JP4896848B2 - Accreditation optimization simulation system and computer-readable storage medium storing program for this system - Google Patents

Description

  The present invention is based on the reference index analysis using the above-mentioned accreditation survey results and past accreditation data when determining the secondary-decision necessity nursing care degree B from the primary determination necessitating care level A based on the accreditation survey results. An accreditation optimization simulation system that estimates how the secondary judgment need care level B changes when the care level candidate C is referred to, and a computer-readable storage that stores a program for this system It relates to the medium.

  Since April 2000, the long-term care insurance system has been introduced, and the certification of long-term care is being implemented in municipalities, such as municipalities, which are insurers. A patent proposal (for example, see Patent Document 1) has already been made for this care-required certification task, and an outline thereof will be described below.

  First, if you wish to use nursing care services, you will apply to the local government for nursing care certification. When an application is made, a person in charge of the local government or a care support specialist such as a home care support company commissioned by the local government conducts a certification survey on the physical condition. This accreditation survey is conducted by an investigator visiting a nursing care accreditation applicant and selecting an option set for each item for a plurality of preset accreditation survey items. In addition, the investigator makes a free statement as a special note about the items that cannot be expressed in the choices of each item. At the same time, the local government asks the attending physician to create a written opinion.

  The certification survey result is input to the computer, and a standard time for certification for long-term care is estimated by a predetermined certification program, and the degree of long-term care required based on the estimated standard time for certification for long-term care is output as a primary determination result.

  After that, at the nursing certification examination committee consisting of health, medical and welfare specialists, the primary judgment results were used to comprehensively confirm the contents of the doctor's opinion, special notes, and reference indicators presented by the Ministry of Health, Labor and Welfare. A secondary determination is made to determine whether care (support) is required, and if care (support) is required, how much care (support) is required. Based on the judgment of the nursing care certification examination committee, the municipality performs nursing care (support required) certification.

  The above-mentioned examination committee has to make secondary judgments in a short time for a large number of certifications requiring long-term care, and prompt and efficient work is required. In addition, since the degree of required care required is directly related to the care service that can be received by a long-term care applicant, it must be fair and it is important that the examination results do not vary.

  The examination committees so far are conducted by using the primary judgment results and comprehensively confirming the contents of the doctor's opinion, special notes and reference indicators presented by the Ministry of Health, Labor and Welfare. However, it takes a lot of time and effort to estimate the secondary determination candidates from each reference index, and there is a possibility that the examination takes time and the examination results for each examination committee vary.

  Therefore, based on the primary judgment need for nursing care based on the certification survey results for those who are subject to certification for long-term care insurance, secondary judgment candidates for the degree of care required used when determining the secondary judgment need for nursing care by the review committee An analysis system for a reference index to be created has been proposed by the present applicant (Japanese Patent Application No. 2007-81677).

  This reference index analysis system obtains secondary determination candidates for each of several preset reference indices, and derives a final secondary determination candidate from the secondary determination candidates obtained for each of these items. For example, it is assumed that secondary determination candidates are obtained for the following four reference indices, respectively.

1. Distribution by degree of care required by combination of independence 2. 2. Indicator of change in degree of care required Similar image example 4 The average score of the intermediate value items as seen by the degree of care required. And for the above 1, by the combination of disability independence and dementia independence of the subject of authorization obtained by the accreditation survey by means of independence combination distribution analysis means, From the statistical data of past certification for long-term care needs, the degree of long-term care required that is certified at the highest rate is selected as a secondary determination candidate.

  For the above 2, the change index analysis means uses the statistical results of the severe change when the secondary determination result is high and the mild change when the secondary determination result is low relative to the primary determination result obtained from the past certification for long-term care certification. From the primary determination result of the target person and the survey result of the item related to the severe or mild change selected in advance from among the certified survey items for each degree of care required of the primary determination result, the primary determination of the target person A secondary determination candidate is determined by determining whether or not the result is changed.

  For the above 3, using the state image of the person to be authorized based on the investigation result of the authorized investigation item by the similar state image example analyzing means, a plurality of each of the degree of care required based on the statistical data of the past nursing care authorization result A plurality of state image examples similar to the state image of the person to be authorized are extracted from the state image examples, and secondary determination candidates are obtained from the degree of care required to which the extracted state image examples belong.

  For the above 4, by dividing the accredited survey items into a plurality of groups by means of intermediate evaluation item estimation means, the scores of the survey results of the accreditation subject are tabulated for each group, and the scores tabulated for each group Then, the average score of each group in each degree of care required obtained from statistical data of past certification of long-term care required is compared, and the degree of need for nursing care with the largest number of groups with the nearest average score is set as a secondary determination candidate.

And among the secondary determination candidates obtained for each of these reference indices, the degree of need for nursing care that is most frequently output as a secondary determination candidate is set as the final secondary determination candidate. For example, if three “care-required 4” are output as secondary determination candidates for each of the above-mentioned reference indices and one “care-required 5” is output, the final secondary determination candidate is “care-required 4”. And
JP 2001-5880 A

  In this way, the above reference index analysis system, based on the primary determination result, when making a secondary determination by comprehensively checking the contents of the reference doctor's opinion and special instructions and the reference index presented by the Ministry of Health, Labor and Welfare at the examination committee, Provide clear secondary decision candidates for the review. For this reason, the examiner can easily refer to the secondary determination candidate by the reference index analysis in the examination, so that the examination is optimized and quick and efficient. Furthermore, there is a possibility that variations in the examination results of each examination committee may be greatly suppressed, and the realization of a fair examination can be expected.

  However, the effect of the optimization by having the examination committee refer to the secondary determination candidate by the reference index analysis appears after performing the optimization for a certain period, for example, one year. For example, in a local government, when the secondary judgment is decided from the primary judgment, the number of cases where the secondary nursing care degree is changed more seriously than the primary judgment nursing care degree at the examination committee How to change compared with the case where the optimization is not performed cannot be grasped unless the optimization is performed for a certain period as described above.

  An object of the present invention is to record an accreditation optimization simulation system and a program for this system that can estimate the effect of optimization by referring to a secondary determination candidate by reference index analysis before the implementation of the optimization. Another object of the present invention is to provide a computer-readable storage medium.

