JP2020123347A - Regional comprehensive care business system - Google Patents

Abstract

To provide a regional comprehensive care business system with which it is possible to quantitatively estimate a benefit expense suppression effect and verify a cost-effectiveness based on the improvement and maintenance of a psychosomatic state owing to various measures.SOLUTION: The present invention finds, from data in which a stagewise change of psychosomatic state of a user prior to an estimation start year/month is recorded, a next stage to change, a change direction, a transition rate and an average period of duration per stage. The present invention finds, from nursing care insurance approval data and benefit track records prior to the estimation start year/month, average stagewise benefit expense information, average start age information, and users number information. the present invention finds the number of transit persons who transition to the next stage from a transition rate by stage of transitioning next and the number of users. The present invention finds the total value of number of transit persons in the average period of duration to the next stage for each relative elapsed month from the estimation start year/month on, and repeats the similar users number estimation process for the next stage to which transitioned after the elapse of the average period of duration. The present invention finds a benefit expense by stage and by elapsed month from the number of users by elapsed month based on the number of transit persons for each relative elapsed month and per-head average benefit expense information by stage.SELECTED DRAWING: Figure 2

Description

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

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

Through these concepts and the realization of PDCA work consisting of P (planning), D (execution of measures), C (performance evaluation), and A (problem analysis), maintenance of service infrastructure and improvement of service quality in healthy life expectancy We are aiming to extend the number of employees, reduce medical and long-term care costs, and eventually realize a sustainable social security system.

The data health business is a business for disease prevention and aggravation prevention (diabetes/depression, etc.). A comprehensive business is a business to reduce the new certification rate and support independence. Nursing care benefit business is a business for independence support and prevention of severity. In addition, the medical/nursing care cooperation business is a business to extend the home medical care period and prevent acute exacerbations.

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

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

Japanese Patent No. 2017-215787

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

An object of the present invention is to provide a regional comprehensive care business system capable of quantitatively estimating a benefit cost restraining effect based on improvement/maintenance of mental and physical conditions by various measures and verifying cost effectiveness.

Regional comprehensive care business system according to an embodiment of the present invention, from the data recording the change in the stage of mental and physical condition of the user in a predetermined period before the estimation start date set in advance, for all the users, For each stage, the next changing stage, the direction of change to this next stage, the transition ratio to the next stage, and the average duration until the change to the next stage are obtained, Based on the mental and physical condition change information acquisition processing unit that constitutes the mental and physical condition change information master by adding a predetermined target value, and the certified data of nursing care insurance before the estimation start date and the benefit record, the average benefit per person for each stage The average start age information for each stage is calculated from the average benefit cost acquisition processing unit per person that constitutes the average benefit cost information master, the certification data of the long-term care insurance before the estimated start date and the actual benefits, and obtains the expense information. Obtained, the average start age acquisition processing unit that constitutes the average start age information master, and the number of users for each stage is obtained from the certified data and benefit record of the nursing care insurance before the estimated start date, and the number of users information User number acquisition processing unit that constitutes a master, transition ratio for each stage that is held next to each stage that is held in the physical and mental state change information master, and use of the stage that is held in the user number information master From the number of persons, the transition number to the next stage is obtained, and the average duration until the change to the next stage, which is held in the mental and physical state change information master, is used to transition to the next stage. The total value of the number of transition people in the period is calculated for the first number of users for each relative elapsed month after the estimation start date, and the same user is used for the next stage after the transition of the average continuation period. The user number transition estimation processing unit that performs the number estimation process and repeats the same user estimation process for each transition thereafter, and the transition number for each relative elapsed month obtained by this user number transition estimation processing unit, Number of users transition table aggregated by stages and by the months elapsed since the estimation start month, number of users aggregated in this number of users transition table, and number of users by the elapsed months and the average benefit cost It is characterized by further comprising a benefit expense transition estimation processing unit for obtaining benefit expenses for each stage and for each elapsed month from the average benefit expense information per person for each stage held in the information master.

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

It is a conceptual diagram of the local comprehensive care business system which concerns on embodiment of this invention. It is a block diagram which shows the whole system structure of the local comprehensive care business system which concerns on embodiment. It is a figure explaining the system configuration for comprising the basic information master in an embodiment. It is a figure explaining the phase change of the physical and mental state of a long-term care insurance user. (A) Quantitatively explains the step change of the mental and physical condition of the long-term care insurance user, and (b) shows the direction and ratio of transition to the next step by step, and the psychosomatic state change information that holds the average maintenance period until the fiber. It is a figure showing a master. It is a figure explaining an example of the method of setting the target value in constructing the mind-body state change information master in the embodiment. It is a figure explaining the average per capita benefit acquisition processing content in an embodiment. It is a figure showing the average benefit cost information master per person in an embodiment. It is a figure explaining the contents of average starting age acquisition processing of the initial user in an embodiment. It is a figure. It is a figure showing the update age classified and average starting age master which hold|maintains the data of the initial user in embodiment. It is a figure explaining the average start age acquisition processing content of the new increase user in an embodiment. It is a figure showing the update age classified and average starting age master which hold|maintains the data of the new increase user in embodiment. It is a figure explaining the user number acquisition processing content of the initial user in an embodiment. It is a figure showing the user number master holding the user number data of the initial user in the embodiment. It is a figure explaining the user number acquisition processing content of the new increase user in embodiment. It is a figure showing the number-of-users master which holds the number-of-users data of a new increase user in an embodiment. It is a figure explaining an outline of acquisition master basic information acquisition processing by public information analysis in an embodiment. It is a figure which shows the actual data, public information, and the data division of a correction coefficient in an embodiment. It is a figure which shows the process which acquires the estimation master basic information intermediate data of a target local government from the public information (Ministry of Health, Labor and Welfare report aggregation, long-term care insurance business situation report) in embodiment. FIG. 21 is a diagram illustrating a specific example of the mental-physical state change (deterioration/improvement) information acquisition processing according to the embodiment with reference to FIG. 20. It is a figure which shows the deterioration rate and improvement rate used as the estimation master basic information intermediate value data of the model local government in embodiment. It is a figure explaining an average benefit cost acquisition processing per person using public information in an embodiment. It is a figure which shows the average number of units per person used as the intermediate|middle value data of the estimation master basic information of the model local government in an embodiment. It is a figure explaining average start age acquisition processing using public information in an embodiment. It is a figure which shows the average starting age according to sex and the degree of long-term care, which is the master basic information for estimation intermediate value data of the model municipality in the embodiment. It is a figure explaining the number acquisition processing of users using public information in an embodiment. It is a figure which shows the service type classified according to the model master municipality master information intermediate value data in an embodiment, according to a sex, according to application classification, and user number data. It is explanatory drawing of the process which makes the ratio of the estimation master basic information intermediate data of the model local government and the estimation master basic information intermediate data of public information analysis local government acquired from the public information in embodiment, into a correction coefficient. 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. It is a figure which shows the mental-physical-condition change (deterioration/improvement) information correction coefficient classified by the degree of long-term care which comprises the master basic information for estimation in an embodiment. It is a figure which shows the average number of units per month for every nursing care degree of the master information for master information for estimation of public information analysis local governments in an embodiment. According to the degree of long-term care required and the average monthly unit number correction coefficient per person obtained from the value of the estimation master basic information intermediate data of the model municipality and the value of the estimation master basic information intermediate data of the public information analysis municipality in the embodiment FIG. It is a figure which shows the average starting age according to gender, degree of long-term care of the master information for master information for estimation of public information analysis municipalities in the embodiment. It is a figure which shows the average start age amendment coefficient according to a man-of-a-kind / degree of long-term care of the master information for estimation in an embodiment. It is a figure which shows the master information for estimation master basic information intermediate data of public information analysis local governments in an embodiment according to a sex, a nursing care degree, and the number of users. It is a figure which shows the correction coefficient of the gender according to the degree of long-term care, and the number of users which comprise the master basic information for estimation in an embodiment. An explanatory diagram of a process of multiplying the estimation master basic information acquired from the actual data of the model municipality in the embodiment by the estimation master basic information correction coefficient to obtain the estimation information basic basic information (estimated value) of the public information analysis municipality Is. (A) is information about changes in mental and physical conditions of model local governments in the embodiment (information regarding changes in mental and physical conditions (deterioration/improvement) information: real-side data), and (b) is the degree of nursing care required for the basic master information correction coefficient for estimation. It is a figure which shows another and mental and physical condition change (deterioration / improvement) information correction coefficient. It is a figure which shows public information analysis according to the degree of long-term care required/mental and physical condition change (deterioration/improvement) information (estimated value) of a local government in the embodiment. (A) shows the average benefit cost per person (actual data) of the model municipality in the embodiment, and (b) is a diagram showing the monthly average number of units correction coefficient per person of the estimation master basic information correction coefficient. .. It is a figure which shows the data which shows the average benefit cost (estimated value) per person of the public information analysis local government in embodiment. (A) shows the model municipalities in the embodiment by sex, by degree of nursing care, and average start age (actual side data), and (b) shows the estimation master basic information correction coefficient by gender, by degree of care, and average It is a figure which shows a start age correction coefficient. It is a figure which shows the average starting age (estimated value) according to gender and the degree of long-term care of the public information analysis local government in the embodiment. (A) shows the gender of each model municipality in the embodiment, the degree of care required, and the number of users (actual side data), and (b) shows the estimated master basic information correction coefficient by gender, degree of care required, and usage It is a figure which shows the number correction coefficient. FIG. 5 is a diagram showing gender-based/need-to-care-degree/number of users (estimated value) of master information for promotion of public information analysis local governments in the embodiment. It is a figure explaining an outline of user number change estimation processing in an embodiment. It is a figure which shows the mind-body state change information master used for the user number transition estimation process in embodiment. It is a figure which shows the specific example of a user number transition estimation process in embodiment. It is a figure explaining the flow of the payment (cumulative) change estimation process in an embodiment. It is a figure explaining the healthy life span transition estimation process in an embodiment. In the embodiment, the cumulative results of the degree of long-term care required, the new policy effect case, and the benefit cost are shown in (a) three years later, (b) six years later, and (c)) nine years later, respectively. It is a figure represented as a table. It is a figure showing the result of having compared the new measure effect cases 1, 2, and 3 according to the degree of long-term care required, the new measure effect case, and the age in the embodiment. FIG. 4 is a diagram for explaining an embodiment in which the cost-effectiveness estimation/verification function is simplified, and (a) shows the establishment group compared with the local government average for both the improvement rate and the average maintenance period until deterioration. , (B) show the effect of suppressing the benefit cost when the maintenance period is extended until the degree of long-term care of the user deteriorates. It is explanatory drawing of the process which calculates|requires the number of users according to the degree of long-term care and the service type in the simplified embodiment. One person at the time of change in service type, degree of need for care, and change of state of mind and body, in order to obtain benefit costs to beneficiaries (users of long-term care insurance) according to degree of care required and type of service in a simplified embodiment It is explanatory drawing of the process which calculates|requires the difference of the benefit cost per hit. In the simplified embodiment, an explanatory diagram of a process of setting a target value (by service type, by light severity of nursing care, improvement rate, and maintenance period until deterioration) to be improved after a predetermined period from the implementation of measures is there. It is a figure explaining the calculation process of the amount of suppression of payment of benefits due to the improvement rate of the degree of long-term care required in the simplified embodiment. It is a figure explaining the calculation processing of the amount of benefit payment restraint by extension of the maintenance period until deterioration in a simplified embodiment. A benefit cost restraint table that is the sum of the benefit cost restraint amount due to the improvement rate of the degree of long-term care required and the benefit cost restraint amount due to the extension of the maintenance period until deterioration after a predetermined period from the implementation of the measures in the simplified embodiment. It is a figure explaining. It is a figure explaining the simulation accompanied by the suppression of the payment cost by the implementation of the measure of the local government in the simplified embodiment. It is a figure which compares and shows each function of a nursing care simple version and a nursing care detailed version. It is a figure explaining a mutual relation at the time of expanding horizontally the cost-effectiveness estimation and verification function in a long-term care benefit business to a general business.

