JP7119013B2 - Regional Comprehensive Care Business System - Google Patents

Description

The embodiment of the present invention is a regional comprehensive system that enables cost-effectiveness estimation when implementing measures by regional management for all four life stages of the elderly (data health, comprehensive, nursing care benefits, medical care cooperation). Regarding the care business system.

Regional management that reflects the actual conditions of each region is being realized based on the following four concepts.
1. Covers all 4 life stages of the elderly (data health, general, nursing care benefits, medical care collaboration).
2. A comprehensive DB (database) will be constructed to centrally manage the history information of elderly people owned by each department of the local government.
3. Create functions such as analysis and extraction that lead to solutions for each task from the big data of the elderly.
4. We will digitally verify the effects of each project and respond to financial incentive systems.

Through the realization of these concepts and PDCA operations consisting of P (planning), D (implementation of measures), C (result evaluation), and A (problem analysis), we will establish a service infrastructure and improve healthy life expectancy by improving service quality. In addition, it aims to reduce medical and long-term care benefit costs and realize a sustainable social security system.

The data health business is a business for disease prevention and prevention of aggravation (diabetes/depression, etc.). Comprehensive projects are projects for reducing the rate of new certifications and supporting independence. The long-term care benefit business is a business for independence support and prevention of aggravation. In addition, the medical and long-term care collaboration business is a business for extending the period of home medical care and preventing acute exacerbations.

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

For example, it is necessary to estimate the improvement and extension of the period of maintenance of the mental and physical conditions of the elderly by service type, and to estimate the benefit costs as a result, but the current situation is that the method is not known (there is no mechanism). In addition, it is necessary to assess the effects fairly and quantitatively after the implementation of the measures, but the current situation is that there is no mechanism for this.

Japanese Patent Publication No. 2017-215787

As described above, the conventional technology cannot estimate the effect of the implementation of measures, and therefore cannot verify the cost-effectiveness.

It is an object of the present invention to provide a comprehensive community care business system that enables quantitative estimation of benefit cost restraint effects based on improvement and maintenance of mental and physical conditions by various measures, and that can verify cost-effectiveness.

The integrated community care business system according to the embodiment of the present invention uses data recording changes in the mental and physical conditions of users in a predetermined period before the preset estimation start date, for all the users, For each stage, the next stage of change, the direction of change to the next stage, the transition ratio to the next stage, and the average duration until the change to the next stage are calculated, and these changes in psychosomatic state A mental and physical condition change information acquisition processing unit that forms a mental and physical condition change information master by adding a predetermined target value to the information, and certified data and benefit results for certifying the stage of the mental and physical condition of the nursing care insurance before the estimation start date. from the average benefit cost information per person for each stage, and the average benefit cost acquisition processing unit per person that constitutes the average benefit cost information master, and the certification data and benefit performance of the long-term care insurance before the estimation start date from the average starting age information of the transition estimation by stage, and from the average starting age acquisition processing unit that constitutes the average starting age information master, and the certified data of nursing care insurance before the estimation start date and the actual benefits, A user number acquisition processing unit that obtains user number information by stage and constitutes a user number information master; And from the number of users at the stage held in the number of users information master, the transition number of people to transition to the next stage is obtained, and the average continuation until the change to the next stage held in the mental and physical state change information master Using the period, perform the first user number estimation process to obtain the total value of the transition number of people in the period until each transition to the next stage for each relative elapsed month after the estimation start year and month, and perform the average continuation For the next stage after transitioning after the period has passed, the same user number estimation processing is performed, and after that, the user number transition estimation processing unit repeats the same user estimation processing for each transition, and this user number transition estimation processing unit A user number transition table in which the obtained transition numbers for each relative elapsed month are tabulated for each stage and for each elapsed month after the estimation start year, and for each stage aggregated in this user number transition table and, from the number of users for each elapsed month and the average benefit cost information per person for each stage held in the average benefit cost information master, the benefit cost transition estimation for each step and for each elapsed month. and a processing unit.

According to the above configuration, it is possible to quantitatively estimate the effect of reducing benefits costs based on the estimation of the improvement and maintenance of mental and physical conditions, and it is also possible to quantitatively estimate the effect of suppressing benefits costs by various measures and to verify the effect after the implementation of the measures. Become.

BRIEF DESCRIPTION OF THE DRAWINGS It is a conceptual diagram of the regional integrated care business system which concerns on embodiment of this invention. BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram which shows the whole system configuration|structure of the regional integrated care business system which concerns on embodiment. It is a diagram for explaining the system configuration for configuring the basic information master in the embodiment. It is a figure explaining the stage change of the mental-and-physical condition of a user of long-term care insurance. (a) quantitatively explains the changes in the mental and physical conditions of long-term care insurance users; FIG. 4 is a diagram representing a master; It is a figure explaining an example of the method of setting the target value in constructing the psychosomatic state change information master in an embodiment. It is a figure explaining the average benefit per person acquisition processing content in embodiment. It is a figure showing the average benefit cost information master per person in embodiment. It is a figure explaining the average starting age acquisition processing content of the initial user in embodiment. It is a diagram. It is a figure showing update age classification and an average starting age master which hold|maintains the data of the initial user in embodiment. It is a figure explaining the average starting age acquisition processing content of the newly increasing user in embodiment. It is a figure showing update age-classified and average starting age master which hold|maintains the data of a new increase user in embodiment. It is a figure explaining the user number acquisition processing content of the initial user in embodiment. It is a figure showing the number-of-users master which holds the number-of-users data of the initial user in embodiment. It is a figure explaining the user count acquisition processing content of the new increase user in embodiment. It is a figure showing the number-of-users master holding the number-of-users data of the new increase user in embodiment. FIG. 10 is a diagram illustrating an outline of estimation master basic information acquisition processing by public information analysis in the embodiment; It is a figure which shows the data classification of the actual data, public information, and a correction coefficient in embodiment. FIG. 10 is a diagram showing a process of acquiring estimated master basic information intermediate data of a target local government from public information (Ministry of Health, Labor and Welfare report summary, long-term care insurance business status report) in the embodiment. FIG. 20 is a diagram for explaining a specific example of psychosomatic state change (deterioration/improvement) information acquisition processing in the embodiment with reference to FIG. It is a figure which shows the deterioration rate and improvement rate used as the master basic information median value data for estimation of the model local government in embodiment. FIG. 10 is a diagram illustrating processing for obtaining an average benefit cost per person using public information in the embodiment; FIG. 10 is a diagram showing the average number of units per person, which is the median value data of master basic information for estimation of a model local government in the embodiment; It is a figure explaining the average starting age acquisition process using the public information in embodiment. It is a figure which shows the master basic information median value data for estimation of the model local government in embodiment, classification by gender, degree of nursing care need, and average starting age. It is a figure explaining the number-of-users acquisition process using the public information in embodiment. It is a figure which shows the number-of-users data by service type, by gender, by application classification, which becomes the master basic information median value data for estimation of the model municipality in embodiment. FIG. 10 is an explanatory diagram of a process in which a ratio of estimated master basic information intermediate data of a model local government acquired from public information and estimated master basic information intermediate data of a public information analysis local government is used as a correction coefficient in the embodiment; It is a figure which shows the master basic information intermediate data for estimation showing the deterioration rate/improvement rate of the public information analysis local government in embodiment. FIG. 10 is a diagram showing a change (deterioration/improvement) information correction coefficient for each level of care need, which constitutes master basic information for estimation in the embodiment. It is a figure which shows the monthly average number of units per person according to the level of care need of the estimation master basic information intermediate data of the public information analysis local government in embodiment. Correction coefficient for monthly average number of units per person for each level of long-term care required obtained from 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 FIG. 4 is a diagram showing; It is a figure which shows the master basic information intermediate data for estimation of the public information analysis local government in embodiment, according to gender, according to a degree of need for nursing care, and an average starting age. It is a figure which shows the master basic information for estimation in embodiment, classification by gender, degree of nursing care need, and an average starting age correction coefficient. It is a figure which shows the master basic information intermediate data for estimation of the public information analysis municipality in embodiment, and the number of users by gender, degree of need for care, and number of users. It is a figure which shows the correction coefficient of the number of users according to gender, degree of need for care, and the number of users which comprise the master basic information for estimation in embodiment. Explanatory diagram of processing for acquiring master basic information for estimation (estimated value) of public information analysis local government by multiplying estimation master basic information correction coefficient by estimation master basic information acquired from actual data of model local government in the embodiment is. (a) is information on changes in mental and physical conditions of a model local government in the embodiment (information on changes in physical and mental conditions (worsening/improvement) by level of care required: real side data), (b) is the level of care required in the master basic information correction coefficient for estimation FIG. 10 is a diagram showing another/psycho-psychological state change (deterioration/improvement) information correction coefficient; FIG. 10 is a diagram showing change (deterioration/improvement) information (estimated values) of mental and physical conditions by level of nursing care required for public information analysis local governments in the embodiment. (a) shows the average benefit cost per person (actual side data) of the model local government in the embodiment, and (b) shows the monthly average number of units per person correction coefficient of the master basic information correction coefficient for estimation. . FIG. 10 is a diagram showing data indicating average per capita benefit costs (estimated values) of public information analysis local governments in the embodiment; (a) shows the average starting age (actual data) by gender, level of care need, and average starting age (actual data) of the model municipality in the embodiment; FIG. 10 is a diagram showing starting age correction coefficients; It is a figure which shows the average starting age (estimated value) according to gender, degree of need for nursing care of the public information analysis local government in embodiment. (a) shows the number of users (actual data) by gender, level of long-term care need, and the number of users (actual data) of the model municipality in the embodiment; It is a figure which shows a number-of-people correction coefficient. It is a figure which shows the master basic information for promotion of the public information analysis local government in embodiment by gender, degree of nursing care need, and the number of users (estimated value). It is a figure explaining the outline|summary of the user number transition estimation process in embodiment. It is a figure which shows the psychosomatic state change information master used for the user number transition estimation process in embodiment. It is a figure which shows the specific example of the user number transition estimation process in embodiment. It is a figure explaining the flow of a benefit expense (cumulative) transition estimation process in embodiment. It is a figure explaining healthy life expectancy transition estimation processing in an embodiment. Cumulative results of benefit costs by level of nursing care required, by case of effect of new measures in the embodiment, after 3 years in (a), after 6 years in (b), and after 9 years in (c)) It is a figure represented as a table|surface. It is a figure showing the result of having compared the age according to the new measure effect cases 1, 2, and 3 according to the nursing-care-required degree, the new measure effect case, and age in embodiment. A diagram explaining a more simplified embodiment of the cost-effectiveness estimation and verification function, (a) shows a group of establishments compared to the municipal average for both the improvement rate and the average maintenance period until deterioration. , (b) shows the benefit cost restraint effect when the maintenance period until the user's degree of care need deteriorates is extended. It is explanatory drawing of the process which calculates|requires the number of users according to a level of care need and according to service type in the simplified embodiment. In order to calculate the benefit costs for recipients (users of long-term care insurance) by care level and service type in the simplified embodiment, one person at the time of change in physical and mental condition by service type, degree of care need It is explanatory drawing of the process which asks for a benefit-cost difference per hit. In the simplified embodiment, an explanatory diagram of the process of setting the target value to be improved after a predetermined period of time from the implementation of the measure (by service type, by level of care required, improvement rate, and maintenance period until deterioration) be. It is a figure explaining a benefit-costs control amount calculation process by having improved the improvement rate of the level of nursing care need in a simplified embodiment. It is a figure explaining the calculation process of the amount of benefit cost suppression by extension of the maintenance period until deterioration in embodiment simplified. After a predetermined period of time from the implementation of the measures in the simplified embodiment, a benefit cost reduction table is created by summing the benefit cost reduction amount due to the improvement rate improvement in the level of nursing care required and the benefit cost reduction amount due to the extension of the maintenance period until deterioration. It is a figure explaining. It is a figure explaining the simulation with benefit-cost control by implementation of the policy of the local government in a simplified embodiment. It is a figure which compares and shows each function of a care simple version and a care detailed version. It is a figure explaining the interrelationship in the case of laterally deploying the cost-effectiveness estimation and verification function in the nursing care benefit business to the general business.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. First, with reference to the conceptual diagram of FIG. 1, the overall flow of the regional comprehensive care business system according to this embodiment will be described. FIG. 1 illustrates the flow of steps 1 to 4 among the main functions in this embodiment. In this embodiment, there are also functions of steps 5 and 6, which will be described later.

