within the same period by gender / age group x subregion x mental and physical condition / average independence period / comprehensive business area policy is analyzed and displayed in a table format.

Here, a structure index (hereinafter, S index) including the content of the measure and its implementation status, and an outcome index (hereinafter, O index) indicating the degree of the physical and mental condition of the user are used.

As measures, there are four items: the health examination consultation rate of S index 1, the participation rate of the energy-building station of S index 2, the utilization rate of the watching service of S index 3, and the utilization rate of the food distribution service of S index 4. Mental and physical condition items are selected from the degree of long-term care required for O index 1, the degree of dementia independence for O index 2, the degree of disability independence for O index 3, and the physical function / moving movement of O index 4 (Group 1: Physical function / moving movement). Items), O-index 5 living function (2 groups: items selected from living functions), O-index 6 cognitive function / social life adaptation (3 groups: cognitive function, 5 groups: items selected from social life adaptation) , O index 7 mental / behavioral disorders (4 groups: items selected from mental / behavioral disorders) (items of paralysis / contracture, sight / hearing that are not expected to lead to improvement / maintenance of mental and physical condition by long-term care services (Excluding) 7 items, and the implementation status of the above-mentioned 4 measures during the average independence period of the mental and physical condition items for each subregion will be investigated and tabulated.

In the example of FIG. 48, the elderly women in the latter stages of analysis are analyzed, and it can be seen that the subregion 1 having a long average independence period of dementia independence has a high health examination consultation rate and a high participation rate of the energy-building station. From these facts, it is considered that the consultation of the health examination and the participation in the energy-building station are effective in extending the average independence period of the degree of independence of dementia, and the subregion 1 is actively using the measures. Therefore, as a future action, it is recommended to call for consultation and participation in areas where the health checkup rate and the participation rate of the energy-building station are low.

FIG. 49 shows an image example created by the second factor analysis unit 53 shown in FIG. 6, and in this embodiment, the purpose is the policy implementation status and mental and physical condition in each subregion in the comprehensive project 12. The purpose is to perform a correlation analysis with the independence period of the state item and extract measures that are effective in extending the independence period of the mental and physical condition. That is, the results of analyzing the elderly gender / age group × comprehensive business area policy × average independence period by mental and physical condition × correlation coefficient are shown in the line graph in (a) and the table in (b).

Here, the analysis target is female late-stage elderly, and as measures, the health examination consultation rate of S index 1, the participation rate of the energy-building station of S index 2, the utilization rate of the watching service of S index 3, and the S index 4 There are four items of utilization rate of food distribution service. Mental and physical condition items are selected from the degree of long-term care required for O index 1, the degree of dementia independence for O index 2, the degree of disability independence for O index 3, and the physical function / moving movement of O index 4 (Group 1: Physical function / moving movement). (Items selected), O-index 5 living function (2 groups: items selected from living functions), O-index 6 cognitive function / social life adaptation (3 groups: cognitive function, 5 groups: items selected from social life adaptation) , O index 7 mental / behavioral disorders (4 groups: items selected from mental / behavioral disorders) (items of paralysis / contracture, sight / hearing that are not expected to lead to improvement / maintenance of mental and physical condition by long-term care services (Excluding) 7 items, the correlation coefficient between all 11 items of the implementation status of the above-mentioned 4 items in each small area and the average independence period of the elderly with 7 items of mental and physical condition is calculated and made into a broken line graph.

In FIG. 49, it can be seen from the polygonal line 49a that the measures that have a strong positive correlation with the degree of independence of dementia are the health examination consultation rate and the energy-building station participation rate. In addition, it can be seen from the polygonal line 49b that the measures that have a strong positive correlation with the degree of disability independence are the health examination consultation rate and the monitoring service utilization rate.

From these facts, it is considered that it is effective to extend the degree of independence of dementia by health management by undergoing a health examination and interaction with people by participating in the energy-building station. In addition, it is thought that it is effective in extending the degree of disability independence by managing health by undergoing a health examination, safety management by using a monitoring service, and reducing the risk of falls.

Therefore, as future actions, we will call on areas with a short degree of independence for dementia to undergo a health examination and use monitoring services, and for areas with a short degree of independence for disabilities to undergo a health examination. It is recommended to call for the use of the energy-building station.

FIG. 50 shows an image example created by the second factor analysis unit 53 shown in FIG. 6, and in this embodiment, the degree of long-term care required, the degree of disability independence, and the degree of independence of a new independence person (business plan unit). The purpose is to aggregate the independence period (non-certification period from the month of birth of 65 years old) according to the degree of dementia independence, and to visualize the speed at which the mental and physical condition of newly certified persons (business plan units) deteriorates. In other words, new independence end persons (business plan unit) × disability independence degree, dementia independence degree, independence period of long-term care required × user distribution is analyzed and visualized.

For that purpose, the mental and physical condition items are set as three items, the degree of long-term care, the degree of disability independence, and the degree of dementia independence. Plot the independence period of the degree of independence of dementia on the vertical axis, the independence period of the degree of independence of dementia on the vertical axis, and the independence period of the degree of long-term care required on the size of the bubble, and make a bubble chart. Here, the value (= Z2) of the independence period of the degree of need for nursing care for the bubble chart uses the original value (= Z1), the minimum value (= MIN), the emphasis power index n of the bubble size, and the emphasis coefficient a. Z2 = Z1 + (Z1-MIN) ⁿ × a By converting to, the difference in bubble size is emphasized and displayed.

As a result, in the example of FIG. 50, only one of the certified persons group (region 1) in which the independence period of the degree of disability independence and the independence period of the dementia independence are both long and the certified persons group in which both are short (region 3) is long. There is a group of certified persons (areas 2 and 4).

From these, there is a moderate positive correlation (correlation coefficient = 0.651) between the independence period of disability independence and the independence period of dementia independence, and only one of them is a long certified group (correlation coefficient = 0.651). Areas 2 and 4) are different from the overall trend, and it is considered that there are individual causes.

As a future action, we will interview certified persons in the group of certified persons (area 1) who have a long independence period for disability independence and dementia independence, and widely disseminate information as a best practice. In particular, it is used for guidance of certified persons in the certified person group (area 3) in which both the independence period of the degree of independence of disability and the independence period of the degree of independence of dementia are short.

In addition, only one of them will hear the certified persons in the long accredited group (areas 2 and 4), grasp the causes and problems of each, give advice, and give guidance as necessary.

FIG. 51 shows an example of an image created by the second factor analysis unit 53 shown in FIG. In this example, the degree of independence required for new independence (business plan unit), the degree of independence of disability, and the degree of independence of dementia are totaled in a small area, and the speed at which the mental and physical condition of the small area deteriorates is visualized. The purpose is. In other words, new independence end persons (business plan unit) x disability independence, dementia independence, average independence period of care required x subregional distribution is analyzed and visualized.

For that purpose, the mental and physical condition items are set as three items, the degree of long-term care, the degree of disability independence, and the degree of dementia independence. The average independence period of dementia independence and the average independence period of long-term care required are plotted on the bubble size to make a bubble chart. Here, the value (= Z2) of the average independence period of the degree of need for nursing care for the bubble chart is the original value (= Z1), the minimum value (= MIN), the emphasis power index n of the bubble size, and the emphasis coefficient a. Using, Z2 = Z1 + (Z1-MIN) ⁿ × a By converting to, the difference in bubble size is emphasized and displayed.

As a result, in the example of FIG. 51, only one of the subregion group (region 1) in which the average independence period of the degree of disability independence and the average independence period of the dementia independence are both long and the subregion group in which both are short (region 1) is available. There are subregions (areas 2 and 4) that are long.

From these, only one of the correlations (moderately positive correlation) and the tendency between the independence period of disability independence and the independence period of dementia independence of newly completed independence (business plan unit) is different. It is believed that there are individual causes in the subregions of the long subregions (Regions 2 and 4).

As a future action, we will hear about the subregions of the subregion group (Region 1) where both the average independence period of disability independence and the average independence period of dementia are long, and widely disseminate information as a best practice. In particular, it is used for guidance in subregions of a subregion group (area 3) where both the average independence period of disability independence and the average independence period of dementia are short.

In addition, it is possible to provide measures such as hearing small areas of a small area group (areas 2 and 4) in which only one of them is long, grasping the causes and problems of each, giving advice, and giving guidance as necessary.

The examples so far relate to the qualitative analysis in the comprehensive project 12, but next, the example of the qualitative analysis in the medical-assistance cooperation 13 described with reference to FIG. 1 will be described.

FIG. 52 shows an image created by the second factor analysis unit 53 shown in FIG. In this example, the ratio of all home medical patients staying at home, the ratio of home care, and the per capita medical / long-term care benefit cost are aggregated in each subregion, and the usage status of home medical care, home care status and medical / long-term care by subregion are tabulated. The purpose is to visualize the relationship between benefit costs. In other words, home medical care patients x home stay period ratio, home care ratio, per capita medical / long-term care benefit costs x subregional distribution are analyzed and visualized.

For that purpose, the ratio of all home medical patients staying at home, the ratio of home care, and the per capita medical / long-term care benefit cost are tabulated by subregion, and the horizontal axis is the average length of stay at home ratio, the vertical axis is the average home care ratio, and the bubble. Plot medical / long-term care benefits per capita on size and make a bubble chart. Here, the value of the per capita medical / long-term care benefit cost (= Z2) for the bubble chart is the original value (= Z1), the minimum value (= MIN), the emphasis power index n of the bubble size, and the emphasis coefficient a. Using, Z2 = Z1 + (Z1-MIN) ⁿ × a By converting to, the difference in bubble size is emphasized and displayed.

As a result, in the example of FIG. 52, a subregion group (region 1) in which both the average stay-at-home period ratio and the average home care ratio are high, a subregion group in which both are short (region 3), and a subregion group in which only one of them is high (region 1). There are areas 2 and 4). Subregions in the subregions (areas 1 and 4) with a high average length of stay at home are considered to have low per capita medical and long-term care benefits and low inpatient medical stipends.

As a future action, we will hear about the small areas of the small area group (area 1) where both the average length of stay at home ratio and the average ratio of home care are high, and widely disseminate information as a best practice. In particular, it is used for guidance in small areas of the small area group (area 3) where both the average length of stay at home ratio and the average ratio of home care are low. In addition, we will hear about the subregions of the subregions (areas 2 and 4) where only one of them is high, grasp the causes and problems of each, give advice, and give guidance as necessary.

FIG. 53 shows an example of an image created by the second factor analysis unit 53 shown in FIG. In this embodiment, in the medical care cooperation 13, the purpose is to aggregate the information cooperation addition at the time of admission and discharge / discharge / discharge addition at each business establishment, and to visualize the medical care cooperation addition request ratio at each business establishment. do. That is, the results of analyzing the disease classification / degree of long-term care required × home care support establishment × medical care cooperation addition at admission / discharge × billing ratio are shown in the line graph in (a) and the table in (b). ..

Here, the analysis target is hospitalization due to a fracture of the thigh, and the medical assistance cooperation addition request at the time of admission and discharge is "information cooperation addition at admission (I)", "information cooperation addition at admission (II)" and "discharge / discharge". Calculate the ratio of "Addition of information" and make a line graph.

In FIG. 53, the ratio of requesting addition for medical assistance cooperation among establishments 1 to 4 is compared, and establishment 1 is "information cooperation addition at admission (I)" (line 53a) and "discharged" from the average of local governments.・ The ratio of "exit addition" (line 53b) is high. In addition, it can be seen that the ratio of "information linkage addition at admission (I)" line 53a) and "discharge / discharge addition" (line 53b) is lower than the average of local governments.

For these reasons, the establishment 1 has a good quality of cooperation between the medical staff on the hospital side and the care manager of the establishment when admitted to and discharged from the acute care hospital, and the mental and physical condition (especially the degree of independence of dementia) deteriorates. Is presumed to be low. In addition, it is presumed that the quality of cooperation between the medical staff on the hospital side and the care manager of the business establishment is poor at the business establishment 2, and the degree of deterioration of the mental and physical condition (particularly the degree of independence of dementia) is high.

Therefore, as a future action, we will interview the care manager of the business establishment 1 about the response status and widely disseminate information as a best practice. On the other hand, it is possible to interview the care manager of the business establishment 2 about the response status, grasp the problem, and give guidance.

FIG. 54 shows an image example created by the second factor analysis unit 53 shown in FIG. 6, and in this example, the mental and physical conditions before and after admission and discharge at each acute care hospital are tabulated in the medical assistance cooperation 13. The purpose is to visualize the rate of change in mental and physical condition before and after hospitalization. That is, the results of analyzing the disease classification / degree of long-term care required × acute care hospital × mental and physical condition change before and after admission and discharge × user ratio are shown in the line graph in (a) and the table in (b).

Here, the analysis target is hospitalization due to a fracture of the thigh, and the ratio of the mental and physical conditions (classification of state requiring nursing care, degree of disability independence, degree of dementia independence) deteriorated by two or more stages before and after admission and discharge is shown as a line graph.

FIG. 54 compares the rate at which the degree of dementia independence deteriorated by two or more stages in the acute phase hospital 1 to the acute phase hospital 4, and the acute phase hospital 1 has a care-requiring state category (break line 54a) and a degree of disability independence. It can be seen that (folded line 54b) and dementia independence degree (folded line 54c) are higher than the local government average (hospital average), and acute phase hospital 2 is lower than the local government average, respectively.

For these reasons, the acute care hospital 1 has a poor quality of cooperation between the care manager of the business establishment and the medical staff on the hospital side when admitted to and discharged from the acute care hospital, and the mental and physical condition (especially the degree of independence of dementia). It is presumed that the rate of deterioration of is high. In addition, it is presumed that the acute care hospital 2 has a good quality of cooperation between the care manager of the business establishment and the medical staff on the hospital side, and the rate of deterioration of the mental and physical condition (particularly the degree of independence of dementia) is low.

Therefore, as a future action, we will interview the medical staff of Acute Hospital 2 and widely disseminate information as a best practice. It is also possible to interview the medical staff of the acute care hospital 1 to understand the problems and give guidance.

