JP7159241B2 - Regional Comprehensive Care Business System - Google Patents

Description

An embodiment of the present invention relates to a community-based integrated care business system equipped with an aggregation analysis method that contributes to fair evaluation of the quality of nursing care services.

Currently, as the super-aging society progresses, it is an urgent issue to make the most effective use of limited resources and provide nursing care services that contribute to the independence support of the elderly. For that reason, it is necessary to set common goals in the region and share them among the people concerned through grasping the actual situation of the region and analyzing the issues.

In addition, the need for initiatives (regional management) to continuously improve activities to achieve goals by creating and executing specific plans to achieve the goals and repeatedly evaluating and reviewing the plans is indicated. It is

In order to strengthen the regional comprehensive care system, each local government will strengthen its insurer functions, and will use data provided by the national government and the data held by each local government to support the independence of the elderly and prevent the need for nursing care from becoming more severe It is important to analyze data, etc., formulate a long-term care insurance business plan, and clarify the contents and goals of measures such as long-term care prevention and prevention of aggravation in the long-term care insurance business plan.

Against this background, local governments are envisioning the construction of a system for evaluating businesses that are working to improve and maintain the conditions of the elderly. In other words, it is necessary to understand the 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 to evaluate the quality of long-term care services in an easy-to-understand manner for the establishments and the general public. Desired.

The applicant has made various proposals in response to these demands. One of them is the development of psychosomatic condition outcome indicators related to service quality. For example, the periods of improvement and worsening of mental and physical conditions (65 items in total of 211 stages) were tabulated for each individual user. In this case, one case is defined as the duration until the phase of the psychosomatic state changes. In other words, when the patient improved to 2nd long-term care level 12 months after being certified as 3rd long-term care level, it was counted as a 3rd long-term care level case, and one case of improvement was counted with a maintenance period of 12 months.

It also proposes to evaluate business establishments using such evaluation indexes and group them. That is, the psychosomatic conditions are aggregated, a scatter diagram is created by the average deviation value of the maintenance period until deterioration and the improvement rate, and grouped by each area and psychosomatic condition. In addition, the aggregation of mental and physical conditions means that the outcome indicators of the physical and mental conditions (65 items in total of 211 stages) are divided into 9 indicators (degree of nursing care, degree of independence with disability, degree of independence with dementia, 3 groups of physical systems, 3 groups of mental systems). group).

JP 2017-215787 A

Based on these activities, we will conduct further investigations and propose a newly enhanced statistical analysis method that contributes to fairer evaluation of service quality. This aggregation analysis method is a method of excluding cases of improvement or deterioration due to factors other than the nursing care service concerned, and the creation of new outcome indicators that include cases of continuous maintenance in addition to past improvement and deterioration.

For example, there is a possibility that factors other than nursing care services may influence the mental and physical condition change factor. Cases related to these factors need to be excluded in order to fairly assess the quality of long-term care services. For that purpose, it is necessary to examine and set the selection conditions for the cases to be evaluated.

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 mental and physical conditions due to factors other than long-term care services (especially medical factors) from the evaluation. necessary.

In addition, until now, "improvement" and "worsening" of mental and physical conditions have been used as an evaluation index for fair evaluation of the quality of nursing care services. However, in addition to these, there is the "maintenance" of the mental and physical state, and it is desired to evaluate this case as well, and it is necessary to index it. In other words, we position continuous maintenance cases (cases where mental and physical conditions have been maintained for a certain period of time) as cases that should be evaluated, and propose a new evaluation index (physical condition outcome index) to further improve the accuracy of service quality evaluation. .

In this way, in order to conduct business establishment grouping based on a fair evaluation of the quality of long-term care services and to extract good practice business establishment candidates, etc. It is necessary to grasp the actual state of service quality (strengths and weaknesses, etc.) for each business establishment from a comprehensive point of view such as degree.

An object of the present invention is to provide a regional comprehensive care business system equipped with a newly enhanced aggregation analysis method that contributes to fairer evaluation of service quality.

A community-based comprehensive care business system according to an embodiment of the present invention includes long-term care certification basic information and long-term care certification data related to mental and physical condition items including the degree of long-term care need of users of long-term care insurance, and improvement and deterioration of the mental and physical condition items. Evaluation index data on the amount of change and maintenance period of the nursing care service, nursing care benefit performance data including the month the user used the nursing care service, the type of service used, and the office code that provided the service to the user, and the nursing care required Physical and mental state transition data for outputting, in chronological order, the changes in stages for each item of mental and physical conditions aggregated from the certification data, for each certification validity period for each stage, and for outputting together the municipalities for which long-term care need certification applications have been applied and the locations of the applications. and an output processing unit, when the stage of the level of care need changes to improvement or deterioration within a preset period for extracting changes in physical and mental conditions, improve or deteriorate after the change in the duration of the stage before the change, which goes back from the point of change. An evaluation index data output unit that outputs an evaluation index of deterioration, and a usage service transition data output unit that outputs the type of service used and the code of the office that provided the service for each month that the user used the nursing care service. It has a user history sheet creation function.

It is a figure explaining the processing step of the integrated community care business system concerning the embodiment of the present invention. It is a figure explaining an example of a user history sheet used for an embodiment. It is a figure which shows an example of a user history sheet same as the above. It is a figure which shows the other example of a user history sheet same as the above. 4 is a functional block diagram showing a user history sheet creation function in the embodiment; FIG. It is a figure which shows an example of the long-term care need certification investigation item comparison table in embodiment. It is a functional block diagram showing a function of creating a long-term care need certification survey item comparison table in the embodiment. It is a functional block which shows the nursing care evaluation object selection function in embodiment. 4 is a timing chart for explaining evaluation indices for cases corresponding to changes or maintenance of psychosomatic conditions in the embodiment. It is a functional block diagram which shows the case classification function in embodiment. It is the scatter diagram which grouped based on the activity evaluation of each establishment in embodiment. It is a figure which shows a part of office list|wrist which grouped in embodiment. FIG. 10 is a diagram showing the rest of the office list after grouping in the embodiment; It is a figure explaining the processing step of the integrated community care business system concerning other embodiments. FIG. 10 is a diagram showing the logic of psychosomatic condition index aggregation. FIG. 4 is a diagram showing logic for time-series estimation of psychosomatic state indices; FIG. 10 is a diagram illustrating a method of calculating the amount of change in psychosomatic state within an index calculation period; FIG. 10 is a diagram illustrating a method for excluding users of multiple business establishments within an index calculation period; FIG. 10 is a diagram illustrating logic for calculating an average maintenance period until deterioration within an index calculation period; FIG. 10 is a diagram illustrating a method of calculating an improvement rate and a maintenance rate aggregated for each group; It is the graph which shows the year transition of an improvement rate, and its numerical table. It is a graph and its numerical table showing improvement / deterioration / end / maintenance number ratio. It is the graph which shows the year transition of an average maintenance period, and its numerical table. It is a figure which shows the logic for the trial calculation of the amount of benefit cost control. It is a figure explaining the calculation method of a benefit cost control effect. FIG. 10 is a diagram for explaining the evaluation of the impact of extension of the valid term of certification on calculation of the maintenance period; It is the graph which shows the annual transition of the average certification period, and its numerical table. It is a figure which shows the effect verification logic by the psychosomatic state index of special nursing care remuneration. It is a figure which shows the aggregation logic of the improvement rate about a service item, and the average maintenance period until deterioration. FIG. 10 is a diagram showing revision history investigation logic for service item selection; FIG. 10 is a diagram showing the logic for tallying the improvement rate and average maintenance period until deterioration according to mental and physical conditions, according to business establishment group; FIG. 10 is a graph and a numerical table showing comparison results of improvement rates by mental and physical conditions for each basic service (room type), which is a service item; FIG. FIG. 10 is a graph and a numerical table showing comparison results of the average maintenance period for each mental and physical condition for each basic service (room type), which is a service item; FIG. It is a graph showing the comparison result of the improvement rate according to mental and physical conditions of the treatment improvement addition which is a service item, and its numerical table. It is the graph which shows the comparison result of the average maintenance period according to psychosomatic conditions of the treatment improvement addition which is a service item, and its numerical table. It is a graph showing the comparison result of the improvement rate according to mental and physical conditions of the nursing system addition which is a service item, and its numerical table. It is the graph which shows the comparison result of the average maintenance period according to mental and physical conditions of the nursing system addition which is a service item, and its numerical table. It is the graph which shows the comparison result of the improvement rate according to mental and physical conditions of the oral hygiene management system addition which is a service item, and its numerical table. It is the graph which shows the comparison result of the mental-and-physical condition-based average maintenance period of oral hygiene management system addition which is a service item, and its numerical table. It is a graph showing the comparison result of the improvement rate according to mental and physical conditions of the psychiatric medical care guidance addition which is a service item, and its numerical table. It is a graph showing the comparison result of the average maintenance period according to mental and physical conditions of the psychiatric medical care guidance addition which is a service item, and its numerical table. FIG. 10 is a scatter diagram and a numerical table showing the effect verification results (first group) relating to the service items, namely, the all-offices calculation addition service (G2, G8); FIG. FIG. 10 is a scatter diagram and a numerical table showing the effect verification results (second group) relating to the all-offices calculation addition service (G2, G8), which is a service item; FIG. FIG. 10 is a graph and a numerical table showing the difference in the rate of improvement by mental and physical conditions for each of five groups of special nursing care remuneration (unit - multi-bed room with additional calculation - without additional calculation). FIG. 10 is a graph and numerical table showing the difference in the average maintenance period for each mental and physical condition (unit - multi-bed room, with additional calculation - without additional calculation) for each of the 5 groups of special nursing care remuneration. (a) is a scatter diagram that groups the business establishments using the aggregation results of the improvement rate and the average maintenance period until deterioration for all users of the business establishment, and (b) is the concept of the new nursing care fee addition system. It is a figure explaining. It is a figure explaining the flow of the application procedure of new nursing-care fee addition. It is a figure explaining the procedure of carrying out the correlation analysis whether the questionnaire result by a questionnaire sheet is related to the improvement or maintenance of a mental-and-physical condition. It is a data processing flow chart of correlation analysis. FIG. 10 is a processing flow diagram of characteristic analysis by special needs nursing care based on the values of the questionnaire index and the psychosomatic condition index. FIG. 4 is a conceptual diagram of selection of psychosomatic condition items related to questionnaire sheet evaluation items. It is a figure explaining the comparison year of a psychosomatic state index and a questionnaire index. FIG. 3 is a scatter plot between correlated diets and aggregate 1 (improvement rate) indices. It is a figure which shows the logic of total processing of an elderly person state image and a service state image. It is a figure which shows the image of an elderly person state image and an in-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 period of use of special nursing homes by effectively utilizing the integrated database for the elderly. It is a figure showing the year-by-year transition of the average period of use for special nursing homes. 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 and nursing care. It is a figure explaining a medical-care/nursing-care integrated database construction method. FIG. 10 is an image diagram of a user history sheet to which medical data has been added;

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

An object of the present invention is to provide a regional comprehensive care business system equipped with a newly enhanced aggregation analysis method for fairly evaluating the quality of nursing care services as described above. A flow of analysis processing for achieving such a purpose will be described with reference to FIG. This flow of analysis processing takes as an example the visualization of the quality of nursing care facility services (special nursing homes), but it can be applied to other nursing care services in the same way.

As shown in Figure 1, this analysis process includes step 1 (anonymization and data provision), step 2 (data collation), step 3 (business establishment survey), step 4 (selection of main business establishment), step 5 ( Selection of cases to be evaluated), step 6 (aggregation of new evaluation indices), and step 7 (office grouping by new evaluation indices). Of these, Steps 1 to 4 have already been proposed by the present applicant in the form of a patent application, so only a brief description will be given.

Step 1: Anonymization and data provision Municipalities anonymize long-term care insurance users in order to aggregate and analyze the long-term care certification data and long-term care benefit performance data they possess. In other words, an irreversible hash function is used to add a private value unique to the local government to the insured person number, and then hash conversion is performed to define the "system management number." In addition, the date of birth of the user will be converted into the month and month of birth, and will be the minimum necessary information for linking with the monthly benefit performance data.

Step 2: Matching Data Matching data for certification of long-term care need and actual data for long-term care benefits. In other words, in order to enable time-series analysis of the mental and physical conditions of users when they use nursing care services, the year and month of service use in the benefit record data and the certification effective period in the certification data for long-term care need are associated, and the two data are collated. conduct. To match both data, the items of "authorization period" of long-term care requirement authorization data, "service provision date" of nursing care benefit performance data, and "system management number" held in common by both data are used. The “authorization period” in the long-term care need certification data indicates the period from the start month to the end month of the certification validity period, and the “service provision date” in the long-term care benefit performance data indicates the month and year in which the service was provided. data that represents

In other words, in order to enable aggregation and analysis on a monthly basis, create a "matching relationship table" that breaks down the period from the start month to the end month of the "certification period" of the certification data for long-term care need into monthly units, Table” is used to collate the nursing care benefit performance data with the same “system management number” and “service provision year and month” on a monthly basis.

Step 3: Fact-finding survey of establishments Fact-finding surveys of establishments investigate the status of use of special nursing home services and the usage status of each establishment, and gain a bird's-eye view of the actual situation of all special nursing homes located within the municipality. Then, the establishments to be analyzed are selected. Specifically, this field survey of establishments includes a survey of service users and a field survey of service-providing establishments (both of which are basic tabulations).

Survey of service users, by location of business location, number of business establishments
・Number of users by care level
・By gender ・By age group ・Number of users Fact-finding survey of service providers ・By business location area ・Number of business establishments
・Number of users by office located within the municipality (ratio to capacity)
・By level of long-term care ・By office ・Percentage of users

Step 4: Selection of main place of business It is common for long-term care service users to use multiple types of long-term care services and places of business during the maintenance period. In the case of nursing care facility services as well, there are cases in which at the beginning of the maintenance period, in-home services or nursing care health facilities (health for the elderly) are used, and then transferred to nursing care welfare facilities. In order to appropriately evaluate the quality of care welfare facility services against such actual conditions, we selected "the maintenance period and business establishment where care welfare facility services had the greatest impact on the mental and physical conditions of users". need to be analyzed.

As a precondition, based on the viewpoint that ``services provided differ according to the stage of mental and physical conditions,'' the same user should be evaluated separately if their mental and physical conditions are different. For this reason, the period during which the psychosomatic state stage continues is defined as "one case", and the quality of service is evaluated for each case. Hereinafter, "changes according to psychosomatic state stages" will be referred to as "changes in psychosomatic state". They will be described in order below.

(1) Setting of psychosomatic state change extraction period The psychosomatic state change extraction period is a period (for example, three years) for extracting changes in the psychosomatic state, and the start and end dates are set in advance. Changes in mental and physical conditions that occurred outside of this period will not be counted. The same psychosomatic state change extraction period is set for all authorized persons.

(2) Checking cases of changes in mental and physical conditions and continued maintenance To check.

(3) Calculation of the maintenance period for each stage of psychosomatic state Going back from the “time of stage change” and “end month of the extraction period” of the psychosomatic state, find the start point of that stage, and calculate the maintenance period for each stage of psychosomatic state period during which the patient remains in the same mental and physical state) and a sustained period (the period during which the patient remains in a certain state of mind and body) are calculated.

(4) Monthly unit number comparison between analysis target service and other services (threshold processing 1)
In the calculated maintenance period until deterioration/improvement and the maintenance duration period, the ratio of the "number of units of service type to be analyzed" to the "total number of units of users by month" is calculated. As a result, months with less than 50% (variable as threshold 1) of service types to be analyzed are excluded.

(5) Comparison of number of units per month for analysis target service (threshold processing 2)
For months in which the number of analysis target services exceeds 50%, the ratio of the “number of units for each business establishment” to the monthly “number of units for analysis target services” is calculated. Then, the number of months in which the ratio exceeds 50% (variable as threshold 2) is counted for each office.

(6) Comparison of the number of units for each maintenance period for each service for analysis (threshold processing 3)
Calculate the "percentage of the number of months of each office" to the "total number of months of the maintenance period" for each maintenance period, and the office where the ratio exceeds 50% (variable as threshold 3) is regarded as the "main office" do.

