JP2020035322A - Medical care demand prediction system and medical care demand prediction program - Google Patents

Abstract

To provide a medical care demand prediction system and a medical care demand prediction program capable of predicting a demand of a hospital or a clinic in cities, towns and villages, and predicting medical care demand in detail.SOLUTION: A medical care demand prediction device 10 comprises a CPU 12 which functions as a medical care behavior number demand prediction calculation part for multiplying respectively, estimated population data, national medical care behavior occurrence rate, and an adjustment factor in the future for every sexuality, every year, every age class in each of cities, towns and villages which are designated investigation objects, for calculating first medical care behavior number demand prediction for every medical care behavior for every sexuality, for each of outpatient and hospitalization, every age class in the future in each of cities, towns and villages.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a medical demand forecasting system and a medical demand forecasting program.

  The demand for medical care is expected to increase as the population ages. However, medical demand in all municipalities will not necessarily increase. Medical demand is expected to increase in aging urban areas, while it is expected to decrease in rural areas where population is declining. For this reason, each medical institution is required to predict future medical demand and to make a planned investment in architecture, facilities, equipment, and personnel.

  Patent Literature 1 proposes a medical data analysis system and a medical data analysis program. In Patent Literature 1, based on various data including data (hereinafter, referred to as DPC data) related to DPC (Diagnosis Procedure Combination: diagnosis group classification) compiled and published by the Ministry of Health, Labor and Welfare, competing hospitals in the surrounding area in the setting area. And the position of the hospital.

  Patent Literature 2 proposes an acute care demand forecast system and an acute care demand forecast program. According to Patent Literature 2, it is possible to predict both acute phase demands such as the number of patients suffering from injuries and illnesses in the target area and the number of drug stamp patients by injuries and illnesses in the target area based on various data including DPC data.

JP 2017-4419 A JP 2013-164721 A

  Patent Literature 1 and Patent Literature 2 cannot use the DPC data of the comprehensive medical treatment fee system adopted by the acute care hospital for analysis, and thus cannot predict the demand of clinics. .

  In addition, since both patent documents predict the demand by using the data of 18 main diagnostic groups (MDC) in the DPC data, detailed medical demand forecasting such as “the number of operations” and “the number of examinations” is performed. I can't.

  An object of the present invention is to solve the above problems and provide a medical demand forecasting system and a medical demand forecasting program capable of forecasting demand in municipalities in any of hospitals and clinics and performing detailed medical demand forecasting. Is to do.

  In order to solve the above problems, the medical demand forecasting system according to the present invention uses a gender, outpatient / hospitalization, and age-specific incidence rate for each medical practice in the base year (hereinafter referred to as medical practice incidence rate). (R1 = B / A), which is a medical treatment incidence rate storage unit, and population data for the base year and future population data for each country, prefecture, and municipalities by gender, year, and age group , And a population data storage unit that stores population data in the past than the base year, and that stores population data by age group and gender nationwide, and a first adjustment coefficient representing a demand trend of a main classification item in medical, medical, and medical expenses, An adjustment coefficient storage unit that stores at least one adjustment coefficient among second adjustment coefficients that indicate the regional tendency of medical care for each prefecture; a survey municipal designation unit that designates a municipal to be surveyed; Multiplying the estimated population data by gender, year, and age group in the municipalities to be surveyed specified by the fixed part, the medical treatment occurrence rate, and the at least one adjustment coefficient, and And a medical treatment frequency demand prediction calculation unit for calculating a first medical treatment frequency demand prediction for each of the future gender, outpatient / hospitalization, and age class medical practices.

  In addition, based on gender, outpatient / hospitalization, and the total number of medical treatments by age group in the base year, and gender and population by age group in the base year, A gender, outpatient / hospitalization, and a medical treatment incidence rate calculator for calculating a medical treatment incidence rate for each age group, wherein the medical treatment incidence storage unit stores a result calculated by the medical treatment incidence calculator. You may do it.

  Also, when the total number of medical treatments performed by the gender, outpatient / hospitalization, and age group in the base year is the first medical treatment result, the gender of the base year in -By hospitalization, provided with a second medical practice count actual storage unit that stores the second medical practice count actual results for each age group, dividing the second medical practice count actual by the medical practice count demand forecast of the reference year, The medical institution may include a market share calculating unit that calculates a market share of a reference year.

  Further, the first medical treatment frequency demand forecast is multiplied by the market share to calculate gender, outpatient / hospital demand, and the medical institution can acquire the demand based on the sum of these. A demand calculation unit for calculating a second number of times of medical treatment demand forecast for each medical treatment may be provided.