In the estimation master basic information acquisition process of step 1, basic information is acquired from certification data of long-term care insurance, benefit performance, etc., in order to configure the estimation master. The basic information to be acquired includes information on changes in mental and physical conditions, information on average benefit costs per user who is certified as needing long-term care, information on average starting age by application category, and information on the number of users by application category. Here, the psychosomatic condition change information is information indicating whether the user's psychosomatic condition stage (level of need for nursing care, etc.) has deteriorated/improved.

In the user number transition estimation in step 2, the transition of the number of users described above is estimated. In this user number transition estimation process, transition estimation is performed as time passes, such as one year, two years, three years, and so on from the start of estimation. One year after the start of the estimation is defined as the current phase, one to two years later is defined as new policy phase 1, two to three years later is defined as new policy phase 2, and so on.

The number of users is divided into renewal change and new application categories. The number of initial updates and changes is the number of users for whom the authorization period for the authorization data of the renewal change application will be required at the start of the estimation. The initial number of new users is the number of users for whom the approval data of new applications will be approved at the start of the estimation. These initial updated estimates and initial new estimates represent initial users who have already benefitted at the time the estimates begin.

On the other hand, new estimates for 1 year later to new estimates for 3 years later are new users who increase due to new applications after the start of estimation. The number of newly added users is the total number of new users applying for the previous year.

The user transition estimation process in step 2 is to estimate how the number of users will transition after a predetermined period of time during which the new measures are implemented. Note that the lower right frame in the figure indicates the decrease in the number of users. This user transition estimation process is performed using the psychosomatic state change information and the number of users by application category information among the basic information for estimation described above. The details of this user transition estimation processing will be described later.

In step 3, the benefit cost (cumulative) transition estimation process, the transition of the benefit cost (cumulative) is estimated based on the user number transition estimation result described above. The right-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 in the basic information for estimation described above. Details of this benefit cost (cumulative) transition estimation processing will be described later.

In the healthy life expectancy transition estimation process of step 4, the user's healthy life expectancy transition estimation process is performed using the average starting age information for each application category among the basic information for estimation described above. Here, "healthy life expectancy" is defined as the age at which the heaviest stage that does not require nursing care ends last for each of the 65 psychosomatic condition items (certified data) such as the degree of care required and the degree of independence with dementia. In addition, the stepped frame rising 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 the overall outline of the processing system in this embodiment. This processing system is realized by a computer system, and includes an estimation master basic information acquisition processing unit 11 corresponding to step 1, a user number transition estimation processing unit 12 corresponding to step 2, and a step 3 and a healthy life expectancy transition estimation processing unit 14 corresponding to step 4 as functions.

Of these, the number of users transition estimation processing unit 12 corresponding to step 2, the benefit (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 Each different new measure is executed, and new measure effect cases 1, 2 and 3 are generated respectively.

In addition, in response to the processing results described above, although not shown in FIG. A comparison processing unit 16 between new policy effect cases of lifespan is provided to perform comparison between each new policy effect case.

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

That is, the master basic information for estimation (psychological state change information master) 201 acquired by the mental and physical state change (deterioration/improvement) information acquisition processing unit 111 is, for example, deterioration by care level (support required 1 to care required 5) rate, completion rate, improvement rate, average maintenance period to deterioration, average maintenance period to completion, average maintenance period to improvement, and the like. In addition, the master basic information for estimation (average benefit cost per person master) 202 acquired by the average benefit cost acquisition processing unit 112 per person is the average benefit cost by care level (support 1 to care need 5). There is, and the estimation master basic information (master by application category/average starting age) 203 acquired by the application category/average starting age acquisition processing unit 113 is a new It is the age of the applicant and the renewal change applicant. It is the number of new applicants for support 1 to long-term care 5) and renewal change applicants.

Here, the function of the psychosomatic state change (worsening/improving) information acquisition processing unit 111 will be described. FIG. 4 shows changes in the physical and mental condition of the user X (degree of need for nursing care). In FIG. 4, the acquisition period of information on changes in mental and physical conditions is from April 2012 to April 2015, and user X newly applied in May 2012 and was certified as needing long-term care 3 . After that, she maintained level 3 of long-term care level for 6 months, which is the renewal period of the level of long-term care level. Six months later, she applied for renewal in November of the same year, and was certified as level of long-term care level 2, which was improved by one level. After that, in February 2013, before reaching the 6-month renewal period for the level of care required, I applied for a change, and the level of care required worsened by two stages and was certified as needing care 4. After maintaining this level of nursing care requirement 4 for 12 months, she applied for renewal, and her level of nursing care requirement worsened by one level and was certified as nursing care requirement 5. After that, there was no application at the end of the renewal period, so it was terminated. Termination refers to the case where there is no application due to the user's death or moving to another municipality.

In this way, from all certification data that includes the certification valid period (end) within the period of acquiring information on changes in physical and mental conditions, the maintenance period of deterioration/completion/improvement to the next stage by user and level of care need and the change direction and change step amount. FIG. 4 is an example of a case where the psychosomatic state change information acquisition period is three years before the estimation start date. The psychosomatic state 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 conditions by user, level of care required, and information on changes in mental and physical conditions by service type, only the certified data and payment results of users who have received benefits for that service type within the period of acquiring information on changes in mental and physical conditions. target. The age at the end of the maintenance period is obtained from the date of birth of the benefit record, and is set as the age at the end of the maintenance period.

When the direction of change is "end", that is, when there is no next certification data, the certification effective period (end) of this certification data is set as the end date of the maintenance period. In addition, if the application category of the next certification data is death, the month before the certification application date of the next certification data shall be the month and year of the end of the maintenance period. Furthermore, if the level of nursing care required in the following certification data is not applicable, the month before the certification application date of the following certification data shall be the month and year of the end of the maintenance period.

FIG. 5A summarizes the state of change in the psychosomatic state shown in FIG. In FIG. 5(a), user X has a level of care need of "3" in the application category of "new", and after maintaining it for 6 months, has improved by one level (-). The level of nursing care requirement "care requirement 2" applied for "renewal" continued for 3 months, and then became "worse" (+) by two stages. After 12 months of continuing for 12 months, the degree of need for care "needed care 4" for which a "change" application was applied was "worse" (+) by one level. The degree of nursing care requirement "5 nursing care requirement" applied for "renewal" is terminated because there is no next application even after the renewal period of 6 months has passed. Information on each item is similarly acquired for other users Y, but the description is omitted.

In this way, since information on changes in the mental and physical conditions of each user can be obtained, based on these, as shown in FIG. next stage), "direction of change" to the next level of care required, "transition ratio" to the next level of care required, and "average maintenance period" to the next level.

For example, the ratio (transition ratio) of a user with a need of care 4 who deteriorates to a need of care 5 (transition ratio) is 70%, and the average maintenance period is 16 months. It is as shown in each item also about the case where it changes to other nursing-care-requirement degrees, and description is abbreviate|omitted.

The physical condition change information master 201 used in the later-described user transition estimation process is composed of information for transition to the next care level for each care level shown in FIG. 5(b).

That is, the psychosomatic state change information master 201 is obtained from data recording changes in the phase of the user's psychosomatic state during a predetermined period (period for acquiring psychosomatic state change information acquisition period) before the preset estimation start date, Mental and physical state change information for all users, for each stage, the next stage of change, the direction of change to the next stage, the transition ratio to the next stage, and the average maintenance period until the change to the next stage is retained as

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

An example of this target value setting method will be described with reference to FIG. In long-term care insurance, various services are provided to users according to their mental and physical conditions. Types of this service include home visits, commuting services, and other in-home services (abbreviated as “home”), special nursing homes for the elderly (abbreviated as “special nursing homes”), nursing homes for the elderly (abbreviated as “elderly health”), and long-term care beds ( There are seven types: ``Recuperation''), Group Homes (abbreviated as ``GH''), Special Facilities (abbreviated as ``Special Treatment''), and Small-Scale Multi-Functions (abbreviated as ``Small Multi'').

The user receives one of the above service types according to his or her psychosomatic state, but may switch to another service type during the psychosomatic state change information acquisition period. For example, a user who has been receiving the "in-home" service may transition to the "elderly health" service. In this way, considering the user's transition to other service types, it is not preferable to perform the processing from step 2 onward based on the psychosomatic state change information for each service type. It is necessary to perform the processing from step 2 onward using the "psycho-psychosomatic state change information for all service types" including all service types.

However, if steps 2 and 3 are processed using "information on changes in mental and physical conditions for all service types" and it is estimated that benefit costs will be reduced, it is not possible to analyze which service type the new measures contributed to. .

Therefore, first, as shown in FIG. 6, the psychosomatic state change information acquisition processing unit 111 prepares certification data 101A for all service types, actual benefits 102A, and a matching table 103A for matching these data so that they can be used mutually. 105A is obtained for all service types, by user, by maintenance period start/end year/month, by level of care required, and mental and physical condition change information 105A. Information on all service types includes system management number (user number), maintenance period start/end date, maintenance period (worsening/end/improvement), direction of change (worsening/end/improvement), and level of care required. be. In addition, one level of care required is determined by one maintenance period start and end year and month.

