JP6896441B2 - Community comprehensive care business system - Google Patents

Description

An embodiment of the present invention relates to a community-based comprehensive care business system for executing a community-based comprehensive care business including a long-term care insurance business.

In general, local governments such as municipalities hold basic data such as residents' addresses in order to realize various businesses and businesses that are closely related to the lives of residents. In addition, the local government implements the long-term care insurance business and holds a lot of data related to long-term care insurance. In addition, various data on medical insurance are also available. In addition to these routine tasks and projects, specific measures related to community-based comprehensive care for residents are being implemented, and specific data such as measures are generated and are also possessed.

Each of the above-mentioned businesses and businesses was carried out independently and individually, and various data associated therewith were also managed individually for each business and business. Therefore, it was difficult to implement a community-based comprehensive care business that oversees these businesses.

In addition, in the community-based comprehensive care business, predetermined measures are implemented for each business area, but it is necessary to set multiple indicators in order to achieve these measures. These indicators need to be set appropriately because they are related to the accuracy of quantitative evaluation such as extraction of issues in the comprehensive community care business and extraction of effective measures by measuring the effects.

For example, as an index showing a mental and physical condition, the following three methods have been proposed as examples, but each has a problem.

1) Indexing the difference in mental and physical condition between 2 time points 2) Indexing the rate of change in mental and physical condition between 2 time points 3) Aggregating the number of transitions by month for newly certified persons at a certain point in time of need for nursing care and certification survey items 1) Problems in indexing the difference in mental and physical condition between two time points ・ In indexing the difference in mental and physical condition between two time points, the period to be aggregated is unclear, and each person has a different time-series change (valid for certification). It is inappropriate as an index based on the period, etc.).

・ Where the certification is renewed varies from person to person, and it is inaccurate to set two time points uniformly.

・ Even if you say that your physical and mental condition has deteriorated, improved, or maintained at two points, you do not know whether or not your healthy life expectancy has been extended, and therefore you do not know the degree of benefit cost restraint, so you can use the benefit cost restraint simulation. I don't get used to it.

2) Problems in indexing the rate of change in mental and physical condition between two time points ・ The rate of change in mental and physical condition (intuition) between two different time points is used as an index. In addition, it is difficult for local government officials to intuitively understand the absolute value of the maintenance duration of each stage. Therefore, it is unknown whether healthy life expectancy is extended. Therefore, the degree of benefit cost restraint is unknown, and it does not fit into the benefit cost restraint simulation.

3) Problems of counting the number of transitions by month for newly certified persons at a certain point in time of need for nursing care and certification survey items (Visualization of how long the number of people in each stage will transition to which stage Problems with how to do it)
・ Different results will be obtained depending on how long the evaluation takes.

・ In principle, it is not known how long each subject stays at each stage.

For these reasons, it is not known whether the healthy life expectancy of individual subjects is extended, and therefore, the degree of benefit cost restraint is unknown, and it does not fit into the benefit cost restraint simulation.

Furthermore, as a problem of the conventional method, the length of the maintenance duration of the physical and mental condition of each subject (shortening of extension), the content (frequency, amount, etc.) of various services that affect them, and the implementation during the same period. There was a problem that the relationship between the efforts of the service establishments, etc. (enhancement of qualified staff, participation in various trainings, etc.) was unclear. Therefore, it was not possible to quantitatively extract and verify what kind of measures (services, business establishments, etc.) should be taken to maintain and improve the physical and mental condition.

In addition, elderly people usually stay at home or in special nursing homes and regularly receive prescribed long-term care services and life support services according to the care plan, but there are individual differences depending on age and chronic illness. , Head fractures and thigh fractures due to strokes and falls occur about once every few years (usually called a care cycle), and patients may be urgently admitted to an acute care hospital. At that time, it is said that there are many cases (especially in the late-stage elderly) in which cognitive function is significantly reduced during hospitalization even if the illness or injury or illness that should be treated is recovered due to a sudden drastic change in the living environment. ing.

The following two efforts are considered to be the cause of this.

・ As soon as possible at the time of admission, the care manager and the people involved in the acute care hospital will share information with the inpatients (especially regarding daily care and living conditions, ADL (Activities of Daily Living) goals at the time of discharge. Whether or not the planning, sharing, etc.) are thoroughly carried out.

・ During hospitalization, not only the treatment (cure) of the disease or injury or illness, but also the relationship between doctors and nurses, etc. Whether all staff are working in parallel at the same time.

If these are unsuccessful, only the illnesses and injuries that should be treated should be recovered and the patient should be discharged with the same cognitive function as before admission. Suddenly worsens. As a result, the originally expected duration of the care-requiring status category before hospitalization is suddenly shortened by hospitalization, and the benefit cost will increase earlier than originally expected.

However, based on the above viewpoint, we will quantify what measures (care managers, acute hospital admission and discharge staff, etc.) should be taken to prevent sudden deterioration of cognitive function before and after admission and discharge of inpatients. Could not be extracted and verified.

Japanese Patent No. 5567046

The present invention is to provide a community-based comprehensive care business system capable of appropriately setting various indicators and solving the above-mentioned various problems, particularly problems when a hospitalization event occurs, by using a centralized database. ..

The community-based comprehensive care business system according to the embodiment of the present invention uses a database having unique data such as basic data of residents, nursing care insurance data, medical insurance data, and measures including measures executed by local governments, and determines predetermined measures. It is a community-based comprehensive care business system that can calculate the values of multiple indexes set in advance for each aggregation unit, and as the index, a structure index including the implementation status of the measures of the implementer who implements the measures. , A process index including the status of service provision from the implementer to the user of the measure, and an outcome index including the degree of gradual change in the mental and physical condition of the user are set, and the hospital during a predetermined check period. The above-mentioned users who have been hospitalized in the hospital are targeted for aggregation, and the degree of change between the stage of the mental and physical condition of the subject before admission and the stage of the mental and physical condition within a predetermined period after discharge is used as an outcome index of the subject of aggregation. The outcome index calculation unit to be calculated and the predetermined period before and after admission to the hospital are set as the hospitalization-related analysis period, and the numerical value representing the performance of the service received from the implementer by the totalized person during this hospitalization-related analysis period is described above. The process index calculation unit that calculates as the user's process index, and the structure index that calculates the numerical value representing the results of the measures taken by the implementer who provided the service to the user during the hospitalization-related analysis period as the structure index. It is characterized by having a calculation unit.

According to the above configuration, objective and quantitative data analysis even when an inpatient event occurs by using a linked OPS index in which outcome indicators, process indicators, and structure indicators are interrelated. Accurate problem extraction will be possible, and the effectiveness of the measures will be concretely shown.

It is a schematic diagram explaining the outline of the community-based comprehensive care business system which concerns on one Embodiment of this invention. It is a figure which shows the structure of the server and the client system used in one Embodiment of this invention. It is a figure explaining the O index calculation part, the P index calculation part, and the S index calculation part used in one Embodiment of this invention. It is an image diagram explaining the difference in the mental and physical state change before and after admission due to the difference in medical care cooperation at the time of admission according to one embodiment of the present invention. It is a schematic diagram explaining the outline of the community-based comprehensive care business system which concerns on other embodiment of this invention. It is a figure which shows the structure of the server and the client system used in the other embodiment of this invention.

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

An embodiment of the present invention comprehensively implements and operates various businesses and businesses including a long-term care insurance business in various insurers operated by local governments such as municipalities and prefectures and countries that are higher-ranking organizations of local governments. To obtain a comprehensive community care business system.

Therefore, the community-based comprehensive care business system implemented and operated by the insurer is defined as the lower-level community-based comprehensive care business system, and the community-based comprehensive care business system implemented and operated by the upper-level prefectures and countries is defined as the higher-level community-based comprehensive care. The business system will be described separately for each embodiment.

<< Embodiment of lower-level community-based comprehensive care system >>
As shown in FIG. 1, a 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, the solution function 15 provided by the database 10 and the community-based comprehensive care business system, and the target business area 16 of the community-based comprehensive care business.

As described above, local governments such as municipalities have data such as resident address data 111 shown in FIG. 1, resident common number (tentative name) 112, regional characteristic data 113, and regional efforts 114 as basic data 11. ing. In addition, the local government is implementing a long-term care insurance business, and has data such as long-term care certification data 121, long-term care receipt data 122, and long-term care establishment ledger 123 as long-term care insurance data 12. Further, as data 13 related to medical insurance, data such as elderly specific medical examination data 131, National Health Insurance / late-stage elderly medical receipt data 132, medical institution ledger 133, and the like are also available.