  In addition, the adjustment coefficient storage unit further stores a future medical cost reduction adjustment coefficient as a third adjustment coefficient, and the demand calculation unit determines the third adjustment coefficient when calculating the second medical practice frequency demand forecast. May be multiplied, and then the amount may be converted.

  Further, the medical demand forecasting program of the present invention includes a medical treatment occurrence rate storage unit that stores a medical treatment occurrence rate for each gender, outpatient / hospitalization, and age group nationwide in the base year, A population data storage unit that stores population data for each, and gender, year, and age group for each municipalities, and that stores base year population data, past population data older than the base year, and estimated future population data. An adjustment coefficient storage for storing at least one of a first adjustment coefficient representing a demand tendency of a main classification item in medical treatment medical expenses and a second adjustment coefficient (K2) representing a regional tendency of medical care actions for each prefecture. Department, a surveyed municipalities designation section that designates the municipalities to be surveyed, and future gender, year-by-age, and age group classifications for the surveyed municipalities designated by the surveyed municipalities designation section. Multiply the fixed population data, the medical treatment occurrence rate, and the at least one adjustment coefficient to obtain the first number of medical treatments for each medical treatment by gender, outpatient / hospitalization, and age group in the municipalities to be surveyed. It is a medical demand forecasting program for functioning as a medical action demand demand forecasting calculation unit for calculating a demand forecast.

  Advantageous Effects of Invention According to the present invention, it is possible to predict demand in municipalities in any of hospitals and clinics, and to perform detailed medical demand prediction.

1 is a block diagram of a medical demand prediction device according to one embodiment. Explanatory drawing of the estimated population data by municipality. Explanatory drawing of the population data according to age class (class width 5 years old) for every area. Explanatory drawing of the national age class and gender population data in the past than the reference year. Explanatory drawing of the actual number of medical treatments nationwide (sex). FIG. 4 is an explanatory diagram of the number of medical treatments performed (by prefecture). FIG. 4 is an explanatory diagram of a second adjustment coefficient. Explanatory drawing of medical treatment market share. (A) is an explanatory view of data in total C _n of medical care medical costs stored in the external storage device _{(n = 0 ~ -10),} the (b) calculation result of the aging rate R3 _n, an external storage device 26, (c) is an explanatory diagram of the main items of medical and medical expenses, and (d) is a demand _{Mn (n ≧ 1)} from 2016 to 2045 in the medical practice of “image diagnosis”. Explanatory drawing of the example displayed on the display image in tabular form. The flowchart which a medical demand prediction system performs.

(1st Embodiment)
An embodiment of a medical demand forecasting apparatus 10 and a medical demand forecasting program embodying a medical demand forecasting system of the present invention will be described below with reference to FIGS.

  As shown in FIG. 1, the medical demand prediction device 10 is a computer, and includes a CPU 12, a ROM 14, a RAM (random access memory) 16, a display 18, an output device 20, an input device 22, a communication control unit 24, and an external device. A storage device 26 is provided, and the units are mutually connected via the system bus 13.

  The CPU 12 is a central processing unit, which communicates with devices connected to the system bus 13 in accordance with programs in a ROM (read only memory) 14 or various programs stored in an external storage device 26. Alternatively, various controls are performed. The CPU 12 communicates with the device to execute data search / acquisition, execute processing of output data in which graphics, images, characters, tables, and the like are mixed, or store the data in the external storage device 26. It performs processing such as execution of management of a database or the like.

The CPU 12 corresponds to a number-of-medical-actions demand prediction calculation unit, a medical treatment occurrence rate calculation unit, a market share calculation unit, a demand calculation unit, and a third adjustment coefficient calculation unit.
The ROM 14 stores an operating system program (OS), which is a basic program for controlling the CPU 12, and the like. The RAM 16 functions as a main memory of the CPU 12, a work area, and the like.

The output device 20 includes a player that writes and reads a storage medium such as a printer, a facsimile, a USB, a CD, a DVD, and a pull-ray disk.
The input device 22 includes input devices such as a keyboard, a pointing device, and a mouse. The input device 22 corresponds to a survey municipalities designation unit.

  The communication control unit 24 controls communication with a website (external device) via the Internet. As a result, various data required by the medical demand prediction device 10 can be obtained from a database held by an external device on the Internet, and various information can be transmitted to the external device.

  The external storage device 26 is composed of a hard disk or the like, and stores various applications, font data, user files, edited files, printer drivers and the like (not shown), a population data storage unit 28, a medical treatment frequency storage unit 30, a medical treatment operation. An occurrence rate storage unit 32, an adjustment coefficient storage unit 34, and a medical institution medical care act number storage unit 36 are provided.

