JP2020191087A - Comprehensive community care business system - Google Patents

Abstract

To provide a comprehensive community care business system that contributes to the fairer evaluation of service quality.SOLUTION: The present invention includes care-needed certification basic information and care-needed certification data relating to the user of a care insurance, evaluation index data relating to changes of improvement and degradation of psychosomatic state items, care benefit record data including the month when a user used a care service, the type of service used and a place of business code which provided service to a user, the invention outputting in time series a stagewise change by psychosomatic state item having been aggregated from the care-needed certification data for each certification effective period by stage, as well as outputting the same together with the filed municipality and filed place of application for care-needed certification; and, when the care-needed stage has changed for improvement or degradation within a preset psychosomatic state change extraction period, outputting an improvement or degradation evaluation index after the change and the service type used by a user and the place of business code which provided the service, as a user history sheet, with respect to the duration of stages preceding the change dating back from the change point.SELECTED DRAWING: Figure 3A

Description

An embodiment of the present invention relates to a community-based comprehensive care business system provided with an aggregate analysis method that contributes to a fair evaluation of the quality of long-term care services.

Currently, with the progress of super-aging, it is an urgent issue to make the best use of limited resources and provide long-term care services that contribute to the independence support of the elderly. For that purpose, it is necessary to set common goals in the region and share them among the parties concerned through grasping the actual situation of the region and analyzing issues.

In addition, the need for efforts (regional management) to continuously improve activities to achieve the goal is shown by creating and executing a concrete plan to achieve it, and repeating evaluation and review of the plan. Has been done.

In order to strengthen the comprehensive community care system, each local government will strengthen the insurer function, and the data provided by the national government and each local government will hold it to support the independence of the elderly and prevent the need for long-term care from becoming more severe. It is important to formulate a long-term care insurance business plan after analyzing data, etc., and clarify the content and goals of efforts such as care prevention and severity prevention in the long-term care insurance business plan.

Against this background, local governments are planning to build a mechanism to evaluate establishments that are working to improve and maintain the condition of the elderly. In other words, it is necessary to identify establishments that are working to support the independence of long-term care service users and prevent their severity, and that have achieved distinctive results, and evaluate the quality of nursing care services in a way that is easy for the establishments and the general public to understand. It is desired.

The applicant has made various proposals in response to these requests. One of them is the development of a mental and physical condition outcome index related to the quality of service. For example, the maintenance period of the improvement and deterioration of the mental and physical condition (65 items, 211 stages in total) was tabulated for each individual user. In this case, the duration until the stage of mental and physical condition changes is defined as one case. That is, if the condition is improved to the need for care 2 12 months after being certified as the need for care 3, the case is the case of the need for care 3, one improvement case, and the maintenance period is 12 months.

It also proposes to evaluate business establishments using such evaluation indexes and group business establishments. That is, the mental and physical states are aggregated, a scatter plot based on the average deviation value and the improvement rate of the maintenance period until the deterioration is created, and grouped by each area and the mental and physical state. In addition, the aggregation of mental and physical conditions means the outcome index of mental and physical condition (65 items, 211 levels in total), 9 indexes (degree of care required, degree of disability independence, degree of dementia independence, 3 physical groups, 3 mental systems). It is to consolidate into groups).

JP-A-2017-215787

Based on these activities, we will further study and propose a newly enhanced aggregate analysis method that contributes to a fairer evaluation of the quality of service. This aggregate analysis method is a method of excluding cases of improvement or deterioration due to factors other than the long-term care service, and creation of a new outcome index that includes cases of continuous maintenance in addition to the improvement and deterioration so far.

For example, factors other than long-term care services may affect the factors of change of mental and physical condition. Cases related to these factors need to be excluded in order to fairly evaluate the quality of long-term care services. For that purpose, it is necessary to examine and set the selection conditions for the evaluation target cases.

In this way, in order to fairly evaluate the quality of long-term care services, it is necessary to exclude cases of improvement or deterioration of the physical and mental condition due to factors other than long-term care services (especially medical factors) from the evaluation target, and the method for that is You will need it.

In addition, "improvement" and "deterioration" of mental and physical conditions have been used as evaluation indexes for fair evaluation of the quality of long-term care services. However, in addition to these, there is "maintenance" of the mental and physical condition, and it is requested to evaluate this case as well, and it is necessary to index it. In other words, we position continuous maintenance cases (cases in which the mental and physical condition is maintained for a certain period of time or longer) as cases that should be evaluated, and propose a new evaluation index (mental and physical condition outcome index) for further improving the accuracy of service quality evaluation. ..

In this way, in order to perform establishment grouping and extraction of good example establishment candidates based on a fair evaluation of the quality of long-term care services, new evaluation indexes are used to determine the degree of care required, disability independence, and dementia independence. It is necessary to grasp the actual state of service quality (pros and cons, etc.) for each business establishment from a comprehensive perspective such as degree.

The present invention is to provide a community-based comprehensive care business system equipped with a newly enhanced aggregate analysis method that contributes to a fairer evaluation of the quality of service.

The community-based comprehensive care business system according to the embodiment of the present invention includes basic information on long-term care certification for long-term care insurance users, data on long-term care certification for mental and physical condition items including the degree of long-term care, and improvement and deterioration of the physical and mental condition items. Evaluation index data regarding the amount of change and maintenance period, long-term care benefit performance data including the month when the user used the long-term care service, the type of service used, and the business establishment code that provided the service to the user, and the long-term care required The mental and physical condition transition data that outputs the step changes for each mental and physical condition item aggregated from the certification data in chronological order for each certification validity period for each stage, and also outputs the application municipality and application location for the long-term care certification application. When the stage of the degree of long-term care changes to improvement or deterioration within the output processing unit and the preset mental and physical condition change extraction period, the continuation period of the stage before the change that goes back from the time of this change is improved or improved after the change. The evaluation index data output unit that outputs as an evaluation index of deterioration and the usage service transition data output unit that outputs the service type used and the office code that provided the service every month when the user uses the long-term care service. It has a user history sheet creation function.

It is a figure explaining the processing step of the community-based comprehensive care business system which concerns on embodiment of this invention. It is a figure explaining an example of the user history sheet used in embodiment. It is a figure which shows an example of the user history sheet of the same as above. It is a figure which shows another example of the user history sheet of the same as above. It is a functional block diagram which shows the user history sheet creation function in an embodiment. It is a figure which shows an example of the care-requiring certification survey item comparison table in embodiment. It is a functional block diagram which shows the creation function of the care-requiring certification survey item comparison table in an embodiment. It is a functional block which shows the long-term care evaluation target selection function in an embodiment. It is a timing chart for explaining the evaluation index of the case corresponding to the change or maintenance of the mental and physical state in the embodiment. It is a functional block diagram which shows the case classification function in an embodiment. It is a scatter plot grouped based on the activity evaluation of each business establishment in an embodiment. It is a figure which shows a part of the business establishment list which performed grouping in an embodiment. It is a figure which shows the rest of the grouping establishment list in an embodiment. It is a figure explaining the processing step of the community-based comprehensive care business system which concerns on other embodiments. It is a figure which shows the logic of the mental and physical condition index aggregation. It is a figure which shows the logic for time series estimation of a mental and physical state index. It is a figure explaining the calculation method of the amount of change of the mental and physical state within the index calculation period. It is a figure explaining the exclusion method of the user of a plurality of establishments within the index calculation period. It is a figure explaining the calculation logic of the average maintenance period until deterioration within an index calculation period. It is a figure explaining the calculation method of the improvement rate and maintenance rate aggregated for each group. It is a graph showing the yearly transition of the improvement rate and its numerical table. It is a graph showing the ratio of improvement / deterioration / termination / maintenance cases and its numerical table. It is a graph showing the yearly transition of the average maintenance period and its numerical table. It is a figure which shows the logic for the trial calculation of the benefit cost restraint amount. It is a figure explaining the calculation method of the benefit cost suppression effect. It is a figure explaining the influence evaluation by extension of certification validity period to maintenance period calculation. It is a graph showing the yearly transition of the average certification period and its numerical table. It is a figure which shows the effect verification logic by the mental and physical condition index of a special nursing care fee. It is a figure which shows the aggregation logic of the improvement rate and the average maintenance period until deterioration about a service item. It is a figure which shows the revision history investigation logic for service item selection. It is a figure which shows the logic for totalizing the average maintenance period by mental and physical condition, by business establishment group, improvement rate and deterioration. It is a graph which shows the comparison result of the improvement rate by the mental and physical condition by the basic service (room type) which is a service item, and the numerical table thereof. It is a graph showing the comparison result of the average maintenance period by the mental and physical condition by the basic service (room type) which is a service item, and the numerical table thereof. It is a graph showing the comparison result of the improvement rate by the mental and physical condition of the treatment improvement addition which is a service item, and the numerical table thereof. It is a graph showing the comparison result of the average maintenance period for each mental and physical condition of the treatment improvement addition which is a service item, and the numerical table thereof. It is a graph showing the comparison result of the improvement rate by the mental and physical condition of the nursing system addition which is a service item, and the numerical table thereof. It is a graph showing the comparison result of the average maintenance period for each mental and physical condition of the nursing system addition which is a service item, and the numerical table thereof. It is a graph showing the comparison result of the improvement rate by the mental and physical condition of the oral hygiene management system addition which is a service item, and the numerical table thereof. It is a graph showing the comparison result of the average maintenance period for each mental and physical condition of the oral hygiene management system addition which is a service item, and the numerical table thereof. It is a graph showing the comparison result of the improvement rate by the mental and physical condition of the psychiatric medical care guidance addition which is a service item, and the numerical table thereof. It is a graph showing the comparison result of the average maintenance period for each mental and physical condition of the psychiatric medical care guidance addition which is a service item, and the numerical table thereof. It is a scatter diagram which shows the effect verification result (the first group) which concerns on the service item calculation addition service (G2, G8) of all establishments, and the numerical table thereof. It is a scatter diagram which shows the effect verification result (the second group) which concerns on the service item all establishments calculation addition service (G2, G8) and the numerical table thereof. It is a graph showing the difference in improvement rate by mental and physical condition (unit-with multi-bed room addition calculation-without addition calculation) for each of 5 groups of special nursing care fees, and its numerical table. It is a graph showing the difference in the average maintenance period (unit-multi-bed room, with addition calculation-without addition calculation) by mental and physical condition for each of the five groups of special long-term care fees, and its numerical table. (a) is a scatter plot of all the users of the business establishment, using the aggregated results of the improvement rate and the average maintenance period until deterioration, and (b) is the concept of a new nursing care fee addition system. It is a figure explaining. It is a figure explaining the flow of application procedure of new care fee addition. It is a figure explaining the procedure of correlation analysis whether the questionnaire result by the questionnaire sheet is related to the improvement and maintenance of the mental and physical condition. It is a data processing flow diagram of a correlation analysis. It is a processing flow chart of the characteristic analysis by special nursing home based on the value of the questionnaire index and the mental and physical condition index. Questionnaire sheet It is a conceptual diagram of selection of mental and physical condition items related to evaluation items. It is a figure explaining the comparison year of a mental and physical condition index and a questionnaire index. It is a scatter plot between a correlated diet and an index of aggregation 1 (improvement rate). It is a figure which shows the logic of the aggregation processing of the elderly state image and the service state image. It is a figure which shows the image of the elderly person state image and the home service state image. It is a data processing flow image diagram for formulating a new facility development plan based on the calculation of the average usage period of special nursing homes by effectively utilizing the elderly integrated database. It is a figure which shows the transition by year of the average usage period by special nursing home. It is a figure explaining the concept of the maintenance plan based on the special nursing average use period. It is a figure explaining the flow of data acquisition such as medical care / nursing care. It is a figure explaining the medical / long-term care integrated database construction method. It is an image diagram of the user history sheet to which medical data is added.

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

The present invention is to provide a community-based comprehensive care business system equipped with a newly enhanced aggregate analysis method for more fairly evaluating the quality of long-term care services as described above. The flow of the analysis process for achieving such an object will be described with reference to FIG. The flow of this analysis process is based on visualization of the quality of long-term care welfare facility services (special care), but it can be applied to other long-term care services as well.

As shown in FIG. 1, this analysis process involves step 1 (anonymization and data provision), step 2 (data matching), step 3 (establishment survey), step 4 (selection of main establishment), and step 5 (selection of main establishment). It is executed in seven steps up to (selection of evaluation target cases), step 6 (aggregation of new evaluation indexes), and step 7 (grouping of establishments by new evaluation indexes). Of these, steps 1 to 4 have already been proposed by the applicant in the form of a patent application, and are only outlined.

Step 1: Anonymization and data provision The local government anonymizes long-term care insurance users in order to aggregate and analyze the long-term care certification data and long-term care benefit performance data that it holds. That is, using an irreversible hash function, the insured number is hash-converted after adding a private value unique to the local government, and defined as a "system management number". In addition, the date of birth of the user will be converted to the date of birth and used as the minimum necessary information for linking with monthly benefit performance data.

Step 2: Data matching The data for certification of long-term care and the actual data for long-term care benefits are matched. In other words, in order to enable time-series analysis of the physical and mental condition of the user when using the long-term care service, the service usage date of the benefit record data and the certification validity period of the long-term care certification data are associated, and the two data are matched. Do. For the matching of both data, the items of "certification period" of the long-term care certification data, "service provision date" of the long-term care benefit record data, and "system management number" held in common by both data are used. The "certification period" of the long-term care certification data is data representing the period from the start month to the end month of the certification validity period, and the "service provision date" of the long-term care benefit record data is the year and month when the service was provided. It is the data to represent.

In other words, in order to enable monthly aggregation and analysis, a "matching relationship table" was created by breaking down the period from the start month to the end month of the "certification period" of the long-term care certification data into monthly units, and the "matching relationship" was created. Using the "table", match the long-term care benefit performance data with the same "system management number" and "service provision date" on a monthly basis.

Step 3: Survey of actual conditions of business establishments The survey of actual conditions of business establishments investigates the usage status of special nursing home services and the usage status of each business establishment, and provides a bird's-eye view of the actual conditions of all special nursing homes located in the local government. Then, the business establishment to be analyzed is selected. Specifically, this business establishment fact-finding survey will be conducted as a service user fact-finding survey and a service-providing establishment fact-finding survey (both are basic tabulations).

Service user fact-finding survey, business location area, number of business establishments (number of business establishment users located in local governments and other areas)
・ By degree of care required ・ Number of users
・ By gender ・ By age group ・ Number of users Survey of service provision establishments ・ By establishment area ・ Number of establishments (Number of establishments located in local governments and other areas)
・ By business establishment in the local government ・ Number of users (ratio to capacity)
・ By degree of care required ・ By business establishment ・ User ratio

Step 4: Selection of main establishments Nursing care service users generally use multiple types of nursing care services and establishments within the maintenance period. In the case of long-term care welfare facility services, there are cases where home-based services and long-term care health facilities (Long-Term Care Health Facility) are used at the beginning of the maintenance period, and then the service shifts to long-term care welfare facilities. In order to properly evaluate the quality of the care and welfare facility service against such an actual situation, "the maintenance period and establishment where the care and welfare facility service had the greatest influence on the physical and mental condition of the user" was selected. Need to be analyzed in.

As a precondition, based on the viewpoint that "the services provided differ depending on the stage of mental and physical condition", the same user should be evaluated separately if the mental and physical condition is different. Therefore, the period during which the mental and physical condition continues is defined as "1 case", and the quality of service is evaluated for each case. Hereinafter, "changes in mental and physical condition stages" will be referred to as "changes in mental and physical condition". Hereinafter, the description will be given sequentially.

(1) Setting of mental / physical state change extraction period The mental / physical state change extraction period is a period (for example, 3 years) for extracting changes in the mental / physical state, and the start / end dates are set in advance. Changes in mental and physical condition that occur outside this period are not included in the total. This mental and physical state change extraction period is set to the same period for all certified persons.

(2) Checking cases of change of state of mind and body and continuation of maintenance During the extraction period of change of state of mind and body of all certified persons, "step change of state of mind and body (improvement, deterioration)" and "continuation of maintenance of state of mind and body" at the end of the extraction period To check.

(3) Calculation of maintenance period for each mental and physical condition stage The maintenance period for each mental and physical condition stage (constant) is obtained by tracing back from the "stage change time point" and "extraction period end month time" of the mental and physical condition. Calculate the duration of maintenance (the period of continuation in a certain state of mind and body) and the duration of maintenance (the period of continuation in a certain state of mind and body).

(4) Comparison of monthly credits between the service to be analyzed and other services (threshold processing 1)
In the calculated maintenance period until deterioration / improvement and maintenance continuation period, "the ratio of" the number of units of the service type to be analyzed "to the" total number of users "by month" is calculated. As a result, months in which the service type to be analyzed is less than 50% (variable as a threshold value 1) are excluded.

(5) Comparison of monthly credits by business establishment of the service to be analyzed (threshold processing 2)
For the month when the service to be analyzed exceeds 50%, "the ratio of the" number of units by business establishment "to the" number of units of the service to be analyzed "by month" is calculated. Then, the number of months in which the ratio exceeds 50% (variable as a threshold value 2) is counted for each business establishment.

(6) Comparison of the number of units by maintenance period for each business establishment of the service to be analyzed (threshold processing 3)
Calculate the "ratio of" months of each business establishment "to" total number of months of maintenance period "by maintenance period", and set the business establishments whose ratio exceeds 50% (variable as threshold value 3) as "main business establishments". To do.

Step 5: Selection of evaluation target cases (new proposals after this step)
In selecting the evaluation target case, the user history sheet creation function, the care-requiring certification survey item comparison function, and the evaluation target selection function are used, respectively. Each of these functions will be described in detail below.