Step 5: Selection of cases to be evaluated
In selecting cases to be evaluated, the user history sheet creation function, the long-term care requirement certification survey item comparison function, and the evaluation target selection function are used. Each of these functions will be described in detail below.

First, as a major premise for the selection of cases to be evaluated, in order to evaluate the quality of nursing care facility services (special nursing homes), changes in the mental and physical conditions (improvement or deterioration) of the service users are the main factors of the service. We need to limit ourselves to the case brought up (of course, the same is true for other services).

Examples of suspected changes in mental and physical conditions due to factors other than nursing care services include the following cases.

・Hospitalized due to acute exacerbation of injury or illness, underwent a survey for certification of need for long-term care at a medical institution while in the hospital in a state of physical and mental deterioration, recovered (mental and physical condition recovered), and certified during the use of nursing care services after discharge If it is judged to be an improvement after receiving an investigation. It is thought that the improvement in mental and physical condition is due to medical treatment and not the nursing care service.

・When you were eating meals with the assistance of nursing care services, but became unable to eat for some reason, and transitioned to tube feeding, etc., which eliminated the need for assistance with meals and reduced the hassle of nursing care. In fact, it is highly possible that the inability to eat is due to a deterioration in physical and mental conditions. It is erroneously judged that the condition has improved.

In order to evaluate the quality of nursing care facility services more accurately and fairly, it is necessary to exclude cases where changes in mental and physical conditions due to factors other than nursing care services, such as those mentioned above, are suspected.

Therefore, in order to investigate in detail what kinds of cases are suspected of causing changes in mental and physical conditions due to factors other than nursing care services, we conducted a time series of changes in the mental and physical conditions of individuals using nursing care services and the status of use of nursing care services. We have developed an in-depth analysis tool that focuses on trends. As tools for in-depth analysis, we have newly developed a "User History Sheet" and a "Long-term Care Need Certification Survey Item Comparison Table".

First, the user history sheet will be explained. This user history sheet is used to fairly evaluate the quality of nursing care services by excluding cases of improvement or deterioration of mental and physical conditions due to factors other than nursing care services (mainly factors related to medical services). In other words, in order to examine and set evaluation target conditions for nursing care services, as shown in FIGS. 2 and 3A, it is possible to comprehensively grasp the improvement/deterioration of the mental and physical conditions of individual users and the transition of service use. is.

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

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

The long-term care requirement certification data 11 is data relating to physical and mental condition items including the basic long-term care requirement certification information and the degree of long-term care requirement of users of long-term care insurance. The basic information for certification of long-term care need includes the insured person number, date of application, municipality of application, gender, age, current situation (a certification survey selected in “facility use” of the long-term care certification questionnaire (summary survey) is conducted. item name of the certification data for long-term care requirement representing the place where the person was placed), and certification valid period. In addition, the psychosomatic condition items are the degree of care required, degree of independence with disability, degree of independence with dementia, and 74 certification survey items. The timing of these occurrences is the start month of the certification validity period.

The evaluation index data 12 are user-by-user, by mental and physical condition stage, maintenance period until improvement/worsening (including maintenance start month and maintenance end month), main office code, and main service type. These occurrence timings are in maintenance period units for each psychosomatic state stage.

The nursing care benefit record data 13 is data including the month in which the user used the nursing care service, the type of service used, and the office code that provided the service to the user.

Each of these data is held by local governments that are long-term care insurers.

Next, the data processing logic section 14 will be described. This data processing logic unit 14 has a mental and physical state transition data output processing unit 15, an evaluation index data output unit 16, and a utilization service transition data output unit 17. Outcome index data 12 and long-term care benefit performance data 13 are tabulated for each insured person and for each month, 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 an annual display area divided into local governments by month in the horizontal direction for a plurality of years (October 2001 to August 2018 in the example shown). up to), and the various data described above can be displayed in chronological order. In addition, in the vertical axis direction, municipalities are divided into the mental and physical condition display area 19, the evaluation index area 20, and the service utilization transition area 21 described above.

The mental and physical condition transition data output processing unit 15 of the data processing logic unit 14 displays the graded change for each mental and physical condition item aggregated from the certification data 11 for long-term care need in the mental and physical condition display area 19 of the user history sheet 18. Output in chronological order for each valid period. At the same time, it outputs the amount of the application municipality for the long-term care certification application and the current situation.

With this output operation, for the level of care required among the physical condition items, the certified effective period is displayed for each level in order to make it easier to grasp the change in level of the level of care required and the maintenance period. In addition, a mark is attached so that the local governments applying for long-term care certification application and the application place can be grasped. In other words, new applications for local governments are indicated by a star ☆, changes by a local government are indicated by a diamond mark ◇, and renewals are indicated by a square mark □. It means that

The fact that the current status is a "medical institution" (other than nursing beds) means that the user is hospitalized in an acute care hospital at the time of the certification survey.

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

In this way, changes in level of nursing care need, amount of change, maintenance period until improvement or change to deterioration at each level are clarified together with the current situation (place of application).

The degree of independence with disabilities and the degree of independence with dementia display the values of each stage. In the special medical care, the number of “present” items among the 12 “special medical care” items of the psychosomatic condition item 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 independent stage is displayed.

Next, data processing of the evaluation index data output unit 16 will be described. The storage unit 10 holds 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 point in time when the stage of the level of care need changes to improvement or deterioration within a preset period for extracting changes in mental and physical conditions is captured, and the maintenance period of the stage before the change, which goes back from this point of change, is improved or deteriorated after the change. is output to the evaluation index area 20 of the user history sheet 18 as an evaluation index of. This is performed for all change points within the psychosomatic state change extraction period.

Here, the psychosomatic state change extraction period is a predetermined period (for example, three years) set in advance to extract the psychosomatic state change. In the examples of FIGS. 2 and 3A, it is the period from April 2015 to March 2018 divided by municipalities by straight lines 18a and 18b.

In the example of the figure, in February 2017 during this psychosomatic state change extraction period, the psychosomatic state changed from level 4 to level 5 in need of care. Therefore, the evaluation index of the maintenance period of the long-term care level 4 before the change that precedes the change to deterioration is output to the evaluation index area 20 as the evaluation index of the deterioration after the change.

In addition, in February 2016 during this psychosomatic state change extraction period, the psychosomatic state changed from level 5 to level 4 with improvement. Therefore, the evaluation index of the maintenance period of the long-term care level 5 before the change that goes back from the point of change to this improvement is output to the evaluation index area 20 as the evaluation index of the improvement after the change.

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

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

The greatest feature of the user history sheet 18 created in this way is that it is not just a time-series output of the mental and physical condition transition area (derived from long-term care certification data) 19 and the use service transition area (derived from nursing care benefit actual data) 21. However, in the evaluation index area 20 between them, the evaluation index related to service quality (indices related to changes in mental and physical conditions by stage and maintenance period of the main business establishment of the main service type) is displayed at the same time.

As a result, which main business establishment of which service type, for example, for each level of nursing care, how long it has been maintained and improved or deteriorated is displayed in combination with the above-mentioned mental and physical condition transition area 19 and the use service transition area 21 can grasp. That is, in the case of outputting only the mental and physical state transition area 19 and the service utilization transition area 21, for example, it is completely impossible to know from where to where the main service is special treatment and which office is the main office. do not have. However, the evaluation index displayed in the evaluation index area 20 is displayed in chronological order and visualized in parallel with the psychosomatic state displayed in the mental and physical state transition area 19 and the service used displayed in the service use transition area 21. By doing so, it becomes clear which service type and which main establishment had the main effect on the improvement, maintenance, or deterioration of the user's mental and physical condition.

This user history sheet 18 can be used as means for confirming factors affecting changes in mental and physical conditions. In other words, in addition to gaining a bird's-eye view of improvements and deterioration in the mental and physical conditions of individual users and transitions in service use, factors that may affect changes in mental and physical conditions (e.g., availability of medical services) differences depending on the type of nursing care service, continuity of the in-home service provider that provides the service, etc.). For this reason, as will be described later, it is also a powerful support tool for "selection of cases to be evaluated."

In addition, the user history sheet is also heavily used as a basic material for individual case review meetings at community care meetings and service staff meetings. In other words, the efficiency of preparing basic materials, which took time and effort, is greatly improved, and the profile of the case, such as what kind of service was provided at what time, can be immediately grasped by the attendees of the conference. As a result, sufficient time will be available for discussions on problem-solving, which should be concentrated on, and the quality of the conference will be dramatically improved.

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

Furthermore, this sheet is also expected to play a role as a material for feedback from local governments on the evaluation results related to the quality of long-term care services by service type and business establishment. In other words, in addition to evaluation indicators such as the average improvement rate and maintenance rate for each business site, we can simultaneously feed back the specific mental and physical conditions of all users at each business site and the transition of service use, so each business site can There is a possibility that it will become a material for concrete efforts to improve the quality of services.

An application example of this user history sheet 18 is shown in FIG. 3B. The user history sheet 18B in FIG. 3B focuses on the transition of mental and physical conditions, and each 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. Along the vertical axis are the level of nursing care required, degree of independence with disability, degree of independence with dementia, 74 physical and mental condition items, elderly condition images, and service condition images. In the middle, 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 achieving maintenance / worsening / not firm), indicating the principal service type and principal place of business for that maintenance period. In the example of the user history sheet 18B, the direction of change to the next stage is △, 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 ~, unachieved maintenance is indicated by <<, deterioration is indicated by ▼, and undetermined is indicated by ◇. Instead of these marks, it is also possible to express the inside of the frame of the sustain period with respective colors or filling patterns. (The direction of change to the next stage is described later in the explanation of the case classification “Step 6: Aggregation of new evaluation indicators”)

As for the elderly person state image and the service state image, it is possible to determine the change direction to the next stage by determining in advance whether each state image is good or bad.

By using this user history sheet 18B, the care manager can grasp the time-series changes in the mental and physical conditions of the user he or she is in charge of, and by confirming the validity of the created care plan, the care manager himself can be expected to improve his skills. .

Next, along with the "user history sheet," we will explain the "long-term care certification survey item comparison chart," which was developed as an in-depth analysis tool for investigating changes in mental and physical conditions due to factors other than nursing care services.

This "long-term care certification survey item comparison table" is used to confirm more detailed changes in mental and physical conditions in the "user history sheet" described above, as shown in Fig. 5. It visualizes changes (differences) at two points in time for all items in

The regional comprehensive care business system according to this embodiment has a long-term care need certification research item comparison function represented by the function block in FIG. Note that the functional blocks shown in FIG. 6 are also implemented by the storage unit, arithmetic processing unit, and set programs of the computer system.

In FIG. 6 , the long-term care requirement certification data 11 held in the storage unit 10 is used for this long-term care requirement certification survey item comparison function. As described above, the long-term care requirement certification data 11 is composed of items of mental and physical conditions including the basic long-term care certification information and the degree of long-term care requirement of users of long-term care insurance. As described above, the long-term care requirement certification survey item comparison function performs data processing to visualize changes (differences) in all items of the long-term care requirement certification survey items at two points in time. The original certification data for long-term care requirement 11A and the certification data for long-term care requirement 11B for comparison are used.

The data processing logic unit 24 that processes the long-term care requirement certification data 11A and the long-term care requirement certification data 11B at these two points in time includes a comparison processing unit 25 for the level of care required and certification application information, a comparison processing unit for the degree of independence with disability and the degree of independence with dementia. 26, a comparison processing unit 27 for intermediate evaluation item scores, and a comparison processing unit 28 for 74 mental and physical condition items. Output to comparison table 30.

The long-term care certification survey item comparison table 30 includes a comparison section 31 for the level of long-term care and certification application information, a comparison section 32 for the degree of independence with disability and the degree of independence with dementia, a comparison section 33 for intermediate evaluation item scores, and 74 physical and mental condition items. of comparison units 34 .

The comparison processing unit 25 of the level of care need and the certification application information of the data output processing logic unit 24 compares the same user's care need certification data 11A between two points of time separated by a predetermined interval and compares the care need certification data 11A and the comparison target. The level of care required and the certification application information are extracted from the care certification data 11B. Then, these extracted data are output to the comparison unit 31 of the level of care level and certification application information of the long-term care level certification survey item comparison table 30 in a state in which they can be compared with each other, as shown in FIG.

That is, in FIG. 5(a), the target data (comparison source data) of user T is the nursing care level and ADL value in January 2016, and the comparison target data (comparison target data) is July 2012 The level of care required and the value of ADL are listed side by side so that they can be compared with each other. In this example, the degree of nursing care requirement is improved from nursing care requirement 4 to nursing care requirement 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 long-term care requirement certification data 11A and 11B defined as the comparison source data and the comparison destination data, respectively. The obtained data is output to comparison section 32 of disability independence degree and dementia independence degree of long-term care requirement certification survey item comparison table 30 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 of user T (marked with a black circle) has the same degree of disability independence as "7" on the horizontal axis compared to the comparison target data (marked with an X). , the dementia independence degree on the vertical axis is displayed as deteriorating from "1" to "4".

Intermediate evaluation item score comparison processing unit 27 selects the first group (physical function/living motion), the second group, which are intermediate evaluation items, from the long-term care requirement certification data 11A and 11B defined as comparison source data and comparison target data. (living function), group 3 (cognitive function), group 4 (mental/behavioral disorders), and group 5 (adaptation to social life) are extracted, and these extracted data are used in the long-term care certification survey. The radar charts shown in FIG.

That is, in FIG. 5C, the target data (solid line) of user T shows a state in which the second group is improved and the fifth group is deteriorated compared to the comparison target data (broken line). It is

The comparison processing unit 28 for the 74 mental and physical condition items extracts the 74 mental and physical condition items from the long-term care requirement certification data 11A and 11B defined as the comparison source data and the comparison destination data, respectively, as shown in FIG. Secondly, they are output in a state that can be compared with each other by a line graph.

In FIG. 5(d), the target data of user T (solid line) is improved in some items in the second group, and is improved in some items in the fifth group, compared with the comparison target data (broken line). is shown to be in a worsening state.

From the display contents of the long-term care need certification survey item comparison table 30 shown in FIG. , The reason for this is that group 5 (applicability to social life), oral administration of drugs and daily decision-making are deteriorating, but group 2 (life function), food intake and swallowing functions are improving. is. This can be assumed because changes (differences) of all items can be viewed simultaneously.

In this way, two points of time for certification of long-term care need were decided, and the differences in the grades of 77 certification survey items (degree of long-term care, degree of independence with disability, degree of independence with dementia, and 74 items of mental and physical condition items) and intermediate evaluation item scores were calculated. By visualizing changes (differences), it is possible to detect changes in mental and physical conditions due to factors other than care services.

Note that the same item comparison table shown in FIG. 5 can be applied not only to comparison of the same person at two points in time, but also to comparison between different persons.

As another comparison area proposal to be added to the comparison table in Fig. 5, the standard time (total standard time) and the standard time for classification by eight types of municipality・Behavioral disorder-related behavior, functional training-related behavior, medical-related behavior) and dementia addition are assumed. This makes it possible to grasp changes (differences) in care work for each type of care.

Next, a method of setting selection conditions based on the output contents of the "user history sheet" and the "comparison table of survey items for certification of long-term care need" as the analysis tools described above and selecting cases to be evaluated will be described.

As mentioned above, in order to evaluate the quality of care and welfare facility services more accurately and fairly, it is necessary to exclude cases in which changes in mental and physical conditions due to factors other than nursing care services (especially medical care) are suspected. be.

Here, the steps for clarifying the conditions for selecting cases to be evaluated (conditions for excluding factors other than nursing care services) will be described.

(1) Visual check of user history sheet Visually check the user history sheet of 100 patterns of evaluation indicators for nursing care welfare facility services (indicators related to the maintenance period until improvement or deterioration for each level of care required).

(2) Pick up a plurality of pattern candidates to be excluded Pick up a plurality of pattern candidates to be excluded through the visual check of the user history sheet. In other words, there are cases in which hospitalization in an acute care hospital is assumed at some point after the start and end of the maintenance period and the level of care required changes. We will pick up cases where .