  In the following, a description will be given of the population data storage unit 28, the number of medical treatments storage unit 30, the medical treatment occurrence rate storage unit 32, the adjustment coefficient storage unit 34, and the medical institution medical treatment times storage unit 36 included in the external storage device 26.

<1. Population data storage unit 28>
The population data storage unit 28 stores the following data (1) to (4).
(1) Estimated population data by municipalities (male H _{mi / n (n ≧ 0)} , female H _{wi / n (n ≧ 0)} ) compiled based on estimates by age group (class width 5 years) for each municipality (Estimated value) (see FIG. 2).

  m indicates a man, w indicates a woman, i indicates the number of classes of 1 to 19 of age classes [0 to 4 years], [5 to 9 years old], ..., [90 years or older], and n indicates the number of years. When n = 0, it is the reference year, when n is 1 or more, it indicates the future years, and when n is -1 or less, it indicates the past years. Hereinafter, these symbols are used in common in this specification.

This data is based on the "Japan's estimated future population by region" in the base year published on the website of the Population Research Institute on the Internet (the base year in this embodiment will be described later). . In the present embodiment, the latest year is the latest year used in “NDB open data” to be described later, and the population (nationwide / prefectural / municipal) data after the reference year is obtained from the aforementioned Population Research Institute. The population (nationwide) before the base year is used from the data of the Statistics Bureau.
In the present embodiment, the reference year is the latest year, but is not limited to this. For example, the previous data may be the previous data. However, it is more preferable that the freshness of the data be high. Although this data was created by the Population Research Institute, it is not limited to this, and may be created by another institution or the like.

  In addition, since the “Japan's estimated future population by region” data is data every 5 years, the difference between the data every 5 years is divided by 5, and if the data has increased in recent years, it is divided. The obtained value is added as a one-year increase value every year from an old year, and data for each year is created by the CPU 12. In the case where the number has decreased in recent years, the value obtained by the above-described division is subtracted every year from the old year as a decrease value for one year, and data for each year is created by the CPU 12. .

  In the following (2) and (3), data for each year is similarly created by calculation. Here, the data of (1) is base year and future population data, and corresponds to population data of each municipalities by gender, year, and age group.

(2) Male E _{mi / n (n ≧ 0) obtained} by summing up the above-mentioned male H _{mi / n (n ≧ 0)} and female H _{wi / n (n ≧ 0)} for each prefecture (each area _). , Female E _{wi / n (n ≧ 0)} , population (estimated value) by age group (class width 5 years) for each area (see FIG. 3).

  Here, the data of (2) is base year and future population data, and corresponds to population data by gender, year, and age group for each prefecture.

(3) Male A _{mi / n (n ≧ 0)} and female A _{wi / n (n ≧ 0 ) in} which all of the above male H _{mi / n (n ≧ 0)} and female H _{wi / n (n ≧ 0)} are totaled ₎ , Including “estimated population by age group (class width 5 years)” (estimated value) (not shown). Here, the data of (3) is base year and future population data, and corresponds to population data by gender, year, and age group in the whole country.

(4) Population data by national age group and gender before the reference year in which male A _{mi / n (-10≤n≤-1)} and female A _{wi / n (-10≤n≤-1)} were tabulated. (Determined value) (see FIG. 4).

  Here, the data of (4) is obtained by acquiring the population for the last 10 years in the future estimated population data by region from "Population Estimation October 1 each year" published on the website of the Statistics Bureau of the General Affairs Bureau. It is.

  Here, the data of (4) is population data past the reference year, and corresponds to nationwide age class and gender population data.

<2. Medical practice act number storage unit 30>
The medical treatment frequency storage unit 30 stores data (B _{ai / n (n = 0 to-} ) for each age class (class width is 5 years), which is the actual number of medical treatments for each medical treatment in the base year (latest year) nationwide. ₅₎ , Bbi / n _{(n = 0 to -5)} , Bci / n _{(n = 0 to -5)} , Bdi / n _{(n = 0 to -5} ), and base year by prefecture (latest year) )), The actual medical treatment frequency data (outpatient Gen _{(n = 0)} , hospitalization G _{fn (n = 0)} ) for each medical practice. The number of medical treatments performed for each medical treatment in the base year (latest year) nationwide corresponds to the first medical treatments performed.

  This data is compiled based on “NDB Open Data” published on the website of the Ministry of Health, Labor and Welfare. It is preferable to supplement the data shortfall of “NDB Open Data” by referring to statistics by social health care practice published on the website of the Ministry of Health, Labor and Welfare. Here, a indicates a man / outpatient, b indicates a man / hospital, c indicates a woman / outpatient, and d indicates a woman / hospital. In addition, e indicates outpatient and f indicates hospitalization.