FIG. 55 shows an image created by the second factor analysis unit 53 shown in FIG. The purpose of this embodiment is to aggregate the mental and physical conditions of users who have been admitted to and discharged from the acute care hospital before and after admission and discharge, and to visualize the stage changes in the mental and physical conditions before and after admission and discharge. In other words, by disease classification × Disability independence, dementia independence, and long-term care required before and after admission × User distribution is analyzed and visualized.

For that purpose, the mental and physical condition items are set as three items, the degree of care required, the degree of disability independence, and the degree of dementia independence. Plot the number of people in each data on the step change of dementia independence and bubble size, and make a bubble chart (The step change of long-term care required is replaced with the step change of disability independence and dementia independence to make a bubble chart. ).

As a result, in the case of hospitalization and discharge due to a fracture of the thigh in FIG. The group of users who improved (area 2), the group of users who maintained / improved both the degree of disability independence and the degree of dementia independence (area 3), and only the degree of disability independence deteriorated (the degree of dementia independence was maintained / improved). There is a user group (area 4).

From FIG. 55, the users of the user group (areas 1, 2, and 4) whose degree of disability independence or dementia independence has deteriorated are the care manager of the business establishment and the medical staff on the hospital side when they are hospitalized and discharged. It is probable that the coordination was poor and the physical and mental condition deteriorated.

As a future action, the users of the user group (areas 1, 2, and 4) whose disability independence or dementia independence has deteriorated, and the care manager of the establishment that provides long-term care services to those users, and the acute hospitalized Hear the staff of the hospital, understand each problem, and give guidance.

FIG. 56 shows an image created by the second factor analysis unit 53 shown in FIG. In this example, in the medical assistance cooperation 13, the physical and mental condition of the user who was admitted to and discharged from the acute care hospital before and after admission and the increase in long-term care benefit cost due to hospitalization (the amount of increase in long-term care benefit cost due to the shortening of the maintenance period due to hospitalization) are at home. The purpose is to tabulate by long-term care support establishments and visualize the relationship between changes in mental and physical conditions before and after hospitalization and the increase in long-term care benefit costs due to hospitalization. In other words, by disease classification × changes in disability independence and dementia independence ・ increase in long-term care benefit costs due to hospitalization per person × distribution of home care support establishments is analyzed and visualized.

For this purpose, the mental and physical condition items are set to three items: the degree of long-term care required, the degree of disability independence, and the degree of dementia independence. Plot the rate of deterioration of disability independence of 2 or more levels, the vertical axis of dementia independence rate of 2 or more levels, and the size of the bubble for the increase in long-term care benefit costs (monthly) due to hospitalization (all users admitted to and discharged from the hospital). , Make a bubble chart.

As a result, in the case of hospitalization and discharge due to a fracture of the thigh in FIG. There is a group of home care support establishments (areas 3) that are both low, and a group of home care support establishments (areas 2 and 4) that are high in only one of them.

From FIG. 56, the home care support establishments of the home care support establishment group (area 3) in which both the deterioration rate of the degree of disability independence of 2 or more stages and the deterioration rate of the dementia independence degree of 2 stages or more are low are the long-term care benefit expenses due to hospitalization. It is considered that the increase is large, the cooperation between the care manager of the business establishment and the medical staff on the hospital side is good, and the rate of deterioration of mental and physical condition is low.

As a future action, care managers of home care support establishments (areas 1, 2, and 4) with a high rate of deterioration of disability independence of 2 levels or more or dementia independence of 2 levels or more. Hear, understand the problems, and give guidance.

FIG. 57 shows an image created by the second factor analysis unit 53 shown in FIG. In this example, the physical and mental condition of the user who was admitted to and discharged from the acute care hospital in the medical assistance 13 and the long-term care benefit cost due to hospitalization (the amount of increase in the long-term care benefit cost due to the shortening of the maintenance period due to hospitalization) are set in the acute care hospital. The purpose is to visualize the relationship between changes in mental and physical condition before and after hospitalization and long-term care benefit costs due to hospitalization. In other words, by disease classification × changes in disability independence and dementia independence ・ increase in long-term care benefit costs due to per capita hospitalization × distribution of acute care hospitals is analyzed and visualized.

For that purpose, the mental and physical condition items are set as three items, the degree of long-term care, the degree of disability independence, and the degree of dementia independence. Dementia independence degree deterioration rate of 2 levels or more, dementia independence degree deterioration rate of 2 levels or more, long-term care benefit cost (monthly amount) per person hospitalized (all users admitted to and discharged) are plotted on the bubble size, bubble chart To.

As a result, in the case of hospitalization and discharge due to a fracture of the thigh in FIG. There is an acute phase hospital group (area 3) and an acute phase hospital group (areas 2 and 4) in which only one of them is high.

From FIG. 57, the acute care benefit cost increase due to hospitalization is large in the acute care hospitals of the acute care hospital group (area 3) in which both the degree of deterioration of disability independence of 2 or more stages and the rate of deterioration of dementia independence of 2 or more stages are low. It is considered that the care manager of the business establishment and the medical staff on the hospital side have good cooperation, and the rate of deterioration of mental and physical condition is low.

On the other hand, in the acute care hospitals of the acute care hospital group (areas 1, 2 and 4) where the rate of deterioration of disability independence is 2 or more or the rate of deterioration of dementia independence is 2 or higher, the cost of nursing care benefits increases due to hospitalization. It is considered that the rate of deterioration of mental and physical condition is high due to poor cooperation between the care manager of the business establishment and the medical staff on the hospital side.

As a future action, we interviewed the medical staff of the acute care hospitals in the acute care hospital group (areas 1, 2 and 4) where the degree of deterioration of disability independence was 2 or higher or the rate of deterioration of dementia independence was 2 or higher. Understand the problems and give guidance.

The explanation so far has been about the regional management support function unit 21 shown in FIG. 2, but next, the regional information management function unit 22 will be described.

As described above, the regional information management function unit 22 displays the activity evaluation results related to the quantity and quality of services and its own history in individual form for each person in charge of the business unit (subregional person in charge or long-term care provider). It is the one to output.

58 and 59 show images created by the regional information management function unit 22. These images are, for example, images (called an activity evaluation sheet) for evaluating the activities of a small area in the local government regarding the home-based service of the long-term care business 11. FIGS. 58 and 59 evaluate the degree of change in the mental and physical condition of the service user as an activity result. Here, the degree of long-term care, the degree of lifelong independence, and the degree of dementia independence are used as the mental and physical conditions. Then, these mental and physical states are assigned to a plurality of evaluation axes extending radially, the degree of change is expressed by the length from the starting point of the central part, and the tips are connected to form a polygon, as a so-called radar chart. expressing.

Both FIGS. 58 and 59 are divided into upper and lower parts, and the upper half uses the improvement rate of the mental and physical condition and the lower half uses the average maintenance period until deterioration as values indicating changes in the mental and physical condition.

FIG. 58 shows the achievement history, and the transition of the achievement for each year can be seen from the display shapes of the previous year's actual value 58a, the target value 58b, and the current year's actual value 58c. By confirming the actual value in this way, the validity of the target setting can be understood. In the figure, the value for this year has not reached the target value, but it has decreased compared to the previous year, and it can be confirmed that it is approaching the target value.

FIG. 59 shows the position, and the position of the small area with respect to other areas can be understood from the display shapes of the city average 59a, the ward / branch average 59b, and the small area 59c. When displayed in this way, the target value to be aimed at becomes clear. In the figure, the value of the own area is lower than the ward / branch average, but higher than the city average, confirming that the city average should be used as a reference when setting goals.

60 and 61 show a case where the above-mentioned radar chart is used as an activity evaluation sheet of a business operator (used as a group home). Even when used in this way, the activity history of each group home (GH) and its position with respect to other GHs can be clearly grasped.

Next, the elderly information management function 23 shown in FIG. 2 will be described. As described above, the elderly information management function 23 centrally manages the basic information of the elderly, the mental and physical condition utilization service status, and the medical status, and provides the past history and the like related thereto.

62 and 63 are images created by the elderly information management function 23. In the image of FIG. 62, information about an individual elderly person is set over a large number of items, and how their states are set in chronological order. It clearly shows whether it is changing.

Such data can be created by centrally managing various data related to medical care and nursing care by the database 16 as described above. By providing such an elderly information history screen to a care manager or the like who creates a care plan for the elderly, an appropriate care plan can be created and appropriate long-term care can be provided.

Note that FIG. 63 shows the meaning of each item shown in FIG. 62 and the evaluation criteria, and can be used as reference material when creating a care plan.

FIG. 64 is an image created by the elderly information management function 23. In addition to the change in the mental and physical condition items at the time of renewal of certification of each user, the maintenance period until the change is compared with the average value, and each user's It calculates the evaluation points for each mental and physical condition item at the time of certification renewal.

Here, as the mental and physical condition items, the degree of care required, the degree of independence of dementia, the degree of independence of disability, and 74 items of the accreditation survey items are combined into 77 items, and the changes in the mental and physical condition items at the time of renewal of accreditation of each user. Compare the maintenance period until change and the average value, and give each user an evaluation point based on the criteria of fast improvement> slow improvement> maintenance> slow deterioration> early deterioration. However, maintenance is not always better than slow deterioration and worse than slow improvement.

Specifically, as shown in FIG. 65, the evaluation points of 77 mental and physical condition items are made into a bar graph. In the example of FIG. 65, it is obvious that the physical system is maintaining or slowly deteriorating, and the mental system is improving quickly or slowly.

In this way, it is possible to visualize the change and the speed of change of the mental and physical condition items at the time of renewal of certification of each user. Therefore, it is possible to reflect the evaluation points at the time of renewal of the user's certification in the care plan.

The embodiments described so far have described a community-based comprehensive care business system implemented and operated by a local government or the like as an insurer. Here, the insurer is the local government for long-term care insurers, the occupational medical insurance for union health insurance and mutual aid health insurance, etc. Is.

It is possible to apply the method of the community-based comprehensive care business system at the local government level to the system of the community-based comprehensive care business at the higher prefectures and countries. This high-level regional comprehensive business system will be described below.

In the above-mentioned regional comprehensive system at the local government level, each insurer of long-term care and medical care implemented it for each business unit such as a subregion or a business establishment. On the other hand, in the high-level regional comprehensive system, prefectures and countries are insurers, and each local government is positioned as a business entity such as a small area or business establishment at the local government level, and the high-ranking prefectures. The government and the national government will consider the local government as a business unit and promote the PDCA cycle to implement a comprehensive community care business.

Even such a high-level community-based comprehensive project has basically the same function as the local government-level community-based comprehensive care system based on each of the above-mentioned examples. That is, the embodiment of the present application includes a community-based comprehensive care project implemented by prefectures, countries, and the like.

The outcome index for each business unit used for the evaluation of the business unit including the subregions and business establishments described in the previous embodiments calculates the maintenance period until the deterioration of the individual unit for each stage of each mental and physical condition item. The maintenance period until this deterioration was aggregated (weighted average) for each mental and physical condition item as it was, and the average value for each business unit was calculated, and the business unit was evaluated. However, if the simple aggregated outcome index for each mental and physical condition item described above is used, it may not be possible to make a correct evaluation for each business unit.

Therefore, for each individual user, an individual evaluation index (for example, deviation value or N quantile value) indicating the position of each user in terms of the position of each mental and physical condition item with respect to the entire user (for example, the entire user of the municipality to which the user belongs). Ask for. Then, the average value of these deviation values or the average value of the N quantile value is obtained for each business unit to which the user belongs, and the business unit is evaluated by the average value of these deviation values or the average value of the N quantile value. suggest.

Here, the evaluation method of the business unit including the small area, the business establishment, etc. described in the previous embodiments is set to the method 1 and the method 2, and the evaluation method of the business unit using the newly proposed individual evaluation index is the method 3. It is explained as.

Outline of Method 1: The average maintenance period is aggregated for each business establishment and 65 items of mental and physical condition, and for each deterioration / improvement, and used for evaluation of each business unit.

Outline of method 2: The average maintenance period and improvement rate are aggregated for each business establishment, mental and physical condition aggregation, 9 indicators, mild severity, deterioration / improvement, and used for evaluation of each business unit.

Outline of method 3: Individual evaluation index and improvement rate are aggregated for each business establishment, mental and physical condition aggregation, 9 indicators, mild severity, deterioration / improvement, and used for evaluation of each business unit.

Here, there are 211 stages of each mental and physical condition, and the aggregation for each stage of the mental and physical condition 211 is as follows. , Dementia independence, disability independence, and 62 accredited survey items, totaling 65 items. Furthermore, this is aggregated into seven items as intensive mental and physical condition items: long-term care required, dementia independence, disability independence, physical function / moving movement, living function, cognitive function / social life adaptation, and mental / behavioral disorder. I explained that I did.

As a method of grouping the aggregated mental and physical state items, as shown in FIG. Grouping is performed as an aggregated 9 index that includes groups of mental system 1 + 2 that summarizes social life adaptation (mental system 1) and mental / behavioral disorders (mental system 2), and the following explanation is explained as an aggregated mental and physical condition item 9 index. ..

FIG. 67 shows a comparison of the outlines of the processing flows of the methods 1, the method 2, and the method 3. In FIG. 67, in both methods 1, 2 and 3, data is collected, anonymized and collated in the first stage. In the second stage, the maintenance period until deterioration / improvement is totaled for 211 stages of 65 items of service type, user, and mental and physical condition.

In the method 1 and the method 2, the aggregated data is used as it is. In Method 1, the average maintenance period for 65 indicators of mental and physical condition by business establishment is tabulated by service type, business establishment, and 65 mental and physical condition by deterioration / improvement in the 3rd stage, and the business establishment in the 4th stage. Make an evaluation. That is, the number of indicators having a long / short average maintenance period of business establishments until the deterioration of 65 items of mental and physical condition is totaled and evaluated.