(3) Examining the validity of hypotheses through interviews with experts Based on the above pick-up, several candidate hypotheses for patterns that should be excluded were formulated, and multiple on-site experts familiar with certification surveys and care plans were interviewed to determine the validity of the hypotheses. think about.

Through this interview, the following viewpoints became clear.
・By focusing on the “current situation (place where the certification survey was conducted)” and “special medical care” in the data items for certification of long-term care need, it is possible to identify the worsening of the disease and the rate of recovery due to acute hospital admission events and special medical care. Changes may be inferred.

"Special medical care" includes the following.
[Content of treatment]
1. Management of infusion2. Central parenteral nutrition3. Dialysis4. Treatment of stoma (artificial anus)5. oxygen therapy6. Respirator (artificial respirator)
7. Tracheotomy procedure8. Pain care9. Tube feeding [special measures]
10. Monitor measurements (blood pressure, heart rate, oxygen saturation, etc.)
11. Treatment of incontinence12. Catheter (condom catheter, indwelling catheter, urostoma, etc.)

・Regarding the above "current situation" and "special medical care", there is little variation in certified surveys due to differences in certified investigators.
・Due to the poor physical and mental condition at the time of admission to the acute care hospital, even if the illness has been cured and the physical and mental condition has improved after discharge, there are almost no applications for change related to improvement, and in many cases the application is not applied until the original certification expires. .

・Among special medical care, only "treatment for incontinence" should not be excluded because it depends on the success or failure of nursing care services, not on the basis of disease.
・"Tube feeding" may be the only special medical care that may reduce the burden of nursing care.

(4) Investigation and Examination of Combining Conditions for Certification of Needed Long-Term Care Data Based on the results of the above interviews, investigations were conducted on conditions for combining multiple items of certification of long-term care need that can be described as conditions for patterns to be excluded.

(5) Determining conditions for selecting cases to be evaluated By sorting out the results of the above investigations and examinations, the conditions for selecting cases to be evaluated (conditions for excluding factors other than nursing care services) were decided. It should be noted that the selection (exclusion) conditions described below can basically be applied in exactly the same way to care service types other than care welfare facility services.
・Pattern 1: Impact of the quality of the main nursing care service (subject to evaluation and selection)
The quality of the main nursing care service (the type of service thought to have had the greatest impact on the user's mental and physical condition) is a factor in the "improvement/maintenance/worsening" of the user's mental and physical condition, and cases of improvement and deterioration occur.
・Pattern 2: Improvement by medical services (excluded from evaluation)

After being hospitalized for acute exacerbation, medical services such as treatment and rehabilitation may be a factor in the improvement of mental and physical conditions. This applies when the application municipality of the case is "new" or "changed" and the current status is "medical institution" (other than long-term care beds).
・Pattern 3: Exacerbation due to acute exacerbation, etc. (excluded from evaluation)

Acute exacerbation of each disease unrelated to the main nursing care service may be a factor in the deterioration of the mental and physical condition. This applies when the application municipality at the start of the next case is "changed" and the current status is "medical institution" (other than long-term care beds).
・Pattern 4: Effects of special medical care (excluding medical treatment) (excluded from evaluation)

The initiation or discontinuation of special medical care is due to the progression or healing of the user's disease, and may be a factor in changes in mental and physical conditions, regardless of the quality of nursing care services. However, Jokuso is an exception because it is considered to be closely related to long-term care services. This applies to cases where there is a change in the implementation status of special medical care, excluding "treatment for incontinence", between the start of the current 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 regional comprehensive care business system according to this embodiment has a function of selecting a subject for evaluation of need for long-term care represented by the functional blocks in FIG. 7 . Note that the functional blocks shown in FIG. 7 are also implemented by the storage unit, arithmetic processing unit, and set programs of the computer system.

In FIG. 7 , this evaluation target selection function uses the above-described nursing care requirement certification data 11 and nursing care benefit performance data 13 held in the storage unit 10 .

Here, if there is a change in the stage of the psychosomatic state during the above-mentioned period for extracting changes in psychosomatic state, the period during which the stage of the psychosomatic state was maintained before this change was taken as the relevant case, and the period during which the stage of the psychosomatic state after the change was maintained was taken as the relevant case. Consider the following case.

The evaluation exclusion case selection logic unit 40 includes a first selection processing unit 41 that determines improvement due to medical service and selects it as non-evaluation, and a second selection processing unit 41 that determines deterioration due to acute exacerbation etc. and selects it as non-evaluation. , and a third selection processing unit 43 that selects non-evaluation when there is a change in the implementation status of special medical care.

The first selection processing unit 41 uses the user's long-term care need certification data 11 and long-term care benefit performance data 13, and the application municipality for long-term care need certification in the case is new or changed, and the current situation is the medical institution (recuperation other than bed), and if the stage of mental and physical condition of the next case is improvement, it is determined that improvement is due to medical services and is not subject to evaluation.

The second selection processing unit 42 uses the user's long-term care requirement certification data and nursing care benefit performance data, and if the application municipality at the start of the next case has changed and the phase change in the mental and physical condition of this next case is worse is judged to be aggravation due to acute exacerbation, etc., and is not subject to evaluation.

The third selection processing unit 43 uses the user's long-term care requirement certification data and long-term care benefit performance data to determine the implementation status of special medical care other than medical treatment during the period from the start of the case to the start of the next case. If there is a change in

The first selection processing unit 41, the second selection processing unit 42, and the third selection processing unit 43 select out of the evaluation target, and the cases after the exclusion are stored in the storage unit 10. It is updated and held as certification data 11 and nursing care benefit performance data 13 .

Step 6: Aggregation of new evaluation indicators Until now, when evaluating the quality of nursing care services, improvement of mental and physical conditions within a predetermined mental and physical condition change extraction period (period between straight lines 18a and 18b in FIG. 3A) We have defined and aggregated evaluation indices (outcome indices by mental and physical conditions: improvement rate, worsening rate, average maintenance period until improvement or worsening, etc.), focusing only on deterioration.

However, as a new analysis, there are two cases: ``the case where each stage of the mental and physical condition is maintained for a certain period of time'' and ``the case where the mental and physical condition worsens but is maintained for a certain period of time'' (these are summarized below). (referred to as “physical condition maintenance cases”) should also be included in the evaluation. In other words, depending on the type of service, there are services that are relatively easy to improve mental and physical conditions (e.g., home-based services) and services that are inherently difficult to improve, but can be maintained for a long time depending on the service content (e.g., This is because there are nursing care facilities.

In order to appropriately evaluate this "case of maintenance of psychosomatic state", it is necessary to define a certain period of time (hereafter referred to as "standard maintenance period") that is appropriate as a threshold.

This standard maintenance period includes cases where the mental and physical conditions have deteriorated (hereinafter referred to as cases of mental and physical deterioration), and the most severe stage (level of care required: 5, degree of independence with disability: C2, independence with dementia). Degree: M), the period from the start to the end of the case (hereinafter referred to as "severe disqualification case") (hereinafter referred to as "maintenance period") Set based on the aggregate results.

From the viewpoint that ``changes in mental and physical conditions may differ depending on gender, age, and stage of mental and physical conditions,'' it is desirable to set multiple reference maintenance periods for each gender, age group, and stage of mental and physical conditions. However, if we establish an age group for each 10-year-old municipality, and tabulate the number of cases of deterioration in mental and physical conditions by gender, age group, and stage of physical and mental conditions, we find that there is a sufficient number of samples in certain age groups and stages of physical and mental conditions. It turned out that the numbers could not be guaranteed.

Therefore, from the viewpoint of securing a sample size, the standard maintenance period does not consider age groups, and all stages of disability independence are aggregated. It was decided to divide it into stages and aggregate and set it.

The following multiple methods are conceivable for setting the reference maintenance period.
1. Average maintenance period: By comparing with the average maintenance period of all deteriorating cases in the municipality, it is possible to visualize the distribution of cases that shorten (or extend) the average maintenance period within the municipality among operators.
2. Mode of maintenance period: By comparing with the period when deterioration is most likely to occur, it is possible to compare the number of cases that have been maintained beyond the period when deterioration is likely to occur among operators.
3. Median duration of maintenance period: By evaluating half of all worsening cases in the local government as "maintained" (or "worse"), it is possible to compare the number of maintained and worsened cases among business establishments from the perspective of competition.
4. Arbitrary period: By setting the period you want to maintain and the period you want to evaluate, you can measure the difference between the period you want to evaluate and the current situation. For example, if a "target maintenance period" is set, the current status relative to the target can be measured.

Any of these methods can be used to determine the standard maintenance period. The standard maintenance period shall be set based on the “average of

By setting the standard maintenance period to the "average maintenance period", cases of deterioration in physical and mental conditions can be classified into "cases exceeding" and "cases not reaching" the average maintenance period for all insurers. The distribution between offices in each case can also be visualized. Visualization of this distribution can be useful information in selecting "business establishments with good practices" and "business establishments requiring countermeasures."

Next, all cases selected as evaluation targets in "Step 5: Selection of evaluation target cases" described above are classified into five categories: "improved", "maintained", "not achieved maintenance", "deteriorated", and "undecided". The conditions for each classification are shown below.

1. Improvements:
1. Improve the stage of mental and physical condition. Maintain:
3. Deterioration after the stage of psychosomatic condition continues for the standard maintenance period or longer Disqualification after the stage of psychosomatic condition continues for the standard maintenance period or longer. Not achieved maintenance:
When the maintenance period of the psychosomatic state 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 reference maintenance period.
4. Aggravation:
5. The stage of psychosomatic state deteriorated within the reference maintenance period. Unsettled:
When the maintenance period of the psychosomatic state stage is within the invalid maintenance period. For example, the phase of the psychosomatic state has just begun.

FIG. 8 shows cases classified into the above five cases.
In FIG. 8, Mr. A improved from level 5 to level 4 within the mental and physical condition change extraction period, so the maintenance period before the point of improvement is the "improvement case". In addition, since the maintenance period after improvement is longer than the invalid maintenance period but shorter than the reference maintenance period, it is a "maintenance failure case".

Mr. B deteriorated from long-term care level 4 to long-term care level 5 within the mental and physical condition change extraction period. ”. Since the maintenance period after deterioration has not reached the invalid maintenance period, it is an “unconfirmed case”.

Since Mr. C improved from level 5 to level 4 within the mental and physical condition change extraction period, the maintenance period before the point of improvement is the "improvement case."

Mr. D has no change in the psychosomatic state during the extraction period of the change in psychosomatic state, and the maintenance period from the start of the psychosomatic state is longer than the reference maintenance period, so he is a "maintenance case".

Mr. E deteriorated from nursing care level 4 to long-term care level 5 within the change extraction period, and the maintenance period from the time of deterioration to deterioration is shorter than the standard maintenance period, so it is a "worsening case". The maintenance period until loss is longer than the invalid maintenance period, but shorter than the standard maintenance period, so it is a "non-maintenance case".
These cases are counted by establishment and category.

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

In FIG. 9, this case classification function performs the case classification logic part 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 has an improvement case classification unit 51 , a maintenance case classification unit 52 , a deterioration case classification unit 53 , and a maintenance failure case classification unit 54 .

The improvement case classification unit 51 classifies the maintenance period before the point of improvement as an "improvement case" when the stage of the user's mental and physical condition changes to an improvement within the psychosomatic condition change extraction period. In the example of FIG. 8, the case where Mr. A's and Mr. C's level of care level is improved from level 5 to level 4 corresponds to this case.

The maintenance case classifying unit 52 classifies, as a "maintenance case", a case where there is no change in the stage of the user's mental and physical condition during the period for extracting changes in mental and physical conditions, and the period of no change exceeds a preset reference maintenance period. do. The maintenance period of nursing care level 4 for Mr. D in FIG. 8 corresponds to this. In addition, even if the stage of the psychosomatic state changed to worse within the period for extracting changes in psychosomatic state, the period of no change in the stage of the psychosomatic state before the change exceeds the preset reference maintenance period, it is also called a "maintenance case". classified as The maintenance period of nursing care level 4 for Mr. B in FIG. 8 corresponds to this.

If the stage of the user's mental and physical state changes to deterioration within the psychosomatic state change extraction period and the period of the mental and physical state stage before deterioration is shorter than the preset reference maintenance period, the worsening case classification unit 53 classifies it as "worse case ”. In the case where Mr. E's level of care requirement 4 in FIG.

The maintenance unattained case classification unit 54 determines that the stage of the psychosomatic state changes to improvement or deterioration within the psychosomatic state change extraction period, and that the period of maintaining the stage of the psychosomatic state after this change is longer than the preset invalid maintenance period and the standard If it is shorter than the maintenance period, it is classified as a “maintenance failure case”. The maintenance period after improvement to nursing care level 4 for Mr. A in FIG.

If none of these cases apply, for example, a maintenance period shorter than the invalid maintenance period after Mr. B's condition has deteriorated to nursing care level 5 in FIG.

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

Next, the evaluation index calculation logic unit 60 that calculates new evaluation indexes (by place of business and by stage of mental and physical conditions) from the cases classified in this way will be described. The evaluation index calculation logic unit 60 has an improvement rate calculation unit 61 , a maintenance rate calculation unit 62 , and an improved maintenance rate calculation unit 63 .

The improvement rate calculation unit 61 calculates an improvement rate by dividing the number of improvement cases for each psychosomatic condition stage obtained for each office by the total number of cases for each psychosomatic condition stage including the number of improvement cases.

In other words, as an indicator of efforts to improve mental and physical conditions at each business establishment, the frequency of occurrence of "improvement cases" (hereinafter referred to as "improvement rate") for all cases is calculated using the following formula. All cases do not include unconfirmed cases.

Improvement rate = Number of improved cases /
(number of improved cases + number of maintained cases + number of cases where maintenance was not achieved + number of deteriorated cases)
The maintenance rate calculation unit 62 calculates the maintenance rate by dividing the number of maintenance cases by psychosomatic condition stage obtained for each office by the total number of cases by psychosomatic condition stage, excluding the number of improvement cases.

In other words, as an indicator of efforts to prevent seriousness at each business establishment, the frequency of occurrence of “maintenance cases” (hereinafter referred to as “maintenance rate”) out of the number of cases excluding improvement cases is calculated using the following formula.

Maintenance rate = number of maintenance cases /
(number of maintenance cases + number of non-maintenance cases + 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 sum of the number of improvement cases and the number of maintenance cases for each stage of mental and physical condition obtained for each place of business as the total number of cases for each mental and physical condition, including the number of improvement cases and the number of maintenance cases. Calculate the improvement retention rate by dividing.

In other words, as an index that comprehensively represents efforts to improve mental and physical conditions at each business site and efforts to prevent aggravation, the frequency of occurrence of “improvement cases” and “maintenance cases” for all cases (hereinafter referred to as “improvement maintenance rate”) ”) is calculated by the following calculation.
Improvement maintenance rate = (number of improved cases + number of maintained cases) /
(number of improved cases + number of maintained cases + number of cases where maintenance was not achieved + number of deteriorated cases)

In this way, by aggregating new evaluation indices, it is possible to visualize the quality of nursing care services of each office in a local government and to perform fair evaluations. That is, by using the obtained evaluation index, the establishment grouping unit 65 can evaluate the establishments within the municipality and perform grouping, which will be described later.

In classifying the above-mentioned cases, as a simpler method, it is conceivable to classify only "improved cases", "maintenance cases" and "worse cases", and classify other cases as "unconfirmed cases". That is, the above-mentioned "maintenance failure case" is not adopted. In this case, when calculating the new evaluation indexes "improvement rate", "maintenance rate", and "improved maintenance rate", the "number of maintenance non-attainment cases" should be deleted from the denominator of each calculation formula described above.

Even with such a method, it is possible to visualize the quality of long-term care services of each office in a local government and to perform a fair evaluation using a new evaluation index.

Step 7: Business establishment grouping by new evaluation index A case of evaluating and grouping business establishments in a municipality using the new evaluation index described above will be described.