The various operations and projects described above have been carried out for many years, and various data 11, 12, and 13 are managed individually but in a fixed format.

In addition to these, the local government has implemented measures related to comprehensive community care unique to the local government for residents, and as the data 14 specific to the measures, etc., the basic checklist (results of the daily living area needs survey) 141, small areas, We have data such as policy plans and achievements 142 for business establishments and users. The specific data 14 for these measures and the like are in various formats according to each measure, and are currently stored in a format unique to each department in charge.

Therefore, in the embodiment of the present invention, the database 10 that unifies the necessary parts of these data 11, 12, 13, and 14 is created.

In this database 10, the related parts of these data 11, 12, 13, and 14 are linked and unified. Therefore, the basic data 11, the long-term care insurance data 12, the medical insurance data 13, and the specific data 14 such as measures are unified to obtain the database 10 which is the basis for analysis.

In addition, by creating a database of unique data 14 for measures unique to local governments, it is possible to quantitatively verify the effect of implementing measures. In particular, it will be possible to quantitatively verify the state image of subregions, business establishments, and users, which was difficult in the past, and the effect of implementing a wide variety of measures that have been invested.

As shown in the figure, the community-based comprehensive care business has a plurality of business areas 161, 162, 163, 164, 165, 166 as the target business areas 16, and various business areas 161, 162, 163, 164, 165, 166. Measures are implemented. Therefore, indicators 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 indicators are registered.

The solution function 15 is realized by a computer, and each business area 161, 162, 163, 164, 165, as described later, is used by using the data 11, 12, 13, 14 centralized in the database 10. The achievement status and implementation status of the measures implemented in 166 are analyzed by aggregating the values of the indicators set for each measure. For this purpose, the aggregation unit 152 and various analysis functions 153 are provided.

FIG. 2 shows the configuration of a computer system that creates the database 10 and executes the above-mentioned community-based comprehensive care business.

This computer system has a client computer (hereinafter, simply referred to as a client) 31 and a server computer (hereinafter, simply referred to as a server) 32 connected to the client 31, and the database 10 uses the server 32 for data. Storage / reading is controlled.

The client 31 is a computer provided in each business department of the local government, and the data acquisition unit 311 is unique to the basic data 11, long-term care insurance data 12, medical insurance data 13, measures, etc. regarding the residents owned by the local government. The data 14 is taken in and provided to the server 32.

The server 32 stores the above-mentioned data 11, 12, 13, and 14 in the original data storage unit 101 of the database 10 as unified data by the database creation unit 321. Centralization by the database creation unit 321 is a unique number that identifies an individual or business establishment held in each data 11, 12, or 13 or a code that encrypts a unique number that identifies an individual or business establishment (for example, a hash). Using the conversion code by conversion), the measure-specific data 14 is created and collected by adding a unique number that identifies the business establishment.

As described above, the data 11, 12, and 13 are individual but standard formats. On the other hand, the measure-specific data 14 has various formats according to each measure, and each time the department in charge implements the measure, an input sheet and a database 10 format (data table) for collecting data. ) Is created. Therefore, the functions of the client 31 and the server 32 create an input sheet and a database 10 format (data table) for collecting the measure-specific data 14, and store the measure-specific data 14 in the database 10. In addition, the server 32 has an index calculation unit 322, an aggregation unit 323, and an analysis unit 324 in order to realize the solution function of the community-based comprehensive care business. The index calculation unit 322 calculates the value of the index registered for each business area in the index menu 151 shown in FIG. 1 by a method described later. The aggregation unit 323 aggregates the index value for each corresponding aggregation unit determined from the aggregation unit menu (subregion unit, user unit, business establishment unit, etc.) registered in the aggregation unit 152 shown in FIG. .. The analysis unit 324 executes each of the analysis functions 153 shown in FIG. Details of these functions, that is, the solution functions of the server 32 will be described later.

Next, the development of indicators used in the business plan of the community-based comprehensive care business will be explained. This index includes a structure index (hereinafter referred to as S index) that includes the implementation status of measures by the implementer of the comprehensive community care business, and a process that includes the service provision status from the implementer of each measure of the comprehensive community care business to the user. An index (hereinafter referred to as P index) and an outcome index (hereinafter referred to as O index) showing improvement results such as the maintenance period of the physical and mental condition of the user will be prepared. The details of these will be described below.

The S index is related to the implementation status of measures and environmental risks by subregion or business establishment that implements each measure for each business area of the comprehensive community care business, and is related to the user (surrounding) business establishment unit or residence. It is defined for each region. This S index is set based on the facility-specific data 14 shown in FIG.

The P index is related to the service usage status of the user, medical care for the same person, etc., and the cooperation status of multiple entities, and the service provision data (medical / long-term care receipt data, etc.) provided for each user is the user, This P index, which is aggregated in each aggregation unit such as business establishments and regions, is set based on the corresponding data of the office system data 11, 12, and 13 shown in FIG.

The O index is data (certification data, etc.) that represents the effect of implementing measures such as the mental and physical condition of each user and the cost, such as the degree of long-term care required, the degree of independence, and the degree of gradual change in the basic checklist score. It is aggregated in each aggregation unit such as area. This O index is set based on the corresponding data of the office system data 11, 12, and 13 shown in FIG.

In addition, when there are multiple items of two choices, applicable or not applicable, as questionnaire items, such as the basic checklist of the activities of daily living needs survey, for each meaningful item group, for example, ADL, IADL ( Instrumental Activity of Daily Living), cognitive function, etc., the value calculated by counting the corresponding items is regarded as the risk stage of the item group, and the degree of change in the mental and physical condition stage is calculated. Conceivable.

In addition, even for items that have continuous numerical values, such as the intermediate evaluation item score of the accredited survey item and the sample test value of the medical examination item, those numerical values are distributed to the predetermined risk stages and code conversion is performed. The degree of gradual change in mental and physical condition can be calculated in the same manner as described above. As a result, for example, by reconsidering the value of HbA1C, which is a sample test item of medical examination, as a risk stage, it becomes possible to calculate the degree of change in each risk stage of diabetes. Similarly, by converting the values of the test items that should be noted and followed for each target disease into each risk stage, it is possible to calculate the degree of change for each risk stage for each disease.

In other words, for any mental and physical state item with various data attributes, by converting it into a discrete code value that represents the optimum risk stage for each and defining the risk stage, the degree of change in the mental and physical state stage can be determined. It becomes possible to calculate a certain O index.

An embodiment of the present invention is characterized in that these S-indexes, P-indexes, and O-indexes are configured as linked connected OPS indexes that are set in relation to each other using the O-index as a key. Specific examples and calculations of the linked O / P / S index will be described later.

These indexes are set and registered for each of a plurality of business areas in the index menu of the index calculation unit 322 that realizes the above-mentioned solution function.

The index calculation unit 322 calculates the value of the index for each business area using the data stored in the original data storage unit 101 of the database 10. The calculated index value is stored in the index storage unit 102 of the database 10.

The aggregation unit 323 aggregates the index values calculated for each index for each index and for each predetermined aggregation unit. The aggregation unit is a subregion or business establishment that implements comprehensive community care, or a user of a comprehensive community care business, and is determined according to the content of the index.

The analysis unit 324 executes a current state analysis function for each index, which captures the size of the disparity between the aggregation units from the numerical value for each aggregation unit and the deviation from the average value of each aggregation unit. Then, the index having a large disparity is extracted from the disparity between the aggregation units and the magnitude of the deviation from the average, and the aggregation unit having many indexes with a large deviation from the average is extracted to clarify the problem.

In addition, the analysis unit 324 calculates the correlation coefficient between each index by using the value of each index in order to extract effective measures. Then, effective measures are a combination with a P index having a high correlation coefficient with the O index, a combination with an S index having a high correlation coefficient with the O index, and a combination with an S index having a high correlation coefficient with the P index. Extract as.

In addition, the analysis unit 324 executes a cost-effectiveness simulation function. In other words, the amount of investment required for effective measures is organized, and the amount of restraint of long-term care benefit costs due to the implementation of each effective measure is calculated. Then, the difference between the investment amount and the restraint amount is calculated, the difference is compared for each effective measure, and the effective measure is prioritized based on the comparison result. This simulation function makes it possible to prioritize cost-effective and effective measures.

Further, the analysis unit 324 executes the index monitoring function and the aggregation unit monitoring function. In the index monitoring function, an index with a large disparity is targeted for monitoring, and the value of the corresponding index is detected for each of a plurality of aggregation units for each monitoring cycle. Then, by calculating the disparity between aggregation units, the transition of the disparity over time is captured. Therefore, it is possible to monitor how the indicators with large disparities change by implementing effective measures.