In addition, in Method 2, in the third stage, the average maintenance period and improvement rate for each deterioration / improvement are calculated for each of the 9 indicators of mental and physical condition by business establishment, by service type, by business establishment, and by mild and severe by 9 items of mental and physical condition. Aggregate and evaluate the establishment in the 4th stage. That is, the average maintenance period of business establishments and the improvement rate until deterioration are visualized and evaluated for each of the nine indicators of mental and physical condition aggregation.

On the other hand, in the method 3, the maintenance period until deterioration / improvement of 65 items of mental and physical condition aggregated as the above-mentioned second stage processing for each of 211 stages is further evaluated individually for each of 211 stages of 65 items of mental and physical condition. Convert to an index. As an individual evaluation index, a deviation value is obtained (method 3-1), or N-quantile, for example, quintile (method 3-2). Details of this deviation value and quintile will be described later.

Then, in the third stage, the individual evaluation indexes and improvement rates for each deterioration / improvement are totaled for each of the 9 indicators of mental and physical condition by business establishment, by service type, by business establishment, and by mild and severe by 9 items of mental and physical condition. Furthermore, in the 4th stage, the average of business establishments and the improvement rate of the individual evaluation index of deterioration are visualized and evaluated according to the 9 indicators of mental and physical condition aggregation.

Hereinafter, the details of methods 1, 2 and 3 will be compared and described.

FIG. 68 describes the aggregation method in the method 1 and the method 2. Table 681 shows the results of aggregating the data collected, anonymized, and collated in the above-mentioned stage 1 into "maintenance period by service type, user, stage, deterioration / improvement" by the processing of stage 2. ing. That is, "users" AP, their "men and women", "business establishments (business units)" where these users receive prescribed services, and "mental and physical condition items" use the degree of long-term care, and " As the "stage of mental and physical condition items", the stage of the degree of long-term care (in the example of the figure, the degree of long-term care is 1 to 4), the distinction between "deterioration / improvement", and the "maintenance period" are totaled. ing.

Table 682 is based on the data of Table 671 aggregated in the above-mentioned "Maintenance period by service type / user / stage / deterioration / improvement", and "average mental and physical condition by business establishment" by the above-mentioned third stage processing. The result of totaling "another index" is shown. That is, for each business establishment, the average of each maintenance period of each stage of mental and physical condition (long-term care required 1-long-term care required 4) and each maintenance period of mild long-term care required (long-term care required 2 or less) and severe long-term care required (long-term care required 3 or more). The values are calculated, and these average values are calculated as an index (average maintenance period until deterioration) for each mental and physical condition (degree of long-term care required) at the business establishment, and the business establishment is evaluated in stage 4 based on this. ..

In the example of FIG. 68, the average maintenance period (deterioration) of the degree of care required (aggregation index) is 26.5 months for establishment 1, 25.5 months for establishment 2, and one month longer for establishment 1. Therefore, the activity related to the degree of care required at the establishment 1 is highly evaluated.

Next, the newly proposed method 3 will be described. In the method 3, the individual evaluation index is totaled in the following two patterns.
・ (Method 3-1) Aggregation of individual evaluation indexes by deviation value ・ (Method 3-2) Aggregation of individual evaluation indexes by quintile groups

First, an outline of aggregation of individual evaluation indexes based on the deviation value of Method 3-1 will be described.

The deviation value itself is well known, and as shown in FIGS. 69 (a) and 69 (b), it is a value standardized using the average value and the standard deviation. The deviation value of the user is calculated by summing up the average value and the standard deviation for each of the 211 stages of the 65 items of the mental and physical condition, and for each of the 211 stages of the 65 items of the mental and physical condition.
<Calculation formula for deviation value>
Deviation value = 50+ (numerical value-average value) / standard deviation x 10
<Advantage of deviation value>
Even if the user's maintenance period and the average maintenance period are the same, if the standard deviation is small, the spread of the distribution is small as shown in FIG. 69 (a), and more effort and care are required to maintain the average value for a longer period of time. It is considered possible to express (more difficult) by the deviation value.

Next, the outline of the aggregation of individual evaluation indexes by the quintile group of Method 3-2 will be described.

As shown in FIG. 70, the maintenance period of the users is arranged from the shortest, and the total number of users is divided into five equal parts, and the number of users is about 20% (first quintile) and about 40% (about 40%), respectively. 5 groups (short maintenance period / shorter than average / average) with about 60% (third quintile) and about 80% (fourth quintile) / Individual evaluation by (longer than average / longer than average).

Next, the aggregation method of the method 3-1 using the deviation value will be described by a specific example. FIG. 71 illustrates a method of calculating the deviation value. In FIG. 71, the table 711 is the same as the table 681 as a result of totaling the “maintenance period by service type / user / stage / deterioration / improvement”. Table 712 obtains "average value and standard deviation of maintenance period by service type, mental and physical condition item (needs care), stage (needs care 1-4), gender, deterioration / improvement" from the data of table 711. The result is shown. Table 713 shows the results of calculating the "deviation value" from the "maintenance period" of each user using these values.

In the example of FIG. 71, the user A is a man who needs nursing care 1, has a maintenance period of 23 months, has an average value of 21.0 months, and has a standard deviation of 4.0 months, so that the deviation value is 55.0. ..

FIG. 72 shows the result of calculating the “deviation value” obtained as described above for each user AP.

"
FIG. 73 describes the process of totaling the “average deviation value of the mental and physical condition index by business establishment” 723 from the “deviation value” data 731 calculated for each user AP shown in FIG. 72. That is, for each business establishment, the average of the deviation values for each stage of the mental and physical condition (needs care 1-needs care 4) and the degree of care required mild (needs care 2 or less) and the degree of care required severe (needs care 3 or more). Find the value. Then, these average deviation values are aggregated as an index for each business establishment's average mental and physical condition, and the business establishment is evaluated based on this.

In FIG. 73, the average deviation value (deterioration) of the degree of care required (aggregation index) is 55.0 for business establishment 1, 56.25 for business establishment 2, and 1.25 higher for business establishment 2. When the average maintenance period described with reference to FIG. 68 is used as the evaluation index, the establishment 1 is one month longer and the evaluation is reversed.

FIG. 74 shows the result 742 of evaluating the deviation value obtained as described above in five stages using the preset five-stage evaluation table 741 and evaluating the average deviation value in each of the 211 stages for each business establishment in five stages. In FIG. 74, since the average deviation of the business establishment 1 and the need for nursing care 1 is 53.75, it corresponds to “45 or more and less than 55” in the 5-grade evaluation table 731 using the deviation value, and becomes 3 of the 5-grade evaluation.

In this way, the average deviation value for each of the 211 stages for each business establishment can be evaluated in five stages.

Next, the aggregation method of the method 3-2, that is, the aggregation method by dividing into N groups (5 groups) by the N quantile (for example, the quintile) will be described.

As mentioned above, the five-group division by quintile means that the maintenance period of users is arranged from the shortest, and the total number of users is divided into five equal parts, and the number of users is about 20% (15th minute). 5 groups with less than the rank), about 40% (less than the 25th quantile), about 60% (less than the 3rd quantile), about 80% (less than the 45th quantile) Individual evaluation (short maintenance period / shorter than average / average / longer than average / long).

FIG. 75 illustrates the five-grouping method based on the quintile, and Table 751 is the aggregated result of "maintenance period by service type, gender, mental and physical condition stage, deterioration / improvement", and is the above-mentioned table. It is the same as 681 and 711. As shown in Table 752, the maintenance period, which is the result of this aggregation, is shown in the quintile value of the maintenance period by service type "user's gender, mental and physical condition stage, deterioration / improvement" (from the 15th quantile value). 4th quintile) ”is set. Further, each user is grouped (table 754) according to the preset "five grouping by quintile value" table 753.

In this way, FIG. 76 shows the results of grouping the individual users into five groups according to the quintile values (from the first quintile value to the fourth quintile value). That is, an example is shown in which each user aggregated in the table 751 of FIG. 75 is divided into 5 groups according to the quintile value and a "group" is set.

FIG. 77 shows the results of grouping the individual users described in FIG. 76 into five groups according to the quintiles (from the first quintile to the fourth quintile), that is, by user / mental and physical condition 65. The process of totaling the points of the average mental and physical condition index (table 772) by business establishment is described using the table 771 of the 211-step group of the items. In other words, using data (groups) for each user's individual mental and physical condition item (long-term care required) stage (long-term care 1-4), points for each long-term care level and long-term care required for each business establishment that receives each user service. The points for each mild / severe degree and the points for the entire degree of long-term care required are calculated and used for mutual evaluation between business establishments.

FIG. 78 shows the aggregated results of the group ratio by establishment and deterioration / improvement. That is, from the result of totaling the maintenance period of each user by service type, gender, mental and physical condition stage, deterioration / improvement by 5 groups, the number of people for each group 5 to 1 by business establishment (about care required 1). W5 to W1) are totaled, and their ratios (a to e for care-requiring 1) are calculated. The method of obtaining the ratio (a to e for the need for nursing care 1) is as follows.

a = W5 / (W5 + W4 + W3 + W2 + W1)
b = W4 / (W5 + W4 + W3 + W2 + W1)
c = W3 / (W5 + W4 + W3 + W2 + W1)
d = W2 / (W5 + W4 + W3 + W2 + W1)
e = W1 / (W5 + W4 + W3 + W2 + W1)
(A + b + c + d + e = 100%)

In this way, the ratio of the number of people who need nursing care 1 at a certain business establishment and are divided into 5 groups according to the quintile is calculated. That is, the ratio of the number of people in the mental and physical condition index unit (in the above case, according to the degree of long-term care required) is calculated. In this case, the high ratio of the group 5 and the sum of the ratios of the group 5 and the group 4 indicate that the maintenance period of the index is long.

FIG. 79 shows the case where the average maintenance period for each business establishment is tabulated by methods 1 and 2 for each index / aggregation index (need care 1 to 4, mild need for long-term care, severe need for long-term care, degree of long-term care). It is shown in comparison with the case where the average deviation value for each business establishment is aggregated by the method 3.

In FIG. 79, as shown in (a), in all of the methods 1, the method 2, and the method 3, the establishment 2 has a longer maintenance period (deterioration) with a mild degree of long-term care than the establishment 1, and the deviation value is high. I understand. In addition, as shown in (a), since the average deviation value of the maintenance period (deterioration) of the establishment 2 with a mild degree of care required in the method 3 is 65.0, the establishment 2 is among all the establishments. It can be seen that the maintenance period (deterioration) is long and the deviation value, which is an evaluation index, is high. Further, as shown in (c), it can be seen that the evaluation indexes (average deviation value) of the maintenance period (deterioration) of the establishment 2 in the method 3 are mild and severe, and are about the same. Moreover. In (d), the maintenance period (deterioration) of the degree of care required is longer in business establishment 1 than in business establishment 2 in method 1 and method 2, but the average deviation value of method 3 is longer in business establishment 2 than in business establishment 1. It becomes higher, and the evaluation is reversed from that of Method 1 and Method 2.

Here, in the methods 1 and 2, the maintenance period until the deterioration of each individual mental and physical condition item for each stage is calculated, and the maintenance period until the deterioration is aggregated (weighted average) as it is for each mental and physical condition item. Therefore, the average value for each business establishment was calculated, and the business establishment was evaluated accordingly. In this way, since the aggregated (weighted average) index for each mental and physical condition item is used, it may not be possible to make a correct evaluation of the business establishment.

Method 3 is an individual evaluation index (for example, deviation value or N quantile) showing the position of an individual user with respect to the entire user (for example, all users of the municipality to which the user belongs) according to the stage of each mental and physical condition item. (Group)). Then, the average value of these deviation values or the average value of the N quantile (group) is obtained for each business establishment to which the user belongs, and the business establishment is determined by the average value of these deviation values or the average value of the N quantile (group). From the evaluation, it seems that more accurate evaluation will be possible. Further, the evaluation result of the individual user can be reflected in the evaluation of the business establishment, and the evaluation of the individual user can be fed back to the business establishment in addition to the evaluation of the business establishment.

However, in order to adopt method 3, a certain number of data is required, and if a large number of data cannot be obtained, a method of replacing the weighted average aggregation in methods 1 and 2 with the arithmetic mean aggregation can be considered. Be done.

In the explanation so far, as an outcome index for each business unit used for evaluation of the business unit including subregions and business establishments, the maintenance period until deterioration / improvement of the individual unit for each stage of each mental and physical condition item is calculated. Based on this maintenance period, the explanation was given when the indexes described as methods 1, 2, and 3 were used, respectively.

In addition to this, as an outcome index for each business unit, as shown in FIGS. 29, 35, etc., there are seven items of mental and physical condition for each business unit (hereinafter, described as a business establishment) (the following explanation is as described above). The improvement rate for each (9 items) is calculated, and the establishment is evaluated based on the improvement rate.

As described above, the improvement rate of a business establishment refers to the percentage of users of the business establishment that have undergone the improvement described with reference to FIG. 24. This ratio is calculated for each of the nine items of mental and physical condition as described above. For example, when the mental and physical condition item is set as the degree of long-term care, the total number of users who have improved the degree of long-term care during the certification renewal check period at the relevant business establishment, and the total number of users whose degree of long-term care has changed. Was defined as the improvement rate of the degree of long-term care required at the business establishment.

That is, regardless of which stage (in this case, the degree of long-term care required) the user was improved, the improvement rate of the entire target mental and physical condition item (in this case, the degree of long-term care) was calculated. For this reason, it is unknown at which stage of the mental and physical condition items have been improved. When the mental and physical condition items are defined as the degree of need for care, in general, the improvement with a mild degree of care required (degree of care required 2 or less) and the improvement with a severe degree (degree of care required 3 or more) are severe improvements at the business establishment. It takes a lot of effort. In other words, it can be said that it takes a lot of time and effort for long-term care to make a severe improvement, and the establishment should be evaluated in consideration of these circumstances. However, with the above-mentioned method, although the improvement rate of all mental and physical condition items at a certain business establishment can be grasped, it is not possible to grasp which stage has been improved.