  FIG. 5 illustrates an example of the actual number of medical treatments (sex). As shown in the figure, the data indicates that the number of medical treatments such as “CT imaging” and “MRI imaging”, which are “male / outpatient”, “female / outpatient”, “male / hospital”, and “female”・ Hospitalization ”and age group.

  Here, the medical care action is an action described in the above-mentioned NDB open data, which is performed by a medical worker on a patient and belongs to a main classification item.

As shown in FIG. 9C, the main classification items include the following.
Basic medical fees (initial / re-examination fees), basic medical fees (hospital fees, etc.), medical management fees, home medical care, examinations, diagnostic imaging, medication, injection, rehabilitation, psychiatric specialty therapy, treatment, surgery, anesthesia, radiation Treatment, pathological diagnosis.

  Further, as shown in FIG. 5, a plurality of items (hereinafter, referred to as lower items) are classified below the main classification items. For example, as shown in the figure, the sub-items of “image diagnosis” of the main classification items include, for example, “CT imaging” and “MRI imaging”.

  The number of medical treatments by gender and outpatient / hospitalization is stored by age group for each sub-item of the main classification item. In the description that will be described later, “to calculate by converting the amount for each main classification item” means to convert the amount for each sub-item belonging to each main classification item, and to total all the sub-items belonging to this main classification item.

  The above main classification items are defined in the NDB open data at the time of the present application, and are not limited to these. That is, if the definition of the main category is changed in the future, it shall be followed.

  FIG. 6 shows an example of the medical treatment frequency data in the reference year (latest year) for each prefecture. As shown in the figure, in the data, the number of medical treatments such as “CT imaging” and “MRI imaging”, which are medical treatments, are classified into “outpatient” and “hospitalization” according to prefectures.

<3. Medical practice occurrence rate storage unit 32>
The medical treatment occurrence rate storage unit 32 stores the medical treatment occurrence rates R1 _{a to di / 0} .

The medical treatment occurrence rate R1 _{a-di / 0} is based on the reference-year male A _{mi / in} the data of the above (3) stored in the population data storage unit 28, in which the actual number of medical treatments B _{a-di / 0} is stored in the base year. _This is calculated by dividing by the CPU 12 by the “estimated population by age class (class width 5 years) in the whole country” including _{n (n = 0)} and female _{Awi / n (n = 0)} .

Specific data of the medical treatment action occurrence rates R1 _{a to di / 0} are as follows.
Male / outpatient: B _{a1 / 0} / A _{m1 / 0} = R1 _{a1 / 0} ……, B _{a19 / 0} / A _{m19 / 0} = R1 _{a19 / 0}
Female / outpatient: B _{b1 / 0} / A _{w1 / 0} = R1 _{b1 / 0} ……, B _{b19 / 0} / A _{w19 / 0} = R1 _{b19 / 0}
Male hospitalization: B _{c1 / 0} / A _{m1 / 0} = R1 _{c1 / 0} ……, B _c19 / 0 / A _{m19 / 0} = R1 _{c19 / 0}
Female / hospitalization: B _{d1 / 0} / A _{w1 / 0} = R1 _{d1 / 0} ……, B _{d19 / 0} / A _{w19 / 0} = R1 _{d19 / 0}
<4. Adjustment coefficient storage unit 34>
The adjustment coefficient storage unit 34 stores the following adjustment coefficients (1) to (3).

(1) First adjustment coefficient K4 _{jn (n ≧ 1)}
The first adjustment coefficient K4 _{jn (n ≧ 1)} is an adjustment coefficient that indicates a demand tendency for each major classification item of medical treatment and medical care expenses. The reason for providing this adjustment coefficient is as follows.

Even if the overall medical demand expands, the demand for individual medical treatments may increase or decrease. When the future population is multiplied by the medical treatment incidence rate R1 _{a ~ di / 0} in the base year, the increase / decrease in demand for individual medical treatment due to changes in the population and age structure can be calculated, but factors such as the emergence of new technologies Does not reflect the trend of

For this reason, it is preferable to grasp the demand trend that does not accompany the increase and decrease of the entire demand for each major classification item of medical treatment and medical expenses, and to reflect the future demand forecast.
The first adjustment coefficient K4 _{jn (n ≧ 1)} is calculated by the CPU 12 as described below.

The actual number of medical treatments B _{a to di / n} obtained from the NDB open data for the base year (latest year) and a plurality of years before that (preferably about 5 years; in this embodiment, 5 years). _{n = 0 to -5)} are converted into monetary amounts and totaled for each major classification item (see Equation 1).

  The conversion of the amount of money is performed by {number of times × points × 10 yen (1 point = 10 yen)} (the same applies to the amount conversion in each explanation of the medical department).