First, as a major premise in selecting cases to be evaluated, in order to evaluate the quality of care and welfare facility services (special training), changes in the physical and mental condition (improvement or deterioration) of the service users are the main factors for the service. It should be limited to the cases brought (of course, the same for other services).

The following are possible examples of suspected changes in mental and physical condition due to factors other than long-term care services.

・ I was hospitalized due to an acute exacerbation of injury or illness, and underwent a long-term care certification survey at a medical institution in the hospital, and after that, healed (recovered mental and physical condition) and was certified while using the long-term care service after discharge. When it is judged to be improved after being investigated. It is considered that the improvement of mental and physical condition is due to medical practice and the long-term care service is not the factor of improvement.

・ If you were eating with assistance from a long-term care service, but for some reason you could not eat, and due to the shift to tube feeding, you no longer need assistance with meals, and the time and effort required for long-term care has decreased. In reality, it is highly possible that the mental and physical condition has deteriorated due to the inability to eat, but tube feeding eliminates the trouble of dietary assistance and shortens the standard time for certification of need for nursing care. It is misjudged as if the condition has improved.

In order to evaluate the quality of long-term care welfare facility services with higher accuracy and fairness, it is necessary to exclude cases in which changes in mental and physical conditions are suspected due to factors other than the above-mentioned long-term care services.

Therefore, in order to investigate in detail what kind of cases are suspected to be changes in the mental and physical condition due to factors other than the long-term care service, a time series of changes in the physical and mental condition of individual long-term care service users and the usage status of the long-term care service. We have developed a deep-drilling analysis tool that focuses on changes. As the in-depth analysis tool, we have newly developed a "user history sheet" and a "comparison table of survey items requiring long-term care".

First, the user history sheet will be described. This user history sheet is used to fairly evaluate the quality of long-term care services, excluding cases of improvement or deterioration of the physical and mental condition due to factors other than long-term care services (mainly factors due to medical services). That is, in order to examine and set the evaluation target conditions for the long-term care service, as shown in FIGS. 2 and 3A, it is possible to grasp the improvement / deterioration of the physical and mental condition of each individual user and the transition of service use from a bird's-eye view. Is.

The community-based comprehensive care business system according to this embodiment has a user history sheet creation function represented by the functional block of FIG. 4 in order to create such a user history sheet. The community-based comprehensive care business system is composed of a computer system, and the functional blocks shown in FIG. 4 are realized by a storage unit, an arithmetic processing unit, and a set program of the computer system. This also applies to other functions described later.

In FIG. 4, this user history sheet creation function uses the data held in the long-term care certification data 11, the evaluation index data 12, and the long-term care benefit record data 13 held in the storage unit 10, respectively.

The long-term care certification data 11 is data related to mental and physical condition items including basic information on long-term care certification and the degree of long-term care of a long-term care insurance user. Basic information on certification for long-term care is the insured person number, application date, municipality for application, gender, age, and current status (certification survey selected by "facility use" on the certification questionnaire for long-term care (overview survey)). The item name of the certification data requiring nursing care) and the certification validity period. The mental and physical condition items are the degree of care required, the degree of disability independence, the degree of dementia independence, and 74 items in the certification survey. The timing of these occurrences is the start month of the certification validity period.

The evaluation index data 12 is the maintenance period (including the maintenance start month and the maintenance end month), the main establishment code, and the main service type for each user, each mental and physical condition, and improvement / deterioration. The timing of these occurrences is a maintenance period unit for each mental and physical condition stage.

The long-term care benefit record data 13 is data including the month when the user used the long-term care service, the type of service used, and the business establishment code that provided the service to the user.

Each of these data is held by the local government, which is the long-term care insurer.

Next, the data processing logic unit 14 will be described. The data processing logic unit 14 has a mental / physical state transition data output processing unit 15, an evaluation index data output unit 16, and a usage service transition data output unit 17, and includes the above-mentioned care-requiring certification data 11 and evaluation index data (mental / physical state). Outcome index data) 12 and nursing care benefit performance data 13 are aggregated on an insured basis and monthly basis and output to the user history sheet 18.

The user history sheet 18 has a mental and physical condition display area 19, an evaluation index area 20, and a service usage transition area 21. As shown in FIGS. 2 and 3A, the user history sheet 18 has a plurality of years of annual display areas divided by local government by month in the horizontal axis direction (in the example of the figure, from October 2001 to August 2018). It is possible to display the various data described above in chronological order. Further, the vertical axis direction is divided into the above-mentioned mental and physical condition display area 19, the evaluation index area 20, and the service usage transition area 21.

The mental and physical condition transition data output processing unit 15 of the data processing logic unit 14 certifies the stage changes for each mental and physical condition item aggregated from the nursing care certification data 11 in the mental and physical condition display area 19 of the user history sheet 18 for each stage. Output in chronological order for each validity period. At the same time, the information for the local governments applying for certification for long-term care and the current status are also output.

By this output operation, as for the degree of need for nursing care among the physical condition items, the certification validity period is displayed by dividing it into stages in order to make it easier to grasp the stage change of the degree of need for care and the maintenance period. In addition, a mark is attached so that the local governments applying for certification for long-term care and the place of application can be identified. In other words, new ones for the application municipality are indicated by a star mark ☆, changes for the municipality are indicated by a diamond mark ◇, and updates are indicated by a square mark □, and the current status of each black fill is "medical institution" (other than medical care beds). It represents that.

The fact that the current situation is "medical institution" (other than medical care beds) means that the user is admitted to the acute care hospital at the time of the accreditation survey.

By the example of FIG. 3A, the step change of the degree of care required of user B will be described together with the current situation (application place). In March 2012, User B was certified as requiring nursing care 2 by a new application at a medical institution (other than a medical care bed), and in October of the same year, was certified as requiring nursing care 2 at home as before. Is maintained until February 2014. After that, his physical and mental condition deteriorated, and he was certified as requiring nursing care 3 in the change application at home in March 2014, and this condition is maintained until December 2014. After that, his physical and mental condition worsened further, and in January 2015, he was certified as requiring long-term care 5 by applying for a change at a medical institution (other than a medical care bed), and this mental and physical condition is maintained until January 2016. After that, his physical and mental condition improved and he was certified as requiring long-term care 4 by applying for renewal in the special nursing care in February 2016, and he maintained this condition until January 2017, but in the special nursing care in February 2017. Upon renewal application, he was certified as requiring nursing care 5 again, and this mental and physical condition has been maintained until August 2018.

In this way, the staged change in the degree of care required, the amount of change, and the maintenance period until the change to improvement or deterioration at each stage are clarified together with the current situation (application place).

The degree of disability independence and the degree of dementia independence are displayed at each stage. In the special medical care, the number of "Yes" items in the 12 items of "Special medical care" of the mental and physical condition items is displayed. Similarly, in ADL, the value obtained by comparing the 18 items of the evaluation axis "ability" of the mental and physical condition items with the independence stage is displayed.

Next, the data processing of the evaluation index data output unit 16 will be described. The storage unit 10 holds the evaluation index data 12 of each user. When outputting the user history sheet of user B, all the evaluation index data of user B are extracted.

Then, the time when the stage of the degree of care required changes to improvement or deterioration within the preset period for extracting the change of state of mind and body is grasped, and the maintenance period of the stage before the change retroactive from the time of this change is improved or deteriorated after the change. Is output to the evaluation index area 20 of the user history sheet 18 as the evaluation index of. This is done for all change-of-state time points within the change-of-state extraction period.

Here, the mental / physical state change extraction period is a predetermined period (for example, 3 years) close to the present time, which is set in advance for extracting the mental / physical state change. In the example of FIGS. 2 and 3A, the period is from April 2015 to March 2018, which is divided by the local governments along the straight lines 18a and 18b.

In the example of the figure, in February 2017 within this period of extracting the change of mental and physical condition, the mental and physical condition changed from 4 requiring nursing care to 5 requiring nursing care. Therefore, the evaluation index of the maintenance period of the care-requiring 4 before the change, which dates back to the time of the change to the deterioration, is output to the evaluation index area 20 as the evaluation index of the deterioration after the change.

Further, in February 2016 within this period of extracting the change of mental and physical condition, the mental and physical condition changed from 5 requiring nursing care to 4 requiring nursing care. Therefore, the evaluation index of the maintenance period of the need for nursing care 5 before the change, which goes back from the time of the change to the improvement, is output to the evaluation index area 20 as the evaluation index of the improvement after the change.

Next, the data processing of the usage service transition data output unit 17 will be described. Since the nursing care benefit record data 13 held in the storage unit 10 includes the month when each user used the nursing care service, the type of service used, and the business establishment code that provided the service to the user, the usage service transition data is output. The unit 17 extracts the service type used and the business establishment code that provided the service every month when the user B uses the nursing care service, and outputs the service to the service transition area 21. By this output operation, the usage period is displayed in the usage service transition area 21 for each service type and business establishment.

Note that the above-mentioned output order of service types is not in order of service type number, but in order of service type with the oldest service provision start month. This makes it possible to easily grasp what kind of services are being used in parallel at the same time.

The greatest feature of the user history sheet 18 created in this way is the simple time-series output of the mental and physical condition transition area (derived from the long-term care certification data) 19 and the service usage transition area (derived from the long-term care benefit record data) 21. However, the evaluation index related to the quality of service (the index related to the change in the mental and physical condition of the main business establishment of the main service type and the maintenance period) is displayed at the same time in the evaluation index area 20 during this period.

As a result, which main business establishment of which service type, for example, according to the degree of care required, and how long it has been maintained for improvement or deterioration, is combined with the above-mentioned mental and physical condition transition area 19 and the service usage transition area 21. Can be grasped. That is, in the case of only the output of the mental and physical condition transition area 19 and the usage service transition area 21, for example, it is completely unknown from where to where the main service is a special nursing home and which business establishment is the main business establishment. Absent. However, the evaluation index displayed in the evaluation index area 20 is written side by side with the mental and physical condition displayed in the mental / physical state transition area 19 and the usage service displayed in the usage service transition area 21, and is displayed and visualized in chronological order. By doing so, it becomes clear which main business establishment of which service type mainly affected the improvement / maintenance or deterioration of the physical and mental condition of the user.

The user history sheet 18 can be used as a means for confirming factors that influence changes in the mental and physical states. In other words, in addition to grasping the improvement / deterioration of the physical and mental condition of each individual user and the transition of service usage from a bird's-eye view, factors that may affect the change in mental and physical condition (possible use of medical services). It is also possible to confirm gender, differences depending on the type of nursing care service, continuity of home service establishments that provide services, etc.). Therefore, as will be described later, it is a powerful support tool for "selection of evaluation target cases".

The user history sheet is also used as basic material for individual case study meetings at community care meetings and service staff meetings. In other words, the efficiency of creating basic materials, which has taken time and effort, is greatly improved, and the profile of the case can be immediately grasped by the meeting attendees, such as what kind of service was provided at what time. For this reason, there will be sufficient time for discussions to resolve issues that should be concentrated, and the quality of meetings will improve dramatically.

In addition, this sheet is expected to play a role not only as a material for examining individual issues of users and their families, but also as a material for improving the skills of care managers.

Furthermore, this sheet is expected to serve as a material for feeding back the evaluation results related to the quality of long-term care services from local governments by service type and business establishment. In other words, in addition to evaluation indexes such as the improvement rate and the average maintenance rate of each business establishment, each business establishment can simultaneously feed back the specific mental and physical condition of all users of each business establishment and the transition of service usage. It may be a material that can be concretely addressed to improve the quality of service.

An application example of this user history sheet 18 is shown in FIG. 3B. The user history sheet 18B of FIG. 3B pays attention to the transition of the mental and physical condition, and describes the degree of care required, the degree of disability independence, the degree of dementia independence, 74 items of the mental and physical condition, the elderly state image, and the service state image, respectively. The main service types and main business establishments are output for each stage and maintenance period.

In FIG. 3B, the horizontal axis direction is the monthly time axis as in the user history sheet 18 of FIG. 3A. In the vertical direction, the degree of long-term care, the degree of independence of disability, the degree of independence of dementia, 74 items of mental and physical condition, the image of elderly condition, and the image of service condition are arranged, and the maintenance period for each stage is displayed in a bar graph. In the meantime, the value of that stage, the direction of change to the next stage (improvement / deterioration after continuing for more than the standard maintenance period / disqualification after continuing for more than the standard maintenance period / continuing for more than the standard maintenance period / not reaching / worsening / not yet (Confirmed), the main service type and main business establishment for the maintenance period are displayed. In the example of the user history sheet 18B, the improvement in the direction of change to the next stage is △, the deterioration after continuing for the standard maintenance period or longer is ⇒, the disqualification after continuing for the standard maintenance period or longer →, while continuing for the standard maintenance period or longer ~, Unachieved maintenance is indicated by ≪, deterioration is indicated by ▼, and unconfirmed is indicated by ◇. Instead of these marks, it is possible to express the inside of the maintenance period frame with each color or fill pattern. (The direction of change to the next stage is described in the explanation of case classification in "Step 6: Aggregation of new evaluation indexes" described later).

Regarding the elderly state image and the service state image, it is possible to determine the direction of change to the next stage by determining the quality of each state image in advance.

With this user history sheet 18B, the care manager can grasp the time-series changes in the mental and physical condition of the user in charge, and confirm the validity of the created care plan, so that the care manager's own skills can be expected to improve. ..

Next, along with the "user history sheet", the "comparison table for long-term care certification survey items" developed as a deep-dive analysis tool for investigating changes in mental and physical conditions due to factors other than long-term care services will be described.

This "comparison table of survey items requiring certification for long-term care" is a survey item requiring certification for long-term care, as shown in FIG. 5, in order to confirm more detailed changes in the mental and physical condition of the above-mentioned "user history sheet". It visualizes the changes (differences) at two time points of all the items in.

The community-based comprehensive care business system according to this embodiment has a care-requiring certification survey item comparison function represented by the functional block of FIG. 6 in order to create such a “care-requiring certification survey item comparison table”. The functional block shown in FIG. 6 is also realized by a storage unit, an arithmetic processing unit, and a set program of the computer system.

In FIG. 6, the care-requiring certification survey item comparison function uses the above-mentioned care-requiring certification data 11 held in the storage unit 10. As described above, the long-term care certification data 11 is composed of the basic information on long-term care certification of the long-term care insurance user and the mental and physical condition items including the degree of long-term care. As described above, the care-requiring certification survey item comparison function performs data processing for visualizing changes (differences) at two time points in all items of the care-requiring certification survey item, and therefore compares them as two-time care-requiring certification data 11. The original nursing care certification data 11A and the comparison destination nursing care certification data 11B are used.

The data processing logic unit 24 that processes the long-term care certification data 11A and the long-term care certification data 11B at these two time points is a comparison processing unit 25 for the degree of long-term care and certification application information, and a comparison processing unit for the degree of disability independence and the degree of dementia independence. It has 26, a comparison processing unit 27 for intermediate evaluation item scores, and a comparison processing unit 28 for 74 items of mental and physical condition, processes the above data, and sets the values of these comparison sources and comparison destinations as long-term care certification survey items. Output to the comparison table 30.

The nursing care certification survey item comparison table 30 shows a comparison unit 31 for the degree of nursing care required and certification application information, a comparison unit 32 for the degree of disability independence and dementia independence, a comparison unit 33 for intermediate evaluation item scores, and 74 items for mental and physical condition items. It has a comparison unit 34 of.

The comparison processing unit 25 of the care-requiring degree and the certification application information of the data output processing logic unit 24 is the comparison source care-requiring certification data 11A of the same user between two time points separated by a predetermined interval, and the comparison destination. From the care certification data 11B, the degree of care required and the certification application information are extracted. Then, these extracted data are output to the comparison unit 31 of the care-requiring degree and certification application information in the care-requiring certification survey item comparison table 30 in a state in which they can be compared with each other as shown in FIG. 5A.

That is, in FIG. 5A, the target data (comparison source data) of the user T is the value of the degree of care required and ADL in January 2016, and the comparison target data (comparison destination data) is July 2012. The degree of care required and the value of ADL are listed side by side in a state in which they can be compared with each other. In this example, the degree of care required is improved from care required 4 to care required 3.

The disability independence degree and dementia independence degree comparison processing unit 26 extracts the disability independence degree and the dementia independence degree from the comparison source data and the comparison destination data and the care-requiring certification data 11A and 11B, respectively, and extracts these. The obtained data is output to the comparison unit 32 of the degree of independence of disability and the degree of independence of dementia in the comparison table 30 for certification survey items requiring long-term care in a state in which they can be compared with each other as shown in FIG. 5 (b).

That is, in FIG. 5 (b), the target data (black circle mark) of the user T) has the same degree of disability independence as “7” on the horizontal axis as compared with the comparison target data (x mark). , The degree of independence of dementia on the vertical axis is displayed in a state of deterioration from "1" to "4".

The comparison processing unit 27 of the intermediate evaluation item score is based on the comparison source data and the comparison destination data and the care-requiring certification data 11A and 11B, which are the intermediate evaluation items, the first group (physical function / wake-up movement) and the second group. Scores of (living function), 3rd group (cognitive function), 4th group (mental / behavioral disorder), and 5th group (adaptation to social life) were extracted, and these extracted data were used as a certification survey for long-term care. The item comparison table 30 outputs data to the comparison unit 32 of the degree of independence of disability and the degree of independence of dementia in a state in which they can be compared with each other by the radar chart shown in FIG. 5 (c).

That is, in FIG. 5C, it is shown that the target data (solid line) of the user T is improved in the second group and deteriorated in the fifth group as compared with the comparison target data (broken line). Has been done.

The comparison processing unit 28 of 74 items of mental and physical condition extracts 74 items of mental and physical condition items from the comparison source data and the comparison destination data defined as the nursing care certification data 11A and 11B, respectively, and as shown in FIG. 5 (d). In addition, it is output in a state where it can be compared with each other by a line graph.