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 (until deterioration/end/improvement) 106A is obtained.

In addition, for each service type, from the data of users who have used the service type (certification data 101B, benefit record 102B, and matching table 103B), we can obtain data by service type, by user, by maintenance period start/end date, required Acquire the degree-of-care/mental-mental state change information 105B. This service type information includes system management number (user number), maintenance period start/end date, maintenance period (worse/end/until improvement), direction of change ((worse/end/improvement), and level of care required. In this case also, one level of care required is determined by one maintenance period start/end year/month.

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

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

First, as shown in the formula (1), the "service type-specific psychosomatic state change information accumulation value" is obtained. This is done by multiplying the information on changes in mental and physical conditions by service type obtained for multiple service types by the ratio of the corresponding benefit cost by service type to the total benefit cost as a contribution rate, and weighting each of these by the ratio of the corresponding benefit cost by service type to the total benefit cost. It is the sum of the types (7 types).

The physical and mental condition change information includes the stage, for example, the direction of change to deterioration/end/improvement, the rate of change to deterioration/end/improvement, and the average duration until deterioration/end/improvement for each level of care required. include. In addition, formulas (1), (2), (3), and (4) are all based on the level of care required.

“Additional value of mental and physical condition change information by service type” = (in-home benefit cost / total benefit cost × in-home mental and physical condition change information) + (special care benefit cost / total benefit cost × special care mental and physical condition change information) + (elderly health benefits) cost/total benefit cost x physical and mental condition change information for the elderly) + (medical care benefit cost/total benefit cost x physical and mental condition change information for medical treatment) + (GH benefit cost/total benefit cost x GH mental and physical condition change information) + (special care benefit cost) / total benefit cost x physical and mental condition change information for special treatment) + (small benefit cost / total benefit cost x small mental and physical condition change information) (1)

Next, the "psychophysiologic state change information ratio" is calculated as shown in Equation (2). This is the ratio between the above-mentioned "mental and physical state change information for all service types" and the "mental and mental state change information accumulated value for each service type".

"Mental and physical state change information ratio" = "Mental and physical state change information for all service types" / "Physical and mental state change information cumulative value for each service type" (2)

Next, as shown in the formula (3), the "target cumulative value of psychosomatic state change information by service type" is obtained. This is because, among multiple service types, the values of changes in mental and physical conditions for a specific service type (for example, “special nursing care”) for which new measures are to be implemented are updated for a predetermined period after the estimation start date. Add a target value (for example, +10%) to improve the mental and physical state change information by the measure, and add the mental and physical state change information of the specific service type ("special care") weighted as described above, and the other remaining This is the sum of the psychosomatic state change information (those weighted as described above) for the six service types.

``Accumulated target physical and mental condition change information for each type of service'' = (in-home benefit/total benefit x in-home mental and physical condition change information) + (special nursing care benefit/total benefit x (special care mental and physical condition change information + 10) %)) + (Elderly health benefits / Total benefits x Old health mental and physical condition change information) + (Medical care benefits / Total benefits x Physical and mental condition change information) + (GH benefits / Total benefits x GH Mental and physical condition change information) ) + (special treatment benefit / total benefit × special treatment mental and physical condition change information) + (small benefit / total benefit × small mental and physical condition change information) (3)

Since the rate of deterioration and the rate of improvement are contradictory, target values for both are set at the same time (rate of deterioration + completion rate + improvement rate = 100%).

Then, as shown in Equation (4), the “target mental and physical state change information for all service types” is calculated. This is obtained by multiplying the above-mentioned "mental and physical state change information ratio" by the "target accumulation value of the mental and physical state change information by service type".

“Target mental and physical state change information for all service types” = “Purpose mental and physical state change information accumulated value for each service type” × “Mental and physical state change information ratio” (4)

The psychosomatic state change information acquisition processing unit 111 uses the value of the "target psychosomatic state change information for all service types" obtained in this way to obtain the information stored in the psychosomatic state change information master 201 shown in FIG. 5(b). It constitutes psychosomatic state change information.

The above description exemplifies "special training" as a specific service type (hereinafter referred to as a "notable service type") in which the information on changes in mental and physical conditions is improved by the new measures, and the target value is +10%. Similarly, by configuring each item value of the psychosomatic state change information master 201 corresponding to other new measures for which target values are set by specifying other service types of interest, as shown in FIG. A new policy effect case is obtained.

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

This per-person average benefit cost acquisition processing unit 112 obtains per-person average benefit cost information by level (level of care required) from the long-term care insurance certification data 101 and benefit record 102 of the estimation start date, It is held in the average benefit cost information master 202 .

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

In this case, first, users with level of nursing care requirement 3 are extracted from the certification data (situation of level of nursing care requirement) 101A shown in FIG. 7(a). That is, all the users of nursing care level 3 are extracted from the authorization data 101A. Next, the average benefit cost is obtained by extracting the total number of units of the average benefit cost acquisition year and month per extracted user from the benefit record 102A shown in FIG. 7(b). Calculate the average benefit cost using the following formula (5).

Average benefit cost per person for long-term care level 3 = (total number of units for user B + total number of units for user E + total number of units for user F) ÷ 3 × unit price by ratio of personnel expenses by region = (11000 + 16,000 + 5,000) ÷ 3 × Unit price by labor cost ratio by region = 106,670 yen (5)

The above formula (5) is an example of calculating the unit price by region-based labor cost ratio with an additional rate of 0% (10 yen) by region. In addition, the "number of service units after determination" of the benefit record data is used.

If the average benefit cost per person is taken only for January, the deviation of the benefit cost within the fiscal year will affect subsequent transition estimates. , the average value of which may be used.

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

Next, the processing contents of the application classification/average starting age acquisition processing unit 113 will be described. Here, the average starting age is divided into the initial user and the newly increasing user. First, the initial user will be described with reference to FIGS. 9 and 10. FIG.

The application classification/average starting age acquisition processing unit 113 acquires the average starting age obtained from the certification data (application classification and level of care required) 101B shown in FIG. , Calculate the average starting age by application category and level of nursing care required. This process is a process of acquiring the average starting age of the initial users by degree of nursing care requirement, which is the basis of transition estimation. The processing for acquiring the average starting age of new users, who are increasing with new applications, will be described later.

Below is an example of how to find the average starting age of long-term care level 3 for renewal change applicants, with the average starting age acquisition year and month as March 2015. For the average start age acquisition year and month, specify any year and month in which data actually exist. In this example, it is assumed to be one month before the estimation start year (April 2015). Note that the results of multiple months may be averaged in order to eliminate the influence of user imbalance by month.

The application classification/average starting age acquisition processing unit 113 first extracts all users with long-term care level 3 who have applied for updating and changing the average starting age acquisition year and month from the certification data 101B. In the example of FIG. 9, user E and user F are the target persons. Next, the average starting ages of the users E and F are obtained from the benefit record 102B shown in FIG. 9(b). However, those certified as requiring long-term care who have not yet used the service are not eligible because they have no track record of benefits. The average age of user E and user F is calculated. This is the average age at which nursing care level 3 begins.

Here, when the age of the average start age acquisition year and month is calculated from the user's birth date in the benefit record, the average start age acquisition year and month is not in units of "days" but in units of "years and months". Age is calculated by the following formula (6) using "date of birth".

(Average starting age acquisition year x 12 + Average starting age acquisition month - Birth year x 12 - Birth month) / 12 (6)
From equation (6), if user E's date of birth 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 old. That is, the average starting age of nursing care level 3 for the renewal change application of the initial user is 78 years old. Similarly, the average starting age is calculated for the other stages (degree of nursing care requirement), and stored in the application classification/average starting age master 203 shown in FIG. 10 as renewal age-specific/average starting age.

In addition, the new application (age) listed in the upper part of the renewal age / average starting age master 203 is the new application of the user of this office, and as described above, the latest certification data at the beginning (at the start of transition) is the average age for all users who are new applicants, and is calculated using the same method as for renewal and change applications.

Next, the case of newly increasing users in the application classification/average starting age acquisition processing unit 13 will be described with reference to FIGS. 11 and 12. FIG. In the case of this newly increasing user, as shown in Fig. 11 (a), the average starting age is calculated based on the user age within the acquisition period, by application category (new) and by degree of nursing care required. do. This process is a process of acquiring the average starting age of newly increasing users by nursing care level, which is the basis of transition estimation, and is used for estimation from the second year onwards.

Below is an example of a method for finding the average starting age of long-term care level 3 for new applicants, assuming that the average starting age acquisition period is one year before March 2015, which is one month before the start of the estimation. For the acquisition period of the average starting age, specify any year, month and period in which the data actually exists. In this example, as described above, the period is one year up to one month before the estimation start date (April 2015).

First, as shown in FIG. 11(a), the application category-by-average starting age acquisition processing unit 13, from the authorization data 101C, selects all users whose new applications are authorized as long-term care level 3 during the user number acquisition period. Extract. In the example, user I and user K are the target persons.

Next, as shown in FIG. 11(b), the age of the corresponding year and month of the average start age acquisition period of user I and user K is obtained from the benefit record 102C. Here, the relevant year and month are the above-mentioned accreditation start date of the new application. However, those certified as requiring long-term care who have not yet used the service are not eligible because they have no track record of benefits. Then, the average ages of these users I and K are calculated.

The following formula (7) is used to calculate the average age at the time of the year and month within the acquisition period from the date of birth of the user in the benefit record. Note that the year and month are not in units of "days" but in units of "years and months", so the user's "date of birth" is used to calculate the age.
(year x 12 + month - year of birth x 12 - month of birth) / 12 (7)

From the above formula (7), if the date of birth of user I is February 17, 1938, as shown in FIG.
(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, and 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 old. In other words, the average age at which care level 3 of newly increasing users begins is 73 years old. Similarly, the average starting age is calculated for the other stages (degree of nursing care requirement), and stored in the application classification/average starting age master 203 shown in FIG. 12 as the average age of the newly increased users. Then, as mentioned above, it will be used for projections from the second year onwards.

Next, the processing contents of the application classification/user number acquisition processing unit 114 will be described. Here, the number of users acquisition process also includes the case of the initial user and the case of the newly increased users. First, the case of the initial user will be described with reference to FIGS. 13 and 14. FIG.

The application classification/user number acquisition processing unit 114 acquires the certification data (application classification and level of care required) 101D shown in FIGS. , the content is irrelevant) From 102D, obtain the number of users by application category and degree of nursing care required at the time of acquisition of the number of users. This process is a process of acquiring the number of users of the initial users by degree of care need, which is the basis of transition estimation. The process of acquiring the number of new users who are increasing with new applications will be described later.