In the aggregation unit monitoring function, the aggregation unit that has many indicators with a large deviation from the average is targeted for monitoring, and the values of multiple indicators related to this aggregation unit are detected for each monitoring cycle and compared with the target value and the reference value. By doing so, the determination results of each index in chronological order can be obtained in chronological order. Therefore, it is possible to monitor how the index with a large deviation from the average changes by implementing effective measures for each aggregation unit.

These analysis results are stored in the analysis result storage unit 103 of the database 10. Then, it is output to the client 31 in response to a request from the client 31, and is used for analysis by the client 31.

FIG. 4 is an image diagram showing the difference in mental and physical condition changes before and after hospitalization due to the difference in medical / long-term care cooperation when the user is hospitalized (occurrence of an inpatient event).

That is, FIGS. 4 (a) and 4 (b) show the state of cognitive deterioration suppression by improving the quality of care during the hospitalization period and before and after the hospitalization period by close cooperation between the care manager at the time of admission and the acute care hospital in the medical care cooperation measures at the time of admission. Is shown. The vertical axis of each upper graph of FIGS. 4 (a) and 4 (b) is the state of deterioration of physical function, the horizontal axis is the short-term period (unit: months), and the vertical axis of each lower graph is the elderly with dementia. The degree of independence in daily life (deterioration of cognitive function) and the horizontal axis are the same as the graphs on the upper side, which are short-term periods (unit: months).

In FIGS. 4 (a) and 4 (b), the change in the value (vertical axis) of the state of deterioration of physical function in the upper graphs is due to acute exacerbation of physical function due to a fracture of the thigh due to a stroke or a fall, etc. It expresses that the patient was hospitalized and was discharged from the hospital because the value of the physical function deterioration state decreased due to the inpatient treatment and the value of the physical function deterioration state became the same as before the hospitalization.

On the other hand, the stairs in the lower graphs express that the level of independence in daily life of the elderly with dementia increases, and the height of the stairs in the degree of independence in daily life (vertical axis) requires that. By simulating the degree of long-term care, the average monthly amount of long-term care benefits (yen) for each degree of long-term care required by a user is shown. The area of the shaded area in the long-term care required degree is the average monthly long-term care benefit cost (yen) x period (months) in the long-term care required degree, and the total long-term care benefit cost (yen) in the long-term care required degree of the user. Is shown.

The patient's physical and mental condition is maintained and improved when there is close cooperation between the care manager and the acute care hospital at the time of admission and consideration is given from the viewpoint of long-term care in addition to the original treatment of the patient during hospitalization. , Cognitive function can be discharged without deterioration. Furthermore, if the medical and long-term care cooperation at the time of discharge is good, it is possible to live without deteriorating the degree of independence in daily life after discharge. The situation is represented by the graph of FIG. 4 (a).

On the other hand, if there is no close cooperation between the care manager and the acute care hospital at the time of admission, and the patient concentrates only on the original treatment during hospitalization, and no consideration is given from the viewpoint of long-term care, during hospitalization. The patient's cognitive function deteriorates. Furthermore, if medical and long-term care cooperation at the time of discharge is not sufficiently performed, application for change certification frequently occurs at the time of discharge (or promptly after discharge), and it is said that the stage of independence in daily life often rises (worsens). .. A graph showing this situation is shown in FIG. 4 (b). As described above, the long-term care benefit cost after discharge of the user (patient) is small in the case of (a) and large in the case of (b) among the two patterns shown in FIG.

FIG. 3 shows a system configuration when the index calculation unit 322 shown in FIG. 2 has the functions of the O index calculation unit, the P index calculation unit, and the S index calculation unit related to the medical care cooperation at the time of admission. It is a figure. The index calculation unit 322 includes a setting unit 1800, an O index calculation unit 1801, a P index calculation unit 1802, and an S index calculation unit 1803.

Hereinafter, the flow of processing for setting and calculating the connected O / P / S index will be described for each functional block diagram of FIG. 3 and the following flow numbers corresponding thereto.

1. 1. Common index (processing of setting unit 1800)
1.1 Determining the target illness: Determined by the illness that causes the emergency hospitalization of the certified person (support 1 and 2, care required 1 to 5).

1.2 Determination of aggregation unit: The long-term care side shall be the home care support office used by inpatients and the individual care manager. In addition, the medical side will be the acute care hospital where the patient will be hospitalized.

1.3 Determining the time of aggregation: Set as appropriate according to the aggregation execution interval (3-month unit, 6-month unit, 1-year unit, etc.).

1.4 Determination of inpatient event extraction period: Although it is determined based on the aggregation implementation interval and other factors, the basic period is one year, which has the longest certification validity period.

1.5 Determination of hospitalization-related analysis period: Determine the prescribed period before and after the hospitalization event. Set a predetermined period from hospitalization to discharge.

The basic data set, the care data set, the medical data set, and the measure-specific data set, which are the data to be analyzed, are created in the database of the data acquisition unit 311 of the client 31 and the server 32 in FIG. This is done by the processing in unit 321. That is, each of the above-mentioned picked-up data is stored in the original data storage unit 101 of the database 10 by the database creation unit 321 of the server 32 from the data acquisition unit 311 of the client 31.

2. Calculation of O index (processing of O index calculation unit 1801)
2.1 Extraction of inpatients: Extracts those aged 65 and over who had an inpatient event during the preset inpatient event extraction period.

The check of the hospitalization event differs depending on whether the medical receipt data is used or the care receipt data is available without the same data. When using medical receipt data, inpatient events are detected in the DPC data of acute care hospitals.

If there is no medical receipt data and only the long-term care receipt data is available, the hospitalization event is detected depending on whether the information provision addition at admission or the joint guidance addition at discharge is requested in the home care support office service addition item.

2.2 Certification renewal check within the hospitalization-related analysis period: Checks whether the stage of mental and physical condition has changed within the preset hospitalization-related analysis period.

2.3 Calculation of the degree of change in the mental and physical condition after discharge by the stage of mental and physical condition before admission: The degree of change between the stage of mental and physical condition before admission and the stage of mental and physical condition after discharge is calculated as the value of the O index.

2.4 Calculation of shortened period due to hospitalization for estimated duration by stage of mental and physical condition before hospitalization: How long the expected duration of mental and physical condition will continue in pre-hospital examination is shortened by hospitalization To calculate,
2.5 Aggregation by aggregation unit: The value of the O index to be aggregated is aggregated in the aggregation unit to which the individual belongs (acute hospital, home care support office, etc.), and the average value of the aggregation value is calculated and calculated. Let the value obtained be the value of the O index of the aggregation unit.

2.6 Gender / Age Category Adjustment: Adjust by multiplying the gender / age category by a predetermined correction coefficient in order to compare the aggregation units (small areas, business establishments, etc.) that aggregated the aggregation targets obtained as described above. Do.

In the above embodiment, the degree of change in mental and physical condition after discharge according to the stage of mental and physical condition before admission is calculated for each stage of mental and physical condition before admission, and this is defined as an O index. However, since there are usually a plurality of mental and physical condition items and the O index for each stage is calculated for each of the plurality of mental and physical condition items, the number of O indexes is expected to be enormous.

If the number of indicators is larger than necessary, there will be too much information, and it may be difficult to grasp the trends and issues of each home care support office (or care manager) or each acute care hospital based on this information. Be done. Therefore, when the number of indicators is large, it is necessary to aggregate them into an appropriate amount. At that time, since the ratio of the total number of people in each stage of the mental and physical condition items is different for each stage, it is not possible to make a comparison between business establishments by their simple average. Therefore, a weighted average based on the aggregated number of people ratio at each stage of the same mental and physical condition item is used as an O index that aggregates all stages of the mental and physical condition item.

Hereinafter, how to obtain this O index will be described.

When the index value for each stage of the mental and physical condition items to be aggregated is V1, V2, V3 ... Vn, and the total number of people (number of records) for each stage index is N1, N2, N3 ... Nn, the index after aggregation The value U is calculated using the following formula.

U = (V1 x N1 + V2 x N2 + V3 x N3 + ... + Vn x Nn)
÷ (N1 + N2 + N3 + ... + Nn)
For example, when the three indexes A, B, and C are aggregated, when the index values of the indexes A, B, and C are 10, 25, and 40, respectively, and the number of records to be aggregated is 3, 2, 1, respectively, the index after aggregation is used. The value is 20 as shown in the formula below.