In FIG. 5D, the target data (solid line) of the user T is improved in some items in the second group and some items in the fifth group as compared with the comparison target data (broken line). Is shown to be deteriorating.

From the display contents of the nursing care certification survey item comparison table 30 shown in FIG. 5, it is a case that the certified person (user) Mr. T has improved the degree of nursing care even though the degree of independence of dementia has deteriorated. The reason is that the oral administration of medicines in group 5 (application to social life) and daily decision-making have deteriorated, but the functions of food intake and swallowing in group 2 (living function) have improved. Is. This can be assumed because changes (differences) in all items can be viewed at the same time.

In this way, two time points of the long-term care certification data are determined, and the difference between the stage of the certification survey item 77 items (degree of long-term care, disability independence, dementia independence and mental and physical condition items 74 items) and the intermediate evaluation item score is totaled. By visualizing changes (differences), changes in mental and physical conditions due to factors other than long-term care services can be detected.

The same item comparison table shown in FIG. 5 can be applied not only to a two-point comparison of the same person but also to a comparison between different persons.

As another comparison area plan to be added to the comparison table in Fig. 5, the reference time (total reference time) and the reference time for sorting eight types of behavioral municipalities (meal, excretion, movement, cleanliness maintenance, indirect life assistance, dementia spirit)・ Behavioral disorder-related acts, functional training-related acts, medical-related acts), and comparison with dementia addition are expected. As a result, changes (differences) in the time and effort required for long-term care for each type of long-term care can be grasped.

Next, a method of determining selection conditions based on the output contents of the "user history sheet" and "comparison table of survey items requiring nursing care" as the above-mentioned analysis tool and selecting the evaluation target case will be described.

As mentioned above, in order to evaluate the quality of long-term care welfare facility services with higher accuracy and fairness, it is necessary to exclude cases in which changes in mental and physical conditions due to factors other than long-term care services (especially medical care) are suspected. is there.

Here, the steps for clarifying the selection conditions (exclusion conditions for factors other than long-term care services) of the evaluation target cases will be described.

(1) Visual check of user history sheet Visually check the user history sheet of 100 patterns of evaluation indexes (index related to the maintenance period until improvement or deterioration according to the degree of care required) of the care and welfare facility service.

(2) Picking up multiple pattern candidates to be excluded Through the visual check of the user history sheet, multiple pattern candidates to be excluded are picked up. In other words, there are cases where hospitalization in an acute care hospital is assumed and the degree of care required changes at any time after the start and end of the maintenance period, and the degree of care required also changes at the start and end of the special medical situation. Pick up cases that change.

(3) Examining the validity of the hypothesis by hearing with experts We made some hypothesis candidates for patterns to be excluded based on the above pickup, interviewed multiple on-site experts who are familiar with certification surveys and care plans, and about the validity of the hypothesis. consider.

Through this hearing, the following viewpoints became clear.
・ By paying attention to the "current situation (where the certification survey was conducted)" and "special medical care" of the data items for certification of long-term care, the exacerbation and cure of illness caused by acute hospital hospitalization events and special medical care The change may be estimated.

The "special medical care" is as follows.
[Corrective action]
1. 1. Management of infusion 2. Central venous nutrition 3. Dialysis 4. Treatment of stoma (artificial anus) 5. Oxygen therapy 6. Respirator (ventilator)
7. Treatment of tracheostomy 8. Pain nursing 9. Tube feeding [special treatment]
10. Monitor measurement (blood pressure, heart rate, oxygen saturation, etc.)
11. Treatment of Jokuso 12. Catheter (condom catheter, indwelling catheter, urostoma, etc.)

-Regarding the above "current situation" and "special medical care", there is little variation in accreditation surveys due to differences in accredited investigators.
・ Even if the illness is cured and the mental and physical condition is improved after discharge from the bad mental and physical condition at the time of admission to the acute hospital, there are few applications for change related to improvement, and in many cases, the application is not made until the initial certification expiration date. ..

-Of the special medical treatments, only "treatment of sickness" depends not on the origin of the disease but on the performance of the nursing care service and should not be excluded.
-The only special medical treatment that may reduce the time and effort of long-term care may be "tube feeding".

(4) Investigation and examination of the conditions for combining the data requiring nursing care Based on the above hearing results, we investigated and examined the conditions for combining multiple items of the data requiring nursing care certification that can be described as the conditions for patterns to be excluded.

(5) Determining the selection conditions for the evaluation target cases The results of the above survey and examination were organized and the selection conditions for the evaluation target cases (exclusion conditions for factors other than long-term care services) were determined. The selection (exclusion) conditions described below can basically be applied to the types of long-term care services other than the long-term care welfare facility services in exactly the same way.
・ Pattern 1: Impact of the quality of the main long-term care service (subject to evaluation selection)
The quality of the main long-term care service (the type of service that is considered to have the most influence on the physical and mental condition of the user) is a factor of "improvement / maintenance / deterioration" of the mental and physical condition, and cases of improvement and deterioration occur.
・ Pattern 2: Improvement by medical services (excluded from evaluation)

After hospitalization due to acute exacerbation, medical services such as treatment and rehabilitation may be a factor in improving the physical and mental condition. This applies when the local government applying for the case is "new" or "changed" and the current situation is "medical institution" (other than medical care beds).
・ Pattern 3: Deterioration due to acute exacerbation (excluded from evaluation)

Acute exacerbations of each illness unrelated to the main long-term care service may be a factor in the deterioration of mental and physical condition. This applies when the application municipality at the start of the next case is "change" and the current situation is "medical institution" (other than medical care beds).
-Pattern 4: Impact of special medical treatment (excluding treatment for Jokuso) (excluded from evaluation)

The start or discontinuation of special medical care is due to the progression or cure of the user's illness, which may be a factor in changes in mental and physical condition, regardless of the quality of long-term care services. However, Jokuso is an exception because it is highly related to long-term care services. This applies when there is a change in the implementation status of special medical care, except for "treatment of sickness", between the start of the case and the start of the next case.

The selection process of the selection target case by the system based on the selection (exclusion) condition of the evaluation target case described above will be described with reference to FIG.

The community-based comprehensive care business system according to this embodiment has a long-term care evaluation target selection function represented by the functional block of FIG. The functional block shown in FIG. 7 is also realized by a storage unit, an arithmetic processing unit, and a set program of the computer system.

In FIG. 7, the evaluation target selection function uses the above-mentioned long-term care certification data 11 and long-term care benefit actual data 13 held in the storage unit 10.

Here, if there is a change in the mental and physical state stage within the above-mentioned mental and physical state change extraction period, the maintenance period of the mental and physical state stage before this change is regarded as the case, and the maintenance period of the mental and physical state stage after the change is defined as the case. The next case.

The selection logic unit 40 of the evaluation target exclusion case determines that the improvement is due to the medical service and selects it as an exclusion target, the first selection processing unit 41, and determines that the deterioration is due to an acute exacerbation or the like and selects it as an exclusion target. It has a selection processing unit 42 of the above, and a third selection processing unit 43 of selecting out of the evaluation target when there is a change in the implementation status of special medical treatment.

The first selection processing unit 41 uses the user's long-term care certification data 11 and long-term care benefit record data 13, and the application for long-term care certification in the case is new or changed, and the current situation is medical institution (medical treatment). If the stage of the mental and physical condition of the next case is improved (other than the bed), it is judged that the improvement is due to medical services and the patient is selected from the evaluation target.

The second selection processing unit 42 uses the user's long-term care certification data and long-term care benefit record data, and the application municipality at the start of the next case is changed, and the stage change of the mental and physical condition of the next case deteriorates. Is judged to be worse due to acute exacerbation, etc. and is not evaluated.

The third selection processing unit 43 uses the user's long-term care certification data and long-term care benefit record data, and from the start of the case to the start of the next case, the implementation status of special medical care excluding the treatment of sickness. If there is a change in, select it out of the evaluation target.

These cases, which are selected from the evaluation targets by the first selection processing unit 41, the second selection processing unit 42, and the third selection processing unit 43 and are excluded, require long-term care after being selected by the storage unit 10. It is updated and retained as certification data 11 and long-term care benefit performance data 13.

Step 6: Aggregation of new evaluation indexes Up to now, when evaluating the quality of long-term care services, improvement of the mental and physical condition within a predetermined mental and physical condition change extraction period (the period sandwiched between straight lines 18a and 18b in FIG. 3A) Evaluation indicators (outcome indicators by mental and physical condition: improvement rate, deterioration rate, average maintenance period until improvement or deterioration, etc.) have been defined and tabulated focusing only on deterioration.

However, as a new analysis, there are two cases, "cases in which each stage of mental and physical condition is maintained for a certain period of time or longer" and "cases in which mental and physical condition deteriorated but were maintained for a certain period of time or longer" (these are summarized below). (Called a "maintenance case of mental and physical condition") should also be evaluated. That is, depending on the type of service, a service that makes it relatively easy to improve the physical and mental condition (for example, a home-based service) and a service that is essentially difficult to improve but can be maintained for a long time depending on the service content (for example). This is because there is a care and welfare facility).

In order to properly evaluate this "maintenance case of mental and physical condition", it is necessary to define a reasonable fixed period as a threshold value (hereinafter referred to as "standard maintenance period").

This standard maintenance period includes cases in which the mental and physical condition deteriorates (hereinafter referred to as cases in which the mental and physical condition deteriorates) and the most severe stage (degree of long-term care required: 5, degree of disability independence: C2, dementia independence). Degree: The period from the start to the end of the case that led to "disqualification" in M) (hereinafter referred to as the "most severe disqualification case") (hereinafter referred to as the "maintenance period") Set based on the aggregation result.

From the viewpoint that "changes in mental and physical condition may differ depending on gender / age and stage of mental / physical condition", it is desirable to set a plurality of standard maintenance periods for each stage of gender / age group and mental / physical condition. However, if an age group is set for 10-year-old local governments and the number of cases such as deterioration of mental and physical condition by gender, age group, and mental and physical condition stage is totaled, a sufficient sample is obtained at a certain age group and stage with mental and physical condition. It turned out that the number could not be secured.

Therefore, the standard maintenance period does not consider age groups from the viewpoint of securing the number of samples, the degree of disability independence is aggregated at all stages, and the degree of dementia independence is divided into mild and severe depending on the presence or absence of long-term care. It was decided to divide it into stages and set it in total.

The following multiple methods can be considered for setting the standard maintenance period.
1. 1. Average maintenance period: By comparing with the average maintenance period of all deterioration cases in the local government, it is possible to visualize the distribution among businesses that shortens (or extends) the average maintenance period of the entire local government.
2. 2. Mode of maintenance period: By comparing with the period when deterioration is most likely to occur, the number of cases that can be maintained beyond the period when deterioration is likely to occur can be compared among businesses.
3. 3. Median maintenance period: By evaluating half of all deterioration cases in the local government as "maintenance" (or "deterioration"), the number of maintenance / deterioration cases can be compared from the viewpoint of competing between business establishments.
4. Arbitrary period: By setting the period you want to maintain and evaluate, you can measure the difference between the period you want to evaluate and the current situation. For example, when a "target maintenance period" is set, the current state of the target can be measured.

The standard maintenance period may be determined by any of these setting methods, but here, from the viewpoint that "it is desirable for the insurer to extend the average maintenance period of the entire insurer", "maintenance period" The standard maintenance period will be set according to the "average of".

By setting the standard maintenance period to "average maintenance period", cases of deterioration of physical and mental condition can be classified into "exceeding cases" and "non-achieving cases" with respect to the average maintenance period of all insurers. The distribution between business establishments in each case can also be visualized. Visualization of this distribution can be useful information in selecting "business establishments with good examples" and "business establishments requiring countermeasures".

Next, all the cases selected as evaluation targets in the above-mentioned "Step 5: Selection of evaluation target cases" are classified into five categories: "improvement", "maintenance", "maintenance not achieved", "deterioration", and "undecided". The conditions for each classification are shown below.

1. 1. Improvement:
The stage of mental and physical condition is improved 2. Maintenance:
Deterioration after the mental and physical condition stage continues for the standard maintenance period or longer. After the mental and physical condition stage continues for the standard maintenance period or longer, the qualification is lost. The mental and physical condition stage continues for the standard maintenance period or longer. Not maintained:
When the maintenance period of the mental and physical condition stage is longer than the invalid maintenance period (set a period shorter than the shortest maintenance period of the maintenance period distribution) and less than the standard maintenance period.
4. Deterioration:
The stage of mental and physical condition deteriorates below the standard maintenance period. Unsettled:
When the maintenance period of the mental and physical condition stage is within the invalid maintenance period. For example, the state where the mental and physical state stage has just begun.

The cases classified into the above five are shown in FIG.
In FIG. 8, since Mr. A has improved from long-term care 5 to long-term care 4 within the mental and physical state change extraction period, the maintenance period before the improvement time is the “improvement case”. In addition, the maintenance period after improvement is longer than the invalid maintenance period, but shorter than the standard maintenance period, so it is a "maintenance unachieved case".

Mr. B deteriorated from needing nursing care 4 to requiring nursing care 5 within the period of extracting changes in mental and physical condition, but since the maintenance period from the time of deterioration to the start time of needing nursing care 4 is longer than the standard maintenance period, "maintenance case" ". The maintenance period after deterioration is an "unconfirmed case" because the invalid maintenance period has not been reached.

Since Mr. C has improved from requiring nursing care 5 to requiring nursing care 4 within the period of extracting changes in mental and physical condition, the maintenance period before the point of improvement is an “improvement case”.

Mr. D is a "maintenance case" because there is no change in the mental and physical condition within the mental and physical state change extraction period, and the maintenance period from the start of the mental and physical condition is longer than the standard maintenance period.

Mr. E deteriorates from needing nursing care 4 to requiring nursing care 5 within the period of extracting changes in mental and physical condition, and since the maintenance period from the time of deterioration to the deterioration is shorter than the standard maintenance period, it becomes a "deterioration case". The maintenance period until loss is longer than the invalid maintenance period, but shorter than the standard maintenance period, so it is a "maintenance unachieved case".
The number of these cases is counted by establishment and classification.

Next, the case classification function for performing such case classification will be described with reference to the functional blocks shown in FIG. Since the community-based comprehensive care business system according to this embodiment is composed of a computer system, the functional blocks shown in FIG. 9 are also realized by the storage unit, the arithmetic processing unit, and the set program of the computer system.

In FIG. 9, this case classification function uses the case classification logic unit using the care-requiring certification data (after selection) 11 that holds all the cases selected as evaluation targets in the above-mentioned “Step 5: Selection of evaluation target cases”. It is executed by 50 and the evaluation index calculation logic unit 60.
First, the case classification logic unit 50 will be described. The case classification logic unit 50 includes an improvement case classification unit 51, a maintenance case classification unit 52, a deterioration case classification unit 53, and a maintenance unachieved case classification unit 54.

When the stage of the user's mental and physical condition changes to improvement within the mental and physical condition change extraction period, the improvement case classification unit 51 classifies the maintenance period before the improvement time as an “improvement case”. In the example of FIG. 8, the case where Mr. A and Mr. C improve from the need for care 5 to the need for care 4 corresponds to.

The maintenance case classification unit 52 classifies the case where there is no change in the stage of the user's mental and physical condition within the mental and physical state change extraction period and the period without the change exceeds the preset standard maintenance period as a "maintenance case". To do. The maintenance period of Mr. D's need for nursing care 4 in FIG. 8 corresponds to this. In addition, if the mental and physical state stage changes to worse during the mental and physical state change extraction period, but the period in which there is no change in the mental and physical state stage before the change exceeds the preset standard maintenance period, a "maintenance case". Classify as. The maintenance period of Mr. B who needs nursing care 4 in FIG. 8 corresponds to this.

In the deterioration case classification unit 53, when the stage of the mental and physical condition of the user changes to deterioration within the period of extracting the change of mental and physical condition and the period of the stage of the mental and physical condition before deterioration is shorter than the preset standard maintenance period, the “deterioration case” ". In the case where Mr. E in FIG. 8 deteriorates from the need for care 4 to the need for care 5, the maintenance period of the need for care 4 corresponds to this.

In the maintenance non-achieved case classification unit 54, the mental and physical condition stage changes to improvement or deterioration within the mental and physical state change extraction period, and the maintenance period of the mental and physical condition stage after this change is longer than the preset invalid maintenance period and is a reference. If it is shorter than the maintenance period, it is classified as a "maintenance unachieved case". The maintenance period after the improvement to Mr. A's need for nursing care 4 in FIG. 8 and the maintenance period until Mr. E's need for nursing care 5 deteriorates and the qualification is lost are applicable.

If each of these cases does not apply, for example, the maintenance period shorter than the invalid maintenance period after the deterioration to the need for nursing care 5 of Mr. B in FIG. 8 is regarded as an "undetermined case".

Further, the improvement case classification unit 51, the maintenance case classification unit 52, the deterioration case classification unit 53, and the maintenance unachieved case classification unit 54 also have a function of counting the number of cases classified respectively.

Next, the evaluation index calculation logic unit 60 for calculating a new evaluation index (by business establishment / by mental and physical condition stage) from the cases classified in this way will be described. The evaluation index calculation logic unit 60 includes an improvement rate calculation unit 61, a maintenance rate calculation unit 62, and an improvement maintenance rate calculation unit 63.

The improvement rate calculation unit 61 calculates the improvement rate by dividing the number of improvement cases for each mental and physical condition stage obtained for each business establishment by the total number of cases for each mental and physical condition stage including the number of improvement cases.

That is, as an index showing the efforts for improving the physical and mental condition of each business establishment, the frequency of occurrence of "improvement cases" (hereinafter, "improvement rate") for all cases is calculated by the following formula. All cases do not include unconfirmed cases.