Therefore, the newly proposed method calculates the improvement rate for each business establishment in 211 stages of 65 items of mental and physical condition. However, since the number of data is too large and it is inconvenient to handle as it is, the improvement rates of severe and mild are obtained from the improvement rates of each of the above-mentioned nine aggregation items. That is, the improvement rate for each of the 9 items of aggregation is calculated for each business establishment, and this is used for the business establishment evaluation.

FIG. 80 shows the deterioration and improvement status of the degree of need for nursing care of users A, B, C, D, and O at a certain business establishment A. During the certification renewal check period of FIG. 79, the user A has one deterioration in the care-requiring degree 2 and one deterioration in the care-requiring degree 3. User A has only one deterioration in the degree of care required 2. For the user c, the deterioration at the care-requiring degree 2 is once, and the improvement at the care-requiring degree 3 is once. User D maintains a care requirement level of 2. The user O has only one improvement in the degree of care required 2.

From these results, when looking at user A, there was one deterioration of long-term care required 2, one case of deterioration of long-term care required 3, and when looking at user A, one case of deterioration of long-term care required 2, and about user c. Looking at it, there is one case of deterioration of long-term care required 2, one case of improvement of long-term care required 3, there is no number of stage changes for user D, and one case of improvement of long-term care 2 is for user O. Looking at these for the need for nursing care 2, there are 1 improvement and 3 deterioration, and the improvement rate is 25% (= 1 / (1 + 3)). Similarly, the improvement rate of care-requiring 3 is 50% (= 1 / (1 + 1)).

In this way, the improvement rate is calculated for each stage of the mental and physical condition items for each business establishment.

FIG. 81 shows the improvement rate for each mental and physical condition stage by service type. In this case, "special nursing home" and "home care support" are illustrated and shown as service types in comparison. As for the mental and physical condition, the number of cases of deterioration and improvement at each stage of "degree of long-term care", "degree of independence of disability", and "degree of independence of dementia" are tabulated, so it is possible to calculate the improvement rate for each mild / severe condition from these. can.
The mild improvement rate of "special nursing home""needs nursing home" is calculated by the arithmetic mean of the improvement rates of "nursing home" from support 1 to 2 of "special nursing home". The "degree of care required" severe improvement rate is calculated by the arithmetic mean of the improvement rates of "degree of care required" 3 and 4 of "special nursing home". The reason why non-applicable is not included in the "degree of long-term care" mild improvement rate is that there is no improvement from the non-applicable stage, and the reason why "long-term care required" severe improvement rate does not include long-term care 5 This is because the improvement rate is 100% without worsening from the need for long-term care 5.
The "degree of care required" mild improvement rate and the "degree of care required" severe improvement rate of "home care support" are also calculated in the same manner. With respect to the "degree of independence of disability" and the "degree of independence of dementia", the improvement rate for each mild / severe can be calculated according to the classification of mild / severe in FIG.

Note that FIG. 81 shows the “degree of long-term care”, “degree of independence of disability”, and “degree of independence of dementia” as mental and physical conditions. do. By obtaining the improvement rate in this way, each business establishment can be accurately evaluated.

Next, another example in which each business entity (explained as each business establishment in this example) is evaluated according to service type and mental and physical condition using the average deviation value of the deterioration maintenance period and the improvement rate. explain. In this example, as shown in FIG. 82, by plotting a plurality of indicators on a scatter plot, the distribution of the entire establishment to be analyzed is captured, and there is a “significant difference” such as a degree of variation and a strong positive correlation. Visualize the "special tendency" with a large bias and the positional relationship with the average value of each index.

FIG. 82 shows an example in which the service type is "special nursing home" and the mental and physical conditions are evaluated for each of the establishments for "degree of long-term care (severe)", "degree of disability independence (severe)", and "degree of dementia independence (severe)". Of course, the mental and physical condition shall be evaluated not only in the above-mentioned example but also in other mental and physical conditions in the same manner.

FIG. 82 is a two-dimensional graph in which the horizontal axis is the improvement rate of the degree of care required for each business establishment and the vertical axis is the average deviation value of the maintenance period until the degree of care required for each business establishment deteriorates. It is a scatter chart which plotted the average deviation value of the improvement rate and the maintenance period until deterioration of the establishment at the coordinate points obtained from the horizontal axis and the vertical axis, respectively. Then, as shown by the broken line, the improvement rate and the average deviation value of the maintenance period until deterioration are divided into three tertiles of the entire business establishment, and the business establishments are classified into 9 groups from G1 to G9. The tertile value of each index is indicated by the broken line described above.

In the example of FIG. 82, the group G1 of the establishments in the upper right area corresponds to the establishments having a high improvement rate, a long maintenance period until deterioration, and both indicators are good. On the contrary, Group G9 of business establishments in the lower left area has a low improvement rate, a short maintenance period until deterioration, and both of the two indicators correspond to bad business establishments.

In this way, by analyzing the two indicators from various angles, in addition to the above-mentioned groups G1 and G9, the group G7 of establishments with a low improvement rate but a long average deviation value of the average maintenance period until deterioration, deteriorated. The average deviation value of the average maintenance period up to is short, but the improvement rate is high, but the group G3 of the establishment, the average deviation value of the average maintenance period until deterioration and the improvement rate are both intermediate values, the group G5 of the establishment, deterioration The average deviation value of the average maintenance period up to is long, but the improvement rate is the group G4 of the establishment with an intermediate value, the average deviation value of the average maintenance period until deterioration is the intermediate value, and the improvement rate is the group G2 of the establishment with a high improvement rate, deterioration Group G6 of establishments where the average deviation value of the average maintenance period up to is short but the improvement rate is intermediate, and the establishments where the average deviation value of the average maintenance period until deterioration is intermediate value and the improvement rate improvement rate is low It can be seen that each group G8 exists.

Here, since the indicators of mental and physical condition are aggregated 9 indicators as described above, first, the degree of need for long-term care, the degree of disability independence, and the degree of dementia independence according to mild and severe are comprehensively analyzed by the combination of groups (FIG. 82). Indicates a case of severe mental and physical condition). Then, if the combination of the groups of the degree of care required, the degree of disability independence, and the degree of dementia independence is subject to the investigation, which will be described later, the remaining 6 aggregated indicators (body system 1, body system 2, body system 1 + 2, mental system). Investigate and analyze in 1, mental system 2, mental system 1 + 2).

In order to comprehensively classify business establishments by group combination as the degree of mental and physical condition, the degree of independence of long-term care, the degree of independence of disability, and the degree of independence of dementia, it is necessary to analyze the nine groups from G1 to G9 as shown in Fig. 82. , The number of combinations is 9 × 9 × 9, which is 729, and there are too many patterns to analyze by group combinations. For this reason, nine groups are classified into three groups A, B, and C as follows according to their physical and mental conditions, and business establishments are classified by a combination of 27 group classifications of 3 × 3 × 3.

That is, the groups G1, G2, and G4 are grouped as Group A. In addition, groups G3, G5, and G7 are grouped as Group B. Furthermore, groups G6, G8, and G9 are grouped as C group. The results of classification into the three groups A, B, and C are shown in FIG. 83 when corresponding to the distribution map of FIG. 82.

Looking at the establishment types based on the combination of groups A, B, and C described above, for example, when the combination of the group classifications of the degree of long-term care required, the degree of disability independence, and the degree of dementia independence of establishment 1 is AAA, the establishment 1 is a candidate for best practice because the quality of care is high in both the physical and mental systems, and the degree of care required is also highly improved and maintained. In addition, when the combination of the group classification of the degree of care required, the degree of disability independence and the degree of dementia independence of the establishment 2 is BAB, the establishment 2 has an average improvement and maintenance of mental care and the degree of nursing care. It is analyzed that the quality of physical care is high. If the combination of the group classifications of the degree of long-term care required, the degree of disability independence, and the degree of dementia independence at the establishment 3 is CCC, the quality of care is low in both the physical and mental systems, and the improvement and maintenance of the degree of long-term care is also low. Place 3 is a candidate for priority guidance. Furthermore, if the combination of the group classifications of the degree of care required, the degree of disability independence, and the degree of dementia independence of the establishment 4 is ACC, the improvement and maintenance of the degree of care required is high, but the quality of care is high in both the physical and mental systems. Since it is low, the establishment 4 is the establishment subject to investigation regarding the care plan and the like why such a combination of mental and physical conditions is obtained.

The above-mentioned three groups A, B, and C are a combination of 27 types of 3 × 3 × 3 group classifications as described above.

FIG. 84 shows an example in which the number of establishments corresponding to the combination of the group classifications of the degree of need for nursing care, the degree of disability independence, and the degree of dementia independence of the main mental and physical condition items is totaled. In FIG. 84, the number of establishments classified as AAA, which are considered as best practice candidates, corresponds to 31 establishments with a combination of mental and physical conditions (mild) and 16 establishments with a combination of mental and physical conditions (severe). ..

In addition, when the group classification combination is ACC, the quality of care for general long-term care is high, but the quality of care for physical and mental systems is low, so it is subject to investigation. As for the number of establishments classified as ACC, there is no establishment corresponding to the combination of mental and physical condition (mild), and 4 establishments correspond to the combination of mental and physical condition (severe).

Similarly, the group combination that requires investigation is the case of CAA, and although the quality of care for general long-term care is low, the quality of care for the physical and mental systems is high. The number of establishments classified as CAA corresponds to 12 establishments with a combination of mental and physical conditions (mild) and 5 establishments with a combination of mental and physical conditions (severe).

When the group combination is CCC, the quality of long-term care, physical and mental care is low, and it is considered as a candidate for priority guidance. As for the number of establishments classified as CCC, there is no combination of mental and physical conditions (mild), and 19 establishments are applicable with combinations of mental and physical conditions (severe).

In addition to the above, if the group classification of the degree of disability independence is A, it can be diagnosed as a business establishment that specializes in the physical system and has high quality of care. In addition, if the group classification of dementia independence is A, it can be diagnosed as a business establishment with high quality of care specializing in the mental system.

Here, as an individual evaluation index for accurately and fairly evaluating the quality of nursing care services for each business unit (business establishment, subregion, etc.), in each of the above-mentioned examples, the maintenance period (real value) until deterioration, the deviation value, The case of using quantiles has been explained, but in addition to these, ranking and aggregation by arithmetic mean of maintenance period (real value) can be considered. Hereinafter, these features will be described.
・ Maintenance period (real value)

From the difference between the maintenance period of the user and the average maintenance period for each service type of the local government, the maintenance period (real value) can be understood as it is how long the user has maintained from the average of the local government.

In addition, the average maintenance period for each business establishment by service type can evaluate the quality of nursing care services for each business establishment, and the difference from the average maintenance period for each service type of the local government indicates that business establishment (weighted average). Value) can be understood as it is how long it has been maintained from the local government average.

Furthermore, the maintenance period (real value) can be used to calculate the amount of restraint of long-term care benefit costs from the difference from the average maintenance period for each service type of the local government. From these facts, it is suitable as an individual index related to the maintenance period, but has the following problems.

In other words, when aggregating indicators, if there is a difference in the average maintenance period for each stage in the index to be aggregated, and if the ratio for each stage is different depending on the establishment, the analysis result of the establishment for each stage and the analysis of the establishment after aggregation Results may vary.
・ Ranking

The ranking of users by maintenance period for each stage is the relative position of each user at that stage. It is an index that the longer the maintenance period until deterioration is considered to be better, and the longest is ranked as 1, 2, 3, .... The ranking has the advantage that it is not affected by the difference in the average maintenance period for each stage. In addition, it is possible to analyze business establishments by stage based on the average value of the user's stage for each business establishment by service type.

However, while the difference before and after the ranking is all 1, the difference in the maintenance period is not constant between the rankings. For example, the difference between the 1st and 2nd place may be 5 months, and the difference between the 2nd and 3rd place may be 1 month. It is possible that the evaluation results differ depending on the average maintenance period for each business establishment in each stage, and it can be seen that it is not suitable for individual indicators related to the maintenance period.
・ Grouping by quantile

When grouping the user retention period by stage by quantile, from all the values of the user retention period by stage, the 15th quantile, the 25th quantile, and the 35th quantile Calculate the number and the 4th quintile, and divide the users into 5 groups according to the stage as shown in the table below, with those 4 quantiles as boundaries. Since it is considered that the longer the maintenance period until deterioration is, the better it is, the long-term group is assigned 5, 4, 3, 2, 1.

In each stage, the group to which each user belongs is a relative position, and has the advantage of being unaffected by the difference in maintenance period for each stage. In addition, in the grouping by quantiles, the number of users belonging to each group is approximately the same. For example, the ratio of users belonging to 5 groups by quantiles is about 20% as described above, which is understood. It has the advantage of being easy to remove.

However, although it is possible to evaluate business establishments by stage based on the average value of the user group numbers for each business establishment by service type, the amount of information such as the user's maintenance period (months) is reduced, and the accuracy is high. It is not possible to compare high-priced establishments.

In addition, when the number of users in each stage is small, the number of users belonging to each group varies widely, and grouping cannot be performed evenly according to the quantiles. Therefore, when grouping by the number of quantiles, it is necessary to have a certain number of users in each stage.

By increasing the number of quantiles (integer m of the m quantile), it becomes possible to make a more accurate comparison of business establishments. For example, it can be divided into 100 groups by grouping by percentiles, but in this case, a larger number of users in each stage (number of different numerical values) is required.

Although there are some problems like this, it is suitable as an individual index related to the maintenance period.
·Deviation value

As described above, the deviation value is a standardized value using the average value and the standard deviation, and the average value and the standard deviation of the maintenance period are totaled for each of the 211 stages of the mental and physical condition of the user according to the service type. The deviation value of the user is calculated by the following formula for each of the 211 stages of the mental and physical states.
Deviation value = 50+ (maintenance period-average value) / standard deviation x 10

The calculation formula of this deviation value is a linear formula (linear transformation) with respect to the maintenance period, and the longer the maintenance period, the higher the deviation value. In addition, since the average value and standard deviation for each stage by service type are common, the relational expression between the average deviation value for each stage by service type and the average maintenance period for each stage for each service type is as follows. Become.
Average deviation value of establishment = 50+ (average maintenance period of establishment-average value) / standard deviation x 10

The evaluation result of the business establishments by service type and stage based on the deviation value is the same as the evaluation result of the average maintenance period by business establishment by service type and stage.