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

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

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

Regarding the number of users, the application classification is classified into update change and new application. Initially, the number of renewal changes is the number of users who take the certification period of the renewal change application certification data at the start of estimation. Initially, the number of new users is the number of users who need the certification period for the certification data of new applications at the start of estimation. These initial renewal change estimates and initial new estimates show the initial users who already have the actual benefits at the start of the estimation.

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

The user transition estimation process in step 2 estimates how the number of these users will change with the lapse of a predetermined period for implementing the new measure. 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 mental and physical condition change information and the application classification user number information among the above-mentioned estimation basic information. Details of this user transition estimation process will be described later.

In the benefit cost (cumulative) transition estimation process in step 3, how the benefit cost (cumulative) is transitioned is estimated based on the above-described user number transition estimation result. The step-like frame in the lower right of the figure shows the decrease in benefit costs. This benefit cost (cumulative) transition estimation process is performed using the average benefit cost acquisition information per person out of the basic information for estimation described above. The details of this benefit cost (cumulative) transition estimation process will be described later.

In the healthy life expectancy transition estimation process in step 4, the user's healthy life expectancy transition estimation process is performed by using the average start age information for each application category among the basic information for estimation described above. Here, the “healthy life expectancy” is the age at which the heaviest stage in which care is not required ends at the end for each of the 65 psychosomatic condition items (certification data) such as the degree of long-term care and the degree of dementia independence. In addition, a stepped frame rising to the right in the figure indicates extension of healthy life span. Details of this healthy life transition estimation process will be described later.

FIG. 2 is a system block diagram showing an 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 described above, a user number transition estimation processing unit 12 corresponding to step 2, and step 3 It has a benefit cost (cumulative) transition estimation processing unit 13 corresponding to and a healthy life expectancy transition estimation processing unit 14 corresponding to step 4.

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

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

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

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

Here, the function of the physical and mental state change (deterioration/improvement) information acquisition processing unit 111 will be examined. FIG. 4 shows changes in the physical and mental state of the user X (degree of care). In FIG. 4, the period of acquisition of physical and mental condition change information is from April 2012 to April 2015, and the user X newly applied in May 2012 and was certified as requiring long-term care 3. After that, for 3 months, which is the renewal period of the degree of long-term care, the requirement for long-term care 3 was maintained, and six months later, an application for renewal was made in November of the same year, and the person was certified as requiring long-term care 2 by one step. After that, a change application was made in February 2013, which is less than the six months, which is the renewal period of the degree of long-term care, and the degree of long-term care deteriorated by two levels, and was certified as requiring long-term care 4. After maintaining this degree of long-term care 4 for 12 months, an application for renewal was made, and the degree of long-term care required deteriorated by one level, and was certified as long-term care 5. After that, there was no application at the end of the renewal period, so it ended. It should be noted that termination means that there is no application due to the death of the user, relocation to another municipality, or the like.

In this way, from all the certification data that includes the certification valid period (end) within the acquisition period of mental and physical condition information, the maintenance period of deterioration/termination/improvement to the next stage by user and degree of care Then, the change direction and the change step amount are obtained. FIG. 4 shows an example in which the acquisition period of the physical and mental condition change information is set to three years before the estimation start date. The acquisition period of the physical and mental condition change information is an arbitrary period (for example, the period of the last business year).

When acquiring information by service type for each user, by degree of nursing care, and changes in mental and physical conditions, only the certified data and the actual results of payment of users who have a benefit record for that service type within the period of acquisition of physical and mental condition change information Target. The age at the end of the maintenance period will be acquired from the date of birth of the actual benefits, and will be the age at the end of the maintenance period.

If the direction of change is “end”, that is, if there is no next certification data, the certification valid period (end) of this certification data is the end date of the maintenance period. If the application category for the next certification data is death, the month preceding the certification application date for the next certification data shall be the end of the maintenance period. Furthermore, if the degree of nursing care required for the next certification data is not applicable, the month preceding the certification application date for the next certification data shall be the end of the maintenance period.

FIG. 5A shows a summary of the changes in the physical and mental condition of FIG. In FIG. 5A, the user X has the degree of long-term care required in the application category “new” of “necessary care of 3”, and after maintaining for 6 months, “improved” (−) by one stage. The “renewal” application for the “necessary care 2” degree required for long-term care continued for 3 months, and then worsened (+) in two stages. The degree of long-term care required for which “change” was applied for “long-term care 4” continued for 12 months, and then worsened by one level (+). The degree of long-term care required for "renewal" application "care required 5" ends because there is no next application even after the renewal period of 6 months. Information of each item is acquired in the same manner for other users Y...

In this way, since the change information of the physical and mental condition of each user is obtained, as shown in FIG. 5(b) based on these, "degree of care required" (stage of mental and physical condition), "degree of next care required" ( Next step), "direction of change" to the next degree of long-term care, "transition ratio" to the next degree of long-term care, and "average maintenance period" up to the next step are configured.

For example, the ratio (transition ratio) at which the users of the long-term care 4 deteriorate to the long-term care 5 is 70%, and the average maintenance period is 16 months. The case of transition to another degree of nursing care is also as shown in each item, and description thereof will be omitted.

The information configured when transitioning to the next degree of nursing care required for each degree of nursing care shown in FIG. 5B is the mental-physical state change information master 201 used in the user transition estimation process described later.

That is, the physical and mental condition change information master 201 is obtained from data that records a change in the physical and mental condition of the user in a predetermined period (a mental and physical condition change information acquisition period) before a preset estimation start date, For each user, the next change step, the change direction to this next step, the transition ratio to the next step, and the average maintenance period until the change to the next step To hold as.

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

An example of this target value setting method will be described with reference to FIG. Nursing insurance provides various services to users according to their physical and mental conditions. This type of service includes home-based services such as visits and out-of-towns (abbreviated as "home"), special elderly nursing homes (abbreviated as "special nursing"), nursing homes for the elderly (abbreviated as "elderly"), and medical care beds ( There are seven types: “medical treatment”, group home (abbreviated as “GH”), specific facility (abbreviated as “special treatment”), and small-scale multifunctional (abbreviated as “small”).

The user receives one of the above-mentioned service types according to the physical and mental state, but may transit to another service type during the psychological and physical state change information acquisition period. For example, a user who has received the "home" service may transition to the "old health" service. In this way, considering the transition to another service type of the user, it is not preferable to perform the processing from step 2 onward with the physical and mental condition change information for each service type, and all service types (the seven types of service mentioned above) It is necessary to perform the processing from step 2 onward by using the “information on mental and physical condition changes of all service types” including all).