U = (10 × 3 + 25 × 2 + 40 × 1) / (3 + 2 + 1) = 20
Regarding the degree of aggregation, not only each stage of the same mental and physical condition items, but also related mental and physical condition items such as the first group to the fifth group of the accredited survey items are used as the O index aggregated in the same manner as described above. It is also possible.

That is, the O index calculation unit has a plurality of items of the mental and physical state, and when the O index value of each stage is calculated for each of the plurality of items of the mental and physical state, the number of index values becomes large. Therefore, for each item of mental and physical condition, the index value at each stage of the user belonging to the aggregation unit and the weighted average value obtained by summarizing all the stages using the number of records of the index value at each stage are obtained. Then, this weighted average value is used as an O index of an item of a certain mental and physical condition in the aggregation unit.

As a result, if the number of indicators is large, the contents of the indicators and the number of indicators that can be understood by the home care support office (or care manager), etc., and the acute care hospital, etc., and lead to concrete practice for business improvement Aggregation is possible.

As a specific effect, when insurers such as local governments provide information to each home care support office (or care manager), acute care hospitals, and care service users and patients, the information granularity is easy for them to understand. Can be realized. In addition, it will be easier for insurers such as local governments to grasp the strengths and weaknesses of each home care support office (or care manager) and acute care hospital, and the expansion to incentive performance indicators will be smooth.

The above aggregation example can be similarly applied to the P index and the S index. In addition, all business areas of the community-based comprehensive care business (long-term care severity deterrence business (mental and physical condition improvement / maintenance continuation business), new comprehensive business (long-term care prevention / life support), home medical care / nursing care cooperation, acute exacerbation hospitalization / discharge medical care / nursing care It can be applied to cooperation, disease prevention, and prevention of severity).

Next, the processing of the P index calculation unit 1802 will be described.

3. 3. Calculation of P index value (processing of P index calculation unit 1802)
3.1 Set of items related to medical care and long-term care cooperation related to P index: Items of service contents that contribute to maintenance and improvement of mental and physical condition are set as P index. Details of the P index will be described later.

3.2 Extraction of the above items within the hospitalization-related analysis period: The service contents (actual results) of each month within the preset hospitalization-related analysis period are extracted for each candidate (individual) to be aggregated.

3.3 Calculation of P-index within the analysis period: The service content (actual results) of each month within the preset hospitalization-related analysis period is calculated and used as the value of the P-index for each candidate (individual) to be aggregated.

3.4 Aggregation unit Aggregation: The value of each P index calculated in the above-mentioned flow number 3.3 is aggregated in the aggregation unit (hospital, business establishment, etc.) to which this aggregation target person belongs, and the average value of the aggregation value is calculated. Calculate and use as the value of the P index of the aggregation unit.

3.5 Gender / age classification adjustment: In order to compare the aggregation units (hospitals, business establishments, etc.), the value of each P index calculated in flow numbers 3 and 4 has a predetermined correction coefficient corresponding to the gender / age classification. To make adjustments.

4. Calculation of S index value (processing of S index calculation unit 1803)
4.1 Set of action items such as medical and long-term care cooperation related to S index: Set the items of efforts such as hospitals and business establishments that contribute to maintenance and improvement of mental and physical condition as S index. Details of the S index will be described later.

4.2 Extraction of the above-mentioned efforts in the aggregation unit within the hospitalization-related analysis period: The efforts (actual results) of each month within the preset hospitalization-related analysis period are extracted for each candidate (individual) to be aggregated.

4.3 Calculation of S index within the analysis period: Calculate the efforts (actual results) of each month within the preset hospitalization-related analysis period, and obtain the value of the S index for each aggregation target (individual).

4.4 Aggregation unit Aggregation: The value of each S index calculated in the above-mentioned flow number 4.3 is aggregated in the aggregation unit to which this target person candidate 2 belongs, and the average value of the aggregated values is calculated and calculated. Is the value of the S index of the aggregation unit.

4.5 Gender / age classification adjustment: In order to compare the aggregation units, the value of each S index calculated in flow numbers 4 and 4 is adjusted by multiplying the value of each S index corresponding to the gender / age classification by a predetermined correction coefficient.

Next, the aggregation conditions and the like regarding the above-mentioned business related to medical and long-term care cooperation at the time of admission will be described.

[Aggregation target area]
Emergency hospitalization-causing illness [Subjects]
Certified person (support 1 and 2, long-term care 1 to 5) (when there are independent service personnel on the care side and medical side at the time of admission)
[Aggregation unit]
<Nursing side> Home care support office, individual care manager <Medical side> Acute hospital [Data source]
<Long-term care side>
・ Nursing care certification data (common throughout Japan)
・ Nursing care receipt data (common throughout Japan)
・ Data on the efforts of home care support establishments and individual care managers (questionnaire data common to insurers-specific home care support establishments and care managers)
・ Data such as attributes of home care support establishments (care manager individuals) (questionnaire data common to insurer-specific home care support establishments and care managers)
・ Data such as user attributes by home care support office (individual care manager) (questionnaire data common to insurer-specific home care support offices and care managers)
<Medical side>
・ Medical examination data (common throughout Japan)
・ Medical receipt data (common throughout Japan)
・ Data on efforts of acute care hospitals (questionnaire data common to acute care hospitals unique to insurers)
・ Data such as attributes of acute care hospitals (questionnaire data common to acute care hospitals unique to insurers)
・ Data such as user attributes by acute care hospital (questionnaire data common to acute care hospitals unique to insurers)
Diseases that cause emergency hospitalization include stroke, head fracture and thigh fracture due to falls, acute myocardial infarction, and acute exacerbation of chronic diseases such as respiratory, digestive, circulatory, and urinary organs.

Home care support establishment (care manager individual) attribute data or acute care hospital attribute data is the scale of establishment or hospital, type of corporation, etc. Some of these data are also included in the long-term care business ledger and medical institution ledger, and are the basic attributes of business establishments or hospitals that are difficult to control by the efforts of business establishments or hospitals.

Here, the data such as user attributes by home care support establishment (individual care manager) or the data such as user attributes by acute hospital are the user's lifestyle, life philosophy, home environment, work environment, neighborhood environment and economy. This is data that aggregates the basic attributes of users, such as the situation, which are not included in the certification data for long-term care and are difficult to control by user efforts, for each business establishment or hospital.

For each attribute data, the code that leads to good or bad is defined in consideration of the magnitude relationship.

As a result, it may be useful for causal analysis between establishments or hospitals that have the same services and establishments or hospitals but have different effects.

It should be noted that the data of one long-term care insurer is not enough to thoroughly evaluate the home care support establishment or the acute care hospital. The reason is not a little, especially when the location of the acute hospital is outside the insured, and there is a possibility that the insured of another insurer in the adjacent secondary medical area, for example, may be hospitalized. In that case, it is necessary to aggregate the long-term care insurance data and medical insurance data that integrate the above-mentioned multiple insurers and perform an aggregate analysis by hospital. However, the logic for aggregating various indicators at that time is the same as for one insurer.

Next, specific examples of OPS indexes in medical care cooperation at the time of admission based on the above-mentioned aggregation conditions and the like will be described.

In the following description, the symbol × does not mean multiplication, but represents a combination of items before and after it.

O index;
<Long-term care side>
[Emergency hospitalization cause illness x Home care support establishment (care manager individual) x Pre-hospital mental and physical condition stage x Post-discharge average stage change]
・ Emergency hospitalization cause illness × Home care support establishment (care manager individual) × Pre-hospital care required status classification Stage × Post-discharge average stage change ・ Emergency hospitalization cause illness × Home care support establishment (care) (By individual manager) x Pre-hospital dementia independence level x Post-discharge average stage change / Emergency hospitalization cause Disease x Home care support establishment (care manager individual) x Pre-hospital disability independence stage x Post-discharge Average stage change / emergency hospitalization cause Illness x Home care support establishment (care manager individual) x Pre-hospital certification survey item Stage x Post-discharge average stage change <Medical side>
[Emergency hospitalization by disease x acute phase hospital x pre-hospital mental and physical condition stage x post-discharge average stage change]
・ Emergency hospitalization cause disease × Acute hospital × Pre-hospital care status classification Stage × Post-discharge average stage change ・ Emergency hospitalization cause disease × Acute hospital × Pre-hospital dementia Independence stage × Post-discharge Average stage change / emergency hospital cause disease x acute hospital x pre-hospital disability independence stage x post-discharge average stage change / emergency hospital cause disease x acute hospital x pre-hospital certification survey item stage x Average post-discharge average stage change / emergency hospitalization cause disease x acute phase hospital x pre-hospital medical examination item Risk stage x post-discharge average stage change Here, the average post-discharge stage change as an O index is the average stage change after discharge. It is the difference value of the stage code value of the mental and physical condition from that before admission when there is an application for change of certification within a predetermined period after discharge from the hospital. If there is no application for change of certification during the above period, it is considered that there has been no change in the stage.