Improvement rate = number of improvement cases /
(Number of improvement cases + Number of maintenance cases + Number of cases not achieved maintenance + Number of deterioration cases)
The maintenance rate calculation unit 62 calculates the maintenance rate by dividing the number of maintenance cases for each mental and physical condition stage obtained for each business establishment by the number of all cases for each mental and physical condition stage excluding the number of improvement cases.

That is, as an index showing the efforts to prevent the severity of the severity of each business establishment, the frequency of occurrence of "maintenance cases" (hereinafter, "maintenance rate") in the number of cases excluding improvement is calculated by the following formula.

Maintenance rate = number of maintenance cases /
(Number of maintenance cases + Number of cases not achieved maintenance + Number of worsening cases)
In the above formula, since the maintenance rate does not include improvement cases, it is assumed that the improvement rate and the maintenance rate are independent of each other.

The improvement / maintenance rate calculation unit 63 calculates the total value of the number of improvement cases and the number of maintenance cases for each mental / physical condition stage, which is obtained for each business establishment, as the total number of cases for each mental / physical condition including the number of improvement cases and the number of maintenance cases. Divide to calculate the improvement maintenance rate.

In other words, the frequency of occurrence of "improvement cases" and "maintenance cases" for all cases (hereinafter, "improvement maintenance rate") as an index that comprehensively represents the efforts to improve the physical and mental condition of each business establishment and the efforts to prevent its severity. ”) Is calculated by the following calculation.
Improvement maintenance rate = (number of improvement cases + number of maintenance cases) /
(Number of improvement cases + Number of maintenance cases + Number of cases not achieved maintenance + Number of deterioration cases)

In this way, by aggregating the new evaluation indexes, the quality of the long-term care service of each business establishment in the local government can be visualized, and a fair evaluation can be performed. That is, the business establishment grouping unit 65 can evaluate the business establishments in the local government using the obtained evaluation index and perform the grouping described later.

In classifying the cases described above, as a simpler method, it is conceivable to classify only "improvement cases", "maintenance cases", and "deterioration cases", and to classify the other cases as "undetermined cases". That is, the above-mentioned "maintenance unachieved case" is not adopted. In this case, in calculating the new evaluation indexes "improvement rate", "maintenance rate", and "improvement maintenance rate", the "number of unachieved cases" may be deleted from the denominator of each of the above-mentioned calculation formulas.

Even with such a method, the quality of long-term care services at each business establishment in the local government can be visualized by a new evaluation index, and a fair evaluation can be performed.

Step 7: Grouping of business establishments using a new evaluation index The case of evaluating and grouping business establishments in a local government using the new evaluation index described above will be described.

The above-mentioned establishment grouping department 65 shows the improvement rate and maintenance rate of the degree of care required for each establishment, the degree of disability independence that aggregates all stages, and the improvement rate and maintenance rate for each degree of dementia independence that is mildly aggregated and severely aggregated. Is used to create a scatter plot shown in FIG. 10 and group it. In this scatter plot, one of the above-mentioned improvement rate and maintenance rate (maintenance rate in FIG. 10) is taken on the vertical axis, and one of the other (improvement rate in FIG. 10) is taken on the horizontal axis on two-dimensional coordinates. It is a plot of the values of each business establishment.

11A and 11B show improvement rates of 6 types of mental and physical condition stages (care required (care required 3 to 5, disability independence (all stages aggregated), dementia independence (mild aggregation, severe aggregation)). The list of establishments that have been grouped is shown according to the maintenance rate. In addition, the mental and physical conditions that are not entered in the scatter plot due to the opening time of establishments and the small number of cases are displayed as blanks.

This list of establishments consists of four areas: the number of cases to be evaluated in FIG. 11A, the improvement rate and maintenance rate area 72, the scatter plot quadrant area 74, and the improvement rate and maintenance rate statistical information area 73 shown in FIG. 11B. It is composed of.

Area 71 of the number of cases to be evaluated lists the number of improvement cases, the number of maintenance cases, and the number of deterioration cases for each business establishment and each mental and physical condition stage. Nursing care required 5 is blank because there are no cases of deterioration. This area 71 is an area for confirming the reliability of the improvement rate and the maintenance rate depending on the number of cases (for example, if the number of cases is several or less, the reliability as the average value of business establishments is low).

The improvement rate and maintenance rate area 72 lists the improvement rate and maintenance rate for each business establishment and each mental and physical condition stage.

Area 73 of the improvement rate and maintenance rate statistical information is the statistical information related to the improvement rate and maintenance rate for each mental and physical condition stage of the entire business establishment (number of business establishments subject to aggregation, average value, standard deviation, maximum value, minimum value). And the range (= maximum value-minimum value)) are listed. This area 73 is an area for comparing the distribution of establishments with the improvement rate and the maintenance rate for each mental and physical condition stage. For example, if the degree of independence of dementia is larger than the degree of independence of disability, the standard deviation of the improvement rate and maintenance rate, and the region are larger, it is possible that dementia care is more difficult than physical care (the difference between strengths and weaknesses depending on the establishment is large). Showing sex, etc.

The scatter plot quadrant area 74 is a list in which the improvement rate and maintenance rate of each business establishment belong to which quadrant in the scatter plot for each business establishment and each mental and physical condition stage shown in FIG. is there. A to D in the table refer to the quadrants of the scatter plot of FIG. In this scatter chart, the first quadrant is A (both improvement rate and maintenance rate are higher than the local government average), the second quadrant is B (improvement rate is higher than the same average, but the maintenance rate is lower), and the second quadrant is based on the local government average. The third quadrant is C (maintenance rate is higher than the average but the improvement rate is lower), and the fourth quadrant is D (both improvement rate and maintenance rate are lower than the same average).

From the list shown in FIGS. 11A and 11B, it is possible to comprehensively grasp the quality of service (pros and cons of care, etc.) for each stage of mental and physical condition of each business establishment, and to lead to the heading of good practices.

In the above examples, the evaluation index of the service establishment such as the long-term care welfare facility in the local government (long-term care insurer) has been described, but in the present invention, the local government is the national or prefectural government and the business establishment is the local government. The same applies to the replaced system for visualizing the quality of service for each municipality at the national or prefectural level.

The present invention provides a new analytical process for more fairly evaluating the quality of the long-term care service as described above. In the above-described embodiment, the analytical process shown in FIG. 1 has been described, but other analytical processes may be used. This will be described with reference to FIG. Quantitatively show the effect of the project by performing a time-series analysis of the mental and physical condition indicators (improvement rate and average maintenance period until deterioration) of special nursing homes for the elderly. The special nursing home is an example and can be applied to other services in the same way. In this analysis process as well, steps 1 to 4 are the flow of pre-processing until the data necessary for the present analysis process is prepared, and the applicant has already proposed in the form of a patent application, and the embodiment of FIG. However, since the outline has been explained, the explanation is omitted here, and the time-series transition aggregation analysis of the index related to the improvement / maintenance of the mental and physical condition by the new processes steps 5 and 6 will be explained.

Step 5: Aggregate analysis of improvement rate Aggregate analysis of annual transition of improvement rate by year and mental and physical condition of index calculation period. That is, as shown in the process 131 of FIG. 13, the time-series transition aggregation of the improvement rate for each mental and physical state is performed.

Step 6: Aggregate analysis of average maintenance period Aggregate analysis of the annual transition of the average maintenance period until deterioration by year and mental and physical condition of the index calculation period. That is, as shown in the process 132 of FIG. 13, the time-series transition aggregation of the average maintenance period until deterioration is performed for each mental and physical condition. Then, as shown in the process 133 of FIG. 13, the effect of suppressing the benefit cost by extending the average maintenance period until the deterioration is calculated by using the average maintenance period until the deterioration obtained for each year.

The logic of the time-series transition aggregation process 131 of the improvement rate for each mental and physical condition in step 5 will be described with reference to FIG. The logic of FIG. 14 is executed by a predetermined program by the arithmetic processing unit of the computer system, and the time series transition aggregation period is set in the processing 141. That is, the period for totaling the time-series transition is divided for each year, and the process up to the improvement rate calculation is repeated for each index calculation period set for each year.

The mental and physical state change amount calculation unit 142 calculates the mental and physical state change amount for each user during the index calculation period. Here, in the above-described embodiment, as shown in FIG. 15A, the number of improvement cases in the index calculation period is counted, and the change of mental and physical state within the index calculation period is totaled. That is, in the example of the figure, the improvement rate was calculated by counting as one improvement case, one deterioration case, and one maintenance case within the index calculation period. In this case, it becomes difficult to grasp the overall mental and physical condition during the index calculation period. Therefore, in this embodiment, the mental and physical condition is grasped from the actual amount of change in the mental and physical condition during the index calculation period.

FIG. 15B describes a method for obtaining the amount of change in the mental and physical state according to this embodiment. FIG. 15 (b) shows the same change in the mental and physical state as in FIG. 15 (a), but the mental and physical state within the index calculation period is improved by two steps from the mental and physical state 3 to the mental and physical state 1, and this implementation is carried out. In the form of, the amount of change is +2. After that, the state of mind and body has deteriorated by one step, and the amount of change is set to -1. The amount of change is set to 0 for maintaining the mental and physical condition, and when these changes are totaled,
2 + (-1) + 0 = 1
Will be. When the total amount of change is a positive value, it is "improved", when it is negative, it is "deteriorated", and when it is 0, it is "maintained".

That is, when the stage of the mental and physical condition changes to improvement within the index calculation period using the user's certification for long-term care, the number of stages that is the amount of improvement is counted. On the contrary, when the stage of mental and physical condition changes to worse within the index calculation period, the number of stages which is the amount of deterioration is counted. Then, the difference between the number of steps improved and the number of steps deteriorated, and the direction of the difference (positive, negative) are obtained, and improvement or deterioration is determined from the direction of the difference.

The mental and physical state change number calculation unit 143 totals the number of times representing the mental and physical state change state within the index calculation period, and calculates the number of mental and physical state changes for each user in the index calculation period. Although the number of changes is not directly related to the calculation of the improvement rate, it is calculated for each index calculation period and is effective as a numerical value indicating the state of mental and physical state change within the index calculation period.

Explaining with the example of FIG. 15B, the amount of change in the mental and physical condition during the index calculation period is +1 improvement as described above, and the amount of change between the two points at the start time and the end time of the index calculation period can be known. However, it is unclear how the mental and physical condition changed during the index calculation period. Therefore, when the stage of the mental and physical condition changes to improvement within the period of extracting the change of state of mind and body, the number of improvements (1 improvement in the example of the figure) and the number of stages of improvement (+2 in the example of the figure) are counted. Further, in the example of the figure, since the stage of the mental and physical condition has changed to deterioration after that, the number of deteriorations (1 deterioration) and the number of deterioration stages (-1) are counted. Then, these counts are output as the number of times indicating the change state of improvement and deterioration within the mental and physical state change extraction period.

As shown in FIG. 16, the aggregation target exclusion unit 144 has a function of excluding the number of changes in mental and physical condition from the aggregation target when the user receives services from a plurality of business establishments within the mental and physical condition change extraction period. .. That is, the number of business establishments used by each user during the index calculation period is calculated, and users with two or more business establishments are excluded from the total as if they are using a plurality of business establishments.

The improvement rate calculation unit 145 determines the number of users who are judged to be improved from the calculation result of the mental and physical state change amount calculation unit 142, the number of users who are judged to be deteriorated, the number of users who have not changed, and the users who have finished. The number of items is totaled for each business establishment, and the improvement rate for each business establishment is calculated from these aggregated values. The calculation formula is as follows.
Improvement rate = number of people improved / (number of people improved + number of people deteriorated + number of people maintained + number of people finished)

Next, the aggregate analysis of the average maintenance period of step 6 in FIG. 12 will be described. This step 6 is to aggregate the time-series transition of the average maintenance period until deterioration for each mental and physical state shown in the process 132 of FIG. 13, and is executed by the logic shown in FIG. In the process 171 of FIG. 17, the period for totaling the time series transition is divided into each year, each year is set as the index calculation period, and the process of the process 172 is repeated for each index calculation period.

The above-mentioned process 172 calculates the average maintenance period until deterioration in the index calculation period. That is, the average maintenance period in the local government until the stage of the mental and physical condition items of each user deteriorates by one stage is calculated for each mental and physical condition item for each year.

The aggregated mental and physical condition improvement rate / maintenance rate is used as a new mental and physical condition outcome index that contributes to the evaluation of independence support and prevention of severity. These calculation methods will be described. The mental and physical condition index is totaled for each mental and physical condition stage in Step 4: “Selection of main business establishment” shown in FIG. In this embodiment, various analyzes are performed by aggregating by group or arbitrary mental and physical condition items. Hereinafter, a method of calculating the maintenance rate based on the improvement rate aggregated from the mental and physical condition stages and the maintenance period until deterioration will be illustrated and described with reference to FIG.

FIG. 18A shows a state in which the improvement rate and the maintenance rate of each stage of the mental and physical condition are counted for each group and item name. In this way, the number of data for each item name is enormous, so it is not suitable for various subsequent analyzes. Therefore, as shown in FIG. 18B, each group is aggregated. That is, Group 1 has items from 1-1-1 to 1-N-3, but items 1-1-1 to 1 for each of the number of improvements, the number of deteriorations, the number of maintenances, the improvement rate, and the maintenance rate. The average value up to −N-3 is calculated and aggregated for each group. The other groups are aggregated in the same way.

Figures 19 (a), (b), and (c) show the time-series transition of the improvement rate by mental and physical condition in the local government, that is, the aggregated result of the improvement rate by year, mental and physical condition, and improvement rate. From the figure, the degree of independence of dementia and the third group tend to increase gradually, the degree of disability independence, the first group, the second group, and the fifth group tend to be flat, and the fourth group and the degree of care required are. It can be seen that although it goes up and down, it is flat as a whole.

Next, FIGS. 20 (a) and 20 (b) show the number and ratio of improvement, deterioration, termination, and maintenance in the local government. The number of users is the total number of people who have improved, deteriorated, completed, and maintained. In addition, each ratio was calculated by the following formula.
Improvement rate = Number of improvements / Number of users Deterioration rate = Number of deterioration / Number of users Termination rate = Number of finishers / Number of users Maintenance rate = Number of maintenance / Number of users

The number of users in FIG. 20 differs depending on the physical and mental condition for the following three reasons.
Reason 1: Because each case excludes cases that do not meet the threshold of the main business establishment. That is, the main business establishment is determined for each month in each case, and if the ratio of the number of months to the maintenance period does not exceed 50%, the data is excluded as data unsuitable for business establishment analysis. Since exclusion is performed for each mental and physical condition stage, the number of users may differ depending on the mental and physical condition.
Reason 2: The aggregated mental and physical condition represents the total number of people in each mental and physical condition stage. That is, while care-requiring 3 to 5 represent the number of people in each stage, the degree of independence of dementia and the degree of independence of disability represent the total number of people in all stages. In addition, the first to fifth groups represent the total number of people who add up all the mental and physical condition stages belonging to each group.
Reason 3: Users who receive services from multiple business establishments are excluded within the index calculation period.

21 (a), (b), and (c) show the time-series transition of the average maintenance period until deterioration by mental and physical condition. As shown in the figure, the average maintenance period has been extended by about 16 months from FY2013 over 5 years. In addition, since the disqualification was not registered in the certification data before 2012, the average maintenance period of the disqualified person is calculated longer than the actual time, so the average maintenance period of the care required 5 in 2012 is calculated longer. ing.

As mentioned above, since the average maintenance period until deterioration is tabulated in chronological order, it is possible to grasp whether the maintenance period is extended for each year compared to the previous year. If it is extended, the benefit cost for the extension can be suppressed and can be regarded as an effect. The following is a trial calculation of the effect of suppressing benefit costs by extending the average maintenance period until deterioration.

This trial calculation is performed by the arithmetic processing unit constituting the computer system with the logic shown in FIG. In FIG. 22, the process 221 divides the period for estimating the benefit cost suppression effect for each year, and causes the calculation of the benefit cost suppression effect by the process 222 to be repeated for each year. In processing 222, as shown in FIG. 23, the average maintenance period of each year of the benefit cost suppression effect trial calculation period and the standard average maintenance period are set as the standard average maintenance period of the previous year of the benefit cost suppression effect trial calculation period. The difference (stretching period) A from the above is obtained. Then, for each mental and physical condition, the difference B of the monthly benefit cost per person from the one-step deterioration stage of the mental and physical condition is multiplied by the number of people C whose mental and physical condition has deteriorated one step, and the benefit cost suppression amount D for each year is calculated. To do. In processing 224, the benefit cost restraint amount for each year in the benefit cost restraint effect trial calculation period is totaled, and the benefit cost restraint amount in the benefit cost restraint effect trial calculation period is calculated.

In FIG. 23, the effect of extending the average maintenance period on controlling benefit costs is calculated. Based on the average maintenance period until deterioration in 2013, the difference (months) from the average maintenance period until deterioration in each year after 2014 is the difference (months) from the need for long-term care 3 to 4 and the need for long-term care 4 to 5 Multiply the difference between the monthly benefit costs per person and the number of people who need nursing care 3 and 4 to calculate the suppressed benefit costs. In the example of FIG. 23, the average maintenance period in the local government from the deterioration of the degree of need for care, which is the mental and physical condition, from the need for care 3 to the need for care 4 was 17.0 months in FY2013, but in FY2014. It is 26.45 months, and the extension period A in 2014 is 9.45 months. Assuming that the difference B of the monthly benefit cost per person between the care-requiring 3 and the care-requiring 4 in 2014 is 22,835 yen, and the number of deteriorated people C in 2014 is 18, the suppressed benefit cost D in 2014 is It will be 3,884,234 yen.