For the number of users acquisition year and month, specify any year and month when the data actually exists. The following shows an example of a method of obtaining the number of users with long-term care level 3 for renewal change applicants, assuming that the number of users was acquired in March 2015. In this example, it is one month before the above-mentioned estimation start date (April 2015). Note that the results of multiple months may be averaged in order to eliminate the influence of user imbalance by month.

The application category/user number acquisition processing unit 114 extracts all users with long-term care level 3 who have applied for updating and changing the user number acquisition year and month from the certification data 101D and the benefit record 102D. In the example, user E and user F are the target persons. Let the number of target persons be the number of users. In the example, there are 6479 persons as shown in FIG. However, those certified as requiring long-term care who have not yet used the service are not eligible because they have no track record of benefits.

The number of people extracted in this way is the user of nursing care level 3 of the update change application of the user at first. The number of users is similarly calculated for other stages (degree of nursing care requirement), and is stored as the initial number of users in the application classification/user number master 204 shown in FIG. 14 .

It should be noted that the new application (person) described in the upper part of the master of the number of users by application category 204 is the initial new number, and as described above, the latest authorization data at the beginning (at the start of transition) is the new application. This is the average number of users for all users, and is obtained by the same method as for renewal change applications.

Next, the processing for the case of newly increased users by the application category/user number acquisition processing unit 114 will be described with reference to FIGS. 15 and 16. FIG. In this process, based on the certification data (application category and level of nursing care required) shown in Fig. 15(a) and the actual benefits shown in Fig. 15(b) (data only, no content), the number of users within the period of acquiring the number of users Acquire the number of users by application category (new) and degree of nursing care required. This process is a process of acquiring the number of users of newly increased users by degree of nursing care required, which is the basis of transition estimation, and is used for estimation after the second year.

Below, an example of a method of obtaining the number of users with long-term care level 3 for new applicants is shown, with the number of users acquisition period set to one year before March 2015. For the period for acquiring the number of users, specify any year and month when the data actually exist. In this example, it is one year up to one month before the above-mentioned estimation start date (April 2015).

The application classification/user count acquisition processing unit 114 extracts all users who have certification data for new applications for long-term care level 3 within the user count acquisition period. In the examples of FIGS. 15(a) and 15(b), the user I and the user K are the target persons. This number of target persons is defined as the number of users. In the example, there are 1904 persons as shown in FIG. However, those certified as requiring long-term care who have not yet used the service are not eligible because they have no track record of benefits.

The number of people extracted in this way is the user of nursing care level 3 of the new application. The number of users is similarly calculated for other stages (degree of nursing care requirement), and is stored as the number of newly increased users in the master 204 of the number of users by application category shown in FIG. 16 . Then, as mentioned above, it will be used for projections from the second year onwards.

In the above explanation, the actual data of the local government is analyzed (referred to as actual data analysis) as shown in FIG. However, there are local governments <long-term care insurers> that cannot analyze actual data like this. For municipalities that cannot analyze actual data, the municipalities that analyzed the actual data are designated as model municipalities, and the analysis is performed using public information issued by the Ministry of Health, Labor and Welfare. ) for estimation master basic information 20 is created.

An overview of the estimation master basic information acquisition processing by public information analysis will be described below with reference to FIG. 17 . First, the basic information acquisition unit 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 (Ministry of Health, Labor and Welfare report summary, long-term care insurance business status report), and estimates each Master basic information (intermediate data) 43, 44 is constructed. Then, the correction coefficient calculation processing unit 31 obtains the difference between the estimation master basic information (intermediate data) 43 and 44 , and uses 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 processing the actual data of the model local government by the correction coefficient 32, and obtains the estimation master basic information (estimated value) 34 of the public information analysis local government. Calculate

Here, since the public information does not have some data divisions (data items), the correction coefficient obtained from the public information also has some data divisions. When estimating the actual data of the public information analysis municipality by multiplying the actual data of the model municipality by the correction coefficient as described above, there is a difference in the data classification between the two. However, the fact that there are no data divisions means that all data divisions are included, so (actual data with data divisions) × (correction coefficient including all data divisions) = (public information analysis municipality with data divisions) actual data) can be estimated. FIG. 18 shows data divisions of actual data, public information, and correction coefficients.

An outline of the method for calculating the correction coefficient from the public information of the model municipality and the public information of the public information analysis municipality shown in FIG. 18 (details will be described later) is listed as follows.
(1) Correction factor for changes in mental and physical condition (worsening/improvement) information: Obtain the deterioration rate and improvement rate for each degree of nursing care required from Report Aggregation 3-7 or the Survey on Nursing Care Benefits Costs, and calculate the correction factor.
(2) Correction coefficients for average benefit cost per person: From Long-Term Care Insurance Business Status Report 08h, by type of service, degree of long-term care required, number of units, and 05-1h, 05-2h, 06-1h, 06-2h, and 07 Calculate the correction coefficient with the number of recipients obtained from -1h.
(3) Correction factor for the average starting age by application category: Obtain the starting age by degree of nursing care need from Report Summary 3-1 and calculate the correction factor.
(4) Correction coefficient for the number of users by application category: The number of users by type of service and level of nursing care required obtained from the long-term care insurance business status report 08h, the male-female ratio of report summary 3-1 and the report summary 3- Calculate the correction factor by multiplying the application classification ratio of 3.

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

In FIG. 19, the psychosomatic state change (worsening/improving) information acquisition processing unit 301 acquires the deterioration/improvement rate from the Ministry of Health, Labor and Welfare Report Compilation 3-7 of the target local government. Since the average maintenance period cannot be obtained from the Ministry of Health, Labor and Welfare Report Summary 3-7, we use the average maintenance period obtained from the actual data of model local governments.

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

In the application classification/average starting age acquisition processing unit 303, the average age obtained from the Ministry of Health, Labor and Welfare Report Summary 3-1 of the target local government is used as the application classification/average starting age.

In the application classification/user number acquisition processing unit 304, the number of users by service type/level of nursing care level acquired from the long-term care insurance business status reports 05-2h, 06-2h, and 07-1h of the target municipality is Multiply the ratio of men and women in Tabulation 3-1 reported by the Ministry of Health, Labor and Welfare by the ratio of application categories in Tabulation 3-3 reported by the Ministry of Health, Labor and Welfare to obtain the number of users by type of service, by gender, by application category, and by application category.

A specific example of processing by the psychosomatic state change (worsening/improving) information acquisition processing unit 301 described above will be described with reference to FIG. From the previous secondary judgment and current secondary judgment by level of long-term care required in the Ministry of Health, Labor and Welfare report tabulation 3-7, obtain changes in mental and physical conditions (worsening/improvement) by level of long-term care. Since the average maintenance period is not available in the public information, the information of the model municipality is used as it is.

An example of obtaining the improvement rate for long-term care level 3 is shown below. The improvement rate for long-term care level 3 is (the number of cases of long-term care level 3 improved from the current level of long-term care level 3) / (number of long-term care level 3 in the previous period), so
(Number of previous care level 3 and current support need 1 + Previous care level 3 and current support level 2 + Previous care level 3 and current care level 1 + Previous care level 3 and current care level 2) / (Number of cases requiring long-term care 3 last time), and in the example of Fig. 20,
(0 + 0 + 274 + 549) / 3169 = 0.26 and the improvement rate is 26%.

Next, an example of obtaining the rate of deterioration of nursing care level 3 will be shown. The deterioration rate for long-term care level 3 is (the number of cases where long-term care level 3 was worse than the current level of long-term care level 3) / (the number of long-term care level 3 in the previous time), so
(The number of nursing care level 4 this time from the previous nursing care requirement 3 + the number of nursing care requirement 5 this time from the previous nursing care level 3) / (the number of nursing care requirement 3 last time), and in the example of Fig. 20,
(756 + 280) / 3169 = 0.33 and the deterioration rate is 33%.

As shown in FIG. 21, the worsening rate and the improving rate are obtained for other nursing care levels as well. Note that the numerical values in FIG. 20 are those of the model local governments, and the rate of deterioration and rate of improvement in FIG. The master basic information median value data 44 for estimation of public information analysis municipalities is constructed in the same manner.

Next, the processing of the per capita average benefit acquisition processing unit 302 will be described with reference to FIG. This process acquires the number of units by type of service, degree of need for long-term care, and the number of units from the long-term care insurance business status report 08h, and the number of recipients by type of service from the reports 05-2h, 06-2h, and 07-1h. , monthly average benefit cost per person by type of service, level of nursing care required.

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

Monthly average benefit cost per person by level of long-term care need = (Average number of units per person per month by level of long-term care level) × Unit price by ratio of labor costs by region ・・・(8)

Taking care level 3 in FIG. 22(b) as an example, the number of units for each service type/level of care level/year total is 220000, and the total number of man-months by service type/level of care level/year total is 21000. , the average number of units per person per month by degree of nursing care need is 10.48, and from formula (8), the average monthly benefit cost per person with nursing care level 3 is 10.48 x 1000 x 10 = 104,800 yen. .

The formula (8) is an example of calculating the unit price by region-based labor cost ratio with an additional rate of 0% (10 yen) by region. In addition, when acquiring the average benefit cost per person regardless of service type, the number of units is 08h "08-1h (number of units 1)" total number of sheets, number of users is 05-1h, 06-1h, 07 -Acquire and calculate the number of recipients from the total number of 1h.

As shown in FIG. 23, the average number of units per person is obtained for other levels of nursing care required by the same method. Note that the numerical values in FIG. 22 are those of the model municipalities, and the average number of units per person in FIG. The master basic information intermediate data 44 for estimation of public information analysis municipalities is constructed in the same manner.

Next, the processing of the application classification/average starting age acquisition processing unit 303 will be described with reference to FIG. In this process, the average starting age by gender, degree of need for nursing care, and average starting age are obtained from the Ministry of Health, Labor and Welfare Report Summary 3-1 (excerpt) shown in FIG. The following shows how to obtain the average starting age of men requiring long-term care 3. In Report Aggregation 3-1, 65 years old to under 100 years old is the age range of 5 years old, so the representative value for each age group is determined. The representative value is the median of the range, with 62.5 for those under the age of 65 and 102.5 for those over the age of 100. Calculate the average age by taking the weighted average of each representative value and the number of cases. In the example of FIG. 24 , the average calculated value for men requiring nursing care 3 is 80.03858, and the average starting age is 80 years old.