In addition, the reason why the risk stages of various test items in medical examinations are only on the <medical side> is that they represent the degree of risk for each disease, and it is assumed that their control is possible only on the medical side.

In addition, the average degree of change is adjusted by gender and age group. As the age classification, the early-stage elderly classification and the late-stage elderly classification are adopted. The timing of determining the age category shall be determined from the age in the month of hospitalization. The month of hospitalization will be determined from the month of occurrence of the medical receipt data.

Furthermore, as another example of the O index, with respect to the average duration for each mental and physical condition stage of the establishment by type of long-term care service used by the user or the period of the entire insurer. A method of focusing on the shortened period from the average duration, which is calculated as the difference between the start month of the pre-hospital mental and physical condition stage and the hospitalization month, is also conceivable. Based on this idea, it is expected that the remuneration will be reduced for medical institutions and home care support establishments whose long-term care status is worsened due to unexpected cognitive decline due to hospitalization.

P index <Nursing care side>
[Emergency hospitalization cause and illness x Home care support establishment (care manager individual) x Pre-hospital mental and physical condition stage medical care cooperation addition item x Average billing ratio]
・ By the same illness × By the same establishment (by the same individual) × By pre-hospital care status classification By stage Medical care cooperation cooperation By item × Average billing ratio ・ By the same illness × By the same establishment (by the same individual) × Before admission Dementia independence degree by stage of medical and nursing care cooperation addition item x average billing ratio / disease by the same business establishment (by the same individual) x pre-hospital disability independence degree by stage of medical care and nursing care cooperation addition item x average billing ratio By illness x by the same establishment (by the same individual) x by pre-hospital certification survey item by stage of medical care and nursing care cooperation by item x average billing ratio / by the same disease x by the same establishment (by the same individual) x home care support establishment Different (individual care manager) User attributes, etc. (index for analyzing the cause of differences between the relevant business establishments (individual care managers))
<Medical side>
[Emergency hospitalization by illness x acute phase hospital x pre-hospital mental and physical condition by medical care cooperation addition item x average billing ratio]
・ Emergency hospitalization cause disease × Acute hospital × Pre-hospital care status classification Stage medical care cooperation Addition item × Average billing ratio ・ Emergency hospitalization cause disease × Acute hospital × Pre-hospital dementia Independence stage Medical care cooperation addition item x average billing ratio / emergency hospitalization cause disease x acute hospital x pre-hospital disability independence stage medical care cooperation addition item x average billing ratio / emergency hospitalization cause disease x acute hospital × Pre-hospital certification survey item by stage Medical care cooperation addition item × Average billing ratio ・ Emergency hospitalization cause disease × Acute hospital × Pre-hospital medical examination item Risk stage by relevant disease treatment item × Average billing ratio ・User attributes, etc. by emergency hospitalization cause disease x acute phase hospital x acute phase hospital Here, the fifth P index on the care side and the sixth P index on the medical side are the lifestyle and life of the user. The attributes of users that are difficult to control on the business establishment or hospital side, such as philosophy, family environment, work environment, neighborhood environment, and economic conditions, which are not included in the certification data for long-term care, are aggregated for each business establishment or hospital. It is data. These may be useful for cause analysis between business establishments or hospitals that provide the same service but have different effects.

The P index indicates the implementation status of cooperation between the home care support office or individual care manager and the acute care hospital staff for each user within the hospitalization-related period including before and after hospitalization extracted by the O index. Calculate and obtain the average billing ratio of.

For the risk stages of various examination items for medical examinations on the <medical side>, the average amount of various treatment items provided for each disease is calculated and calculated, not the billing ratio of the medical care cooperation addition items.

The following are assumed as additional items for medical care cooperation at the time of admission on the <nursing side>. The number of credits is as of May 2012. Here, the unit means that the number of units is multiplied by 10 and the unit price is adjusted by region (adjustment is made higher in urban areas by adjusting due to differences in price levels). It will be the amount to be charged.

1. 1. At the time of admission / information linkage addition (visit: 200 credits, non-visit: 100 credits)
2. Discharge and discharge addition: 300 credits (can be calculated up to 3 times during hospitalization, etc., 1 of which participates in a conference at a medical institution)
Prepare for life support after discharge (interview), collect assessment information, reflect in the care plan, and submit the care plan at the request of the medical institution.

3. 3. Home conference addition for emergencies: 200 credits (up to twice a month at the request of hospitals, etc.)
The following are assumed as the items to be added to the medical care cooperation at the time of admission on the <medical side>. The number of credits is as of May 2012. Here, the score is the amount that the medical institution charges to the National Health Insurance Federation, the social insurance payment fund, etc. by multiplying the score by 10.

1. Discharge adjustment addition (acute phase): 340 points from 1 to 14 days, 150 points from 15 to 30 days, 31 to 50 points (once at discharge)
Established screening system, started to formulate discharge support plan within 7 days of hospitalization, contact / discharge adjustment, and provided written adjustment details.

2. Joint guidance fee at discharge: 300 points Joint guidance with doctors or nurses in hospitals and doctors in charge at home (provided in writing, once during hospitalization).

Addition: Joint with home doctor: 300 points Joint with 3 or more home care staff: 2000 points (doctor, nurse, dentist, dental hygienist, pharmacist, visiting nurse, care support specialist).

3. 3. Long-term care support cooperation guidance fee: 300 points Joint guidance on medical points to be noted when receiving long-term care insurance services and points to be noted in medical treatment (up to 2 times during hospitalization).

Doctors, nurses, social workers, etc. collaborate with care support specialists.

Consent required from the hospitalized patient, obtain a copy of the care plan from the care support specialist with the patient's consent.

4. Pre-discharge visit guidance fee: 555 points 5. Medical information provision fee: 250 points S index <Nursing care side>
[Emergency hospitalization by illness x home care support establishment (care manager individual) x pre-hospital mental and physical condition by establishment (individual) efforts x average implementation amount / illness x establishment (individual) ) × Pre-hospital care status classification Business establishment (individual) by stage × Average implementation amount / disease x Business establishment (individual) × Pre-hospital dementia degree of independence Business establishment (individual) By x average implementation amount / by the same disease x by the same establishment (by the same individual) x by the degree of independence of pre-hospital disability by establishment (individual) efforts x average implementation amount, by the same disease x by the same establishment (same individual) (Separate) x Pre-hospital certification survey items By stage (individual) Efforts x Average implementation amount / Disease x By business (by individual) x By business (same individual) Attributes, etc. (between business establishments, etc.) Index for analyzing the cause of the difference)
<Medical side>
[Emergency hospitalization by illness x acute phase hospital x pre-hospital mental and physical condition by hospital effort x average amount]
・ By emergency hospitalization cause disease × By acute phase hospital × By pre-hospital care status classification By stage hospital effort × Average amount ・ By emergency hospitalization cause disease × By acute phase hospital × By pre-hospital dementia independence stage By hospital effort × Average implementation amount / emergency hospital cause disease × acute phase hospital × pre-hospital disability independence stage by hospital approach × average dose / emergency hospitalization cause disease × acute phase hospital × pre-hospital certification survey item staged hospital By Effort x Average Implementation / Emergency Hospitalization Cause Disease x Acute Phase Hospital x Pre-hospital Medical Examination Item Risk Stage By Hospital Initiative x Average Implementation Volume / Emergency Hospitalization Cause Disease x Acute Phase Hospital x Acute Phase Hospital Attribute Etc. (Indicators for analyzing the causes of differences between hospitals)
Here, the fifth S index on the nursing side and the sixth S index on the medical side are the scale of the establishment or acute care hospital, the type of corporation, etc., and some of them are in the nursing establishment ledger or medical institution ledger. Data included in mourning, such as attributes of business establishments or hospitals that are difficult to control on the business establishment or hospital side. As a result, each approach (S index) provision service is the same, but the effect (O index) may be different, which may be useful for cause analysis between business establishments or hospitals.

The S index is calculated by calculating the average amount of organizational efforts of home care support establishments or individual care managers and acute care hospital staff within the hospitalization-related period including before and after hospitalization extracted by the O index.

