JP2017215787A - Community-based integrated care service system and community-based integrated care service promotion method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a community-based integrated care service system which integrally operates various operations and services including a care insurance service in an autonomous body and a community-based integrated care service promotion method.SOLUTION: By using a database 10 in which basic data, care insurance data, medical insurance data on each resident held by an autonomous body and particular data including measures taken by the autonomous body are integrated, a current state analysis part 31 calculates values of a plurality of indexes preset in order to achieve objectives of a specific measure for each calculation unit and captures the magnitudes of differences and deviations from the average among the calculation units for each index on the basis of the value of each calculated index. An issue extraction unit 32 extracts an index presenting a large difference and calculation unit(s) having a large number of indexes presenting large deviations from the averages on the basis of the magnitudes of the differences obtained by the current state analysis part. The community-based integrated care service system has a check system to draw up improvement measures for indexes presenting large differences and for calculation unit(s) having a large number of indexes presenting the large negative deviations from the average.SELECTED DRAWING: Figure 3

Description

  Embodiments of the present invention relate to a community comprehensive care business system and a community comprehensive care business promotion method for executing a community comprehensive care business including a care insurance business performed in a local government.

  In general, municipalities such as municipalities have basic data such as addresses of residents for realizing various operations and businesses closely related to the lives of residents. In addition, the local government has a long-term care insurance business and has a lot of data related to long-term care insurance. In addition, various data on medical insurance is available. In addition to these routine tasks and businesses, specific measures related to the care of residents are implemented, and specific data such as measures are generated and retained.

  Each business and business described above is performed independently and individually, and the various types of data associated therewith are also individually managed for each business and business.

Japanese Patent No. 5567046

  In each business and business described above, for example, the nursing care insurance business and the medical-related business have many parts related to each other, and it is desirable that data regarding each business and business can be used mutually. However, until now, each business and business has been carried out independently and the various data associated with each business and business has been managed separately for each business and business. It was not done.

  It is an object of the present invention to provide a comprehensive community care business system and a comprehensive community care business promotion method that comprehensively implement and operate various businesses and businesses including a long-term care insurance business in a local government.

  The regional comprehensive care business system according to the embodiment of the present invention includes a database in which basic data related to inhabitants owned by a local government, nursing care insurance data, medical insurance data, and unique data including measures executed by the local government are unified. Using the data for a predetermined period held in the database, a plurality of index values set in advance for achieving a predetermined measure are calculated for each aggregation unit, and the calculated index-specific values are used for each index. The current status analysis unit that captures the difference between the aggregation units and the deviation from the average, and extracts the index with a large difference from the magnitude of the difference obtained by the current status analysis unit, and the deviation from the average And a task extraction unit that extracts the aggregation unit having a large number of large indicators, formulates improvement measures for the large disparity indicators, and has many large indicators that are poorly deviated from the average With a check system to develop an improvement plan to the total units.

It is a schematic diagram explaining the outline | summary of the community comprehensive care business system which concerns on embodiment of this invention. It is a figure explaining the PDCA cycle implemented by embodiment of this invention. It is a figure which shows the structure of the check system used by embodiment of this invention. It is a figure which shows the structure of the measure formulation system used by embodiment of this invention. It is a figure which shows the structure of the business plan formulation system used by embodiment of this invention. It is a figure which shows the structure of the measure execution system used by embodiment of this invention. It is a figure for demonstrating the N quantile calculation method used by embodiment of this invention. It is a figure which shows the attention parameter | index list used by embodiment of this invention. It is a figure which shows the relationship between the business area | region and user in embodiment of this invention. It is a figure which shows the relationship between the analysis object data source according to business area and total unit in embodiment of this invention. It is the figure which summarized the present condition analysis and subject extraction result about the "mental and physical condition maintenance / improvement measure" in the business area "preventive benefit / nursing benefit business" used in the embodiment of the present invention. It is a figure which shows the effective measure extraction result about the "mental and physical condition maintenance / improvement measure" in the business area "preventive benefit / nursing care benefit business" used in the embodiment of the present invention. It is a figure which shows the present condition analysis and subject extraction result about "the maintenance / improvement measure of dementia (severity prevention measure)" in business field "dementia measure". It is a figure which shows the effective measure extraction result about "maintenance / improvement measure of dementia (severity prevention measure)" in business field "dementia measure". It is a figure which shows the present condition analysis and problem extraction result about "prevention and early detection measure of dementia" in business field "dementia measure". It is a figure which shows the effective policy extraction result about "prevention and early detection measure of dementia" in business field "dementia measure". It is a figure which shows the present condition analysis and subject extraction result about "care prevention and life support measure for authorized persons" in business field "care prevention and daily life support comprehensive business (new comprehensive business)". It is a figure which shows the effective policy extraction result about "care prevention and life support measure for authorized persons" in business field "care prevention and daily life support comprehensive business (new comprehensive business)". It is a figure which shows the present condition analysis and problem extraction result about "care prevention and life support measure for non-certified persons" in business field "care prevention and daily life support comprehensive business (new comprehensive business)". It is a figure which shows the effective policy extraction result about "the nursing prevention / life support measure for the non-certified person" in the business field "the care prevention / daily life support comprehensive business (new comprehensive business)". It is a figure which shows the present condition analysis and subject extraction result about "medical care cooperation concerning hospitalization support" in business field "home medical care and long-term care cooperation promotion business". It is a figure which shows the effective policy extraction result about "medical care cooperation concerning hospitalization discharge support" in business field "home medical care and care cooperation promotion business". FIG. 10 is a diagram showing another display example corresponding to the display example shown in FIG. 9. It is the figure which summarized the result of total unit monitoring

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

  As described above, the present invention is to obtain a regional comprehensive care business system and a comprehensive local care business promotion method that comprehensively implements and operates various businesses and businesses including a long-term care insurance business in a local government. As described above, the database 10 that unifies data related to various businesses and businesses is created. FIG. 1 is a schematic diagram showing the data types configured in the database 10 and the relationship between the database 10 and the solution function 15 provided by the regional comprehensive care business system and the target business area 16 of the regional comprehensive care business.