In addition, by standardizing the deviation value from the average maintenance period of users by service type to 50, it is thought that the establishment can be evaluated correctly by the indicators aggregated into 65 items of mental and physical condition by service type and 9 indicators. Be done.

Further, even if the maintenance period until the deterioration of the user and the average maintenance period of the user of the corresponding service type are the same, if the standard deviation is small, as shown in FIG. 66A described above, the spread is small and the average maintenance is performed. Deviations can indicate that more effort and care is required (more difficult) to maintain longer over a period of time.

Since the distribution of deviation values is the same as the distribution of the data (maintenance period) before conversion to deviation values, if the distribution of the maintenance period is not a normal distribution (significantly deviates from the normal distribution), the distribution of deviation values is also normal. The distribution is not a distribution (largely deviates from the normal distribution). If the distribution of deviation values is not normally distributed, the percentile or percentile rank of each deviation value is different from the value in the case of normal distribution. However, the effort required to maintain longer than the average maintenance period and the degree of care (difficulty of care) are evaluated by deviation values, and the difference from the percentile or percentile ranking in the case of normal distribution is the business. It is considered that it does not affect the evaluation of the place.

Thus, the deviation value is most suitable as an individual index related to the maintenance period. However, the number of users for each stage (the number of different numbers) above a certain level is required.
・ Arithmetic mean of maintenance period (real value)

When aggregating indicators of 211 stages of mental and physical condition into indicators in units of 65 items of mental and physical condition, and further aggregating into 9 indicators of mental and physical condition, when aggregating indicators with different average values for each stage (weighted average), it is aggregated. Since the ratio of the number of data (number of users) for each stage is different for each unit (business establishment), the average value (weighted average value) after aggregation is the number of data for each stage (users) for each aggregation unit (business establishment). It depends on the ratio of number).

For example, the number of data (number of users) of disability independence degree B1 of business establishment 1 is 8, and the maintenance period until deterioration of each user is 21,15,23,19,27,23,13,12 months. Then, the average maintenance period (weighted average value) for each stage is 19.75 months. Assuming that the number of data (number of users) of disability independence degree B2 of the same establishment 1 is 2 and the maintenance period until deterioration of each user is 30 or 36 months, the average maintenance period for each stage (weighted average value). Will be 33.0 months.

On the other hand, assuming that the number of data (number of users) of disability independence degree B1 of business establishment 2 is 2 and the maintenance period until deterioration of each user is 15 or 13 months, the average maintenance period (weighted) for each stage is assumed. The average value) is 14.0 months. Assuming that the number of data (number of users) of disability independence degree B2 of the same business establishment 2 is 8, and the maintenance period until deterioration of each user is 32,28,30,28,22,28,20,30 months. The average maintenance period (weighted average value) for each stage is 27.25 months.

As a result, both the disability independence degree B1 and the disability independence degree B2 are longer in the establishment 1 than in the establishment 2. However, when the maintenance period (weighted average) of the disability independence degree B, which is the sum of the disability independence degree B1 and the disability independence degree B2, until deterioration by establishment, is calculated, the establishment 1 has 22.4 months and the establishment 2 has 24. At 6 months, the establishment 2 is longer than the establishment 1, and a so-called reversal phenomenon occurs, which is the opposite of the maintenance period until deterioration in each stage. This is because the ratio of disability independence degree B2 of establishment 2 is as high as 80%, and that of establishment 1 is as low as 20%.

Therefore, when the arithmetic mean is taken when totaling the maintenance period until the deterioration of the disability independence degree B by establishment, which is the sum of the disability independence degree B1 and the disability independence degree B2, the establishment 1 has (19.75 + 33.0). / 2 = 26.38 months, at business establishment 2 (14.0 + 27.25) / 2 = 20.63 months, regardless of the ratio of the number of data (number of users) for each stage, as described above. The reversal phenomenon does not occur.

However, arithmetic mean cannot be said to evaluate the effect of each user receiving long-term care services at each stage.

Therefore, it should be used when the number of data (number of users) is small and grouping by deviation value or quantile is not suitable.
·summary

As a result, it is judged that the deviation value is the most suitable as an evaluation index for accurately and fairly evaluating the quality of long-term care services of each business unit (business establishment, subregion, etc.).

In each embodiment of the present invention, the maintenance period of the mental and physical condition is used as an index indicating the mental and physical condition. Conventionally, for this maintenance period index, indexing of the difference in mental and physical condition between two time points according to a preset date has been used. Hereinafter, the conditions required for the outcome index that contributes to the quantitative evaluation of improvement and maintenance of the physical and mental condition of the two-time point comparison index (hereinafter referred to as the conventional method) and the maintenance period index (hereinafter referred to as the present method) are described. Compare from the viewpoint of whether or not to meet. In other words, in order to realize quantitative evaluation of the quality of services by service type and business establishment, and to estimate the effect of suppressing long-term care benefit costs by improving the quality, the following explanations (1) to (8) will be given. The conditions must be met, and both equations are compared under each condition.

In conclusion, it can be understood that the maintenance period index of this method is likely to satisfy the conditions as an accurate and fair outcome index that contributes to the improvement and maintenance of the physical and mental condition. Hereinafter, each condition will be described in detail.
(1) Is the range of target mental and physical condition items wide?

By using the long-term care certification data for both the two-time point comparison index (conventional method) and the maintenance period index (this method), 77 mental and physical condition items (long-term care required, dementia independence, disability independence, certification) Each stage of the survey 74 items) and items having continuous values such as the reference time for each action category related to the primary judgment logic and the score of the intermediate evaluation item can be counted.
(2) Is the accuracy of the improvement rate of mental and physical condition high?

Since the improvement rate by this method is calculated after specifying the start month and end month of the maintenance period of the mental and physical condition stage, it is not affected by the length of the certification renewal check period in principle. On the other hand, in the conventional method, the improvement rate, the maintenance rate, etc. fluctuate greatly depending on the length of the evaluation period.
(3) Is the accuracy of the maintenance period until the deterioration of the physical and mental condition is high?

In the conventional method, even if the end point is fixed, the value of the data to be calculated for the maintenance period may change due to the difference in the setting of the start time of the evaluation period. There is an essential problem that it cannot be uniquely identified. In addition, when the evaluation period is short, the number of users whose value of the calculation target data does not change increases, and the accuracy becomes low as an index. On the contrary, as a countermeasure, by lengthening the evaluation period, the percentage of users whose value of the calculation target data changes will increase, but (4) identification of service type and (5) identification of dominant business establishment will be described later. The problem of reduced accuracy arises.

On the other hand, in this method, the maintenance period is uniquely specified regardless of the setting of the certification renewal check period. In addition, if the data cannot specify the start month, the accuracy can be improved by excluding it from the calculation target. However, there is a problem that the index cannot be calculated (decrease in the number of subjects that can be indexed) while the stage of the mental and physical condition item remains unchanged within the certification renewal check period.
(4) Is it possible to identify the main service type during the maintenance period?

Even with the conventional method, it is possible to specify the main service type from the cumulative total number of units for each service type within the evaluation target period. However, if the evaluation period is lengthened, the number of users who change the service received during the same period increases, and it becomes difficult to identify the main service type.

On the other hand, in this method, by using the calculation target data within the maintenance period in the same mental and physical state stage, it can be expected that there is little change in the service type, and the main service type during the maintenance period can be accurately specified.

For example, it is assumed that the maintenance period of a certain mental and physical condition is 7 months, and the service of the service type (for example, special nursing home) to be analyzed is received from the business establishment A, the business establishment B, and the business establishment C. For the 7 months of this maintenance period, it is determined whether the usage rate of the service type to be analyzed for each month is equal to or higher than the preset first threshold value (for example, 50%) in the total service of the month. ..

As a result, for example, a month in which the usage rate of the fourth month is equal to or less than the first threshold value of the service type is excluded from the months in which the influence of the service type is strong. If there is at least one service provision month that does not satisfy the above exclusion conditions during the maintenance period (7 months described above), the service type is specified as the main service type during the maintenance period of 7 months.
(5) Is it possible to identify the main business establishment during the maintenance period?

Even with the conventional method, it is possible to identify the dominant business establishment by the number of credits provided by the business establishment within the evaluation period. However, if the evaluation period is lengthened, the number of business establishments that provide services received within the evaluation period increases, and it becomes difficult to identify the main business establishments.

On the other hand, in this method, it is possible to identify the main business establishment by the number of credits provided by each business establishment during the maintenance period.

For example, from the establishments A, B, and C for each of the remaining 6 months excluding the month in which the maintenance period of a certain mental and physical condition explained in (4) above is 7 months and does not reach the first threshold value. The number of units of the long-term care service received is compared with a preset second threshold value (for example, 50% of the total number of units of establishments A, B, and C).

As a result, for example, if the number of credits of each of the establishments A, B, and C in the sixth month is equal to or less than the second threshold value, this month is excluded. Then, for the remaining months excluding the 6th month and the 4th month mentioned above, the number of credits of the A establishment is equal to or higher than the 2nd threshold in the 1st, 2nd, 3rd and 7th months. Therefore, it is assumed that the number of credits of the B establishment is equal to or more than the second threshold value in the fifth month. At this time, it is determined that the business establishment A that provides the service has an influence of 4 months and the business establishment B that provides the service has an influence of 1 month during the continuous 7 months of the physical and mental condition. do.

In this way, as a result of the judgment by the second threshold value, of the 7 months in which the mental and physical condition continued, the establishment A was for 4 months (about 57%) and the establishment B was for 1 month (about 14%). ) Has an influence. Therefore, when the third threshold value is set to, for example, 50%, the establishment A having an influence period ratio of about 57% satisfies the third threshold value, and therefore is specified as the main establishment during the maintenance period.
(6) Is it possible to calculate the amount of services affected during the maintenance period?

Even with the conventional method, it is possible to calculate the degree of influence of each service item on the two-time comparison index from the number of units of each service item provided within the evaluation period, but the maintenance period is not specified. , The amount of service that affects the service cannot be calculated correctly.

On the other hand, in this method, the number of units of each service item provided during the maintenance period can be considered as the degree of influence on the index value of the maintenance period of each service item, and each service item provided during the maintenance period. It is also possible to evaluate the relationship between the number of units and the index value of the maintenance period, and the amount of services that affect the service can be calculated correctly.
(7) Is it possible to aggregate mental and physical condition indicators?

It is necessary to realize an aggregation index that is easy for administrative staff and business establishments to understand and can be grasped immediately, and in both formulas, each stage of mental and physical condition items can be aggregated.

In the conventional method, the improvement rate and the maintenance rate of the aggregation unit are calculated from the improvement and maintenance data of each stage of the mental and physical condition items, and the aggregation is possible by calculating the arithmetic mean value of them.

This method can be aggregated by calculating the weighted average of the average value of the maintenance period of the aggregation unit from the maintenance period of each stage of the mental and physical condition items.
(8) Is it easy to apply to the estimation of the effect of suppressing long-term care benefit costs?

In the conventional method, it is not known at what point in the evaluation period the deterioration / improvement occurs, but the difference in benefit costs can be calculated by calculating the average value of all combinations in which the degree of care required deteriorates / improves. .. However, the accuracy is not high.

On the other hand, in this method, the degree of long-term care required and the average long-term care benefit cost per person, and the maintenance period for each degree of long-term care and the cumulative amount of long-term care benefit cost during that period are consistent with each other, so the accuracy is relatively high. It is possible to estimate the effect of suppressing long-term care benefit costs.

As a result of considering these conditions (1) to (8), as described above, the maintenance period index of this method satisfies the conditions as an accurate and fair outcome index that contributes to the improvement and maintenance of the physical and mental condition. It is clear that.

The above-mentioned index of the maintenance period can be calculated by a preset program by the computer system constituting the community-based comprehensive care business system 15 using the long-term care insurance data 161 of the database 16 shown in FIG. ..

In the above-described embodiment, as an image created by the regional information management function unit 22 shown in FIG. 2, the activity evaluation sheet shown in FIGS. 58 to 61 (evaluating the activities of business entities such as small areas and other business establishments in the local government). Image) is illustrated. The images of the activity evaluation sheets shown in FIGS. 58 to 61 represent the mental and physical conditions (degree of long-term care required, degree of lifelong independence, degree of dementia independence) as a so-called radar chart.

This activity evaluation sheet may be expressed as an image as shown in FIG. 85. FIG. 85 targets, for example, special nursing homes as business entities, and outputs and provides a business establishment activity evaluation sheet for each business establishment A, B, C, ... Separately.

This business activity evaluation sheet consists of the actual results and plans of the business establishment efforts in the upper row and the actual results and plans of the outcome indicators in the lower row. The upper row shows examples of efforts that may contribute to the improvement of mental and physical condition, and the lower row shows the year-to-year transition of the outcome index of mental and physical condition that shows the results of these efforts.

In other words, in the upper row, "ratio of unit-type private rooms," "strengthening the service provision system," and "dementia specialist care status" are listed as examples of efforts to improve the physical and mental condition, with the horizontal axis being the time axis and the vertical axis being the vertical axis. Is the evaluation axis, and shows the status of efforts by the actual value against the planned value for each year. In the figure, the actual values up to 2017 are shown with respect to the planned values up to 2020.

Here, the unit-type private room is not a completely private room, but a shared kitchen, etc., so that communication with others can be maintained while respecting the privacy of individuals. It is believed that the use of this unit-type private room will contribute to improving the physical and mental condition, and one of the efforts is to increase the ratio at business establishments.

The vertical axis in strengthening the service provision system means that the higher the level, the more generous the system (the number of staff assigned, etc.).

The vertical axis in the dementia specialty care situation indicates that the higher the staff, the higher the level of staff who can care for dementia.