The business establishment grouping unit 65 described above calculates the improvement rate and maintenance rate for each stage of the level of nursing care required for each business establishment, the degree of disability independence that aggregates all stages, and the degree of independence with dementia that aggregates mild and severe levels. is used to create the scatter diagram shown in FIG. 10 for grouping. In this scatter diagram, one of the above-described improvement rate and maintenance rate (maintenance rate in FIG. 10) is taken on the vertical axis, and the other (improvement rate in FIG. 10) is taken on the horizontal axis. This is a plot of the values for each establishment.

Figures 11A and 11B show the improvement rate of 6 types of mental and physical condition stages (by level of care required (care level 3 to 5, disability independence level (all stages aggregated), dementia independence level (mild aggregated, severe aggregated)). The list of establishments grouped according to the rate and retention rate.Mental and physical conditions that are not entered in the scatter diagram due to the time of establishment or the small number of cases are shown as blanks.

This office list consists of four areas: an evaluation target case number area 71, an improvement rate and maintenance rate area 72, a scatter diagram quadrant area 74, and an improvement rate and maintenance rate statistical information area 73 shown in FIG. 11B. consists of

The area 71 for the number of cases to be evaluated lists the number of improved cases, the number of maintained cases, and the number of worsened cases by place of business and by each psychosomatic condition stage. Nursing care level 5 is left blank because there are no worsening cases. This area 71 is an area for confirming the reliability of the improvement rate and maintenance rate depending on the number of cases (for example, if the number of cases is several or less, the reliability as an office average value is low, etc.).

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

Statistical information area 73 for improvement rate and maintenance rate shows statistical information related to the improvement rate and maintenance rate for each stage of mental and physical condition of the entire workplace (number of establishments subject to aggregation, average value, standard deviation, maximum value, minimum value and range (=maximum value - minimum value)). This area 73 serves as an area for comparing the establishment distribution of the improvement rate and maintenance rate for each psychosomatic condition stage. For example, the standard deviation of the improvement rate and maintenance rate of dementia independence is larger than the disability independence degree, and it is difficult to care for dementia compared to physical care (there is a large difference between strengths and weaknesses depending on the business). showing sexuality, etc.

The scatter diagram quadrant area 74 is a list showing, in tabular form, which quadrant the improvement rate and maintenance rate of each establishment belong to in the scatter diagram by establishment and by each psychosomatic condition stage shown in FIG. be. A to D in the table refer to the quadrants of the scatter diagram in FIG. This scatter diagram is based on the local government average, with A in the first quadrant (both the improvement rate and maintenance rate are higher than the local government average), B in the second quadrant (the improvement rate is above the average but the maintenance rate is lower), and the The 3rd quadrant is C (the maintenance rate is higher than the same average, but the improvement rate is lower), and the 4th quadrant is D (both the improvement rate and the maintenance rate are lower than the same average).

The lists shown in FIGS. 11A and 11B can be used to comprehensively grasp the quality of services (strengths and weaknesses in care, etc.) for each stage of mental and physical conditions at each business establishment, and may lead to headlines of good examples.

In the above examples, evaluation indexes for service establishments such as nursing care welfare facilities in local governments (long-term care insurers) have been described. The replaced system relating to the visualization of service quality for each municipality at the national or prefectural level can also be applied in exactly the same way.

The present invention is to provide a new analysis process for more fairly evaluating the quality of nursing care services as described above. In the above embodiment, the analysis process shown in FIG. This will be described with reference to FIG. A time-series analysis of mental and physical condition indicators (improvement rate and average maintenance period until deterioration) in special nursing homes (special nursing homes for the elderly) is performed to quantitatively show the effects of the project. Note that special nursing care is just an example and can be applied to other services in the same way. Even in this analysis process, steps 1 to 4 are the flow of preprocessing up to the preparation of data necessary for this analysis process, and have already been proposed by the applicant in the form of a patent application. 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 and maintenance of the mental and physical condition by the new processing, Step 5 and Step 6, will be explained.

Step 5: Aggregate analysis of improvement rate Aggregate analysis of yearly changes in improvement rate by year during the index calculation period and by mental and physical condition. That is, as indicated by processing 131 in FIG. 13, time-series transition aggregation of the improvement rate is performed for each psychosomatic condition.

Step 6: Aggregate analysis of the average maintenance period Aggregate and analyze the annual changes in the average maintenance period until deterioration by year of the index calculation period and by mental and physical condition. That is, as indicated by processing 132 in FIG. 13, time-series transition aggregation of the average maintenance period until deterioration is performed for each psychosomatic condition. Then, as shown in process 133 in FIG. 13, the average maintenance period until deterioration obtained for each year is used to estimate the benefit cost restraint effect by extending the average maintenance period until deterioration.

The logic of time-series transition aggregation processing 131 of the rate of improvement by mental and physical conditions 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 in process 141, setting of the time-series transition aggregation period is performed. That is, the period for aggregating the time-series transition is divided into fiscal years, and the processing up to the calculation of the improvement rate is repeated for each index calculation period set for each fiscal year.

The psychosomatic state change amount calculator 142 calculates the psychosomatic 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 during the index calculation period is counted, and the psychosomatic state changes within the index calculation period are tabulated. That is, in the example of the figure, the improvement rate is calculated by counting 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 psychosomatic state is grasped from the actual amount of change in the psychosomatic state during the index calculation period.

FIG. 15(b) explains a method for determining the amount of change in the psychosomatic state according to this embodiment. FIG. 15(b) shows the same changes in psychosomatic state as in FIG. In the form of , the amount of change is +2. After that, it deteriorated by one step to psychosomatic condition 2, and the amount of change is assumed to be -1. Maintaining the mind-body state is assumed to have a change of 0, and if these changes are totaled,
2 + (-1) + 0 = 1
becomes. If the total amount of change is positive, it is "improved", if it is negative, it is "deteriorated", and if it is 0, it is "maintained".

That is, using the user's long-term care requirement certification data, when the stage of the mental and physical condition changes to an improvement within the index calculation period, the number of stages, which is the amount of improvement, is counted. Conversely, when the stage of the psychosomatic condition changes to worse within the index calculation period, the number of stages, which is the amount of change to worsen, is counted. Then, the difference between the number of improved stages and the number of deteriorated stages and the direction of this difference (positive, negative) are obtained, and improvement or deterioration is determined from the direction of this difference.

The psychosomatic state change count calculation unit 143 counts the number of times the psychosomatic state change state is expressed within the index calculation period, and calculates the number of times of psychosomatic state change for each user during the index calculation period. Although the number of times of change 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 representing the state of change in the psychosomatic state within the index calculation period.

In the example of FIG. 15(b), the amount of change in the psychosomatic state during the index calculation period is +1 improvement as described above, and the amount of change between the two points at the start and end of the index calculation period can be understood. However, it is unclear how the psychosomatic state changed during the index calculation period. Therefore, when the stage of the psychosomatic state changes to an improvement within the psychosomatic state change extraction period, the number of times of improvement (1 improvement in the example of the figure) and the number of stages of improvement (+2 in the example of the figure) are counted. In addition, in the example of the figure, since the stage of the psychosomatic condition has changed to deterioration after that, the number of times of deterioration (1 case of deterioration) and the number of stages of deterioration (-1) are counted. Then, these count numbers are output as the number of times indicating the change state of improvement and deterioration within the psychosomatic state change extraction period.

As shown in FIG. 16, the tabulation target exclusion unit 144 has a function of excluding the number of mental and physical state changes from being tabulated if the user receives services from a plurality of establishments during the psychosomatic state change extraction period. . In other words, 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 tally as users of multiple business establishments.

The improvement rate calculation unit 145 calculates the number of users determined to be improved, the number of users determined to be deteriorated, the number of users not to change, and the number of users who have completed are counted for each business site, and the improvement rate for each business site is calculated from these aggregated values. The calculation formula is as follows.
Improvement rate = No. of improved / (No. of improved + No. of worsened + No. of maintained + No. of completed)

Next, the aggregation analysis of the average maintenance period in 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 psychosomatic condition shown in the process 132 of FIG. 13, and is executed by the logic shown in FIG. Processing 171 in FIG. 17 divides the period for aggregating the time-series transition by year, sets each year as an index calculation period, and repeats the processing of processing 172 for each index calculation period.

The process 172 described above calculates the average maintenance period until deterioration during the index calculation period. In other words, the average maintenance period in the municipality until the level of the mental and physical condition item of each user deteriorates by one level is calculated for each mental and physical condition item for each year.

Aggregated mental and physical condition improvement rate and maintenance rate are used as new mental and physical condition outcome indicators that contribute to the evaluation of independence support and prevention of aggravation. These calculation methods will be described. The psychosomatic condition index is tabulated according to the psychosomatic condition stage in step 4: "selection of main place of business" shown in FIG. In this embodiment, various analyzes are performed by aggregating by groups or arbitrary psychosomatic condition items. Hereinafter, a method of calculating a maintenance rate based on the maintenance period until the improvement rate and deterioration aggregated from each psychosomatic condition stage will be described with reference to FIG. 18 .

FIG. 18(a) shows the state in which the improvement rate and maintenance rate of each stage of the psychosomatic condition were counted for each group and item name. Data for each item name in this way is enormous in number, and is not suitable for various subsequent analyses. Therefore, as shown in FIG. 18(b), each group is aggregated. That is, Group 1 has items from 1-1-1 to 1-N-3. The average value up to -N-3 is calculated and aggregated for each group. Other groups are aggregated similarly.

FIGS. 19A, 19B, and 19C show time-series changes in improvement rates by mental and physical conditions in local governments, that is, aggregate results of improvement rates by year and by mental and physical conditions. From the figure, the degree of independence with dementia and Group 3 are on a moderate upward trend, the degree of independence with disability in Groups 1, 2, and 5 are on a flat trend, and Group 4 and the level of nursing care need are on a flat trend. It can be seen that although it fluctuates, it is generally flat.

Next, Figures 20(a) and 20(b) show the numbers and ratios of improvement, deterioration, termination, and maintenance in local governments. The number of users is the sum of the number of improved users, the number of worsened users, the number of completed users, and the number of maintained users. Moreover, each ratio was calculated by the following formula.
Improvement rate = improvement number / number of users Deterioration rate = number of deterioration / number of users Completion rate = number of completion / number of users Maintenance rate = number of maintenance / number of users

The number of users shown in FIG. 20 differs for each psychosomatic condition for the following three reasons.
Reason 1: Because we exclude cases that do not meet the threshold of the main place of business for each case. In other words, the main place of business was determined for each month in each case, and if the number of months did not account for more than 50% of the maintenance period, it was excluded as data unsuitable for the analysis of the place of business. Since exclusion is performed for each psychosomatic condition stage, the number of users may differ for each psychosomatic condition.
Reason 2: The aggregated psychosomatic conditions represent the total number of people for each psychosomatic condition stage. In other words, although long-term care level 3 to 5 represents the number of people in each stage, dementia independence degree and disability independence degree represent the total number of people who combined the number of people in each stage. Groups 1 to 5 represent the total number of people who added up all psychosomatic state stages belonging to each group.
Reason 3: Users who receive services from multiple establishments during the index calculation period are excluded.

FIGS. 21(a), (b), and (c) show the time-series transition of the average maintenance period until deterioration according to mental and physical conditions. As shown in the figure, the average maintenance period has increased by about 16 months over five years since FY2013. Before FY2012, the loss of eligibility was not registered in the certification data, so the average maintenance period of those who lost their eligibility was calculated longer than the actual period. ing.

As mentioned above, since the average maintenance period until deterioration is aggregated in chronological order, it is possible to grasp whether the maintenance period has been extended compared to the previous year for each fiscal year. If it is extended, the benefit costs for the extension can be suppressed, and it can be seen as an effect. In the following, we will explain the trial calculation of the effect of suppressing benefits costs by extending the average maintenance period until deterioration.

This trial calculation is performed by the logic shown in FIG. 22 by the arithmetic processing unit that constitutes the computer system. In FIG. 22, the process 221 divides the period for trial calculation of the benefit cost restraint effect into each year, and repeats the calculation of the benefit cost restraint effect by the process 222 for each year. In process 222, as shown in FIG. 23, the average maintenance period for each fiscal year of the benefit cost control effect trial calculation period is set as the reference average maintenance period, and the average maintenance period for each year of the benefit cost control effect trial calculation period is calculated. and the difference (stretching period) A is obtained. Then, for each mental and physical condition, the difference B in the monthly benefits per person from the one-step deterioration stage of that mental and physical condition is multiplied by the number of people C whose mental and physical conditions have deteriorated by one stage, and the benefit cost reduction amount D for each fiscal year is calculated. do. In process 224, the amount of benefit cost suppression for each year during the benefit cost control effect trial calculation period is totaled to calculate the benefit cost control amount for the benefit cost control effect trial calculation period.

Figure 23 shows a trial calculation of the effect of extending the average maintenance period on benefit costs. Based on the average maintenance period until deterioration in fiscal 2013, the difference (number of months) from the average maintenance period until deterioration in each fiscal year since fiscal 2014 is from 3 to 4 in nursing care and from 4 to 5 in nursing care. Multiply the difference in the monthly benefit cost per person by the number of people with worsening levels of care need 3 and 4 to calculate the suppressed benefit cost. In the example of Figure 23, the average maintenance period in local governments until the level of care required, which is a physical condition, deteriorated from care need 3 to care need 4 in 2013 was 17.0 months in 2014. 26.45 months, and the extension period A in 2014 will be 9.45 months. Assuming that the difference B in the monthly benefits per person between those requiring long-term care 3 and 4 in fiscal 2014 is 22,835 yen, and the number of worsened persons C in fiscal 2014 is 18, the suppressed benefits D in fiscal 2014 is It becomes 3,884,234 yen.

That is, the average maintenance period calculation unit (corresponding to processing 171 and 172 in FIG. 17), which the arithmetic processing unit of the computer system has as a function, calculates the average maintenance period in the municipality until the stage of the psychosomatic condition item of each user deteriorates by one stage. Calculate the period for each psychosomatic condition item for each year. Similarly, the benefit cost suppression effect calculation unit (corresponding to processes 220, 222, and 223 in FIG. 22), which the arithmetic processing unit also has as a function, sets the benefit cost suppression effect calculation year to the current year for each stage of mental and physical conditions, and calculates the benefit cost suppression effect calculation year for the previous year. Calculate the extension period of the average maintenance period of the corresponding stage of psychosomatic condition (average maintenance period that serves as the reference) for the current year. Then, using this extension period, the difference in benefits per person between the corresponding stage of mental and physical condition in this year and the one level mild stage, and the number of people whose mental and physical condition deteriorated in the corresponding stage in this year, Calculate the benefit cost reduction amount due to the extension of the period of maintaining mental and physical conditions.

Table 1 below shows the results of trial calculations of benefit cost control amounts for long-term care level 3 and long-term care level 4 calculated using the method for calculating benefit cost control effects described above. FY2013 is the starting year for the annual transition. The amount of benefit cost reduction (provisional calculation) shows a comparison with FY 2013, so the years in Table 1 start from FY 2014.

In Table 1, the annual change in monthly benefits per person shows the difference in monthly benefits per person for those requiring long-term care 3 and those requiring long-term care 4, as well as the difference in monthly benefits per person for those requiring long-term care 4 and those requiring long-term care 5. It is around 20,000 yen. This is also consistent with the difference in the number of units of the basic service item of special nursing care × 30 days. As a result of the benefit cost suppression amount (trial calculation), the amount of benefit expense suppression (trial calculation) is about 38.47 million yen for long-term care level 3 and about 12.53 million yen for long-term care level 4, for a total of about 51 million yen.