  As described above, the work of local governments such as municipalities is to realize various businesses closely related to the lives of residents, and as shown in FIG. ) 112, regional characteristic data 113, regional specific case examples 114, and the like. In addition, the local government conducts a long-term care insurance business, and as the long-term care insurance data 12, data such as long-term care certification data 121, long-term care receipt data 122, and long-term care establishment ledger 123 are held. Furthermore, as the data 13 related to the medical insurance, data such as the elderly specific medical examination data 131, the national health insurance / late elderly medical receipt data 132, the medical institution ledger 133, and the like are also available.

  The various operations and businesses described above are fixed-type data that has been implemented for many years, and the various data 11, 12, and 13 are managed in an individual but fixed format.

  In addition to these, local governments are implementing measures related to the care of residents specific to local governments. As specific data 14 related to such measures, basic checklists (daily life sphere needs survey results) 141, small regions and businesses・ Data such as measure plans and results 142 for users are generated and held. These measures-specific data 14 are in various formats according to each measure, and are currently stored in a unique format for each department in charge.

  Therefore, in the embodiment of the present invention, the database 10 in which these data 11, 12, 13, and 14 are unified is created.

  That is, in this database 10, the related parts of these data 11, 12, 13, and 14 are encoded with a unique number for identifying an individual or a business operator, or a code obtained by encrypting a unique number for identifying an individual or a business operator ( For example, they are linked and unified by a conversion code by hash conversion. For this reason, the database 10 which is the base for analysis in which the basic data 11, the care-related data 12, the medical-related data 13, and the specific data 14 such as measures are unified is obtained. Further, by creating the database 14 of the unique data 14 such as the measures unique to the local government, it is possible to quantitatively verify the effect of implementing the measures. In particular, it is possible to quantitatively verify the state of small regions, businesses, and users, and the effectiveness of implementing various measures that have been implemented, which has been difficult in the past.

  The comprehensive community care business has a plurality of business areas 161, 162, 163, 164, 165, and 166 as target business areas 16 as shown in the figure, and various business areas 161, 162, 163, 164, 165, and 166 have various types. Measures are implemented. For this reason, indices for achieving these measures are set for each business area 161, 162, 163, 164, 165, 166, and the solution function 15 has an index menu 151 in which these indices are registered.

  The solution function 15 is realized by a computer, and uses the data 11, 12, 13, and 14 unified in the database 10, and as will be described later, the business areas 161, 162, 163, 164, 165, and so on. The achievement status and implementation status of the measures executed in 166 are analyzed by collecting the values of the indices set for each measure. For this purpose, a totaling unit 152 and various analysis functions 153 are provided. That is, the solution function 15 functions as the main body of each system in each phase of PDCA described later. The index menu 151, the aggregation unit 152, and the various analysis functions 153 in the solution function 15 are described with respective unique numbers in the systems 21, 22, 23, and 24 described later that execute the phase PDCA. .

  Here, the comprehensive community care business is implemented by a PDCA cycle comprising a business plan formulation phase P, a measure execution phase D, a problem extraction phase C, and a measure development phase A as shown in FIG. That is, each phase PDCA is executed by the database 10 shown in FIG. 1 and the systems 21, 22, 23, and 24 (shown in FIGS. 3 to 6) by computers that function for each phase PDCA.

  Normally, the PDCA cycle literally starts from the business plan development phase P, but since the nursing care insurance business and the medical insurance business have already been implemented individually, in the embodiment of the present invention, Start from issue extraction phase C.

  First, the check system 21 that executes the task extraction phase C will be described. As shown in FIG. 2, the problem extraction phase C is a current state analysis function. It has an issue extraction function and a priority measure identification function. In order to realize these functions, the check system 21 includes a current state analysis unit 31 and a problem extraction unit 32 (corresponding to the analysis function 153 in FIG. 1), as shown in FIG.

  Here, the comprehensive community care business has a plurality of business areas 161, 162, 163, 164, 165, and 166 as described with reference to FIG. 1, and executes a predetermined measure for each business area. In order to achieve these measures, indices are set and registered in the index menu 211 (corresponding to the index menu 151 in FIG. 1).

  The current status analysis unit 31 described above indicates the degree of achievement of a measure based on a plurality of indexes set in advance to achieve a predetermined measure, for each count unit registered in the count unit menu 212 (corresponding to the count unit 152 in FIG. 1). From the calculated achievement value for each index (hereinafter referred to as the index value), the difference between the aggregation units and the magnitude of the deviation from the average are captured for each index. Further, the task extraction unit 32 extracts an index having a large disparity from the magnitude of the disparity obtained by the current state analysis unit 31, and extracts an aggregation unit having many indexes having a large deviation from the average.

  Hereinafter, these detailed functions will be described.

  The current state analyzing unit 31 calculates the index value for each index registered in the index menu 211 by the counting unit 311 for each counting unit. This aggregation is executed using the basic data 11, the care insurance data 12, the medical insurance data 13, and the unique data 14 regarding the residents of the centralized municipality that the database 10 has. In addition, the total unit is determined according to the contents of the index by a small area or business operator who performs comprehensive community care registered in the total unit menu 212 or a user of the comprehensive community care business.

  The aggregation unit 311 captures the difference between the aggregation units and the magnitude of the deviation from the average of the index values by the analysis unit 312 from the index values calculated for each index for each index.

  The disparity between the aggregation units is calculated for each index by the disparity calculating unit 313 using the maximum value and the minimum value of the index values calculated for each aggregation unit as the difference = maximum value / minimum value. At this time, the average value, standard deviation, value range width, and the like are also calculated. The calculated disparity values for each index are collected as a disparity list 314.

  The magnitude of deviation from the average is obtained by the N quantile calculation unit 315. The function of this N quantile calculation unit 315 will be described below.