  The authorization optimization simulation system according to the present invention determines the degree of care required for secondary determination B from the degree of care A required for primary determination based on the result of approval investigation for the degree of care required. It is a nursing care level certification simulation system that estimates how the secondary judgment nursing care degree B changes when the appropriateness of the nursing care degree candidate C is referred to by reference index analysis using authorized data, Using the data of the primary determination nursing care degree A for each authorized person certified for a certain period of time and the secondary judgment nursing care degree B before the optimization, the secondary determination nursing care degree B is required to be primary judgment. Number of severe changes (B> A) changed in a more severe direction than the degree of care A, the number of secondary judgment requiring care B equals the degree of primary judgment requiring care A (B = A), the number of secondary judgment requiring care Degree B is lighter than primary decision need care degree A Pre-optimization judgment and comparison means for determining the number of minor changes (B <A) changed in the direction, severe change before optimization (B> A), no change (B = A), minor change (B About <A), the secondary judgment need for care level B before optimization and the need for care level candidate C are compared, the secondary judgment need for care level B is more severe than the need for care level C, and the optimization The number of cases with the possibility of becoming milder (C <B), the number of cases where the secondary judgment need for care B is milder than the need for care level C, and the possibility of becoming serious due to the optimization (C> B) Changeability calculation means to be obtained for each degree of care required, and severe change before optimization (B> A), no change (B = A), and mild change (B <A) for each degree of care required The number of cases (C <B) that may become mild due to the above optimization and the possibility that it may become severe (C Based on the number of cases of B), using the pre-set acceptance rate of the reference index analysis data, change the degree of care required in the mild direction by the optimization (C <B) change in the number and severity direction (C > B) Number of cases calculated by optimization for each of the above-mentioned severe changes (B> A), no changes (B = A), and minor changes (B <A) for each degree of care required, and this optimization By using the number of changes in the minor direction (C <B) and the number of changes in the severe direction (C> B) obtained for each degree of care required by the number-of-changes calculation means, self-care is required for each degree of care required. The number of changes from each degree to the level of other care required and the number of changes from the level of other care required to the level of self-care required are totaled to determine the number of changes for each degree of care required. Use post-optimization case count calculation to find the number of secondary-determination required care level B after optimization Means.

  The accreditation optimization simulation system of the present invention is based on the number of care facility service users according to the degree of care required before optimization in the predetermined period determined in advance, from the number of cases before the secondary determination need for care B is optimized. Multiply by the rate of increase / decrease by the degree of care required to the number of cases after optimization to determine the number of people changing by degree of care required due to the optimization of care facility service users, and the degree of care required during the preset period Based on another per capita benefit cost upper limit, there is provided a benefit cost change calculation means for obtaining a change in benefit cost in the certain period from the number of people changing according to the degree of care required.

  The benefit cost change calculation means calculates the number of certified persons requiring long-term care according to the degree of long-term care required in the predetermined period from the number before the optimization of the secondary determination need for long-term care B to the number after the optimization. Multiply the rate of change by degree of care required to obtain the number of people changing by degree of care required due to the appropriateness of certified care recipients, and the average benefit cost per person for each degree of care required in the predetermined period Based on the amount, the amount of change in benefit costs during the certain period may be obtained from the number of people changing according to the degree of care required.

  In addition, the authorized optimization simulation system of the present invention uses data obtained when past optimization is performed, and the number of cases of each secondary care need B for each nursing care degree in a certain period before the optimization is performed. The change value to the actual number of cases for each degree of care required in the secondary judgment need for care level B after the predetermined period after the optimization has been carried out It is preferable to provide an acceptance rate calculating means for obtaining the acceptance rate for each degree of care required for the reference index analysis data, which improves the simulation accuracy by dividing by the number of cases where the degree of care change is possible.

  In addition, the acceptance rate calculating means is a change of severity (B> A), no change (B = A), or a minor change (B <A), which are comparison results of primary / secondary determination in a certain period of time when optimization is not performed. ), Which is a comparison result between the secondary determination required care level B and the long-term care level candidate C, which may be mild (C <B) by the reference to the care level candidate C, and may be severe ( C> B), the data obtained for the ratio of the total including no change (C = B), and the severe change that is the comparison result of the primary / secondary determination in a certain period when the optimization was performed ( B> A), no change (B = A), minor change (B <A), refer to this care degree candidate C, which is a comparison result of the secondary determination necessity care degree B and the need for care degree C The possibility of becoming milder (C <B) and the possibility of becoming severe (C> B) (C = B) and the ratio of the total to the total, and the number of cases that may not be optimized (C <B) and the possibility of becoming severe (C> B) ) And the number of cases that may become mild (C <B) and the number of cases that may become severe (C> B). C> B) separately, and divide these values by the corresponding value of the number of cases (C <B) and the number of cases that may become severe (C> B) The acceptance rate may be determined based on the result.

  Furthermore, the adoption rate calculation means uses data collected through questionnaires to the members of the committee, and whether or not the committee members are placing importance on the reference index, the primary analysis method is the same as the reference index at the time of secondary determination. If the judgment data is analyzed and the degree of implementation of the above analysis, the ratio of the number of people in each category and the need for nursing care based on the reference index analysis of the members for each category The average adoption rate of the degree of care required candidate C in the examination committee is obtained from the adoption rate by referring to C, and is output as the adoption rate for each degree of care required in the reference index analysis data for the next simulation. It may be a thing.

  The storage medium of the present invention stores a program for realizing each function of the above-described authorized optimization simulation system.

  According to the present invention, it is possible to specifically estimate the effect of performing optimization before implementation of optimization.

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

  FIG. 1 shows an outline of a system configuration that realizes a qualified optimization simulation system according to this embodiment.

  In FIG. 1, a local government 11 such as a municipality who is an insurer for long-term care insurance is provided with a computer 12 for processing a long-term care insurance certification service. This computer 12 has a database 13 in which various data including past certification record data relating to certification for long-term care are held. The database 13 includes all past performance data related to certification for long-term care, such as data on the results of certification surveys for applicants requiring long-term care, data on the degree of primary care required, data on the level of secondary care required, etc. Is held.