That is, the average maintenance period calculation unit (corresponding to the processes 171 and 172 in FIG. 17) that the arithmetic processing unit of the computer system has as a function maintains the average in the local government until the stage of the mental and physical condition items of each user deteriorates by one stage. The period is calculated for each mental and physical condition item for each year. Similarly, the benefit cost suppression effect calculation unit (corresponding to the processes 220, 222, 223 in FIG. 22), which the calculation processing unit has as a function, sets the benefit cost suppression effect calculation year as this year for each stage of mental and physical condition, and the previous year. Calculate the extension period of the average maintenance period of the same stage of this year with respect to the average maintenance period (standard average maintenance period) of the corresponding mental and physical condition stages. Then, using this extension period, the difference in per capita benefit costs between the corresponding mental and physical condition stage and the one-stage mild stage of this year, and the number of people whose mental and physical condition deteriorated in the corresponding stage of this year, Calculate the amount of benefit cost restraint by extending the maintenance period of mental and physical condition.

Table 1 below shows the results of trial calculation of the amount of benefit cost restraint for Nursing Care 3 and Nursing Care 4 calculated by the above-mentioned calculation method of benefit cost restraint effect. The starting year of the yearly transition is 2013. Since the amount of benefit cost restraint (estimate) shows a comparison with FY2013, the year in Table 1 will be from FY2014.

In Table 1, first, in the yearly transition of the monthly benefit cost per person, the difference between the monthly benefit cost per person in the need for care 3 and the monthly benefit cost per person in the need for care 4 and the difference in the monthly benefit cost per person in the need for care 4 It is about 20,000 yen. This is also the same as the difference in the number of units of basic service items for special nursing homes x 30 days. The result of the benefit cost restraint amount (estimate) is about 38.47 million yen for the care-requiring 3 and about 12.53 million yen for the care-requiring 4, for a total of about 51 million yen (estimate).

Here, it will be examined whether the certification validity period is often extended due to system revisions and the like, and whether it affects the maintenance period calculation in the present invention. First, the background of the system revision related to the extension of the certification validity period is shown below.

Background of system revisions related to extension of certification validity period (2000 (when the system was established))
・ The "valid period for certification in principle" is set to 6 months.
-The "valid period that can be set by the municipality" was set to 3 to 12 months if the result of the renewal application was "support required → support required" or "care required → care required". For new applications, classification change applications and renewal applications other than the above cases, it was set to 3 to 6 months.
(2004)
-Regarding the "valid period of certification in principle", the validity period has been extended to 12 months when "support required → support required" or "care required → care required" as a result of the renewal application.
-Regarding the "valid period that can be set by the municipality", the upper limit of the valid period when "need care → need care" as a result of the renewal application has been extended to 24 months.
(2011)
-Regarding the "valid period that can be set by the municipality," the upper limit of the valid period for classification change applications and renewal applications, excluding new applications, has been extended to 12 months.
(2012)
-Regarding the "validity period that can be set by the municipality," the upper limit of the valid period for new applications has been extended to 12 months.
(2015)
When a municipality shifts to a comprehensive care prevention / daily life support project
-Regarding the "valid period of certification in principle", the valid period of renewal application has been extended to 12 months.
-For the "valid period that can be set by the municipality," the upper limit of the valid period for renewal applications has been extended to 24 months.
(See Table 2 below for 2018)

Impact assessment method for maintenance period calculation In this way, the certification validity period is being extended year by year due to system revisions. Therefore, we have grasped the fact that the validity period of certification at the time of certification for long-term care is extended year by year due to system revisions, etc., and in fact, the next certification for long-term care is being carried out in the middle of the certification validity period. , The differences in the truncated certification period were tabulated and shown in FIG.

In FIG. 24, the certification period determined at the time of certification for long-term care is referred to as the “certification validity period”, and the certification period cut down by the change application is referred to as the “actual certification period”. As is clear from FIG. 24, even if the certification validity period is extended due to system revisions, etc., if the physical and mental condition changes, a change application will be made at that point, and the above-mentioned maintenance period is based on the actual certification period. , It is clear that the maintenance period calculation is not affected.

FIG. 25 shows the transition of the annual average of the certification validity period and the transition of the annual average of the actual certification period cut down by the change application. Compared with the increase in the certification validity period, the growth in the actual certification period has remained at 15 months or less, which indicates that the actual certification period is not affected by the length of the certification validity period. This is because, as described above, if the physical and mental condition deteriorates, an immediate change application is made at that time. After FY2017, all change applications were not applied at the time of extraction of this certification data, so they were excluded from the analysis.

Next, regarding the basic service items and additional service items in special nursing homes, the relationship between the differences and presence of each special nursing home and the evaluation indexes (improvement rate, maintenance rate, etc.) is investigated, and independence support / severity for each service item is investigated. Verify the effect of prevention of conversion. Since the calculation requirements for service items are revised each time, it is necessary to analyze based on the latest calculation requirements. In this embodiment, the analysis is performed based on the calculation status of the latest year (2018) data for which one year's worth of data is available.

This analysis is performed in three steps shown in FIG. That is, Step 1: Service item selection 1 for effect verification (1 point-in-time survey), Step 2: Service item selection 2 for effect verification (time series survey), Step 3: Effect verification by mental and physical condition index of special nursing care fee Is. By going through these steps, the data is aggregated and analyzed.

This analysis process is executed in the arithmetic processing unit of the computer system. As described above, this analysis process verifies the effect of independence support / prevention of severity for each service item by using evaluation indexes (improvement rate, maintenance rate, etc.). Therefore, the arithmetic processing unit evaluates the above-mentioned evaluation. The average maintenance period calculation unit (corresponding to the logic process 172 described in FIG. 17) that calculates the average maintenance period at each business establishment until the stage deteriorates for each stage of the mental and physical condition items using the index data, and For each stage of mental and physical condition items, an improvement rate calculation unit (corresponding to the logic processing 145 described in FIG. 14) that calculates the ratio of users who improve at that stage at each business establishment as the improvement rate of each business establishment. Have.

In addition, among the services provided by each business site, select service items that have been provided to users within the most recent predetermined period and have continued from the time when the average maintenance period can be calculated. The group of the service item selection department (details will be described later with reference to FIGS. 27 and 28) and the business establishment that provides the selected service item is the first group, and the selected service item is not provided. The effect verification department that compares the average maintenance period and improvement rate of these first and second groups for each mental and physical condition item, with the business establishment group as the second group (details will be described later with reference to FIG. 29). And have as a function.

In addition, since this effect verification department cannot compare and analyze the service items provided to users by all the business establishments of the local government by grouping them into the first group and the second group mentioned above, each business establishment The average maintenance period and the improvement rate are aggregated, and those values are analyzed and evaluated by outputting a scatter plot.

First, the service item selection unit will be described. There are hundreds of service items prepared for special nursing homes, but there are dozens of items actually provided to users, and service items are selected as to which items are to be verified for effectiveness. This is called the above-mentioned one-time survey. In this one-point survey, as described above, the data for FY2018, which has the latest data for one year, is selected as the aggregation target.

The one-time point survey is executed by the arithmetic processing unit of the computer system according to the logic shown in FIG. In processing 271, in order to determine the candidates for the service items to be aggregated, the calculated number of people by service item and business establishment is aggregated and aggregated from the benefit record data of March 2019, which is the last month of the latest year (2018). Narrow down the target service item candidates. At this time, service items that have no calculation or have only a few calculations are excluded.

Next, the calculation status of the service item for one year (FY2018) is totaled, and the service item for which the number of months of calculation in the year is sufficient for analysis is selected as the aggregation target. That is, with reference to the service items narrowed down by the calculation status of only the last month of the aggregation target year by the above processing 271, the calculation status for 12 months of the aggregation target year (FY2018) is aggregated and aggregated in the processing 272. Select the target service item.

Table 3 below shows an example of the results of a one-point survey of service items subject to effectiveness verification. Table 3 exemplifies the relationship between the service items and the aggregate analysis group to be verified for the effects selected from the calculation status of the special nursing home in FY2018. That is, as described above, after totaling the calculated number of business establishments for each service item as of March 2019, determining the service item candidates to be verified, and then calculating the calculation status of the service item for one year in FY2018. Aggregated.

In Table 3, when the aggregate analysis group is, for example, "G1: basic item (by room type)", the service items are "welfare facility II3", ... "Yu-type welfare facility I5", and they are The summary of is "Multi-bed room / Nursing required 3", ... "Unit type private room / Nursing required 5", and the analysis type is "By room type". In addition, "multi-bed room / care required 3" means that the user of the care required 3 lives in one room with a capacity of two or more people. Multi-bed rooms allow users to communicate with each other, but it is difficult to ensure the privacy of individual users. Further, "unit type private room / care required 5" means that each user of the care required 5 uses a shared living room, although each person has one room. This unit-type private room can ensure the privacy of individual users, but there are some problems in communication with others.

Service items, summaries thereof, and classification types are also set for the other aggregate analysis groups G2 ... G8. Of these, "G6; daily life continuation support addition" is decided at the time of admission and is not subject to verification because it has nothing to do with the quality of service.

Next, the time-series survey of step 2 shown in FIG. 26 will be described. This time-series investigation is executed by the arithmetic processing unit of the computer system by the logic shown in FIG. 28. In FIG. 28, in the process 281, the revision history survey by the process 282 is repeatedly performed for the service item selected in the above-mentioned one-time survey. Then, from the survey results of the past revision history, it is investigated whether the service item exists for a period sufficient for analysis, and the effect of the change in the name of the service item or the calculation condition on the analysis is clarified, and the service suitable for analysis. Select an item. In other words, the calculation of the index of mental and physical condition, which is the basis of the effect analysis, requires the value of a certain period back from the evaluation time, and the service item suitable for the analysis is not targeted for the service items that do not reach that period. This is to select.

In the process 282, as described above, it is investigated from the past revision history of the service item whether or not the service item exists for a period sufficient for analysis. Here, the period sufficient for analysis may be, for example, the period of "3 years", which is the business year of long-term care insurance, or the period of the case project carried out by the local government. The continuity of the service item history is determined by the service item code regardless of the service item name. Therefore, when investigating the continuity of history, it is investigated whether or not service items having the same service item code exist. For example, even if the same service item name does not exist in the past, if service items with different service item names with the same service item code exist, it is considered that the continuity is maintained as the same service item.

The results of this time-series survey are shown in Table 4 below.

In Table 4, if the same service item name is listed in each cell of the service item revision date, there is no major revision of the service item in each fiscal year, but minor revisions (for example, revisions such as points). Indicates when there is. In the example of Table 4, the revision date of the service item of the service item number (= service item code, the same applies hereinafter) "1135" is "April 1, 2012" ... "April 1, 2018". The same service item name "Welfare Facility II3" is listed in all cells, but this is because the service item "Welfare Facility II3" has not been significantly revised on the relevant revision date, but minor revisions have been made. It shows that there is. In addition, service items that have not been revised at all on the relevant revision date are indicated by "→". Furthermore, service items that do not yet exist on the revised date are indicated by "x".

In addition, in Table 4, the aggregate analysis group "Treatment improvement addition" added service items and existing service items on April 1, 2015 and April 1, 2017 due to the establishment of additional calculation requirements for additional evaluation. Although the name of is changed, the service item number is not changed, so the continuity of the history is maintained and there is no problem in analysis. That is, since the service item number "6018" was newly established on April 1, 2015 under the name of "treatment improvement addition I", the name of the service item of the service item number "6014" was changed to "treatment improvement addition II". Was done. In addition, on April 1, 2017, the service item number "6110" was newly established under the name of "treatment improvement addition I", so the name of the service item of the service item number "6018" is "treatment improvement addition II". The name of the service item of the service item number "6014" was changed to "Treatment improvement addition III". In this way, looking at the service item number "6014", the name of the service item is changed every time the service item is added, but since the service item number is not changed, the continuity of the history is maintained. There is no problem in analysis.

Next, the effect verification unit for verifying the effect of the special nursing care fee shown in FIG. 26 by the mental and physical condition index will be described. The effect verification unit based on the mental and physical condition index of the special nursing care fee is executed by the arithmetic processing unit of the computer system according to the logic shown in FIG. The outline will be described below.

In process 291, a business establishment group is determined for the service items selected as described above. The business establishment group is grouped by the business establishments that have the calculation of the selected service item and the business establishments that do not have the calculation. The establishment group is the comparison unit for analysis. In the process 292, the business establishment groups to be compared are grouped into an aggregate analysis group. In the processing 293, the aggregation processing of the improvement rate for each mental and physical condition index for each business establishment group and the average maintenance period until the deterioration is executed by the aggregation analysis group for each aggregation analysis group, and is used as analysis data. In the process 294, the improvement rate for each mental and physical condition and the average maintenance period until deterioration of the business establishment group to be compared are calculated and compared for each aggregate analysis group. In processing 295, if it is for the country, the care fee system is based on the aggregated analysis results of each business establishment if it cannot be divided into the business establishment group to be compared or the business establishment group as described later. Supports the decision of the content of measures such as the necessity of reviewing the policy and the necessity of business guidance for local governments.

Table 5 shows the results of verification of the effects of the above-mentioned special nursing care fee based on the mental and physical condition index. Table 5 shows the relationship between the above-mentioned aggregate analysis group for effect verification and the calculation establishment. As can be seen from Table 5, the aggregate analysis groups subject to effect verification are classified by room type (G1), calculation of all business establishments (G2, G8), and presence / absence of addition calculation (G3.G4.G5.G7). There will be 3 groups.

In Table 5, in the aggregate analysis group (G1) whose analysis type is by room type, the service item explained in Table 3 is "Unit type care and welfare facility service (I)", and the cost is calculated for I, J. The first business group 11 consisting of the two business establishments, and the eight businesses A, ... H, for which the service item "Nursing care and welfare facility service (II) <multi-bed room>" was adopted and the cost was calculated. The analysis target is selected for the second business establishment group 12 consisting of the offices. In other words, the group 11 of the I and J establishments where the cost of the service item "unit type care and welfare facility service (I)" is calculated is the first group 11, and the service item "unit type care and welfare facility service (I)". Group 12 of A, ... H establishments for which the cost is not calculated becomes the second group 12.

For the aggregate analysis group group (G3.G4.G5.G7) whose analysis type is with or without additional calculation, adopt the service items shown in Table 3, and adopt the first group for which the cost was calculated and the service item. Instead, it is divided into a second group for which the cost is not calculated.

Then, these aggregate analysis groups G1 and G3. G4. G5. A mental and physical condition index is calculated for each of the first and second business establishment groups for each G7, and comparative analysis (described later) is performed.
In addition, the aggregate analysis group (G2, G8) whose analysis type is calculated by all business establishments adopts the service items shown in Table 3 and calculates the cost, so divide them into business establishment groups. I can't. In this case, the mental and physical condition indicators of all business establishments are calculated and comparative analysis (described later) is performed.
The results of effect verification will be described below for these aggregate analysis groups.

Aggregate analysis group G1: The effect verification results of the basic items (by room type) will be described with reference to FIGS. 30 and 31. In the basic item (by room type) G1, the establishment group 11 that calculates the unit type shown in Table 5 is the first establishment group, and the establishment group 12 that calculates the multi-bed room is the second. As a business group, we compared and analyzed these mental and physical condition indicators. as a result,
The improvement rate is higher in the unit type in all mental and physical conditions, and conversely, the average maintenance period is longer (better) in the multi-bed room in all mental and physical conditions. As for the average maintenance period, since the calculation of the unit type facility started in 2016, it is considered that the average maintenance period is calculated shorter than the actual one. In addition, in order to correctly compare and evaluate the unit type and the multi-bed room, the average maintenance period of the multi-bed room is less than 50 months, so the unit type calculation period must be at least 5 years or more. From this analysis result, it is possible to understand how the room type affects the improvement and maintenance of the physical and mental condition of the user, and it can be used for examining the necessity of reviewing the facility equipment plan and the care fee system.

Next, the results of verifying the effect of the aggregate analysis group G3: treatment improvement addition will be described with reference to FIGS. 32 and 33. In the treatment improvement addition G3, among the service items "treatment improvement addition I to V" shown in Table 3, the service items actually calculated are only the treatment improvement addition I and II, so as shown in Table 5. , The mental and physical condition indexes of the business establishment group 31 that calculates the treatment improvement addition I and the business establishment group 33 that calculates the treatment improvement addition II are compared and analyzed as the first group and the second group, and FIGS. 32 and 33. The results shown in are obtained.

The hypothesis is that the establishment group 31 that calculates the treatment improvement I should show a better tendency for the mental and physical condition index than the establishment group that calculates the treatment improvement II, but the actual analysis results are shown in the figure. As shown in 32, in terms of improvement rate, the establishment group 31 that calculates the treatment improvement I is high except for the second group, but the difference is small, and even if it is large, the difference is 1.22% in the fifth group. The result is contrary to the hypothesis that II is long except for the 4th group and the degree of independence of dementia in the average maintenance period.

Next, the results of verifying the effect of the aggregate analysis group G4: nursing system addition will be described with reference to FIGS. 34 and 35. As shown in Table 4, there are two types of nursing system addition G4, Nursing system addition I2 and II2. Nursing system addition is a cumulative addition that can be calculated by the establishment that calculated I2 in addition to II2. That is, since the nursing system addition I2 is added by all business establishments, as shown in Table 5, the establishment group 41 that calculates II2 by paying attention to the addition of the nursing system addition II2 and the nursing system addition The mental and physical condition indexes of the establishment group 42 for which II2 was not calculated were compared and analyzed.

The hypothesis was that the establishment group that calculated the nursing system addition II2 tended to have a better mental and physical condition index than the establishment group that did not calculate the nursing system addition II2. Actually, as shown in FIG. 34, the improvement rate is higher in the business group that does not calculate the nursing system addition II2 in all the mental and physical condition indexes, but the difference is small. As for the average maintenance period, as shown in Fig. 35, the group 5 and the group of establishments that did not calculate the nursing system addition II2 for the degree of independence of dementia are longer, but other than that, the nursing system addition II2 is calculated. The business group is longer. Especially in the third group, the difference is as large as 10.1 months. For this reason, the establishment group that calculates the nursing system addition II2 according to the hypothesis shows a good tendency.