  Here, it is assumed that there are 200 practitioners of a community-based comprehensive care business of a local government, for example, business managers such as a care management organization that implements a long-term care insurance business (hereinafter, described as an aggregation unit). In addition, it is assumed that a certain index A has a maximum value of 35% and a minimum value% of 5 for each counting unit. Therefore, the N quantile in FIG. 7 is set by dividing the distribution from 5% (minimum value) to 35% (maximum value) into N equal parts. That is, the distribution of the 200 aggregation units is equally divided into N, and the first N quantile and the (N-1) N quantile in the index A are determined.

  That is, out of 200 aggregation units, if the value of the index A of the aggregation unit of the first N quantile (for example, the first decile) is 10%, the first N quantile in this case is 10%. In addition, if the value of the index A of the (N-1) Nth quantile (for example, the 9th decile) of the aggregation units of 200 is 30%, the (N-1) th The N quantile is 30%.

  The N quantile calculation unit 315 further specifies a unit of aggregation included in the range of the first N quantile or the range of the NN quantile among the plurality of aggregate units, obtains the number thereof, and calculates these as the first N quantile. The rank list 316 and the NN quantile list 317 are collected. The range of the first N quantile is a range where the index A is greatly deviated in a small direction, and the range of the NN quantile is a range where the index A is largely deviated in a large direction. Therefore, the first N quantile range or the aggregation unit included in the NN quantile range is an aggregation unit in which the index A greatly deviates from the average.

  Note that, when the calculated difference is less than or equal to the set value, or when the standard deviation is less than or equal to the set value, or the range width (= maximum value−minimum value) and the interquartile range (= third fourth range). When the ratio between the quantile and the first quartile is equal to or less than the set value, the above-described divergence analysis by the N quantile calculation unit 315 is not performed. Furthermore, when the ratio of “median-minimum value” and “median-first quartile” is less than or equal to the set value, the deviation analysis in the direction of smaller values is not performed, and “maximum value-median” When the ratio of “third quartile-median” is equal to or smaller than the set value, it is possible to configure so that the divergence analysis in the direction of larger values is not performed.

  The task extraction unit 32 extracts an index having a large disparity from the magnitude of the disparity obtained by the current state analysis unit 31 as described above, and extracts an aggregation unit having many indexes having a large deviation from the average. Therefore, a disparity extraction unit 321 and a divergence extraction unit 322 are included.

  A threshold for determination is set in the inequality extraction unit 321, the inequality for each index calculated by the inequality calculation unit 313 and output to the inequality list 314 is taken in, and the inequality is compared with the threshold value. Indexes exceeding 1 are extracted and summarized as a large disparity index list 325.

  The divergence extraction unit 322 takes in the first N quantile list 316 and the NN quantile list 317 output from the N quantile calculation unit 315, and for each index, the range of the first N quantile or the range of the NN quantile. The best practice candidate calculation unit 323 identifies the best practice candidate from the aggregation units included in the table, and the priority guidance target candidate calculation unit 324 identifies the priority guidance target candidate. These results are collected as a best practice candidate list 326 and a priority guidance target candidate list 327.

  For example, the best practice candidate calculation unit 323 arranges the aggregation units in the descending order of the number of indices included in the first N quantile range of each index, and calculates the aggregation units included in the first N quantile range of the arranged aggregation units. A best practice candidate list 326 is created as a best practice candidate. The priority guidance target candidate calculation means 324 arranges the aggregation units in descending order of the number of indices included in the NN quantile range of each index, and the aggregation units included in the NN quantile range of the arranged aggregation units. As a priority guidance target candidate, a priority guidance target candidate list 327 is created.

  In addition, as for the index, the index with the larger index value is better, the index with the smaller index value is better (for example, benefit cost rate, etc.), and the index is irrelevant whether the index value is large or small There is. Therefore, by providing these information to the first N quantile list 316 and the NN quantile list 317, the aggregate unit information of the corresponding index is obtained as the best practice candidate calculation unit 323 and the priority guidance target candidate calculation unit 324. Can be sent to properly.

  That is, of the information in the first N quantile list 316, the aggregation unit information of the index with the smaller value is sent to the best practice candidate calculation means 323, and the aggregation unit information of the index with the smaller value is the priority guidance target It is sent to the destination candidate calculation means 324. In addition, among the information in the range of the NN quantile, the aggregation unit information of the index with the larger value is sent to the best practice candidate calculation means 323, and the aggregation unit information of the index with the smaller value is the priority guidance target It is sent to the destination candidate calculation means 324.

  The best practice candidate calculation unit 323 aggregates the sent information, summarizes the aggregation units and the indexes as the best practice, and outputs them to the best practice candidate list 326. Further, the priority guidance target candidate calculation means 324 aggregates the sent information, summarizes the aggregation units that are the priority guidance target candidate and its index, and outputs them to the priority guidance target candidate list 327.

  By specifying the higher percentage (percentile) of deviations in the positive direction or the lower percentage (percentile) of deviations in the bad direction, apart from the above-mentioned first N quantile range or NN quantile range, It is also possible to calculate “an index with a large deviation in a good direction” or “an index with a large deviation in a bad direction”.

  In addition to the range of the first N quantile, “minimum value” and “average value−standard deviation × a” (where a is an arbitrary positive number, for example, an arbitrary setting from 0.8 to 3.0 It is also possible to extract a list of index values included in the first quantile list 316 from an average value by a divergence value (a divergence degree that is not a relative rank). Similarly, apart from the range of the NN quantile, “average value + standard deviation × b” (b is a positive set value, for example, an arbitrary set value from 0.8 to 3.0) and “maximum It is also possible to set a range by two numerical values, “value”, and extract a list of index values included in the NN quantile list 317 from an average value by a divergence value (a divergence degree that is not a relative rank). is there.

  Further, the attention index list 328 shown in FIG. 8 can be created using these pieces of information. This attention index list 328 is a collection of “index with a large deviation in a good direction” and “an index with a large deviation in a bad direction” for each centralized unit.