(Equation 1)
B _{a ~ di / 0 (n = 0 ~ -5)} × money amount conversion → L _{j / n (n = 0 ~ -5)}
Actual medical expenses L _{j / n} obtained by tabulation (j is the ratio of the major classification items to the medical care medical expenses C _{n (n = 0 to -5)} for the past 6 years including the base year R4 _{jn (n = 0 to -5)} are calculated (see Equation 2). From the national medical expenses published on the website of the Ministry of Health, Labor and Welfare, the total C _{n (n = 0 to -5)} of medical and medical expenses corresponding to “NDB open data” for the base year and five years from the base year The data is stored in the external storage device 26.

(Equation 2)
_{L j / 0 / C 0 =} R4 j0, L j / -1 / C -1 = R4 j-1, L j / -2 / C -2 = R4 j-2, ... L j / -5 / C - ₅ = R4j- ₅
Then, a ratio R4 _{jn (n ≧ 1)} predicted in the future from the tendency of the ratio R4 _{jn (n = 0 to −5)} is calculated as follows.

Specifically, it is assumed that the technological progress depends on the number of elapsed years, and the number of years N before the base year (here, n is used as a numerical value) is used as an explanatory variable, and R4 _{jn (n ≦ 0)} is used as an explanatory variable. A regression equation (see equation 3) is determined as a dependent variable, and the value of future years N is substituted into the equation to determine a future ratio (ratio) R4 _{jn (n ≧ 1)} .

(Equation 3)
R4 _jn = a + bN
b = (σNR4 _jn ) / σ ² N
(Note that σ ² N is the variance of N, σ NR4 _jn is the covariance of N and R4 _jn .)
a = E (R4 _j ) −b · E (N)
(E (R4 _j ) and E (N) are the average of R4 _j and N)
If any of the future ratios R4 _jn of the main classification items is less than 0 as a result of the calculation, the immediately preceding values of the future ratios R4 _jn of all the main classification items are fixed and used.

Further, the CPU 12 multiplies the medical treatment incidence rate R1 _{a to di / 0} in the base year by future population data: male A _{mi / n (n ≧ 1)} and female A _{wi / n (n ≧ 1),} and multiplies this value. The amount of money is converted to obtain a medical cost forecast L _{j / n (n ≧ 1)} for each major classification item, and a future medical care medical cost In _{(n ≧ 1)} obtained by summing all the major classification items (Equation 4). reference).

(Equation 4)
A _{mi / n (n ≧ 1)} , A _{wi / n (n ≧ 1)} × R1 _{a ~ di / 0} × Amount conversion → L _{j / n (n ≧ 1)} , In _{(n ≧ 1)}
Then, the CPU 12 divides the medical cost prediction L _{j / n (n ≧ 1)} for each major classification item by the future medical treatment / medical medical cost In _{(n ≧ 1)} to obtain a ratio R5 _{j / n (n ≧ 1). )} Is obtained (see Equation 5).

(Equation 5)
_{L j / n / I 1 =} R5 j1, L j / 2 / I 2 = R5 j2, ..., L j / n / I n1 = R5 jn
Thereafter, the CPU 12 calculates the first adjustment coefficient K4 _{jn (n ≧ 1)} by dividing the ratio R5 _{jn (n ≧ 1)} by the future ratio (ratio) R4 _{jn (n ≧ 1)} (see Equation 6). .

(Equation 6)
R5 _j1 / R4 _j1 = K4 _j1 , R5 _j2 / R4 _j2 = K4 _j2 , ..., R5 _jn / R4 _jn = K4 _jn

(2) Second adjustment coefficient: outpatient K2 _{en (n = 0)} , hospitalization K2 _{fn (n = 0)}
The second adjustment coefficient K2 _{en (n = 0)} is an adjustment coefficient related to outpatient and hospitalization indicating a regional tendency of each prefecture in the base year (see FIG. 7). FIG. 7 shows only one of the prefectures, and the same adjustment coefficients are stored in the external storage device 26 for the other remaining prefectures.

The reason for providing this adjustment coefficient is as follows.
Even for the same disease, a nationwide uniform medical practice is not selected, but there is a regional tendency. However, the tendency is not taken into account for the medical practice occurrence rates R1 _{a to di / 0} . For this reason, it is preferable to reflect the regional tendency of each prefecture in the demand forecast.

Prefectural population in the base year: Male E _{mi / 0} , Female E _{wi / 0} multiplied by the medical treatment incidence rate R1 _{a ~ di / 0} , and summed up to obtain the demand for the number of medical treatments by prefecture for each medical treatment The prediction _{Fa to dn (n = 0)} is calculated (see Equation 7). The medical treatment frequency demand forecast _{Fa to dn (n = 0)} for each prefecture is hereinafter referred to as a regional demand forecast.

