JP2022105135A - Regional comprehensive care business system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a regional comprehensive care business system that allows quantitative estimation of a benefit cost restriction effect based on improvement and maintenance of a mind and body state through various measures to perform verification of cost-effectiveness.

SOLUTION: A regional comprehensive care business system includes: in estimation master basic information acquisition processing, a step 1 of acquiring basic information from authorization data of a nursing care insurance and results of benefits in order to constitute an estimation master; a user number transition estimation step 2 of estimating the transition of the number of users; a (cumulative) benefit cost transition estimation processing step 3 of estimating how a (cumulative) benefit cost makes a transition from a result of estimating the transition of the number of users; a healthy life expectancy transition estimation processing step 4 of performing processing of estimating the transition of the healthy life expectancy of the user by using, of basic information for estimation mentioned above, average start age information for every application section; a comparison processing step 5 between new measure effect cases for a (cumulative) benefit cost estimation result; and a comparison processing step 6 between new measure effect cases for the healthy life expectancy.

SELECTED DRAWING: Figure 2

COPYRIGHT: (C)2022,JPO&INPIT

Description

The embodiment of the present invention is a regional inclusion that enables cost-effectiveness estimation when implementing measures by regional management for all life stage 4 projects (data health, comprehensive, long-term care benefits, medical assistance cooperation) for the elderly. Regarding the care business system.

Regional management that reflects the actual conditions of each region is being realized with the following four concepts.
1. 1. It covers all four life stage projects for the elderly (data health, comprehensive, long-term care benefits, medical assistance cooperation).
2. 2. Build a comprehensive database (database) that centrally manages the history information of the elderly owned by each department of the local government.
3. 3. Create functions such as analysis and extraction that derive solutions for each business from big data of the elderly.
4. Digitally verify the effects of each business's efforts and respond to financial incentive systems.

Through the realization of PDCA work consisting of these concepts, P (plan formulation), D (measure execution), C (performance evaluation), and A (problem analysis), the service infrastructure will be improved and the quality of service will be improved for a healthy life. We are working to extend the cost of medical care and nursing care, and to realize a sustainable social security system.

The data health business is a business for disease prevention and aggravation prevention (diabetes / depression, etc.). A comprehensive business is a business for reducing the new certification rate and supporting independence. The long-term care benefit business is a business for supporting independence and preventing the severity of the disease. Furthermore, the medical / long-term care cooperation business is a business for extending the period of home medical care and deterring acute exacerbations.

When implementing measures by regional management, it is necessary to estimate the cost-effectiveness before implementation and to verify the same after implementation. The effect refers to the effect of suppressing medical / long-term care benefits based on the improvement / maintenance of the physical and mental condition. The costs include personnel costs, facility maintenance / operation costs, computer system investment costs, etc., which are policy costs.

For example, it is necessary to estimate the improvement of the mental and physical condition of the elderly and the extension of the maintenance period for each service type, and estimate the benefit cost as a result, but the current situation is that the method is unknown (there is no mechanism). In addition, after implementing the measures, it is necessary to grasp the effects fairly and quantitatively, but the current situation is that there is no mechanism for that.

Japanese Patent Publication No. 2017-215787

In this way, the conventional technique could not estimate the effect of implementing the measures, and therefore could not verify the cost-effectiveness.

The present invention is to provide a community-based comprehensive care business system capable of quantitatively estimating the effect of suppressing benefit costs based on the improvement and maintenance of mental and physical conditions by various measures and verifying cost effectiveness.

The community-based comprehensive care business system according to the embodiment of the present invention is based on the first public data in which the number of benefits and benefit costs to users by service type of nursing care insurance are disclosed, by service type, and the degree of nursing care required. The monthly user number data of another user is acquired and disclosed by the user number acquisition processing unit constituting the user number master, the number of users possessed by the user number master, and the second public data. The number of mental and physical condition changes by service type and the number of mental and physical condition changes according to the degree of care required, which is calculated from the rate of change in mental and physical condition according to the degree of care required, is aggregated according to the predetermined mild and severe degree of care required. The mental and physical change information acquisition processing unit that constitutes the master, and the salary acquisition processing unit that constitutes the benefit expense master by acquiring the monthly benefit amount per person for each service type and the degree of care required from the first public data. , Benefits per person when improving mental and physical condition, calculated from the difference in benefit costs per person when the mental and physical condition changes according to service type and degree of care required, calculated using the monthly benefit data held in this benefit cost master. The difference in cost and the difference in benefit cost per person when the physical and mental condition deteriorates are the benefit cost difference table held for each service type and for each of the mild and severe levels of care required, and the service type and level of care required. The current value of the improvement rate and maintenance period until deterioration of the mental and physical condition by mild and severe, and the target value of the improvement rate and maintenance period until deterioration of the mental and physical condition by these service types and mild and severe degree of care required. Difference between the difference master with the target value in which the difference value is held, the number of people with changes in the mental and physical state held by the mental and physical state change number master, and the target value of the improvement rate held in the difference master. From the above, the number of persons subject to benefit cost suppression is calculated for each service type and for the mild and severe level of care required, and the mental and physical aspects of the service type and the mild and severe level of care required are held in the benefit cost difference table. The first benefit for calculating the amount of benefit cost restraint due to improvement of mental and physical condition from the difference in benefit cost per person at the time of condition improvement and the number of persons subject to benefit cost restraint according to the service type and the degree of care required. The difference between the cost suppression amount calculation unit, the number of people whose mental and physical condition has deteriorated in the mental and physical condition change master, and the target value of the maintenance period until deterioration held in the difference master, and the benefit cost difference table. Calculate the amount of benefit cost restraint due to the difference in maintenance period until deterioration from the difference in benefit cost per person when the mental and physical condition deteriorates according to the type of service held in and the degree of care required is mild and severe. It is characterized by having a benefit cost restraint amount calculation unit of 2.

According to the above configuration, it is possible to quantitatively estimate the benefit cost suppression effect based on the estimation of improvement and maintenance of mental and physical condition, and it is also possible to quantitatively estimate the benefit cost suppression effect by various measures and verify the effect after the measures are implemented. Become.

It is a conceptual diagram of the community-based comprehensive care business system which concerns on embodiment of this invention. It is a block diagram which shows the overall system composition of the community-based comprehensive care business system which concerns on embodiment. It is a figure explaining the system configuration for configuring the basic information master in embodiment. It is a figure explaining the stage change of the mental and physical condition of a long-term care insurance user. (A) quantitatively explains the stage change of the mental and physical condition of the long-term care insurance user, and (b) is the mental and physical condition change information that maintains the direction and ratio of transition to the next stage for each stage and the average maintenance period up to the fiber. It is a figure which shows the master. It is a figure explaining an example of the method of setting the target value in constructing the mental and physical state change information master in an embodiment. It is a figure explaining the content of the average benefit cost acquisition processing per person in an embodiment. It is a figure which shows the average benefit cost information master per person in an embodiment. It is a figure explaining the content of the average start age acquisition processing of the initial user in an embodiment. It is a figure. It is a figure which shows the update age-specific, average start age master which holds the data of the initial user in embodiment. It is a figure explaining the content of the average start age acquisition processing of the newly increased users in an embodiment. It is a figure which shows the update age-specific, average start age master which holds the data of the new increase user in an embodiment. It is a figure explaining the content of the user number acquisition processing of the initial user in an embodiment. It is a figure which shows the user number master which holds the user number data of the initial user in an embodiment. It is a figure explaining the content of the user number acquisition processing of the newly increased users in an embodiment. It is a figure which shows the user number master which holds the user number data of the newly increased user in an embodiment. It is a figure explaining the outline of the master basic information acquisition process for estimation by the public information analysis in an embodiment. It is a figure which shows the data classification of the actual data, the public information, and the correction coefficient in an embodiment. It is a figure which shows the process of acquiring the estimation master basic information intermediate data of a target municipality from the public information (Ministry of Health, Labor and Welfare report aggregation, long-term care insurance business status report) in an embodiment. It is a figure explaining the specific example of the mental and physical state change (deterioration / improvement) information acquisition processing in an embodiment with reference to FIG. It is a figure which shows the deterioration rate and improvement rate which becomes the median value data of the master basic information for estimation of a model municipality in an embodiment. It is a figure explaining the average benefit cost acquisition processing per person using the public information in an embodiment. It is a figure which shows the average number of units per person which becomes the median value data of the master basic information for estimation of a model municipality in an embodiment. It is a figure explaining the average start age acquisition process using the public information in an embodiment. It is a figure which shows the median value data of the master basic information for estimation of a model municipality in an embodiment, by gender, by the degree of long-term care, and the average starting age. It is a figure explaining the user number acquisition process using the public information in an embodiment. It is a figure which shows the service type, gender, application category, and the number of users data which becomes the median value data of the master basic information for estimation of a model municipality in an embodiment. It is explanatory drawing of the process which uses the ratio of the estimation master basic information intermediate data of a model municipality acquired from the public information in an embodiment, and the estimation master basic information intermediate data of a public information analysis municipality as a correction coefficient. It is a figure which shows the master basic information intermediate data for estimation which shows the deterioration rate / improvement rate of a public information analysis local government in an embodiment. It is a figure which shows the information correction coefficient of the mental and physical condition change (deterioration / improvement) according to the care-requiring degree which constitutes the estimation master basic information in an embodiment. Public information analysis in the embodiment It is a figure which shows the monthly average number of units per person by the degree of care required of the master basic information intermediate data for estimation of a local government. The value of the master basic information intermediate data for estimation of the model municipality in the embodiment and the value of the master basic information intermediate data for estimation of the public information analysis municipality are obtained by the degree of care required and the monthly average unit number correction coefficient per person. It is a figure which shows. Public information analysis in the embodiment It is a figure which shows the master basic information intermediate data for estimation of a local government by gender, the degree of care required, and the average start age. It is a figure which shows the gender-separation, the care-requiring degree, and the average start age correction coefficient of the master basic information for estimation in an embodiment. Public information analysis in the embodiment It is a figure which shows the master basic information intermediate data for estimation of a local government, by gender, by the degree of care required, and the number of users. It is a figure which shows the correction coefficient of the number of users, by gender, by the degree of long-term care, which constitutes the basic information for estimation master in the embodiment. Explanatory diagram of the process of acquiring the estimation master basic information (estimated value) of the public information analysis municipality by multiplying the estimation master basic information correction coefficient acquired from the actual data of the model municipality in the embodiment. Is. (A) is information on changes in the mental and physical condition of the model municipality in the embodiment (information on changes in the mental and physical condition (deterioration / improvement) by degree of care required: real-side data), and (b) is the degree of care required in the estimation master basic information correction coefficient. It is a figure which shows the information correction coefficient of another, mental and physical state change (deterioration / improvement). Analysis of public information in the embodiment It is a figure which shows the information (estimated value) of the change (deterioration / improvement) of the mental and physical condition according to the degree of care required of a local government. (A) is a diagram showing the average benefit cost per person (real side data) of the model municipality in the embodiment, and (b) is a diagram showing the monthly average unit number correction coefficient per person of the master basic information correction coefficient for estimation. .. It is a figure which shows the data which shows the average benefit cost (estimated value) per person of a public information analysis municipality in an embodiment. (A) shows the model municipality in the embodiment by gender, degree of long-term care, and average starting age (real side data), and (b) is the estimation master basic information correction coefficient by gender, degree of long-term care, and average. It is a figure which shows the start age correction coefficient. It is a figure which shows the public information analysis in an embodiment, by gender, by the degree of care required, and the average starting age (estimated value) of the local government. (A) shows the model municipality by gender, degree of care required, and number of users (real side data) in the embodiment, and (b) shows the master basic information correction coefficient for estimation by gender, degree of care required, and use. It is a figure which shows the person number correction coefficient. It is a figure which shows the public information analysis | master basic information for promotion of a local government by gender, the degree of care required, and the number of users (estimated value) in an embodiment. It is a figure explaining the outline of the user number transition estimation process in an embodiment. It is a figure which shows the mental and physical state change information master used for the user number transition estimation process in an embodiment. It is a figure which shows the specific example of the user number transition estimation process in an embodiment. It is a figure explaining the flow of the benefit cost (cumulative) transition estimation processing in an embodiment. It is a figure explaining the healthy life expectancy transition estimation process in an embodiment. The cumulative results of the degree of long-term care required, the effect case of the new measure, and the benefit cost in the embodiment are shown in (a) after 3 years, (b) after 6 years, and (c) after 9 years. It is a figure shown as a table. It is a figure which shows the result of having compared the care-requiring degree, the new measure effect case, and the age in the embodiment by the new measure effect cases 1, 2, and 3. In the figure explaining the embodiment in which the cost-effectiveness estimation / verification function is simplified, (a) shows the business establishment group compared with the local government average in terms of both the improvement rate and the average maintenance period until deterioration. , (B) show the effect of suppressing the benefit cost when the maintenance period until the deterioration of the user's need for nursing care is extended. It is explanatory drawing of the process which obtains the number of users by the degree of care-requiring and the service type in the simplified embodiment. One person at the time of service type, long-term care level light and heavy, and mental and physical condition change in order to request the benefit cost to the beneficiary (long-term care insurance user) according to the level of long-term care and service type in the simplified embodiment. It is an explanatory diagram of the process for finding the difference in benefit costs per. In the simplified embodiment, it is an explanatory diagram of the process of setting the target value (improvement rate by service type, mild and severe need for long-term care, maintenance period until deterioration) that should be improved after a predetermined period from the implementation of the measure. be. It is a figure explaining the benefit cost restraint amount calculation process by improving the improvement rate of the care-requiring degree in the simplified embodiment. It is a figure explaining the calculation process of the benefit cost restraint amount by extending the maintenance period until deterioration in a simplified embodiment. A benefit cost restraint table that combines the benefit cost restraint amount by improving the improvement rate of the degree of care required and the benefit cost restraint amount by extending the maintenance period until deterioration after a predetermined period from the implementation of the measures in the simplified implementation form. It is a figure explaining. It is a figure explaining the simulation with the reduction of the benefit cost by the implementation of the measure of the local government in the simplified embodiment. It is a figure which compares each function of the long-term care simple version and the long-term care detailed version. It is a figure explaining the mutual relationship when the cost-effectiveness estimation / verification function in a long-term care benefit business is horizontally developed in a comprehensive business.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. First, the overall flow of the community-based comprehensive care business system according to this embodiment will be described with reference to the conceptual diagram of FIG. FIG. 1 describes the flow from step 1 to step 4 among the main functions in this embodiment. In this embodiment, there are functions of steps 5 and 6 in addition to these, which will be described later.