In this way, the gender, degree of care need, and average starting age are obtained as shown in FIG. 25, respectively. Note that the numerical values in FIG. 24 are those of the model municipalities, and the gender-based, care-required degree-based, and average starting ages in FIG. The master basic information intermediate data 44 for estimation of public information analysis municipalities is constructed in the same manner.

Next, the processing of the application category/user count acquisition processing unit 304 will be described with reference to FIG. 26 . In this process, the number of users by type of service, degree of need for long-term care, and the number of users obtained from Long-Term Care Insurance Business Status Reports 05-2h, 06-2h, and 07-1h, the male-to-female ratio in the Ministry of Health, Labor and Welfare Report 3-1, Obtain the number of users by service type, gender, application category, and number of users by multiplying the application category ratio in the Ministry of Health, Labor and Welfare Report Summary 3-3. An example of the acquisition method is shown below.

The method of obtaining the number of users is as explained in the processing for obtaining the average benefit cost 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 male/female ratio is obtained from the number of certifications by degree of need for nursing care, by gender, and in the aggregate report 3-1 (excerpt) shown in Fig. 26(a). The application category ratio is obtained from the number of certifications by degree of nursing care required, by application category, and in the aggregate report 3-3 (excerpt) shown in Fig. 26(b). Based on these, the number of users by service type, by gender, by application category, and by the following formula (9) is obtained.

Number of users by type of service, by gender, by application category = (number of users) × (gender ratio) × (application category ratio) ・・・(9)

In the case of care level 3 in Figure 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 category ratio = (3078 + 888) / (5239 - 97) = 77%, so the number of male new application users is 21000 × 39% × 23% = 1884 (persons) from formula (9)
becomes.

Other service types, men and women, application classifications, and the number of users are obtained by the same method as shown in FIG. The figures in FIG. 26 are those of the model municipality, and the numbers of users by type of service, by gender, by application category, and by number of users in FIG. The master basic information intermediate data 44 for estimation of public information analysis municipalities is constructed in the same manner.

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

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

The per capita average benefit cost correction coefficient calculation processing unit 312 acquires from the master basic information intermediate data 43 for estimation of the model local government based on public information and the master basic information intermediate data 44 for estimation of the public information analysis local government. 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 master basic information correction coefficient 32 for estimation.

The application classification-by-average starting age correction coefficient calculation process 313 is the application classification obtained from the estimation master basic information intermediate data 43 of the model local government based on public information and the estimation master basic information intermediate data 44 of the public information analysis local government. The mutual ratio of different average age is obtained, and this ratio is used as the correction coefficient of the application classification/average age in the estimation master basic information correction coefficient 32 .

The application classification/user number correction coefficient calculation processing unit 314 obtains applications from the estimation master basic information intermediate data 43 of the model local government based on public information and the estimation master basic information intermediate data 44 of the public information analysis local government. The mutual ratio of the number of users by category is obtained, and this ratio is used as the correction coefficient of the number of users by application category in the estimation master basic information correction coefficient 32 .

Next, specific processing of the psychosomatic state change (deterioration/improvement) information correction coefficient calculation processing unit 311 described above will be described. This processing is based on the mental and physical condition change information (change in mental and physical condition (worsening/improving) information by level of care required) of the master basic information intermediate data 43 for estimation of the model local government shown in FIG. 21, and the public information analysis shown in FIG. The ratio of the master basic information intermediate data 44 for estimation of the municipality to the mental and physical state change information (mental and mental state change (deterioration/improvement) information for each level of care required) is calculated and used as a correction coefficient.

The correction coefficients are obtained by the following equations (10) and (11).
Deterioration rate correction coefficient = (Deterioration rate by degree of long-term care need in public information analysis municipality) / (Deterioration rate by degree of long-term care need in model municipality) ・・・(10)
Improvement rate correction coefficient = (improvement rate by degree of long-term care need in public information analysis local government) / (improvement rate by degree of long-term care need in model municipality) ・・・(11)

Based on the values of the estimation master basic information intermediate data 43 of the model local government shown in FIG. 21 and the values of the estimation master basic information intermediate data 44 of the public information analysis local government shown in FIG. A change (worsening/improvement) information correction coefficient is obtained and constitutes the master basic information correction coefficient 32 for estimation.

Next, specific processing of the per capita average benefit cost correction coefficient calculation processing unit 312 will be described. This processing is based on the model local government's estimation master basic information intermediate data 43 shown in FIG. The ratio of the data 44 to the monthly average number of units per person for each level of nursing care required is calculated and used as a correction coefficient.

A correction coefficient is obtained by the following equation (12).
Correction coefficient for average number of units per person per month
= (Average number of credits per person per month in public information analysis municipalities) / (Average number of credits per person per month in model municipalities) ・・・（12）

Based on the values of the estimation master basic information intermediate data 43 of the model local government shown in FIG. 23 and the values of the estimation master basic information intermediate data 44 of the public information analysis local government shown in FIG. A monthly average number of units correction coefficient per person is obtained, and constitutes the master basic information correction coefficient 32 for estimation.

Next, specific processing of the application classification/average starting age correction coefficient calculation processing unit 313 will be described. This processing is based on the estimation master basic information intermediate data 43 of the model local government shown in FIG. The ratio of 44 by sex, degree of care required, and average starting age is calculated, and this is used as a correction coefficient.

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

Based on the values of the estimation master basic information intermediate data 43 of the model municipality shown in FIG. 25 and the values of the estimation master basic information intermediate data 44 of the public information analysis municipality shown in FIG. A correction coefficient for each degree of care need and average starting age is obtained, and constitutes a correction coefficient for the master basic information 32 for estimation.

Next, the specific processing of the application classification×user number correction coefficient calculation processing unit 314 will be described. This processing is based on the estimation master basic information intermediate data 43 of the model local government shown in FIG. 44 by gender, degree of need for nursing care, and the number of users are calculated, and this is used as a correction coefficient.

A correction coefficient is obtained by the following equation (14).
User number correction coefficient = (number of users in public information analysis municipality) / (number of users in model municipality) (14)

Based on the values of the estimation master basic information intermediate data 43 of the model municipality shown in FIG. 27 and the values of the estimation master basic information intermediate data 44 of the public information analysis municipality shown in FIG. The user correction coefficients classified by level of care need are obtained, and constitute the master basic information correction coefficients 32 for estimation.

Next, the processing of the estimation master basic information estimation processing section 33 shown in FIG. 17 will be described with reference to FIG. In this process, the estimation master basic information (the same as the estimation master basic information 20 described in FIG. 3) obtained from the actual data of the model municipality is added to the estimation master calculated in the correction coefficient calculation process described in FIG. Multiplying the basic information correction coefficient 32, master basic information (estimated value) 34 for estimation of public information analysis municipality is obtained.

The mental and physical state change (worsening/improvement) information actual data estimation processing unit 331 adds the correction coefficient of the mental and physical state change information in the master basic information for estimation correction coefficient 32 to the mental and physical state change information of the master basic information for estimation (model local government) 20. By multiplying by , the psychosomatic state change information constituting the estimation master basic information 34 of the public information analysis municipality is obtained.

The per capita average benefit cost actual data estimation processing unit 332 corrects the per capita average benefit cost in the estimation master basic information correction coefficient 32 to the per capita average benefit cost of the estimation master basic information (model local government) 20. Multiply by the 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 classification/average starting age actual data estimation processing unit 333 adds the average starting age per application classification in the estimation master basic information correction coefficient 32 to the average benefit cost per person in the estimation master basic information (model local government) 20. is multiplied by the correction coefficient to obtain the average starting age for each application category that constitutes the estimation master basic information 34 of the public information analysis local government.

The actual data estimation processing unit for the number of users by application category 334 adds the number of users by application category in the basic master information for estimation (model local government) 20 to the number of users by application category in the basic master information for estimation correction coefficient 32 . By multiplying the number of correction coefficients by the correction coefficient of the number, the number of users for each application classification constituting the estimation master basic information 34 of the public information analysis municipality is obtained.

Next, specific processing examples of the respective processing units 331, 332, 333, and 334 described above will be described. The psychosomatic state change (worsening/improvement) information actual data estimation processing unit 331 is the same as the psychosomatic state change information 201 of the estimation master basic information (actual data) 20 described in FIG. Change in physical and mental condition information (change (deterioration/improvement) information by level of care required: real side data) 201A shows the master basic information correction coefficient 32 for estimation shown in FIG. By multiplying the state change (worsening/improvement) information correction coefficient, the mental and physical state change (worsening/improvement) information (estimated value) by nursing care level of the mental and physical state change information estimated value 34 of the public information analysis local government shown in FIG. get.

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

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

The per capita average benefit cost estimation processing unit 332 is the same as the average per capita benefit cost 202 of the estimation master basic information (actual data) 20 described in FIG. Multiplying the average monthly benefit cost per person (actual side data) 202A by the correction coefficient of the monthly average number of units per person of the estimation master basic information correction coefficient 32 shown in FIG. Acquire the average benefit cost (estimated value) per person in the promotion master basic information 34 .

This actual data estimated value is obtained by the following equation (17).
Estimated average benefit cost per person = (Average benefit cost per person based on actual data of model local government) × (Correction coefficient for average number of credits per person per month) ・・・(17)

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

The application classification/average starting age estimation processing unit 333 is the same as the application classification/average starting age 203 of the estimation master basic information (actual data) 20 described in FIG. Gender-specific care level-specific average starting age (actual data) 203A is multiplied by the gender-specific care level-specific average starting age correction coefficient of the estimation master basic information correction coefficient 32 shown in FIG. 43, the average starting age (estimated value) by gender, by level of nursing care required, and the master basic information 34 for promotion of the public information analysis municipality shown in FIG. 43 is acquired.

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

From the values shown in FIGS. 42(a) and 42(b), the values shown in FIG. 43 can be obtained by gender, by level of care required, and estimated average starting age.

The number of users by application category estimation processing unit 334 is the same as the number of users by application category 204 of the estimation master basic information (actual data) 20 described in FIG. The number of users by gender and degree of need for care (actual data) 204A is multiplied by the correction coefficient for the number of users by gender and degree of need for care of the estimation master basic information correction coefficient 32 shown in FIG. Then, the number of users (estimated value) by gender, by level of care required, and the number of users (estimated values) of the public information analysis local government promotion master basic information 34 shown in FIG. 45 are acquired.

This actual data estimated value is obtained by the following equation (19).
Number of users = (number of users of actual data of model local government) × (number of users correction factor)
(19)

From the values shown in FIGS. 44(a) and 44(b), the values shown in FIG. 45 can be obtained by gender, by level of nursing care, and estimated number of users.

In the above-mentioned master basic information acquisition process for estimation (public information analysis), it is necessary to analyze the actual data of the model municipality. However, if there are no model local governments with similar scales and forms, it will be difficult to estimate trends. As a result, the accuracy of the master basic information for estimation decreases, but the method that enables transition estimation without analysis of the actual data of model local governments will be explained below.