(Equation 7)
Male, outpatient: E _{m1 / 0} × R1 _{a1 / 0} + ... + E _{m19 / 0} × R1 _{a19 / 0} = F _a0 (= Σ _{i = 1} ¹⁹ E _{m1 / 0} R1 _{a1 / 0} )
Female / outpatient: E _{w1 / 0} × R1 _{b1 / 0} + ... + E _{w19 / 0} × R1 _{b19 / 0} = F _b0 (= Σ _{i = 1} ¹⁹ E _{w1 / 0} R1 _{b1 / 0} )
Male / hospitalization: E _{m1 / 0} × R1 _{c1 / 0} + ... + E _{m19 / 0} × R1 _{c19 / 0} = F _c0 (= Σ _{i = 1} ¹⁹ E _{m1 / 0} R1 _{c1 / 0} )
Female / hospitalization: E _{w1 / 0} × R1 _{d1 / 0} + ... + E _{w19 / 0} × R1 _{d19 / 0} = F _d0 (= Σ _{i = 1} ¹⁹ E _{w1 / 0} R1 _{d1 / 0} )
Then, CPU12, the intervention number of prefecture of medical treatment each _{G en (n = 0),} G fn (n = 0) the local demand prediction _{F an (n = 0) +} F bn (n = 0) , F _{cn (n = 0)} + F _{dn (n = 0)} , respectively, to obtain a second adjustment coefficient K2 for each prefecture, that is, a second adjustment coefficient K2 _{en (n = 0) for} outpatients, and for hospitalization. Calculate the second adjustment coefficient K2 _{fn (n = 0)} (see Equation 8)

(Equation 8)
Outpatient: G _e0 / (F _a0 + F _b0 ) = K2 _e0
Hospitalization: G _f0 / (F _c0 + F _d0 ) = K2 _f0

(3) Third adjustment coefficient K3 _{n (n ≧ 1)}
The third adjustment coefficient K3 _{n (n ≧ 1)} is an adjustment coefficient indicating a future medical cost reduction tendency due to the medical cost policy (that is, a future medical cost reduction adjustment coefficient). The reason for providing this adjustment coefficient is as follows.
Population increase / decrease / age structure when multiplying the base year medical treatment incidence rate R1 _{a ~ di / 0} by future population data: male A _{mi / n (n ≧ 1)} and female A _{wi / n (n ≧ 1)} The medical demand reflecting the fluctuation of the medical demand can be predicted, but the fluctuation factor of the medical demand is not limited to this. In particular, medical expenses for the elderly account for 50% or more of the total medical expenses, and for the time being, the aging rate will increase, and it is thought that the tendency to reduce medical expenses will increase. Therefore, as the aging rate increases, medical costs increase, but the population of producers decreases. As a result, the tax revenues of the country decrease. As a result, medical costs are reduced more strongly, and medical costs are reduced more politically.

Therefore, it is preferable to reflect this prediction. The method of obtaining the third adjustment coefficient K3 _{n (n ≧ 1)} is as follows.

Here, first, from the national medical expenses published on the website of the Ministry of Health, Labor and Welfare, "NDB Open Data" for the past several years including the base year (preferably about 10 years. , The data of the total Cn _{(n = 0 to -10)} of the corresponding medical care costs are stored in the external storage device 26 (see FIG. 9A).

The CPU 12 converts the total number of medical practice times B _{a to di / 0} into monetary values and sums them to calculate a medical care medical cost In _{(n = 0)} . Incidentally, C ₀ = I ₀ .

The CPU 12 calculates the medical treatment occurrence rate R1 _{a to di / 0} for the base year as population data for 10 years before the base year: male A _{mi / n (n = -1 to -10)} and female A _{wi / n ( n = -1 to -10)} to calculate the number of past medical treatment demand forecasts, convert this to a monetary value, and then add them all together to account for changes in population and changes in age structure. Of the medical treatment medical expenses I _{n (n = -1 to -10)} are calculated.

Here, in the case where there is a strong tendency that the past medical medical treatment medical expenses I _{n (n = -1 to -10)} are calculated to be lower than the actual medical medical treatment medical expenses C _{n (n = -1 to -10).} If the unit cost of medical expenses tends to be reduced, and if the unit cost is calculated to be high, the unit cost of medical expenses is considered to be increasing.

Then, the CPU 12 divides the actual medical / medical medical cost Cn _{(n = 0 to -10)} by the past medical / medical medical cost forecast In _{(n = 0 to -10)} to obtain an adjustment coefficient K3 _{n ( n = 0 to -10)} is calculated (see Equation 9). From the national medical expenses published on the Ministry of Health, Labor and Welfare's website, the total C _{n (n = -1 to-} The data of ₁₀₎ is stored in the external storage device 26.