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

History of system revisions related to the extension of the valid period of certification (2000 (when the system was established))
・The “effective period of accreditation in principle” is uniformly set at 6 months.
・The “Validity period that can be set by municipalities” is set to 3 to 12 months if the renewal application results in “Support required → Support required” or “Nursing care required → Nursing care required”. For new applications, classification change applications, and renewal applications excluding the above cases, 3 to 6 months has been set.
(2004)
・Regarding the “principle certification validity period”, the validity period has been extended to 12 months when “support required → support required” or “long-term care required → long-term care required” as a result of renewal application.
・Regarding the “Valid period that can be set by municipalities”, the upper limit of the valid period has been extended to 24 months in the case of “Nursing care requirement → Nursing care requirement” as a result of renewal application.
(2011)
・Regarding the “Valid period that can be set by municipalities,” the upper limit of the valid period for classification change applications and renewal applications, excluding new applications, has been uniformly extended to 12 months.
(2012)
・Regarding the “Valid period that can be set by municipalities,” the upper limit of the valid period for new applications has been extended to 12 months.
(2015)
When the municipality shifts to long-term care prevention and daily life support comprehensive business,
・Regarding the “principle certification validity period,” the validity period for renewal applications has been uniformly extended to 12 months.
・Regarding the “Valid period that can be set by municipalities,” the upper limit of the valid period for renewal applications has been uniformly extended to 24 months.
(See Table 2 below for 2018)

Method of evaluating the impact on the calculation of the maintenance period As described above, the effective period of certification is being extended year by year due to system revisions and other factors. Therefore, we have grasped the actual situation that the certification valid period at the time of certification of long-term care need is extended year by year due to system revision etc. , the differences in the truncated certification periods were tabulated and illustrated in FIG.

In FIG. 24, the certification period determined at the time of certification of long-term care need is called "certification effective period", and the certification period shortened by change application is referred to as "actual certification period". As is clear from Figure 24, even if the valid period of certification is extended due to system revisions, etc., if the physical and mental conditions change, an application for change will be made at that time, and the above-mentioned maintenance period is based on the actual certification period. , is not affected by the maintenance period calculation.

FIG. 25 shows changes in the year-by-year average of the certification effective period and the year-by-year average of the actual certification period shortened by the change application. Compared to the extension of the effective period of certification, the extension of the actual period of certification has remained at 15 months or less. This is because, as mentioned above, when the mental and physical conditions deteriorate, the change application is made immediately at that time. After 2017, it was excluded from the analysis because all change applications had not been filed at the time of extracting this certification data.

Next, regarding the basic service items and additional service items in special nursing homes, we investigated the differences and existence of each special nursing home and the relationship with the evaluation index (improvement rate, maintenance rate, etc.) To verify the effect of anti-corrosion prevention. 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, analysis is performed based on the calculation status of data for the latest fiscal year (2018) for which data for one fiscal year are available.

This analysis is performed in three steps shown in FIG. That is, Step 1: Selection of service items for effect verification 1 (one-time survey), Step 2: Selection of service items for effect verification 2 (time-series survey), Step 3: Effect verification by mental and physical condition index of special nursing care fee is. Through these steps, the data is aggregated and analyzed.

This analysis processing is executed by the arithmetic processing unit of the computer system. As described above, this analysis process verifies the effect of self-reliance support and severity prevention for each service item using evaluation indicators (improvement rate, maintenance rate, etc.). An average maintenance period calculation unit (corresponding to the logic processing 172 described with reference to 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 item using the index data, and An improvement rate calculation unit (corresponding to the processing 145 of the logic described in FIG. 14) that calculates the rate of improvement for each business office of the user who improves in that stage for each stage of the mental and physical condition item. have.

In addition, among the services provided by each office, select service items that have been provided to users within the most recent predetermined period, and that have continued since the time period that can be calculated for the average maintenance period. A service item selection unit (details will be described later with reference to FIGS. 27 and 28) and a group of establishments that provide the selected service item are defined as the first group, and the selected service item is not provided. A group of establishments is defined as the second group, and an effect verification unit compares the average maintenance period and improvement rate between the first group and the second group for each psychosomatic condition item (details will be described later with reference to FIG. 29). and as a function.

In addition, since this effect verification department cannot group the service items provided to users by all the business establishments of the local government into the above-mentioned first group and second group and perform comparative analysis, all business establishments Aggregate the average maintenance period and improvement rate, and analyze and evaluate those values by outputting a scatter diagram.

First, the service item selection unit will be described. Although there are hundreds of service items available at Tokuyo, only dozens of items are actually provided to users. This is called a one-time survey as described above. In this one-point survey, as described above, the data for fiscal 2018, which has the most recent year's worth of data, is selected as the object of aggregation.

The one-time survey is executed by the logic shown in FIG. 27 by the arithmetic processing unit of the computer system. In process 271, in order to determine candidates for service items to be tallied, the calculated number of people for each service item and each office is tallied from the benefit performance data in March 2019, which is the last month of the latest fiscal year (2018), and tallied. Narrow down the target service item candidates. At this time, service items with no calculations or with only a few calculations are excluded.

Next, the calculation status of the service item for one year (fiscal year 2018) is totaled, and the service item whose calculation number of months per year is sufficient for analysis is selected as an object of totalization. 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-described process 271, in the process 272, the calculation status for 12 months of the aggregation target year (FY 2018) is aggregated and aggregated. Select the target service item.

Table 3 below shows an example of the results of a one-time survey of service items whose effects are to be verified. Table 3 exemplifies the relationship between the aggregate analysis group selected from the 2018 calculation status of special nursing homes and the service items for which the effects are to be verified. In other words, as mentioned above, after totaling the calculated number of people at each business office for each service item as of March 2019 and determining the service item candidates to be verified, the calculation status for the service item for the year of 2018 will be calculated. Aggregated.

In Table 3, if the aggregation analysis group is, for example, "G1: basic item (by room type)", the service items are "welfare facility II3", ... "U-type welfare facility I5". is "multi-bed room/need nursing care 3", ... "unit type private room/need nursing care 5", and the analysis type is "by room type". In addition, "Multi-bed room/Long-term care level 3" means that a user with level of long-term care level 3 lives in one room with a capacity of 2 or more people. In multi-bed rooms, users can communicate with each other, but it is difficult to secure the privacy of individual users. In addition, "unit type private room/long-term care level 5" means that each user of long-term care level 5 has one room but uses a shared living room. This unit-type private room can secure the privacy of individual users, but there are some problems in communication with others.

Service items, their summaries, and classification types are set for other aggregate analysis groups G2 to G8, respectively. Among these items, "G6; additional support for continuing daily life" is determined 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 investigation of step 2 shown in FIG. 26 will be described. This time-series investigation is executed by the logic shown in FIG. 28 by the arithmetic processing unit of the computer system. In FIG. 28, in process 281, the revision history survey in process 282 is repeated for the service items selected in the one-time survey. Then, from the results of past revision history surveys, we investigate whether the service item exists for a period sufficient for analysis, clarify the impact of changes in service item names and calculation conditions on analysis, and make services suitable for analysis. Select an item. In other words, the calculation of the psychosomatic state index, which is the basis of the effect analysis, requires values for a certain period of time before the time of evaluation. This is for selecting

In process 282, as described above, it is checked from the past revision history of the service item whether the service item has existed for a period sufficient for analysis. Here, the period sufficient for analysis may be, for example, "three years", which is the business year of long-term care insurance, or the period of a case project implemented by a local government. The continuity of the service item history is judged by the service item code, not by the service item name. Therefore, when investigating the continuity of history, it is investigated whether there is a service item having the same service item code. For example, even if the same service item name does not exist in the past, if there are service items with the same service item code and different service item names, they are regarded as the same service item and continuity is maintained.

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

In Table 4, when the same service item name is described in each cell of the service item revision date, there is no major revision in the service item in each fiscal year, but minor revisions (for example, revision of points etc.) indicates if there is In the example of Table 4, the revision date of the service item with the service item number (=service item code, hereinafter the same) "1135" is "April 1, 2012" ... "April 1, 2018". The same service item name “welfare facility II3” is listed in all the cells. indicates that there is In addition, service items that were not revised on the relevant revision date are indicated by "→". Further, service items that do not exist on the revision date are indicated by "x".

In addition, in Table 4, the aggregation analysis group "treatment improvement addition" added service items and existing service items on April 1, 2015 and April 1, 2017, along with the establishment of additional evaluation calculation requirements. is changed, but there is no change in the service item number, so the continuity of the history is maintained and there is no problem in analysis. That is, on April 1, 2015, service item number "6018" was newly established under the name of "treatment improvement addition I", so the name of the service item with service item number "6014" was changed to "treatment improvement addition II". was done. Also, on April 1, 2017, service item number "6110" was newly established under the name of "treatment improvement addition I", so the name of the service item with service item number "6018" is "treatment improvement addition II". , and the name of the service item with the service item number "6014" has been changed to "treatment improvement addition III". In this way, looking at the service item number "6014", the name of the service item is changed each time a service item is added, but since the service item number is not changed, the continuity of the history is maintained. There are no analytical problems.

Next, the effect verification unit for verifying the effect of the special nursing care reward based on the mental and physical condition index in step 3 shown in FIG. 26 will be described. The effect verification unit based on the mental and physical condition index of this special nursing care reward is executed by the logic shown in FIG. 29 by the arithmetic processing unit of the computer system. An outline will be described below.

In step 291, the office group is determined for the service items selected as described above. The business establishment group is made up of business establishments with and without calculation for the selected service item. A group of establishments is the unit of comparison for analysis. In process 292, the business establishment groups to be compared are grouped together to form a total analysis group. In process 293, the process 294 is executed for each aggregation analysis group to aggregate the improvement rate for each mental and physical condition index for each business establishment group and the average maintenance period until deterioration, and the results are used as data for analysis. In process 294, the improvement rate and the average maintenance period until deterioration for each mental and physical condition of the business establishment group to be compared are calculated for each aggregation analysis group, and compared. In process 295, for each aggregation analysis group, based on the aggregation analysis result of each business establishment group to be compared, or if it cannot be divided into business establishment groups as described later, the long-term care fee system if it is for the country We will support the decision of the content of the measures, such as whether it is necessary to review the measures and whether it is necessary to give guidance to the business operator if it is for local governments.

Table 5 shows the results of verification of the effect of the above-mentioned special nursing care reward using the mental and physical condition index. Table 5 shows the relationship between the above-mentioned tabulation analysis group for verifying the effects and the accounting establishments. As can be seen from Table 5, the aggregation analysis group subject to effect verification is, from the analysis type, by room type (G1), calculation for all offices (G2, G8), and presence or absence of addition calculation (G3, G4, G5, G7). 3 groups.

In Table 5, in the analysis type aggregate analysis group (G1) by room type, the service item explained in Table 3 adopts "unit type care welfare facility service (I)", and the cost is calculated I, J The first business establishment group 11 consisting of 2 establishments and 8 projects A, ... H, which adopted the service item "Nursing care facility service (II) <Multi-bed room>" and calculated the cost A second business establishment group 12 consisting of business establishments is selected as an analysis target. In other words, the first group 11 is the group 11 of business establishments I and J, which have the calculation of the cost of the service item "unit type nursing care facility service (I)", and the service item "unit type nursing care facility service (I) , the group 12 of the A, .

For the aggregation analysis group group (G3, G4, G5, G7) with or without additional calculation for the analysis type, the service items shown in Table 3 are also adopted, and the first group whose costs are calculated and the service items are adopted. and a second group for which there is no cost calculation.

These aggregate analysis groups G1, G3 . G4. G5. For each G7, the psychosomatic condition index is calculated for each of the first and second business establishment groups, and a comparative analysis (described later) is performed.
In the aggregation analysis group (G2, G8) whose analysis type is calculation for all business establishments, all business establishments adopt the service items shown in Table 3 and the costs are calculated, so they should be divided into business establishment groups. can't In this case, a comparative analysis (described later) is performed by calculating the psychosomatic condition index for each of the establishments.
Below, the results of verification of the effects will be explained to these aggregation 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. FIG. In the basic item (by room type) G1, the establishment group 11 for which the unit type shown in Table 5 is calculated is the first establishment group, and the establishment group 12 for which the multi-bed room is calculated is the second establishment group. We compared and analyzed these mental and physical condition indices as a group of establishments. as a result,
The improvement rate was higher in the unit type in all psychosomatic conditions, and conversely, the average maintenance period was longer (better) in the multi-bed room in all psychosomatic conditions. As for the average maintenance period, calculation of unit-type facilities started in FY2016, so it is considered that the average maintenance period is calculated shorter than the actual period. In addition, in order to correctly compare and evaluate unit-type and multi-bed rooms, the average maintenance period for multi-bed rooms is a little less than 50 months, so the unit-type calculation period must be at least five years. From this analysis result, it is possible to understand how the room type affects the improvement and maintenance of the user's mental and physical condition, and it can be used to examine the necessity of revising the facilities plan and nursing care fee system.

Next, the result of verifying the effect of the aggregation analysis group G3: treatment improvement addition will be described with reference to FIGS. 32 and 33. FIG. In treatment improvement addition G3, among the service items "treatment improvement addition I to V" shown in Table 3, the service items that are actually calculated are only treatment improvement addition I and II, so as shown in Table 5 , the establishment group 31 calculating the treatment improvement addition I and the establishment group 33 calculating the treatment improvement addition II were compared and analyzed as the first group and the second group, and Fig. 32, Fig. 33 obtained the results shown in

According to the hypothesis, the establishment group 31 that calculated Treatment Improvement I should show a better mental and physical condition index than the establishment group that calculated Treatment Improvement II. As shown in 32, although the improvement rate is higher in the business establishment group 31 that calculates treatment improvement I other than Group 2, the difference is slight, and even the largest difference is 1.22% compared to Group 5. This result is the opposite of the hypothesis that the average maintenance period is longer in II except Group 4 and dementia independence.

Next, the result of verifying the effects of the aggregation analysis group G4: Nursing system addition will be described with reference to FIGS. 34 and 35. FIG. As shown in Table 4, there are two types of nursing system additions I2 and II2 calculated in the nursing system addition G4. Nursing system addition is a cumulative addition that allows establishments that have calculated I2 to also calculate II2. In other words, since all establishments add Nursing System Addition I2, as shown in Table 5, establishment group 41 that calculates II2 focusing on addition of Nursing System Addition II2 and A comparative analysis was made of the psychosomatic condition index of 42 business establishment groups that did not calculate II2.

According to the hypothesis, the group of establishments that calculated Nursing System Addition II2 should have tended to show a better mental and physical condition index than the group of establishments that did not calculate Nursing System Addition II2. Actually, as shown in FIG. 34, although the improvement rate is higher in all the mental and physical condition indexes in the establishment group that does not calculate the nursing system addition II2, the difference is slight. Regarding the average maintenance period, as shown in Figure 35, Group 5 and the establishment group for which the dementia independence degree did not calculate the nursing system addition II2 was longer, but other than that, the nursing system addition II2 was calculated. The group of business establishments with Especially in the third group, the difference is as large as 10.1 months. For this reason, as hypothesized, the business establishment group that calculated the nursing system addition II2 shows a favorable trend.

Next, the results of verifying the effects of the aggregation analysis group G5: oral hygiene management system addition will be described with reference to FIGS. 36 and 37. FIG. Oral hygiene management system addition G5, as shown in Table 5, was a comparative analysis of the mental and physical condition indicators of the establishment group 51 that calculated the oral hygiene management system addition and the establishment group 52 that did not calculate the addition. .

According to the hypothesis, the group of establishments that calculated the oral hygiene management system addition should show a tendency to have better mental and physical conditions than the group of establishments that did not. In fact, as shown in FIG. 36, the improvement rate is higher in the second group, the fourth group, and the group of establishments that do not calculate the dementia independence degree, but the difference is slight. Regarding the average maintenance period, as shown in FIG. , 2nd group, 3rd group, and the group of business establishments not calculated for the disability independence degree are longer, so the result cannot be said to be as hypothesized.

Next, the results of verifying the effects of the aggregation analysis group G7: additional psychiatric care guidance will be described with reference to FIGS. 38 and 39. FIG. As shown in Table 5, the psychiatric medical care guidance addition G7 is the difference between the establishment group 71 that has calculated the psychiatric care guidance addition and the business establishment group 72 that has not calculated the psychiatric medical care guidance addition. The psychosomatic state index was comparatively analyzed.