Next, the results of verification of the effect of the aggregate analysis group G5: addition of the oral hygiene management system will be described with reference to FIGS. 36 and 37. As shown in Table 5, the oral hygiene management system addition G5 is a comparative analysis of the mental and physical condition indexes of the establishment group 51 that calculates the oral hygiene management system addition and the establishment group 52 that does not calculate the addition. ..

The hypothesis was that the establishment group that calculated the oral hygiene management system addition tended to be in better physical and mental condition than the establishment group that did not calculate it. Actually, as shown in FIG. 36, the improvement rate is higher in the second group, the fourth group, and the establishment group in which the degree of independence of dementia is not calculated, but the difference is small. Regarding the average maintenance period, as shown in FIG. 37, the 4th group, the 5th group, and the establishment group that calculates the oral hygiene management system addition in the input of the degree of independence of dementia are longer, and the 1st group. , The second group, the third group, and the establishment group that did not calculate the degree of disability independence are longer, so the result cannot be said to be as hypothesized.

Next, the results of verifying the effects of the aggregate analysis group G7: psychiatric medical care guidance addition will be described with reference to FIGS. 38 and 39. As shown in Table 5, the psychiatric medical care guidance addition G7 is a combination of a business group 71 that calculates the psychiatric medical care guidance addition and a business group 72 that does not calculate the psychiatric medical care guidance addition. Psychiatric and physical condition indicators were compared and analyzed.

The hypothesis was that the business group that calculated the psychiatric medical care guidance addition tended to have a better mental and physical condition index than the business group that did not calculate it. Actually, as shown in Fig. 38, the improvement rate is higher in the establishment group that calculates the psychiatric medical care guidance addition in the 1st group, 2nd group, 4th group, and 5th group. The group of establishments, which did not calculate the degree of independence of disability and dementia, was higher in the third group. As for the average maintenance period, as shown in Fig. 39, the group 4 and the group of establishments that do not calculate the psychiatric medical care guidance addition in the degree of disability independence are long, but the other mental and physical condition indicators are psychiatric medical care guidance. The establishment group that calculates the addition is longer. Therefore, the result is not as hypothesized.

Next, the aggregate analysis group G2: the effect verification result of the individual function training addition and the G8: the effect verification result of the nutrition management addition calculated by all the business establishments will be described. As shown in Table 5, the individual function training addition G2 and the nutrition management addition G8 are calculated by all business establishments, and it is not possible to compare the mental and physical conditions of the business establishments grouped by the presence or absence of the addition calculation. Therefore, as shown in FIGS. 40 and 41 for each mental and physical condition, the first group (physical function / occupancy) related to the addition is used by using a scatter plot of the improvement rate of all business establishments and the average maintenance period until deterioration. For the movement) and the second group (living function), the mental and physical conditions of the business establishments were aggregated and analyzed.

As a result of the above-mentioned aggregate analysis, looking at the relationship between the improvement rate of Group 1 and the average maintenance period, as shown in FIG. 40, facilities with a high improvement rate have a short average maintenance period, and conversely, facilities with a long average maintenance period. Shows a low improvement rate. The minimum improvement rate is 0.7%, the maximum is 2.9%, the shortest average maintenance period is 33.5 months, the longest is 53.6 months, and the difference between the shortest and longest in the average maintenance period is 20. It has grown to one month. As shown in FIG. 41, the second group also shows the same tendency as the first group, but the shortest average maintenance period is 34.9 months and the longest is 45.2 months, and the difference is 10 .3 10.3 months, about half of the first group.

Below, FIGS. 42 and 43 show the difference between the improvement rate of the business establishment group and the average maintenance period until the deterioration, and the direction thereof, for each of the aggregated analysis groups analyzed as described above. Aggregate analysis group G1: The average maintenance period for each room type is excluded as described above.

In FIGS. 42 and 43, the aggregate analysis group G1 is a unit type because the difference is (unit type-multi-bed room), G3 is a treatment improvement I because the difference is (I-II), and G4 to G7 are. , If there is an addition because the difference is (with addition-without addition), but it shows that each mental and physical condition improves (if the value in the bar graph becomes 0 or more), the independence support and severity of the nursing care fee will be increased. It may be a preventive effect.

The main results of the above-mentioned effect verification are as follows.
-Compared to the multi-bed room, the unit type showed a significant difference in the improvement rate for all mental and physical conditions.
・ The treatment improvement addition I was different from II in the improvement rate for almost all mental and physical conditions.
-For other additions, the difference in improvement rate (effect) varied depending on the physical and mental condition.
-As for the average maintenance period until deterioration, the effect of each addition varied depending on the physical and mental condition.

Based on the results of the above-mentioned "time-series analysis of mental and physical condition indicators" and "verification of the effect of special nursing care fees", as a proposal to the national government, "a new nursing care fee addition system based on the evaluation of independence support and prevention of severity" Recommendations "can be considered. As shown in Fig. 44 (b), the concept of the new addition system is to add performance-based addition based on the results of improvement and maintenance of the desired mental and physical condition in addition to the existing establishment system addition. Is. In other words, "result-type addition" is certified and added to establishments where the results of efforts to improve and maintain the physical and mental condition of all users meet certain conditions. FIG. 44 (a) shows the improvement rate and the average of all the users of the business establishment until the deterioration, as an index showing that the efforts for improving and maintaining the physical and mental condition of all the users of the business establishment satisfy certain conditions. It is shown that the business establishments are grouped using the aggregated results of the maintenance period. In addition, as a method of setting each new number of credits, a method of dividing the total amount of the conventional establishment system addition into two additions based on the new allocation (the idea that the number of units of the existing establishment system addition is reduced). It is conceivable to maintain the existing number of credits for addition and then set a new number of credits for performance-based addition. According to the idea of total amount regulation on nursing care fees these days, the former method seems to be a realistic choice.

The procedure for newly adding the result addition may be set as shown in FIG.
・ Procedure 1: At the service establishment, improve and identify the maintainers every year.
-Procedure 2: Apply for registration of the calculation requirements for the new addition, for example, the ratio of improvement / maintainers of physical function and cognitive function to the insurer (local government).
・ Procedure 3: The insurer examines the accepted requirements and registers the ledger.
-Procedure 4: The service establishment is a calculation requirement, for example, if the ratio of improvement / maintenance of physical function and cognitive function exceeds the set level, a new addition is requested to the National Health Insurance Federation.
・ Procedure 5: The National Health Insurance Federation will examine the new additions received and pay the service establishment.
・ Procedure 6: The National Health Insurance Federation confirms the benefits with the insurer.

Next, the correlation analysis between the service self-evaluation table (hereinafter, questionnaire sheet) and the mental and physical condition index (hereinafter, referred to as the correlation analysis of the questionnaire sheet) will be described. Local governments, which are insurers, conduct questionnaires to the staff of each business establishment for the purpose of improving services, and have them self-evaluate the implementation status of many service items. The questionnaire sheet used for this self-evaluation systematically summarizes the matters to be tackled and the service level to be achieved. Therefore, we will investigate which item of the questionnaire result by the questionnaire sheet, that is, the evaluation result of the self-evaluation of the service is related to the improvement and maintenance of the mental and physical condition by the statistical method of correlation analysis. The details will be described below.

The correlation analysis of this questionnaire sheet is executed by the arithmetic processing unit of the computer system in four steps shown in FIG.
Step 1: Definition of questionnaire index Step 2: Creation of questionnaire index Step 3: Selection of evaluation period of data to be compared Step 4: Correlation analysis between mental and physical condition index and questionnaire index Step 5: Characteristic analysis by special needs

The arithmetic processing unit functions as a questionnaire index creation unit 461 that executes steps 1 and 2 of FIG. 46, a correlation analysis unit 462 that executes steps 3 and 4, and a special training characteristic analysis unit 463 that executes step 5. Have as.

The questionnaire index creation unit 461 defines a service item associated with a mental and physical condition item as a questionnaire item. From the results of the questionnaire to the staff of each business establishment, the implementation rate of the questionnaire item whose response rate is equal to or higher than the predetermined value among the defined questionnaire items is used as the questionnaire index of the questionnaire item. The correlation analysis unit 462 includes a mental and physical condition index consisting of an improvement rate or a maintenance period until deterioration of the mental and physical condition items associated with the questionnaire items in a preset period, and a questionnaire index of the questionnaire items related to these mental and physical condition items. Correlation analysis is performed. The relationship between the correlation coefficient, which is the result of this correlation analysis, and the questionnaire index value and the mental and physical condition index value of each business establishment is output.

Hereinafter, the operation of each step shown in FIG. 46 will be described in detail with reference to FIGS. 47 and 48 and related tables.

First, an outline of selection of mental and physical condition items related to the questionnaire sheet will be described. In step 1 of FIG. 46, as shown in FIG. 47, the questionnaire index definition unit 4611 reads the service item and the mental and physical condition index data 472 from the questionnaire data 471, and determines the association between the two.

As shown in FIG. 49, the evaluation items (major classification: medium items / questionnaire items) of the questionnaire sheet and the related mental and physical condition items (certified survey items, etc.) are selected. For example, the content of the item "1. Meal" in the questionnaire sheet is considered to affect the improvement and maintenance of the mental and physical condition items "2-3 under" and "2-4 meal intake", and these are related. Similarly, the content of the middle item "2. Excretion" is considered to affect the improvement and maintenance of the mental and physical condition items "2-5 urination" and "2-6 defecation", and these are related to each other. FIG. 49 shows a part of the selection of the evaluation items (major classification: medium items / questionnaire items) of the questionnaire sheet and the related mental and physical condition items (certified survey items, etc.), but in reality, many questionnaire sheets There are evaluation items (major classification: medium items / questionnaire items), and these are associated with the corresponding items of the mental and physical condition items. However, since it is diverse, the illustration is omitted.

Returning to FIG. 47, the data 473 is the mental and physical condition item associated with the evaluation item (major classification: middle item / questionnaire item) of the questionnaire sheet in the above-mentioned questionnaire index definition unit 4611.

Next, the creation of the questionnaire index of step 2 shown in FIG. 46 will be described. The questionnaire index 474 is created by the questionnaire index creation unit 4612 from the questionnaire data 471 associated with the mental and physical condition items in the questionnaire index definition unit 4611 of FIG. 47.

As shown in FIG. 49, the evaluation items of the questionnaire sheet are composed of major categories, medium items, and questionnaire items. For example, if the major classification is "direct life assistance", there are "meal", "excretion", "movement", etc. as middle items, and more specific items are set as questionnaire items for each of these middle items. .. There are the following two methods for creating the questionnaire index.

-Method 1 Aggregate the implementation ratio of "A: 80% higher response" in the middle item for each business establishment and use this as a questionnaire index.
・ Method 2 Weighted points were assigned to the questionnaire items "A: 80% or more", "B: 60-80%", and "C: less than 60%", and the average of business establishments for each medium item was totaled. This is used as a questionnaire index.

In the above-mentioned method 1, the implementation ratio of "A: 80% higher correspondence" is totaled for each business establishment for each medium item. In addition, "A: 80% higher correspondence" means a service index of rank A having a response rate to users of 80% or more. At this time, the "non-applicable" questionnaire items are excluded from the total. In the above method 2, for example, "A: 80% or more correspondence": 5 points, "B: 60-80% correspondence": 3 points, "C: less than 60% correspondence": 1 Score points and total the business establishment average value for each medium item. At this time, the "non-applicable" questionnaire items are excluded from the total. Either of these methods 1 and 2 may be adopted, but here, method 1 is adopted.

next. The evaluation period of the comparison target data in step 3 shown in FIG. 46 is set by the evaluation period selection unit 4621 of FIG. 47. In this analysis, as described above, the correlation analysis between the mental and physical condition index and the questionnaire index is performed in step 4 of FIG. 46, but in order to perform the correlation analysis, it is necessary to match the timing of the mental and physical condition index with the time of the questionnaire survey. .. The survey period (questionnaire survey period) of the questionnaire index used this time is each year from 2016 to 2018, and the evaluation period of the mental and physical condition index is set to each year from 2016 to 2018 as shown in Fig. 50. The mental and physical condition index (data 475 in FIG. 47) in the period is calculated, and the corresponding questionnaire index for each year is subjected to correlation analysis for each year.

Next, the correlation analysis between the mental and physical condition index and the questionnaire index according to step 4 in FIG. 46 is performed by the correlation analysis unit 4622 in FIG. 46, and the correlation coefficient is calculated by the following formula. The correlation coefficient is Pearson's product moment correlation coefficient.

In general, the strength of the following correlation can be found from the value of the correlation coefficient.
± 0.7 to ± 1 Strong correlation ± 0.4 to ± 0.7 Medium correlation ± 0.2 to ± 0.4 Weak correlation
0 to ± 0.2 Almost no correlation

As a result of the above-mentioned correlation analysis, as shown in FIG. 47, the correlation analysis result and the scatter plot data 476 are created. These will be described later. Further, the characteristic analysis by special nursing home in step 4 shown in FIG. 46 is performed by the characteristic analysis unit 463 by special nursing home in FIG. 47 by the logic shown in FIG. 48, and as shown in FIG. 47, the number of indicators exceeds the local government average. Data 464 is output.

That is, in the correlation analysis of this questionnaire sheet, the questionnaire index creation unit 461 defines the questionnaire index from the questionnaire items associated with the mental and physical condition items. Then, from the results of the questionnaire to the staff of each business establishment, the implementation rate of the questionnaire item whose response rate is equal to or higher than the predetermined value among the questionnaire items is used as the questionnaire index of the questionnaire item. The correlation analysis unit 462 includes a mental and physical condition index (an index aggregated when there are a plurality of mental and physical condition items) consisting of an improvement rate in a preset period or a maintenance period until deterioration of the mental and physical condition items associated with the questionnaire items. Correlation analysis with the questionnaire index of the questionnaire items related to the mental and physical condition items is performed. Then, the relationship between the correlation coefficient, which is the result of this correlation analysis, and the questionnaire index value and the mental and physical condition index value of each business establishment is output.

Next, the result of the correlation analysis between the questionnaire index and the mental and physical condition index will be described.

There is a positive correlation between the indicators shown in Table 6 below.

FIG. 51 shows an example of a scatter plot between the indicators in Table 6 above. From the scatter plot between the indicators, a strong positive correlation can be seen between the indicators. It can be seen that the questionnaire index and the mental and physical condition index of the business establishments 3, 7, and 10 are significantly higher than the local government average among these indicators.

Next, the results of the above-mentioned characteristic analysis by special nursing home will be described. From the results of the correlation analysis, it was found that the questionnaire index and the mental and physical condition index of the business establishments 3, 7, and 10 were significantly higher than the local government average among the plurality of indicators. Find out if the other questionnaire indicators are similar. From the value of the mental and physical condition index that correlates with the questionnaire index of each business establishment, the number of mental and physical condition indicators whose values of both the questionnaire index and the mental and physical condition index are larger than the local government average is determined by the improvement rate and the maintenance period until deterioration. Count. The results of this count are shown in Table 7.

In Table 7, establishments 3 and 7 have many questionnaire indicators that exceed the local government average throughout the three years. In addition, it can be seen that there are more mental and physical condition items related to them than in other nursing homes, which is higher than the local government average. In addition, it can be seen that the number of questionnaire indexes for business establishments 10 exceeding the local government average increased in FY2018, and the number of mental and physical condition items related to them increased above the local government average.

From these analysis results, it is found that the correlation analysis between the questionnaire index created from the questionnaire sheet and the mental and physical condition index is performed, and that there is a positive correlation between the questionnaire index and the mental and physical condition index in the relationship for each medium item (care content). In addition, the characteristics of each special nursing home, such as the questionnaire index and special nursing homes whose mental and physical condition is much higher than the local government average, will be clarified.

Next, the analysis of regional issues will be described. One of the regional issues is the definition of the elderly state image and the service state image, and the provision of regional aggregate analysis means related to the same state image and the grasp of the actual situation. That is, it is necessary to clarify the concrete definitions of the elderly state image and the service state image, including the existing literature survey. In addition, it is necessary to propose a method of aggregate analysis by region based on these state images. For this reason, the image of the elderly condition should take into consideration the nursing ability and economic ability in addition to the mental and physical condition. In addition, in the service status image, it is necessary to consider the existence of medical services and facility-based and residential-related services for home-based services. Therefore, we propose the creation and implementation of a comprehensive analysis program for the elderly status image, etc. by region based on these as appropriate.

First, the purpose, method, results, and consideration of the analysis of the state image of the elderly will be explained. The purpose of the analysis is to define the image of the elderly state as a combination of the mental and physical condition, long-term care ability, and economic power of the elderly and the image of the service state as a combination of home services and the like. After that, the elderly state image analysis by subregion and the service state image analysis by the elderly state image of the entire local government will be performed. This makes it possible to grasp the actual state of the elderly state image for each region and the actual state of the service status image provided for each elderly state image, and in the future, businesses based on the actual state of service supply and demand in each region. I think it will lead to support for planning.

Below, the elderly state image and the service state image are defined. The definition of the elderly state image is performed by the arithmetic processing unit of the computer system in the flow shown in FIG. 52 (a). That is, in the process 641, first, "definition of the degree of independence of disability" and "definition of the degree of independence of dementia" are performed, and these are combined to form "definition of mental and physical condition image". Next, "definition of nursing power" and "definition of economic power" are performed, and these are combined to form "definition of nursing / economic power status image". Finally, as shown in FIG. 53 (a), the "definition of disability independence", "definition of dementia independence", "definition of long-term care ability", and "definition of economic ability" are combined to form an "image of the elderly state". Definition of ".