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

Therefore, as shown in FIG. 6, the mental-physical condition change information acquisition processing unit 111 first creates the authorization data 101A of all service types, the benefit record 102A, and the match table 103A that matches these data so that they can be used mutually. Using all service types, users, maintenance period start/end year/month, degree of long-term care, and mental/physical condition change information 105A. Information on all service types includes system management number (user number), maintenance period start/end date, maintenance period (deterioration/termination/improvement), direction of change (deterioration/termination/improvement), and degree of care required. is there. It should be noted that one degree of long-term care is determined at the start and end of one maintenance period.

From these information 105A, information on the degree of long-term care for all service types, direction of change (worse/end/improvement), rate of change (worse/end/improvement), and average maintenance period (until worse/end/improvement) 106A is obtained.

In addition, for each service type, from the data of the user who has used the service type (authorization data 101B, benefit record 102B, and match table 103B), the service type, the user, the maintenance period start/end year/month/required Acquiring the care degree-dependent physical/mental state change information 105B. This service type information includes system management number (user number), maintenance period start/end date, maintenance period (deterioration/termination/improvement), direction of change ((deterioration/termination/improvement), and degree of nursing care required. Also in this case, one degree of long-term care is determined at the end and the end of one maintenance period.

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

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

First, as shown in the equation (1), the “accumulated value of mental/physical condition change information by service type” is obtained. This is to weight the changes in mental and physical condition information for each service type obtained by multiple service types by multiplying the ratio of the corresponding benefit cost by service type to the total benefit cost as a contribution rate. It is the total of the types (7 types).

Note that the physical and mental condition change information is a stage, for example, each direction of change to deterioration/end/improvement according to degree of nursing care, rate of change to deterioration/end/improvement, and average duration until deterioration/end/improvement. Including. Further, the expressions (1), (2), (3), and (4) are all classified by the degree of need for nursing care.

``Accumulated value of information on changes in mental and physical conditions by service type'' = (Home benefits/total benefits × information on changes in physical and mental conditions at home) + (Special benefit expenses / Total benefits × information on changes in physical and mental conditions) + (Personal health benefits Expenses/Total Benefits × Health and Mental and Physical Condition Change Information) + (Medical Benefits / Total Benefits × Medical and Physical Condition Change Information) + (GH Benefits / Total Benefits × GH Physical and Physical Condition Change) + (Special Benefit Benefits / Total benefit cost x Special treatment mental and physical condition change information) + (Small and large benefit costs / Total benefit cost x Small and multiple mental and physical condition change information) (1)

Next, the “mental and physical condition change information ratio” is obtained as shown in Expression (2). This is the ratio of the above-mentioned “physical and physical condition change information for all service types” and “accumulated physical and mental condition change information by service type”.

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

Next, as shown in Expression (3), the “accumulated value of target physical and mental condition change information for each service type” is obtained. This is because the value of the physical and mental condition change information of a specific service type (for example, “special nursing care”) for which a new measure is to be implemented among a plurality of service types is set to a new value for a predetermined period after the estimation start date. The target value (for example, +10%) for improving the physical and mental condition change information by the measure is added, and the physical and mental condition change information of the specific service type (“special diet”) weighted as described above and other remaining It is the sum of the mental and physical state change information (weighted as described above) for the six types of services.

“Target accumulated information on changes in mental and physical conditions by service type” = (Home benefits/total benefits × information on changes in mental and physical conditions at home) + (Special benefit expenses / Total benefits × (Special changes in mental and physical conditions + 10) %)) + (Old health benefits / Total benefits × Health and psychological change information) + (Retirement benefits / Total benefits × Health and psychological change information) + (GH benefits / Total benefits × GH health change information) ) + (Special benefits/total benefits x Special changes in mental/physical condition) + (Small benefits/total benefits x Small changes in mental/physical condition) (3)

Since the deterioration rate and the improvement rate are contradictory, both targets are set at the same time (deterioration rate + termination rate + improvement rate = 100%).

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

"Goal all-service-type psychosomatic condition change information" = "Accumulated value of each-service type psychosomatic condition change information" x "Mental-physical condition change information ratio" (4)

The mental-physical state change information acquisition processing unit 111 is stored in the mental-body state change information master 201 shown in FIG. 5B using the value of the “targeted mental and physical state change information of all service types” thus obtained. It constitutes the physical and mental condition change information.

The above description exemplifies "special nursing" as a specific service type (hereinafter referred to as a service type of interest) in which the physical and mental condition change information is improved by the new measure, and the target value is +10%. Similarly, by configuring each item value of the mental and physical condition change information master 201 corresponding to another new measure in which another target service type is specified and a target value is set, as shown in FIG. A new policy effect case is obtained.

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

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

FIG. 7 shows an example of a method for calculating the average benefit cost per person for the long-term care 3 with the average benefit cost acquisition date per person being March 2015. For the average acquisition date of benefits per person, specify any date for which data actually exists. In this example, the estimated start date is one month before (April 2015).

In this case, first, the user of the long-term care 3 is extracted from the certification data (status of degree of long-term care) 101A shown in FIG. That is, all the users of the long-term care 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 per user of the extracted users from the benefit record 102A shown in FIG. 7(b). The average benefit cost is calculated by the following formula (5).

Average benefit cost per person for long-term care 3 = (total number of units of user B + total number of units of user E + total number of units of user F) ÷ 3 × unit cost by personnel cost ratio by region category = (11000 + 16000 + 5000) ÷ 3 × Unit cost by labor cost ratio by region = 106670 yen (5)

The above formula (5) is an example in which the unit cost by region personnel cost ratio is calculated at an additional rate of 0% (10 yen) by region classification. Also, use the "number of service units after determination" in the benefit record data.

If the average benefit payment per person is set to only January, the average benefit payment per person will be acquired in several months because the bias in the benefit payment within the fiscal year will affect the estimation of subsequent trends. , Its average value may be used.

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

Next, the processing content of the application classification/average start age acquisition processing unit 113 will be described. Here, the average starting age includes the case of an initial user and the case of a new increasing user. First, the case of an initial user will be described with reference to FIGS. 9 and 10.

Based on the user age at the time of acquisition of the average start age acquired from the certification data (status of application classification and degree of long-term care) 101B shown in FIG. , Calculate the average start age by application category and degree of nursing care required. This process is a process for obtaining the average starting age of initial users by degree of nursing care, which is the basis of the transition estimation. The average starting age acquisition process for new users, which increases with new applications, will be described later.

The following is an example of a method for determining the average start age of the care request 3 of the update change applicant, assuming that the average start age acquisition date is March 2015. Note that the average start age acquisition year/month specifies an arbitrary year/month for which data actually exists. In this example, it is assumed to be one month before the above-mentioned estimation start year (April 2015). It should be noted that, in order to exclude the influence of the bias of users by month, the results of a plurality of months may be averaged.

The application classification/average start age acquisition processing unit 113 first extracts all the users of the long-term care 3 of the update change application of the average start age acquisition date from the authorization data 101B. In the example of FIG. 9, users E and F are the target people. Next, the age of the average start age acquisition date of the user E and the user F is obtained from the benefit record 102B shown in FIG. 9B. However, those who are certified as requiring long-term care and who have not used the service are not eligible as they have no record of benefits. The average ages of user E and user F are calculated. This is the average start age for nursing care 3.

Here, when the average start age acquisition date is calculated from the user's birth date of the benefit record, the average start age acquisition date is not in “day” units but in “year/month” units. The age is calculated by the following equation (6) using the date of birth.

(Average starting age acquisition year × 12 + average starting age acquisition month − birth year × 12 − birth month) / 12 ・・・(6)
From the formula (6), when the date of birth of the user E is February 11, 1935,
(2015×12+3−1935×12−2)/12=80.0833...
= 80 years old.
If the date of birth of user F is October 14, 1940,
(2015×12+3−1940×12−10)/12=74.416...
= 74 years old.

The average age is (80+74)/2=78 years old. That is, the average starting age of the long-term care 3 for the user's renewal change application is 78 years old. Similarly, the average start age is calculated for the other stages (degree of nursing care), and is stored as the update age/average start age in the application classification/average start age master 203 shown in FIG.

Note that the new application (years) described in the upper part of the update age-based/average start age master 203 is the new application for the users of this site, and as described above, the latest certified data at the beginning (at the start of transition). Is the average age for all users who are new applications, and is calculated by the same method as for renewal change applications.

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

The following is an example of a method for determining the average starting age of the new applicants requiring long-term care 3 by setting the average starting age acquisition period as one year before March 2015, which is one month before the estimation start. As the average start age acquisition period, an arbitrary year and month in which data actually exists is designated. In this example, as described above, the year is one month before the start of the estimation (April 2015).

First, as shown in FIG. 11A, the application-classified/average starting age acquisition processing unit 13 identifies all users whose new application is certified as requiring long-term care 3 from the certification data 101C during the number of users acquisition period. Extract. In the example, the users I and K are the target users.

Next, as shown in FIG. 11B, the ages of the years of the average start age acquisition period of the user I and the user K are obtained from the benefit record 102C. Here, the year and month is the year and month when the above new application is approved. However, those who are certified as requiring long-term care and who have not used the service are not eligible as they have no record of benefits. Then, the average ages of the users I and K are calculated.

If the age at the relevant year within the average start age acquisition period is obtained from the birth date of the user of the actual benefits, use the following formula (7). Since the year/month is not in “day” units but in “year/month” units, the user's “birth date” is used to calculate the age.
(The year x 12 + the month-birth year x 12-birth month) / 12 ... (7)

Assuming that the birth date of the user I is February 17, 1938 from the above formula (7), as shown in FIG.
(2014×12+5−1938×12−2)/12=76.25
= 76 years old.