In the estimation master basic information acquisition process of step 1, basic information is acquired from the long-term care insurance certification data and the benefit record in order to configure the estimation master. The basic information to be acquired is information on changes in mental and physical condition, information on average benefit cost per user who is certified as requiring long-term care, information on average starting age for each application category, and information on the number of users by application category. Here, the mental and physical state change information is information indicating whether the stage of the mental and physical state of the user (degree of care required, etc.) has deteriorated / improved.

In the user number transition estimation in step 2, the above-mentioned user number transition estimation is performed. In this process of estimating the number of users, the transition is estimated with the passage of time, such as one year, two years, three years, and so on from the start of the estimation. The current phase is from 1 year after the start of estimation, the new policy phase 1 is from 1 year to 2 years, and the new policy phase 2 is from 2 years to 3 years.

The number of users is divided into update and change and new application categories. The number of initial renewal changes is the number of users who need the certification period of the certification data of the renewal change application at the start of estimation. The initial number of new users is the number of users who need the certification period for the certification data of new applications at the start of estimation. These initial renewal change estimates and initial new estimates indicate the initial users who already have a benefit record at the start of the estimation.

On the other hand, the new estimation after 1 year to the new estimation after 3 years is a new increase in users who will increase by new application after the estimation starts. The number of newly increased users is the total number of newly applied users in the previous year.

The user transition estimation process in step 2 estimates how the number of these users will change as the predetermined period for implementing the new measure elapses. The downward-sloping frame in the figure indicates a decrease in the number of users. This user transition estimation process is performed using the mental and physical condition change information and the user number information for each application category among the above-mentioned basic information for estimation. The details of this user transition estimation process will be described later.

In the benefit cost (cumulative) transition estimation process in step 3, how the benefit cost (cumulative) changes is estimated from the above-mentioned user number transition estimation result. The downward-sloping stepped frame in the figure indicates a decrease in benefit costs. This benefit cost (cumulative) transition estimation process is performed using the average benefit cost acquisition information per person among the above-mentioned basic information for estimation. The details of this benefit cost (cumulative) transition estimation process will be described later.

In the healthy life expectancy transition estimation process in step 4, the healthy life expectancy transition estimation process of the user is performed using the average start age information for each application category among the above-mentioned basic information for estimation. Here, "healthy life expectancy" is defined as the age at which the heaviest stage that does not require long-term care is finally completed for each 65 mental and physical condition items (certification data) such as the degree of long-term care required and the degree of independence of dementia. The stepped frame that rises to the right in the figure indicates the extension of healthy life expectancy. The details of this healthy life expectancy transition estimation process will be described later.

FIG. 2 is a system block diagram showing an overall overview of the processing system according to this embodiment. This processing system is realized by a computer system, and includes an estimation master basic information acquisition processing unit 11 corresponding to the above-mentioned step 1, a user number transition estimation processing unit 12 corresponding to step 2, and step 3. It has a benefit cost (cumulative) transition estimation processing unit 13 corresponding to the above and a healthy life expectancy transition estimation processing unit 14 corresponding to step 4.

Of these, the number of users transition estimation processing unit 12 corresponding to step 2, the benefit cost (cumulative) transition estimation processing unit 13 corresponding to step 3, and the healthy life expectancy transition estimation processing unit 14 corresponding to step 4 are mutually exclusive. It is executed for each different new measure, and new measure effect cases 1, 2 and 3 are generated respectively.

In addition to this, in response to the above-mentioned processing results, although not shown in FIG. 1, the new measure effect case-to-case comparison processing unit 15 of the benefit cost (cumulative) estimation result corresponding to step 5 and the health corresponding to step 6 A comparison processing unit 16 between new measure effect cases of life expectancy is provided to compare each new measure effect case.

The estimation master basic information acquisition processing unit 11 includes a mental and physical condition change (deterioration / improvement) information acquisition processing unit 111, an average benefit cost acquisition processing unit 112 per person, an application category / average start age acquisition processing unit 113, and an application. It has a classification / user number acquisition processing unit 114. As shown in FIG. 3, each of these processing units 111, 112, 113, 114 acquires the estimation master basic information (actual data) 20 from the certification data 101 and the benefit record 102 possessed by the local government (long-term care insurer). ..

That is, the estimation master basic information (mental and physical state change information master) 201 acquired by the mental and physical state change (deterioration / improvement) information acquisition processing unit 111 is, for example, deteriorated according to the degree of care required (support 1 to 5). Rate, end rate, improvement rate, average maintenance period until deterioration, average maintenance period until end, average maintenance period until improvement, etc. In addition, the estimation master basic information (average benefit cost master per person) 202 acquired by the average benefit cost acquisition processing unit 112 per person is the average benefit cost according to the degree of long-term care (support 1 to 5). Yes, the estimation master basic information (application category / average start age master) 203 acquired by the application category / average start age acquisition processing unit 113 is new according to the degree of long-term care (support 1 to 5). The age of the applicant and the renewal change applicant, and the estimation master basic information (application category / user number master) 204 acquired by the application category / user number acquisition processing unit 114 is the degree of long-term care required (needed). Support 1 to long-term care required 5) The number of new applicants and renewal change applicants.

Here, let us look at the function of the mental and physical state change (deterioration / improvement) information acquisition processing unit 111. FIG. 4 shows changes in the stage (degree of care required) of the mental and physical condition of the user X. In FIG. 4, the mental and physical condition change information acquisition period is from April 2012 to April 2015, and User X made a new application in May 2012 and was certified as requiring nursing care 3. After that, he maintained the need for care 3 for 6 months, which is the renewal period of the degree of need for care, and applied for renewal in November of the same year 6 months later, and was certified as needing care 2 which was improved by one step. After that, in February 2013, when the renewal period of the long-term care level of 6 months was not reached, a change application was made, and the long-term care level deteriorated by two levels, and it was certified as long-term care required 4. After maintaining this long-term care level 4 for 12 months, an application for renewal was made, and the long-term care level deteriorated by one level, and it was certified as long-term care required 5. After that, there was no application at the end of the renewal period, so it was terminated. The term "termination" refers to the case where there is no application due to the death of the user or moving to another local government.

In this way, from all the certification data that includes the certification validity period (end) within the mental and physical condition change information acquisition period, the maintenance period of deterioration / termination / improvement to the next stage according to the user and the degree of long-term care required. And the change direction and change stage amount are obtained. FIG. 4 shows an example in which the mental and physical condition change information acquisition period is set to 3 years before the estimation start date. The mental and physical condition change information acquisition period is an arbitrary period (for example, the period of the previous business year, etc.).

When acquiring information on changes in mental and physical condition by user, degree of long-term care, and mental and physical condition change by service type, only the certified data and benefit performance of users who have a record of benefits for that service type within the period for acquiring information on changes in mental and physical condition. Is targeted. The age at the end of the maintenance period is obtained from the date of birth of the actual benefits, and is used as the end of the maintenance period.

If the direction of change is "end", that is, if there is no next certification data, the certification validity period (end) of this certification data will be the end date of the maintenance period. If the application category for the next certification data is death, the month before the certification application date for the next certification data shall be the end date of the maintenance period. Furthermore, if the degree of care required for the next certification data is not applicable, the month before the certification application date for the next certification data shall be the end date of the maintenance period.

The changes in the mental and physical states of FIG. 4 are summarized in FIG. 5 (a). In FIG. 5A, the user X has a degree of long-term care required of “3” in the application category “new”, and after maintaining for 6 months, the user X is “improved” (-) by one stage. The degree of care required "care required 2" for which "renewal" was applied continued for 3 months, and then became "deteriorated" (+) in two stages. The degree of need for care "4" for which "change" was applied was continued for 12 months and then "worse" (+) by one level. The degree of care required for "renewal" application "Need care 5" will be terminated because there is no next application even after the renewal period of 6 months has passed. Information on each item is also acquired for other users Y ..., but the description is omitted.

Since information on changes in the mental and physical condition of each user can be obtained in this way, as shown in FIG. 5 (b), the "degree of long-term care" (stage of mental and physical condition) and the "next degree of long-term care" ( The data for each item of (next stage), "direction of change" to the next level of long-term care, "transition ratio" to the next level of long-term care, and "average maintenance period" to the next stage are constructed.

For example, the rate at which the user of the long-term care 4 deteriorates to the long-term care 5 (transition rate) is 70%, and the average maintenance period is 16 months. The case of transitioning to another degree of long-term care is also as shown in each item, and the description is omitted.

The mental and physical state change information master 201 used in the user transition estimation process, which will be described later, is composed of the information in the case of transitioning to the next long-term care level according to the long-term care required level shown in FIG. 5 (b).

That is, the mental / physical state change information master 201 is obtained from the data recording the change in the stage of the physical / mental state of the user in a predetermined period (the period for acquiring the mental / physical state change information) before the estimation start date set in advance. Mental and physical state change information for all users, step by step, next step, direction of change to this next step, transition ratio to the next step, and average maintenance period until the change to the next step It is to be held as.

In constructing such a mental and physical state change information master 201, it is necessary to set a target value to be improved by the new measures implemented during the estimation period.

An example of this target value setting method will be described with reference to FIG. Long-term care insurance provides various services to users according to their physical and mental conditions. The types of services include home-based services such as visits and outpatient services (abbreviated as "home"), special nursing homes for the elderly (abbreviated as "special nursing home"), nursing homes for the elderly (abbreviated as "old health"), and nursing homes (abbreviated as "old health"). There are seven types: "medical treatment"), group home (abbreviated as "GH"), specific facility (abbreviated as "special treatment"), and small-scale multifunctional (abbreviated as "kota").

The user receives one of the above-mentioned service types according to the mental and physical condition, but may transition to another service type in the middle of the mental and physical condition change information acquisition period. For example, a user who has received the "home" service may shift to the "long-term care" service. In this way, considering the transition of the user to another service type, it is not preferable to perform the processing after step 2 with the mental and physical state change information for each service type, and all the service types (the above-mentioned seven types of services). It is necessary to perform the processing after step 2 using "information on mental and physical state changes of all service types" including all).