(Equation 9)
_{C 0 / I 0 = K3 0} (= 1), C -1 / I -1 = K3 -1, C -2 / I -2 = K3 -2, ... C -10 / I -10 = K3 -10
The CPU 12 obtains the ratio (age rate) R3 _{n (n = 0 to -10)} of the population over the age of 65 to the total population from the population data of the whole country: male A _{mi / n} and female A _{wi / n} . A regression equation using the rate R3 _{n (n = 0 to -10)} as an explanatory variable and the adjustment coefficient K3 _{n (n = 0 to -10)} as an explained variable is obtained. Then, in this regression equation, the value of the future aging rate R3 _{n (n ≧ 1)} is substituted, and the third adjustment coefficient K3 _{n (n ≧ 1)} of the future medical fee is obtained (see Equation 10).

FIG. 9B is an explanatory diagram in which the calculation result of the aging rate R3 _n is stored in the external storage device 26.

(Equation 10)
K3 _n = a + bR3 _n
b = (σR3 _n K3 _n ) / σ ² R3 _n
(Note that σ ² R3 _n is the variance of R3 _n and σR3 _n K3 _n is the covariance of R3 _n and K3 _n .)
_{a = E (K3 n) -bE} (R3 n)
(E (K3 _n ) and E (R3 _n ) are the average of K3 _n and R3 _n )

<5. Medical institution medical practice frequency storage unit 36>
The medical institution medical treatment frequency storage unit 36 stores the number of medical treatment activities of the medical institution to be surveyed input via the input device 22 or the communication control unit 24.
That is, the number of medical treatments performed by the medical institution to be surveyed is determined by the number of medical treatments by the age class and the patient's resident municipalities, which are input from the external device or the input device 22 via the communication control unit 24. Number of times (man / outpatient: J _{ai / n (n = 0)} , woman / outpatient: J _{bi / n (n = 0)} , man / hospital: J _{ci / n (n = 0)} , woman / outpatient: J _{di / n (n = 0)} ) is totaled by the CPU 12 and stored. The results of the number of medical treatments for each medical treatment performed by the age group and the resident municipalities of the base year in the medical institutions to be surveyed correspond to the second medical treatments.

  As shown in FIG. 8, the number of medical treatments for each medical treatment of the medical institution to be surveyed is stored for each age group by the number of medical treatments for each sex and outpatient / hospitalization. The medical institution medical treatment frequency storage unit 36 corresponds to a second medical treatment frequency actual storage unit.

(Operation of the embodiment)
Next, the operation of the medical demand prediction device 10 configured as described above will be described with reference to FIG.
When the medical treatment frequency demand forecasting program stored in the external storage device 26 is started, the CPU 12 determines in S10 whether or not the operator has operated the input device 22 to specify the municipalities to be surveyed. The municipalities to be surveyed are the municipalities designated by the medical institution stored in the medical institution medical practice frequency storage unit 36 of the external storage device 26.

When the municipalities to be surveyed are specified, the CPU 12 reads the population of municipalities to be surveyed: male H from the population data storage unit 28, the medical treatment occurrence rate storage unit 32, and the adjustment coefficient storage unit 34 of the external storage device 26. _{mi / n (n ≧ 0)} , female H _{wi / n (n ≧ 0)} , medical treatment occurrence rate R1 _{a-di / 0} , first adjustment coefficient K4 _{jn (n ≧ 1)} , and second adjustment coefficient: outpatient K2 _{en (n = 0)} and hospitalization K2 _{fn (n = 0)} are read.

In S30, the CPU 12 calculates the first medical treatment frequency demand forecast Qa _{to di / n (n ≧ 0)} for each medical treatment in the municipalities to be surveyed (see Equation 11).
In the present embodiment, the first adjustment coefficient K4 _jo is “1” for 2015 as the reference year.

(Equation 11)
(2015)
Man / outpatient: _{Hm1 / 0} x R1 _{a1 / 0} x K4 _j0 x K2 _e0 = Qa1 _{/ 0}
:::
H _{m19 / 0} × R1 _{a19 / 0} × K4 _j0 × K2 _e0 = Qa _19/0
Woman - _{foreign: H w1 / 0 × R1 b1} / 0 × K4 j0 × K2 e0 = Qb 1/0
:::
Hw _19/0 × R1 _{b19 / 0} × K4 _j0 × K2 _e0 = Qb _19/0
Male hospitalization: _{Hm1 / 0} x R1 _{c1 / 0} x K4 _j0 x K2 _f0 = _{Qc1 / 0}
:::
Hm _19/0 × R1 _{c19 / 0} × K4 _j0 × K2 _f0 = Q _{c19 / 0}
Female, hospitalization: H _{w1 / 0} × R1 _{d1 / 0} × K4 _j0 × K2 _f0 = Q _{d1 / 0}
:::
H _{w19 / 0} × R1 _{d19 / 0} × K4 _j0 × K2 _f0 = Q _{d19 / 0}
(FY2016)
:::
(FY2020)
Man, outpatient: _{Hm1 / n (n = 5)} × R1 _{a1 / 0} × K4 _{jn (n = 5)} × _K2e0 = _{Qa1 / n (n = 5)}
:::
…… …… …… …… …… …… ……
The CPU 12 outputs the calculation result to the output device 20 such as a printer and the display 18.