  Here, a correlation coefficient is calculated between “an index with a large deviation in a good direction” and “an index with a large deviation in a bad direction” for each aggregation unit, and the absolute value of the correlation coefficient is a set value (from 0.3 to 1). Any number of combinations up to .0) can be extracted and displayed with emphasis, or a list of such combinations for each aggregation unit can be created.

  FIG. 11 shows a display example in which the analysis results obtained by the current status analysis unit 31 and the problem extraction unit 32 are summarized. Fig. 11 summarizes the current status analysis and problem extraction results for "physical and mental condition maintenance / improvement measures" in the business area "preventive benefit / nursing care benefit business". The indicators for achieving the above measures are listed in the vertical axis direction. In the horizontal axis direction, tabulation units (place care offices (place care) 1 to 5 for executing this measure, place rehabilitation places 1 to 5) are listed. In the cell where the vertical axis and the horizontal axis intersect, the index value calculated for each aggregation unit is written for each index. In addition, the average value and the disparity of these index values are also described in the cell for each index. In the display of the disparity, an indicator with a large disparity (= maximum value / minimum value) is displayed by color, for example, an orange background cell indicator and a yellow indicator with a medium disparity.

  In this way, the index to be corrected becomes clear as shown by the display column A. In addition, the value of the index greatly deviating from the average in a good direction displays the cell background in blue, for example. In this way, as shown by the display column B, it is possible to easily determine that there is a possibility that a business with a large blue index value may be a best practice candidate. On the other hand, the index value that is greatly deviated from the average in the bad direction displays the cell background in orange. In this way, as shown by the display column C, it is possible to easily determine that there is a possibility that a business with a lot of orange may be a candidate for priority guidance target.

  Here, the regional comprehensive care business has a plurality of business areas 161 to 166 as described above, and each implements various measures. The relationship between this business area and users is shown in FIG. FIG. 9 shows the relationship between users and the examples of efforts in each business area aimed at maintaining and improving the health and mental and physical state of the elderly.

  In FIG. 9, each business area is shown in the vertical axis direction, and the mental and physical state of the user is shown in the horizontal axis direction. From the left side, the users list two groups of non-certified people, healthy people and frail people. The two groups on the right side are authorized persons (support required) and authorized persons (care required). In other words, the more the user goes to the right side of the figure, the more the user is in a mental and physical state.

  In FIG. 9, for example, regarding the business area 161 “nursing care insurance business plan”, the measure “preventive benefit service regional resource plan” is for authorized persons (support is required), and the measure “nursing benefit service regional resource plan” Qualified persons (care required) are eligible. In addition, the measure “New Integrated Business Regional Resource Plan” covers non-certified persons (health), non-certified persons (weakness), and certified persons (support required).

  Hereinafter, the relationship between each measure and the user who becomes the object is similarly shown about other business fields 162-166.

  FIG. 10 shows the relationship between the analysis target data source and the aggregation unit for each business area. That is, it shows the relationship between the business area to be analyzed, the users of each business, the aggregation unit, and the data that is the basis of the analysis. This figure shows that “office work system data” and “measure-specific data” are analyzed in a unified manner. As a result, it is possible to quantitatively verify the state of small regions, businesses, and users that have been difficult in the past and the effectiveness of implementing various measures that have been implemented.

  For each of the following measures in these business areas 161 to 166, an index for achieving them is set as shown in FIG. 11 described above, and the value indicating the degree of achievement and the implementation state is set as the index value. Aggregated by aggregation unit. Therefore, it is possible to grasp the status of each measure by analyzing the result of tabulating the index values.

  In the same manner, FIG. 13 shows the current status analysis / problem extraction results for “dementia maintenance / improvement measures (severity prevention measures)” in business area 163 “dementia measures”, and FIG. Figure 17 shows the results of the current status analysis and problem extraction of “dementia prevention / early detection measures” in “Measures”. Fig. 19 shows the results of current situation analysis and problem extraction for "prevention / life support measures". Figure 19 shows "care prevention / life support measures for non-certified persons" in business area 164 "General care prevention / daily life support general business (new general business)". Fig. 21 shows the current status of "medical care cooperation related to hospital admission support" in the business area 165 "home care / nursing care cooperation promotion project". The analysis and issue extraction results are displayed together.

  Also for these, an indicator with a large disparity (= maximum value / minimum value) can be clarified as an indicator to be corrected if, for example, the cell background is displayed in orange and an indicator with a medium disparity is displayed in yellow. In addition, the value of the index greatly deviating from the average in a good direction displays the cell background in blue, for example. In this way, it is possible to easily determine that there is a possibility that an operator with a large blue index value is a best practice candidate. On the other hand, the index value that is greatly deviated from the average in the bad direction displays the cell background in orange. In this way, it is possible to easily determine that there is a possibility that a business operator with a lot of orange color may be a priority guidance target candidate.

  In addition to these display examples, the screen may be displayed in a display format as shown in FIG. The example of FIG. 23 corresponds to the display example shown in FIG. 11, and the display area α of FIG. 23 is the same as the display content of FIG. In FIG. 23, a mode switching area β is provided above the display area α, a horizontal summary display area γ is provided to the right of the display area α, a vertical summary display area δ is provided below the display area α, A horizontal summary display area γ is provided on the right side, and a polygonal display area ε is provided on the right side of the vertical summary display area δ below the horizontal summary display area γ.

  In the mode switching area β, the business area name and the measure name shown in FIG. 1 are displayed. The horizontal summary display area γ displays values as in a uniaxial scatter diagram so that the tendency of data displayed for each index can be seen and compared at a glance in the display area α, and the bias and the trend are visualized. That is, the maximum value, the average value, and the minimum value are clearly indicated with large white circles together with the numerical values. Other values are displayed as small filled circles. When the values are dense, the bias can be read by overlapping small filled circles. The background bar not only clarifies the width between the minimum value and the maximum value, but also clarifies the plot axis of the data.