However, if it is estimated that the benefit cost reduction effect can be obtained by performing the processes of steps 2 and 3 using "information on changes in mental and physical condition of all service types", it is not possible to analyze which service type the new measures have contributed to. ..

Therefore, as shown in FIG. 6, the mental / physical state change information acquisition processing unit 111 first obtains the certification data 101A for all service types, the benefit record 102A, and the matching table 103A for collating so that these data can be used mutually. Use it to acquire information 105A for all service types, by user, by year and month when the maintenance period starts, by degree of care required, and mental and physical condition change information. Information on all service types is based on the system management number (user number), maintenance period start / end date, maintenance period (deterioration / end / improvement), change direction (deterioration / end / improvement), and degree of care required. be. In addition, one degree of care required is determined by the start and end date of one maintenance period.

From this information 105A, further information on the degree of care required for all service types, direction of change (deterioration / end / improvement), rate of change (deterioration / end / improvement), and average maintenance period (deterioration / end / improvement). 106A is obtained.

In addition, by service type, from the data of users who have used the service type (certification data 101B, benefit record 102B, and matching table 103B), by service type, by user, by maintenance period start end date, required. Acquire the mental and physical condition change information 105B according to the degree of care. The information for each service type is the system management number (user number), maintenance period start / end date, maintenance period (deterioration / end / improvement), change direction ((deterioration / end / improvement), and degree of care required. In this case as well, one degree of care required is determined in the start and end years of one maintenance period.

From this information 105B, information 106B of the degree of care required for each service type, the direction of change (deterioration / end / improvement), the rate of change (deterioration / end / improvement), and the average maintenance period (deterioration / end / improvement) can be obtained. Be done.

Using these information 106A and 106B, the operations represented by the following equations (1) to (4) are performed.

First, as shown in the equation (1), the "value of accumulated information on mental and physical condition changes by service type" is obtained. This weights the mental and physical condition change information for each service type obtained by multiple service types by multiplying the contribution rate by the ratio of the corresponding service type benefit cost to the total benefit cost, and these are all services. It is the sum of the types (7 types).

The mental and physical condition change information refers to the stage, for example, the direction of change to deterioration / end / improvement according to the degree of care required, the rate of change to deterioration / end / improvement, and the average duration until deterioration / end / improvement. include. Further, the equations (1), (2), (3) and (4) are all classified according to the degree of long-term care required.

"Physical and physical condition change information accumulated value by service type" = (Home benefit cost / All benefit cost x Home mental and physical condition change information) + (Special benefit cost / All benefit cost x Special mental and physical condition change information) + (Elderly health benefit Expenses / total benefit expenses x information on changes in physical and mental condition of the elderly) + (medical treatment benefit expenses / total benefit expenses x information on changes in mental and physical condition) + (GH benefit expenses / total benefit expenses x GH information on changes in mental and physical condition) + (special benefit expenses) / Total benefit cost x Special mental and physical condition change information) + (Small benefit cost / Total benefit cost x Small mental and physical condition change information) ・ ・ ・ (1)

Next, the "mental and physical state change information ratio" is obtained as shown by the equation (2). This is the ratio of the above-mentioned "mental and physical condition change information for all service types" and "accumulated value of mental and physical condition change information for each service type".

"Mental and physical condition change information ratio" = "Mental and physical condition change information for all service types" / "Physical and physical condition change information accumulated value for each service type" ・ ・ ・ (2)

Next, as shown in the formula (3), the "target accumulated value of mental and physical condition change information by service type" is obtained. This is a new value for the mental and physical condition change information of a specific service type (for example, "special training") for which a new measure is to be implemented, out of multiple service types, for a predetermined period after the estimation start date. Add the target value (for example, + 10%) to improve the mental and physical condition change information by the measure, add the above-mentioned weighted specific service type (“special training”) mental and physical condition change information, and the rest. It is the sum of the mental and physical condition change information (the above-mentioned weighting) of the six types of services.

"Physical and physical condition change information accumulated value by target service type" = (Home benefit cost / total benefit cost x home mental and physical condition change information) + (Special benefit cost / total benefit cost x (Special care mental and physical condition change information + 10) %)) + (Elderly health benefit cost / total benefit cost x old health mental and physical condition change information) + (medical treatment benefit cost / total benefit cost x medical treatment mental and physical condition change information) + (GH benefit cost / total benefit cost x GH mental and physical condition change information) ) + (Special benefit cost / Total benefit cost x Special mental and physical condition change information) + (Small benefit cost / All benefit cost x Small mental and physical condition change information) ・ ・ ・ (3)

Since the deterioration rate and the improvement rate are contradictory, set the target value at the same time (deterioration rate + end rate + improvement rate = 100%).

Then, as shown by the equation (4), "information on change in mental and physical condition of all target service types" is calculated. This is the product of the "accumulated value of mental and physical condition change information by target service type" and the above-mentioned "mental and physical condition change information ratio".

"Mental and physical condition change information for all target service types" = "Physical and physical condition change information accumulated value for each target service type" x "Mental and physical condition change information ratio" ・ ・ ・ (4)

The mental / physical state change information acquisition processing unit 111 is held in the mental / physical state change information master 201 shown in FIG. 5B using the value of the “mental / physical state change information of all target service types” thus obtained. It composes information on changes in mental and physical conditions.

In the above explanation, "special nursing home" is exemplified as a specific service type (hereinafter referred to as "attention service type") in which the mental and physical condition change information is improved by the new measure, and the target value is set to + 10%. Similarly, by configuring each item value of the mental and physical condition change information master 201 corresponding to other new measures that specify other attention service types and set target values, as shown in FIG. 2, each item is configured. A new measure effect case can be obtained.

Next, the processing contents of the average benefit cost acquisition processing unit 112 per person will be described with reference to FIGS. 7 and 8.

The average benefit cost acquisition processing unit 112 per person obtains the average benefit cost information per person for each stage (degree of care required) from the long-term care insurance certification data 101 and the benefit record 102 of the estimation start date, and obtains this. Have the average benefit cost information master 202 hold it.

FIG. 7 shows an example of a method of calculating the average benefit cost per person for the care-requiring 3 with the average benefit cost acquisition date per person as March 2015. For the average benefit cost acquisition date per person, specify any date in which the data actually exists. In this example, it is one month before the above-mentioned estimation start date (April 2015).

In this case, first, the user requiring nursing care 3 is extracted from the certification data (status of the degree of nursing care required) 101A shown in FIG. 7 (a). That is, all the users requiring nursing care 3 are extracted from the certified data 101A. Next, from the benefit record 102A shown in FIG. 7 (b), the total number of units of the extracted average benefit cost acquisition date per user is extracted to obtain the average benefit cost. The average benefit cost is calculated by the following formula (5).

Average benefit cost per person in need of nursing care 3 = (total number of units of user B + total number of units of user E + total number of units of user F) ÷ 3 × unit price by labor cost ratio by region = (11000) + 16000 + 5000) ÷ 3 × Unit price by labor cost ratio by region = 106670 yen ・ ・ ・ (5)

The above formula (5) is an example of calculating the unit price for each labor cost ratio by region at an additional ratio of 0% (10 yen) according to the region. In addition, the "number of service units after determination" in the actual benefit data is used.

If the average benefit cost per person is acquired only in January, the bias in the benefit cost within the fiscal year will affect the subsequent transition estimation, so the average benefit cost per person will be acquired in several months. , The average value may be used.

For other stages (degree of care required), the average benefit cost per person is obtained by the same method, and these values are held in the average benefit cost information master 202 shown in FIG.

Next, the processing contents of the application category / average start age acquisition processing unit 113 will be described. Here, the average starting age may be an initial user or a newly increased user, but first, the case of the initial user will be described with reference to FIGS. 9 and 10.

The average start age acquisition processing unit 113 for each application category is based on the user age as of the acquisition date of the average start age acquired from the certification data (application category and status of care required) 101B shown in FIG. 9 (a). , Calculate the average starting age for each application category and degree of care required. This process is a process to obtain the average starting age of the initial user according to the degree of long-term care, which is the basis of the transition estimation. The process of acquiring the average starting age of new users, which is increasing with new applications, will be described later.

The following is an example of a method for obtaining the average starting age of the care-requiring 3 of the renewal change applicant, with the average starting age acquisition date being March 2015. For the average start age acquisition date, specify any year and month in which the data actually exists. In this example, it is one month before the above-mentioned estimation start year (April 2015). In addition, in order to exclude the influence of the bias of users by month, the results of multiple months may be averaged.

The application category / average start age acquisition processing unit 113 first extracts from the certification data 101B all the users requiring nursing care 3 who apply for renewal / change of the average start age acquisition date. In the example of FIG. 9, user E and user F are the target persons. Next, the average starting age of the user E and the user F is obtained from the benefit record 102B shown in FIG. 9 (b). However, even if you are certified as requiring long-term care, if you are not using the service, you will not be eligible because there is no actual benefit. Calculate the average age of user E and user F. This is the average starting age of care required 3.

Here, when the age of the average starting age acquisition date is calculated from the user's date of birth of the benefit record, the average starting age acquisition date is not in the "day" unit but in the "year / month" unit. The age is calculated by the following formula (6) using up to "date of birth".

(Average starting age acquisition year x 12 + Average starting age acquisition month-Year of birth x 12-Birth month) / 12 ... (6)
From equation (6), if the date of birth of user E is February 11, 1935,
(2015 x 12 + 3-1935 x 12-2) / 12 = 80.0833 ...
= 80 years old.
If User F's date of birth is October 14, 1940
(2015 x 12 + 3-1940 x 12-10) / 12 = 74.416 ...
= 74 years old.

The average age is (80 + 74) / 2 = 78 years. That is, the average starting age of the care-requiring 3 for the initial user's renewal change application is 78 years old. For other stages (degree of care required), the average starting age is calculated in the same manner, and the application category / average starting age master 203 shown in FIG. 10 is held as the renewal age / average starting age.

The new application (years) listed in the upper part of the average start age master 203 by renewal age is the new application of our users, and as mentioned above, the latest certification data at the beginning (at the start of transition). Is the average age for all users who are new applications, and is calculated by the same method as the renewal change application.

Next, the case of a newly increased user of the application category / average start age acquisition processing unit 13 will be described with reference to FIGS. 11 and 12. In the case of this newly increased user, as shown in FIG. 11A, the average starting age for each application category (new) and the degree of care required is calculated based on the user age within the average starting age acquisition period. do. This process is a process to acquire the average starting age of newly increased users by the degree of long-term care, which is the basis of the transition estimation, and is used for the estimation from the second year onward.

The following is an example of a method for obtaining the average starting age of the long-term care required 3 of a new applicant, with the average starting age acquisition period being one year before March 2015, which is one month before the estimation start time. For the average start age acquisition period, specify any year, month, and period in which the data actually exists. In this example, as described above, one year is set up to one month before the estimation start date (April 2015).

As shown in FIG. 11A, the application category / average start age acquisition processing unit 13 first selects all users whose new application is certified as requiring nursing care 3 from the certification data 101C during the user number acquisition period. Extract. In the example, user I and user K are the target persons.

Next, as shown in FIG. 11B, the age of the average start age acquisition period of the user I and the user K is obtained from the benefit record 102C. Here, the relevant date is the above-mentioned certification start date of the new application. However, even if you are certified as requiring long-term care, if you are not using the service, you will not be eligible because there is no actual benefit. Then, the average ages of these users I and K are calculated.

When calculating the age as of the relevant year and month within the average starting age acquisition period from the date of birth of the user who has actually received benefits, use the following formula (7). Since the year and month are not in "day" units but in "year / month" units, the age is calculated using up to the user's "date of birth".
(Year x 12 + Month-Birth year x 12-Birth month) / 12 ... (7)

From the above formula (7), assuming that the date of birth of user I is February 17, 1938, as shown in FIG. 11 (b), when the date is May 2014,
(2014 x 12 +5-1938 x 12-2) / 12 = 76.25
= 76 years old.

Assuming that the date of birth of user K is September 28, 1940, if the date is June 2014 as shown in Fig. 11 (b),
(2014 x 12 + 6-1943 x 12-9) / 12 = 70.75
= 70 years old.