In this case, the basic information acquisition unit 30 explained in FIG. Intermediate data) 44 is constructed. Then, the estimation master basic information (intermediate data) 44 of the public information analysis municipality is used as it is for transition estimation.

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

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

In this process, based on the state of the number of users at the time of the estimation start, the mental and physical state change information 201 of the master basic information for estimation 20 (including the mental and physical state change information 201A of the public information analysis municipality, but hereinafter unified with 201 ), and the number of users by application category 204. ) is used to estimate changes in the number of users.

Here, the users include update change application users and new application users as described above. The initial number of users for renewal and change applications shall be the number of users whose renewal and change applications are subject to the approval period at the start of estimation. Newly applied users in the first year (at the start of estimation) are not limited to the range of the past one year, but all users for whom the latest certified data at the time of the start of estimation are newly applied. . On the other hand, for the first users who applied for the second year and later, the number of users who applied for the first year before the start of the estimation is used for the estimation. When considering changes in the population, increase or decrease the number of users.

For example, if there are 700 users with level 4 of long-term care among the update change application users, the users with level of long-term care 4 go to the next stage (end, level of long-term care 5, level of long-term care 3, level of long-term care 2, . . . ). ) is estimated using the psychosomatic state change information 201 . After that, for the next stages (other than the end) that have changed, nursing care level 5, nursing care level 3, nursing care level 2, . FIG. 47 shows the psychosomatic state change information master 201 used for this estimation.

For new application users, transition estimation is performed in the same way as for update change application users described above.

A specific example of the user number transition estimation process described above will be described with reference to FIG. In FIG. 48, the mental and physical condition change information master 201 is the same as those shown in FIGS. This is a master that holds the "transition ratio" and "average maintenance period" for each level of care required. This is created in advance by the estimation master basic information acquisition processing unit 11 described above.

The application category/user count master 204 is the same as those shown in FIGS. 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 is explained below.

First, user number transition estimation processing (hereinafter referred to as “estimation processing”) for each update/change user and new user for all levels of care need is started at the same time. That is, "estimation start date", "level of care need", "initial number of users", "application category", and "sex" are set as attribute information for each estimation process. Based on the level of care required and gender, the number of people to start estimating is obtained from the application classification/user count master by care level and gender. In the example of FIG. 48, there are 700 women who require nursing care 4.

In each estimation process, from the mental and physical condition change information master 201, the estimation start date of each estimation process is within the application period of the mental and physical condition change information master. Acquired by level of care. In FIG. 48 , the “average maintenance period”, “next level of nursing care requirement” and “transition ratio” of a woman with nursing care requirement 4 are acquired in the first estimation process 12A. These are acquired from the psychosomatic state change information master 201 from the row where the care level and the next care level match in the phase in which the estimation start date is included in the application period.

In addition, the "level of care required" in the first estimation process 12A indicates the level of care required within this estimation process. "Average maintenance period" is the maintenance period until the next level of care required, and is shown for each level of care required. The "next level of care requirement" indicates the level of care requirement to which the transition is made after maintaining the level of care requirement for the average maintenance period. "Transition ratio" indicates the ratio of the number of people who transition to the next level of care required.

Next, each estimation process calculates (initial number of users × next transition ratio by degree of care need) to obtain the “number of users by degree of care need”, and divides this into the number of months of the average maintenance period. Only the number of users by month based on the relative number of months elapsed since the start of the estimation is expanded.

Taking the second line from the top of the diagram of the first estimation process 12A (the next level of care need is care need 5) as an example, "70% of 700 users with care need 4 (490 people) have care need 4 After maintaining for 16 months, transition to long-term care level 5”. In each month column of "Relative number of months from estimation start year and month" in the first estimation process 12A, the number of users obtained by the above (initial number of users × transition ratio) is entered from the estimation start year and month. Set for the number of months of the average maintenance period. Taking the fourth line from the top (the next level of care requirement is 3) as an example, 10% (70 people) out of 700 users maintain the level of care requirement 4 for 16 months.

The total value of the users with the current level of care required in the maintenance period until the next level of care required is calculated for each month. In the first estimation process 12A, the number of users is summed up by the period of maintenance to the next level of care level of the users with the current level of care level 4, that is, by the relative number of months since the start of the next level of care level estimation. Calculate the total number of monthly users. At the beginning of the estimation, the total number remained at 700 for a while, but after 16 months of relative progress, it reached 644, and after that, 84 in 17 months, 39 in 18 months, and so on. Decrease in sequence.

The monthly total number of users obtained in this way is registered in the number of users by year and month in the number of users by level of care requirement transition table 12T. At this time, the registration destination registers the same nursing care level as the nursing care level of each estimation process for the year and month obtained by "estimation start year and month + relative number of elapsed months from estimation start year and month". Also, the number of users is registered by adding it to the number of users already registered.

Each estimation process executes "each next estimation process" for each level of nursing care required. In Fig. 48, as the attribute information for the next estimation process 12B, "estimation start year and month" is set to "previous estimation start year and next average maintenance period by degree of nursing care", and "initial number of users" is set to Set the number of users by level of nursing care required for the next single month, which is the number of people as a result of the previous estimation, and set the next level of nursing care requirement in "Level of nursing care requirement". Set “Update/Change” for “Application category” and set the same gender as for each previous estimation process for “Gender”.

In FIG. 48, when the next level of care need is care need 3, the "estimation start year and month" in the next estimation process 12B is "previous estimate start year (April 2015) + next care need level The average maintenance period (16 months)” is August 2016. The "initial number of users" is assumed to be 70, which is the next single month number of users according to the level of care required, which is the number of people estimated as a result of the previous estimate. In the "level of care required", a care level 3, which is the next level of care required, is set. For "application category" and "sex", set "update/change" as in the previous estimation process, and for "sex" set to "female".

Also, in the same way as in the previous estimation process, we obtain the number of users by relative number of months since the start of the next estimation by degree of care need, add them up to obtain the total number of users by month, and then , is registered in the number of users by year and month in the transition table 12T for the number of users by level of long-term care need.

Regarding the number of users by relative number of elapsed months, the following estimation processing is not performed for rows where the next level of nursing care requirement has ended or the number of users by month has become less than 1. In addition, if the number of monthly users is a fraction of less than 1, the total number of users by month will be calculated as a decimal number, and the number of users by level of nursing care will be calculated below the decimal point when registering the transition table for the number of users by level of care required. is rounded to an integer value.

The next estimation process is similarly executed for other care need levels, and the estimation process is similarly executed one after another thereafter. When all estimation processes are completed, the user number transition estimation process ends.

The transition table 12T of the number of users by level of nursing care requirement described above is a table that holds the transition of the number of users by level of nursing care requirement and by year and month. note that,
The year and month of the registration destination is calculated as the estimation start date + the number of relative elapsed months from the estimation start date - 1. In the example of the dashed arrow in FIG. 48, the relative number of elapsed months is 3, (April 2015) + 3 - 1, and the registration previous year and month in the user transition table T by level of care need is June 2015. becomes.

The change in the number of users by level of care requirement becomes clear from the values in this table 12T of changes in the number of users by level of care level.

Next, FIG. 49 will be used to explain the benefit cost (cumulative) transition estimation process in step 3 explained in FIG. The average benefit cost per person 202 is the same as shown in FIGS. The benefit cost (cumulative) transition estimation processing unit 13 shown in FIG. Estimate the transition of benefit costs (cumulative) by using the degree of need for nursing care, monthly, and the number of users in the number transition table 12T.

That is, the number of users by level of long-term care need/month/number of users in the transition table 12T for the number of users by level of long-term care level is added to the average benefit cost per person (monthly amount) of the average benefit cost per person 202 obtained in step 1. ) to calculate the benefit cost (monthly amount) for each level of nursing care required, by month, and the sum and cumulative amount thereof, and register them in the benefit cost transition table 49 . Tables 202, 12T, and 49 shown in FIG. 49 are all classified by service type and gender.

From the values of this salary transition table 49, the transition of the benefit expenses (cumulative) becomes clear.

Next, FIG. 50 demonstrates the healthy life span transition estimation process of step 4 demonstrated in FIG. In this process, for each of 65 psychosomatic condition items (certified data) such as the level of nursing care required and the degree of independence with dementia, the age at which the heaviest stage that does not require nursing care ends last is defined as the "healthy life expectancy".

The healthy life expectancy transition estimation processing unit 14 shown in FIG. The age is converted to the average starting age of 203M by application category. Next, a method of obtaining a healthy life expectancy using the first estimation process 12A in step 2 described above with reference to FIG. 48 will be described.

In the first estimation process 12A in FIG. 48, the number of users who need long-term care 4 to transition to the next stage is obtained for each next stage, and from the average duration of each of them, the months elapsed until transition to the next stage The number of people who transitioned was registered every time. In this first tallying process 12A, when to transition to the next stage can be known for each next stage.

If the content of this first estimation process 12A is directly applied to the estimation process 50A in FIG. Assuming 4, the average starting age (age in months) by application category is 203M to 924 (months). 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 in months, +1 is added each time one month passes.

Looking at the next row of nursing care requirement 3, the average duration is 16 months, so the age in months 924 is maintained for 16 months while +1 is added. Then, after 16 months have passed, the process transitions to the estimation process 50B for the next level of nursing care level 3 . Therefore, the age at which care level 4 finally ends is 939, which is 16 months later.

In the above explanation, the numerical value of the first estimation process 12A in FIG. In general, it is said to be Level 2 of long-term care. Therefore, if the initial care level in the estimation process 50A is set to care level 2 and the same process as described above is performed, the timing at which the care level transitions to the next level, i.e., care level 3, can be grasped. Since it will be the year and month when the stage (long-term care level 2) where no load is applied will end, the age in months can be estimated as the healthy life expectancy.

Next, referring to FIG. 51, the process of comparing the benefit cost (cumulative) estimation results between new policy effect cases in step 5 explained with reference to FIG. 2 will be explained. The benefit cost (cumulative) estimation unit 15 in FIG. 2 performs comparative analysis of the estimated results for each new policy effect case in step 3 of FIG. 2 described above. For example, if new policy case 1 is a policy that sets the target value of mental and physical state change information for a specific service type (for example, “special nursing care”) as +10% as described above, new policy case 2 is the same. If the target value is set to 15%, and if the new measure case 3 is a measure that sets the target value of the mental and physical state change information of another service type (for example, "health for the elderly") to +10%, step 3 The benefit cost (cumulative) transition estimation process is performed, and based on the results, the effects are compared and analyzed.