According to the hypothesis, the group of establishments that calculated the addition of psychiatric care guidance should have tended to show a better mental and physical condition index than the group of establishments that did not. In fact, as shown in Figure 38, the improvement rate is higher in the business establishment group that calculates the psychiatric care guidance addition in the 1st, 2nd, 4th and 5th groups, In Group 3, degree of independence with disabilities, and degree of independence with dementia, the business establishment group not calculated is higher. As shown in Fig. 39, the average maintenance period is longer in Group 4 and in the group of establishments that do not calculate additional psychiatric care guidance in terms of disability independence degree, but in the other mental and physical condition indices, psychiatric care guidance is longer. It is longer for the establishment group that calculates the addition. Therefore, the result cannot be said to be as hypothesized.

Next, the aggregation analysis group G2 of the addition calculated by all establishments: the effect verification result of the individual functional training addition, and G8: the effect verification result of the nutrition management addition will be explained. As shown in Table 5, the individual functional training addition G2 and the nutrition management addition G8 are calculated by all establishments, and it is not possible to compare the mental and physical conditions by grouping establishments with or without addition calculation. Therefore, as shown in Figures 40 and 41 for each mental and physical condition, using the scatter diagram of the improvement rate and the average maintenance period until deterioration of all business establishments, the first group (physical function / living habits) related to the addition The mental and physical conditions of the workplace were tabulated and analyzed for the second group (movement) and group 2 (life function).

As a result of the above aggregate analysis, looking at the relationship between the improvement rate and the average maintenance period in Group 1, as shown in Figure 40, facilities with a high improvement rate have a short average maintenance period, and conversely, facilities with a long average maintenance period. indicates a low improvement rate. The minimum improvement rate was 0.7%, the maximum was 2.9%, the minimum average maintenance period was 33.5 months, the maximum was 53.6 months, and the difference between the shortest and longest average maintenance period was 20. It's been a month and it's getting bigger. As shown in FIG. 41, the second group also showed the same tendency as the first group, but the shortest and longest average maintenance period was 34.9 months and 45.2 months, respectively, with a difference of 10 months. .310.3 months, about half of the first group.

42 and 43 show the difference and direction of the improvement rate and the average maintenance period until deterioration of the compared business establishment groups for each aggregate analysis group analyzed as described above. Aggregate analysis group G1: Average maintenance period by room type is excluded as described above.

In Figures 42 and 43, aggregate analysis group G1 is unit type because the difference is (unit type - multi-bed room), G3 is treatment improvement I because the difference is (I - II), and G4 to G7 are , Since the difference is (with addition - without addition), if the addition indicates that each person's mental and physical condition is improving (if the numerical value in the bar graph is 0 or more), the independence support and severity of the nursing care fee There is a possibility that it has a preventive effect.

The main results of the verification of the effects described above are as follows.
・A significant difference was observed in the rate of improvement in all mental and physical conditions in the unit type compared to the multi-bed room.
・There was a difference in treatment improvement addition I compared to II in the improvement rate for almost all mental and physical conditions.
・As for other additions, there were variations in the improvement rate difference (effect) for each mental and physical condition.
・Regarding the average maintenance period until deterioration, there were variations in the effect of each addition for each mental and physical 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," we proposed to the government "a new nursing care fee addition system based on the evaluation of independence support and prevention of aggravation." proposal” can be considered. As shown in Fig. 44(b), the concept of the new premium system is to add a result-type premium based on the results of improving and maintaining the desired mental and physical condition in addition to the existing business establishment system premium. is. In other words, business establishments that meet certain conditions for efforts to improve and maintain the mental and physical conditions of all users are certified as "performance-based additions" and added. Figure 44 (a) shows the average improvement rate and deterioration rate for all users of the business establishment as an index showing that efforts to improve and maintain the mental and physical conditions of all users of the business establishment satisfy certain conditions. It indicates that the establishments are grouped using the aggregate results of the maintenance period. In addition, as a method of setting each new number of units, a method of dividing the total amount of the existing business establishment system addition into two additions based on the new allocation (the idea that the number of units of the existing business establishment system addition will be reduced) Alternatively, it is conceivable to maintain the number of credits for existing additions and set up new credits for performance-based additions. The former method seems to be a realistic choice according to the recent idea of total amount regulation related to nursing care remuneration.

The procedure for newly adding the result addition should be set as shown in FIG.
・Procedure 1: At the service office, grasp the improvement/maintenance personnel every fiscal year.
・Step 2: Apply to the insurer (local government) for registration of the calculation requirements for the new addition, such as the percentage of those who have improved and maintained physical and cognitive functions.
・Step 3: The insurer examines the accepted requirements and registers them in the ledger.
・Procedure 4: Service establishments request a new addition to the National Health Insurance Federation if, for example, the percentage of those who have improved or maintained physical function or cognitive function, which is a requirement for calculation, exceeds the set level.
・Step 5: The National Health Insurance Federation examines the received new addition and makes payment to the service office.
・Step 6: The National Health Insurance Federation confirms benefits to the insurer.

Next, the correlation analysis between the service self-evaluation table (hereinafter referred to as questionnaire sheet) and the psychosomatic condition index (hereinafter referred to as questionnaire sheet correlation analysis) will be described. For the purpose of improving services, local governments, which are insurers, conduct questionnaire surveys for employees of each business establishment and have them self-evaluate the implementation status of many service items. The questionnaire sheet used for this self-evaluation is a systematic summary of matters to be addressed and service levels to be achieved. Therefore, a statistical method of correlation analysis is used to investigate which items in the results of the questionnaire using the questionnaire sheet, that is, the evaluation results of the self-evaluation of the service, are related to the improvement or maintenance of the mental and physical conditions. Details will be described below.

Correlation analysis of this questionnaire sheet is executed in four steps shown in FIG. 46 by the arithmetic processing section of the computer system.
Step 1: Definition of questionnaire index Step 2: Creation of questionnaire index Step 3: Selection of evaluation period for comparison target data Step 4: Correlation analysis between psychosomatic state index and questionnaire index Step 5: Analysis of special needs characteristics

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

The questionnaire index creation unit 461 defines service items associated with psychosomatic condition items as questionnaire items. From the results of questionnaires to the staff of each office, the implementation rate of the questionnaire items whose correspondence rate is equal to or higher than a predetermined value among the defined questionnaire items is used as the questionnaire index of the questionnaire item. The correlation analysis unit 462 obtains the psychosomatic condition index consisting of the improvement rate or the maintenance period until the deterioration of the psychosomatic condition item associated with the questionnaire item in a preset period, and the questionnaire index of the questionnaire item related to these psychosomatic condition items. perform correlation analysis. The relationship between the correlation coefficient, which is the result of this correlation analysis, and the questionnaire index value and psychosomatic state index value of each office is output.

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

First, an overview of selection of psychosomatic 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 items from the questionnaire data 471 and the psychosomatic state items from the psychosomatic state index data 472, and determines the association between them.

As shown in FIG. 49, the psychosomatic condition items (certified survey items, etc.) related to the evaluation items of the questionnaire sheet (major classification: medium items/questionnaire items) are selected. For example, the contents of the medium item "1. Meals" in the questionnaire sheet are considered to affect the improvement or maintenance of the psychosomatic condition items "2-3 Suffering" and "2-4 Dietary intake", and are associated with each other. Similarly, the contents of the middle item "2. Excretion" are considered to affect the improvement and maintenance of the psychosomatic condition items "2-5 Urination" and "2-6 Defecation", and are associated with each other. FIG. 49 shows some of the selected mental and physical condition items (certified survey items, etc.) related to the evaluation items (major classification: middle items/questionnaire items) of the questionnaire sheet. There are evaluation items (major classification: medium items/questionnaire items), and these are associated with the corresponding psychosomatic condition items. However, since it covers a wide range, illustration is omitted.

Returning to FIG. 47 , data 473 are psychosomatic condition items associated with the evaluation items (major classification: medium items/questionnaire items) of the questionnaire sheet in the definition section 4611 of the questionnaire index described above.

Next, creation of the questionnaire index in step 2 shown in FIG. 46 will be described. A questionnaire index creation unit 4612 creates a questionnaire index 474 from the questionnaire data 471 associated with the psychosomatic condition item in the questionnaire index definition unit 4611 of FIG.

The evaluation items of the questionnaire sheet consist of major categories, intermediate items, and questionnaire items, as shown in FIG. For example, if the major category is "direct living assistance", there are "diet", "excretion", "movement", etc. as middle items, and more specific items are set as questionnaire items for each middle item. . There are the following two methods for creating a questionnaire index.

・Method 1 Aggregate the implementation rate of medium item “A: 80% higher” for each business establishment and use this as a questionnaire index.
・Method 2 Allocate weighted points to the questionnaire items “A: 80% or more correspondence”, “B: 60 to 80% correspondence”, and “C: Less than 60% correspondence”, and aggregate the office average for each middle item. This is used as a questionnaire index.

Method 1 described above aggregates the implementation ratio of "A: 80% higher" for each medium item for each office. Note that “A: 80% higher response” means a service index of rank A with a response rate to users of 80% or higher. At this time, "not applicable" questionnaire items are excluded from the tally. Method 2 described above, for questionnaire items, for example, "A: 80% or more correspondence": 5 points, "B: 60 to 80% correspondence": 3 points, "C: Less than 60% correspondence": 1 The points are allotted, and the average value of each medium item is calculated. At this time, "not applicable" questionnaire items are excluded from the tally. Either method 1 or method 2 may be adopted, but method 1 is adopted here.

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 in FIG. In this analysis, as described above, the correlation analysis between the psychosomatic state index and the questionnaire index is performed in step 4 of FIG. . The survey period for the questionnaire index used this time (questionnaire survey period) is each fiscal year from 2016 to 2018, and the evaluation period for the mental and physical condition index is each fiscal year from 2016 to 2018 as shown in Figure 50. The psychosomatic state index (data 475 in FIG. 47) for the period is calculated, and correlation analysis is performed on the corresponding questionnaire index for each fiscal year.

Next, the correlation analysis between the psychosomatic condition index and the questionnaire index in step 4 of FIG. 46 is performed by the correlation analysis unit 4622 of FIG. 46, and the correlation coefficient is calculated by the following equation. The correlation coefficient is the Pearson product-moment correlation coefficient.

Generally, the following correlation strengths can be found from the value of the correlation coefficient.
±0.7 to ±1 Strong correlation ±0.4 to ±0.7 Moderate correlation ±0.2 to ±0.4 Weak correlation
0 to ±0.2 Almost no correlation

As a result of the correlation analysis described above, correlation analysis results and scatter diagram data 476 are created as shown in FIG. These will be described later. In addition, the characteristic analysis by special nursing home in step 4 shown in FIG. 46 is performed by the special nursing care characteristic analysis unit 463 in FIG. Data 464 is output.

That is, in the correlation analysis of this questionnaire sheet, the questionnaire index creating unit 461 defines questionnaire indexes from the questionnaire items associated with the psychosomatic condition items. Then, from the results of questionnaires to the staff of each office, the implementation rate of the questionnaire items whose correspondence rate is equal to or higher than a predetermined value among the questionnaire items is used as the questionnaire index of the questionnaire item. The correlation analysis unit 462 includes a psychosomatic condition index (aggregated index if there are multiple psychosomatic condition items) consisting of the improvement rate or the maintenance period until the deterioration of the psychosomatic condition item associated with the questionnaire item in a preset period; Correlation analysis of questionnaire items related to mental and physical condition items and questionnaire indices is performed. Then, the correlation coefficient, which is the result of this correlation analysis, and the relationship between the questionnaire index value and the psychosomatic state index value of each office are output.

Next, the results of the correlation analysis between the questionnaire index and the psychosomatic state 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 diagram between indices in Table 6 above. A strong positive correlation can be seen between the indicators from this scatter plot of the indicators. It can be seen that both the questionnaire index and the mental and physical condition index of business establishments 3, 7 and 10 significantly exceed the local government average.

Next, we will explain the results of the above-mentioned characteristics analysis for special nursing homes. From the results of the correlation analysis, it was found that both the questionnaire index and the mental and physical condition index significantly exceeded the local government average among the plurality of indices for business establishments 3, 7, and 10. Check whether other questionnaire indicators are the same. Based on the values of the questionnaire index and the mental and physical condition index for which correlation analysis is performed for each business establishment, the number of mental and physical condition indexes with both the questionnaire index and the mental and physical condition index values greater than the local government average is calculated as 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, there are more mental and physical condition items related to these than other special nursing homes, and it can be seen that they exceed the local government average. In addition, it can be seen that in fiscal 2018, the number of questionnaire indicators that exceeded the local government average for business establishment 10 increased, and the number of items that exceeded the local government average for mental and physical condition items related to them increased.

Based on these analysis results, a correlation analysis was performed between the questionnaire index created from the questionnaire sheet and the psychosomatic state index, and it was found that there was a positive correlation between the questionnaire index and the psychosomatic state index in the relationship for each middle item (care content). In addition, we will clarify the characteristics of each special nursing home, such as special nursing homes whose questionnaire index and mental and physical conditions are much higher than the local government average.

Next, I will explain the analysis of regional issues. One of the regional issues is the definition of the image of the state of the elderly and the state of service, and the provision and grasp of the actual state of the regional aggregation and analysis means related to the same state image. In other words, it is necessary to clarify the concrete definitions of the image of the state of the elderly and the image of the service state, including existing literature surveys. In addition, it is necessary to propose a method of aggregate analysis by region based on these state images. For this reason, in addition to mental and physical conditions, the state of the elderly should also be considered in terms of care capabilities and economic capabilities. In addition, in the service state image, it is necessary to consider the presence or absence of medical services, facility services, and residential services for in-home services. Therefore, we propose the creation and implementation of a program for tabulating and analyzing the state of the elderly by region, based on the above as appropriate.

First, the purpose, method, results, and considerations of the analysis of the state images of the elderly will be explained. The purpose of the analysis is to define an image of the state of the elderly as a combination of the mental and physical conditions of the elderly, care capacity, and economic power, and an image of the service state as a combination of in-home services. On top of that, we will analyze the image of the state of elderly people by small area and the image of service state by image of the state of elderly people in the whole municipality. As a result, it will be possible to grasp the actual state of the elderly in each region and the state of services provided for each image of the elderly. We think that it will lead to support for planning.

The definition of the elderly state image and the service state image is given below. The definition of the elderly condition image is performed by the arithmetic processing unit of the computer system according to the flow shown in FIG. 52(a). That is, in process 641, first, "definition of disability independence degree" and "definition of dementia independence degree" are performed, and these are combined to obtain "definition of psychosomatic state image". Next, "definition of nursing care ability" and "definition of economic ability" are performed, and these are combined to make "definition of state of nursing care and economic ability". Finally, as shown in Fig. 53(a), we combine four of "definition of degree of independence with disability", "definition of degree of independence with dementia", "definition of nursing care ability", and "definition of economic ability" to obtain the "elderly condition image". definition of

Next, a process 642 defines a service state image. That is, first, the "use service classification" related to the current service use status of the certification data for long-term care need is defined. Next, 39 types of service status images are defined based on combinations of service statuses used by the elderly. FIG. 53(b) shows an example of the home service status image. In addition, facilities and residential services are also included in the state image.

The state images described above are those of individual users, but they are tabulated for analysis as shown in FIG. 52(b). In other words, the process 643 counts the number of certified persons by small area and by condition image of the elderly. Aggregate the ratio of Process 644 is to cross-tabulate the state image of elderly people and service state images of the entire municipality. Aggregate another ratio. A process 645 generates a master showing the distribution of the optimal service condition image for each elderly person condition image. In step 646, the actual number of recipients is tabulated for each small area of the local government, for each elderly person state image, and for each service state image. A process 647 calculates excess and deficiency of demand and supply of each service state image from the difference between the master of the process 645 and the actual situation of the process 646 .

Table 8 below shows an outline of the method for defining the level of disability independence described above. In Table 8, (Light/Heavy) and (Light/Medium/Heavy) are prepared as grades to indicate the degree of light/heavy. It is set in two stages (light and heavy).

Table 9 below shows an overview of how to define the degree of self-reliance with dementia. In Table 9, (Light/Heavy) and (Light/Medium/Heavy) are prepared as grades representing the degree of light/heavy. Set in 3 levels (light, medium and heavy).