The average age is (76 + 70) / 2 = 73 years. That is, the average starting age of the need for nursing care 3 for newly increased users is 73 years. The average starting age is calculated in the same manner for other stages (degree of care required), and the average starting age master 203 for each application category shown in FIG. 12 is held as the average age of newly increased users. And, as mentioned above, it is used for the estimation after the second year.

Next, the processing contents of the application category / user number acquisition processing unit 114 will be described. Here, regarding the process of acquiring the number of users, there are cases of initial users and cases of newly increased users. First, the case of initial users will be described with reference to FIGS. 13 and 14.

In the case of the initial user, the application category / user number acquisition processing unit 114 has the certification data (application category and status of care required) 101D shown in FIGS. 13 (a) and 13 (b), and the benefit record (data presence / absence only). , The content does not matter) From 102D, the number of users by application category and degree of care required as of the acquisition date of the number of users is acquired. This process is a process to acquire the number of initial users by the degree of long-term care required, which is the basis of the transition estimation. The process of acquiring the number of new users, which will increase with new applications, will be described later.

Number of users For the acquisition date, specify any date in which the data actually exists. The following is an example of a method for obtaining the number of users requiring nursing care 3 of a renewal change applicant, with the acquisition date of the number of users being March 2015. In this example, it is one month before the above-mentioned estimation start date (April 2015). In addition, in order to exclude the influence of the bias of users by month, the results of multiple months may be averaged.

The application category / user number acquisition processing unit 114 extracts all the users requiring long-term care 3 from the certification data 101D and the benefit record 102D for the renewal change application of the user number acquisition date. In the example, user E and user F are the target persons. The number of target users is the number of users. In the example, it is assumed that there are other subjects in addition to the user E and the user F, and the number is 6479 as shown in FIG. However, even if you are certified as requiring long-term care, if you are not using the service, you will not be eligible because there is no actual benefit.

The number of people extracted in this way is the user requiring nursing care 3 of the initial user's renewal change application. The number of users is calculated in the same manner for other stages (degree of care required), and the number of users by application category / user number master 204 shown in FIG. 14 is retained as the initial number of users.

The new application (person) described in the upper part of the user number master 204 by application category is the initial number, and as mentioned above, the latest certification data at the beginning (at the start of the transition) is the new application. It is the average number of people for all users, and is calculated by the same method as the renewal change application.

Next, the process for the case of a newly increased user of the application category / user number acquisition processing unit 114 will be described with reference to FIGS. 15 and 16. In this process, the number of users is within the acquisition period based on the certification data (application category and degree of care required) shown in FIG. 15 (a) and the benefit record shown in FIG. Obtain the number of users by application category (new) and degree of care required. This process is a process to acquire the number of newly increased users by the degree of long-term care, which is the basis of the transition estimation, and is used for the estimation from the second year onward.

The following is an example of a method for obtaining the number of users requiring nursing care 3 for a new applicant, assuming that the acquisition period for the number of users is one year before March 2015. For the number of users acquisition period, specify any year and month when the data actually exists. In this example, the year is set to one month before the above-mentioned estimation start date (April 2015).

The application category / user number acquisition processing unit 114 extracts all users who have the certification data of the new application for nursing care 3 within the user number acquisition period. In the example of FIGS. 15A and 15B, the user I and the user K are the target persons. The number of users is defined as the number of users. In the example, 1904 people are used as shown in FIG. 16 assuming that there are other subjects in addition to the user I and the user K. However, even if you are certified as requiring long-term care, if you are not using the service, you will not be eligible because there is no actual benefit.

The number of people extracted in this way is the user of the new application requiring nursing care 3. The number of users is calculated in the same manner for other stages (degree of care required), and the number of users by application category master 204 shown in FIG. 16 is retained as the number of newly increased users. And, as mentioned above, it is used for the estimation after the second year.

In the above explanation, as shown in FIG. 3, the actual data of the local government is analyzed (referred to as actual data analysis), and the estimation master basic information 20 to be used after "Step 2 User number transition estimation" described later is acquired. However, there are some local governments (nursing care insurers) who cannot analyze such actual data. For local governments that cannot analyze actual data, the local governments that have performed actual data analysis will be used as model local governments, and the local governments that cannot analyze actual data will be analyzed using public information issued by the Ministry of Health, Labor and Welfare (public information analysis local governments). ), The estimation master basic information 20 is created.

The outline of the estimation master basic information acquisition process by public information analysis will be described below with reference to FIG. First, the basic information acquisition department 30 acquires the public information 41 of the model local government and the public information 42 of the public information analysis local government from the public information (total of reports from the Ministry of Health, Labor and Welfare, long-term care insurance business status report), and estimates each. Master basic information (intermediate data) 43 and 44 are constructed. Then, the correction coefficient calculation processing unit 31 obtains the difference between the estimation master basic information (intermediate data) 43 and 44, and sets this difference as the correction coefficient 32. The estimation master basic information estimation processing unit 33 multiplies the estimation master basic information 20 obtained by the actual data processing of the model municipality by the correction coefficient 32, and the public information analysis municipality estimation master basic information (estimated value) 34. Is calculated.

Here, since the public information does not have some data divisions (data items), the correction coefficient obtained from the public information also does not have some data divisions. As mentioned above, when the actual data of the model municipality is multiplied by the correction coefficient to estimate the actual data of the public information analysis municipality, there is a difference in the data classification between the two. However, the fact that there is no data classification means that all data classifications are included, so (actual data with data classifications) × (correction coefficient including all data classifications) = (public information analysis municipality with data classifications) Actual data) can be inferred. FIG. 18 shows the actual data, the public information, and the data classification of the correction coefficient.

The outline (details will be described later) of the method of calculating the correction coefficient from the public information of the model local government and the public information of the public information analysis local government shown in FIG. 18 are listed as follows.
(1) Correction coefficient for mental and physical condition change (deterioration / improvement) information: Obtain the deterioration rate and improvement rate for each degree of long-term care required from the report total 3-7 or the long-term care benefit cost fact-finding survey, and obtain the correction coefficient.
(2) Correction coefficient of average benefit cost per person: From the long-term care insurance business status report 08h, by service type, degree of long-term care, number of units, 05-1h, 05-2h, 06-1h, 06-2h, 07 The correction coefficient is calculated from the number of beneficiaries obtained from -1h.
(3) Correction coefficient for each application category / average start age: Obtain the correction coefficient for each degree of care required / start age from the report summary 3-1.
(4) Correction coefficient for each application category and number of users: Gender ratio of report total 3-1 and report total 3- Obtain the correction coefficient from the product of 3 application category ratios.

FIG. 19 illustrates a process of acquiring the estimation master basic information intermediate data (43, 44 of FIG. 17) of the target local government from the above-mentioned public information (Ministry of Health, Labor and Welfare report aggregation, long-term care insurance business status report). The estimation master basic information acquired here is input information for the “correction coefficient calculation process” described later. The target local government is a "model local government" or a "public information analysis local government".

In FIG. 19, the mental and physical condition change (deterioration / improvement) information acquisition processing unit 301 acquires the deterioration / improvement rate from the Ministry of Health, Labor and Welfare report tabulation 3-7 of the target local government. Since the average maintenance period cannot be obtained from the Ministry of Health, Labor and Welfare report tabulation 3-7, the average maintenance period obtained from the actual data of the model municipality is used.

The average benefit cost per person acquisition processing unit 302 acquires the average benefit cost per person from the long-term care insurance business status report (annual report) 08h of the target municipality.

In the application category / average start age acquisition processing unit 303, the average age obtained from the Ministry of Health, Labor and Welfare report tabulation 3-1 of the target municipality is defined as the application category / average start age.

By application category / number of users The acquisition processing unit 304 is thickened by the service type / degree of care required / number of users acquired from the long-term care insurance business status reports 05-2h, 06-2h, 07-1h of the target municipality. Multiply the gender ratio of the Ministry of Health, Labor and Welfare report total 3-1 by the application category ratio of the Ministry of Health, Labor and Welfare report total 3-3 to obtain the service type, gender, application category, and number of users.

A specific example of the processing by the above-mentioned mental and physical state change (deterioration / improvement) information acquisition processing unit 301 will be described with reference to FIG. From the previous secondary judgment and the current secondary judgment for each degree of long-term care in the Ministry of Health, Labor and Welfare report total 3-7, the change in mental and physical condition (deterioration / improvement) according to the degree of long-term care is acquired. Since the average maintenance period is not included in the public information, the information of the model municipality is used as it is.

The following is an example of finding the improvement rate of care-requiring 3. The improvement rate of the need for nursing care 3 is (the number of cases that required nursing care 3 last time and improved from the number of cases requiring nursing care 3 this time) / (the number of cases requiring nursing care 3 last time).
(The number of cases requiring long-term care 1 in the previous long-term care 3 + the number of cases requiring long-term care 2 in the previous long-term care 3 + the number of cases requiring long-term care 1 in the previous long-term care 3 + the number of cases requiring long-term care 2 in the previous long-term care 3) / (Number of cases requiring long-term care 3 last time) In the example of Fig. 20,
(0 + 0 + 274 + 549) / 3169 = 0.26, and the improvement rate is 26%.

Next, an example of finding the deterioration rate of the care-requiring 3 is shown. The deterioration rate of the need for nursing care 3 is (the number of cases requiring nursing care 3 last time and worse than the number of cases requiring nursing care 3 this time) / (the number of cases requiring nursing care 3 last time).
(The number of cases requiring long-term care 3 in the previous time + the number of cases requiring long-term care 4 in the previous time + the number of cases requiring long-term care 5 in the previous time 3) / (Number of cases requiring long-term care 3 in the previous time)
(756 + 280) / 3169 = 0.33, and the deterioration rate is 33%.

As shown in FIG. 21, the deterioration rate and the improvement rate are obtained for other degrees of long-term care. The numerical values in FIG. 20 are the numerical values of the model local government, and the deterioration rate and the improvement rate in FIG. 21 are the estimation master basic information median value data 43 of the model local government. Public information analysis Master basic information for estimation of local governments Median value data 44 is also configured in the same manner.

Next, the processing of the average benefit cost acquisition processing unit 302 per person will be described with reference to FIG. In this process, the number of beneficiaries by service type, degree of care required, and number of credits are obtained from the long-term care insurance business status report 08h, and the number of beneficiaries by service type is obtained from the reports 05-2h, 06-2h, and 07-1h. , Service type, long-term care required, monthly average benefit per person.

For example, in the case of outpatient care, the average benefit cost per person is calculated by service type and required from the "08-1h (number of units 1)" sheet (excerpt) of the long-term care insurance business status report 08h shown in Fig. 22 (a). Obtain the number of credits by degree of long-term care and cumulative total for the year, and from the "05-2-1t" sheet (excerpt) of the long-term care insurance business status report 05-2h shown in Fig.・ Obtain the cumulative number of months for the cumulative total of the fiscal year, calculate (by service type / degree of long-term care / cumulative number of credits for the fiscal year) / (by service type / degree of long-term care / cumulative total of the fiscal year), and calculate. Calculate the monthly average number of units per person. Next, the average monthly benefit cost per person is calculated by the following formula (8).

Monthly average benefit cost per person by degree of long-term care = (monthly average number of units per person by degree of long-term care) × Unit price by labor cost ratio by region (8)

Taking the long-term care required 3 in FIG. 22 (b) as an example, the number of units for each service type / degree of long-term care / cumulative total for the year is 220,000, and the number of units for each service type / degree of long-term care / cumulative total for the year is 21,000. By degree of long-term care ・ The monthly average number of units per person is 10.48, and from equation (8), the monthly average benefit cost per person for long-term care 3 is 10.48 x 1000 x 10 = 104800 yen. ..

The formula (8) is an example of calculating the unit price for each labor cost ratio by region at an additional ratio of 0% (10 yen) according to the region. In addition, when acquiring the average benefit cost per person regardless of the service type, the number of units is 08h, the total number of "08-1h (number of units 1)" sheets, and the number of users is 05-1h, 06-1h, 07. -Calculate by acquiring the number of beneficiaries from the total number of 1h.

As shown in FIG. 23, the average number of units per person is obtained for other degrees of long-term care by the same method. The numerical value in FIG. 22 is the numerical value of the model local government, and the average number of units per person in FIG. 23 is the estimation master basic information intermediate data 43 of the model local government. The master basic information intermediate data 44 for estimation of the public information analysis local government is also configured in the same manner.