  In such a municipality 11, when performing optimization by referring to a secondary determination candidate by reference index analysis to the examination committee, it is preferable to perform a simulation in advance to estimate and grasp the effect of the optimization. When performing such a simulation, first, the analysis software 14 is applied to the computer 12 of the local government 11, and the simulation data 15 is created from various data related to the certification of care required stored in the database 13. As data 15 for this simulation, primary judgment necessity nursing care degree A for every authorized person, secondary judgment nursing care degree B before optimization, secondary used for optimization for a certain period (for example, the latest one year) There is data of a judgment needing nursing care level candidate (hereinafter simply referred to as a nursing care degree candidate) C and the like. This long-term care need candidate C is data calculated for each long-term care request applicant by the reference index analysis system using the authorization survey data, the primary determination required long-term care degree A, and the like.

  In addition, the simulation data 15 includes the actual number of nursing care facility service users according to the degree of care required before optimization in the certain period, the upper limit of the benefit cost per person according to the degree of care required, and the need for care during the certain period. There are the number of certified persons requiring long-term care according to the degree, benefit costs according to the degree of long-term care required for the certain period, and so on.

  The actual number of persons requiring long-term care is the actual number of persons who have received recognition of long-term care and have actually received benefits and received long-term care services. The actual number of nursing care facility service users is the actual number of nursing care authorized persons who have used nursing care facilities such as nursing homes. These actual numbers are known in the local government. In general, the higher the degree of care required, the higher the utilization rate of care facilities.

  In addition, the upper limit of the per capita benefit costs for each degree of care required is an amount that is determined by the level of care required throughout the country as a national system. The average benefit utilization rate of benefit costs by degree of care required is the ratio of the amount actually paid to the maximum per capita benefit cost by degree of care required. The higher the degree of care required, the higher the average benefit utilization rate of benefit costs. This is because, as described above, the higher the degree of care required, the higher the utilization rate of the care facility, and the utilization rate of the benefit cost at the care facility is almost 100% of the upper limit of the benefit cost.

  These simulation data are output as CSV data, for example, and applied to a computer (not shown) as a simulator for simulation, or the analysis software 14 has a simulation program and is simulated by the computer 12 of the local government 11. A simulation may be performed using the business data 15.

  Hereinafter, the simulation function will be described.

  Figure 2 shows how the number of serious changes in the degree of care required and the number of minor changes change due to the adequacy of accreditation (making the examination committee refer to candidates for the degree of care required for secondary determination based on reference indicator analysis). It shows the function to simulate.

  In FIG. 2, in the area of the simulation data 15, data 1501 of the primary determination necessity care level A for all authorized persons who have been certified for a certain period (for example, the most recent year), and the corresponding data 1501. Data 1502 of the secondary determination necessity care level B before optimization is held. In addition, data 1503 of the degree of care required candidate C 150 and data 1504 regarding the adoption rate of the reference index analysis data (care required degree candidate C) are held.

  The pre-optimization determination comparison means 21 compares the primary determination requiring care level A and the secondary determination needing care level B at the examination committee where the optimization is not performed, and obtains the number of cases where the required care level has been changed. . That is, for all subjects to be certified, the secondary determination required care level B is changed more severely than the primary determination required care level A (B> A), the secondary determination required care level B and the primary determination required care level There is no change equal to A (B = A), and the secondary decision necessary care level B is divided into minor changes (B <A) that have been changed in a lighter direction than the primary determination need care level A, and the number of people data for each category 1601, 1602, 1603 are obtained respectively. Each of these data 1601, 1602, 1603 is held in the area of the operation result data 16.

  The change possibility calculation means 22 uses the respective data 1601, 1602, 1603 classified as described above, and changes of severe change before optimization (B> A), no change (B = A), and light change (B <A). About each, the secondary determination required care level B before optimization and the data 1503 of the corresponding required care level candidate C corresponding to this are compared. As a result, the number of cases in which the degree of care required for secondary judgment B is more severe than the candidate for need for care C, that is, the number of cases where the degree of need for care may become lighter due to optimization (C <B), The degree of care B is milder than the degree of care required candidate C, that is, the number of cases where the degree of care required may become severe due to optimization (C> B), and the secondary determination need for care B is equal to the need for care required C The number of cases, that is, the number of cases where the degree of care required is not changed by optimization (C = B) is determined for each degree of care required. Ask for each. Data 1604, 1605, and 1606 in which these are severely changed (B> A), unchanged (B = A), and lightly changed (B <A) are held in the area of the operation result data 16.

  Here, the primary judgment requiring nursing care degree A of a certain nursing care certification applicant is "Needing nursing care 3", and the secondary judgment requiring nursing care degree B is severely changed to "Needing nursing care 4" by the review board before optimization. (B> A). As a result of analyzing the data of the applicant requiring certification for long-term care using the reference index analysis system described above, when the candidate for long-term care C is “Need for Long-Term Care 3”, the degree of long-term care required for this applicant is changed to light by optimization. (C <B). In this way, the possibility of changing the degree of care required due to the optimization can be obtained by comparing all cases of the secondary determination necessity care level B before optimization with the corresponding candidate for long-term care C.

  FIG. 8 shows the above relationship by applying specific numerical values. In this municipality, there are 2148 secondary judgments requiring nursing care B for 1 year before optimization, for example, "Needs requiring nursing care 4" is 251 (the number of cases is also displayed for other nursing needs) ) Of the 251 cases of “Need Care 4”, the number of cases with severe change (B> A) is 61, the number of cases with no change (B = A) is 160, and the change is minor. The number of cases (B <A) is 30. And about each of these cases, as a result of comparing with the corresponding candidate for long-term care C, the change between primary / secondary is a severe change (B> A). 16 cases (C <B) may be changed to 39, the number of cases requiring no care change due to optimization (C = B) 39 cases, may be severe due to optimization The number of cases (C> B) is six. Similarly, in the case of no change (B = A) where there is no change between primary / secondary, among the 160 cases, the number of cases that may be changed mildly by optimization (C <B) is 52. The number of cases where the degree of care required does not change even when the cases are optimized (C = B) is 89, and the number of cases that may become severe due to the optimization (C> B) is 19. Furthermore, for those with minor changes (B <A) between the primary and secondary, the number of cases that can be changed mildly by optimization (C <B) is 3, and care is also required by optimization The number of cases in which the degree is not changed (C = B) is 23, and the number of cases that may become severe due to optimization (C> B) is 4.