The vertical summary display area δ is displayed in the display area α so that the trend of the data displayed for each aggregation unit can be seen and compared at a glance, and the top 10% of good values and the bottom 10% of bad values are bar graphs. This makes it easier to understand trends. The higher the number of good items, the longer the bar on the upper side, and it can be seen at a glance that it is a good counting unit (business). Conversely, the lower the number of bad items, the longer the lower bar, and it can be seen at a glance that this is a counting unit (operator) with many improved items.

  The multi-angle display area ε is used to visualize the analysis viewpoint. The three analysis viewpoints and the evaluation axis are represented by a cube, and the combination of the viewpoint of the data currently visualized and the evaluation axis is intuitively understood. ing.

  By adopting such a display configuration, it is possible to visually grasp the display contents visually.

  The check system 21 shown in FIG. 3 can formulate an improvement plan for an index having a large disparity from such an analysis result, and can formulate an improvement plan for an index to be corrected for each small region and each business operator. In addition, best practice candidates and priority guidance candidate candidates can be identified, and as a result, priority measures by index and priority measures by aggregation unit can be formulated. That is, by identifying issues, it is possible to identify measures that have not been implemented for each index and aggregation unit, and to identify insufficient measure-related indicators, and set them as priority measures.

  Next, the policy formulation system 22 for executing the policy formulation phase A in the PDCA cycle shown in FIG. 2 will be described. This measure formulation phase A has an index maintenance function, an effective measure extraction function, and an effective measure determination function as shown in FIG. In order to realize these functions, the measure formulation system 22 includes an effective measure extraction unit 41 as shown in FIG.

  The effective measure extraction unit 41 first prepares an index to be used in the next business plan of the comprehensive community care business. As this index, a structure index (hereinafter referred to as “S index”) including the measure implementation status of the practitioner of the regional comprehensive care business, a process index (hereinafter referred to as “P index”) including the service provision status from the implementer to the user, In addition, an outcome index (hereinafter referred to as “O index”) representing the improvement result of the maintenance period of the user's mental and physical state is prepared.

  The S index relates to the state of implementation of measures and environmental risks by small region or by business, and is defined by the business unit related to (or surrounding) the user or the local unit of residence. For example, the S index in the “Psychosomatic state maintenance / improvement measure” of the “Preventive benefit / nursing benefit business” shown in FIG. And so on. These are set based on the facility specific data 14 shown in FIG.

  The P index is related to the service usage status of the user and the status of multi-person collaboration such as medical care for the same person. The service provision data provided for each user (medical / care receipt data, etc.) Aggregated by each aggregation unit, such as business operators and regions. For example, the P index in the “Psychological and physical condition maintenance / improvement measure” of the “Preventive benefit / nursing benefit business” shown in FIG. 11 is “Train care service provision enhancement addition type (0-4)” or “Train rehabilitation service provision” For example, “enhancement type (0 to 4)”. These are set based on the corresponding ones of the office system data 11, 12, 13 shown in FIG.

  O index is data (accreditation data, etc.) that shows the effect of measures such as mental and physical condition and cost of each user, such as degree of care required, independence, and basic checklist score extension period, etc. The data is tabulated in each unit such as region. For example, the O index for “Measures for maintaining and improving mental and physical conditions” in the “Preventive Benefits and Long-term Care Benefit Business” shown in FIG. The ratio of maintenance / improvement period by stage of independence of disability during the certification period ”. These are set based on the corresponding ones of the office system data 11, 12, 13 shown in FIG.

  These indicators are set not only for the “mental and physical condition maintenance / improvement measures” in the above-described business area 162 “preventive benefit / nursing care benefit business” but also for measures in other business areas. Specific examples thereof are as described in FIGS.

  Here, the above-mentioned O index can be further subdivided into O1 index: average duration for each stage of psychosomatic state, O2 index: new recognition rate, O3 index: degree of change in mind and body state before and after hospitalization.

  The O1 index is positioned as a central index for verifying the implementation effect of each measure in each business area of comprehensive community care. Although there are differences in the mind and body status data that should be noted for each business area, whether or not the extension of each average duration is the barometer of the evaluation. Furthermore, the application can be smoothly applied to the estimation (simulation) of the average benefit cost incurred during that period.

  The target of aggregation is only those who have undergone certification renewal or investigation within the period before the predetermined period from the time of aggregation, and the psychosomatic state has changed, and the psychosomatic state stage before the renewal or investigation is the same. All of the multiple records in are counted.

  The O2 index is positioned as a central index for verifying the implementation effect of each measure for non-certified persons. Aggregation is performed within a certain period (year, half, and quarter). It is the ratio of the number of newly certified persons to all elderly people in each region in a given period. It should be noted that the above-described aggregation target period when calculating the S index is set as an aggregation target of the S index as an affected period in the past predetermined period including the period for counting new certification.

  The O3 index is positioned as a central index for verifying the change in the mind and body condition before and after hospitalization when a person is admitted to an acute hospital due to some acute hatred in the care cycle of the certified person. Aggregation is a totalization of the degree of change in the state of mind and body before and after hospitalization when a hospitalization event occurs within a certain period (such as several years corresponding to a care cycle). Note that “by stage” corresponds to each stage of psychosomatic state before hospitalization. The hospitalization event within the analysis target period is detected for each user from the medical reception.

  Each index of these SPOs is registered in the index menu 221 shown in FIG. The aggregation means 411 of the effective measure extraction unit 41 calculates the index value for each index registered in the index menu 221 for each aggregation unit. This aggregation is executed using data for a predetermined period in the past, such as the basic data 11, the care insurance data 12, the medical insurance data 13, and the unique data 14 relating to the residents of the centralized municipality that the database 10 has. In addition, the total unit is determined according to the contents of the index by a small area or business operator who performs comprehensive community care registered in the total unit menu 222 or a user of the comprehensive community care business.