Next, the processing of the application category-specific / average start age acquisition processing unit 303 will be described with reference to FIG. 24. In this process, the gender, the degree of long-term care required, and the average starting age are obtained from the Ministry of Health, Labor and Welfare report tabulation 3-1 (excerpt) shown in FIG. The following shows how to obtain the average starting age of males requiring nursing care 3. In the report summary 3-1, the age categories from 65 to under 100 are in the 5-year range, so the representative values for each age category are determined. The median value of the range is 62.5 for those under 65 years old and 102.5 for those over 100 years old. A weighted average is taken for each representative value and number of cases, and the average age is calculated. In the example of FIG. 24, the average calculated value of the male requiring nursing care 3 is 80.03858, and the average starting age is 80 years.

In this way, the gender, the degree of long-term care, and the average starting age are calculated as shown in FIG. 25. The numerical values in FIG. 24 are the numerical values of the model local government, and the numerical values by gender, the degree of long-term care required, and the average starting age in FIG. 25 are the estimation master basic information intermediate data 43 of the model local government. The master basic information intermediate data 44 for estimation of the public information analysis local government is also configured in the same manner.

Next, the processing of the application category / user number acquisition processing unit 304 will be described with reference to FIG. 26. In this process, the ratio of men and women in the Ministry of Health, Labor and Welfare report total 3-1 is added to the service type, degree of care required, and number of users obtained from the long-term care insurance business status reports 05-2h, 06-2h, and 07-1h. Multiply the application category ratio of the Ministry of Health, Labor and Welfare report total 3-3 to obtain the service type, gender, application category, and number of users. An example of the acquisition method is shown below.

The method of acquiring the number of users is as described in the above-mentioned processing for acquiring average benefit expenses per person. That is, from the "05-2-1t" sheet (excerpt) of the long-term care insurance business status report 05-2h shown in FIG. The gender ratio is obtained from the report total 3-1 (excerpt) shown in Fig. 26 (a) by degree of long-term care, gender, and number of certified cases. The application category ratio is obtained from the report total 3-3 (excerpt) shown in Fig. 26 (b) by the degree of long-term care required, by application category, and the number of certified cases. Based on these, the following formula (9) is used to calculate the service type, gender, application category, and number of users.

Service type / Gender / Application category / Number of users = (Number of users) x (Gender ratio) x (Application category ratio) ... (9)

In the case of 3 requiring nursing care in FIG. 26, the number of users = 21000, male ratio = 2009/5142 = 39%, female ratio = 3133/5142 = 61%, new application category ratio = 1176 / (5239-97) = 23%. , Renewal change application classification ratio = (3078 + 888) / (5239-97) = 77%, so the number of male new application users is 21000 x 39% x 23% = 1884 (persons) from formula (9).
Will be.

Other service types, gender, application categories, and number of users are also calculated by the same method as shown in FIG. 27. The numerical values in FIG. 26 are the numerical values of the model local government, and the service type, gender, application category, and number of users in FIG. 27 are the estimation master basic information intermediate data 43 of the model local government. The master basic information intermediate data 44 for estimation of the public information analysis local government is also configured in the same manner.

Next, the correction coefficient is the ratio of the estimation master basic information intermediate data 43 of the model municipality and the estimation master basic information intermediate data 44 of the public information analysis municipality obtained from the public information (total of reports from the Ministry of Health, Labor and Welfare, long-term care insurance business status report). The processing of the correction coefficient calculation processing unit 31 will be described with reference to FIG. 28.

The mental and physical condition change (deterioration / improvement) information correction coefficient calculation processing unit 311 acquires from the estimation master basic information intermediate data 43 of the model municipality based on the public information and the estimation master basic information intermediate data 44 of the public information analysis municipality, respectively. The ratio of the mental and physical state change information is obtained, and this ratio is used as the mental and physical state change information correction coefficient in the estimation master basic information correction coefficient 32.

The average benefit cost correction coefficient calculation processing unit 312 per person acquired from the model municipality estimation master basic information intermediate data 43 based on public information and the public information analysis municipality estimation master basic information intermediate data 44, respectively. The ratio of the average benefit cost per person is obtained, and this ratio is used as the correction coefficient of the average benefit cost per person in the estimation master basic information correction coefficient 32.

The application category / average start age correction coefficient calculation process 313 is obtained from the model municipality estimation master basic information intermediate data 43 based on public information and the public information analysis municipality estimation master basic information intermediate data 44, respectively. The ratio between different and average ages is obtained, and this ratio is used as the correction coefficient for each application category and average age in the estimation master basic information correction coefficient 32.

The application category / user number correction coefficient calculation processing unit 314 is an application obtained from the model municipality estimation master basic information intermediate data 43 based on public information and the public information analysis municipality estimation master basic information intermediate data 44, respectively. The ratio between each category and the number of users is obtained, and this ratio is used as the correction coefficient for each application category and the number of users in the estimation master basic information correction coefficient 32.

Next, the specific processing of the above-mentioned mental and physical state change (deterioration / improvement) information correction coefficient calculation processing unit 311 will be described. This processing is performed by the mental and physical condition change information (information on the degree of care required / mental and physical condition change (deterioration / improvement) information) of the estimation master basic information intermediate data 43 of the model municipality shown in FIG. 21 and the public information analysis shown in FIG. The ratio of the mental and physical condition change information (information on the degree of care required / mental and physical condition change (deterioration / improvement) information) of the intermediate data 44 of the master basic information for estimation of the local government is obtained, and this is used as the correction coefficient.

The correction coefficient is obtained by the following equations (10) and (11).
Deterioration rate correction coefficient = (Public information analysis by degree of long-term care / deterioration rate) / (Model municipality by degree of long-term care / deterioration rate) ・ ・ ・ (10)
Improvement rate correction coefficient = (Public information analysis by degree of long-term care / improvement rate of local government) / (Model local government by degree of long-term care / improvement rate) ・ ・ ・ (11)

From the value of the estimation master basic information intermediate data 43 of the model municipality shown in FIG. 21 and the value of the estimation master basic information intermediate data 44 of the public information analysis municipality shown in FIG. Another ・ Mental and physical condition change (deterioration / improvement) information correction coefficient is obtained, and constitutes the estimation master basic information correction coefficient 32.

Next, the specific processing of the average benefit cost correction coefficient calculation processing unit 312 per person will be described. This process is performed between the monthly average number of units per person for each degree of care required in the model municipality estimation master basic information intermediate data 43 shown in FIG. 23 and the public information analysis municipality estimation master basic information intermediate shown in FIG. Obtain the ratio of the data 44 according to the degree of long-term care required and the monthly average number of units per person, and use this as the correction coefficient.

The correction coefficient is calculated by the following equation (12).
Monthly average number of units correction coefficient per person
= (Monthly average number of units per person in public information analysis municipalities) / (Monthly average number of units per person in model municipalities) ・ ・ ・ (12)

From the value of the estimation master basic information intermediate data 43 of the model municipality shown in FIG. 23 and the value of the estimation master basic information intermediate data 44 of the public information analysis municipality shown in FIG. 31, the degree of care required shown in FIG. 32. Separately, a monthly average unit number correction coefficient per person is obtained, and constitutes a master basic information correction coefficient 32 for estimation.

Next, the specific processing of the application category-specific / average start age correction coefficient calculation processing unit 313 will be described. In this process, the model municipality's estimation master basic information intermediate data 43 shown in FIG. 25 is divided by gender, the degree of long-term care required, and the average starting age, and the public information analysis municipality's estimation master basic information intermediate data shown in FIG. 33. Obtain the ratio of 44 by gender, by degree of long-term care, and average starting age, and use this as the correction coefficient.

The correction coefficient is obtained by the following equation (13).
Average starting age correction coefficient = (Average starting age of public information analysis municipality) / (Average starting age of model municipality) ・ ・ ・ (13)

From the value of the estimation master basic information intermediate data 43 of the model municipality shown in FIG. 25 and the value of the estimation master basic information intermediate data 44 of the public information analysis municipality shown in FIG. The correction coefficient for each degree of care required and the average start age is obtained, and the correction coefficient for the estimation master basic information 32 is configured.

Next, the specific processing of the application category x user number correction coefficient calculation processing unit 314 will be described. This processing is performed by the estimation master basic information intermediate data 43 of the model municipality shown in FIG. 27 by gender, the degree of care required, and the average starting age, and the public information analysis municipality estimation master basic information intermediate data shown in FIG. Find the ratio of 44 by gender, degree of care required, and number of users, and use this as the correction coefficient.

The correction coefficient is obtained by the following equation (14).
Number of users correction coefficient = (Number of users of public information analysis municipality) / (Number of users of model municipality) ・ ・ ・ (14)

From the value of the estimation master basic information intermediate data 43 of the model municipality shown in FIG. 27 and the value of the estimation master basic information intermediate data 44 of the public information analysis municipality shown in FIG. The care-requiring degree / user correction coefficient is obtained, and the estimation master basic information correction coefficient 32 is configured.

Next, the processing of the estimation master basic information estimation processing unit 33 shown in FIG. 17 will be described with reference to FIG. 37. This process is performed by using the estimation master basic information (same as the estimation master basic information 20 described in FIG. 3) acquired from the actual data of the model municipality and the estimation master calculated by the correction coefficient calculation process described in FIG. 28. The basic information correction coefficient 32 is multiplied to obtain the estimation master basic information (estimated value) 34 of the public information analysis local government.

Psychosomatic state change (deterioration / improvement) information The actual data estimation processing unit 331 adds the mental and physical state change information of the estimation master basic information (model municipality) 20 to the correction coefficient of the mental and physical state change information in the estimation master basic information correction coefficient 32. To acquire the mental and physical condition change information constituting the estimation master basic information 34 of the public information analysis local government.

The average benefit cost per person actual data estimation processing unit 332 corrects the average benefit cost per person in the estimation master basic information correction coefficient 32 to the average benefit cost per person in the estimation master basic information (model municipality) 20. Multiply by a coefficient to obtain the average benefit cost per person that constitutes the estimation master basic information 34 of the public information analysis municipality.

The application category / average start age actual data estimation processing unit 333 adds the average benefit cost per person of the estimation master basic information (model municipality) 20 to the application category / average start age in the estimation master basic information correction coefficient 32. By multiplying by the correction coefficient of, the average start age for each application category that constitutes the estimation master basic information 34 of the public information analysis local government is obtained.

By application category / number of users The actual data estimation processing unit 334 determines the number of users by application category of the estimation master basic information (model municipality) 20 and the application category / user in the estimation master basic information correction coefficient 32. Multiply the correction coefficient of the number correction coefficient to obtain the number of users by application category that constitutes the estimation master basic information 34 of the public information analysis local government.

Next, specific processing examples of each of the above-mentioned processing units 331,332,333,334 will be described. The mental and physical condition change (deterioration / improvement) information actual data estimation processing unit 331 is the same as the mental and physical condition change information 201 of the estimation master basic information (actual data) 20 described in FIG. 3, and is the model municipality shown in FIG. 38 (a). Mental and physical condition change information (degree of care required / change of mental and physical condition (deterioration / improvement) information: real side data) 201A, the estimation master basic information correction coefficient 32 shown in Fig. By multiplying the state change (deterioration / improvement) information correction coefficient, the information (estimated value) of the mental and physical condition change (deterioration / improvement) information (estimated value) according to the degree of care required and the mental and physical condition change (deterioration / improvement) of the mental and physical condition change information estimated value 34 of the public information analysis municipality shown in FIG. get.

This estimated value of actual data is obtained by the following equations (15) and (16).
Estimated value of deterioration rate = (deterioration rate of actual data of model municipality by degree of long-term care / deterioration rate) × (deterioration rate correction coefficient of information correction coefficient by degree of long-term care / change in mental and physical condition (deterioration / improvement)) ・ ・ ・ (15 )
Estimated value of improvement rate = (actual data of model municipality by degree of long-term care / improvement rate) × (change in mental and physical condition (deterioration / improvement) by degree of long-term care / improvement rate correction rate of information correction coefficient) ・ ・ ・ (16) )

From the values shown in FIGS. 38 (a) and 38 (b), the estimated values of the mental and physical state change information of the values shown in FIG. 39 can be obtained by the equations (15) and (16).