Next, the service state image is defined in process 642. That is, first, the "service classification" related to the current service usage status of the nursing care certification data is defined. Next, 39 types of service status images are defined based on the combination of service statuses used by the elderly. FIG. 53 (b) shows an example of the home service status image. In addition to this, facilities and residential services are also included in the status image.

The above-mentioned state image is that of the individual user, but in the analysis, this is tabulated as shown in FIG. 52 (b). That is, processing 643 aggregates the number of certified persons by subregion / elderly state image, and by subregion, the number of certified persons by elderly state image and the number of certified persons by elderly state image in each region. Aggregate the ratio of. Process 644 performs cross tabulation of the elderly state image and the service status image of the entire municipality, and the service status image for each elderly status image of the entire municipality includes the number of certified persons and the service status image for each elderly status image. Aggregate another ratio. Process 645 generates a master showing the optimum distribution of service state images for each elderly state image. Process 646 aggregates the actual number of beneficiaries by subregion, elderly person status image, and service status image of local governments. Process 647 calculates the excess or deficiency of supply and demand of each service state image from the difference between the master of process 645 and the actual state of process 646.

Table 8 below outlines the method for defining the degree of independence of disability as described above. In Table 8, (light and medium weight) and (light and medium weight) are prepared as stages to indicate the degree of light and heavy, but in this analysis, "mild" and "severe" shown in a thick frame from the viewpoint of the presence or absence of assistance. It is set in two stages (light weight).

Table 9 below outlines how to define the degree of independence of dementia. In Table 9, (light weight) and (light to medium weight) are prepared as stages to indicate the degree of light weight, but in this analysis, "mild", "moderate", and "severe" are selected from the viewpoint of the presence or absence of assistance. Set in 3 stages (light, medium and heavy).

Table 10 below outlines a method for defining the level of long-term care. In this analysis, living alone is grouped into "low" and households with two or more people are grouped into two stages indicated by a thick frame of "high".

As a method of further improving the definition accuracy of long-term care ability, there is a method of calculating a detailed pattern of the elderly household composition from relationship information (for example, whether the partner of two households is a spouse or a child), and a method of not living together. A method of defining the degree of care required including certification can be considered. The long-term care ability defined above is only a guideline obtained from the long-term care insurance qualification data, etc., and does not necessarily match the long-term care ability (human relationships, etc.) that the care manager considers when creating a home care plan. You need to be careful. Therefore, it is necessary to separately verify the consistency between the two and determine the usefulness of this long-term care ability model.

Table 11 below outlines how to define economic power. In Table 11, all 14 levels of insurance premiums are summarized into two levels, "high" and "low", depending on whether or not the person is taxed by the inhabitant tax, as shown in the bold frame.

Here, the principle of defining economic power is as follows.
-Define economic power after converting the income level peculiar to the insurer to the income level according to the Local Tax Law common throughout the country.
・ Economic power (income level basis) is calculated based on income and does not include savings.

As a method to further improve the definition accuracy of economic power, it is conceivable to extract the income stage of the elderly and the resident of the same household, and determine the economic power of the household by focusing only on the person and spouse. .. The reason for having only two people, the person and the spouse, is that the basic unit of economic independence is limited to the person and the spouse. As a specific example, if the income level of the person under the Local Tax Law is 4 or less, the spouse's economic power is low, but if it is 5 or more, the spouse's economic power may be high. If the livelihood of the person and the spouse is one, and the income level of the person is 5 or more, the total economic power of the person and the spouse is regarded as "economic power". ..

The economic power defined above is only a guideline obtained from the data held by the long-term care office system. It should be noted that it does not always match the financial strength (human relations, etc.) that the care manager considers when creating a home care plan. Therefore, it is necessary to separately verify the consistency between the two and determine the usefulness of this economic power model.

Next, the definition of the mental and physical state image will be described. As shown in FIG. 53A, 6 types of mental and physical condition images are defined in Table 12 below as a combination of the degree of disability independence of two-stage aggregation and the degree of dementia independence of three-stage aggregation.

Next, the definition of the long-term care / economic power status image will be explained. As shown in FIG. 53 (a), Table 13 below shows four types of “nursing / economic power state images” as a combination of long-term care power and economic power.

Next, the definition of the elderly state image will be described. As shown in FIG. 53A, the combination of the degree of independence of disability, the degree of independence of dementia, the long-term care ability, and the economic ability is defined as 24 types of elderly state images shown in Table 14 below. In Table 14, it can be seen that the larger the state image code, the heavier the mental and physical condition and the lower the nursing ability / economic ability, that is, the unfavorable and undesired situation.

Next, the definition of the service status image will be described. The service status image is defined based on the current status of the nursing care certification data and the service usage status. Table 15 below shows the "service classification" related to the current service usage status of the nursing care certification data. The way to read the table is as follows.
-Each service type (No. 1-37) is arranged in the order of service items used in the certification data, and is classified into the current service category (long-term care benefit and preventive benefit).
-The relationship between each service type and the current status flag is shown.
・ There are four types of home-based services: visit type, outpatient type, short-term type, and complex type.
-In addition, which service is a medical care service is indicated by ●.
At the same time, the distinction between medical visit type and medical outpatient type is also shown.

Next, 39 types of service status images shown in Table 16 below are defined based on the combination of service statuses used by the elderly.

In Table 16, the beneficiary (service user) corresponds to any one of the 39 types of service status images. The service status image is roughly classified into the following seven groups.

1. 1. 14 types of home services for long-term care benefits (service status image codes 1 to 14)
From the perspective of the elderly's condition (mental and physical condition, long-term care, financial strength), there are four types of visits (only visits, only outpatients, both outpatients, none) and the presence or absence of medical services (home-visit nursing, etc.) 14 types are defined based on whether or not medical care services are used) and whether or not short-term (short stay) use is used. See the special notes in Table 17 for details of each.
2. 2. Two types of small-scale multifunctional (complex type) long-term care benefits (15-16)
3. 3. Two types of long-term care benefits residential (group home, specific facility) (17-18)
4. Four types of long-term care benefits (special care, elderly health, medical treatment, long-term care medical clinic) (19-22)
5. 14 types of home-based services for preventive benefits (23-36) * Same as 1 above 6. One type of small-scale multifunctional preventive benefits (37)
7. Two types of preventive benefits, residential (group home, specific facility) (38-39)

Originally, it is necessary to provide an appropriate service status image for each type of elderly status image. However, in reality, there is a possibility that the ideal service supply and demand conditions have not been met due to the amount of service resources in each region and the thorough lack of care management that contributes to independence support and prevention of severity.

Next, these analysis results and discussions will be described. First, the results of analysis of the state image of the elderly by subregion will be explained. Table 17 below shows the aggregated analysis results of the relevant number of elderly people in the image of the elderly status by subregion as of March 2019.

In Table 17, 24 types of elderly state images are arranged in the order of lightness from the left on the front side and small areas on the front side. The third column from the left shows the number of certified persons in each region. Except for the small number of state image codes 17 to 20, all state images have a certain number of people. It can be seen that there is some variation between regions in each image of the elderly state.

Next, the analysis results of the elderly state image and the service state image will be described. Table 18 below shows the aggregated analysis results of the corresponding number of people related to the service status image for each elderly status image as of March 2019.

In Table 18, 39 types of service status images are arranged in code order on the front side, and 24 types of elderly status images are arranged in light order from the left on the front side. The rightmost column shows the number of beneficiaries in each region of the entire municipality. Here, it is assumed that there should be a service status image that should be in each elderly status image, for example, that contributes to independence support and prevention of severity of each elderly status image. In this case, the fact that the distribution of the elderly state image in each region is different means that the required service state image distribution and, by extension, the required maintenance amount for each service type will be different. In other words, responding to this will lead to fine-tuned service supply and demand adjustment by subregion.

In this way, 24 types of elderly person status images (combination of mental and physical conditions, long-term care and economic power, etc.) and 39 types of service status images (combination of home services, etc.) are defined, and the actual conditions of the entire local government and each subregion are grasped. By conducting an analysis related to, it will be a basic material for the supply and demand of services by region for the formulation of the next business plan.

The above-mentioned analysis processing of the elderly state image and the service state image is performed by the elderly state image aggregation analysis unit (corresponding to the process 641 in FIG. 52A) and the service state image, which the arithmetic processing unit of the computer system has as a function. The aggregation analysis unit (corresponding to the process 642 of FIG. 52 (a)) and the analysis unit performing the service state analysis correspond to the processes 643 and 644 of FIG. 52 (b)).

As shown in FIG. 53 (a), the Elderly State Image Aggregation Analysis Department has dementia independence classified into mild, moderate, and severe, and disability independence classified into mild and severe. The elderly state image is defined into 24 types by the combination of the classified long-term care power and the economic power classified as having or not. Then, as shown in Table 18, the number of people in the preset small areas (large area, middle area, small area) of the local government is calculated for each image of the elderly state.

As shown in Table 17, the service status image aggregation analysis unit uses, for example, the calculation function shown in FIG. 53 (b) to provide only visits, only outpatients, or both outpatients in the home service of long-term care benefits. There are 14 types of groups defined by the combination of whether or not medical care services are used and whether or not short stays are used, two types of small-scale multifunctional long-term care benefits, and a group that is a residential long-term care benefit. Defined as 14 types, which is the same as the combination of 2 types of groups of home and specific facilities, 4 types of groups of long-term care benefits such as special needs, old health, medical treatment, and long-term care clinics, and the above-mentioned home service of preventive benefits. The service status image is defined in 39 types consisting of the group, one type of small-scale multifunctional preventive benefit, group home which is a residential system of preventive benefit, and two types of specific facilities, and this defined service. For each state image, the number of people in the preset small area of the local government is calculated.

As shown in Table 19, the analysis department that performs service status analysis cross-tabulates the elderly status images defined in these 24 types and the service status images defined in 39 types, and classifies the elderly status of the entire municipality. Analyze the service status of.

Next, a method for obtaining the average utilization institution (turnover rate) of special nursing homes, which can be used as a material for formulating a maintenance plan for nursing homes (hereinafter, will be explained by taking special nursing homes as an example), will be described.

The importance of formulating a service development plan is increasing in promoting regional management that contributes to the independence support and prevention of the severity of long-term care recipients. In particular, the development of special nursing homes with many waiting people incurs a large amount of money, so it is necessary to carefully consider restrictions from various perspectives when investing. Specifically, it is necessary to consider the number of elderly people, the number of waiting people, and the transition (past / future) of the average usage period (turnover rate) of special nursing homes in local governments.

However, at present, it is difficult to quantitatively grasp the transition of the average usage period of special nursing homes, and it is difficult to formulate an appropriate and accurate maintenance plan based on the data. In this embodiment, the average usage period of special nursing care is achieved by effectively utilizing the integrated database for the elderly owned by the local government (basic resident registry, qualifications, certification for long-term care, and long-term care receipts are matched for each individual for about 10 years). Is calculated, and a new facility development plan formulation based on this calculation is proposed.

As shown in FIG. 54, the local government has Juki data 661, qualification data 662, long-term care certification data 663, and long-term care receipt data 664, and these are collated for each individual for about 10 years. Has been built. Therefore, by effectively utilizing this integrated database for the elderly 665, the average usage period of special nursing homes is calculated. That is, the processing 667 by the arithmetic processing unit of the computer system calculates the average usage period of the special nursing home, and outputs the transition table 668 for each service type and average usage period year. Then, using this data, a new facility development plan is formulated (described later with reference to FIG. 56).

FIG. 55 (a) shows a yearly transition table of the entire special nursing home and the average usage period of each special nursing home in the local government. Examples of future estimates are shown after 2020. As a way of thinking of estimation, in addition to natural estimation from past results, it is necessary to take into account factors such as reviewing management policies by special nursing home managers. In general, the average usage period tends to increase with each passing year, but the average usage is based on the management policy of the special nursing home manager, for example, by adopting a policy of leaving early, such as special nursing home 4. The period may be shortened year by year, and it is estimated that the future value of such special nursing homes will gradually decrease. FIG. 55 (b) shows the long-short relationship with the average of each special nursing home, and the tendency of increase / decrease in the period for each year.

The calculation process of the average usage period is performed as follows, that is, the target persons who have used the special nursing home in the past are extracted, and the special nursing home of each user is started from the start date and the last usage date of the same special nursing home. Calculate the usage period. At this time, the continuous usage ratio during the same period (the ratio of the usage period excluding the period when the person was absent after moving to another facility) is also taken into consideration. Furthermore, the users are divided into groups for each year of starting use, and the average usage period of each special nursing home in the same year is calculated.

The same calculation can be performed for each degree of care required, degree of dementia independence, and other service types. The figures for each year in the figure are the average period of users who started using the service in that year.

FIG. 56 shows the concept of the maintenance plan based on the special nursing average usage period obtained by the processing shown in FIG. 54. Based on the annual transition data 6681 of the special nursing average usage period calculated from the average usage period calculation processing, the elderly vital data 681 and the special nursing waiting information 682, the number of special nursing waiting users 684 is estimated by processing 638. To do. Estimate the number of users who cannot enter the special nursing home within a predetermined period (for example, 3 years) from the estimation results, and formulate a new special nursing home development plan.

In formulating a new special nursing home maintenance plan, it is necessary to consider the scale of the new special nursing home (normal or mini), the large-scale repair plan for the existing special nursing home, the trend of revision of nursing home fees related to the special nursing home, etc. There is.

The above-mentioned calculation of the average usage period of the special nursing home is performed by the arithmetic processing unit of the computer, and the arithmetic processing unit is described in the basic resident register of the local government stored in the storage unit as shown in FIGS. 54 and 56. Juki data 661, special nursing home waiting information 682 that totals the number of people waiting to enter each special nursing home belonging to the local government, and long-term care receipt data 664, which is the actual nursing care benefit data, are read out and used to each belong to the local government. For each special nursing home, the special nursing care usage period of each user is calculated from the start date and last usage date of the same special nursing home. The average usage period of users is calculated for each year when the special nursing home is used, and the transition of the average usage period for each year is calculated for each special nursing home. Further, it has an average usage period calculation unit that estimates the average usage period for each special nursing home in the future year from the special nursing home waiting information 682 and the elderly population data 681 obtained from the Juki data 661.

As a result, when formulating a service maintenance plan for promoting regional management, we quantitatively grasp the changes (past / future) of the number of elderly people, the number of waiting people, and the average usage period (turnover rate) of local governments, and formulate an accurate maintenance plan. Can be formulated.

Next, a medical / long-term care integrated DB (database) using national insurance database data (hereinafter, KDB data) and local government long-term care data will be described.

By adding medical data to long-term care data, medical data can be utilized in the four businesses of long-term care benefit business, medical / long-term care cooperation business, general business, and data health business, and solutions that were difficult with long-term care data alone. Can be proposed. There are the following two methods for obtaining medical data.
-Method of utilizing KDB data (hereinafter, method 1)
・ How to utilize data owned by local governments (hereinafter, method 2)

In the case of the method 1, the KDB data has the service type code, but does not have the service item code. In addition, when utilizing the certification data requiring nursing care for analysis, 77 items of certification survey items are required in addition to the degree of nursing care required, but KDB data holds only the degree of nursing care required among them, and other We do not have accredited survey items. Therefore, KDB data is unsuitable for analysis of long-term care receipt data and analysis utilizing long-term care certification data, that is, analysis of the physical and mental condition of the user.

In the case of method 2, as for the medical data held by the local government, etc., the medical receipt data for the elderly aged 75 or over is obtained from the Prefectural Regional Union for Medical Care for the Elderly, and the national insurance (early elderly) medical receipt data is obtained from the national insurance department. In order to obtain medical receipt data for the elderly aged 75 or over, it is necessary to obtain materials related to data acquisition from the insurance section inspection section of the federation and carry out the procedure. In addition, in order to obtain the National Health Insurance (early elderly) medical receipt data, it is necessary to coordinate the two departments, the National Health Insurance department and the long-term care insurance department. It is necessary to solve these problems, and a new method is proposed below.

Here, the medical examination / medical data of the KDB data is the same as the medical receipt data of the national insurance (early elderly) and the medical receipt data of the late elderly, and the medical examination / medical data is the nursing care benefit business, medical care / nursing care. It has sufficient items for data analysis in four businesses: collaborative business, general business, and data health business. Therefore, the medical examination / medical data of KDB data is obtained, and the long-term care certification data, long-term care receipt data and basic data (common number, qualification data, household data, etc.) are obtained from the long-term care insurance department and collated.

Currently, the Prefectural National Health Insurance Federation uses the KDB system, etc. to provide medical examination / medical expense analysis information (KDB data) to insurers (national health insurance / hygiene / long-term care / late-stage elderly departments). .. When the KDB data is collated with the long-term care certification data, the long-term care receipt data, and the basic data, if the KDB data includes the insured person number, the collation is possible in the same manner as the long-term care data. If the insured person's number is not included, use the items that can be identified by the elderly (gender, date of birth, degree of care required for the specified month, area of residence, etc.) to match.

FIG. 57 shows the flow of data acquisition and matching described above. The outline of each step shown in FIG. 57 will be described below.
・ Step 1: Obtain long-term care data, etc. Obtain long-term care certification data, long-term care receipt data, and basic data (common number, qualification data, household data, etc.) from the long-term care insurance department.
・ Step 2: Obtain medical examination / medical data Obtain National Health Insurance (early elderly) medical receipt data and late elderly medical receipt data from KDB.
・ Step 3: Collation of medical / long-term care data The obtained medical / long-term care data is collated for each elderly person to create an elderly integrated DB consisting of medical / long-term care data.