  The calculated value of each index is used by the correlation coefficient calculation unit 412 to calculate the correlation coefficient. That is, the correlation coefficient between each index is calculated. The calculated correlation coefficient is compared with a threshold set in advance in the correlation coefficient determination unit 413, and an S index having a high correlation coefficient with the O index among combinations of indexes with a high correlation coefficient exceeding the threshold. , A combination with the P index having a high correlation coefficient with the O index, and a combination with the S index having a high correlation coefficient with the P index are extracted as effective measures. For example, when the absolute value of the correlation coefficient is greater than 0.7, it can be said that the measure can have a strong correlation, that is, a high effect.

  FIG. 12 shows a display example in which the correlation coefficient calculation results in the effective measure extraction unit 41 described above are summarized. FIG. 12 summarizes the correlation coefficient calculation results for the “mental and physical condition maintenance / improvement measures” in the business area 162 “preventive benefit / nursing care benefit business”, and the index for achieving the above measures is listed in the vertical axis direction. In the horizontal axis direction, an index (the same index as that listed in the vertical axis direction) combined with the index listed in the vertical axis direction is listed. In the cell where the vertical axis and the horizontal axis intersect, the value of the correlation coefficient by the combination of the indices described on the intersecting vertical axis and the horizontal axis is described. In FIG. 12, the portion surrounded by the frame line D is a case where the psychosomatic state maintenance / improvement ratio during the entire certification period tends to increase as the implementation rate of the psychosomatic state maintenance / improvement approach increases. It can be said that these two indexes have a positive correlation (correlation coefficient approaches +1).

  Similarly, FIG. 14 shows a correlation coefficient calculation result for “dementia maintenance / improvement measures (severe prevention measures)” in business area 163 “dementia measures”, and FIG. 16 shows business area 163 “dementia measures”. 18 shows the correlation coefficient calculation results for “Prevention and Early Detection Measures for Dementia” in FIG. 18, and FIG. 18 shows “Nursing Care Prevention for Authorized Persons” in Business Area 164 “Nursing Care and Daily Life Support Business (New Comprehensive Business)”. Figure 20 shows the results of the effective measures extracted for “Life Support Measures”, and FIG. 20 shows the effective measures for “Nursing Care Prevention and Life Support Measures for Non-Certified Persons” in the Business Area 164 “Nursing Care Prevention and Daily Life Support Business (New Comprehensive Business)” FIG. 22 summarizes the extraction results and the effective policy extraction results for “medical care cooperation related to hospital admission support” in the business area 165 “home medical care / nursing care cooperation promotion project”. Is displayed.

In this way, the measure formulation system 22 shown in FIG. 4 determines the effective measure by judging the extraction result of the effective measure from the past measure implementation status. As a result, the effective measure is confirmed and the effective measure list 414 is created.
Next, the business plan formulation system 23 for executing the business plan formulation phase P in the PDCA cycle shown in FIG. 2 will be described. This business plan formulation phase P has a cost-effectiveness simulation function and a long-term care insurance business plan formulation function, as shown in FIG. In order to realize these functions, the business plan formulation system 23 has a cost-effectiveness simulation unit 51 as shown in FIG.

  The cost-effectiveness simulation unit 51 sorts out the investment amounts related to the effective measures formulated by the measure formulation system 22, calculates the amount of nursing care benefit cost restraint due to the implementation of each effective measure, and makes these investment amount and restraint amount effective Compare each measure, and prioritize the effective measures based on the comparison results. Details will be described below.

  The cost-effectiveness simulation unit 51 takes in the effective measure list 414 created by the measure formulation system 22, and the investment amount management unit 511 holds the investment amount for the effective measure described in the effective measure list 414 in the database 10. Calculated from the collected data and organized by effective measure.

  In addition, the suppression amount calculation unit 512 calculates the suppression amount for each effective measure, how much care benefit costs are suppressed by the implementation of the effective measure. For example, calculate the average benefit cost for each stage by calculating the average of the mental and physical condition and the maintenance period depending on whether each measure is implemented (implementation rate). That is, if the period for maintaining the current stage becomes longer without the user's degree of care required changing to the upper stage, an increase in the nursing care benefit cost can be suppressed accordingly. This period maintenance state is calculated from the value of the O index. From these viewpoints, the amount of protection benefit protection is calculated for each effective measure.

  Then, the cost comparison unit 513 compares the investment amount of these effective measures with the suppression amount due to the implementation for each effective measure, and the priority setting unit 514 gives priority to each effective measure based on the result. Give a ranking. In other words, a significant difference between the investment amount and the amount of restraint such as benefit costs is scrutinized, and a measure with a significantly large amount of restraint (several times more) than the investment amount is preferentially selected.

  The business plan formulation system 23 shown in FIG. 5 formulates a regional resource plan such as a long-term care insurance business plan using the effective measures that have been given priority in this way. In other words, the Long-Term Care Insurance Business Plan, Elderly Health and Welfare Plan, Regional Comprehensive Care Plan, Comprehensive Medical Care Plan, Municipality Plan, etc. are formulated, and it is effective to determine targets and timing for effective measures and implementation in these plans. Formulate an action plan.

  Next, the measure execution system 24 that executes the measure execution phase D in the PDCA cycle shown in FIG. 2 will be described. As shown in FIG. 2, in the measure execution phase D, the execution details are communicated to related parties, measure execution, indicator monitoring, aggregate unit monitoring, and business plan execution management are performed. As shown in FIG. 6, an index monitoring unit 61 and a total unit monitoring unit 62 are provided so that changes in index values associated with the execution of the plan are captured in time series and monitored.

  That is, the index monitoring unit 61 grasps the change in the value of the target index in time series by executing the measures based on the business plan formulated by the business plan formulation system 23 described above, and the index disparity having a large disparity between aggregation units is obtained. Monitor how it changes. In addition, the total unit monitoring unit 62 monitors, for each total unit, how the divergence of the indicator having a large deviation from the average changes due to the execution of the measure. These will be described in detail below.