Table 10 below shows an overview of how to define the level of caregiving ability. In this analysis, living alone is grouped into two levels, indicated by a thick frame: "low" for those living alone, and "high" for households with two or more people.

As a method to further improve the definition accuracy of caregiving capacity, there is a method of calculating detailed patterns of elderly household composition from relationship information (for example, whether two household members are spouses or children), A method of defining by taking into account the level of care required, including certification, is conceivable. It should be noted that the long-term care ability defined above is only a guideline obtained from long-term care insurance qualification data, etc., and does not necessarily match the long-term care ability (including consideration of human relationships, etc.) that the care manager considers when creating an in-home care plan. Be careful. For this reason, it is necessary to verify the consistency between the two separately and determine the usefulness of this model of nursing care ability.

Table 11 below outlines how economic strength is defined. In Table 11, all 14 stages of insurance premiums are grouped into two stages, "high" and "low," depending on whether the individual is subject to resident tax or not, as indicated by the bold frame.

Here, the principles for defining economic strength are as follows.
・Economic power will be defined after converting the income level unique to insurers to the income level based on the local tax law that is common throughout the country.
・Economic strength (based on income level) 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 levels of the elderly and those living in the same household, and to determine the economic power of the household by focusing only on the elderly and their spouses. . The reason why only the person and his/her spouse are included is that the basic unit of economic independence is the person and his or her spouse. As a specific example, if the person's income level according to the local tax law is 4 or less, it can be seen that the spouse's economic strength is low, but if it is 5 or more, the spouse's economic strength may be high. If the person and his/her spouse have the same livelihood, if the person's income level is 5 or higher, the sum of the person's individual economic power and the spouse's economic power is regarded as "economic power." .

It should be noted that the economic power defined above is only a guide based on the data possessed by the nursing care business system. It should be noted that it does not necessarily match the economic capacity (including consideration of human relationships, etc.) that the care manager considers when creating an in-home care plan. For this reason, it is necessary to separately verify the consistency between the two and determine the usefulness of this economic capacity model.

Next, the definition of the psychosomatic state image will be explained. As shown in FIG. 53(a), Table 12 below shows the method of defining six types of psychosomatic state images as combinations of disability independence degree aggregated into two stages and dementia independence degree aggregated into three stages.

Next, we will explain the definition of the state of nursing care and economic capacity. As shown in FIG. 53( a ), Table 13 below shows how to define four types of "nursing care/financial ability state images" as combinations of nursing care ability and economic ability.

Next, the definition of the elderly condition image will be explained. As shown in FIG. 53(a), 24 types of elderly condition images shown in Table 14 below are defined as a combination of disability independence degree, dementia independence degree, care ability, and economic ability. 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 and economic ability, that is, the worse and undesirable situation.

Next, the definition of the service state image will be explained. The service status image is defined based on the current status of certification data for long-term care and service usage status. Table 15 below shows the "use service classification" relating to the current service use status of the certification data for long-term care need. The view of the table is as follows.
- Each service type (No. 1 to 37) is arranged in the order of the service items used in the authorization data, and is divided into the current service categories (nursing care benefits and preventive benefits).
• The relationship between each service type and the current status flag is shown.
・There are four types of in-home services: visiting type, outpatient type, short-term type and combined type.
・In addition, ● indicates which services are medical care services.
At the same time, a 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 combinations of service statuses used by the elderly.

In Table 16, the recipient (service user) corresponds to one of the 39 types of service status images. Service state images are roughly classified into the following seven groups.

1. 14 types of in-home services for long-term care benefits (service state image codes 1 to 14)
From the perspective of the state of the elderly (mental and physical condition, care capacity, economic capacity), four types of outpatient visits (visit only, outpatient only, both outpatient visits, neither) and the presence or absence of medical services (home-visit nursing, etc.) 14 types are defined based on the presence or absence of medical care service use) and the presence or absence of short-term (short stay) use. See special notes in Table 17 for details of each.
2. Two types of small-scale multifunctional system (complex type) of long-term care benefits (15-16)
3. Two types of long-term care benefits: residence system (group home, specific facility) (17-18)
4. 4 types of long-term care benefits facility system (special nursing care, elderly health care, medical treatment, nursing care clinic) (19 to 22)
5. 14 types of in-home 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 for residents (group homes, specific facilities) (38-39)

Originally, it is necessary to provide an appropriate service state image for each type of elderly person state image. However, in reality, there is a possibility that the supply and demand conditions for services are not being met due to the lack of service resources in each region and the lack of thorough care management that contributes to independence support and prevention of aggravation.

Next, the results of these analyzes and considerations will be described. First, the results of analysis of the state images of the elderly by small region will be explained. Table 17 below shows the aggregate analysis results of the corresponding number of people according to the state image of the elderly by subregion as of March 2019.

In Table 17, small regions are arranged on the front side, and 24 types of elderly condition images are arranged in ascending order from the left on the front side. The third column from the left lists the number of certified persons in each region. All the state images have a certain number of people, except for the state image codes 17 to 20, which have a small number of people. In addition, it can be seen that there is a certain degree of variation between regions in each image of the condition of the elderly.

Next, we will explain the analysis results of the state images of the elderly and service state images. Table 18 below shows the aggregate analysis results of the corresponding number of people related to the service status image for each elderly person status image as of March 2019.

In Table 18, 39 types of service state images are arranged in code order on the front side, and 24 types of elderly person state images are arranged in ascending order from the left on the front side. The far right column lists the number of beneficiaries in each region of the municipality as a whole. Here, it is assumed that each elderly person's condition image should have, for example, a service condition image that contributes to independence support and prevention of aggravation for each elderly person's condition image. In this case, the fact that the distribution of the condition image of the elderly is different in each region means that the required service condition image distribution and, in turn, the required amount of maintenance for each service type will be different. In other words, responding to this will lead to detailed service supply and demand adjustment for each subregion.

In this way, 24 types of elderly state images (combination of mental and physical conditions, nursing care ability and economic capacity, etc.) and 39 types of service state images (combination of in-home services, etc.) were defined, and the actual situation of the entire municipality and each stratum sub-region was identified. By performing an analysis related to this, it will be a basic material related to the supply and demand of services by region for the formulation of the next business plan.

The analysis processing of the elderly state image and the service state image described above is performed by an elderly person state image total analysis unit (corresponding to processing 641 in FIG. 52(a) corresponds to the processing 642, and the analysis unit that performs the service state analysis corresponds to the processing 643 and 644 in FIG. 52(b)).

As shown in FIG. 53( a ), the elderly state image aggregation and analysis unit determines the independence degree of dementia classified into mild, moderate and severe, the independence degree of disability classified into mild and severe, 24 types of elderly condition images are defined by the combination of the classified care ability and the economic ability classified as non-existent or non-existent. Then, as shown in Table 18, the number of people in a preset small area (large area, medium area, small area) of the municipality is calculated for each elderly condition image.

For example, the service state image aggregation and analysis unit uses the arithmetic function shown in FIG. , and 14 groups defined by the combination of whether or not they use medical nursing care services and whether or not they use short stays, 2 types of groups for small-scale multifunctional nursing care benefits, and a residential group for nursing care benefits. Defined as 2 types of groups: homes and specific facilities, 4 types of groups: special nursing homes, elderly health care, recuperation, and long-term care clinics, which are facility-based long-term care benefits, and 14 types, which are the same as the above combination of in-home services for preventive benefits. We defined 39 types of service status images, consisting of one type of small-scale multifunctional group with preventive benefits, group homes that are residential systems with preventive benefits, and two types of groups of specific facilities. For each state image, the number of people in a preset small area of the municipality is calculated.

As shown in Table 19, the analysis unit that analyzes the service status cross-tabulates the elderly status images defined in these 24 types and the service status images defined in 39 types, and service status analysis.

Next, we will explain the method of obtaining the average user institution (turnover rate) of special nursing homes, which can also be used as a material for formulating development plans for nursing care facilities (hereinafter, taking special nursing homes as an example).

In the promotion of regional management that contributes to the independence support and prevention of aggravation of people requiring long-term care, the importance of formulating a service development plan is increasing more and more. In particular, the establishment of special nursing homes with many people on waiting lists incurs a large amount of money, so it is necessary to carefully consider restrictions from various perspectives when making such investments. Specifically, it is necessary to consider the transition (past and future) of the number of elderly people, the number of people on waiting lists, and the average length of use of special nursing homes (turnover rate) in local governments.

At present, however, it is difficult to quantitatively grasp changes in the average duration of use of special nursing homes, making it difficult to formulate appropriate and accurate development plans based on data. In this embodiment, by effectively utilizing the integrated database of elderly people owned by local governments (combining resident registration, qualifications, certification for long-term care, and long-term care receipts for each individual for about 10 years), the average length of use of special nursing homes is calculated, and a proposal is made for formulating a new facility development plan based on this calculation.

As shown in FIG. 54, local governments have resident registry data 661, qualification data 662, long-term care certification data 663, and long-term care receipt data 664, and an integrated elderly person database 665 that matches these for each individual over a period of about 10 years. is constructed. Therefore, by effectively utilizing this integrated database for elderly people 665, the average utilization period of special nursing homes is calculated. That is, in the process 667 by the arithmetic processing unit of the computer system, the average usage period of the special treatment is calculated, and the transition table 668 of the average usage period by service type by year is output. Then, this data is used to formulate a new facility development plan (described later with reference to FIG. 56).

FIG. 55(a) shows an annual change table of all special nursing homes and the average usage period of each special nursing home in local governments. For 2020 and beyond, examples of future projections are shown. As for the way of thinking about the estimation, in addition to the natural estimation based on the past performance, it is necessary to add factors such as the review of the management policy by the special nursing care manager. In general, the average usage period tends to be longer each year, but due to the management policy of the special nursing home manager, the average usage period is reduced by adopting a policy of leaving early, such as special nursing care 4. In some cases, the period becomes shorter each fiscal year, and the future values for such special nursing homes are estimated to gradually decrease. FIG. 55(b) shows the long/short relationship of each special nursing home with respect to the local government average, and the increase/decrease trend of the period for each year.

The calculation process for the average period of use is performed as follows. Calculate the usage period. At this time, the ratio of continuous use within the same period (the ratio of the period of use excluding the period of absence due to moving to another facility, etc.) is also considered. In addition, users are grouped by year of use, and the average usage period of each special treatment in the same year is calculated.

In addition, the same calculation is possible for each degree of nursing care required, degree of self-reliance with dementia, and other service types. The figure for each year in the figure is 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 average special nursing use period obtained by the processing shown in FIG. Based on the year-by-year transition data 6681 of the average use period of special nursing care calculated from the average use period calculation process, the demographic data of the elderly 681 and the special nursing waiting information 682, the number of special nursing waiting users 684 is estimated in the process 638. do. Based on the estimation results, the number of users who cannot enter special nursing homes within a predetermined period (for example, three years) is estimated, and a development plan for new nursing homes is formulated.

In addition, when formulating a new special nursing home development plan, it is necessary to consider the scale of the new special nursing home (regular or mini), the large-scale repair plan for the existing special nursing home, the trend of nursing care fee revisions related to special nursing homes, etc. There is

Calculation of the above-mentioned special nursing average use period is performed by the arithmetic processing unit of the computer. The resident registry data 661, the special nursing waiting information 682 that counts the number of people waiting to enter each special nursing home belonging to the local government, and the nursing care receipt data 664 that is nursing care benefit performance data are read out, and using these, each nursing care belonging to the local government For each special nursing home, the period of use for each special nursing home is calculated from the month and year when the same special nursing home was first used and the month and year when it was last used. Calculate the average usage period of users for each year of use of this special nursing home, and obtain the transition of the average usage period by year for each special nursing home. Furthermore, it has as a function an average usage period calculation unit for estimating the average usage period for each special nursing home in the future year from the special nursing waiting information 682 and the elderly population data 681 obtained from the resident registry data 661 .

As a result, in formulating a service development plan for promoting regional management, we have quantitatively grasped the transition (past and future) of the number of elderly people, the number of people on the waiting list, and the average usage period (turnover rate) in the local government, and have made an accurate development plan. can be formulated.

Next, a medical/nursing integrated DB (database) using national health insurance database data (hereinafter referred to as KDB data) and local government nursing care data will be described.

By adding medical data to nursing care data, we can utilize medical data in four businesses: nursing care benefit business, medical/nursing care collaboration business, general business, and data health business. can be proposed. There are two ways to obtain medical data.
・How to utilize KDB data (Method 1 below)
・Method of utilizing data held by local governments (Method 2 below)

In the case of method 1, the KDB data has a service type code, but does not have a service item code. In addition, when utilizing certification data for long-term care need for analysis, etc., 77 certification survey items, etc. are required in addition to the level of long-term care level. We do not have certified survey items. Therefore, the KDB data is unsuitable for the analysis of the nursing care receipt data and the analysis utilizing the certification data for long-term care, that is, the analysis related to the mental and physical condition of the user.

In the case of Method 2, the medical data held by local governments is obtained by obtaining medical insurance claim data for the elderly aged 75 or over from the prefectural wide-area association of medical care for the elderly aged 75 or over, and obtaining medical insurance claim data for the elderly aged 75 or over from the national health insurance department. In order to obtain medical receipt data for the elderly aged 75 or over, it is necessary to obtain materials related to obtaining the data from the health insurance section inspector of the federation and follow the necessary procedures. Also, in order to obtain National Health Insurance (early elderly) medical receipt data, it is necessary to coordinate two departments, the National Health Insurance Department and the Nursing Care Insurance Department. It is necessary to solve these problems, and a new method is proposed below.

Here, the medical checkup and medical data of the KDB data is the same as the medical receipt data of the National Health Insurance (older elderly) and the medical receipt data of the elderly aged 75 and older. We have sufficient items for data analysis in four businesses: collaborative business, comprehensive business, and data health business. Therefore, the health checkup/medical data of the KDB data is obtained, and the long-term care certification data, 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, prefectural federations of national health insurance utilize the KDB system, etc., and provide medical checkup and medical expense analysis information (KDB data) to insurers (national health insurance, health, nursing care, and senior elderly departments of municipalities). . When matching the KDB data with the certification data requiring nursing care, the nursing care receipt data, and the basic data, if the KDB data includes the insured person's number, matching can be performed in the same way as with the nursing care data. If the insured person's number is not included, the items that can identify the elderly individual (sex, date of birth, level of nursing care required for a given month, residential area number, etc.) are used for comparison.

FIG. 57 shows the data acquisition and matching flow described above. The outline of each step shown in FIG. 57 will be described below.
・Step 1: Acquisition of nursing care data, etc. Obtain nursing care certification data, nursing care receipt data, and basic data (common number, qualification data, household data, etc.) from the nursing care insurance department.
・Step 2: Acquisition of 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 and nursing care data Obtained medical and nursing care data are collated for each elderly person to create an integrated elderly database consisting of medical care and nursing care data.

Specific processing of step 3 in FIG. 57 described above will be described with reference to FIG. This process is also executed by the arithmetic processing unit of the computer system. In process 671, it is determined whether or not the insured person's number is included in the obtained KDB data. If it is included, the process 672 matches the KDB data and nursing care data with this insured number. If the insured person's number is not included, a process 673 extracts items that can identify the elderly individual (sex, date of birth, degree of required monthly nursing care, residential area number, etc.), and a process 674 extracts this information. The extracted data are used to match with the KDB data.

Since the integrated elderly DB can be created according to the flow of processing shown in Fig. 57, data analysis is performed in the four businesses of nursing care benefit business, medical and nursing care collaboration business, general business, and data health business by utilizing this elderly integrated DB. can do

In this way, we made a realistic proposal for constructing an integrated medical and nursing care database by comparing qualifications and nursing care data owned by local governments with medical data in the KDB. In the future, we will be able to make specific efforts to prevent care by disease and care management by disease (support for independent care and prevention of aggravation).