  Returning to FIG. 2, the number-of-changes calculation means 23 by the optimization calculates the number of cases in which the degree of care required is actually changed in a severe or mild direction by performing the optimization using the preset adoption rate data 1504. . That is, with regard to the severe change (B> A), no change (B = A), and mild change (B <A), the change possibility calculation means 22 can be made light by optimization calculated for each degree of care required. Based on the number of cases of gender (C <B) and the possibility of becoming severe (C> B), using the adoption rate of reference index analysis data set in advance, the degree of nursing care required by the implementation of the optimization The number of changes to the direction (C <B) and the number of changes to the severe direction (C> B) are determined for each degree of care required. It should be noted that the number of cases where the degree of care required is not changed by optimization (C = B) is not handled here. Data 1607 and 1608 of the number of changes in the degree of care required in the mild direction (C <B) and the number of changes in the severe direction (C> B) due to the implementation of the optimization are also held in the area of the calculation result data 16.

  Here, the adoption rate of the reference index analysis data is the ratio of the screening committee adopting this need for long-term care C when it receives presentation of the need for long-term care C obtained by the reference index analysis at the review committee. That is. In other words, even when the candidate for need for nursing care C is presented, the judge does not adopt the candidate C for need for nursing care 100% to determine the degree of need for secondary care B, but is adopted at a certain rate. Become. This acceptance rate can be obtained by a method described later if there is an example of simulation, but in the case of the first simulation, it is determined to be an arbitrary fixed value (for example, 50%).

  The number of cases calculating means 24 after optimization uses the data 1607 for the number of changes to the minor direction (C <B) and the number of cases for the change to the severe direction (C> B) 1608 determined for each degree of care required, For each degree of long-term care required, the number of changes from the level of self-care required to the level of other care required and the number of changes from the level of other care required to the level of self-care required are secondary by appropriate implementation according to the level of care required The number of increase / decrease of the judgment needing care degree B is obtained.

  FIG. 9 shows the above relationship by applying specific numerical values. For example, in “Need Care 4”, as shown in FIG. 8, the number of cases in which the degree of care required may become mild due to optimization (C <B) is a severe change from primary to secondary (B> A ) 16 cases, no change (B = A) 52 cases, and mild change (B <A) 3 cases. In contrast, the number of cases where the degree of care required may become severe due to optimization (C> B) is 6 for severe changes from primary to secondary (B> A), and no change (B = A) 19 cases and 4 cases with minor changes (B <A). When these numbers are multiplied by the acceptance rate (50% as described above), as shown in FIG. 9, the number of changes in the minor direction (C <B) is 8 in the case of severe changes (B> A). If there is no change (B = A), there will be 26 cases, and if there is a minor change (B <A), there will be 2 cases. On the other hand, the number of changes in the severe direction (C> B) is 3 for severe change (B> A), 10 for no change (B = A), and 2 for mild change (B <A). Become.

  When the number of these cases is counted, in “Need Care 4”, the number of changes from the level of self-care required to another level of care required, that is, the number of minor direction changes to “Low Need 3” is −8−26. 2 = −36 cases, and the higher number of severe direction changes to “care-requiring 5” is −3−10−2 = −15 cases, which is a total of −51 cases. On the other hand, the number of changes from the level of other care required to the level of self-care required, that is, the number of minor changes from the higher “care required 5” is 8 + 14 + 0 = 22, and the severity from the lower “care required 3” The number of changes is 2 + 12 + 3 = 17, for a total of 39 cases. Therefore, when these are totaled, −51 + 39 = −12. That is, the number of increase / decrease cases from before the optimization of “care-required 4” to after optimization is −12, and the number of “care-required 4” necessary for the secondary determination requiring care B before optimization shown in FIG. The value obtained by subtracting the decrease number 12 is 239 after the optimization, as shown in FIG. When each degree of care required is totaled, the increase / decrease number from before optimization to after-optimization becomes −32, and the total number of secondary determination necessity care B before optimization shown in FIG. The subtracted value is 2116 cases after the implementation of optimization as shown in FIG.

  In the above description, the rank of the degree of care required that is changed to the mild direction and the severe direction after optimization is set to one rank. Actually, there may be a case where the secondary determination need-for-care level B before optimization is “rank 5 requiring care” and the need-for-care candidate C is two ranks different from “care required 3”. However, even if it is presented as “care-requiring 3” need-for-care candidate C, the judge who originally determined “care-required 5” and secondary-determination need-for-care B may immediately lower the 2-rank decision. Is very low. Therefore, the simulation rule was determined based on the assumption that the change in the mild direction and the severe direction due to optimization is only one rank movement.

  In this way, it is possible to estimate how the number of cases of secondary determination requiring care changes due to the implementation of the optimization before the implementation of the optimization, and to grasp the effect of the optimization.

  Next, the case of simulating how much the benefit cost can be reduced by performing the above-described optimization using FIG. 3 will be described.

  In FIG. 3, the benefit cost change calculation means 31 calculates the benefit cost change by using data 1505, 1506, 1507 held in the area of the simulation data 15. The calculated benefit cost change data 1610 is held in the calculation result data 16 area.

  The data 1505 is the actual number of care facility service users according to the degree of care required before optimization for a certain period (for example, the latest one year). Further, the data 1506 is the upper limit amount of benefit cost per person for each degree of care required in a preset period. Furthermore, the data 15076 is a rate of increase / decrease for each degree of care required from the number before the optimization of the secondary determination need for care B to the number after the optimization. This increase / decrease rate can be obtained by dividing the number of increase / decrease cases from before optimization to after optimization obtained in FIG. 2 by the number of secondary determination requiring care level B before optimization.

  In other words, the actual number of nursing care facility service users is multiplied by the rate of increase / decrease in the number of cases before and after optimization, to determine the number of people changing according to the level of care required due to the optimization of nursing facility service users. Based on the number of care facility service users that change and the upper limit of the benefit cost per person for each degree of care required for a certain period of time, a change in benefit cost for the certain period is obtained.

  In addition, the actual number of nursing care facility service users according to the degree of care required before optimization described above and the upper limit of benefit costs per person according to the level of care required are stored in the database 12 of the local government 11 shown in FIG. Based on the data, it is created as simulation data 15.

  FIG. 10 shows an example in which specific numerical values are applied to the above data and the like. For example, the actual number of persons requiring nursing care prior to optimization (data 1508) and the actual number of nursing care facility service users (data 1505) are calculated according to the degree of nursing care required. On the other hand, the total number of people in the latter year is 457 people. As for “Need Care 4”, the former is 224 people per year, while the latter is 113 people per year. As the degree of care required increases, the proportion of facility service users among the certified care recipients increases.