  Since the indicator monitoring unit 61 monitors how the disparity of the indicator having a large disparity changes, the indicator monitoring unit 61 takes in the disparity large indicator list 325 created by the check system 21 shown in FIG. 3 and becomes a monitoring target. Select indicators with large disparities. The setting unit 611 sets a monitoring period, a monitoring cycle, a reference value, a target value, and a determination value for determining whether the target value has been achieved.

  The monitoring period is, for example, 3 years (36 months) in accordance with the business plan (3 years). The monitoring cycle is, for example, every month. The standard value is used as an evaluation standard for monitoring results. The year or month or year before the start of monitoring is specified, and the monthly index value or the monthly average of the annual index value is used as the standard value. The target value is set as a set of an achievement target time and a determination value for determining whether or not the target value has been achieved at a plurality of time points in the monitoring period. For example, after the start of monitoring for a monitoring period of 36 months, First to fifth target values are set for every month. When the fifth target value is set as the final target value, the value is set so as to gradually approach the final target value from the first target value to the fourth target value. The target value is set for each of a plurality of aggregation units (for example, 50 small areas).

  The index calculation unit 612 calculates an index value for each of a plurality of tabulation units for each month that is a monitoring cycle after monitoring starts for an index with a large disparity to be monitored.

  The statistical information calculation unit 613 calculates the number of objects, the average value, the standard deviation, the minimum value, the first quartile, the median, and the third quadrant from the values of the indexes for each of the total units calculated every month. The position, maximum value, disparity (= maximum value / minimum value), and range width (= maximum value−minimum value) are calculated.

  These various time series data values calculated for each month are stored in the storage unit 614 for each of a plurality of aggregation units.

  For the target value, as a determination value for determining whether or not the target value has been achieved, an upper determination value that deviates above the target value by a predetermined range and a lower determination value that deviates from the target value by a predetermined range below Is set. Then, if the value of the aggregate unit of the achievement target time with respect to the target value is within the range between the upper judgment value and the lower judgment value, the aggregation unit index is judged to have achieved the target value. Is done.

  As an indication of the achievement status, for example, the indicator below the lower judgment value is orange in the cell background, the indicator of the upper judgment value excess is, for example, yellow in the cell background, the upper judgment value and the lower judgment value For the index within the range, for example, the background of the cell is displayed in blue. Furthermore, the background of the cell before the target time for the target value, the indicator below the lower judgment value is light orange in the cell background, the indicator above the upper judgment value is pale yellow, the cell background is light yellow, the upper judgment value and the lower Indicators within the range between the side determination values display the cell background in light blue. In this way, the achievement status with respect to the target value can be easily determined.

  The above is an example of a better index with a larger index value, but when the smaller index value is a better index, the orange and light orange colors of the background of the cell above are yellow and light yellow, respectively. By replacing the yellow and light yellow of the cell background with orange and light orange, respectively, the achievement status with respect to the target value can be displayed more appropriately.

  In this manner, during the monitoring period, the index value is calculated for each of a plurality of aggregation units for the monitoring target index for each monitoring period. In addition, various statistical values including the magnitude of the disparity for each aggregation unit are calculated and stored as time series data. For this reason, during the monitoring period, there are changes in the value of indicators by multiple aggregation units, changes in inequality, changes in other statistical information, achievement status of multiple target values including final target values, etc. The implementation status of the measures can be grasped quantitatively.

  Since the total unit monitoring unit 62 monitors, for each total unit, how the magnitude of the deviation of the indicator having a large deviation from the average due to the execution of the measure is changed, it is a priority instruction target created by the check system 21. The candidate list 327 is taken in, and a counting unit having an index with a large deviation from the average to the worse is selected as a monitoring target.

  The setting unit 621 sets a monitoring period, a monitoring cycle, a reference value, a target value, an achievement target time, and a determination value for determining whether the target value has been achieved.

  The monitoring period is, for example, 3 years (36 months) in accordance with the business plan (3 years). The monitoring cycle is, for example, every month. The reference value is used as an evaluation standard for monitoring results. The year, month or year before the start of monitoring is specified, and the index value for the year or month average of the index value for the year is used as the reference value. The target value is set at a plurality of points in the monitoring period by setting the target time and the judgment value that determines whether the target value has been achieved. For example, for the monitoring period of 36 months, 7 months have elapsed since the start of monitoring. First to fifth target values are set every time. When the fifth target value is set as the final target value, the value is set so as to gradually approach the final target value from the first target value to the fourth target value. In addition, as the target value, an individual value is set for each of a plurality (for example, 50) of indicators corresponding to the aggregation unit to be monitored.

  The index calculation unit 622 calculates a value of each index for each month that is a monitoring cycle after the start of monitoring for a plurality of indices to be monitored.

  The time series diagnosis unit 623 determines whether the index value calculated every month has reached the target value for each of the plurality of indices, and diagnoses the transition state of the index.

  For the target value, as a determination value for determining whether or not the target value has been achieved, an upper determination value that deviates above the target value by a predetermined range and a lower determination value that deviates from the target value by a predetermined range below Is set. Then, if the value of the index of the achievement target time with respect to the target value is within the range between the upper determination value and the lower determination value, it is determined that the index has achieved the target value.

  As an indication of the achievement status, for example, the indicator below the lower judgment value is orange in the cell background, the indicator of the upper judgment value excess is, for example, yellow in the cell background, the upper judgment value and the lower judgment value For the index within the range, for example, the background of the cell is displayed in blue. Furthermore, the background of the cell before the target time for the target value, the indicator below the lower judgment value is light orange in the cell background, the indicator above the upper judgment value is pale yellow, the cell background is light yellow, the upper judgment value and the lower Indicators within the range between the side determination values display the cell background in light blue. In this way, the achievement status with respect to the target value can be easily determined.

  The above is an example of a better index with a larger index value, but when the smaller index value is a better index, the orange and light orange colors of the background of the cell above are yellow and light yellow, respectively. By replacing the yellow and light yellow of the cell background with orange and light orange, respectively, the achievement status with respect to the target value can be displayed more appropriately.