The lower row shows the year-to-year transition of the mental and physical condition outcome indicators that show the results of these efforts, and examples of typical outcome indicators for the mental and physical condition are "degree of long-term care", "degree of independence of disability", and "cognition". Dementia independence degree ". In the lower part of FIG. 85, the horizontal axis is the improvement rate and the vertical axis is the average maintenance period until deterioration, and the change of the outcome index for each year is shown by the actual value with respect to the planned value for each year. In addition, the numerical value written next to the code represents the year (15 = 2015, ...) In the figure, the actual value up to 2017 is shown with respect to the planned value up to 2020. ..

By providing such a business establishment activity evaluation sheet to the corresponding business establishments, each business establishment can grasp the activity results and the progress status with respect to the plan in a timely manner, and can formulate an appropriate management plan.

Next, the service supply and demand analysis by subregion will be described. As the necessity and importance of work in the comprehensive community care business, it is required to optimize the service supply and demand balance for each service type and subregion. It is also necessary to verify whether the current service infrastructure for each subregion truly meets the demand for services.

The following items can be mentioned as problems in such business execution.
・ I do not know how to quantitatively grasp the gap and balance between service demand and service supply by subregion with respect to the main image of the elderly status by service type.
・ I don't know what kind of particle size subregional unit should be considered for service supply and demand adjustment for each service type.

As a function to solve such problems, the demand (utilization rate) and supply of services by subregion corresponding to the main image of the elderly state (mental and physical condition image x long-term care power x economic power, etc.) for each service type ( It is important to visualize the balance and excess / deficiency of the provision rate).

When such visualization is performed, the following effects occur.
・ Clarification of specific policies for optimizing regional service infrastructure.
・ Under the current status of service infrastructure development, specific studies will be conducted on whether services that really meet the needs of users living in each subregion (services required for each user status image) can be provided without excess or deficiency. It will be possible.

Also in the above-described embodiment, as described with reference to FIGS. 11 to 15, the utilization rate of residents at business establishments in each subregion is determined by the number of the regional management support function unit 21 shown in FIG. 6 for each service type. It is proposed that the factor analysis unit 52 of No. 1 performs quantitative analysis in the nursing care business 11 and visualizes it by a band graph or the like.

The embodiment described with reference to FIG. 86 shows other analysis results different from the analysis results described with reference to FIGS. 11 to 15 described above, and is mainly based on the service type based on the analysis by the first factor analysis unit 52. It outputs an image that visualizes the balance and excess / deficiency of service demand (utilization rate) and supply (provision rate) for each subregion, corresponding to the image of the elderly state (mental and physical condition image x long-term care power x economic power, etc.).

That is, FIG. 86 shows an image of the service supply and demand analysis results by subregion, and visualizes the balance and gap of service demand and supply by service type and elderly state image. For this reason, it is possible to lead to optimal infrastructure development through verification of the sufficiency status of each service in each subregion.

In FIG. 86, the service type is group home (mainly community-based service), and the main image of the elderly state (detailed later) is no paralysis × mild disability independence × mild dementia independence. In addition, the subregional hierarchy in local governments (detailed later) will be the middle hierarchy. The horizontal axis is the service utilization rate (%), that is, the ratio of users using the business establishment located in the residential area, and the vertical axis is the service provision rate (%), that is, the business establishment is the location. It is the ratio of providing services to users residing in the area, and each small area is plotted on the coordinates corresponding to its own service utilization rate and service provision rate.

In FIG. 86, it is divided into four quadrants, and the central point of these quadrants is taken as the local government average. In addition, it is analyzed that the small area within the area 86a of the first quadrant has a good balance between supply and demand, and the current status will be maintained as a future action. In other words, the business establishments in the region are able to supply the demand in the region in a well-balanced manner, and the condition will be maintained as it is.

The small area within the area 86b of the 4th quadrant is oversupplied, and the future action will be to curb entry without increasing the number of business establishments. That is, the business establishments in the area are able to sufficiently supply the demand in the area. However, the low rate of service provision to users residing in the area means that there are too many business establishments in the area for users and supply to users in other areas. It is judged to be in an excessive state. For this reason, even if a business establishment is set up in the area, no further effect can be expected, and as a future action, entry will be restrained.

The small area within the second quadrant area 86c is in short supply, and as a future action, the service infrastructure will be enhanced by increasing the number of business establishments in the area. That is, since there are few business establishments in the region, the supply is not sufficient only by the business establishments in the region to meet the demand in the region, and the supply is received from the business establishments in other regions. Therefore, as a future action, we will make a plan to set up a business office in the area.

Subregions within the third quadrant area 86d are subject to investigation, and individual regional measures will be taken. In other words, it means that the user who lives in the area does not use the place of business where he / she is located, but uses the place of business in another area. Therefore, as a future action, we will investigate the reasons for using business establishments in other regions and extract regional issues.

FIG. 87 shows the system configuration of the first factor analysis unit 52 for obtaining such an image. In FIG. 87, the received data 87a is read from the database 16 shown in FIG. 1, and as shown in the figure, certification data, benefit record data, long-term care insurance qualification data, Juki data, small area information, business establishment information. Is.

The analysis data creation unit 87b converts and processes each of these received data 87a, and creates analysis data 87c for performing subregion analysis in combination with other data. As shown in the figure, the analysis data 87c is each of the subregion data, the elderly state image data, the long-term care service data, and the business establishment data. Each of the analytical data 87c will be described later.

The basic tabulation unit 87d grasps the actual conditions of various analytical data 87c (number of insured persons, number of certified persons, number of beneficiaries, number of non-users of service, etc.), which is the basis of analysis of supply and demand of nursing care services, and basic tabulation. Result data 87e is created. The analysis conditions are determined from the aggregated results. The details of this basic tabulation will be described later.

The nursing care service supply and demand analysis department 87f analyzes the supply and demand situation of nursing care services (demand and supply) from the four viewpoints of "small area", "image of elderly condition", "nursing service" and "business establishment" for each service type. Gap analysis, etc.) is performed, analysis result data 87g is created, and analysis result 87h is output.

In order to analyze the supply and demand situation of nursing care services (small area analysis), as described above, four types of analysis data 87c are used: small area data, elderly state image data, nursing care service data, and business establishment data. , We will analyze the supply and demand situation of nursing care services from the four viewpoints of "small area", "image of elderly condition", "nursing care service", and "business establishment". The viewpoints of each analysis will be described below.

(1) Subregion: The local government is divided into the first level "local government", the second level "large area", the third level "medium area", and the fourth level "small area".

(2) Elderly state image: The elderly state image is defined by the "degree of care required", "mental and physical condition image", "nursing ability" and "economic ability" of the elderly. The "image of mental and physical condition", "nursing power", and "economic power" will be described later.

(3) Long-term care service: The service type is analyzed by classifying it into "home-based", "facility-based", and "residential-based". In addition, in each service, the basic service and the addition service that characterize them are also considered.

(4) Business establishments: "Service type", "Business establishment number", "Small area code including location", "Capacity", "Number of staff by qualification", "Corporate", and "Basic service to be provided" Analyze from the perspective of "bis and addition services".

Next, the basic tabulation performed by the basic tabulation unit 87d described above will be described in detail. As mentioned above, the basic tabulation is a tabulation for grasping the actual conditions of various analytical data 87c, which is the basis of the supply and demand analysis of nursing care services. The number, number of users, number of non-users (number of certified persons-number of users), and monthly benefit amount per person are totaled.

This basic tabulation includes a single tabulation and a combined tabulation. In the single tabulation, the actual situation of the elderly in each subregion of each level is grasped from the viewpoint of the subregion. In addition, from the viewpoint of the state of the elderly, the physical and mental condition of the certified / beneficiary (special medical care / paralysis / dementia independence / disability independence), nursing ability and financial ability will be grasped. From the viewpoint of long-term care services, grasp the long-term care services (home, facility, residence) of the beneficiary. Furthermore, from the perspective of business establishments, the attributes of business establishments will be analyzed based on the list of business establishments located within the insurer (local government) and benefit record data.

In the combination tabulation, the actual condition (bias, etc.) of the elderly state image is visualized for each subregion from the viewpoint of subregion x elderly state image, and the elderly state image and long-term care service from the viewpoint of elderly state image x long-term care service. Understand the relationship with. In addition, from the perspective of long-term care services x business establishments, the actual conditions (bias, etc.) of long-term care services by business establishment will be visualized. From the perspective of subregion x business establishment, aggregate the business information and grasp the actual situation of the business establishment. Furthermore, the actual condition (bias, etc.) of the elderly state image for each business establishment is visualized from the viewpoint of the elderly state image × business establishment.

FIG. 89 summarizes these aggregated contents.

Next, the flow of the process of creating the image data shown in FIG. 86 by the system described in FIG. 87, that is, the flow of the service supply and demand analysis will be outlined with reference to FIG. 88.

The service supply and demand analysis by subregion shown in FIG. 86 mainly targets community-based services. "Service utilization rate (how much elderly people living in each subregion use the business establishments located in the same area)" and "Service provision rate (business establishments located in each subregion are located in the same area) How much service is provided to the elderly living in the area) ”is visualized. In this way, it is clarified in which subregion of the hierarchy there is a balance or gap between supply and demand for each service type. In addition, the subregional hierarchy is divided into three layers, which will be described later, for analysis.

This service supply and demand analysis is sequentially processed according to steps (1) to (6) shown in FIG. 88. Hereinafter, each step (1) to (6) will be described in detail.

(1) First, determine the type of service to be analyzed. FIG. 90 shows the whole picture of the long-term care service. Nursing care services are broadly divided into "home services," "facility services," and "residential services." "Home-based services" include "visit-based welfare services," "visit-based medical services," "outpatient-based welfare services," "outpatient-based medical services," "short stays," and "welfare equipment rental." There is also.

"Facility services" include "special care", "elderly health" and "nursing facility (nursing home)". "Residential services" include "group homes," "living care for residents of specific facilities," and "small-scale multifunctional home care." Many service types are configured by these. The details are shown in FIG. In the example of FIG. 86 described above, "group home" is set as the service type.

(2) Returning to FIG. 88, the basic tabulation of the elderly state image is performed for each service type. As shown in FIG. 92, the elderly state image is defined by a combination of "degree of long-term care", "mental and physical state image", "long-term care ability", and "economic ability". Furthermore, the mental and physical condition image is defined by a combination of "special medical care", "paralysis", "fracture", "dementia independence" and "disability independence". The definition of each component stage of this mental and physical state image is shown in FIG. 93.

That is, for each component of the above-mentioned mental and physical condition image shown in FIG. 92, each component stage, certification survey item options, and corresponding special notes / remarks are combined in FIG. 93. For example, the component in FIG. 92 is "special medical treatment", and the No. 4 In the case of “Nursing care facility × Yes”, in FIG. 93, the No. 1 in the “Special medical care” column. The component stages of line 4, the certification survey item options, and the definitions of special notes and remarks correspond. Other "paralysis", "fracture", "dementia independence" and "disability independence" are similarly defined.

The "long-term care ability" that constitutes the image of the elderly state is determined based on the composition of the elderly household (relationship to the elderly person) and the degree of care required of each person who constitutes the elderly household. .. That is, first, the data regarding the elderly household to be cared for shown in FIG. 94 is created from the Juki data, the certification data, and the like.

In FIG. 94, the elderly and cohabitants constituting the household are A, B, and C, and the relationship of the Juki data is that A is the head of the household, B is the wife, and C is the child. Since it is not possible to determine who is the elderly person to be cared for by the relationship of this Juki data, the elderly person to be cared for is taken as the person himself / herself and converted into the relationship to this person. As shown in FIG. 94, when the head of household is the person himself / herself, the wife is the spouse and the child is the child as it is. If the wife is the person, the head of the household will be the spouse.

Among the people who make up these households, the degree of care required (light and heavy classification) of the target person (65 years old or older) is defined. In FIG. 94, it is assumed that the person requiring nursing care is severe and the spouse requiring nursing care is mild.

Based on the data of the elderly household, as shown in FIG. 95, the long-term care ability is determined from the combination of the elderly household composition pattern, the physical and mental condition of the cohabiting subject, and its mild severity. In the household shown in FIG. 94, the elderly household composition pattern is "person + spouse + child", and the physical and mental condition of the cohabiting subject and its mild severity are "mild", so that the long-term care ability is No. It becomes "medium" of 6.

The "economic power" that composes the image of the elderly state is to extract the income level of the resident of the same household as the elderly person, and determine the economic power of the household by focusing only on the person and spouse. The reason for having only two people is that the basic unit of financial independence is the person and the spouse. Therefore, the financial strength information of the elderly person's household is limited to the No. 1 insured person aged 65 or over. In addition, economic power is calculated based on income and does not include savings.

In order to obtain this "economic power", first, data representing the economic power of the elderly person and the resident of the same household shown in FIG. 96 (a) is created from the Juki data, the long-term care office work data, and the like. The household composition is the above-mentioned person A, spouse B, and child C. Set income levels for each of these individuals. There are two levels of income, one is the level of the local government to which the person belongs and the other is the stage of the local tax law, and both stages are used. The stages set by the local government are set in more detail as shown in Fig. 97 than the stages set by the Local Tax Law.

Next, using the economic power judgment table of the person shown in FIG. 96 (b) and the economic power judgment table of the spouse shown in FIG. 96 (c), the economy of the person and the spouse as shown in FIG. Determine the force.

In the case of a resident of the same household as the elderly person shown in Fig. 96 (a), the income of the person A is 15 million yen or more. There are 9 stages. Since the income of spouse B is 800,000 yen or less, the income stage of the local government is the sixth stage from Fig. 97.

Therefore, the person's economic power corresponds to the bottom row of the economic power judgment table FIG. 96 (b) and is judged to be "high". In addition, from the economic power judgment table Fig. 96 (b), it can be seen that the spouse's economic power is also low when the income level of the person is 4th stage or lower of the Local Tax Law, but when the income level of the spouse is 5th stage or higher of the Local Tax Law. The spouse's financial strength may be high. If the livelihood of the person and the spouse is one, and the income level of the person is the fifth stage or higher of the Local Tax Law, the economic power of the person alone and the spouse judged by the economic power judgment table Figure 96 (c). The sum of the economic powers of the persons is regarded as "economic power".