Next, an embodiment in which the functions of the user history sheet described above are enhanced will be described. The user history sheet visualizes the history of the elderly obtained from nursing care data and nursing care insurance qualification information so that attendees of individual issue review meetings at community care meetings, nursing care service office staff, and care managers can refer to it. It is. At community care meetings and service staff meetings, there is a problem that it is difficult for attendees of individual problem review meetings, nursing care office staff, and care managers to grasp the medical history and hospitalization history of the elderly. It has been a challenge to integrate medical data (causative diseases, medical examination history, hospitalization history, medication history, etc.) and nursing care data (mental and physical conditions, nursing care services used, etc.) and visualize individual history. These issues may be resolved by adding medical data to the user history sheet.

As an appropriate care management method that contributes to independence support, we focused on the assessment process in the care management process, systematically organized hypotheses of support content that should be considered possible according to the user's condition, and concretely identified in each case. Investigative research has been reported that summarizes assessment items for judging how much and what kind of support is generally required for each disease group that is often the cause of certification for long-term care (development of appropriate care management methods). , April 10, 2019, The Japan Research Institute).

In this survey research, we focus on femoral neck fracture, cerebrovascular disease, heart disease, and dementia as causative diseases for long-term care certification, and examine the expected support content and related assessment/monitoring items. . For this reason, by adding medical data (history of home medical care, hospitalization, medication history, etc.) to the user history sheet, it is possible to ) is thought to contribute to the implementation of care management.

FIG. 59 shows an example (image) of a user history sheet with medical data added, Table 19 below shows item descriptions for enhancing user history sheet functions, and Table 20 below shows the data sources of the user history sheet.

In Table 19, the mental and physical condition refers to the level of nursing care required, degree of independence with dementia, degree of independence with disability, and 74 certification survey items (groups 1 to 5, special medical care). In addition, ADL (ability) refers to the certified survey items such as turning over, sitting up, maintaining a standing position on both feet, walking, standing up, standing on one foot, eyesight, hearing, swallowing, communicating intentions, and daily activities. Comprehension of daily routines, saying date of birth and age, short-term memory, saying one's name, understanding the current season, understanding places, and daily decision-making are the sum of survey results of 18 items.

As is clear from these, in addition to the level of long-term care required, it is possible to add and select outcome indicators (improvement, maintenance, etc.) for all certification survey items, and the results of the goals for the care plan (improvement and maintenance of which mental and physical conditions are the goals). etc.). In addition, by adding the causative disease, history of home medical care, hospitalization, medication history, etc. as medical data, it is possible to visualize the history of an individual combined with medical care and nursing care. The user's long-term mental and physical condition and the history of the service used can be grasped without omission. Medical data can be added based on the needs of care managers and others.

As a result, the function of the user history sheet that contributes to care management by disease will be enhanced. In other words, it is possible to visualize history information for the past 10 years such as "incorporation of all mental and physical conditions in certification surveys" and "morbidity of diseases such as cerebral apoplexy, femoral fracture, dementia, and locomotive syndrome".

While several embodiments of the invention have been described, these embodiments have been presented by way of example and are not intended to limit the scope of the invention. These novel embodiments can be embodied in various other forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are included in the scope of the invention described in the claims and equivalents thereof.

10 Storage unit 11 Long-term care requirement certification data 12 Evaluation index data 13 Nursing care benefit performance data 14, 24 Data output processing logic unit 15 Mental and physical state 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 Use service transition area 25 Long-term care level and certification application information comparison processing unit 26 Independent degree of disability and dementia Independence level comparison processing unit 27 Intermediate evaluation item score comparison processing unit 28 Physical condition item comparison processing unit 74 items 30 Long-term care requirement certification survey item comparison table 31 Long-term care level and certification application information comparison 32 Disability Comparison of degree of independence and degree of independence with dementia 33 Comparison of intermediate evaluation item scores 34 Comparison of 74 mental and physical condition items 40 Selection logic part for cases excluded from evaluation 41 Selection processing part for improvement by medical services 42 Acute exacerbation, etc. Deterioration selection processing unit 43 Influence selection processing unit due to special medical treatment 50 Case classification logic unit 51 Improvement case classification unit 52 Maintenance case classification unit 53 Deterioration case classification unit 54 Maintenance unattained case classification unit 60 Evaluation Index calculation logic part 61 Improvement rate calculation part 62 Maintenance rate calculation part 63 Improvement maintenance rate calculation part 65 Office grouping part 71 Evaluation target case number area 72 Improvement rate and maintenance rate area 73 Improvement rate and Statistical information area for maintenance rate 74 … Scatter plot quadrant area

Claims (18)

A storage unit that constitutes a computer system, and an arithmetic processing unit that performs arithmetic processing according to a predetermined program using data held in the storage unit,
The storage unit
Long-term care insurance certification basic information and long-term care certification data related to mental and physical condition items, including the degree of long-term care need of users of long-term care insurance;
Evaluation index data regarding the amount of change in the improvement and deterioration of the mental and physical condition items and the maintenance period;
Holding nursing care benefit performance data including the month the user used the nursing care service, the type of service used, and the office code that provided the service to the user,
The arithmetic processing unit is
Physical and mental to output the step change for each mental and physical condition item aggregated from the long-term care need certification data in chronological order for each step-by-step certification valid period, and to output together the application municipality for the long-term care need certification application and the application place. a state transition data output processing unit;
When the stage of the degree of care need changes to improvement or deterioration within a preset period for extracting changes in physical and mental conditions, the duration of the stage before the change, which goes back from the time of this change, is used as an evaluation index for improvement or deterioration after the change. an evaluation index data output unit for output;
It has as a function a usage service transition data output unit that outputs the type of service used and the code of the office that provided the service for each month when the user uses the nursing care service Regional comprehensive with a user history sheet creation function Care business system. The arithmetic processing unit is
From each certification data of long-term care need defined as comparison source data and comparison target data between two time points separated by a predetermined interval of the same user, the level of care requirement and certification application information can be extracted and compared with each other. A comparison processing unit of the level of care required and certification application information output to the state;
The degree of independence with disability and the degree of independence with dementia are extracted from each certification data requiring long-term care defined as the comparison source data and the data to be compared, respectively, and the comparison of the degree of independence with disability and the degree of independence with dementia output in a mutually comparable state. a processing unit;
An intermediate evaluation item score comparison processing unit that extracts intermediate evaluation item scores from each of the certification data for long-term care requirement defined as the comparison source data and the comparison target data and outputs them in a mutually comparable state;
A comparison processing unit for the 74 mental and physical condition items that extracts 74 physical and mental condition items from each of the certification data for long-term care required defined as the comparison source data and the comparison target data and outputs them in a mutually comparable state. have
2. The regional comprehensive care business system according to claim 1, comprising a function of comparing survey items for certification of long-term care need. The arithmetic processing unit is
If there is a change in the phase of the psychosomatic state within the psychosomatic state change extraction period, the period of the phase of the psychosomatic state before this change is regarded as the case, and the period of the phase of the psychosomatic condition after the change is regarded as the next case,
Using the long-term care requirement certification data and the nursing care benefit performance data of the user, the application municipality for the long-term care requirement certification in the case is new or changed, the application location is an acute hospital, and the physical and mental health of the next case a first selection processing unit that determines that a case in which the stage of the condition is improved is an improvement due to medical services and selects it as not to be evaluated;
Using the certification data of long-term care need and the actual data of long-term care benefits of the user, if the amount of the application municipality at the start of the next case is changed and the change in the physical and mental condition of the next case is worse, it is due to acute exacerbation, etc. A second selection processing unit that determines deterioration and selects out of the evaluation target;
When there is a change in the implementation status of special medical care, excluding medical treatment, between the start of the case and the start of the next case, using the data for certification of long-term care need and the actual data for nursing care benefits of the user has as a function a third selection processing unit that selects not to be evaluated,
3. The regional comprehensive care business system according to claim 1 or 2, which has an evaluation target selection function. The arithmetic processing unit is
an improvement case classifying unit that classifies, as an improvement case, a case where the mental and physical condition stage changes to an improvement within the mental and physical condition change extraction period, using the nursing care requirement certification data of the user;
Using the certification data for long-term care need of the user, if there is no change in the stage of the mental and physical condition within the period for extracting changes in physical and mental conditions, and the period of no change exceeds a preset reference maintenance period, and the physical condition A maintenance case is classified as a maintenance case when the phase of the psychosomatic state has changed to a deterioration within the change extraction period, but the period of no change in the phase of the psychosomatic state before the change exceeds the preset reference maintenance period. a classifier;
If the stage of the mental and physical condition changes to deterioration within the period of extracting the change in physical and mental condition using the certification data for long-term care need of the user, and the period of the stage of the physical condition before deterioration is shorter than the preset reference maintenance period Having as a function a worse case classification unit that classifies as a worse case,
4. The integrated community care business system according to any one of claims 1 to 3, comprising a case classification function. The arithmetic processing unit is
Using the certification data for long-term care need of the user, the stage of the mental and physical condition changes to improve or worsen within the period of extracting changes in physical and mental conditions, and the period of the stage of the physical and mental condition after this change is longer than the preset invalid maintenance period. 5. The community-based integrated care business system according to claim 4, further comprising a maintenance failure case classifying unit that classifies a maintenance failure case as a maintenance failure case if it is longer and shorter than the reference maintenance period. The arithmetic processing unit is
an improvement rate calculation unit that calculates an improvement rate by dividing the number of improvement cases for each psychosomatic condition obtained for each place of business by the total number of cases for each psychosomatic condition including the number of improvement cases;
a maintenance rate calculation unit that calculates a maintenance rate by dividing the number of maintenance cases for each mental and physical condition obtained for each place of business by the total number of cases for each mental and physical condition excluding the number of improved cases;
Improvement by dividing the sum of the number of improvement cases and the number of maintenance cases for each physical and mental condition obtained for each place of business by the total number of cases for each mental and physical condition, including the number of improvement cases and the number of maintenance cases. Having as a function an improved maintenance rate calculation unit that calculates the maintenance rate,
The regional comprehensive care business system according to claim 4 or claim 5. The arithmetic processing unit is
The values of the improvement rate and maintenance rate for each stage of the level of long-term care required for each place of business, the degree of disability independence aggregated for all stages, the degree of independence for dementia aggregated mildly and severely, 7. The regional comprehensive care business system according to claim 6, further comprising a business establishment grouping unit that plots each business establishment on a scatter diagram with the vertical axis of , and the horizontal axis of the other. The arithmetic processing unit is
Using the certification data for long-term care need of the user, if the stage of the mental and physical condition changes to an improvement within the index calculation period set for each year, the number of improved stages is counted, and the mental and physical condition is calculated within the index calculation period. If the stage changes to deterioration, count the number of deteriorated stages, obtain the difference between the number of improved stages and the number of deteriorated stages, and the direction of this difference, and determine improvement or deterioration from the direction of this difference The integrated community care business system according to claim 1, characterized by having a calculation unit for the amount of change in mental and physical conditions as a function. The arithmetic processing unit is
The number of users determined to be improved, the number of users determined to be worsened, the number of users not to change, and the number of users whose mental and physical conditions ended with deterioration The integrated community care business system according to claim 8, characterized by having an improvement rate calculation unit that counts the number of each of the business establishments and calculates the improvement rate for each business establishment from the aggregated value. . The arithmetic processing unit is
10. The method according to claim 9, characterized by having, as a function, an aggregation target exclusion unit that excludes the number of mental and physical condition changes from aggregation targets when the user receives services from a plurality of establishments within the index calculation period. Regional Comprehensive Care Business System. The arithmetic processing unit is
When the stage of the psychosomatic condition changes to improvement within the index calculation period, the number of improved stages is counted, and when the stage of the psychosomatic condition changes to deterioration, the number of deteriorated stages is counted, and these counts are calculated. 9. The integrated community care business system according to claim 8, further comprising: a mental and physical condition change frequency calculation unit that calculates the number of times of improvement and deterioration within the index calculation period. The arithmetic processing unit is
an average maintenance period calculation unit that calculates, for each stage of the mental and physical condition item, an average maintenance period in the local government until the stage deteriorates by one stage for each year;
For each stage of mental and physical condition mentioned above, the fiscal year for calculating the effect of reducing benefits is set as the current fiscal year, and the extension period for the average maintenance period for the same stage for the current fiscal year relative to the average maintenance period for the corresponding stage for the previous fiscal year is calculated, and this extension is calculated. Maintenance of mental and physical condition based on the period, the difference in the amount of benefits per person between the corresponding stage of mental and physical condition in this year and the level 1 mild stage, and the number of people whose mental and physical condition deteriorated to the corresponding stage in the said year. It has as a function a benefit cost suppression effect calculation unit that calculates the benefit cost suppression amount due to the extension of the period,
The integrated community care business system according to claim 1, characterized by: The arithmetic processing unit is
using the evaluation index data, for each stage of the physical condition item, an average maintenance period calculation unit for calculating an average maintenance period at each business establishment until the stage deteriorates; an improvement rate calculation unit that calculates the percentage of users whose stages 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 that have continued since the time period that can be calculated for the average maintenance period. a service item selection unit to
A group of establishments that provide the selected service item is defined as a first group, and a group of establishments that do not provide the selected service item is defined as a second group. 2. The integrated community care business system according to claim 1, further comprising, as a function, an effect verification unit that compares the average maintenance period and the rate of improvement for each of the psychosomatic condition items. 14. The effect verification unit outputs a scatter diagram of the average maintenance period and improvement rate of all business establishments for service items provided to users by all business establishments. Local inclusion care business system of mention. The arithmetic processing unit is
A service item associated with the mental and physical condition item is defined as a questionnaire item, and from the results of the questionnaire to the staff of each office, the implementation rate of the questionnaire item whose response rate is equal to or higher than a predetermined value among the questionnaire items is determined. a questionnaire index creation unit as an index;
Correlation analysis between the psychosomatic condition index consisting of the rate of improvement in a preset period or the maintenance period until deterioration of the psychosomatic condition item associated with the questionnaire item, and the questionnaire index of the questionnaire item related to the psychosomatic condition item. and a correlation analysis unit that outputs the correlation coefficient, which is the result of the correlation analysis, and the relationship between the questionnaire index value and the psychosomatic condition index value of each office;
The integrated community care business system according to claim 1, characterized by having as a function: The storage unit
Has resident registry data recorded in the basic resident register of the local government that is the insurer, and special nursing waiting information that includes the number of people waiting to enter each special nursing home belonging to the said local government,
The arithmetic processing unit is
Using the nursing care benefit record data, for each special nursing home belonging to the above municipality, calculate the special nursing usage period of each user from the start date and last usage date of the same special nursing home, and start using this special nursing home Calculate the average period of use of users for each year and obtain the transition of the average period of use for each year for the special nursing home, and from the above-mentioned special nursing waiting information and the elderly population data obtained from the above-mentioned resident registry data 2. The regional comprehensive care business system according to claim 1, having as a function an average usage period calculation unit for estimating an average usage period for each special nursing home in future years. The arithmetic processing unit is
A combination of dementia independence categorized as mild, moderate and severe, disability independence categorized as mild and severe, care ability categorized as non-existent, and economic ability categorized as non-existent an elderly person state image aggregation and analysis unit that calculates the number of people in a preset small area of the local government for each of the 24 types of elderly person state images defined by
14 groups defined by combinations of home visits for nursing care benefits, outpatient only, both outpatient visits, neither, use of medical care services, and use of short stays, and long-term care benefits. Two types of small-scale multi-functional groups, group homes that are residential systems with nursing care benefits, two types of groups of special facilities, and special nursing care, elderly health, recuperation, and nursing care clinics that are facility systems with nursing care benefits. 4 types of groups, 14 groups defined as the same combination of preventive benefits at home services, 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 and 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 a group of types;
It has an analysis unit as a function that cross-tabulates the elderly condition images defined in these 24 types and the service condition images defined in 39 types, and analyzes the service condition images by elderly condition in the entire municipality. The regional comprehensive care business system according to claim 1, characterized by: The arithmetic processing unit is
An optimal service state image distribution master for each elderly state image defined based on the analysis results of the analysis unit that analyzes the service state image for each elderly state in the entire municipality;
Having as a function a surplus/deficiency calculation unit for calculating the surplus/deficiency of supply and demand for each service state image from the difference between the service state image distribution for each elderly person state image for each small area and the optimal service state image distribution master. The integrated community care business system according to claim 17.

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