The following are assumed as examples of efforts by home care support establishments or individual care managers on the <nursing side>.

・ Thoroughly request users to carry care management information at all times ・ Thorough emergency contact rules with the user's family and acute care hospital staff to grasp the hospitalization time in a timely manner ・ User's life at the acute care hospital at the time of admission・ Preparation of input forms for mental and physical / nursing information, etc. The following are assumed as examples of efforts by <medical side> acute care hospitals.

・ Establishing a thorough system to require timely initial notification to the care manager at the time of emergency hospitalization of the user (patient) ・ Mandatory holding of a place to provide information on life, mind, body, and long-term care about the user (patient) from the care manager at the time of admission Input form of user's life / mental / physical / nursing information to care manager at the time of maintenance / hospitalization Based on the patient's life / mental / physical / nursing information from the care manager, not only the treatment of the target disease but also the viewpoint of long-term care In this way, by using the connected OPS index for each of the long-term care side and the medical care side, it is possible to accurately analyze the data objectively and quantitatively on each of the long-term care side and the medical care side. It will be possible to identify issues, concretely show the effectiveness of measures, and accurately implement community-based comprehensive care projects using highly accurate indicators.

The above-mentioned embodiment explained a low-level community-based comprehensive care business system implemented and operated by a local government or the like as an insurer. Here, the insurer is the local government in the case of a care insurer, the occupational medical insurance in the case of a medical insurer, such as union health insurance and mutual aid health insurance, and the regional medical insurance in association, national insurance (local government), medical association for the elderly, etc. Is.

It is possible to apply the method of the comprehensive community care business system at the insurer level to the system of the comprehensive community care business at the higher prefecture or country level. Hereinafter, embodiments of this higher-level regional comprehensive business system will be described.

<< Embodiment of high-level community-based comprehensive care system >>
In the above-mentioned lower-level community-based comprehensive system, each insurer of long-term care and medical care grasped the value of the OPS index for each business unit for each aggregation unit such as a subregion or a business establishment. On the other hand, in the higher-level regional comprehensive system according to this embodiment, the aggregation unit is each insurer, and each insurer obtains the disparity and the magnitude of the divergence of the OPS index of each insurer. To be able to grasp the status of community-based comprehensive care business at the level of prefectures and countries.

The system configuration in this case will be described with reference to FIGS. 5 and 6. Since the basic system configuration has many parts in common with those shown in FIGS. 1 and 2, the common parts will be described below with the same reference numerals.

This high-level community-based comprehensive care business system also has a solution function 15 for the database 10 and the target business area 16 as shown in the schematic diagram of FIG.

However, since the aggregation unit is each insurer and it is a regional comprehensive business system at the prefectural and national levels higher than those, various insurers A, B, C, ... The following various data are provided.

-Basic data 11, resident address data 111, resident common number (tentative name) 112, regional characteristic data 113, regional efforts 114.

-As long-term care insurance data 12, long-term care certification data 121, long-term care receipt data 122, and long-term care establishment ledger 123.

-As data 13 related to medical insurance, elderly specific medical examination data 131, National Health Insurance / late-stage elderly medical receipt data 132, medical institution ledger 133.

-Basic checklist (results of daily living area needs survey) 141, measure plans and achievements for small areas, business establishments, and users 142 as specific data 14 for measures related to comprehensive community care unique to local governments for residents.

The database 10 centralizes and stores the necessary parts of these data 11, 12, 13, and 14. That is, in this database 10, the related parts of these data 11, 12, 13, and 14 are linked and unified.

In order to achieve the measures implemented by each business area 161, 162, 163, 164, 165, 166 of the community-based comprehensive care business, the indicators set for each business area 161, 162, 163, 164, 165, 166 are It is registered in the index menu 151 of the solution function 15.

The solution function 15 is realized by a computer and is executed in each business area 161, 162, 163, 164, 165, 166 using each data 11, 12, 13, 14 centralized in the database 10. The achievement status and implementation status of measures are analyzed by aggregating the values of the indicators set for each measure. For this purpose, in addition to the index menu 151 described above, the aggregation unit menu 152 and various analysis functions 153 are provided.

That is, in the upper community comprehensive care business system shown in FIG. 5, various data including individual level data provided by each insurer A, B, C, ... Is under the jurisdiction of the upper prefecture or country level. In the solution function 15, the index obtained from these data is aggregated and analyzed for each insurer, which is the aggregation unit, by the same method as the above-mentioned comprehensive community care business system at the lower level. It is a system. Here, as mentioned above, the insurers that are the aggregation unit are long-term care insurers (local governments), occupational medical insurers (union health insurance, mutual aid health insurance), and regional medical insurers (Japan Health Insurance Association, National Health Insurance, Medical Union for the Elderly). ).

FIG. 6 shows the configuration of a computer system that creates a database 10 and executes a community-based comprehensive care business. Also in FIG. 6, it is shown that the database 10 is a computer system having a client 31 and a server 32 connected to the client 31, and the storage / reading of data is controlled by the server 32.

The client 31 is a computer provided to each insurer A, B, C, ..., And the data acquisition unit 311 has basic data 11, long-term care insurance data 12, medical insurance data 13, measures, etc. regarding residents. The unique data 14 is taken in and provided to the server 32.

The server 32 stores the above-mentioned data 11, 12, 13, and 14 in the original data storage unit 101 of the database 10 as unified data by the database creation unit 321.

In addition, the server 32 has an index calculation unit 322, an aggregation unit 323, and an analysis unit 324 in order to realize the solution function of the community-based comprehensive care business. The index calculation unit 322 calculates the value of the index registered for each business area in the index menu 151 of FIG.

The tabulation department 323 is a tabulation unit registered in the tabulation unit menu 152 in FIG. 5 (long-term care insurer (local government), occupational medical insurer (union health insurance, mutual aid health insurance), regional medical insurer (association health insurance, national health insurance, late-stage elderly). The value of the index is totaled for each medical association), etc.). The analysis unit 324 executes each of the analysis functions 153 shown in FIG.

In this higher-level community-based comprehensive care business system, as in the case of the lower-level community-based comprehensive care system, O / P / S indicators are required for each of the nursing-care side and the medical-side regarding the business related to medical care cooperation at the time of admission. , Each insurer is evaluated by the consolidated OPS index. Therefore, a specific example of the OPS index in each of the above-mentioned projects will be described.

First, we will explain the aggregation conditions, etc. regarding the business related to medical and long-term care cooperation at the time of admission.

[Aggregation target area]
Emergency hospitalization-causing illness [Subjects]
Certified person (support 1 and 2, long-term care 1 to 5) (when there are independent service personnel on the care side and medical side at the time of admission)
[Aggregation unit]
<Long-term care side> Local government (because all elderly people who are residents of the local government and are insured by long-term care insurance are included in the total)
<Medical side> Local government (to include the early-stage elderly who are residents of the local government and are insured by the National Health Insurance medical insurance and the late-stage elderly who are insured by the wide-area medical care for the late-stage elderly)
[Data source]
<Long-term care side>
・ Nursing care certification data (common throughout Japan)
・ Nursing care receipt data (common throughout Japan)
・ Data on the efforts of home care support establishments and individual care managers (questionnaire data common to insurers-specific home care support establishments and care managers)
・ Data such as attributes of home care support establishments (care manager individuals) (questionnaire data common to insurer-specific home care support establishments and care managers)
・ Data such as user attributes by home care support office (individual care manager) (questionnaire data common to insurer-specific home care support offices and care managers)
<Medical side>
・ Medical examination data (common throughout Japan)
・ Medical receipt data (common throughout Japan)
・ Data on efforts of acute care hospitals (questionnaire data common to acute care hospitals unique to insurers)
・ Data such as attributes of acute care hospitals (questionnaire data common to acute care hospitals unique to insurers)
・ Data such as user attributes by acute care hospital (questionnaire data common to acute care hospitals unique to insurers)
Diseases that cause emergency hospitalization include stroke, head fracture and thigh fracture due to falls, acute myocardial infarction, and acute exacerbation of chronic diseases such as respiratory, digestive, circulatory, and urinary organs.

Home care support establishment (care manager individual) attribute data or acute care hospital attribute data is the scale of establishment or hospital, type of corporation, etc. Some of these data are also included in the long-term care business ledger and medical institution ledger, and are the basic attributes of business establishments or hospitals that are difficult to control by the efforts of business establishments or hospitals.