Figure 51 shows the cumulative results of benefit costs by degree of nursing care required, by case of effect of new measures, after three years in the same figure (a), after six years in the same figure (b), and in the same figure (c). and 9 years later are shown as tables.

As a result of the above-mentioned comparative analysis, it is possible to select and implement plans for measures that are effective in reducing benefit costs, by service type or by subregion.
Next, FIG. 52 demonstrates the comparison process between new policy effect cases of healthy life expectancy of step 6 demonstrated in FIG. The new policy effect case comparison processing unit 16 in FIG. 2 performs a comparative analysis of the estimation results for each new policy effect case in step 4 of FIG. 2 described above.

FIG. 52 shows the results of comparing the degree of care required, new policy effect cases, and age for new policy effect cases 1, 2, and 3. FIG. From the results of this comparison, it is possible to determine the type of service or measures for each sub-region that are effective in extending healthy life expectancy, making it possible to determine the selection and implementation plan.

Next, an embodiment in which the function of estimating and verifying cost-effectiveness is further simplified will be described. If the embodiments described so far are so-called detailed nursing care versions, the embodiments described below will be simplified nursing care versions. In this simple version of nursing care, as the current status 1, the number of users (users of long-term care insurance) and benefit costs by level of long-term care need and type of service are obtained from the long-term care insurance business status report of the Ministry of Health, Labor and Welfare, which is public information. In addition, as the current status 2, the improvement rate and average maintenance period until deterioration of the level of long-term care required are aggregated by level of long-term care level, type of service, and group of business establishments.

Next, formulate an improvement plan for each care level, service type, and establishment group. The establishment group refers to four groups classified by comparing the improvement rate and the average maintenance period until deterioration with the local government average. That is, the target improvement rate and maintenance period of the level of care required for each care level and service type are planned for each establishment group. For example, as shown in Figure 53(a), for users with a need for long-term care (severe), both the improvement rate and the average maintenance period until deterioration are below the local government average. Three years after the implementation of the plan, an improvement plan will be made to improve the local government average in the center of the figure.

In addition, we will calculate the benefit cost restraint effect after implementing the improvement plan. That is, the amount of benefit cost reduction due to the improved rate of improvement in the need for nursing care and the amount of benefit cost reduction due to extension of the maintenance period shown in FIG. 53(b) are calculated. FIG. 53(b) shows that the user's degree of nursing care requirement deteriorated from nursing care requirement 3 to nursing care requirement 4 after the maintenance period indicated by the solid line. As shown, when the maintenance period until deterioration is extended, it represents that the salary to the user is suppressed by the area of the hatched line.

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

Examples are described below. As one of the steps in the above-mentioned current situation understanding 1, the number of users by level of care required and by service type is obtained as described above. For this reason, as shown in FIG. The number-of-users acquisition processing unit 52 acquires data on the number of users requiring nursing care (by month) by service type, and configures a number-of-users master 53 .

The number of users master 53 registers the number of users for each type of service according to the level of care required. For example, for long-term care level 1, service types are registered as 8.0 (1,000 people) for in-home care, 1.0 (1,000 people) for special nursing care, 0.3 (1,000 people) for elderly health care, and so on. ing.

Next, the mental and physical state change rate by level of nursing care required is obtained from the second public data (survey on nursing care benefit costs) 54, in which the rate of mental and physical state change by level of nursing care is disclosed, by the mental and physical change information acquisition processing unit 55. get. In the second public data 54, "lightening" means that the level of nursing care requirement is lowered by one level or more, and "severity" means that the level of nursing care requirement is higher by one level or more. In the example shown in the figure, 5% of the care level was lowered by one level to long-term care level 1, and 25% was raised by one level to long-term care level 1. It shows that 70% of the patients remain in care level 1.

By multiplying the number of users in the user number master 53 by this rate of change in mental and physical conditions, the number of persons with changed mental and physical conditions is calculated by service type and level of care required, and the master of the number of persons with changed mental and physical conditions is constructed. In the example of the figure, the number of people with in-home long-term care requirement 1 is 8.0 (thousands) as shown in the number of users master 53, and the rate of reduction is 5% from the second public information 54 , the number of improved people is 0.4 (1,000 people). In the same way, the number of people with maintenance and deterioration is also calculated and registered in the master 56 of the number of people with changed mental and physical conditions.

The psychosomatic change information acquisition processing unit 55 further collects the number of persons whose mental and physical conditions have changed according to the predetermined degree of care required, and configures the mental and physical state changed number of persons master 56 with the aggregated number of persons whose mental and physical conditions have changed. Here, mild refers to the range of care needs 1 and 2, and severe refers to the range of care needs 3, 4, and 5. In the example shown in the figure, the number of people with care levels 1 and 2 are summed up as the number of people with mild care needs, and the number of people with care levels 3, 4, and 5 corresponding to severe care levels are summed up and aggregated. Register at 56.

In this way, the service type, level of care required, and the number of people with changes in mental and physical conditions can be obtained.

As another step of understanding the current situation 1, we will calculate the benefit costs for recipients (users of long-term care insurance) by level of nursing care and service type. For this purpose, the difference in benefit costs per person according to the type of service, degree of care required, and change in mental and physical conditions is sought.

That is, as shown in FIG. 55, the salary acquisition processing unit 58 acquires the monthly benefit amount per person by service type and by level of care requirement from the first public data 51, and configures the benefit master 59. do.

In the benefit cost master 59, the monthly benefits per person are registered for each type of service (in-home care, special nursing care, health care for the elderly, . . . ) according to the level of care required. In the figure, when looking at service types: in-home services, the monthly benefit per person is 45 (thousand yen) for support level 2, 85 (thousand yen) for long-term care level 1, and 120 (thousand yen) for long-term care level 2. 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 at the time of change in mental and physical conditions by service type and level of care required, and calculates the benefit cost difference. Register in table 61. In the example of the figure, looking at nursing care level 1, the number of people with improved mental and physical conditions obtained from the mental and physical condition change number master 56 is 0.4 (1,000 people), and the number of people with worsening is 2.0 (1,000 people). Benefit cost difference per person at the time (monthly amount of reduction due to improvement from 1 to 2 support required) - 40 (thousand yen) and annual amount - 480 (thousand yen), and benefits per person at the time of deterioration 35 (1,000 yen) is registered for each of the expense differences (monthly increase due to deterioration of nursing care level 1 to nursing care level 2).

Similarly, for other levels of nursing care required, the number of people whose mental and physical conditions have changed and the benefit cost difference per person when their mental and physical conditions have changed are registered.

The benefit cost difference extraction processing unit 60 further extracts the benefit cost difference per person when the mental and physical condition is improved and the benefit cost difference per person when the mental and physical condition is deteriorated into light (long-term care level 1, 2) and severe (long-term care level 3). , 4, 5) and registered in the benefit cost difference table 61. This aggregate value is the weighted average value of long-term care level 1 and 2 when looking at the degree of severity. That is, the light benefit cost difference (annual amount) is [{0.4×(-480)}+{0.6×(-420)}]/1.0=-444 (thousand yen). The severity (monthly amount) is similarly calculated and registered in the benefit cost difference table 61 .

In grasping the current situation 2, the improvement rate and maintenance period of the level of long-term care required are tabulated by level of long-term care level, service type, and establishment group.

At the stage of formulating an improvement plan for each service type and business group, the target improvement rate and maintenance period for the level of long-term care need and service type are planned for each business group. As mentioned above, in Figure 52, the current values for both the improvement rate and the average maintenance period until deterioration are below the local government average. Make an improvement plan to improve to the municipal average.

Therefore, as shown in FIG. 56, the current value 63 is first grasped. For municipalities that do not have actual data, use the current values (by service type, degree of care required, improvement rate, and maintenance period until deterioration) based on actual data from model municipalities with similar environments and forms. Next, by the target value setting unit 64, the target value that should be improved after a predetermined period (for example, 3 years) from the implementation of the measure (by service type, by degree of care required, improvement rate, and maintenance period until deterioration) set.

The difference extracting means 65 obtains the improvement rate difference between the current value and the target value and the maintenance period difference until deterioration, and constructs the difference master 66 . In the figure, the difference in mild improvement rate for home-based patients is 4%, and the difference in the maintenance period until deterioration is 2 months.

Next, we calculate the benefit cost restraint effect after implementing the improvement plan. That is, the amount of benefit cost reduction due to the improvement rate of the need for nursing care and the amount of benefit cost reduction due to the extension of the maintenance period are calculated.

First, the benefit cost suppression amount calculation processing based on the improved rate of improvement in the need for nursing care will be described. The first benefit cost suppression amount calculation unit 68 of FIG. The difference (improvement rate difference) is acquired, and from these, the number of persons subject to benefit cost reduction is calculated by service type and degree of degree of nursing care required, and registered in the corresponding item of the first benefit cost reduction amount table 69. do.

In the example shown in the figure, the number of people with changes in mental and physical conditions for mild at-home patients is 14.0 (thousand people), and the improvement rate difference from the target value is 4%, so the number of people subject to benefit cost reduction is 560 people. .

The first benefit cost restraint amount calculation unit 68 further calculates the benefit cost difference per person when the mental and physical condition is improved by service type and by the level of care required and by the level of care required held in the benefit cost difference table 61, Based on the above-mentioned service type and the number of people subject to benefit cost reduction according to the level of care required, the amount of benefit cost reduction due to improvement in mental and physical conditions is calculated, and the corresponding item in the first benefit cost reduction amount table 69 is calculated. sign up.

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

Next, the processing for calculating the amount of benefit cost reduction by extending the maintenance period until deterioration will be described. The second benefit cost suppression amount calculation unit 71 in FIG. 58 calculates the difference (worsening (maintenance period difference up to) is acquired and registered in the corresponding item of the second benefit cost suppression amount table 72 .

In the example shown in the figure, the number of home-based patients with mild deterioration is 3.2 (thousands), and the difference in maintenance period until deterioration is two months.

The second benefit cost suppression amount calculation unit 71 further multiplies the benefit cost difference per person (monthly amount) at the time of deterioration by the difference in maintenance period until deterioration (extended value). , and registers it in the corresponding item of the second benefit cost suppression amount table 72 .

In the example in the figure, the number of people who deteriorated is 3.2 (thousand people), the benefit cost difference (monthly amount) per person at the time of deterioration is 41 (thousand yen), and the difference in the maintenance period until deterioration is 2 months. , the amount of benefit cost reduction due to the difference in the maintenance period until deterioration is -262.4 (million yen).