  The time series value of the index calculated for each monitoring period by the index calculation unit 622 and the time series diagnosis result of the index determined for each monitoring period by the time series diagnosis unit 623 are stored in the storage unit 624, respectively. Keep it.

  As described above, during the monitoring period, the index value is calculated for each of the plurality of indicators in the monitoring target aggregation unit, and the calculated index values reach the plurality of preset target values, respectively. This is stored as time series data, so that the transition of the values of multiple indicators, the achievement status of multiple target values including the final target value, the comparison status with the reference value, etc. during the monitoring period, etc. The implementation status of the planned measures can be grasped quantitatively.

  As a result, it is possible to grasp how the index has changed in time series for each monitoring target totaling unit. Therefore, the totaling unit monitoring result can be summarized as shown in FIG.

  In FIG. 24, as a result of the monitoring of the total unit, the index that has improved with time and the index that has deteriorated with time as a result of the implementation of the measure are summarized for each target total unit. It is possible to clarify the generation process (cause) of the degree of deviation by unit index.

As described above, according to the embodiment of the present invention, the measure to be executed is incorporated into the index, and the effectiveness of the measure is specifically shown by objective and quantitative data analysis such as correlation analysis. Also,
Since analysis functions and the like are prepared in accordance with each phase PDCA, quantitative business verification based on a unified standard with continuity becomes possible. That is. Compared with the conventional research and analysis projects that are carried out in a single fiscal year or in each business plan period, continuity of project evaluation and verification can be expected. In addition, maintenance of indicators across business plan periods is simplified.

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

DESCRIPTION OF SYMBOLS 10 ... Database 11 ... Basic data 12 ... Care insurance data 13 ... Medical insurance data 14 ... Measures specific data 15 ... Solution function 16 ... Target business area 21 ... Check system 22 ... Measure formulation system 23 ... Business plan formulation system 24 ... Measure Execution system 31 ... Current state analysis unit 32 ... Issue extraction unit 41 ... Effective measure extraction unit 51 ... Cost-effectiveness simulation unit 61 ... Indicator monitoring unit 62 ... Total unit monitoring unit

Claims (5)

A database that integrates basic data about residents held by local governments, long-term care insurance data, medical insurance data, and unique data including measures implemented by local governments;
Using the data for a predetermined period held in this database, a plurality of index values set in advance to achieve a predetermined measure are calculated for each aggregation unit, and each index is calculated from the calculated index-specific values. In addition, the current state analysis department that captures the disparity between aggregation units and the magnitude of deviation from the average,
From the magnitude of the disparity obtained in the current state analysis unit, an issue extraction unit that extracts an index having a large disparity and extracts a unit of aggregation having a large number of indicators having a large deviation from the average,
A regional comprehensive care business system having a check system for formulating an improvement plan for an indicator having a large disparity, and an improvement plan for an aggregation unit having many large indicators for those with a large deviation from the average. As indices used in the business plan of the comprehensive community care business, a structure index including the implementation status of the implementer's measures, a process index including the service usage status from the implementer to the user, and a maintenance period of the mental and physical state of the user The outcome index is set, and the value of each index is calculated using data for a predetermined period held in the database, and the correlation coefficient between each index is calculated using the calculated value of each index. An effective measure extraction unit that calculates and extracts combinations of indicators with high correlation coefficients as effective measures,
The community comprehensive care business system according to claim 1, further comprising a measure development system for formulating an effective measure from the extracted effective measure. By using the data for a predetermined period held in the database, the investment amount related to the effective measures formulated by the measure formulation system is organized, and the amount of nursing care benefit costs restrained by the implementation of each effective measure is calculated, and these investments A cost-benefit simulation unit is provided that compares the amount and amount of restraint for each effective measure and prioritizes the effective measure based on the comparison result.
The regional comprehensive care business system according to claim 2, further comprising a business plan formulation system that formulates a business plan including a formulation of an effective measure implementation plan based on a simulation result of the cost-effectiveness simulation unit. An indicator monitoring unit that monitors how the disparity value of an indicator with a large disparity changes due to the implementation of measures by the business plan formulated by the business plan formulation system;
An aggregate unit monitoring unit that monitors how the deviation of the indicator having a large deviation from the average by the execution of the measure changes by the aggregate unit;
The regional comprehensive care business system according to claim 3, further comprising a measure execution system comprising: Using a database that integrates basic data on residents, data on long-term care insurance, medical insurance data, and specific data including measures implemented by the local government,
In order to achieve predetermined measures using data for a predetermined period held in this database, a plurality of indicators set in advance for users of community comprehensive care and those who implement community comprehensive care Calculate the value for each aggregation unit, extract the difference between the aggregation units and the deviation from the average of each aggregation unit for each indicator from the calculated value, and analyze the current situation to extract the indicator with the large difference In addition, the aggregation unit having many indicators with large deviation from the average is extracted, and the improvement measure for the indicator with large disparity is formulated, and the improvement measure to the aggregation unit with many large indicators toward the one where the deviation from the average is bad Check stage to formulate,
As an index used in the business plan of comprehensive community care, a structure index including the implementation status of the measure of the implementer, a process index including a service usage status from the implementer to the user, and a maintenance period of the mental and physical state of the user Are set using the data of the past predetermined period stored in the database, and the calculated coefficient is used to calculate the correlation coefficient between each index. Calculating and extracting a combination of indicators with a high correlation coefficient as effective measures and finalizing them,
Using the data for the past predetermined period held in the database, the investment amount for the formulated effective measure is organized, the amount of benefit cost restraint due to the implementation of each effective measure is calculated, and these investment amount and restraint amount The cost-effectiveness simulation that prioritizes the effective measures based on the comparison results, and the business plan formulation stage that formulates the business plan including the formulation of the effective measure implementation plan based on the simulation results ,
Monitor how the indicators with large disparity change due to the implementation of measures based on the formulated business plan, and monitor how the indicators with large deviation from the average change due to the implementation of the measures Measure implementation stage,
Promotion method of community comprehensive care business that executes sequentially.