The average benefit cost estimation processing unit 332 per person is one of the model municipalities shown in FIG. 40 (a), which is the same as the average benefit cost 202 per person in the estimation master basic information (actual data) 20 described in FIG. The average benefit cost per person (actual data) 202A is multiplied by the monthly average unit number correction coefficient of the estimation master basic information correction coefficient 32 shown in Fig. 41, and the public information analysis municipality shown in Fig. 41. Obtain the average benefit cost (estimated value) per person in the promotion master basic information 34.

This estimated value of actual data is obtained by the following equation (17).
Estimated average benefit cost per person = (Average benefit cost per person in actual data of model municipality) × (Monthly average unit number correction coefficient per person) ・ ・ ・ (17)

From the values shown in FIGS. 40 (a) and 40 (b), the estimated average benefit cost of the values shown in FIG. 41 can be obtained by the formula (17).

The application category / average start age estimation processing unit 333 is the same as the application category / average start age 203 of the estimation master basic information (actual data) 20 described in FIG. 3, of the model municipality shown in FIG. 42 (a). Multiply the average start age (real side data) 203A by gender / degree of long-term care required by the estimation master basic information correction factor 32 shown in Fig. (B) by gender / degree of long-term care / average start age correction coefficient. Then, the public information analysis local government promotion master basic information 34 shown in FIG. 43 is acquired by gender, by degree of long-term care, and average starting age (estimated value).

This estimated value of actual data is obtained by the following equation (18).
Average starting age = (Average starting age of actual data of model municipality) × (Average starting age correction coefficient) ・ ・ ・ (18)

From the values shown in FIGS. 42 (a) and 42 (b), the estimated values of the values shown in FIG. 43 by gender, degree of long-term care, and average starting age can be obtained by the formula (18).

The application category / user number estimation processing unit 334 is the same as the application category / user number 204 of the estimation master basic information (actual data) 20 described in FIG. 3, of the model municipality shown in FIG. 44 (a). Multiply by gender / degree of care required / number of users (actual side data) 204A by gender / degree of care required / number of users correction coefficient 32 for estimation master basic information correction coefficient 32 shown in Fig. (B). Then, the public information analysis local government promotion master basic information 34 shown in FIG. 45 is acquired by gender, degree of care required, and number of users (estimated value).

This actual data estimation value is obtained by the following equation (19).
Number of users = (Number of users of actual data of model municipality) × (Number of users correction coefficient)
... (19)

From the values shown in FIGS. 44 (a) and 44 (b), the values shown in FIG. 45 can be obtained by gender, degree of long-term care, and estimated number of users by the formula (19).

In the above-mentioned estimation master basic information acquisition process (public information analysis), it is necessary to analyze the actual data of the model municipality. However, if there is no model municipality with similar scale and form, it will be difficult to estimate the transition. Therefore, the accuracy of the master basic information for estimation is reduced, but the method that enables transition estimation without actual data analysis of the model municipality will be described below.

In this case, the basic information acquisition unit 30 described with reference to FIG. 17 acquires the public information 42 of the public information analysis municipality from the public information (Ministry of Health, Labor and Welfare report tabulation, long-term care insurance business status report), and the estimation master basic information (estimation master basic information). Intermediate data) 44 is constructed. Then, the master basic information (intermediate data) 44 for estimation of this public information analysis local government is used as it is for the transition estimation.

In this way, even if there is no model municipality with a similar scale and form, it is possible to estimate the transition of public information analysis municipalities.

Next, the user number transition estimation process in step 2 described above will be described. First, the outline of the process will be described with reference to FIG.

In this process, based on the situation of the number of users at the start of estimation, the mental and physical condition change information 201 of the estimation master basic information 20 (including the mental and physical condition change information 201A of the public information analysis local government, but unified with 201 below. The rate of change and average maintenance period according to the degree of care required (explained), and the number of users 204 by application category (similarly, the number of users by application category of public information analysis municipality 204A is included, but unified with 204 below. To estimate the number of users.

Here, the users include a renewal change application user and a new application user as described above. The initial number of users of the renewal change application shall be the number of users who need the certification period of the renewal change application at the start of the estimation. In addition, among new application users, new application users in the first year (at the start of estimation) are not within the range of the past year, but all users whose latest certification data at the start of estimation is a new application. .. On the other hand, for the initial users of new applications after the second year, the number of users who newly applied for one year before the start of estimation is used for estimation. When considering the increase or decrease in the population, increase or decrease the number of users.

Assuming that the number of users requiring long-term care 4 is 700 among the users applying for renewal change, for example, the users of this long-term care 4 need the next stage (end, long-term care 5, care required 3, long-term care 2, ... ) Is estimated by using the mental and physical condition change information 201. After that, for the next stage (other than the end) that has changed, the number of people requiring nursing care 5, the number of people requiring nursing care 3, the number of people requiring nursing care 2, ... The mental and physical state change information master 201 used for this estimation is shown in FIG. 47.

For new application users, the transition is estimated by the same method as for the above-mentioned update change application users.

A specific example of the above-mentioned user number transition estimation process will be described with reference to FIG. 48. In FIG. 48, the mental and physical state change information master 201 is the same as that shown in FIGS. 3, 5 (b), and 47, and the next care-requiring degree according to the application period and the next care-requiring degree according to the application period, and the next It is a master that holds the "transition ratio" and "average maintenance period" for each degree of long-term care required. This is created in advance by the estimation master basic information acquisition processing unit 11 described above.

The user number master 204 by application category is the same as that shown in FIGS. 3, 14, and 45, and is a master that holds the number of users as of the acquisition date of the number of users according to the degree of care required. be. This is also created in advance by the estimation master basic information acquisition processing unit 11 described above.

The first estimation process (12A in FIG. 48) of the user number transition estimation processing unit 12 shown in FIG. 2 is the starting point of the user number transition estimation. The estimation process will be described below.

First, the user number transition estimation process (hereinafter referred to as "each estimation process") for each of the updated / changed users and the new users for all the care-requiring levels is started at the same time. That is, "estimation start date", "degree of care required", "initial number of users", "application category", and "gender" are set as attribute information of each estimation process. The estimated number of people to start is obtained from the application category / user number master by the degree of care required and gender based on the degree of care required and gender. In the example of FIG. 48, the number of women requiring nursing care 4 is 700.

For each estimation process, from the mental and physical condition change information master 201, the estimation start date of each estimation process is the "transition ratio" and "average maintenance period" within the application period of the mental and physical condition change information master. Obtained according to the degree of care. In FIG. 48, in the first estimation process 12A, the “average maintenance period”, “next care required degree”, and “transition ratio” of the women requiring long-term care 4 are acquired. These are acquired from the line of the mental and physical state change information master 201 in which the degree of care required and the next degree of care required match in the phase in which the estimation start date is included in the application period.

Further, the "degree of long-term care required" in the first estimation process 12A indicates the degree of long-term care required in this estimation process. The "average maintenance period" is the maintenance period until the next degree of long-term care, and is shown according to the degree of long-term care. The "next long-term care level" indicates the level of long-term care required after the transition after maintaining the long-term care level for the average maintenance period. "Transition ratio" indicates the ratio of the number of people who transition to the next degree of long-term care.

Next, each estimation process calculates (initial number of users x transition ratio by next degree of long-term care) to obtain "number of users by next degree of long-term care", which is the number of months of the average maintenance period. Only, it expands to the number of monthly users of the relative elapsed months from the estimation start date.

Taking the second line from the figure of the first estimation process 12A (the next degree of long-term care is 5) as an example, "70% (490) out of 700 users of long-term care 4 need long-term care 4". After maintaining for 16 months, it will transition to long-term care 5. " In each month column of "Relative elapsed months from the estimation start date" of this first estimation process 12A, the number of users calculated by the above (initial number of users x transition ratio) is entered from the estimation start date. Set only for the number of months of the average maintenance period of. Taking the fourth line from the top (the next degree of long-term care is 3) as an example, 10% (70) out of 700 users indicate that the long-term care 4 is maintained for 16 months.

As described above, the total value of the users of the current long-term care level during the maintenance period until the next long-term care level is calculated by month. In the first estimation process 12A, the number of users is added up according to the maintenance period of the current long-term care 4 users to the next long-term care requirement, that is, the relative elapsed months from the next estimation start date for each long-term care requirement. Find the total number of monthly users. This total value will change for a while at the beginning of the estimation start date of 700 people, but after 16 months of relative elapsed months, it will be 644 people, and after that, 84 people in 17 months, 39 people in 18 months, and so on. It will decrease gradually.

The total number of monthly users obtained in this way is registered in the number of users by year and month in the number of users transition table 12T by degree of long-term care. At this time, the registration destination is registered in the same degree of care required as the degree of care required for each estimation process in the year and month obtained by "estimation start date + relative elapsed months from the estimation start date". In addition, the number of users is added to the number of users registered earlier and registered.

For each estimation process, "next estimation process" is executed according to the next degree of long-term care required. In FIG. 48, as the attribute information of the next estimation process 12B, "pre-estimation start date + next average maintenance period according to the degree of long-term care required" is set in "estimation start date", and "initial number of users" is set. Set the number of users according to the next level of long-term care required for a single month, which is the number of people required for the previous estimation, and set the next level of long-term care required for "degree of long-term care". Set "Update / Change" for "Application Category" and set the same gender as each previous estimation process for "Gender".

In FIG. 48, when the next degree of long-term care is set to the degree of long-term care 3, the “estimation start date” of the next estimation process 12B is “pre-estimation start date (April 2015) + next degree of long-term care required”. The average maintenance period (16 months) ”is August 2016. The "initial number of users" is 70, which is the number of users according to the degree of long-term care required after the month, which is the number of users as a result of the previous estimation. The next long-term care required level, long-term care required 3, is set in the “long-term care required level”. For "application category" and "gender", set the same "update / change" as for each previous estimation process, and for "gender", set "female".

In addition, as in the previous estimation process, the number of users by the number of relative elapsed months from the next estimation start date by the degree of long-term care is calculated, and the total value of the number of users by month is calculated by adding them up. , Register in the number of users by year and month of the user number transition table 12T by degree of long-term care.

As for the number of users by relative elapsed months, the next estimation process is not executed for the line where the next degree of care required has ended or the number of monthly users is less than 1. In addition, the total number of monthly users is the total number of monthly users if the number of monthly users has a fraction of less than 1, and the total number of monthly users is calculated as a decimal number. Is rounded to an integer value.

The following estimation process is similarly executed for other care-requiring degrees, and the estimation process is executed one after another in the same manner thereafter. When all the estimation processes are completed, the user number transition estimation process is completed.

The above-mentioned table for changing the number of users by the degree of long-term care 12T is a table that holds the transition of the number of users by the degree of long-term care and by year and month, and the total number of monthly users of each estimation process is added by year and month. note that,
The year and month of the registration destination is calculated by the estimation start date + the relative elapsed months from the estimation start date-1. In the example of the broken line arrow in FIG. 48, the relative elapsed months are 3 (April 2015) + 3-1 and the registration in the user transition table T according to the degree of care required is June 2015. Will be.

From the value of the user number transition table 12T according to the degree of long-term care, the transition of the number of users according to the degree of long-term care becomes clear.

Next, the benefit cost (cumulative) transition estimation process of step 3 described with reference to FIG. 2 will be described with reference to FIG. 49. The average benefit cost 202 per person is the same as that shown in FIGS. 3, 8, and 41. The benefit cost (cumulative) transition estimation processing unit 13 shown in FIG. 2 uses the average benefit cost 202 per person and the usage according to the degree of care required obtained by the user number transition estimation processing in step 2 described in FIG. Estimate the transition of benefit costs (cumulative) using the number of people transition table 12T by degree of care required, monthly, and number of users.

That is, the average benefit cost per person 202 obtained in step 1 is the average benefit cost per person (monthly amount) according to the degree of care required / monthly / number of users in the user number transition table 12T. ) To calculate the degree of long-term care required, monthly, benefit cost (monthly amount), and the total and cumulative total, and register them in the benefit cost transition table 49. Tables 202, 12T, and 49 shown in FIG. 49 are all service types and genders.

From the value of this stipend transition table 49, the transition of the benefit cost (cumulative) becomes clear.