If the birth date of the user K is September 28, 1940, and the relevant year/month is June 2014, as shown in FIG.
(2014 x 12 + 6-1943 x 12-9)/12 = 70.75
= 70 years old.

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

Next, the processing content of the application classification/number of users acquisition processing unit 114 will be described. Regarding the number-of-users acquisition process, there are cases of initial users and cases of new increased users. First, the case of initial users will be described with reference to FIGS. 13 and 14.

In the case of the initial user, the application classification/number-of-users acquisition processing unit 114 determines the authorization data (the status of the application classification and the degree of long-term care) 101D shown in FIGS. , The content does not matter) From 102D, the number of users according to application category and degree of long-term care as of the number of users acquisition year is acquired. This process is a process of acquiring the number of users of initial users by degree of need for nursing care, which is the basis of the transition estimation. The process of acquiring the number of new users, which will increase with new applications, will be described later.

As the number of users acquisition year/month, specify an arbitrary year/month when data actually exists. The following is an example of a method for determining the number of users of the need for long-term care 3 of the update change applicant by setting the number of users acquisition date to March 2015. In this example, the estimated start date is one month before (April 2015). It should be noted that, in order to exclude the influence of the bias of users by month, the results of a plurality of months may be averaged.

The application classification/number-of-users acquisition processing unit 114 extracts, from the authorization data 101D and the benefit record 102D, all the users of the long-term care 3 of the update change application of the number of users acquisition. In the example, users E and F are the target people. The number of users is the number of users. In the example, it is assumed that there are target users in addition to the users E and F, and there are 6479 as shown in FIG. However, those who are certified as requiring long-term care and who have not used the service are not eligible as they have no record of benefits.

The number of people extracted in this way is the user of the long-term care need 3 for the initial user update change application. Similarly, the number of users is calculated for the other stages (degree of need for nursing care), and the number of users is stored as the initial number in the application classification/user number master 204 shown in FIG.

Note that the new application (person) described in the upper part of the application classification user count master 204 is the initial new number, and as described above, the latest (at the start of transition) latest certification data is the new application. It is the average number of users for all users, and is calculated by the same method as for renewal change applications.

Next, the processing for the case of a new increased user of the application classification/number of users acquisition processing unit 114 will be described with reference to FIGS. 15 and 16. In this process, within the acquisition period of the number of users, from the certification data (application classification and status of degree of long-term care requirement) shown in FIG. 15(a) and the benefit record (only data is available, content is not required) shown in FIG. 15(b). Acquire the number of users by application category (new) and degree of need for nursing care. This process is a process to obtain the number of new increasing users by degree of long-term care, which is the basis of the transition estimation, and is used for estimation after the second year.

The following is an example of a method of obtaining the number of users of the long-term care 3 of a new applicant, assuming that the number of users acquisition period is one year before March 2015. Note that the user number acquisition period specifies an arbitrary year and month when the data actually exists. In this example, it is set as one year until one month before the above-mentioned estimation start date (April 2015).

The application classification/number-of-users acquisition processing unit 114 extracts all the users who have the authorization data for the new application for the long-term care 3 within the number-of-users acquisition period. In the example of FIGS. 15A and 15B, the users I and K are the target people. The number of users is the number of users. In the example, it is assumed that there are target users in addition to the users I and K, and there are 1904 people as shown in FIG. However, those who are certified as requiring long-term care and who have not used the service are not eligible as they have no record of benefits.

The number of people extracted in this way is the user of the long-term care need 3 for new application. Similarly, the number of users is calculated for the other stages (degree of nursing care), and the number of users by application category shown in FIG. 16 is held as the number of newly increased users. And, as mentioned above, it is used for the estimation after the second year.

In the above description, the actual data of the local government is analyzed (referred to as actual data analysis) as shown in FIG. 3, and the master basic information 20 for estimation to be used after “Step 2 Estimating the number of users transition” to be described later is acquired. However, some municipalities <care insurers> cannot analyze such actual data. For local governments that cannot analyze actual data, the municipality that performed the actual data analysis is used as a model municipality, and the analysis is performed using public information issued by the Ministry of Health, Labor and Welfare, and the local government that cannot analyze the actual data (public information analysis municipality ) The estimation master basic information 20 is created.

The outline of the estimation master basic information acquisition process based on the public information analysis will be described below with reference to FIG. First, the basic information acquisition unit 30 acquires the public information 41 of the model municipality and the public information 42 of the public information analysis local government from the public information (the Ministry of Health, Labor and Welfare report aggregation, the long-term care insurance business status report), and estimates each of them. 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 sets this difference as the correction coefficient 32. The estimation master basic information estimation processing unit 33 multiplies the estimation master basic information 20 obtained by the actual data processing of the model municipality by the correction coefficient 32, and public information analysis municipal estimation master basic information (estimated value) 34. To 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. As described above, when the actual data of the model local government is multiplied by the correction coefficient to estimate the actual data of the public information analysis local government, there is a difference between the data categories of the two. However, the fact that there is no data division means that all data divisions are included, so (actual data with data divisions) × (correction coefficient including all data divisions) = (public information analysis local government with data divisions) It will be possible to estimate (actual data). Data classification of actual data, public information, and correction coefficient is shown in FIG.

The outline of the method of calculating the correction coefficient from the public information of the model local government and the public information of the public information analysis local government shown in FIG. 18 (details will be described later) is listed as follows.
(1) Correction coefficient for changes in physical and mental condition (deterioration/improvement): Obtain the correction coefficient by obtaining the deterioration rate and improvement rate for each degree of long-term care, from the report aggregation 3-7 or the survey on nursing care benefits.
(2) Correction coefficient for average benefit cost per capita: 05-1h, 05-2h, 06-1h, 06-2h, 07 from nursing care insurance business status report 08h by service type, degree of nursing care required, number of units -Calculate the correction coefficient based on the number of beneficiaries acquired from 1h.
(3) Correction coefficient for each application category and average starting age: Obtain the correction coefficient by obtaining the starting age for each nursing care level from the report aggregate 3-1.
(4) By application category, correction coefficient of the number of users: By the service type, the degree of long-term care required, and the number of users acquired from the Long-term Care Insurance Business Status Report 08h, the ratio of males and females in the report aggregate 3-1 and the report aggregate 3- Calculate the correction coefficient by multiplying the application classification ratio of 3.

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

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

The per-capita average benefit cost acquisition unit 302 acquires the average per-capita benefit cost from the nursing insurance business status report (annual report) 08h of the target municipality.

The application classification/average start age acquisition processing unit 303 sets the average age obtained from the Ministry of Health, Labor and Welfare report aggregation 3-1 of the target municipality as the application classification/average start age.

The application classification/number-of-users acquisition processing unit 304 indicates the type of service, the degree of long-term care, and the number of users acquired from the long-term care insurance business status reports 05-2h, 06-2h, and 07-1h of the target municipality. Multiply the ratio of males and females from the Ministry of Labor Report aggregate 3-1 by the ratio of application categories from the Ministry of Health, Labor and Welfare reports 3-3 to obtain the service type, gender, application category, and number of users.

A specific example of the processing by the above-described mental/physical state change (deterioration/improvement) information acquisition processing unit 301 will be described with reference to FIG. Acquire changes (deterioration/improvement) of mental and physical conditions by degree of care required from the previous secondary judgment and the current secondary judgment by degree of nursing care in the Ministry of Health, Labor and Welfare report aggregation 3-7. Since there is no average maintenance period in the public information, the information of the model municipality is used as it is.

An example of obtaining the improvement rate of the long-term care 3 is shown below. Since the improvement rate of Nursing Care 3 is (Number of cases of previous Nursing Care 3 that has improved over this time of Nursing 3)/(Number of previous Nursing Care 3),
(Number of cases requiring last nursing care 3 this time 1 + number of cases requiring previous care 3 this time support 2 + number of cases requiring previous care 3 this time requiring care 1 + number of cases requiring previous care 3 this time requiring care 2) / (The number of cases requiring nursing care 3 last time)
(0+0+274+549)/3169=0.26, and the improvement rate is 26%.

Next, an example of obtaining the deterioration rate of the long-term care 3 will be shown. Since the deterioration rate of long-term care 3 is (the number of cases that were worse than last time 3 in case of long-term care 3 this time)/(the number of cases of last time long-term care 3),
(The number of cases requiring care 4 this time in the previous care 3 + the number of cases requiring care 5 this time in the case requiring care 3 last time)/(the number of cases 3 requiring care last time)
With (756 + 280) / 3169 = 0.33, the deterioration rate is 33%.

As shown in FIG. 21, the deterioration rate and the improvement rate are calculated for the other degrees of long-term care. The numerical values in FIG. 20 are those of the model municipality, and the deterioration rate and improvement rate in FIG. 21 are the estimation master basic information intermediate value data 43 of the model local government. The public information analysis municipal basic information for estimation master value intermediate value data 44 is similarly constructed.

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

For example, in the case of outpatient care, the average benefit cost per person can be calculated from the "08-1h (unit number 1)" sheet (excerpt) of the long-term care insurance business status report 08h shown in Fig. 22(a) by type of service Acquired the number of units for each nursing care level and the cumulative total for the year, and from the "05-2-1t" sheet (excerpt) of the long-term care insurance business status report 05-2h shown in (b) of the figure, classified by service type and nursing care level・Obtain the total number of deferred months for the year, and calculate (by service type, degree of long-term care, total number of units for the year) / (by service type, degree of long-term care, total number of deferred months for the year), Calculate the average number of units per person per month. Next, the average monthly benefit payment per person is calculated using the following formula (8).