  As described above, the upper limit of the per-capita benefit cost (data 1506) for each degree of care required is a nationally defined amount, and the monthly amount per person is shown in the figure. For example, regarding “Need Care 4”, 306,000 yen / person per month is set as the upper limit. This amount is an upper limit, and the amount actually paid is lower. However, the benefit rate when using nursing care facilities is high, and it is assumed that nearly 100% will be paid. Looking at “Nursing Care 4”, the average benefit utilization rate including facility-based home care is 64.8%, and the annual benefit cost is 306000 yen x December x 224 people x 64.8% = 533 million yen. It becomes.

  The rate of increase / decrease according to the level of long-term care required from the number of cases prior to optimization of the secondary judgment need for long-term care B to the number of cases after optimization is shown as the number of changes from the level before 4 ”is obtained by dividing −12 cases) by the number of cases of the secondary determination necessity care level B before optimization (251 cases for“ care required 4 ”). As shown in FIG. 11, “Nursing Care 4” is −4.8% = − 12 ÷ 251. FIG. 11 shows the simulation results of the optimization implementation with specific numerical values for each degree of care required.

  By multiplying the actual reduction number of facility service users shown in FIG. 10 by the reduction rate obtained in this way, the number of changed people after the optimization of the facility service users is obtained. In “Need Care 4”, the actual number of facilities service usage before optimization is 113, and the rate of change is −4.8%, so the number of changes after optimization is −5.4 (−5 in FIG. 11). Is described). Furthermore, by multiplying the number of nursing care facility service users who have changed by the preset upper limit of the per capita benefit cost for each degree of care required (in the case of facility use, the benefit rate is assumed to be 100%). Changes in annual benefit costs are required. In “Nursing Care 4”, the number of people who have changed after optimization is -5.4 people, and the upper limit of benefits is 306000 yen, so 306000 yen x December x -5.4 people ≒ -20 million yen Become. That is, for “Need Care 4”, the result of the simulation is that 20 million yen is reduced annually due to the implementation of optimization.

  By using the same method, the annual amount of change is also obtained for other degrees of care required. In the example in the figure, it is estimated that a total of 78 million yen will be reduced by optimization.

  Here, in determining the change in benefit costs, the change in the number of facility service users is obtained, and this is multiplied by the upper limit of benefit for each degree of care required. This is because the benefit utilization rate is high and a value close to the upper limit is paid, and changes in the number of people directly affect it. On the other hand, the average benefit use rate for home care is around 50%, and even if the rank of the level of care required decreases by one level due to optimization, the actual service benefit cost is likely not to change. . In other words, the benefit cost reduction effect comes from the fact that the facility service only provides higher accuracy and the minimum line of benefit cost reduction effect can be grasped.

  However, more simply, like the benefit cost change calculation means 41 shown in FIG. 4, the actual number of persons requiring long-term care (data 1508) according to the degree of long-term care required for a certain period is set to Multiplying the rate of increase / decrease by the degree of care required (data 1510) from the number before the optimization to the number after the optimization to obtain the number of people changing by the level of care required by the optimization, the required care required Based on the average per capita benefit cost per degree, the amount of change in benefit cost over a certain period may be obtained.

  Next, the case of obtaining the adoption rate of the reference index analysis data will be described with reference to FIG. As described above, this acceptance rate is determined when the examiner receives the presentation of the candidate for need for nursing care C obtained by the reference index analysis and determines the need for care for secondary determination B as described above. It is a ratio representing how much this C need for care level C is employed. In the case of the first simulation, as shown in FIG. 9, for example, 50% is determined. However, the result of the simulation based on the acceptance rate and the result of the actual optimization (for example, the start of the implementation of the optimization) The actual acceptance data after 1 year) can be used to determine the acceptance rate at which high simulation accuracy is obtained.

  That is, the adoption rate calculation means 50 in FIG. 5 uses the data when past optimization is performed, and the actual number of cases of each secondary care level B for each nursing care level (for a certain period before the optimization is performed) ( Subtract the change value (data 503) from the data 501) to the actual number of cases (data 502) for each degree of care required for the secondary determination need care level B after a predetermined period (for example, one year) after the optimization. Obtained by means 51. Then, the change value (data 503) based on the actual measurement value is divided by the dividing means 52 by the number of cases in which the degree of care required can be changed due to the implementation of optimization (data 504) for each degree of care required before the implementation of the optimization. The adoption rate (data 505) for each degree of care required is obtained.

  Here, the actual number of cases (data 502) for each degree of care required of the secondary determination need for care B after a predetermined period in which the optimization has been performed is data corresponding to item number 4-0 in FIG. Obtained by actual measurement. That is, the data currently written in the item No. 4-0 in FIG. 9 is a value obtained by simulation, but the number of cases after optimization actually obtained by carrying out optimization was actually measured 1 Use the number of cases per year. A value obtained by multiplying the actual number of cases after optimization thus obtained by the number of natural increase corrections (<1.0), and a certain period before the implementation of optimization entered in item number 3-1 in FIG. The subtraction means 51 obtains the difference between the secondary determination nursing care degree B and the actual number of cases requiring care (data 501), and the change value (data 503) based on the actual measurement values (item number 4 in FIG. 9). -1).

  In addition, the number of possible changes in the level of care required due to the implementation of optimization (data 504) is required from the number of possible changes from the level of self-care required to the level of other care required and the level of other care required prior to the implementation of optimization. This is the total number of cases that can be changed to the degree of care. For example, in the case of “care required 4”, the number of cases (C <B) that may become mild due to optimization is 16 + 52 + 3 = 71 from the value in FIG. 8, and may become severe (C> B) The number of cases is 6 + 19 + 4 = 29 cases, and the total value of these 100 cases is the number of possibility of change from the self-care level to other care levels. In addition, the number of cases where there is a possibility of change from the level of other care required adjacent to the level of self-care required, that is, the number of cases that may become milder due to the optimization of “care required 5” (C <B) is 16 + 28 + 0 = 44 And the number of cases (C> B) that could become severe due to the optimization of “Nursing Care 3” is 3 + 24 + 5 = 32, and the total of these 76 cases is from the degree of other nursing care to the level of self-care The number of changeable cases. Then, −100 + 76 = −24 cases where these are totaled is the number of cases in which the degree of care need change possibility due to the implementation of optimization (data 504).