As mentioned above, the person's economic power is "high" and the spouse's income is 800,000 yen or less. Therefore, in Figure 96 (c) of the economic power judgment table, the economic power is judged to be "low". The economic power of the spouse is judged to be "high" from the figure (d).

By combining in this way, an image of the elderly state can be obtained. This image of the elderly state is calculated not for each subregion but for each municipality to which the elderly belong, and is calculated for each service type.

(3) Determine the main image of the elderly condition for each service type. That is, the elderly state image obtained in (2) has a wide variety of combinations of the degree of care required, the mental and physical state image, the long-term care ability, and the economic ability, and it becomes a huge amount of data. The most common image of the elderly state is determined to be the main image of the elderly state.

(4) As shown in FIG. 86, the service utilization rate and the service provision rate are aggregated for each service type × main elderly state image × subregional hierarchy, and plotted at the corresponding coordinate positions. In FIG. 86, as described above, the service type is a group home, and the most common image of the elderly state of the users of this group home is set as the main image of the elderly state, and further as a subregional hierarchy. The middle area is set.

Here, the subregional hierarchy means that the local government is divided into, for example, the first layer "local government", the second layer "large area", the third layer "middle area", and the fourth layer "small area". The "small area" on the 4th level literally refers to a small area such as a daily living area. The "medium area" in the third layer refers to a medium range in which a plurality of small areas are assembled. The "large area" on the second level refers to a large area such as an administrative district where a plurality of medium areas are gathered. The "local government" on the first level literally refers to the local government itself, and is used for comparison with other neighboring local governments.

As shown in FIG. 98, codes are attached to the data of each of these layers for arithmetic processing, and a subregion master that combines these data constitutes a subregion master.

(5) Identify the optimal subregional hierarchy for supply and demand analysis. The subregional hierarchy that should be considered in the service supply and demand analysis differs depending on the service type, and the analysis results may differ in the upper hierarchy. Therefore, we will analyze the supply and demand of services for the areas one level higher, and comprehensively consider the development policy for each subregion of group homes.

For example, in FIG. 99, when the service type is a group home of community-based service, first, supply and demand analysis is performed in a small area which is a daily living area, and then a middle area which is a higher hierarchy including each small area. Perform the analysis in. In Case 1-2, the supply and demand analysis of the subregion A2 in the "subregion" of the 4th level revealed a supply shortage. Next, when a demand analysis was conducted for the middle region B1 including other small regions A2, the balance between supply and demand was good. In such a case, even if the subregion A1 is in short supply, the supply and demand balance is good in the subregion B1 including the other subregions A2, so the future assumed action is the subregion A1 constituting the subregion B1. , A2 will both maintain the current service infrastructure.

As in Case 1-3, if the supply and demand analysis in the "small area" of the 4th layer shows that the supply is insufficient in both the small areas A1 and A2, the supply can be supplied even if the supply and demand analysis is performed in the medium area B1 including these small areas A1 and A2. It will be insufficient. In such a case, the assumed action in the future will enhance the maintenance of the service infrastructure in the middle area B1.

From these facts, when the service type is a group home of community-based services, the optimal subregional hierarchy for supply and demand analysis is the middle area of the third hierarchy. Therefore, for group homes, we will consider regional development policies based on the results of supply and demand analysis on the third level.

In addition, in the case of outpatient care, since it is not a community-based service, service supply and demand analysis is performed for the two layers, the middle area and the large area, which are the upper layers of the small area, which is the daily living area, and the small area of the outpatient care service. Consider a separate maintenance policy. In the case of outpatient long-term care services, it is advisable to consider regional development policies based on the results of the supply and demand analysis of large regions in the second stage, as shown in the figure.

(6) Group small areas with similar supply and demand. That is, the subregions that fall into the four regions 86a, 86b, 86c, and 86d shown in FIG. 86 are grouped into four groups as shown below, and actions are set for each to make an action plan for the entire municipality. be able to.
・ Areas with good supply and demand balance ⇒Maintaining the status quo ・ Areas with oversupply ⇒Restrictions on entry ・ Areas with insufficient supply ⇒Enriching service infrastructure ・ Areas requiring investigation ⇒Individual regional measures

Here, in the community-based comprehensive care business, it is necessary to improve the quality of services of the parties concerned in order to support independence and prevent the severity of the disease, and for that purpose, it is important to visualize the quality of services. High-quality services are services that contribute to the improvement and maintenance of physical and mental conditions.

However, as a problem in business execution, it is difficult to define an outcome index that can fairly evaluate the quality of service, and the quality of service is visualized by service type and business establishment to identify issues and best practices of business establishments. / It was difficult to extract the worst practice concretely.

In order to solve such problems, it was proposed to introduce an outcome index (improvement rate / maintenance period until deterioration) based on the physical and mental condition of the elderly as described above. As a result, the quality of service of each business establishment can be visualized, and it becomes possible to concretely grasp the business establishments that are producing results and the business establishments that should be given priority guidance, and to formulate a detailed guidance plan. In addition, there is a possibility that many notices regarding the quality of service can be obtained by comparing with the results of self-inspection by conventional business establishments. Furthermore, it may be a material to correct the disagreement about the quality of service between the management (manager) of the business establishment and the field staff.

In this way, visualization of the quality of service using the outcome index (improvement rate / maintenance period until deterioration) based on the physical and mental condition of the elderly is also explained in this application by the example shown in FIG. 82. There is. In the embodiment described with reference to FIG. 100, the quality of service is visualized by the same means as that of the embodiment shown in FIG. It visualizes whether or not it has such characteristics.

In this embodiment, the establishments are grouped from the viewpoint of the quality of long-term care services by scatter plotting the "improvement rate" and "average maintenance period until deterioration" of the establishments according to the mildness of the physical and mental condition. In the embodiment of FIG. 100, the service type is set to "special nursing home" and the mental and physical condition of the service user is set to "degree of long-term care (severe)". That is, in FIG. 100, the horizontal axis is the improvement rate of the degree of care required for each business establishment, the vertical axis is the average maintenance period until the degree of care required for each business establishment deteriorates, and each business establishment is obtained from the horizontal axis and the vertical axis. It is a scatter plot plotted at the coordinate points.

In the example of FIG. 100, the group of establishments B1, B2, and B3 in the area 100a on the upper right corresponds to establishments having a high improvement rate, a long average maintenance period until deterioration, and both indicators are good. .. On the contrary, the group of establishments W1, W2, W3 in the lower left area 100b has a low improvement rate, a short average maintenance period until deterioration, and both of the two indicators correspond to bad establishments.

The actions based on the above analysis results are as follows. Business establishments B1, B2, and B3 within the area 100a are best practice candidate business establishments and are subject to good practice hearings. In other words, it is possible to improve the quality of service of the entire business establishment by hearing good practices from business establishments with high quality of service and publicizing them to other business establishments.

On the other hand, the business establishments W1, W2, and W3 in the area 100b are the worst practice candidate business establishments and are subject to priority guidance. That is, the quality of service can be effectively improved by extracting the establishments having low quality of service and implementing specific improvement guidance suitable for the actual situation by the characteristic analysis described later.

Next, it is assumed that there is a strong correlation with the quality of service for each group of best practice candidate establishments B1, B2, B3 and worst practice candidate establishments W1, W2, W3 extracted as described above. Focus on the characteristic items of business establishments and visualize the characteristic items that have significant differences between the two business establishment groups. We will provide feedback to each business site using such information as a casebook, etc., and encourage behavior change.

FIG. 101 shows a state in which the characteristic items of business establishments that are expected to have a strong correlation with the quality of service are compared between the best practice candidate business establishments B1, B2, B3 and the worst practice candidate business establishments W1, W2, W3. ing. In this embodiment, since the service type is "special nursing home", the number of users per employee and the unit-type private room ratio are used as the characteristic items of the business establishment that are expected to have a strong correlation with the quality of service.

In FIG. 101, looking at the number of users per employee, the worst practice candidate establishments W1, W2, W3 have the same number of users per employee as the best practice candidate establishments B1, B2, B3. Significantly more. For this reason, it is considered that the worst practice candidate business establishments W1, W2, and W3 have an increased work load due to a shortage of staff, which leads to a decrease in the quality of service.

Looking at the ratio of unit-type private rooms, the ratio of unit-type private rooms is higher in the best practice candidate establishments B1, B2, and B3 than in the worst practice candidate establishments W1, W2, and W3. That is, regarding the balance between resident privacy and communication. Compared to private rooms and multi-bed rooms, the ratio of unit-type private rooms is significantly higher, which is thought to lead to improved quality of service.

The procedure of the analysis process described above can be summarized as follows.
(1) Selection of target service type.
(2) Aggregation of outcome indicators (maintenance period until improvement / deterioration) by user.
(3) Aggregation of outcome indicators (improvement rate, average maintenance period until deterioration) by business establishment.
(4) Grouping business establishments from the scatter plot of outcome indicators by business establishment (best practice,
Extraction of worst practice establishment candidates, etc.).
(5) Selection of service type / establishment characteristic items.
(6) Aggregation of service types and business establishment characteristic items.
(7) Check for significant differences between service types, business establishment characteristic items, best practices, and worst practice business establishment candidates.
(8) Feed back the analysis results of the characteristic items of business establishments with high significant differences to each business establishment.

Next, an example of visualization of the quality of service in the comprehensive business shown in FIG. 2 will be described using the same method. That is, FIG. 102 shows, as an example of the business unit of the comprehensive business service, targeting a plurality of places of commuting provided in the local government, and visualizing the quality of the program of these places of commuting.

In FIG. 102, users are divided according to their physical and mental condition, and the “improvement rate” and “average maintenance period until deterioration” of the place of attendance are scatter plots from the viewpoint of the quality of the program (comprehensive business service). The places to go are grouped. For example, the mental and physical condition is divided into independence / weakness / support-requiring groups, etc., and each group is regarded as the mental / physical condition stage, and the maintenance period until improvement and deterioration is indexed.

In other words, the horizontal axis is the improvement rate, and the vertical axis is the average maintenance period until deterioration. / Aggregate the maintenance period until deterioration, aggregate the improvement rate for each mental and physical condition aggregation stage and the average maintenance period until deterioration for each place of visit, and correspond to the improvement rate and average maintenance period of each place of visit Plot the coordinate points to be used, make a scatter plot, and group the places to go.

Then, consider the corresponding actions for each group of places to go. That is, the places B1, B2, and B3 that belong to the area (group) 102a of FIG. 102 are best practice candidates, and good case hearings are conducted. As a good example, the quality of the program of all the places of commuting is done by hearing from the places of commuting with high quality of the program and publishing the cases to other places of commuting and those who wish to open the place of commuting. Raise.

On the other hand, the places W1, W2, and W3 that belong to the area (group) 102b of FIG. 102 are the worst practice candidates and give priority guidance. In other words, the quality of the program will be improved by providing specific improvement guidance using good practices, etc., to the places where the quality of the program is low.

Next, an example of regional characteristic analysis in the comprehensive business service by the first factor analysis department shown in FIG. 6 will be described with reference to FIG. 103. In the example shown in FIG. 103, for all elderly people who require support or less, the new certification rate by subregion and the participation rate of the commuting place in the same area are scatter plots to show the regional characteristics related to the commuting place. Based on the results, we will increase the number of bases for attendance, guide participation, and provide guidance on improving the quality of the program.

In other words, the horizontal axis is the participation rate for commuting to the elderly (number of elderly people), and the vertical axis is the new certification rate (number of elderly people). The number of persons / the total number of elderly people who require support in the same area) and the new certification rate by subregion (= the number of newly certified persons in each subregion / the total number of elderly people in the same area) are totaled and each subregion Plot the coordinate points corresponding to the attendance rate and the new certification rate, and make a scatter map to group the subregions.

Then, consider the response actions for each subregional group. In other words, the subregions belonging to the area (group) 103a in Fig. 103 have a high participation rate in the place of commuting, a low rate of new certification, and the results expected for the place of commuting have been achieved. do.

In the subregion belonging to the area (group) 103b of FIG. 103, although the participation rate of the place of attendance is high, the new certification rate is high, so that the quality of the program is judged to be low, and individual guidance is provided. That is, the difference from the place of attendance in other small areas (for example, the small area belonging to the above group 103a) is investigated, and good examples are introduced.

In the subregion belonging to the area (group) 103c of FIG. 103, the participation rate of the place of commuting is low, but the new certification rate is low due to another factor, so it is necessary to investigate another factor.

In the subregion belonging to the area (group) 103d of FIG. 103, the participation rate of the commuting place is low and the new certification rate is high. expand.

Next, the business plan formulation support (P) and the performance evaluation (C) in the community-based comprehensive care business system 15 shown in FIGS. 1 and 2 will be described with reference to FIG. 104.

Generally, in order to plan the provision of services, it is necessary to grasp and plan the usage status image (service demand) for each subregion, and it is necessary to make a plan for improving the quality of services for each service type. .. Until now, there was no mechanism to extract the usage status image (service demand) for each subregion, and there was no definition of an outcome index that could fairly evaluate the quality of service.

In the embodiment of the present invention, each of the above-mentioned functions as a solution function can support the formulation of a mental and physical condition improvement / maintenance plan for each service, and can formulate a service infrastructure plan based on the results of service supply and demand analysis for each subregion. , It has become possible to formulate progress management and recovery measures for service infrastructure plans by subregion and mental and physical condition improvement / maintenance plans by service.

To that end, we aggregate the service provision rate (ratio of services provided to users residing in the area where the business is located) by subregion, service type, and service provision rate, formulate a plan for each fiscal year, and proceed as planned. Measure regularly (monthly, etc.). Then, if there is a discrepancy between the planned value and the measured value, the actual situation is grasped, factor investigation, countermeasures (suppression measures, guidance measures, etc.) are examined, and the plan is reviewed.