Monthly average benefit cost per person by degree of long-term care = (monthly average number of units per person by degree of long-term care requirement) x unit cost by personnel cost ratio by region category (8)

Taking the example of long-term care 3 in FIG. 22(b), the number of units by service type, degree of long-term care, and year-to-date total is 220,000, and the number of postponement months by service type, degree of long-term care and year is 21,000. , The average number of units per person by degree of long-term care is 10.48, and from Equation (8), the average monthly benefit cost per person for long-term care 3 is 10.48 × 1000 × 10 = 104800 yen. ..

Formula (8) is an example in which the unit cost by labor ratio by region classification is calculated at an additional rate of 0% (10 yen) by region classification. In addition, when acquiring the average benefit cost per person regardless of service type, the number of units is 08h, the total number of "08-1h (1 unit)" sheets, and the number of users are 05-1h, 06-1h, 07 -Calculate by obtaining the number of beneficiaries from the total number of 1h.

The average number of units per person is calculated for other degrees of nursing care as well, as shown in FIG. The numerical values in FIG. 22 are those of the model local government, and the average number of units per person in FIG. 23 is the estimation master basic information intermediate data 43 of the model local government. The public information analysis local master information for estimation master basic information 44 is similarly constructed.

Next, the processing of the application classification/average start age acquisition processing unit 303 will be described with reference to FIG. In this process, the average start age for each gender by degree of nursing care is acquired from the Ministry of Health, Labor and Welfare report aggregation 3-1 (excerpt) shown in FIG. The method for obtaining the average starting age of male 3 requiring nursing care is shown below. In the report summary 3-1, the age groups within the 5-year-old range are between 65 and 100 years old, so a representative value for each age group is determined. The typical value is the median of the range, 62.5 for those under the age of 65, and 102.5 for those over the age of 100. The average age is calculated by taking the weighted average of each representative value and the number of cases. In the example of FIG. 24, the average calculated value of the long-term care 3 for men is 80.03858, and the average start age is 80 years.

In this way, the gender-specific, care-giving-degree-specific, and average starting ages are obtained as shown in FIG. The numerical values in FIG. 24 are those of the model municipality, and the male/female-specific/need-to-care level/average start age of FIG. 25 is the estimation master basic information intermediate data 43 of the model municipality. The public information analysis local master information for estimation master basic information 44 is similarly constructed.

Next, the processing of the application classification/number of users acquisition processing unit 304 will be described with reference to FIG. In this process, the ratio of males and females of the Ministry of Health, Labor and Welfare report 3-1 is added to the number of users by service type, degree of long-term care, and the number of users acquired from the Long-term Care Insurance Business Status Report 05-2h, 06-2h, 07-1h. Multiply the application classification ratio of the Ministry of Health, Labor and Welfare report aggregation 3-3 to obtain the service type, gender, application category, and number of users. An example of the acquisition method is shown below.

The method for acquiring the number of users is as described in the above-mentioned processing for acquiring 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. 22(b), the type of service, the degree of long-term care required, and the cumulative total deferred month of the year are acquired. The ratio of males and females is obtained from the report aggregation 3-1 (excerpt) shown in Figure 26(a), by degree of nursing care required, by gender, and the number of certified cases. The application classification ratio is obtained from the report aggregation 3-3 (excerpt) shown in Fig. 26(b) based on the degree of long-term care required, application classification, and the number of certified cases. Based on these, the service type, gender, application category, and number of users are calculated using the following equation (9).

By service type, gender, application category, number of users = (number of users) x (ratio of males and females) x (ratio of application categories) (9)

In the case of long-term care 3 in FIG. 26, the number of users = 21000, male ratio = 2009/5142 = 39%, female ratio = 3133/5142 = 61%, new application classification ratio = 1176 / (5239-97) = 23% Since the renewal change application classification ratio = (3078+888) / (5239-97) = 77%, the number of male new application users is 21000 x 39% x 23% = 1884 (person) from formula (9).
Becomes

Other service types/genders/application categories/number of users are also obtained by the same method as shown in FIG. The numerical values in FIG. 26 are those of the model municipality, and the service type, gender, application classification, and number of users in FIG. 27 are the estimation master basic information intermediate data 43 of the model municipality. The public information analysis local master information for estimation master basic information 44 is similarly constructed.

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

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

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

The application classification and average start age correction coefficient calculation processing 313 is the application classification acquired from the model master local information master data intermediate data 43 of the model municipality based on public information and the public information analysis local master information intermediate data 44 of estimation. The ratio between the different age groups and the average age group is calculated, and this ratio is used as the correction coefficient for the average age group for each application in the estimation master basic information correction coefficient 32.

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

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

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

From the value of the estimation master basic information intermediate data 43 of the model municipality shown in FIG. 21 and the value of the estimation master basic information intermediate data 44 of the public information analysis municipality shown in FIG. 29, the degree of long-term care required shown in FIG. A different/mental and physical condition change (deterioration/improvement) information correction coefficient is obtained, and constitutes the estimation master basic information correction coefficient 32.

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

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

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

Next, a specific process of the application classification/average start age correction coefficient calculation processing unit 313 will be described. This processing is performed by estimating the master basic information intermediate data 43 of the model municipality shown in FIG. The ratio of 44 to the average starting age for each gender by degree of care is calculated and used as the correction coefficient.

The correction coefficient is calculated by the following equation (13).
Average start age correction coefficient = (Average start age of public information analysis local government) / (Average start age of model local government) (13)

From the value of the estimation master basic information intermediate data 43 of the model municipality shown in FIG. 25 and the value of the estimation master basic information intermediate data 44 of the public information analysis municipality shown in FIG. An average start age correction coefficient for each degree of long-term care is obtained and constitutes the correction coefficient of the estimation master basic information 32.

Next, a specific process of the application classification×number of users correction coefficient calculation processing unit 314 will be described. This process is carried out by estimating the master basic information intermediate data 43 of the model municipality shown in FIG. The ratio of 44 to the number of users by gender, degree of nursing care, and the number of users is calculated and used as the correction coefficient.

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

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

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

The physical/physical condition change (deterioration/improvement) information actual data estimation processing unit 331 uses the correction coefficient of the mental/physical condition change information in the estimation master basic information correction coefficient 32 as the mental/physical condition change information of the estimation master basic information (model municipality) 20. By multiplying by, the physical and mental condition change information that constitutes the public master information 34 for estimating public information is acquired.

The average benefit cost per person actual data estimation processing unit 332 corrects the average benefit cost per person in the estimate master basic information correction coefficient 32 to the average benefit cost per person in the estimation master basic information (model municipality) 20. The coefficient is multiplied to obtain the average benefit cost per person that constitutes the public master information 34 for estimation of public information analysis local governments.

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

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

Next, a specific processing example of each of the processing units 331, 332, 333, 334 described above will be described. The physical/mental state change (deterioration/improvement) information actual data estimation processing unit 331 is the same as the physical/mental state change information 201 of the estimation master basic information (actual data) 20 described in FIG. (A) Physical/physical condition change information (by the degree of care required/mental/physical condition change (deterioration/improvement) information: real-side data) 201A of the estimation master basic information correction coefficient 32 shown in FIG. Multiplying the state change (deterioration/improvement) information correction coefficient, the public information analysis shown in FIG. get.

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

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

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

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

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

The application classification/average start age estimation processing unit 333 is the same as the application classification/average start age 203 of the estimation master basic information (actual data) 20 described in FIG. The average start age (actual data) 203A by gender, multiplied by the master master information correction coefficient for estimation 32 shown in FIG. 7B is multiplied by the average start age correction coefficient by gender. 43, the average starting age (estimated value) by gender and nursing care level of the master basic information 34 for promotion of public information analysis local government shown in FIG. 43 is acquired.

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

From the values shown in FIGS. 42(a) and 42(b), it is possible to acquire the estimated values for the average starting age by gender according to the values shown in FIG. 43 by the expression (18).

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

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

From the values shown in FIGS. 44(a) and 44(b), it is possible to obtain the estimated values for the number of users by gender/degree of nursing care of the values shown in FIG. 45 by the equation (19).

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

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

In this way, it is possible to estimate the transition of public information analysis local governments even if there are no model local governments of similar sizes or forms.

Next, the user number transition estimation processing of 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 situation of the number of users at the time of starting the estimation, the mental and physical state change information 201 of the estimation master basic information 20 (including the physical and mental state change information 201A of the public information analysis local government, but is unified as 201 below. Change rate and average maintenance period for each degree of nursing care), and application category/number of users 204 (also includes application classification/user number 204A of public information analysis local governments, but unified as 204 below) User).

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

If the number of users who need long-term care 4 is 700 among the update change application users, the user who needs long-term care 4 is in the next stage (end, need long-term care 5, long-term care 3, long-term care 2,... The number of people who change (deteriorate or improve) to () is estimated using the physical and mental condition change information 201. After that, for the changed next stage (other than the end), long-term care 5, long-term care 3, long-term care 2,..., The number of persons changing to each next stage is repeatedly determined in order to estimate the number of users. FIG. 47 shows the mind-body state change information master 201 used for this estimation.