  If the change value (data 503) based on the actual measurement value is the same as -12 cases as in the simulation, the acceptance rate is 50% as before. For example, if the change value (data 503) based on the actual measurement value is −18, the acceptance rate in the next simulation is 75%. This adoption rate can be obtained for each degree of care required.

  Thus, the accuracy of the simulation can be improved by obtaining the acceptance rate for the next simulation based on the past results of optimization.

  The method of obtaining the acceptance rate is not limited to the above method, and may be as follows.

  In this case, a severe change (B> A), no change (B = A), and a minor change (B <A), which are comparison results of primary / secondary determination in a certain period (for example, one year) when optimization is not performed. Severe change (B> A), no change (B = A), mild change (B), which is a comparison result of primary / secondary judgment in a certain period (also 1 year) in which optimization was implemented <A) data is used. And about each data of these severe change (B> A), no change (B = A), and mild change (B <A), the comparison result of those secondary determination required care level B and the required care level candidate C The number of cases that may become mild (C <B), the number of cases that may become severe (C> B), and the number of cases that do not change (C = B) are obtained. Then, regarding the possibility of becoming mild (C <B) and the possibility of becoming severe (C> B), the ratio of the total including the number of cases where there is no change (C = B) is obtained. Furthermore, regarding the possibility of becoming mild (C <B) and the possibility of becoming severe (C> B), the difference between the ratio when the optimization is not performed and the ratio when the optimization is performed is obtained. The adoption rate is obtained by dividing this difference by the ratio when optimization has not been performed.

  FIG. 6 shows a case where data 601 for a certain period when optimization is not performed for a severe change (B> A) and data 602 for a certain period when optimization is performed are used. As the data 601, the data of “care required 2” shown in FIG. 8 is used. That is, the total number of severe changes (B> A) when optimization has not been performed is 146 cases. Of these, 117 cases are likely to be mild (C <B), 3 cases are likely to be severe (C> B), and 26 cases are unchanged (C = B). On the other hand, in the data 602 for a certain period in which the optimization is performed, for example, the total number of severe changes (B> A) is 128. Of these, 60 cases are likely to be mild (C <B), 2 cases are likely to be severe (C> B), and 66 cases are unchanged (C = B). Then, the proportion of each possibility is calculated. As for the possibility of becoming mild (C <B), it was 80% when optimization was not implemented, but it was 47% when optimization was implemented. Therefore, a difference (33%) is taken by the subtracting means 61 for these ratios. This difference is assumed to be caused by carrying out optimization and referring to the presented candidate C for nursing care degree. Therefore, by dividing this difference (33%) by the ratio (80%) in which optimization has not been performed by the dividing means 62, a future appropriate adoption rate (41%) can be obtained.

  The above explanation has been made with regard to the possibility of becoming severe (C <B) of the severe change (B> A) in “Need Care 2”, but each possibility if it is made with regard to the possibility of becoming severe (C> B) In the future, an appropriate adoption rate can be obtained. Of course, the adoption rate can be obtained by the same method for other no changes (B = A), minor changes (B <A), and other classes requiring care.

  Thus, each degree of care required, severe change (B> A), no change (B = A), mild change (B <A), and the possibility of becoming mild (C <B), becoming severe Since the acceptance rate can be obtained for each possibility (C> B), a more accurate simulation is possible.

  The method of obtaining the acceptance rate can be considered in addition to the above, and may be as follows.

  That is, data on the handling of the reference index when performing the secondary determination is collected by a questionnaire to the committee members when past optimization is performed. Then, using this data, whether or not the review committee members place importance on the reference index, whether the primary determination data is analyzed by the same analysis method as the reference index at the time of the secondary determination, and the above analysis And classify the ratio of the number of people in each category. Then, an acceptance rate is obtained by referring the member of each category to the need-for-care candidate C based on the reference index analysis, and from these data, the average acceptance rate of the need-for-care candidate C in the examination committee is obtained. It is calculated and output as the acceptance rate for each degree of care required in the reference index analysis data for the next simulation.

  For example, the acceptance rate calculating means 70 shown in FIG. 7 has already selected the members of the examination committee from the aggregated data based on the questionnaire for the examination committee members when the past optimization has been carried out, at the time of the secondary determination with an emphasis on the reference index. The member a (data 701) performing the same analysis and the reference index are emphasized, but the analysis at the time of the secondary determination is slightly insufficient member b (data 702), and the reference index is emphasized. The member c (data 703) that is completely insufficient in the analysis at the next determination is classified into the member d (data 704) that ignores the reference index and performs the original analysis at the secondary determination. For example, the constituent ratios of the members a, b, c, and d are a = 10%, b = 50%, c = 30%, and d = 10%.

  In addition, the adoption rate is set for each of the members a, b, c, and d when the candidate for need for nursing care C by reference index analysis is referred. For example, the member a attaches great importance to the reference index, and sufficiently performs the same analysis at the time of the secondary determination. Therefore, even if the candidate for need for nursing care C based on the reference index analysis is presented, the possibility of adopting it is low. Assume that the adoption rate of member a is 10% (data 710). Member b attaches great importance to the reference index, but the analysis at the time of the secondary determination is somewhat insufficient, so when C-care candidate C is presented, the possibility of adopting it is relatively high. The acceptance rate of b is 50% (data 720). The member c attaches great importance to the reference index, but the analysis at the time of the secondary determination is quite inadequate. Therefore, when the candidate C requiring nursing care is presented, the possibility of adopting it is extremely high. The adoption rate of 80% is assumed to be 80% (data 730). The member d ignores the reference index and performs its own analysis at the time of the secondary determination. Therefore, even if the care need candidate C is presented by the reference index analysis, the member d is unlikely to adopt it. Assume that the adoption rate of member c is 10% (data 740).

  Using each of these data, the average adoption rate of this jury is obtained. That is, the composition ratio of each member is multiplied by the adoption rate, and the sum of the multiplication results is obtained as the average adoption rate (data 750). In the above example, 10% × 10% + 50% × 50% + 30% × 80% + 10% × 10% = 51% is the average acceptance rate, and this value is determined as the acceptance rate in this examination committee.

  Thus, by determining the acceptance rate based on the member composition of the examination committee, a highly accurate simulation can be performed for each examination committee.