Finally, in the cost-effectiveness verification stage, as shown in FIG. 59, the benefit cost reduction amount due to the improvement rate improvement in the level of care required after a predetermined period (for example, 3 years) after the implementation of the measures obtained as described above (Table value) 69 and the benefit cost reduction amount (table value) 72 due to the extension of the maintenance period until deterioration are added by the cost-effectiveness verification unit 74, and the target value for the implementation of measures by service type, level of care required, and severity Calculates the total amount of the benefit cost suppression amount by calculating the total benefit cost suppression amount table 75 .

Using the numerical values registered in this benefit cost suppression amount table 75, as shown in FIG. 60, it is possible to perform a simulation involving benefit cost suppression due to implementation of measures by the local government.

So far, the cost-effectiveness estimation/verification function of the simple care version has been explained, and FIG. 61 compares this with the detailed care version described above. The detailed nursing care version is more accurate than the simple nursing care version in all respects. However, the difficulty of realizing the estimation verification tool is higher than the simplified version. Each comparison item will be briefly described below with reference to FIG.

In FIG. 61, looking at the comparison item "consideration of the number of users by application classification", in the simple nursing care version, consideration is "none" for each application classification. On the other hand, in the long-term care detailed version, consideration is "Yes", and it is distinguished between new and renewal / change.

Looking at the comparative item, "notable level of long-term care required", in the simplified version of long-term care, "levels of long-term care required are aggregated into light and severe levels and estimated." On the other hand, the detailed version of long-term care "accurately estimates all stages of the need for long-term care", indicating that the detailed version of long-term care is more accurate.

Looking at the comparison item, "level of change in level of nursing care required," the simple version of nursing care assumes that "only one stage of deterioration and improvement is assumed." On the other hand, the detailed version of nursing care assumes "all stages of deterioration and improvement", and it can be seen that the detailed version of nursing care has higher accuracy.

Looking at the comparison item, "improvement rate and accuracy of the average maintenance period until deterioration," in the simple nursing care version, "it is impossible to specify the figures for the local government, which has only figures for model local governments." On the other hand, in the nursing care detailed version, it is possible to obtain "highly accurate values using actual data".

Looking at the comparison item, "Phase considering change in improvement rate and average maintenance period until deterioration", in the simple nursing care version, "current and target are set only between two points (for example, 3 years) at the start and end of the estimation. "is doing. On the other hand, in the detailed nursing care version, "repeated calculation of transitions in all stages after the maintenance period of the level of nursing care required (including the case of termination)" shows that the detailed nursing care version has higher accuracy.

Regarding the comparison item "time-series resolution accuracy", in the simplified nursing care version, it is "only linear interpolation between two points". On the other hand, in the detailed version of nursing care, it is possible to calculate monthly estimates, and it can be seen that the detailed version of nursing care has higher accuracy.

Looking at the comparison item “whether healthy life expectancy can be calculated”, the calculation is “impossible” in the simple nursing care version. On the other hand, in the nursing care detailed version, it is "calculation logic similar", and calculation is "possible" by this similar calculation logic.

Looking at the comparative item "Difficulty of realizing estimation verification tool", the simple version of nursing care is "realizable with Excel", and the degree of difficulty is relatively low. On the other hand, in the detailed version of nursing care, "Program development is essential (difficult to use Excel)", and the detailed version of nursing care is relatively more difficult.

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

All of the above cost-effectiveness estimation and verification functions were estimated and verified for service users of the long-term care benefit business. Such estimation and verification functions can be similarly applied to general projects. In the case of a comprehensive project, the approach targets include how to extend the period of independence and how to make those who need support return to being eligible for the project or becoming self-reliant.

Here, in the case of the long-term care benefit business, the long-term care requirement certification data and long-term care benefit data are used as the source data for estimation and verification, and estimation and verification are performed for each service type. On the other hand, in general projects, data for certification of long-term care need and nursing care benefit data are used as estimation verification data, but in addition, estimation and Validate.

Even in the case of a comprehensive project, it is possible to estimate and verify cost-effectiveness based on the same logic based on the following correspondence relationships. For example, as shown in Fig. 62, similar to the level of care required (long-term care level 1 to 5) for persons eligible for long-term care services, the mental and physical conditions of persons eligible for comprehensive business are divided into 5 stages (independence to support required 2). Just do it. In FIG. 62, frailty 1 and frailty 2 are subjects of so-called frailty, and their severity may be defined by the risk points of the basic checklist.

If defined in this way, for example, if the mental and physical condition of the person eligible for the comprehensive business is "independent", the cost-effectiveness can be estimated and verified.

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

11 . Master basic information acquisition processing unit for estimation 111 . Mental and physical condition change (deterioration/improvement) information acquisition processing unit 112 . Per capita average benefit cost acquisition processing unit 113 . Classification × number of users acquisition processing unit 20 Master basic information for estimation 201 Mental and physical condition change information master 202 Average benefit cost master per person 203 Average starting age master by application classification 204 User by application classification Number master 30... Master basic information acquisition unit for estimation from public information 31... Correction coefficient calculation processing unit 311... Mental and physical condition change (worsening/improvement) information correction coefficient calculation processing unit 312... Per person average benefit cost correction coefficient calculation processing unit 313 ... Application classification/average starting age correction coefficient calculation processing unit 314 ... Application classification/user number correction coefficient calculation processing unit 32 ... Estimation master basic information correction coefficient 33 ... Estimation master basic information estimation processing unit 331 ... Mind and body State change (worsening/improvement) information actual data estimation processing unit 332 Average benefit cost per person actual data estimation processing unit 333 Application classification/average starting age actual data estimation processing unit 334 Application classification/number of users 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 municipality)
43 … Master basic information for estimation (model municipality: intermediate data)
44 … Master basic information for estimation (public information analysis municipality: intermediate data)
51 First public data 52 User number acquisition processing unit 53 User number master 54 Second public data 55 Psychosomatic 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 extracting means 66 Difference master 68 First benefit cost suppression amount calculator 69 First benefit cost suppression amount table 71 Second benefit suppression amount calculator 72 Second benefit suppression amount table 74 Cost-effectiveness verification unit 75 Benefit cost suppression amount table

Claims (5)

From the data recording the changes in the mental and physical states of the users for a predetermined period before the preset estimation start date, for each of the above-mentioned stages, for all the users, the next stage of change, the next stage The direction of change to the next stage, the transition ratio to the next stage, and the average duration until the change to the next stage are calculated, and a predetermined target value is added to this mental and physical state change information to create the mental and physical state change information master. A psychosomatic state change information acquisition processing unit to constitute,
Based on the certification data that certifies the stage of the mental and physical condition of the long-term care insurance before the estimation start date and the actual benefits, the average benefit cost information per person for each stage is obtained, and the average benefit cost information per person constituting the average benefit cost information master. an average benefit acquisition processing unit of
An average starting age acquisition processing unit that forms an average starting age information master to obtain average starting age information for transition estimation for each stage from certification data of long-term care insurance and benefit results before said estimation starting year, and said estimation starting year a user count acquisition processing unit that obtains the user count information for each stage from the nursing care insurance certification data and payment performance before the month and configures the user count information master;
Transition to the next stage from the transition ratio for each stage to be transitioned to next for each stage held in the mental and physical state change information master and the number of users at the stage held in the user number information master In addition to obtaining the transition number of people, using the average duration until the change to the next stage held in the mental and physical state change information master, the total value of the transition number in the period until the transition to the next stage, Perform the first user number estimation process for each relative elapsed month after the estimation start date, perform the same user number estimation process for the next stage after the transition after the average duration period has elapsed, and perform the same user number estimation process for each transition thereafter a user number transition estimation processing unit that repeats similar user estimation processing;
a user number transition table in which the transition number of people for each relative elapsed month obtained by the user number transition estimation processing unit is tabulated for each stage and for each elapsed month after the estimation start year and month;
Based on the number of users for each stage and for each elapsed month aggregated in the user number transition table and the average benefit cost information per person for each stage held in the average benefit cost information master, and a benefit cost transition estimation processing unit that obtains the benefit cost for each elapsed month;
Community comprehensive care business system with. If the municipality that is the long-term care insurer does not have the actual data on the basic information consisting of the physical and mental condition change information, the average benefit cost information, the average starting age information, and the number of users information, a correction coefficient calculation unit that compares public data on long-term care insurance of a local government that does not have public data on long-term care insurance with public data on long-term care insurance of a model local government that has actual data on the basic information, and obtains a correction coefficient based on the difference between them;
Basic information estimation processing of correcting the actual data related to the basic information of the model municipality using the correction coefficient obtained by the correction coefficient calculation unit, and estimating the basic information of the public information analysis municipality that does not have the actual data. Department and
The integrated community care business system according to claim 1, further comprising: If the municipality that is the long-term care insurer does not have the actual data related to the basic information consisting of the physical and mental condition change information, the average benefit cost information, the average starting age information, and the number of users information, it does not have the actual data. Data on the basic information is acquired from public data on long-term care insurance of the local government, and the acquired data is analyzed from the public information that constitutes the corresponding master as the basic information of the public information analysis local government that does not have the actual data. 2. The regional comprehensive care business system according to claim 1, further comprising a basic information acquisition unit. The psychosomatic state change information acquisition processing unit
Each direction of change (worsening, ending, or improving) by level of long-term care required for all types of services provided by long-term care insurance, the rate of change (worsening, ending, or improving) obtained from long-term care insurance certification data and benefit performance, and Mental and physical condition change information of all service types including the average duration of deterioration, termination, and improvement;
Information on changes in mental and physical conditions by service type obtained by multiple service types provided by long-term care insurance is weighted by the ratio of the total benefit cost and the corresponding benefit cost by service type, and these are added up for all the service types. The mental and physical condition change information accumulated value by service type,
a psychosomatic state change information ratio, which is the ratio of the psychosomatic state change information for all service types to the psychosomatic state change information accumulated value for each service type;
Said value obtained by adding a target value for improving said psychosomatic state change information by measures for a predetermined period after said estimation start date to the value of said psychosomatic state change information for a specific service type among said plurality of service types Set the mental and physical state change information accumulated value by service type as the target mental and physical state change information accumulated value by service type,
Calculating the target mental and physical state change information by care level for all service types from the accumulated value of the target mental and physical state change information by service type and the above-mentioned mental and physical state change information ratio,
2. The comprehensive regional community according to claim 1, wherein the mental and physical state change information held in the mental and physical state change information master is configured using the values of the information on changes in mental and physical state classified by degree of care need for all the target service types. Care business system. Using the average starting age information for each stage of the psychosomatic condition held in the average starting age information master and the average duration until the stage changes to the next stage held in the psychosomatic condition change information master and a healthy life expectancy transition estimation processing unit that sets the average starting month age information of the stage in the final year and month of the average duration corresponding to the stage to the age of healthy life expectancy at which the heaviest stage that does not require nursing care ends. The regional comprehensive care business system according to claim 1.

Images