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

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

The application classification/user count master 204 is the same as that shown in FIGS. 3, 14, and 45, and is a master that holds the number of users at the time when the number of users is acquired according to the degree of nursing care required. is there. This is also created in advance by the estimation master basic information acquisition processing unit 11 described above.

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

First, the user number transition estimation process (hereinafter, “each estimation process”) for each of updated/changed users and new users of all nursing care levels is started at the same time. That is, “estimation start date”, “degree of nursing care”, “initial number of users”, “application category”, and “sex” are set as attribute information of each estimation process. Based on the degree of long-term care and gender, the estimated number of persons to start is acquired from the application classification/user master by sex and degree of long-term care. In the example of FIG. 48, the number of women requiring nursing care 4 is 700.

Each estimation process requires the “transition ratio” and “average maintenance period” within the applicable period of the psychosomatic state change information master from the psychosomatic state change information master 201 to indicate the estimation start date of each estimation process. Get by care level. In FIG. 48, the “average maintenance period”, the “degree of next care required”, and the “transition ratio” of the woman in need of care 4 are acquired in the first estimation process 12A. These are acquired from the row of the mental-physical condition change information master 201 in which the degree of long-term care and the next degree of long-term care match in the phase in which the estimated start date is included in the applicable period.

Further, the "degree of care required" in the first estimation process 12A indicates the degree of long-term care required in this estimation process. The “average maintenance period” is the maintenance period until the next degree of long-term care and is shown for each degree of long-term care. The "next care requirement" indicates the degree of care need at the transition destination after maintaining the care requirement for the average maintenance period. The “transition ratio” indicates the ratio of the number of people who transition to the next degree of need for nursing care.

Next, in each estimation process, (number of initial users x transition ratio by next nursing care level) is calculated to obtain "the number of users by next nursing care level", which is calculated for the number of months of the average maintenance period. Only the number of monthly users of the relative number of months elapsed from the start of the estimation is expanded.

Taking the second line from the top of the figure of the first estimation processing 12A (nursing care required is 5 for next care) as an example, “70% (700 people of 700 users of nursing care 4) After maintaining for 16 months, it will transition to nursing care 5”. In each month column of “Relative number of months elapsed from the estimation start date” of this first estimation process 12A, the number of users obtained by the above (initial number of users × transition ratio) is calculated from the estimation start date. Set for the number of months of the average maintenance period of. Taking the fourth line from the top (nursing care required next is 3) as an example, 10% (70) of 700 users show that they need to maintain 4 for 16 months.

The total value of the users of the current degree of long-term care in the maintenance period up to the next degree of long-term care is calculated for each month. In the first estimation process 12A, the number of users is summed up according to the maintenance period of the user of the current long-term care 4 to the next degree of long-term care, that is, the number of months that have elapsed since the next start of estimation by degree of long-term care. Calculate the total number of monthly users. This total value is 700 at the beginning of the estimation start month, but it will change for a while, but it will become 644 after 16 months have passed since then, then 84 at 17 months, 39 at 18 months, and so on. It gradually decreases.

The total value of the number of users by month thus obtained is registered in the number of users by degree of care by year in the number-of-users-by-care level transition table 12T. At this time, the registration destination is registered to the degree of long-term care that is the same as the degree of long-term care required for each estimation process for the year and month obtained by “estimation start month + relative number of months elapsed from the estimation start month”. In addition, the number of users is added to the number of users already registered before registration.

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

In FIG. 48, when the next degree of long-term care required is 3, the “estimation start date” of the next estimation process 12B is “previous estimation start month (April 2015)+next degree of long-term care required”. The average maintenance period (16 months)" will be August 2016. The "initial number of users" is 70, which is the number of users by degree of long-term care required in the month following the previous estimation result. The next degree of long-term care 3, which is the degree of long-term care, is set in the "degree of long-term care". For “Application classification” and “Gender”, set the same “update/change” as in the previous estimation process, and for “Gender”, set “Female”.

In addition, as in each of the previous estimation processes, the number of users by the relative number of months elapsed from the start of the next estimation based on the degree of long-term care is calculated, and the total is calculated for the total number of users by month. , The number of users by degree of long-term care table 12T is registered in the number of users by degree of long-term care requirement.

Regarding the number of users by relative elapsed months, the following estimation process is not executed for the rows for which the degree of care required has ended or the number of users for each month is less than 1. In addition, the total number of monthly users is less than 1 when the number of monthly users is less than 1, and the total number of monthly users is output as a minority. Is rounded off to obtain an integer value.

The following estimation processing is similarly performed for other nursing care degrees, and the estimation processing is similarly performed one after another. When all the estimation processes are completed, the user number transition estimation process is completed.

The above-mentioned number-of-users-by-care-degree transition table 12T is a table that retains the transition of the number of users by degree-of-need-of-care and year and adds the total number of users by month of each estimation process by year and month. In addition,
The year and month of the registration destination is calculated by the estimated start date + the relative number of months elapsed since the estimated start date -1. In the example of the broken line arrow in FIG. 48, the number of relative elapsed months is 3, (April 2015) + 3−1, and the registration destination year in the user transition table T by degree of care need is June 2015. Becomes

From the value of the number-of-users-by-care-degree transition table 12T, the transition of the number of users-by-care degree becomes clear.

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

That is, the average number of users 202 by the degree of long-term care required, the number of users by degree of long-term care in the table 12T ) Is multiplied by the required nursing care amount, the monthly amount, the benefit cost (monthly amount), and the total and the total are calculated, and these are registered in the benefit cost transition table 49. Each of the tables 202, 12T, and 49 shown in FIG. 49 is classified by service type and sex.

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

Next, the healthy life span transition estimation process of step 4 described with reference to FIG. 2 will be described with reference to FIG. In this process, the age at which the heaviest stage, which requires less care, ends at the end for each of the 65 psychosomatic condition items (certification data) such as the degree of long-term care and the degree of independence of dementia is defined as “healthy life expectancy”.

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

In the first estimation process 12A of FIG. 48, the user of the long-term care 4 obtains the number of persons transitioning to the next stage for each next stage, and the elapsed months from the respective average durations to the transition to the next stage. The number of people who changed each time was registered. In this first tabulation process 12A, it is possible to know when to transit to the next stage for each next stage.

If the content of this first estimation process 12A is applied to the estimation process 50A in FIG. 50 as it is, the initial degree of long-term care required in the first estimation process 12A was 4 requiring long-term care. If it is 4, the average starting age (monthly age) will be 924 (months) from the average starting age 203M according to application category. This average starting month age is registered for each elapsed month until the next stage (the upper row in the figure is the number of people). Since it is the age of the moon, +1 is added every month.

Looking at the line for the next long-term care 3, since the average duration is 16 months, the age 924 is maintained by 16 months while adding +1. Then, after 16 months have passed, the process proceeds to the estimation process 50B of the next long-term care 3 that is required. Therefore, the age at which the long-term care 4 ends is 16 months, which is 939.

In the above description, since the numerical value of the first estimation process 12A in FIG. 48 is used as it is, the degree of long-term care required for this place was 4, but the heaviest stage where care is taken is the degree of long-term care required. Generally, it is said that nursing care 2 is required. Therefore, if the same degree of care as described above is performed with the initial degree of long-term care required in the estimation process 50A as 2 for long-term care, the timing at which the degree of long-term care required transitions to the next stage, long-term care 3, can be grasped. Since it is the year and month when the non-working stage (care required 2) ends, the age of that month can be estimated as a healthy life expectancy.

Next, the comparison process between the new policy effect cases of the benefit cost (cumulative) estimation result of step 5 described in FIG. 2 will be described with reference to FIG. The benefit cost (cumulative) estimation unit 15 in FIG. 2 performs a comparative analysis of the estimation results performed for each new policy effect case in step 3 of FIG. 2 described above. For example, when the new measure case 1 is a measure in which the target value of the physical and mental condition change information of a specific service type (for example, "special diet") is +10% as described above, the new measure case 2 is the same. If the target value is set to 15%, and the new policy case 3 is a policy in which the target value of the physical and mental condition change information of other service types (for example, "old health") is +10%, step 3 This is a process for estimating the transition of the payment costs (cumulative) of the above, and comparing and comparing the effects with each other based on the results.

In Fig. 51, the cumulative results of the degree of long-term care, new policy effect cases, and benefit costs are shown in Fig. (a) after 3 years, in Fig. (b) after 6 years, and in Fig. (c). 9 years later, each is shown as a table.

As a result of the above-mentioned comparative analysis, it is possible to select a policy and an implementation plan for each service type or sub-region that is effective in controlling benefit costs.
Next, the comparison process between new policy effect cases of healthy life expectancy in step 6 described in FIG. 2 will be described with reference to FIG. The new policy effect case comparison processing unit 16 of FIG. 2 performs comparative analysis of the estimation results performed for each new policy effect case in step 4 of FIG. 2 described above.

FIG. 52 shows the result of comparison between new measure effect cases 1, 2, and 3 by degree of long-term care, new measure effect case, and age. From the comparison result, the effective service type or the sub-regional measure for the extension of the healthy life is known, and the selection and the implementation plan can be determined.