Next, the healthy life expectancy transition estimation process of step 4 described with reference to FIG. 2 will be described with reference to FIG. 50. In this process, for each 65 mental and physical condition items (certification data) such as the degree of care required and the degree of independence of dementia, the age at which the heaviest stage that does not require long-term care is finally completed is defined as "healthy life expectancy".

The healthy life expectancy transition estimation processing unit 14 shown in FIG. 2 uses the application category-specific / average start age 203 obtained in the application category-specific / average start age acquisition process of step 1 described in FIG. Convert the age to 203M for each application category and average starting month. Next, a method for obtaining a healthy life expectancy by using the first estimation process 12A of step 2 described with reference to FIG. 48 will be described.

In the first estimation process 12A of FIG. 48, the number of people requiring nursing care 4 to transition to the next stage is calculated for each next stage, and the elapsed months from each average duration of them to the transition to the next stage. The number of people who transitioned each time was registered. In this first aggregation process 12A, it is possible to know when to transition to the next stage for each next stage.

When the content of this first estimation process 12A is directly applied to the estimation process 50A in FIG. 50, the initial degree of care required in the first estimation process 12A was 4, so the initial degree of care required is also required in the estimation process 50A. If it is 4, the average starting age (month age) is 924 (months) from 203M to the average starting month age by application category. This average starting month age is registered for each elapsed month up to the next stage (the upper row in the figure is the number of people). Since it is the age of the moon, +1 is added every month.

Looking at the next row of 3 requiring nursing care, the average duration is 16 months, so the age of 924 is maintained for 16 months while being added by +1. Then, after 16 months have passed, the process proceeds to the estimation process 50B of the next stage requiring nursing care 3. Therefore, the age at which the care-requiring 4 is finally completed is 939, which is 16 months old.

In the above explanation, since the numerical value of the first estimation process 12A in FIG. 48 was used as it is, the degree of long-term care required at our office was 4, but the heaviest stage that does not require long-term care is the degree of long-term care. Looking at it, it is generally said that it requires long-term care 2. Therefore, if the initial care-requiring degree in the estimation process 50A is set to the long-term care-requiring 2 and the same processing as described above is performed, the timing at which the long-term care-requiring degree shifts to the next stage, the long-term care-requiring 3, can be grasped, which is the time and effort of long-term care. Since it is the year when the stage where it does not take care (long-term care 2) ends, the age can be estimated as the healthy life expectancy.

Next, the comparison process between the new measure effect cases of the benefit cost (cumulative) estimation result of step 5 described with reference to FIG. 2 will be described with reference to FIG. 51. The benefit cost (cumulative) estimation unit 15 in FIG. 2 performs a comparative analysis of the estimation results carried out for each new measure effect case according to step 3 in FIG. 2 described above. For example, if the new measure case 1 is a measure in which the target value of the mental and physical condition change information of a specific service type (for example, "special training") is + 10% as described above, the new measure case 2 is the same. If the target value is 15% and the new measure case 3 is a measure that sets the target value of the mental and physical condition change information of other service types (for example, "old health") to + 10%, step 3 for each. The benefits cost (cumulative) transition estimation process is performed, and the effects are compared and analyzed with each other based on the results.

In FIG. 51, the cumulative results of the degree of long-term care required, the effect cases of new measures, and the benefit costs are shown in Fig. (A) after 3 years, in Fig. (B) after 6 years, and in Fig. (C). Each of them is shown as a table after 9 years.

As a result of the above-mentioned comparative analysis, it is possible to determine the selection and implementation plan of measures by service type or subregion that are effective in controlling benefit costs.
Next, the effect case-to-case comparison process of the new measures for healthy life expectancy in step 6 described with reference to FIG. 2 will be described with reference to FIG. 52. The new measure effect case-to-case comparison processing unit 16 of FIG. 2 performs comparative analysis of the estimation results carried out for each new measure effect case according to step 4 of FIG. 2 described above.

FIG. 52 shows the results of comparing the degree of long-term care required, the new measure effect case, and the age by the new measure effect cases 1, 2, and 3. From this comparison result, the effective service type for extending healthy life expectancy or the measures for each subregion can be clarified, and the selection and implementation plan can be decided.

Next, an embodiment in which the cost-effectiveness estimation / verification function is simplified will be described. Assuming that the embodiment described so far is a so-called detailed nursing care version, the embodiment described below is a simplified nursing care version. In this simplified long-term care version, the number of users (users of long-term care insurance) and benefit costs for each degree of long-term care required and service type are acquired from the public information of the long-term care insurance business status report of the Ministry of Health, Labor and Welfare as the status quo 1. In addition, as the current status grasp 2, the improvement rate of the long-term care level and the average maintenance period until deterioration are tabulated for each long-term care level, service type, and business establishment group.

Next, make an improvement plan for each level of long-term care, service type, business establishment group, and so on. The business establishment group is a group in which the improvement rate and the average maintenance period until deterioration are classified into four groups in comparison with the local government average. In other words, the target improvement rate and maintenance period for the degree of long-term care required for each degree of long-term care and service type are planned for each business establishment group. For example, as shown in FIG. 53 (a), it is an effective measure for business establishment groups whose improvement rate and average maintenance period until deterioration are both below the local government average for users who require nursing care (severe). Three years after the implementation, an improvement plan will be made to improve the average of local governments in the center of the figure.

Furthermore, the effect of curbing benefit costs after the implementation of the improvement plan is calculated. That is, the amount of benefit cost restraint due to the improvement in the improvement rate of the degree of care required and the amount of benefit cost restraint due to the extension of the maintenance period shown in FIG. 53 (b) are calculated. In FIG. 53 (b), the degree of long-term care required by the user deteriorated from the long-term care required 3 to the long-term care required 4 after the maintenance period shown by the solid line. As shown, if the maintenance period until deterioration is extended, it means that the salary to the user will be suppressed by the area of the shaded line.

Finally, as a cost-effectiveness verification, after a predetermined period (for example, 3 years) from the implementation of the measure, the improvement rate and maintenance period of the long-term care required by the degree of long-term care, service type, and business operator are totaled and the long-term care required. Calculate the improvement rate and the amount of benefit cost restraint due to the extension of the maintenance period.

An embodiment will be described below. In the above-mentioned current situation grasp 1, as one step thereof, the number of users according to the degree of long-term care required and the service type is obtained as described above. For this purpose, as shown in FIG. 54, first, the first public data (long-term care insurance business status report) 51, in which the number of benefits and benefit costs to users by long-term care insurance service type are disclosed, is used. The number of persons acquisition processing unit 52 acquires data of the number of users requiring long-term care (monthly) by service type, and constitutes the number of users master 53.

In the number of users master 53, the number of users for each service type according to the degree of long-term care is registered. For example, in Nursing Home 1, the service type is registered as 8.0 (thousands) for home-based, 1.0 (thousands) for special nursing homes, 0.3 (thousands) for elderly people, and so on. ing.

Next, from the second public data (nursing care benefit cost fact-finding survey) 54 in which the rate of change in mental and physical condition according to the degree of long-term care is disclosed, the rate of change in mental and physical condition according to the degree of long-term care is obtained by the mental and physical change information acquisition processing unit 55. get. In the second public data 54, mildness means that the care-requiring stage is lowered by one stage or more, and severeness means that the long-term care-requiring stage is raised by one stage or more. In the example shown in the figure, the percentage of people requiring long-term care decreased by one level to become one requiring long-term care was 5%, and the percentage of people requiring long-term care increased by one level to become one requiring long-term care was 25%. It shows that the ratio of long-term care required 1 remains 70%.

This mental and physical state change rate is multiplied by the number of users possessed by the above-mentioned user number master 53 to calculate the number of mental and physical state changes by service type and degree of long-term care, and the mental and physical state change number master 56 is configured. In the example of the figure, the number of people requiring nursing care 1 in the home system is 8.0 (thousands) as shown in the user number master 53, and the rate of mildness is 5% from the second public information 54. , The number of people to improve will be 0.4 (thousands). Similarly, the number of people who maintain or deteriorate is calculated and registered in the mental / physical state change number master 56.

The mental and physical change information acquisition processing unit 55 further aggregates the number of mental and physical state changes according to the mild and severe degree of care required, and the aggregated number of mental and physical state changes constitutes the mental and physical state change number master 56. Here, mild refers to the range of long-term care required 1 and 2, and severe refers to the range of long-term care required 3, 4, and 5. In the example of the figure, the number of people requiring nursing care 1 and 2 are added up and aggregated as the number of people requiring care, respectively, and the number of people requiring nursing care 3, 4 and 5 corresponding to the severe number is added up and aggregated. Register at 56.

In this way, it is possible to obtain the number of people whose mental and physical condition changes according to the service type, the degree of care required, and the weight.

As another step of grasping the current situation 1, the benefit costs to the beneficiaries (users of long-term care insurance) are requested according to the degree of long-term care required and the type of service. For this purpose, the difference in benefit costs per person when the service type, the degree of care required is light and heavy, and the physical and mental condition changes is determined.

That is, as shown in FIG. 55, the stipend acquisition processing unit 58 acquires the monthly benefits per person for each service type and each degree of long-term care from the first public data 51, and constitutes the stipend master 59. do.

In the benefit cost master 59, the monthly benefit amount per person is registered for each service type (home-based, special nursing, elderly health, ...) according to the degree of long-term care required. In the figure, when looking at the service types: home-based, the monthly benefit per person is 45 (thousand yen) for support required 2, 85 (thousand yen) for nursing care 1, 120 (thousand yen) for nursing care 2, and so on.・ ・ It is registered as.

The benefit cost difference extraction processing unit 60 uses the monthly benefit data held in the benefit cost master 59 to obtain the benefit cost difference per person when the mental and physical condition changes according to the service type and the degree of long-term care, and the benefit cost difference. Register in table 61. In the example of the figure, when looking at the long-term care 1 required, the number of people who have changed their mental and physical condition is 0.4 (thousand) and the number of people who have deteriorated is 2.0 (thousand), which are improved from the master 56. Difference in benefit costs per person at the time (monthly amount of reduction due to improvement of long-term care 1 to support 2) -40 (thousand yen) and its annual amount -480 (thousand yen), and benefits per person at the time of deterioration The cost difference (monthly amount of increase due to deterioration of long-term care 1 to long-term care 2) 35 (thousand yen) is registered respectively.

Similarly, for other levels of long-term care, the number of people with physical and mental changes and the difference in benefit costs per person at the time of physical and mental changes are registered.

The benefit cost difference extraction processing unit 60 further sets the per capita benefit cost difference when the mental and physical condition is improved and the per capita benefit cost difference when the mental and physical condition deteriorates as mild (needs care 1 and 2) and severe (needs care 3). , 4, 5) They are aggregated separately and registered in the benefit cost difference table 61. This aggregated value is a weighted average value of care required 1 and 2 when viewed as mild. That is, the slight difference in benefit costs (annual amount) is [{0.4 × (-480)} + {0.6 × (-420)}] /1.0=-444 (thousand yen). The severity (monthly amount) is calculated in the same manner and registered in the benefit cost difference table 61.

In grasping the current situation 2, the improvement rate and maintenance period of the degree of long-term care are totaled for each degree of long-term care, service type, and business establishment group.

At the stage of formulating an improvement plan for each service type / business establishment group / improvement plan, the target improvement rate / maintenance period for the long-term care required level / service type is planned for each business establishment group. As mentioned above, in Fig. 52, three years after implementing effective measures for business establishment groups whose current values of both the improvement rate and the average maintenance period until deterioration are below the average of local governments, the center of the figure is shown in the center of the figure. Make an improvement plan to improve to the average of the local government.

Therefore, as shown in FIG. 56, the current value 63 is first grasped. For local governments that do not have actual data, the current values (by service type, by mild and severe need for long-term care, improvement rate, and maintenance period until deterioration) of model local governments with similar environments and forms are used. Next, the target value setting unit 64 should improve the target value after a predetermined period (for example, 3 years) from the implementation of the measure (service type, care-requiring mild / severe, improvement rate, and maintenance period until deterioration). To set.

The difference extracting means 65 obtains the difference in the improvement rate between the current value and the target value and the difference in the maintenance period until the deterioration, respectively, and constitutes the difference master 66. In the figure, the difference in the mild improvement rate of the home system is 4%, and the difference in the maintenance period until deterioration is 2 months.