In the embodiment of FIG. 104, it is evaluated whether the required service supply amount is secured by totaling the utilization rate (actual value) of the elderly in a specific mental and physical condition image for each subregion. Therefore, it is possible to formulate a plan that suits the actual situation. In this example, although not shown, the service type is group home (mainly community-based), the elderly state image is no paralysis × mild disability independence × severe dementia independence, and the subregional hierarchy is middle hierarchy. And. Then, the local government average and the subregion 1 and the subregion 2 are compared and shown.

From this figure, it can be seen that in 2017, subregion 1 has a higher service provision rate than planned. For this reason, the action for subregion 1 is to consider measures to curb the service provision rate and review the plan. On the other hand, it can be seen that the service provision rate in the subregion 2 is lower than the planned value. For this reason, as for the action for subregion 2, measures will be examined and plans will be reviewed to increase the service provision rate.

In this way, the service provision rate (ratio of services provided to users residing in the area where the business is located) is aggregated by subregion, service type, and service provision rate, and it is regularly checked whether the service is proceeding as planned every year. If there is a discrepancy between the planned value and the measured value, the actual situation can be grasped, and factor investigation, countermeasure examination, and plan review can be performed.

As described above, in the embodiment of the present invention, regional management is promoted based on big data analysis, so that it is an extremely advanced approach and is consistent with the current state and future direction of the country. In other words, by data analysis, it is possible to quantitatively and comprehensively grasp the current situation and identify issues regarding the quality of services in business entities such as subregions and business establishments. In addition, similar business establishment groups to be approached (best practice candidates, priority guidance destination candidates, etc.) can be extracted, and an action plan can be formulated based on specific effective contents. In addition, for long-term care establishments, the position of all of their own establishments and the history of the results of their efforts can be grasped in an easy-to-understand and immediate manner, and can be used as a reference when formulating a management plan for each establishment. Information can be provided on a regular basis, and by updating the elderly comprehensive database daily for users every time there is a change in certification or credential information, etc., risk target persons in each business area (comprehensive) It will be possible to timely extract comprehensive historical information of the general elderly, etc. who should be the target of the project) and the persons who are the subjects of individual issue examination of the community care conference. Furthermore, it is possible to quantitatively manage the execution of the actual results for the plans of each measure / measure implemented for each similar business group or individual user, and quantitatively evaluate the actual results every three years, for example. This has the effect of enabling timely follow-up and rewinding of measures that are behind schedule.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other embodiments, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are also included in the scope of the invention described in the claims and the equivalent scope thereof.

11 ... Long-term care business 12 ... Comprehensive business 13 ... Medical cooperation 15 ... Community-based comprehensive care business system 16 ... Database 160 ... Basic data for the elderly 161 ... Long-term care insurance data 162 ... Medical insurance data 163 ... Community policy data 17 ... "Visualization" System 21 ... Regional management support function department 22 ... Regional information management function department 23 ... Elderly person information management function

Claims (21)

It is a community-based comprehensive care business system that executes each business of the long-term care business including the independence support / long-term care severity prevention business, the comprehensive business including the long-term care prevention / daily life support business, and the medical / long-term care cooperation promotion business.
Elderly basic data including resident address data and regional characteristic data, nursing care insurance data including nursing care certification data and nursing care receipt data, medical insurance data including elderly specific medical examination data and medical receipt data, community facilities and care prevention facilities And a database with regional policy data on life support supporters,
For each of the above businesses, qualitative data based on medical / nursing data, quantitative analysis reports on elderly information and service usage status by business unit including business establishments and subregions, and indicators showing changes in the state of mental and physical condition items. Regional management support function department that outputs reports,
For each business unit, the regional information management function department that outputs the activity evaluation results related to the quantity and quality of services in individual form,
Elderly information management that centrally manages basic information, mental and physical condition utilization service status, and medical status of the elderly, confirms the effect of the care plan by referring to the past history related to these, and supports the review policy examination of the next plan Functional part and
The regional management support function department
For the self-insurer and non-self-insurer, the corresponding data including the amount received per beneficiary in the long-term care business are compared, and the comparison position between the self-insurer and the non-self-insurer is visually output. , Positioning grasping department to find indicators that need to be examined,
The first factor analysis unit that analyzes the quantitative problem including the service receipt rate for each business unit and for each mental and physical condition and outputs the quantitative analysis report based on the analysis result.
A second factor analysis unit that analyzes qualitative problems related to changes in mental and physical condition and outputs the qualitative report for each business unit and mental and physical condition.
The regional management support function department, the regional information management function department, and the elderly information management function department are realized by a computer system that data-driven the data of the database. .. The regional information management function department uses the data for problem analysis of the business unit extracted from the data of the database, and targets the amount of benefits per beneficiary for each of a plurality of types of physical and mental conditions for each of a plurality of business units. Quantitative evaluation sheet showing historical changes of actual values with respect to values, and the position of comparing own business unit area with other business unit areas, improvement rate for each type of mental and physical condition, and target value of average maintenance period until change The community-based comprehensive care business system according to claim 1, which is characterized by having a function of creating a qualitative evaluation sheet showing the historical change of the actual value with respect to the actual value and the position of comparing the own business unit with another business unit. .. The positioning grasping unit has a coordinate plane divided into four regions of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant by the x-axis and the y-axis intersecting each other, and the x-axis and the y-axis One of the above is the numerical axis of the monthly benefit per home service beneficiary, the other is the numerical axis of the monthly benefit per facility / residential service beneficiary, and the intersection of the x-axis and the y-axis is the national average beneficiary. When the coordinates of the monthly benefit per person are used, the coordinates of the monthly benefit per beneficiary of the local government and the coordinates of the monthly benefit per beneficiary of the other municipality to be compared indicate in which quadrant. The community-based comprehensive care business system according to claim 1 , wherein an image is generated. The positioning grasping unit has a plurality of numerical axes extending radially, and the monthly benefit per beneficiary of a plurality of different types of facilities / residential services, which are different from each other, can be obtained from the plurality of types of facilities. - allocation to residential system services by said plurality of value axis separately, represent those values in length from the center point of the corresponding value axis, is formed a tip of value axis the authorities, and signed by comparison The community-based comprehensive care business system according to claim 1 , wherein each polygonal graph is generated. The first factor analysis unit has a coordinate plane divided into four regions of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant by the x-axis and the y-axis intersecting each other, and the x-axis One of the x-axis and the y-axis is the numerical axis of the monthly benefit per beneficiary of the mild physical and mental condition, and the other is the numerical axis of the monthly benefit per beneficiary of the severe physical and mental condition. When the intersection is the coordinates of the average monthly benefit per beneficiary in the insurer, it indicates in which quadrant the coordinates of the monthly benefit per beneficiary in each of the plurality of business units belonging to the insurer are located. The community-based comprehensive care business system according to claim 1 , wherein an image is generated. The first factor analysis department describes various services including home services, group home services, and facilities / residential services other than group homes, which are provided by the self-insurer's multiple business units according to their physical and mental conditions. An image that obtains the user ratio for each service, displays the user ratio for each service as a graph for each business unit, and displays the average value of the ratio for each business unit for each service as the average value for self-insurers. The community-based comprehensive care business system according to claim 1 , wherein the service is generated. In the first factor analysis unit, a user residing in a plurality of subregions of a self-insurer exists in the subregion in which the user resides and in each region other than the subregion. The first aspect of claim 1 , wherein an image for displaying a graph of the utilization rate for each of the areas where the business is located is generated for each of the subregions where the user resides, which of the business establishments is used. Community-based comprehensive care business system. The first factor analysis department describes the business establishments existing in a plurality of subregions of the self-insurer as the subregion in which this business establishment exists, other subregions within the local government, and outside the local government. It is characterized by generating an image that graphs the usage rate of the users who use the business establishment by the residential area, either by one of the users living in the area or by each area where the business establishment exists. The community-based comprehensive care business system according to claim 1. The second factor analysis unit has a coordinate plane divided into four regions of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant by the x-axis and the y-axis intersecting each other, and the x-axis Either one of the and y-axis is used as the numerical axis of the average duration until the change in the mental and physical condition of the person with mild mental and physical condition, and the other is used as the numerical axis of the average duration until the change in the mental and physical condition of the person with severe mental and physical condition. When the intersection of the y-axis is the coordinate of the average value of the average duration up to the change of the mental and physical condition in the self-insurer area, the coordinates of the average duration up to the change of the mental and physical condition of the plurality of small areas are located in each quadrant. The community-based comprehensive care business system according to claim 1 , wherein an image showing whether or not to do is generated. The second factor analysis unit has a coordinate plane divided into four regions of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant by the x-axis and the y-axis intersecting each other, and the x-axis Either one of the and y-axis is used as the numerical axis of the average duration until the change in the mental and physical condition of the person with mild mental and physical condition, and the other is used as the numerical axis of the average duration until the change in the mental and physical condition of the person with severe mental and physical condition. The intersection of the y-axis is the coordinates of the average value of the average duration up to the change in the physical and mental condition of multiple business establishments in the local government that is implementing a specific facility / residential service among multiple facilities / residential services. If this is the case, the community-based comprehensive care business system according to claim 1 , wherein an image showing which quadrant each of the coordinates of the average duration until the change in the mental and physical condition of the plurality of business establishments is located is generated. .. The second factor analysis unit connects the average duration until the change in the mental and physical state of a plurality of aggregated mental and physical condition items of a plurality of business units for each service type among the plurality of business units, and connects the plurality of aggregated mental and physical states. The community-based comprehensive care business system according to claim 1 , wherein an image to be displayed as a line graph for each state item is generated. The second factor analysis unit obtains the average duration until the mental and physical condition changes of a plurality of detailed mental and physical condition items for each service type for each of a plurality of business units, and obtains a plurality of aggregated mental and physical conditions for each of the plurality of business units. The community-based comprehensive care according to claim 1 , wherein the average duration value of the item is connected between the plurality of detailed mental and physical condition items to generate an image displayed as a line graph for each of a plurality of business establishments. Business system. The second factor analysis unit connects the mental and physical condition improvement rates of a plurality of aggregated mental and physical condition items of a plurality of business units for each service type among the plurality of business units, and is used for each of the plurality of aggregated mental and physical condition items. The community-based comprehensive care business system according to claim 1 , wherein an image to be displayed as a line graph is generated. The second factor analysis unit obtains the improvement rate of the mental and physical condition of a plurality of detailed aggregated mental and physical condition items for each of a plurality of business units for each service type, and the above-mentioned said of the plurality of aggregated mental and physical condition items for each of the plurality of business units. The community-based comprehensive care business system according to claim 1 , wherein the value of the improvement rate is connected between the plurality of detailed aggregated mental and physical condition items to generate an image displayed as a line graph for each of a plurality of business units. The second factor analysis unit, the individual user, the mental and physical state item tiered sustain period, of the individual user is operating units to receive service, and upper engine this business unit belongs, the psychosomatic state item From the average value of the maintenance period of all users corresponding to the stage and the standard deviation, the deviation value of the individual user is obtained, and based on the deviation value of the individual user, the business unit to which this user receives the service The community-based comprehensive care business system according to claim 1 , wherein the deviation value is determined. Regarding the maintenance period for each mental and physical condition item stage, the second factor analysis department shortens the maintenance period for all users for each mental and physical condition item stage of the higher-ranking institution to which the business unit to which the individual user receives the service belongs. The entire user is divided into N equal parts (divided into N quantiles) side by side from the side, and the physical and mental condition item stage of the individual user is determined by the N quantile values from the 1st N quantile to the (N-1) N quantile. Claim 1 characterized in that a user is personally evaluated according to which of the N groups the other maintenance period belongs to, and based on this personal evaluation, the points of the business unit to which the user receives the service are determined. Community-based comprehensive care business system described in. The second factor analysis department aggregates the number of cases where the physical and mental condition has improved and the number of cases where the physical and mental condition has deteriorated during the preset period for each business unit and for each stage of the mental and physical condition items, and for the number of improvements and the number of deteriorations. The community-based comprehensive care business system according to claim 1 , wherein the ratio of the number of improvements is obtained for each stage as an improvement rate for each business unit, and is aggregated as a mild / severe improvement rate preset from the stage. .. Since the second factor analysis unit performs qualitative analysis of a plurality of business units, the improvement rate of these business units is set on either the horizontal axis or the vertical axis of the two-dimensional graph until the deterioration of the business units. The average maintenance period is taken for either one, and the intersections of the horizontal axis and the vertical axis corresponding to the improvement rate and the numerical value of the average maintenance period until the deterioration are plotted for each of the plurality of business units to create a scatter diagram. The community-based comprehensive care business according to claim 1 , wherein a plurality of groups are formed by the business units in which the plotted positions of the intersections fall into a predetermined area, and the analysis is performed for each group. system. In order to analyze the service supply and demand of a plurality of subregions, the first factor analysis unit provides the service utilization rates of these subregions on either the horizontal axis or the vertical axis of the two-dimensional graph. take rates in the other, by plotting the intersection of the horizontal and vertical axes corresponding to value of the service utilization and the service providing rate thereof to the plurality of small regional creates a scatter plot, this The community-based comprehensive care business system according to claim 1 , wherein a plurality of groups are formed by the subregions in which the plotted positions of the intersections fall into a predetermined area, and analysis is performed for each group. Claim 19 is characterized in that when the subregion is composed of a plurality of layers, a service supply and demand analysis is performed for each of the plurality of layers, and the optimum layer for the service supply and demand analysis can be identified from these analysis results. The listed community-based comprehensive care business system. In order to analyze the regional characteristics of a plurality of subregions, the first factor analysis unit sets the participation rate of these subregions in the comprehensive business service on either the horizontal axis or the vertical axis of the two-dimensional graph. A scatter diagram is created by taking the new accreditation rate of a region as one of the other and plotting the intersections of the horizontal axis and the vertical axis corresponding to the numerical values of the participation rate and the new accreditation rate for each of the plurality of subregions. The community-based comprehensive care business system according to claim 1 , wherein a plurality of groups are formed by the subregions in which the plotted positions of the intersections fall into a predetermined region, and the analysis is performed for each group. ..

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