Here, the data such as user attributes by home care support establishment (individual care manager) or the data such as user attributes by acute hospital are the user's lifestyle, life philosophy, home environment, work environment, neighborhood environment and economy. This is data that aggregates the basic attributes of users, such as the situation, which are not included in the certification data for long-term care and are difficult to control by user efforts, for each business establishment or hospital.

For each attribute data, the code that leads to good or bad is defined in consideration of the magnitude relationship.

As a result, it may be useful for causal analysis between establishments or hospitals that have the same services and establishments or hospitals but have different effects.

It should be noted that the data of one long-term care insurer is not enough to thoroughly evaluate the home care support establishment or the acute care hospital. The reason is not a little, especially when the location of the acute hospital is outside the insured, and there is a possibility that the insured of another insurer in the adjacent secondary medical area, for example, may be hospitalized. In that case, it is necessary to aggregate the long-term care insurance data and medical insurance data that integrate the above-mentioned multiple insurers and perform an aggregate analysis by hospital. However, the logic for aggregating various indicators at that time is the same as for one insurer.

Next, specific examples of OPS indexes in medical care cooperation at the time of admission based on the above-mentioned aggregation conditions and the like will be described.

In the following description, the symbol × does not mean multiplication, but represents a combination of items before and after it.

O index;
<Long-term care side>
[Emergency hospitalization cause disease x municipality x pre-hospital mental and physical condition stage x post-discharge average stage change]
・ Emergency hospitalization cause illness × Municipality × Pre-hospital care required status classification Stage × Post-discharge average stage change ・ Emergency hospitalization cause illness × Municipality × Pre-hospital dementia independence stage × Post-discharge average stage change・ Emergency hospitalization cause disease x municipality x pre-hospital disability independence stage x post-discharge average stage change ・ Emergency hospitalization cause disease x municipality x pre-hospital certification survey item stage x post-discharge average stage change <medical care Side>
[Emergency hospitalization cause disease x municipality x pre-hospital mental and physical condition stage x post-discharge average stage change]
・ Emergency hospitalization cause illness × Municipality × Pre-hospital care required status classification Stage × Post-discharge average stage change ・ Emergency hospitalization cause illness × Municipality × Pre-hospital dementia independence stage × Post-discharge average stage change・ Emergency hospitalization cause disease × Municipality × Pre-hospital disability independence stage × Post-discharge average stage change ・ Emergency hospitalization cause disease × Municipality × Pre-hospital certification survey item Stage × Post-discharge average stage change ・ Emergency Cause of hospitalization By disease x Municipality x Pre-hospital medical examination item Risk stage x Average stage change after discharge Here, the average stage change after discharge as an O index is changed from during hospitalization to within a predetermined period after discharge. This is the difference between the stage code value of the relevant mental and physical condition when there is an application and that before admission. If there is no application for change of certification during the above period, it is considered that there has been no change in the stage.

In addition, the reason why the risk stages of various test items in medical examinations are only on the <medical side> is that they represent the degree of risk for each disease, and it is assumed that their control is possible only on the medical side.

In addition, the average degree of change is adjusted by gender and age group. As the age classification, the early-stage elderly classification and the late-stage elderly classification are adopted. The timing of determining the age category shall be determined from the age in the month of hospitalization. The month of hospitalization will be determined from the month of occurrence of the medical receipt data.

Furthermore, as another example of the O index, with respect to the average duration for each mental and physical condition stage of the establishment by type of long-term care service used by the user or the period of the entire insurer. A method of focusing on the shortened period from the average duration, which is calculated as the difference between the start month of the pre-hospital mental and physical condition stage and the hospitalization month, is also conceivable. Based on this idea, it is expected that the remuneration for local governments will be reduced due to the unexpected deterioration of cognitive function due to hospitalization.

As described in the implementation of the lower-level community-based comprehensive care system in the first half of this specification, since the O index is defined for each stage of the mental and physical condition items in the above embodiment, it is assumed that the number of O indexes will be enormous. Will be done.

If the number of indicators is larger than necessary, information overload will occur. Therefore, if the number of indicators is large, the amount will be aggregated to an appropriate amount by the same method as described above. That is, since the ratio of the total number of people in each stage of the mental and physical condition items is different for each stage, it is not possible to compare between the total units (local governments, insurers, etc.) with their simple averages. Therefore, the weighted average based on the aggregated number of people ratio at each stage of the same mental and physical condition item is calculated by the following formula as an O index that aggregates all stages of the mental and physical condition item.

Here, the index values for each stage of the mental and physical condition items to be aggregated are V1, V2, V3 ... Vn, and the total number of people (number of records) for each stage index is N1, N2, N3 ... Nn. Let U be the index value.

U = (V1 x N1 + V2 x N2 + V3 x N3 + ... + Vn x Nn)
÷ (N1 + N2 + N3 + ... + Nn)
Regarding the degree of aggregation, it is possible to use an O index that aggregates not only each stage of the same mental and physical condition items but also related mental and physical condition items such as the first group to the fifth group of the accredited survey items. ..

As a result, when the number of indicators is large, it is possible to aggregate the contents of the indicators and the number of indicators that are understandable to the insurer at the site and lead to concrete practice for business improvement.

The above aggregation example can be similarly applied to the P index and the S index. In addition, all business areas of the community-based comprehensive care business (long-term care severity deterrence business (mental and physical condition improvement / maintenance continuation business), new comprehensive business (long-term care prevention / life support), home medical care / nursing care cooperation, acute exacerbation hospitalization / discharge medical care / nursing care It can be applied to cooperation, disease prevention, and prevention of severity).

P index <Nursing care side>
[Emergency hospitalization by illness x municipality x pre-hospital mental and physical condition by medical and nursing care cooperation addition item x average billing ratio]
・ By disease x By local government x By insurer x Pre-hospital care status category Medical care cooperation addition by stage x Average billing ratio ・ By disease x By local government x Pre-hospital dementia Degree of independence Medical care cooperation addition Item x average billing ratio / disease x local government x pre-hospital disability independence stage medical care cooperation addition item x average billing ratio / disease x local government x pre-hospital certification survey item medical care cooperation addition By item x average billing ratio, by the same disease x by local government x by local government User attributes, etc. <Medical side>
[Emergency hospitalization by illness x municipality x pre-hospital mental and physical condition by medical and nursing care cooperation addition item x average billing ratio]
・ Emergency hospitalization cause disease × Municipality × Pre-hospital care status category Medical care cooperation addition item × Average billing ratio ・ Emergency hospitalization cause disease × Municipality × Pre-hospital dementia degree of independence Medical care cooperation addition By item x average billing ratio / emergency hospitalization cause disease x municipality x pre-hospital disability independence stage medical care cooperation addition item x average billing ratio / emergency hospitalization cause disease x municipality x pre-hospital certification survey item stage Medical care cooperation addition item x average billing ratio / emergency hospitalization cause disease x municipality x pre-hospital medical examination inspection item by risk stage x average billing ratio / emergency hospitalization cause disease x municipality x local government Different user attributes, etc. Here, the 5th P index on the nursing side and the 6th P index on the medical side are the lifestyle, life philosophy, home environment, work environment, neighborhood environment, economic situation, etc. of the user. This is data that aggregates the attributes of users that are difficult to control on the business establishment or hospital side, which are not included in the data requiring medical care, for each business establishment or hospital. These may be useful for cause analysis between local governments that provide the same service but have different effects.

The P index indicates the implementation status of cooperation between the home care support office or individual care manager and the acute care hospital staff for each user within the hospitalization-related period including before and after hospitalization extracted by the O index. Calculate the average billing ratio for each municipality.

Regarding the risk stages of various medical examination items on the <medical side>, the average amount of various treatment items provided for each disease is calculated and calculated for each local government, not the billing ratio for the medical care cooperation addition items.

The following are assumed as additional items for medical care cooperation at the time of admission on the <nursing side>. The number of credits is as of May 2012. Here, the unit means that the number of units is multiplied by 10 and the unit price is adjusted by region (adjustment is made higher in urban areas by adjusting due to differences in price levels). It will be the amount to be charged.

1. 1. At the time of admission / information linkage addition (visit: 200 credits, non-visit: 100 credits)
2. Discharge and discharge addition: 300 credits (can be calculated up to 3 times during hospitalization, etc., 1 of which participates in a conference at a medical institution)
Prepare for life support after discharge (interview), collect assessment information, reflect in the care plan, and submit the care plan at the request of the medical institution.