Next, the effect of suppressing benefit costs after the implementation of the improvement plan is calculated. That is, the amount of benefit cost restraint due to the improvement in the improvement rate of the degree of care required and the amount of benefit cost restraint due to the extension of the maintenance period are calculated.

First, the process of calculating the amount of benefit cost restraint due to the improvement in the improvement rate of the degree of care required will be described. The first benefit cost suppression amount calculation unit 68 of FIG. 57 sets the number of people with changes in mental and physical condition held in the above-mentioned number of people with physical and mental state changes master 56 and the target value of the improvement rate held in the difference master 66. Obtain the difference (improvement rate difference), calculate the number of people subject to benefit cost restraint according to the service type and the degree of care required, respectively, and register it in the corresponding item of the first benefit cost restraint amount table 69. do.

In the example of the figure, the number of people with mild changes in mental and physical condition is 14.0 (thousands), and the difference in improvement rate from the target value is 4%, so the number of people subject to benefit cost suppression is 560. ..

The first benefit cost restraint amount calculation unit 68 further determines the benefit cost difference per person at the time of mental and physical condition improvement according to the service type and the degree of long-term care required, which is held in the benefit cost difference table 61. From the above-mentioned number of persons subject to benefit cost restraint by service type and mild and severe need for long-term care, the amount of benefit cost restraint due to improvement of mental and physical condition is calculated, and it is included in the corresponding item of the first benefit cost restraint amount table 69. sign up.

In the example of the figure, the number of people subject to mild benefit cost restraint in the home system is 560 as described above, and the difference in benefit cost per person is -444 (thousand yen), so the amount of benefit cost restraint due to the difference in improvement rate. Is -248.6 (million yen).

Next, the calculation process of the benefit cost restraint amount by extending the maintenance period until deterioration will be described. The second benefit cost suppression amount calculation unit 71 in FIG. 58 shows the difference (deterioration) between the number of deteriorated persons held by the above-mentioned mental and physical condition change number master 56 and the target value of the maintenance period until deterioration held in the difference master 66. (Difference in maintenance period up to) and is registered in the corresponding item of the second benefit cost restraint amount table 72.

In the example of the figure, the number of people with mild deterioration in the home system is 3.2 (thousands), and the difference in maintenance period until deterioration is 2 months.

The second benefit cost restraint amount calculation unit 71 further multiplies the benefit cost difference (monthly amount) per benefit at the time of deterioration by the maintenance period difference (extension value) until the deterioration, so that the maintenance period difference until the deterioration occurs. The amount of benefit cost restraint is calculated and registered in the corresponding item of the second benefit cost restraint amount table 72.

In the example shown in the figure, the number of people who have deteriorated is 3.2 (thousands), the difference in benefit costs per person (monthly amount) at the time of deterioration is 41 (thousand yen), and the difference in maintenance period until deterioration is two months. , The amount of benefit cost restraint due to the difference in maintenance period until deterioration is -262.4 (million yen).

At the final cost-effectiveness verification stage, as shown in FIG. 59, the amount of benefit cost restraint by improving the improvement rate of the degree of long-term care required after a predetermined period (for example, 3 years) from the implementation of the measures obtained as described above. (Table value) 69 and the amount of benefit cost restraint (table value) 72 due to extension of the maintenance period until deterioration are added up by the cost-effectiveness verification department 74, and the target value for each service type, the degree of care required, and the implementation of measures The total amount of the benefit cost restraint amount is calculated, and the benefit cost restraint amount table 75 is constructed.

Using the numerical values registered in the benefit cost restraint amount table 75, as shown in FIG. 60, it is possible to perform a simulation accompanied by the benefit cost restraint by implementing the measures of the local government.

So far, the cost-effectiveness estimation / verification function of the simplified long-term care version has been described, but FIG. 61 compares this with the detailed long-term care version described above. The long-term care detailed version is more accurate than the long-term care simplified version in every respect. However, the difficulty of realizing the estimation verification tool is higher than that of the simplified version. Hereinafter, each comparison item will be briefly described with reference to FIG. 61.

Looking at the comparison item “Consideration of the number of users by application category” in FIG. 61, in the long-term care simplified version, consideration is “none” for each application category. On the other hand, in the long-term care detailed version, it is considered "yes", and it is distinguished by new and updated / changed.

Looking at the comparison item "attention level of long-term care required", the simplified long-term care version "estimates the degree of long-term care required by aggregating it into mild and severe". On the other hand, in the long-term care detailed version, "the degree of long-term care is faithfully estimated at all stages", and it can be seen that the long-term care detailed version is more accurate.

Looking at the comparison item "stage of change in the degree of long-term care", the simplified version of long-term care assumes that "deterioration and improvement are only one stage". On the other hand, in the long-term care detailed version, "the number of stages of deterioration and improvement is assumed to be all", and it can be seen that the long-term care detailed version is more accurate.

Looking at the comparison item "improvement rate and accuracy of average maintenance period until deterioration", in the long-term care simplified version, "the numerical value of the local government, which has only the numerical value of the model local government, cannot be specified". On the other hand, in the long-term care detailed version, "high accuracy value using actual data" can be obtained.

Looking at the comparison item "Phase for considering changes in the improvement rate and the average maintenance period until deterioration", in the long-term care simplified version, "the current and target are set only between the two points (for example, 3 years) at the start and end of the estimation. "is doing. On the other hand, in the long-term care detailed version, "the transitions of all stages (including the end) after the maintenance period of the long-term care level are repeatedly calculated", and it can be seen that the long-term care detailed version is more accurate.

Looking at the comparison item "time-series decomposition accuracy", in the long-term care simplified version, it is "only linear interpolation between two points". On the other hand, in the long-term care detailed version, "monthly estimation can be calculated", and it can be seen that the long-term care detailed version is more accurate.

Looking at the comparison item "whether or not healthy life expectancy can be calculated", the calculation is "impossible" in the simplified nursing care version. On the other hand, in the long-term care detailed version, it is "similar to the calculation logic", and it is "possible" to calculate by this similar calculation logic.

Looking at the comparison item "Difficulty of realizing the estimation verification tool", the simple version of long-term care is "Feasible with Excel", and the difficulty level is relatively low. On the other hand, in the long-term care detailed version, "program development is essential (Excel difficulty)", and the long-term care detailed version is relatively difficult.

From the comparison results between the simple long-term care version and the detailed long-term care version, one can be selected according to the actual situation of the local government (long-term care insurer).

All of the above cost-effectiveness estimation / verification functions were estimated / verified for service users of the long-term care benefit business. Such an estimation / verification function can be similarly applied to a comprehensive business. In the case of a comprehensive project, the approach targets are how to extend the independence period and how to return the person requiring support to the project target or the independence.

Here, in the case of the long-term care benefit business, the long-term care certification data and long-term care benefit data are used as the estimation / verification source data, and the estimation / verification is performed for each service type. On the other hand, in the comprehensive business, it is the same to use the long-term care certification data and long-term care benefit data as the estimation verification data, but further estimate using the comprehensive business data (basic checklist, visitation record, etc.). Perform verification.

And even in the case of a comprehensive business, it is possible to estimate and verify cost-effectiveness with exactly the same logic based on the following correspondence. For example, as shown in FIG. 62, similar to the degree of long-term care required (long-term care required 1 to 5) of the long-term care business target person, the mental and physical condition of the comprehensive business target person is divided into five stages (independence to support required 2) and associated with each other. Just do it. In FIG. 62, frailty 1 and frailty 2 are so-called frailty subjects, and their weight may be defined by risk points or the like in the basic checklist.

With this definition, for example, when the mental and physical condition of the general business target person is "independence", the cost-effectiveness is based on the same logic as when the long-term care business target person needs nursing care level 1 ". Can be estimated and verified.

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

11 ... Master basic information acquisition processing unit for estimation 111 ... Mental and physical condition change (deterioration / improvement) information acquisition processing unit 112 ... Average benefit cost acquisition processing unit per person 113 ... Application category x average start age acquisition processing unit 114 ... Application Category x number of users acquisition processing unit 20 ... estimation master basic information 201 ... mental and physical condition change information master 202 ... average benefit cost master per person 203 ... application category / average start age master 204 ... application category / user Number master 30 ... Master for estimation from public information Basic information acquisition unit 31 ... Correction coefficient calculation processing unit 311 ... Mental and physical condition change (deterioration / improvement) information correction coefficient calculation processing unit 312 ... Average benefit cost correction coefficient calculation processing unit per person 313 ... Application category / average start age correction coefficient calculation processing unit 314 ... Application category / user number correction coefficient calculation processing unit 32 ... Estimating master basic information correction coefficient 33 ... Estimating master basic information estimation processing unit 331 ... Mind and body Status change (deterioration / improvement) Information actual data estimation processing unit 332 ... Average benefit cost per person Actual data estimation processing unit 333 ... By application category / Average start age Actual data estimation processing unit 334 ... By application category / Number of users Actual Data estimation processing unit 34 ... Master basic information for estimation (public information analysis municipality: estimated value)
41 ... Public information (model municipality)
42 ... Public information (public information analysis local government)
43 ... Master basic information for estimation (model municipality: intermediate data)
44 ... Master basic information for estimation (public information analysis municipality: intermediate data)
51 ... 1st public data 52 ... Number of users acquisition processing unit 53 ... Number of users master 54 ... Second public data 55 ... Mental and physical change information acquisition processing unit 56 ... Mental and physical condition change number master 58 ... Salary acquisition processing unit 59 … Benefit cost master 60… Benefit cost difference extraction processing unit 61… Benefit cost difference table 63… Current value 64… Target value setting unit 65… Difference extraction means 66… Difference master 68… First benefit cost suppression amount calculation unit 69… 1st benefit cost restraint table 71 ... 2nd benefit cost restraint calculation unit 72 ... 2nd benefit cost restraint table 74 ... Cost effectiveness verification unit 75 ... Benefit cost restraint table

Claims (1)

From the first public data in which the number of benefits to users by long-term care insurance service type and the benefit cost are disclosed, the data of the monthly number of users of the above users by service type and degree of care required is acquired. , The number of users acquisition processing unit that constitutes the number of users master,
The number of users whose mental and physical condition changes according to the service type and the number of people who need long-term care calculated from the number of users possessed by the number of users master and the rate of change in mental and physical condition according to the degree of long-term care disclosed by the second public data. , The mental and physical change information acquisition processing unit that constitutes the mental and physical condition change number master aggregated according to the mild and severe degree of long-term care required,
From the first public data, the salary acquisition processing unit that acquires the monthly benefit amount per person for each service type and the degree of care required and constitutes the benefit expense master,
The per capita benefit cost for improving the mental and physical condition, which is calculated from the difference in the per capita benefit cost when the mental and physical condition changes according to the service type and the degree of long-term care required, calculated using the monthly benefit data held in this benefit cost master. The difference between the benefits and the difference in benefits per person when the physical and mental condition deteriorates, and the benefit cost difference table, which is held for each service type and for each of the mild and severe levels of long-term care required.
The improvement rate of the mental and physical condition according to the service type, mild and severe need of care, and the current value of the maintenance period until deterioration, and the improvement rate of the mental and physical condition according to the service type, mild and severe need of care, and The difference master with the target value in which the value of the difference from the target value of the maintenance period until deterioration is held, and
Persons subject to benefit cost restraint according to service type and mild / severe degree of long-term care based on the difference between the number of people whose mental and physical condition has changed and the target value of the improvement rate held in the difference master. The number of people is calculated, and the difference in benefit costs per person at the time of improving the physical and mental condition according to the service type held in the benefit cost difference table and the degree of care required is mild and severe, and the service type and required. The first benefit cost restraint amount calculation unit that calculates the benefit cost restraint amount due to improvement of mental and physical condition from the number of people subject to benefit cost restraint according to the degree of care
The difference between the number of people whose mental and physical condition has deteriorated in the mental and physical state change master, the target value of the maintenance period until deterioration held in the difference master, and the service type held in the benefit cost difference table. Separately, from the difference in benefit costs per person when the mental and physical condition deteriorates according to the degree of care required, and the second benefit cost restraint amount calculation unit that calculates the amount of benefit cost restraint due to the difference in maintenance period until deterioration. ,
A community-based comprehensive care business system that is characterized by being equipped with.

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