The specific process of step 3 of FIG. 57 described above will be described with reference to FIG. 58. This process is also executed by the arithmetic processing unit of the computer system, and in process 671, it is determined whether or not the insured person number is included in the obtained KDB data. If it is included, processing 672 collates the KDB data with the long-term care data using this insured person number. If the insured person's number is not included, process 673 extracts items that can be identified by the elderly (gender, date of birth, prescribed month's degree of care required, residential area number, etc.), and process 674 is used for this. Use the extracted data and collate with KDB data.

Since the elderly integrated DB can be created by the processing flow shown in FIG. 57, data analysis can be performed in the four businesses of the long-term care benefit business, the medical / nursing care cooperation business, the comprehensive business, and the data health business by utilizing this elderly integrated DB. Can be done.

In this way, we made a realistic proposal for the construction of a medical / long-term care integrated DB by matching the qualification / long-term care data owned by the local government with the medical data of KDB. In the future, concrete efforts can be made to prevent long-term care by illness and care management by illness (support for independence and prevention of severity of long-term care).

Next, an example in which the functions of the user history sheet described above have been enhanced will be described. The user history sheet visualizes the history of the elderly obtained from long-term care data and long-term care insurance qualification information so that attendees of individual issue review meetings at community care meetings, long-term care service establishment staff, and care managers can refer to it. It is a thing. At community care meetings and service staff meetings, there is a problem that it is difficult for attendees of individual problem review meetings, care facility staff, and care managers to grasp the consultation history and hospitalization history of the elderly. It has been an issue to integrate medical data (causative disease, consultation history, hospitalization history, drug history, etc.) and long-term care data (mental and physical condition, long-term care service, etc.) to visualize individual history. It is considered that these problems may be solved by adding medical data to the user history sheet.

As an appropriate care management method that contributes to independence support, we focus on the assessment process in the care management process, systematically organize the hypothesis of the support content that should recall the possibility according to the user's condition, and concretely in each case. A survey and research content has been reported that summarizes the assessment items for determining how much and what kind of support is needed for each disease group that is often the causative disease of certification for long-term care (formulation of appropriate care management method). (April 10, 2019, Japan Research Institute, Limited).

In this research study, we focus on femoral neck fracture, cerebrovascular disease, heart disease and dementia as the causative diseases of certification for long-term care, and examine the expected support contents and related assessment / monitoring items. .. For this reason, by adding medical data (home medical care consultation history, hospitalization history, drug history, etc.) to the user history sheet, by disease / stage (time to stabilize after discharge / time to move to self-management) ) It is considered to contribute to the implementation of care management.

An example (image) of the user history sheet to which medical data is added is shown in FIG. 59, the item description of the user history sheet function enhancement is shown in Table 19 below, and the data source of the user history sheet is shown in Table 20 below.

In Table 19, the mental and physical condition refers to the degree of care required, the degree of dementia independence, the degree of disability independence, and 74 items of the accreditation survey (groups 1 to 5, special medical care). In addition, ADL (ability) is certified survey items such as turning over, getting up, holding a sitting position, holding a standing position on both feet, walking, standing up, standing on one foot, sight, hearing, swallowing, communication of intention, daily. It is the sum of the survey results of 18 options of understanding daily routine, saying date of birth and age, short-term memory, saying one's name, understanding the current season, understanding place, and daily decision making.

As is clear from these, in addition to the degree of care required, outcome indicators (improvement / maintenance, etc.) for all items in the certification survey can be added / selected, and the goal results for the care plan (which mental and physical condition improvement / maintenance is the goal). It is possible to flexibly respond to the evaluation of (whether to do it, etc.). In addition, by adding the causative disease, home medical care consultation history, hospitalization history, drug history, etc. as medical data, the personal history including medical care and long-term care can be visualized, and medical care / long-term care cooperation and medical care / long-term care can be combined. The long-term mental and physical condition of the user and the history of the services used can be grasped without omission. Medical data can be added based on the needs of care managers and the like.

As a result, the user history sheet that contributes to disease-specific care management will be enhanced. That is, it is possible to visualize the history information of the past 10 years of "incorporation of the whole mental and physical condition of the accreditation survey" and "the morbidity of diseases such as stroke, thigh fracture, dementia, locomotive syndrome".

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.

10 ... Storage unit 11 ... Nursing care certification data 12 ... Evaluation index data 13 ... Nursing care benefit actual data 14, 24 ... Data output processing logic unit 15 ... Mental and physical condition transition data output processing unit 16 ... Evaluation index data output processing unit 17 ... Use Service transition data output processing unit 18 ... User history sheet 19 ... Mental and physical condition transition area 20 ... Evaluation index area 21 ... Usage service transition area 25 ... Comparison processing unit of care required and certification application information 26 ... Disability independence and dementia Independence degree comparison processing unit 27 ... Intermediate evaluation item score comparison processing unit 28 ... Mental and physical condition item 74 item comparison processing unit 30 ... Nursing care certification survey item comparison table 31 ... Nursing care required and certification application information comparison 32 ... Disability Comparison of degree of independence and degree of independence of dementia 33 ... Comparison of intermediate evaluation item scores 34 ... Comparison of 74 items of mental and physical condition items 40 ... Selection logic department for evaluation target exclusion cases 41 ... Improvement selection processing department by medical services 42 ... Acute exacerbation, etc. Deterioration selection processing unit 43 ... Impact selection processing unit due to special medical treatment 50 ... Case classification logic department 51 ... Improvement case classification unit 52 ... Maintenance case classification unit 53 ... Deterioration case classification unit 54 ... Maintenance unachieved case classification unit 60 ... Evaluation Index calculation logic section 61 ... Improvement rate calculation section 62 ... Maintenance rate calculation section 63 ... Improvement maintenance rate calculation section 65 ... Business establishment grouping section 71 ... Number of cases to be evaluated Area 72 ... Improvement rate and maintenance rate area 73 ... Improvement rate and Maintenance rate statistical information area 74 ... Scatter map quadrant area

Claims (18)

It has a storage unit that constitutes a computer system, and an arithmetic processing unit that performs arithmetic processing by a predetermined program using the data held in this storage unit.
The storage unit
Long-term care insurance user certification basic information and long-term care certification data related to mental and physical condition items including the degree of long-term care,
Evaluation index data on the amount of change and maintenance period of improvement and deterioration of the mental and physical condition items,
Holds the month when the user used the long-term care service, the type of service used, and the long-term care benefit record data including the business establishment code that provided the service to the user.
The arithmetic processing unit
The mental and physical condition data aggregated from the nursing care certification data is output in chronological order for each stage of certification validity period, and the mental and physical status of the application for the nursing care certification application and the application location are also output. State transition data output processing unit and
When the stage of the degree of care required changes to improvement or deterioration within the preset period for extracting changes in mental and physical condition, the duration of the stage before the change that goes back from the time of this change is used as an evaluation index for improvement or deterioration after the change. Evaluation index data output unit to output and
Regional comprehensive with a user history sheet creation function that has a function of the service type used and the usage service transition data output unit that outputs the office code that provided the service every month when the user uses the long-term care service. Care business system. The arithmetic processing unit
The degree of care required and the certification application information can be extracted from each of the comparison source data and the comparison destination data defined as the comparison source data and the comparison destination data at two time points separated by the same user at a predetermined interval, and can be compared with each other. A comparison processing unit for the degree of care required and certification application information that is output to the status,
Comparison of disability independence and dementia independence by extracting the disability independence degree and dementia independence degree from each of the comparison source data and the comparison destination data defined as each of the care-requiring certification data and outputting them in a state in which they can be compared with each other. Processing unit and
An intermediate evaluation item score comparison processing unit that extracts intermediate evaluation item scores from the comparison source data and comparison destination data and outputs them in a state in which they can be compared with each other.
As a function, a comparison processing unit of 74 items of mental and physical condition items that extracts 74 items of mental and physical condition items from each of the comparison source data and the comparison destination data and outputs them in a state in which they can be compared with each other. Have, have
The community-based comprehensive care business system according to claim 1, which has a function of comparing survey items requiring nursing care. The arithmetic processing unit
If there is a change of state of mind and body within the period of extracting the change of state of mind and body, the period of the stage of state of mind and body before this change shall be the case, and the period of the stage of state of mind and body after the change shall be the next case.
Using the long-term care certification data and the long-term care benefit record data of the user, the application for the long-term care certification in the case is new or changed, the application place is an acute hospital, and the mind and body of the next case. The first selection processing unit that determines that the improvement is due to medical services and selects it as a non-evaluation target when the state stage is improved.
Using the long-term care certification data and the long-term care benefit record data of the user, the case where the application municipality at the start of the next case is changed and the stage change of the mental and physical condition of the next case worsens is due to acute exacerbation or the like. The second selection processing unit, which judges that it has deteriorated and selects it out of the evaluation target,
When there is a change in the implementation status of special medical care other than the treatment of sickness between the start time of the case and the start time of the next case using the long-term care certification data and the long-term care benefit record data of the user. Has a function of a third selection processing unit that is selected outside the evaluation target.
The community-based comprehensive care business system according to claim 1 or 2, which has an evaluation target selection function. The arithmetic processing unit
The improvement case classification unit that classifies the case where the stage of the mental and physical condition changes to improvement within the period of extracting the change of mental and physical condition using the user's certification data requiring nursing care, and the improvement case classification unit.
Using the user's certification data for requiring long-term care, when there is no change in the stage of the mental and physical condition within the extraction period for changing the mental and physical condition and the period without the change exceeds the preset standard maintenance period, and the mental and physical condition. Maintenance cases in which the stage of the mental and physical condition changed to worse during the change extraction period, but the period in which there was no change in the stage of the mental and physical condition before the change exceeded the preset standard maintenance period, respectively. Classification department and
When the mental and physical condition stage changes to deterioration within the mental and physical condition change extraction period using the user's certification data requiring nursing care, and the period of the mental and physical condition stage before deterioration is shorter than the preset standard maintenance period. It has a function of a deterioration case classification unit that classifies it as a deterioration case.
The community-based comprehensive care business system according to any one of claims 1 to 3, which has a case classification function. The arithmetic processing unit
Using the user's certification data for requiring long-term care, the mental and physical condition stage changes to improvement or deterioration within the mental and physical condition change extraction period, and the period of the mental and physical condition stage after this change is from the preset invalidation maintenance period. The community-based comprehensive care business system according to claim 4, which has a maintenance-unachieved case classification unit as a function, which classifies the maintenance-unachieved cases when it is long and shorter than the standard maintenance period. The arithmetic processing unit
An improvement rate calculation unit that calculates the improvement rate by dividing the number of improvement cases for each mental and physical condition obtained for each business establishment by the total number of cases for each mental and physical condition including the number of improvement cases.
A maintenance rate calculation unit that calculates the maintenance rate by dividing the number of maintenance cases for each mental and physical condition obtained for each business establishment by the total number of cases for each mental and physical condition, excluding the number of improvement cases.
Improvement by dividing the total value of the number of improvement cases and the number of maintenance cases for each mental and physical condition obtained for each business establishment by the total number of cases for each mental and physical condition including the number of improvement cases and the number of maintenance cases. It has an improvement maintenance rate calculation unit that calculates the maintenance rate as a function.
The community-based comprehensive care business system according to claim 4 or 5. The arithmetic processing unit
One of the values of the improvement rate and the maintenance rate for each stage of the degree of need for nursing care obtained for each business establishment, the degree of disability independence that aggregates all stages, and the degree of dementia independence that is mildly aggregated and severely aggregated. The community-based comprehensive care business system according to claim 6, further comprising a business establishment grouping unit that plots by business establishment on a scatter plot in which one of the two is plotted on the vertical axis and one of the other is plotted on the horizontal axis. The arithmetic processing unit
When the mental and physical condition stage changes to improvement within the index calculation period set for each year using the user's certification for long-term care, the number of improved stages is counted and the mental and physical condition is counted within the index calculation period. When the stage changes to deterioration, the number of deteriorated stages is counted, the difference between the number of improved stages and the number of deteriorated stages, and the direction of this difference are obtained, and improvement or deterioration is determined from the direction of this difference. The community-based comprehensive care business system according to claim 1, which has a function of calculating the amount of change in mental and physical condition. The arithmetic processing unit
The number of users judged to be improved from the calculation result of the calculation unit of the amount of change in mental and physical condition, the number of users judged to be deteriorated, the number of users who did not change, and the number of users whose mental and physical condition ended while being deteriorated. The community-based comprehensive care business system according to claim 8, which has an improvement rate calculation unit as a function, which aggregates the number of items for each business establishment and calculates the improvement rate for each business establishment from these aggregated values. .. The arithmetic processing unit
The ninth aspect of the present invention is characterized in that, when the user receives services from a plurality of business establishments within the index calculation period, the user has a function of excluding a totaling target exclusion unit that excludes the number of mental and physical state changes from the totaling target. Community-based comprehensive care business system. The arithmetic processing unit
If the stage of the mental and physical condition changes to improvement within the index calculation period, the number of improved stages is counted, and if the stage of the mental and physical condition changes to worse, the number of deteriorated stages is counted, and these counts are counted. The community-based comprehensive care business system according to claim 8, further comprising a mental and physical state change count calculation unit for determining the number of improvements and deteriorations within the index calculation period. The arithmetic processing unit
For each stage of the mental and physical condition items, the average maintenance period calculation unit that calculates the average maintenance period in the local government until that stage deteriorates by one stage for each year, and
For each stage of the mental and physical condition, the year of calculating the benefit cost suppression effect is set as this year, and the extension period of the average maintenance period of the same stage of this year with respect to the average maintenance period of the corresponding mental and physical condition stages of the previous year is calculated and this extension. Maintenance of mental and physical condition from the period, the difference between the per capita beneficiaries of the corresponding mental and physical condition stage and the one-stage mild stage of this year, and the number of people whose mental and physical condition deteriorated in the corresponding stage of this year. It has a function of a benefit cost restraint effect calculation unit that calculates the benefit cost restraint amount due to the extension of the period.
The community-based comprehensive care business system according to claim 1, characterized in that. The arithmetic processing unit
Using the evaluation index data, the average maintenance period calculation unit that calculates the average maintenance period at each business establishment until the stage deteriorates for each stage of the mental and physical condition item, and each stage of the mental and physical condition item. The improvement rate calculation unit that calculates the percentage of users who improve at each business site as the improvement rate of each business site,
Among the services provided by each business establishment, select service items that have been provided to the user within the most recent predetermined period and have continued from the time when the average maintenance period can be calculated. Service item selection department and
The first group is the group of establishments that provide the selected service items, and the second group is the group of establishments that do not provide the selected service items. These first and second groups The community-based comprehensive care business system according to claim 1, further comprising an effect verification unit that compares the average maintenance period and the improvement rate for each of the mental and physical condition items. Claim 13 is characterized in that the effect verification unit outputs a scatter plot of the average maintenance period and the improvement rate of all the business establishments for the service items provided to the users by all the business establishments. The listed community-based comprehensive care business system. The arithmetic processing unit
The service item associated with the mental and physical condition item is defined as a questionnaire item, and the implementation rate of the questionnaire item whose response rate is equal to or higher than the predetermined value among the questionnaire items is determined from the questionnaire results for the staff of each business establishment. Questionnaire index creation department as an index and
Correlation analysis between the mental and physical condition index consisting of the improvement rate in a preset period or the maintenance period until deterioration of the mental and physical condition item associated with the questionnaire item and the questionnaire index of the questionnaire item related to the mental and physical condition item. And the correlation analysis unit that outputs the relationship between the correlation coefficient, which is the result of this correlation analysis, and the questionnaire index value and the mental and physical condition index value of each business establishment.
The community-based comprehensive care business system according to claim 1, wherein the system is characterized by having. The storage unit
It has the Juki data recorded in the Basic Resident Register of the local government that is the insurer, and the special nursing home waiting information that totals the number of people waiting to enter each special nursing home belonging to the local government.
The arithmetic processing unit
Using the long-term care benefit record data, the special nursing home usage period of each user is calculated from the start date and last usage date of the same special nursing home for each special nursing home belonging to the local government, and the use of this special nursing home is started. The average usage period of users is calculated for each year to obtain the transition of the average usage period for each year for each special nursing home, and from the special nursing home waiting information and the elderly population data obtained from the Juki data. The community-based comprehensive care business system according to claim 1, characterized in that it has an average usage period calculation unit that estimates the average usage period for each special nursing home in the future year. The arithmetic processing unit
A combination of mild, moderate, and severe dementia independence, mild and severe disability independence, long-term care and non-existent economic power The elderly state image aggregation analysis unit that calculates the number of people in the preset small area of the local government for each of the 24 types of elderly state images defined by
There are 14 types of groups defined by the combination of visits only, outpatients only, and both outpatients for long-term care benefits, whether or not medical care services are used, and whether or not short stays are used, and long-term care benefits. Two types of small-scale multifunctional groups, a group home that is a residential system for long-term care benefits, and two types of groups that are specific facilities, and special nursing care, old health, medical treatment, and long-term care medical clinics that are facility systems for long-term care benefits. 4 types of groups, 14 types defined as the combination of the above-mentioned home service of preventive benefits, 1 type of small-scale multifunctional preventive benefits, group homes that are residential systems of preventive benefits, and 2 of specific facilities A service status image aggregation analysis unit that calculates the number of people in a preset small area of the local government for each service status image defined in 39 types consisting of group groups
As a function, it has an analysis unit that cross-tabulates the elderly state image defined in these 24 types and the service state image defined in 39 types and analyzes the service state image according to the elderly state of the entire municipality. The community-based comprehensive care business system according to claim 1. The arithmetic processing unit
An optimal service status image distribution master for each elderly status image defined based on the analysis results of the analysis department that analyzes the service status image for each elderly status of the entire municipality.
A claim characterized by having an excess / deficiency calculation unit of supply and demand of each service status image based on the difference between the service status image distribution by elderly status image for each subregion and the above-mentioned optimum service status image distribution master. The community-based comprehensive care business system described in 17.