It is a conceptual diagram which shows the example of creation of the data for simulation used for the nursing care degree authorization simulation system by this invention. 1 is a system block diagram showing an embodiment of a nursing care level authorization simulation system according to the present invention. It is a figure which shows the numerical example of the simulation result after the optimization used for this invention. It is a system block diagram which shows other embodiment which calculates | requires the part for payment cost change in this invention. It is a system block diagram which shows embodiment which calculates | requires the acceptance rate in this invention. It is a system block diagram which shows other embodiment which calculates | requires the acceptance rate in this invention. It is a system block diagram which shows other embodiment which calculates | requires the acceptance rate in this invention. It is a figure which shows the numerical example of the analysis data before optimization used for this invention. It is a figure which shows the numerical example of the simulation data after the optimization used for this invention. It is a system block diagram which shows embodiment which calculates | requires the part for payment cost change in this invention. It is a figure which shows the numerical example of the basic data before optimization used for this invention.

Explanation of symbols

11 Local government 12 Database 13 Local government computer 14 Analysis software 15 Simulation data 21 Pre-adjustment judgment change number calculation means 22 Changeable number calculation means 23 Post-optimization change number calculation means 24 Post-optimization number calculation means 31, 41 Benefit costs Change calculation means 50, 60, 70 Acceptance rate calculation means

Claims (12)