Next, an embodiment in which the cost-effectiveness estimation/verification function is further simplified will be described. When the embodiment described so far is a so-called care detailed version, the embodiment described below is a simplified care version. In this nursing care simplified version, as the current situation grasp 1, the number of users (users of long-term care insurance) and benefit costs by degree of long-term care and service type are obtained from the Ministry of Health, Labor and Welfare's long-term care insurance business status report. In addition, as the current situation grasp 2, the average rate of improvement until the degree of nursing care required and the average maintenance period until deterioration deteriorates by the degree of nursing care required, service type, and business group.

Next, make a plan for improvement by degree of care required, type of service, business group. In addition, the establishment group refers to a group that is classified into four groups by comparing the improvement rate and the average maintenance period until deterioration to the average of local governments. That is, the target improvement rate and maintenance period of the degree of long-term care required for each degree of long-term care and service type are planned for each business group. For example, as shown in FIG. 53(a), effective measures are taken for business establishment groups whose improvement rate and average maintenance period until deterioration are both below the average of local governments for users with a nursing care level (severe). Three years after implementation, the improvement plan to improve to the average of local governments in the center of the figure is made.

In addition, the effect of curbing benefit costs after the implementation of the improvement plan is calculated. That is, the amount of reduction of the benefit cost due to the improvement in the improvement rate of the degree of long-term care and the amount of the reduction of benefit cost due to the extension of the maintenance period shown in FIG. 53(b) are calculated. In FIG. 53(b), the degree of long-term care of the user deteriorated from long-term care 3 to long-term care 4 after the maintenance period shown by the solid line, but by applying a new measure (some improvement measure), As shown in the figure, if the maintenance period is extended until the deterioration, it means that the amount of pay for the user is suppressed by the area of the diagonal line.

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

Examples will be described below. In the current situation grasp 1 described above, as one of the steps, the number of users according to the degree of long-term care and the type of service is obtained as described above. For this reason, as shown in FIG. 54, first, from the first public data (nursing insurance business status report) 51 in which the number of benefits and the cost of benefits to users by service type of nursing care insurance are disclosed, The number-of-persons acquisition processing unit 52 acquires data on the number of service-required users (by month) for each type of care, and constitutes a user number master 53.

In the user number master 53, the number of users for each service type, which is classified by the degree of long-term care, is registered. For example, in the case of long-term care 1, the service type is registered as 8.0 (thousand) for home, 1.0 (thousand) for special nursing, 0.3 (thousand) for elderly, and so on. ing.

Next, by the second public data (nursing care benefit cost fact-finding) 54 in which the rate of change in mental and physical conditions by degree of long-term care is disclosed, the rate of change in mental and physical state by degree of long-term care is calculated by the mental-physical change information acquisition processor 55 get. In the second public data 54, the degree of lightening means that the level of need for nursing care has decreased by one step or more, and the degree of seriousness means that the level of need for nursing care has increased by one step or more. In the example in the figure, the ratio of nursing care required decreased by 1 step to become nursing care 1 is 5%, and the ratio of nursing care required increased by 1 step to become nursing care 1 is 25%. It shows that the ratio of the patients requiring care 1 is 70%.

The rate of change in mental and physical conditions is multiplied by the number of users in the above-mentioned number-of-users master 53 to calculate the number of persons in physical and mental condition change for each service type and degree of long-term care to form a master for changing physical and mental state 56. In the example shown in the figure, the number of home-care 1 is 8.0 (thousands) as shown in the user count master 53, and the rate of reduction is 5% from the second public information 54. , The improvement number is 0.4 (thousands). Similarly, the number of persons who maintain and deteriorate is also calculated and registered in the number-of-physical-body-state-changed-person master 56.

The mental-physical change information acquisition processing unit 55 further aggregates the number of mental-physical state-changed persons according to a predetermined degree of care required, mild and severe, and configures the mind-body-state changed number master 56 with the aggregated number of mental-physical state changed persons. Here, “severe” refers to the range of nursing care required 1 and 2, and “severe” refers to the range of nursing care required 3, 4, and 5. In the example shown in the figure, the number of persons requiring nursing care 1 and 2 is added together as a minor number, and the number of persons requiring nursing care 3, 4, and 5 corresponding to severe degree is also added together and summarized. Register at 56.

In this way, it is possible to obtain the number of persons by service type, degree of nursing care required, and changes in mental and physical conditions.

As another step of grasping the current situation 1, request payments to beneficiaries (users of long-term care insurance) by degree of long-term care and service type. For this purpose, the difference in benefit costs per person by service type, degree of care required, and changes in mental and physical conditions is calculated.

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

In the benefit expense master 59, the monthly benefit amount per person is registered according to the service type (home-based system, special nursing care, senior citizen,...) According to the degree of nursing care required. In the figure, looking at the service type: home-based system, the monthly payment per person is 45 (thousand yen) for support required, 85 (thousand yen) for long-term care 1, 120 (thousand yen) for long-term care 2. .. is registered.

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 a change in mental and physical conditions by service type and degree of long-term care, and calculates the benefit cost difference. Register in the table 61. In the example of the figure, regarding the need for nursing care 1, as the number of persons changing the mental and physical condition mentioned above, the number of persons improved from the master 56 of the number of persons changing their physical and mental state was 0.4 (thousands), and the number of worsening was 2.0 (thousands). Benefit difference per person at the time (monthly amount of reduction due to improvement of nursing care 1 to support 2)-40 (thousand yen) and its annual amount-480 (thousand yen), and per capita benefit at the time of deterioration Expense difference (monthly amount of increase due to deterioration of long-term care 1 to long-term care 2) 35 (thousand yen) is registered.

Similarly, regarding other nursing care levels, the number of people who have changed their physical and mental condition and the difference in benefit costs per person when the physical and mental condition changes are registered.

The benefit cost difference extraction processing unit 60 further sets the per capita benefit cost difference when the physical and mental condition is improved and the per capita benefit cost difference when the physical and mental condition is deteriorated to be mild (needs to care 1, 2) and severe (needs to care 3). , 4, 5) are separately collected and registered in the benefit difference table 61. This aggregated value is a weighted average value of nursing care needs 1 and 2 in terms of mildness. That is, the slight difference (annual amount) in the benefit expense 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 the current situation grasp 2, the improvement rate and maintenance period of the degree of long-term care are calculated by degree of long-term care, service type, and business group.

At the stage of making improvement plans for each service type, each business group, and at the stage of making an improvement plan, the target improvement rate and maintenance period for the care required by each nursing care level and service type are planned for each business group. As described above, in FIG. 52, the current values of both the improvement rate and the average maintenance period until deterioration deteriorate in the center of the figure three years after effective measures have been implemented for the business establishment groups below the local government average. Develop an improvement plan to improve the local government average.

Therefore, as shown in FIG. 56, the current value 63 is first grasped. For local governments that do not have actual data, the current values (by service type, by degree of minor care need, improvement rate, and maintenance period until deterioration) of model municipalities with similar environments and configurations are used. Next, by the target value setting unit 64, a target value that should be improved after a predetermined period (for example, 3 years) from the implementation of the measures (by service type, by nursing care severity, improvement rate, and maintenance period until deterioration) To set.

The difference extracting means 65 determines the improvement rate difference between the current value and the target value and the maintenance period difference until deterioration, and constitutes a difference master 66. In the figure, the difference in the slight improvement rate for home type is 4%, and the difference in the maintenance period until deterioration is 2 months.

Next, the effect of suppressing the benefit cost after the implementation of the improvement plan is calculated. In other words, the amount of benefit cost reduction due to the improvement in the degree of improvement in the degree of long-term care and the amount of benefit cost reduction due to the extension of the maintenance period are calculated.

First, a description will be given of the processing for calculating the amount of the benefit cost restraint due to the improvement in the improvement rate of the degree of long-term care. The first benefit expense restraint amount calculation unit 68 of FIG. The difference (improvement rate difference) is acquired, and the number of persons subject to benefit cost restraint is calculated for each service type and the degree of nursing care required, and registered in the corresponding item of the first benefit expense restraint amount table 69. To do.

In the example in the figure, when looking at the mildness of the home-based system, the number of people with mental and physical changes is 14.0 (thousands), and the improvement rate difference from the target value is 4%, so the number of people targeted for benefit cost control is 560. ..

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

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

Next, a description will be given of the calculation process of the amount of reduction of benefit costs by extending the maintenance period until deterioration. The second benefit cost restraint calculation unit 71 of FIG. 58 calculates the difference (deterioration) between the deteriorated number of people in the physical and mental condition change number master 56 and the target value of the maintenance period until deterioration held in the difference master 66. Up to the maintenance period difference) and register it in the corresponding item of the second benefit cost restraint amount table 72.

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

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

In the example in the figure, the number of worsening people is 3.2 (thousands), and the difference in benefit costs (monthly amount) per benefit at the time of deterioration is 41 (thousand yen), and the difference in maintenance period until deterioration is 2 months. However, the amount of reduction in benefit costs due to the difference in the maintenance period until deterioration will be -262.4 (million yen).

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

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

Up to now, the cost-effectiveness estimation/verification function of the nursing care simplified version has been described, but FIG. 61 compares this with the nursing care detailed version described above. The care detailed version is more accurate than the care simple version in all respects. However, the difficulty of realizing the estimation verification tool is higher than that of the simplified version. Hereinafter, each comparison item will be briefly described with reference to FIG.

Referring to the comparison item “considering the number of users by application category” in FIG. 61, in the simplified nursing care version, consideration by application category is “none”. On the other hand, in the detailed version of nursing care, “Yes” is considered, and it is distinguished by new and update/change.