3. 3. Home conference addition for emergencies: 200 credits (up to twice a month at the request of hospitals, etc.)
The following are assumed as the items to be added to the medical care cooperation at the time of admission on the <medical side>. The number of credits is as of May 2012. Here, the score is the amount that the medical institution charges to the National Health Insurance Federation, the social insurance payment fund, etc. by multiplying the score by 10.

1. Discharge adjustment addition (acute phase): 340 points from 1 to 14 days, 150 points from 15 to 30 days, 31 to 50 points (once at discharge)
Established screening system, started to formulate discharge support plan within 7 days of hospitalization, contact / discharge adjustment, and provided written adjustment details.

2. Joint guidance fee at discharge: 300 points Joint guidance with doctors or nurses in hospitals and doctors in charge at home (provided in writing, once during hospitalization).

Addition: Joint with home doctor: 300 points Joint with 3 or more home care staff: 2000 points (doctor, nurse, dentist, dental hygienist, pharmacist, visiting nurse, care support specialist).

3. 3. Long-term care support cooperation guidance fee: 300 points Joint guidance on medical points to be noted when receiving long-term care insurance services and points to be noted in medical treatment (up to 2 times during hospitalization).

Doctors, nurses, social workers, etc. collaborate with care support specialists.

Consent required from the hospitalized patient, obtain a copy of the care plan from the care support specialist with the patient's consent.

4. Pre-discharge visit guidance fee: 555 points 5. Medical information provision fee: 250 points S index <Nursing care side>
[Emergency hospitalization by illness x local government x pre-hospital mental and physical condition by business establishment (individual) efforts x average implementation amount / same illness x local government x pre-hospital care required status classification by stage care insurer efforts x average Implementation amount / disease x municipality x pre-hospital dementia independence stage by long-term care insurer's efforts x average implementation amount / disease x local government x pre-hospital disability independence stage by long-term care insurer's efforts x average implementation Amount / Disease-specific x Local government x Pre-hospital certification survey item Stage-based long-term care insurer efforts x Average amount / Disease-specific x Local government x Local government-specific attributes, etc. <Medical side>
[Emergency hospitalization by illness x municipality x pre-hospital mental and physical condition by hospital approach x average amount]
・ By emergency hospitalization cause disease × by local government × by pre-hospital care status classification by stage hospital effort × average implementation amount ・ By emergency hospitalization cause disease × by local government × pre-hospital dementia degree of independence by hospital effort × average implementation amount・ Emergency hospitalization cause disease x municipality x pre-hospital disability independence stage hospital effort x average implementation amount-Emergency hospitalization cause disease x municipality x pre-hospital certification survey item stage hospital effort x average implementation amount-emergency Hospitalization cause illness x Municipality x Pre-hospital medical examination inspection item Risk stage by hospital effort x Average amount / emergency hospitalization Cause of illness x Municipality x Municipal attribute, etc. Here, the fifth S index on the care side , And the sixth S index on the medical side is the size and corporation type of the care insurer or acute care hospital in the local government, and the attributes of the local government that are difficult to control by the care insurer or the hospital side. By these, each approach (S index) provision service is the same, but there is a possibility that it will be useful for cause analysis between local governments with different effects (O index).

The S index is calculated by calculating the average amount of systematic efforts of the long-term care insurer and the acute care hospital staff for each local government within the hospitalization-related period including before and after hospitalization extracted by the O index.

The following are assumed as examples of efforts by local governments on the <nursing side>.

・ Thoroughly request users to carry care management information at all times ・ Thorough emergency contact rules with the user's family and acute care hospital staff to grasp the hospitalization time in a timely manner ・ User's life at the acute care hospital at the time of admission・ Preparation of input forms for mental and physical / nursing information, etc. The following are assumed as examples of efforts by <medical side> acute care hospitals.

・ Establishing a thorough system to require timely initial notification to the care manager at the time of emergency hospitalization of the user (patient) ・ Mandatory holding of a place to provide information on life, mind, body, and long-term care about the user (patient) from the care manager at the time of admission Input form of user's life / mental / physical / nursing information to care manager at the time of maintenance / hospitalization Based on the patient's life / mental / physical / nursing information from the care manager, not only the treatment of the target disease but also the viewpoint of long-term care In this way, by using the connected OPS index for each of the long-term care side and the medical care side, it is possible to accurately analyze the data objectively and quantitatively on each of the long-term care side and the medical care side. It will be possible to identify issues, concretely show the effectiveness of measures, and accurately implement community-based comprehensive care projects using highly accurate indicators.

Here, in promoting the community-based comprehensive care business, efforts for each individual, etc. are carried out at the local government or insurer level, but it is preferable to supervise these businesses at the higher prefecture or national level. For that purpose, higher-ranking organizations such as prefectures and countries need to make top-down comparisons between local governments and insurers. This mutual comparison (visualization) has the effect of inducing efforts to local governments and insurers.

The high-level community-based comprehensive care business system explained in the latter half of the specification of the present application evaluates each local government as an aggregation unit by a consolidated OPS index based on this top-down, so that each local government's community-based comprehensive care is evaluated. It is possible to evaluate the status of business efforts in a mutually quantitative and correct manner.

On the other hand, local governments and insurers themselves can understand their position in the whole country by the above-mentioned mutual comparison (visualization), but how to act to improve their position, high-level community comprehensive care business Specific measures cannot be seen only by evaluating the system.

In the lower-level community-based comprehensive care business system described in the first half of the specification of the present application, each local government and each insurer evaluates the businesses and subregions belonging to them as a total unit using a consolidated OPS index. Therefore, these businesses and subregions can be evaluated quantitatively and correctly. For this reason, it is possible to give specific and effective guidance to them and enhance their own evaluation nationwide.

From these facts, it is the best situation that both solutions of the comprehensive community care system at the local government and insurer level and the comprehensive community care system at the upper prefecture and national level are operated in parallel (applied in parallel). ..

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

10 ... Database 11 ... Basic data 12 ... Nursing care insurance data 13 ... Medical insurance data 14 ... Measures and other specific data 16 ... Target business area 31 ... Client computer 311 ... Data acquisition department 32 ... Server computer 321 ... Database creation department 322 ... Index Calculation unit 1801 ... O index calculation unit 1802 ... P index calculation unit 1803 ... S index calculation unit

Claims (5)

Using a database that has basic data of residents, long-term care insurance data, medical insurance data, and unique data such as measures including measures implemented by local governments, the values of multiple indicators set in advance to achieve a predetermined measure can be set. It is a community-based comprehensive care business system that can be calculated for each aggregation unit.
As the index, a structure index including the implementation status of the measure of the implementer who implements the measure, a process index including the service provision status from the implementer to the user of the measure, and a stage of the mental and physical condition of the user. Outcome indicators including the degree of change are set,
The user who was admitted to the hospital during the predetermined check period is the subject of aggregation, and the degree of change between the stage of the mental and physical condition before admission and the stage of the mental and physical condition within the predetermined period after discharge. The outcome index calculation unit that calculates the outcome index of the totalized person,
A predetermined period before and after admission to the hospital is set as a hospitalization-related analysis period, and a numerical value representing the performance of the service received from the implementer by the aggregation target person during this hospitalization-related analysis period is calculated as a process index of the user. Process index calculation unit and
A structure index calculation unit that calculates a numerical value representing the results of measures taken by the implementer who provided the service to the user during the hospitalization-related analysis period as a structure index.
Community comprehensive care business system with. The value of each index of the individual unit calculated by the outcome index calculation unit, the process index calculation unit, and the structure index calculation unit is totaled for each aggregation unit to which the user belongs, and the average value thereof is the aggregation unit. The community-based comprehensive care business system according to claim 1, which is an outcome index, a process index, and a structure index. The community-based comprehensive care business system according to claim 1 or 2, wherein the outcome index is provided with an amendment unit that makes amendments according to gender and age classification. The outcome index calculation unit has a plurality of items of the mental and physical condition, and when the outcome index value of each stage is calculated for each of the plurality of items of the mental and physical condition, the user belonging to the aggregation unit for each item of the mental and physical condition. The weighted average value that aggregates all stages is obtained by using the index value at each stage of the above and the number of records at each stage of this index value, and this weighted average value is used as the outcome index of the mental and physical condition item of the aggregation unit. The community-based comprehensive care business system according to claim 2 , characterized in that. The aggregation unit is a long-term care service establishment, a medical institution, a small area within the long-term care insurer, a stratified group with the occupational medical insurer at the top, a stratified group with the regional medical insurer at the top, a long-term care insurer, and a occupational area. The community-based comprehensive care business system according to claim 2 or 4, characterized in that it is one of a medical insurer, a community medical insurer, and a local government.

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