When determining the secondary judgment need for nursing care B from the primary judgment need for nursing care A based on the certification survey results in long-term care insurance, the need for long-term care by reference index analysis using the certification survey results and past authorization data An authorization optimization simulation system for estimating how the degree of care required for secondary determination B changes when optimization is performed by referring to the degree candidate C.
Using the data of the primary determination nursing care degree A for each authorized person certified for a certain period of time and the secondary judgment nursing care degree B before the optimization, the secondary determination nursing care degree B is required to be primary judgment. Number of severe changes (B> A) changed in a more severe direction than the degree of care A, the number of secondary judgment requiring care B equals the degree of primary judgment requiring care A (B = A), the number of secondary judgment requiring care A pre-optimization determination comparing means for determining the number of minor changes (B <A) in which the degree B is changed in a milder direction than the primary judgment requiring care degree A;
About the serious change before the optimization (B> A), no change (B = A), and the minor change (B <A), the secondary judgment need for care B before the optimization and the need for care required C The number of cases in which the secondary decision requiring care B is more severe than the need for nursing care candidate C and may become lighter due to the above optimization (C <B), the degree of need for secondary decision requiring care B is A change possibility calculation means for obtaining the number of cases that are milder than the degree C and that may become severe due to the optimization (C> B) for each degree of care required;
Severity change before optimization (B> A), no change (B = A), and minor change (B <A) may be mild by the optimization calculated for each degree of care required (C < B) Based on the number of cases and the possibility of becoming severe (C> B), the adoption rate of the reference index analysis data set in advance is used to change the degree of care required in a mild direction by the optimization ( C <B) Number of cases and changes in the severe direction (C> B) Number of cases for each degree of care required for the severe change (B> A), no change (B = A), and mild change (B <A) A means for calculating the number of changes due to optimization, and
By using the number of changes in the light direction (C <B) and the number of changes in the severe direction (C> B) determined for each degree of care required by the means for calculating the number of changes by this optimization, Calculate the number of changes from the level of self-care required to the level of other care required and the number of changes from the level of other care required to the level of self-care required to determine the number of changes for each level of care required. An authorized optimization simulation system comprising: a post-optimization case number calculating means for obtaining the number of cases of secondary judgment requiring nursing care B after optimization using the number of changes.   The actual number of care facility service users according to the degree of care required before optimization in the fixed period obtained in advance, the degree of care required from the number before the optimization of the secondary judgment need for care B to the number after optimization Multiply by another rate of change to find the number of people changing by degree of care required due to the optimization of care facility service users. The authorization optimization simulation system according to claim 1, further comprising: a benefit cost change calculation unit that calculates a change in benefit cost during the certain period from the number of people changing according to the degree of care required.   For the actual number of persons requiring long-term care required by the degree of long-term care required in the predetermined period, the rate of increase / decrease by degree of long-term care from the number before the appropriateness of the secondary determination need for long-term care B to the number after the appropriateization Multiply to find the number of people changing by the degree of care required due to the appropriateness of the person requiring care, and based on the average amount of benefit costs per person for each degree of care required in the given period, the degree of care required The authorized optimization simulation system according to claim 1, further comprising a benefit cost change calculation unit that obtains a change in benefit cost during the certain period from another changing number of people.   Based on the number of cases for each degree of care required for the degree of care required for secondary determination B during a certain period of time prior to the implementation of the optimization, the data after the previous optimization was applied. Divide the change value of the next judgment required nursing care level B into the actual number of nursing care needs by the degree of nursing care required, and divide it by the number of cases where the need for nursing care can be changed due to the implementation of optimization for each degree of nursing care required. The accreditation optimization simulation system according to any one of claims 1 to 3, further comprising an acceptance rate calculating means for obtaining an acceptance rate for each degree of care required of the reference index analysis data for improving accuracy. Secondary judgment for severe changes (B> A), no changes (B = A), and minor changes (B <A), which are comparison results of primary / secondary judgments over a certain period when optimization is not performed The change between the possibility of becoming mild (C <B) and the possibility of becoming severe (C> B) as a result of comparison between the degree of care required B and the need for care required C Data for each percentage of the total including none (C = B),
Secondary determination required for severe changes (B> A), no changes (B = A), and minor changes (B <A), which are comparison results of primary / secondary judgments over a certain period of time when optimization was implemented There is no change in the possibility of becoming mild (C <B) and the possibility of becoming severe (C> B) by referring to the candidate for care degree C, which is a comparison result between the care degree B and the need for care level C. (C = B) and the ratio of the total to the total data
Number of cases that may become mild (C <B) and may become severe (C> B) during the non-optimization period, and number of cases (C <B) that may become mild during the implementation period And the difference from the number of cases (C> B) that become severe (C> B) is obtained for each possibility (C <B) (C> B), and these values become mild in the non-optimized period. 2. An arithmetic means for dividing the number of possibilities (C <B) and the possibility of becoming severe (C> B) by the corresponding value and determining the acceptance rate based on the result. The certification optimization simulation system according to any one of 1 to 3. Using data collected through questionnaires to members of the judging committee,
Depending on whether the members of the review committee place importance on the reference index, whether the primary determination data is analyzed by the same analysis method as the reference index at the time of the secondary determination, and how well the above analysis is performed When divided, the ratio of the number of members for each division,
From the acceptance rate by referring to the candidate for need for nursing care C by reference index analysis of the member for each category,
2. An arithmetic means for obtaining an average adoption rate of the need for nursing care degree C in the examination committee and outputting as an acceptance rate for each nursing care degree of reference index analysis data for the next simulation. The certification optimization simulation system according to any one of 1 to 3. When determining the secondary judgment need for nursing care B from the primary judgment need for nursing care A based on the certification survey results in long-term care insurance, the need for long-term care by reference index analysis using the certification survey results and past authorization data A computer-readable storage medium that records an accreditation optimization simulation program that estimates how the degree of care required for secondary determination B changes when the optimization is performed by referring to the degree candidate C.
Using the data of the primary determination nursing care degree A for each authorized person certified for a certain period of time and the secondary judgment nursing care degree B before the optimization, the secondary determination nursing care degree B is required to be primary judgment. Number of severe changes (B> A) changed in a more severe direction than the degree of care A, the number of secondary judgment requiring care B equals the degree of primary judgment requiring care A (B = A), the number of secondary judgment requiring care A pre-optimization determination comparison step for determining the number of minor changes (B <A) in which the degree B has been changed in a milder direction than the primary judgment requiring care degree A;
About the serious change before the optimization (B> A), no change (B = A), and the minor change (B <A), the secondary judgment need for care B before the optimization and the need for care required C The number of cases in which the secondary decision requiring care B is more severe than the need for nursing care candidate C and may become lighter due to the above optimization (C <B), the degree of need for secondary decision requiring care B is A change possibility calculation step for obtaining the number of cases (C> B) that is milder than the degree candidate C and may become severe due to the optimization (C> B),
Severity change before optimization (B> A), no change (B = A), and minor change (B <A) may be mild by the optimization calculated for each degree of care required (C < B) Based on the number of cases and the possibility of becoming severe (C> B), the adoption rate of the reference index analysis data set in advance is used to change the degree of care required in a mild direction by the optimization ( C <B) Number of cases and changes in the severe direction (C> B) Number of cases for each degree of care required for the severe change (B> A), no change (B = A), and mild change (B <A) The number of changes due to the optimization required,
By using the number of changes in the light direction (C <B) and the number of changes in the severe direction (C> B) determined for each degree of care required by the means for calculating the number of changes by this optimization, Calculate the number of changes from the level of self-care required to the level of other care required and the number of changes from the level of other care required to the level of self-care required to determine the number of changes for each level of care required. A computer-readable storage medium storing an authorized optimization simulation program characterized by comprising: a post-optimization count calculation step for obtaining a post-optimization secondary decision requiring nursing care degree B using the number of changes .   The actual number of care facility service users according to the degree of care required before optimization in the fixed period obtained in advance, the degree of care required from the number before the optimization of the secondary judgment need for care B to the number after optimization Multiply by another rate of change to find the number of people changing by degree of care required due to the optimization of care facility service users. 8. A computer recording an accreditation optimization simulation program according to claim 7, further comprising a benefit cost change calculation step for obtaining a change in benefit cost during the predetermined period from the number of people changing according to the degree of care required. A readable storage medium.   For the actual number of persons requiring long-term care required by the degree of long-term care required in the predetermined period, the rate of increase / decrease by degree of long-term care from the number before the appropriateness of the secondary determination need for long-term care B to the number after the appropriateization Multiply to find the number of people changing by the degree of care required due to the appropriateness of the person requiring care, and based on the average amount of benefit costs per person for each degree of care required in the given period, the degree of care required 8. The computer-readable storage medium storing the accreditation optimization simulation program according to claim 7, further comprising a benefit cost change calculation step for obtaining a change in benefit cost during the predetermined period from another change number of people.   Based on the number of cases for each degree of care required for the degree of care required for secondary determination B during a certain period of time prior to the implementation of the optimization, the data after the previous optimization was applied. Divide the change value of the next judgment required nursing care level B into the actual number of nursing care needs by the degree of nursing care required, and divide it by the number of cases where the need for nursing care can be changed due to the implementation of optimization for each degree of nursing care required. The accreditation optimization simulation program according to any one of claims 7 to 9, further comprising an acceptance rate calculation step for obtaining an acceptance rate for each degree of care required of the reference index analysis data for improving accuracy. A computer-readable storage medium. Secondary judgment for severe changes (B> A), no changes (B = A), and minor changes (B <A), which are comparison results of primary / secondary judgments over a certain period when optimization is not performed The change between the possibility of becoming mild (C <B) and the possibility of becoming severe (C> B) as a result of comparison between the degree of care required B and the need for care required C Data for each percentage of the total including none (C = B),
Secondary determination required for severe changes (B> A), no changes (B = A), and minor changes (B <A), which are comparison results of primary / secondary judgments over a certain period of time when optimization was implemented There is no change in the possibility of becoming mild (C <B) and the possibility of becoming severe (C> B) by referring to the candidate for care degree C, which is a comparison result between the care degree B and the need for care level C. (C = B) and the ratio of the total to the total data
Number of cases that may become mild (C <B) and may become severe (C> B) during the non-optimization period, and number of cases (C <B) that may become mild during the implementation period And the difference from the number of cases (C> B) that become severe (C> B) is obtained for each possibility (C <B) (C> B), and these values become mild in the non-optimized period. 8. An operation step of dividing by the corresponding value of the number of possibility (C <B) cases and the possibility of becoming severe (C> B) and determining the acceptance rate based on the result. A computer-readable storage medium recording the accreditation optimization simulation program according to any one of 1 to 9. Using data collected through questionnaires to members of the judging committee,
Depending on whether the members of the review committee place importance on the reference index, whether the primary determination data is analyzed by the same analysis method as the reference index at the time of the secondary determination, and how well the above analysis is performed When divided, the ratio of the number of members for each division,
From the acceptance rate by referring to the candidate for need for nursing care C by reference index analysis of the member for each category,
8. An operation step of obtaining an average adoption rate of the care-requiring degree candidate C in the examination committee and outputting it as a adoption rate for each care-requiring degree of reference index analysis data for the next simulation. A computer-readable storage medium recording the accreditation optimization simulation program according to any one of claims 9 to 9.

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