As for the comparative item “Notable nursing care level”, the simplified nursing care version “estimates the nursing care level to be mild and severe”. On the other hand, the detailed nursing care version "estimates all levels of care required faithfully", indicating that the detailed nursing care version is more accurate.

As for the comparative item “Change in nursing care level”, the simplified version of nursing care assumes “deterioration and improvement in only one step”. On the other hand, in the detailed nursing care version, “the number of stages of deterioration and improvement is assumed”, and it can be seen that the detailed care version has higher accuracy.

Looking at the comparison item “improvement rate and accuracy of average maintenance period until deterioration”, in the nursing care simplified version, “the number of the relevant local government which is only the number of the model local government cannot be specified”. On the other hand, in the detailed version of nursing care, it is possible to obtain "high accuracy value using actual data".

As for the comparative item “Improvement rate and change consideration phase of average maintenance period until deterioration”, in the nursing care simplified version, “current and target are set only between two points at the start and end of estimation (for example, 3 years) "doing. On the other hand, in the detailed care version, “the calculation is repeated for all transitions (including termination) after the maintenance period of the degree of long-term care”, and it can be seen that the detailed care version has higher accuracy.

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

As for the comparison item “whether or not the healthy life expectancy can be calculated”, the calculation is “impossible” in the nursing care simplified version. On the other hand, in the detailed nursing care version, the calculation logic is “similar”, and the calculation is “OK” by the similar calculation logic.

As for the comparative item “Difficulty of realization of estimation verification tool”, the simplified nursing care version is “realizable with Excel”, which is relatively low in difficulty. On the other hand, in the detailed nursing care version, “program development is essential (Excel difficult)”, and the detailed nursing care version is relatively difficult.

From the comparison result between the simplified care version and the detailed care version, one can be selected according to the actual situation of the local government (care insurer).

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

In the case of long-term care benefit business, the long-term care certification data and long-term care benefit data are used as the estimation/verification source data, and estimation/verification is performed for each service type. On the other hand, in the general business, the same data is used as the estimation verification data, such as certified long-term care data and long-term care benefit data, but it is also estimated using general business data (basic checklist, commuting place usage record, etc.). Perform verification.

And even in the case of a comprehensive project, it is possible to estimate and verify the cost-effectiveness using the exact same logic, based on the following correspondence relationships. For example, as shown in FIG. 62, like the degree of long-term care required by the care business target person (necessary care 1 to 5), the physical and mental condition of the general business target person is divided into five stages (independence to support required 2) and associated. Good. In addition, in FIG. 62, weakness 1 and weakness 2 are so-called flail subjects, and their lightness may be defined by risk points or the like in the basic checklist.

If defined in this way, for example, when the mental and physical condition of the comprehensive business target person is “independence”, the cost-effectiveness is the same as in the case where the nursing care target person's degree of long-term care is “1”. Can be estimated and verified.

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

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

Claims (6)

From the data recording the changes in the stages of the physical and mental state of the user in a predetermined period before the preset start date of the estimation, the next changing stage for each of the above users, the next changing stage Change direction, the transition ratio to the next stage, and the average duration until the change to the next stage are obtained, and a predetermined target value is added to these mental and physical state change information to form a mental and physical state change information master. A physical and mental condition change information acquisition processing unit,
From the certified data of the long-term care insurance before the estimation start date and the benefit record, obtain 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,
From the certified data of the long-term care insurance before the estimated start date and the benefit record, the average start age information for each stage is obtained, and the average start age acquisition processing unit that constitutes the average start age information master and the estimated start date before From the certification data of the long-term care insurance and the benefit record, the number-of-users information for each stage is obtained, and the number-of-users acquisition processing unit that constitutes the user number information master,
Transition to the next stage from the transition ratio for each stage to be transited to next, which is held in the mental-physical condition change information master, and the number of users of the stage retained in the user number information master Along with determining the transition number, using the average duration until the change to the next stage held in the mental and physical condition change information master, the total value of the transition number in the period until each transition to the next stage, The first number of users estimation process is performed for each relative elapsed month after the estimation start month, and the same number of users estimation process is performed for the next stage after the transition of the average continuation period. A user number transition estimation processing unit that repeats similar user estimation processing,
A number-of-users transition table in which the number of transitions for each relative elapsed month obtained by this user number transition estimation processing unit is aggregated by each of the stages and by the elapsed months after the estimation start year and month,
According to the stages aggregated in the user number transition table, and from the average number of users per stage and the average benefit cost information per person of the stages held in the average benefit cost information master, by stage, And a benefit cost transition estimation processing unit that seeks benefit costs by the elapsed months,
Local comprehensive care business system equipped with. Having the actual data when the municipality to be a care insurer does not have the actual data concerning the basic information consisting of the physical and mental condition change information, the average benefit cost information, the average start age information, and the number-of-users information. Comparing public data on long-term care insurance of local municipalities with public data on model long-term care insurance having actual data on the basic information, and calculating a correction coefficient based on the difference between them.
A basic information inferring process for correcting the actual data relating to the basic information of the model local government by the correction coefficient obtained by the correction coefficient calculation unit and estimating the basic information of the public information analysis local government that does not have the actual data. Department,
The regional comprehensive care business system according to claim 1, further comprising: If the municipality to be a care insurer does not have the actual data regarding the basic information consisting of the physical and mental condition change information, the average benefit cost information, the average start age information, and the number-of-users information, it does not have the actual data. Data concerning the basic information is acquired from public data concerning long-term care insurance of the local government, and the acquired data is analyzed from the public information constituting the master corresponding to the basic information of the public information analysis local government that does not have the actual data. The regional comprehensive care business system according to claim 1, further comprising a basic information acquisition unit. The mental and physical condition change information acquisition processing unit,
Integrating all types of services provided by long-term care insurance, obtained from certified data of long-term care insurance and the results of payments Mental and physical condition change information of all service types including the average duration until deterioration, termination and improvement,
The mental and physical condition change information 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 summed up for all the above service types. Accumulated value of physical and mental condition change information by service type,
A psychosomatic state change information ratio which is a ratio of the psychosomatic state change information of all the service types and the accumulated value of the psychosomatic state change information for each service type,
Among the plurality of service types, the value of the mental and physical condition change information of a specific service type is obtained by adding a target value for improving the mental and physical condition change information by a measure for a predetermined period after the estimation start date. The accumulated value of mental and physical condition change information by service type is set as the target accumulated value of mental and physical condition change information by service type,
From the accumulated value of this target service type-specific physical and mental condition change information and the ratio of the physical and mental condition change information, the target physical and mental condition change information for all service types is calculated,
2. The regional inclusion according to claim 1, wherein the target physical and mental condition change information is configured to be held in the physical and mental condition change information master by using values of the target physical and mental condition change information for all service types. Care business system. Using the average start age information for each stage of the physical and mental condition held in the average start age information master, and the average duration until the stage held in the physical and mental condition change information master changes to the next stage Further comprising a healthy life expectancy transition estimation processing unit that sets the average start age information of the stage in the final year and month of the average duration corresponding to the stage as the age of the healthy life expectancy at which the heaviest stage that does not take care of care ends The regional comprehensive care business system according to claim 1. Data on the number of monthly users of the above-mentioned users by service type and degree of long-term care is obtained from the first public data that discloses the number of benefits and expenses for users by service type of long-term care insurance. , A user number acquisition processing unit that constitutes the user number master,
The number of users who change the mental and physical condition for each service type and the degree of long-term care calculated from the number of users of the user number master and the rate of change of mental and physical condition for each degree of long-term care disclosed by the second public data , A mental-physical change information acquisition processing unit that constitutes a master of the number of mental-physical condition changes, which is aggregated according to a predetermined degree of care required, mild and severe,
From the first public data, a salary acquisition processing unit that acquires a monthly benefit amount per person for each service type and degree of long-term care and constitutes a benefit cost master,
Benefits per person at the time of improvement of mental and physical condition, which is calculated from the difference in per capita costs when the physical and mental conditions change by service type and degree of long-term care calculated using the monthly benefit data held in this benefit master The difference amount and the difference amount of the benefit cost per person when the physical and mental condition deteriorates are held according to the service type and the degree of long-term care required, respectively, and
The improvement rate of mental and physical conditions by the type of service, mild and severe degree of long-term care and the current value of the maintenance period until deterioration, and the improvement rate of mental and physical states by the type of service, mild and severe degree of long-term care, and Difference master with the target value that holds the value of the difference between the target value of the maintenance period until deterioration,
Persons subject to suppression of benefit costs by the type of service and mild/severe degree of long-term care, based on the difference between the number of persons in the physical/mental state change master and the target value of the improvement rate held in the difference master The number of persons is calculated, and the difference in the amount of benefits per person at the time of improving the mental and physical condition by the service type and the degree of need for nursing care, which is held in the benefit difference table, and the type of service, A first benefit expense reduction amount calculation unit that calculates the benefit expense reduction amount due to improvement of mental and physical condition from the number of persons subject to benefit expense reduction by degree of caregiving,
The difference between the number of worsened mental and physical conditions in the master for changing physical and mental condition and the target value of the maintenance period until deterioration held in the difference master, and the service type held in the benefit cost difference table A second benefit expense reduction amount calculation unit that calculates the benefit expense reduction amount due to the difference in the maintenance period until deterioration, based on the difference in the benefit expense per person when the physical and mental condition deteriorates according to the degree of mild and severe care needs ,
Community comprehensive care business system characterized by having

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