In the next step S40, the CPU 12 calculates the number of medical treatments J _{a to di / 0} for each medical treatment according to the age class and the resident municipality of the patient in the reference year and the first number of medical treatments for each corresponding medical treatment. Divide by the demand forecast Qa- _{di / 0} to calculate the market share R2a _{-di / n (n = 0)} by municipalities (see Equation 12).

(Equation 12)
Man / outpatient: J _{a1 / 0} / Q _{a1 / 0} = R2 _{a1 / 0} … J _{a19 / 0} / Q _{a19 / 0} = R2 _{a19 / 0}
Female / outpatient: J _{b1 / 0} / Q _{b1 / 0} = R2 _{b1 / 0} … J _{b19 / 0} / Q _{b19 / 0} = R2 _{b19 / 0}
Male hospitalization: J _{c1 / 0} / Q _{c1 / 0} = R2 _{c1 / 0} … J _{c19 / 0} / Q _{c19 / 0} = R2 _{c19 / 0}
Woman, hospitalization: J _{d1 / 0} / Q _{d1 / 0} = R2 _{d1 / 0} … J _{d19 / 0} / Q _{d19 / 0} = R2 _{d19 / 0}
The CPU 12 outputs the calculation result to the output device 20 such as a printer and the display 18.

In the next step S50, the CPU 12 calculates a third medical treatment frequency demand forecast S _{a to dn (n ≧ 1),} which can be obtained by the medical institution to be surveyed, by gender, outpatient / hospitalization for each medical treatment.

Specifically, the CPU 12 multiplies the first medical treatment frequency demand forecast Qa _{-di / n (n ≧ 1)} by the market share R2 _{a-di / n (n = 0)} in the base year, and For each gender, outpatient / hospitalization, the third medical treatment frequency demand forecast S _{a to dn (n ≧ 1)} is calculated (see equation 13).

(Equation 13)
Man / outpatient: Q _{a1 / n} × R2 _{a1 / 0} … + Q _{a19 / n} × R2 _{a19 / 0} = Σ _{i = 1} ¹⁹ Q _{ai / n} × R2 _{ai / 0} = S _an
Female / outpatient: Q _{b1 / n} × R2 _{b1 / 0} … + Q _{b19 / n} × R2 _{b19 / 0} = Σ _{i = 1} ¹⁹ Q _{bi / n} × R2 _{bi / 0} = S _bn
Male / hospitalization: Q _{c1 / n} × R2 _{c1 / 0} … + Q _{c19 / n} × R2 _{c19 / 0} = Σ _{i = 1} ¹⁹ Q _{ci / n} × R2 _{ci / 0} = S _cn
Female / hospitalization: Q _{d1 / n} × R2 _{d1 / 0} … + Q _{d19 / n} × R2 _{d19 / 0} = Σ _{i = 1} ¹⁹ Q _{di / n} × R2 _{di / 0} = S _dn
Further, the CPU 12 calculates the second number-of-medical-actions demand demand prediction _{Sn (n ≧ 1)} for each medical procedure that can be obtained by summing up these (see Equation 14).

(Equation 14)
S _an + S _bn + S _cn + S _dn = S _n
Further, the CPU 12 converts the second number-of-medical-actions demand demand prediction S _{n (n ≧ 1)} for each medical treatment into a monetary value, and then multiplies it by a third adjustment coefficient K3 _{n (n ≧ 1)} to obtain the amount-based medical treatment that can be obtained. The demand M _{n (n ≧ 1)} for each action is calculated (see Equation 15).

(Equation 15)
S _{n (n ≧ 1)} × money conversion × K3 _{n (n ≧ 1)} = M _{n (n ≧ 1)}
The CPU 12 outputs the above calculation result to the output device 20 such as a printer and the display 18.
FIG. 9D illustrates an example in which the demand _{Mn (n ≧ 1)} from 2016 to 2045 in the medical practice of “image diagnosis” is displayed on a display image in a table format. In the image of FIG. 9D, actual values are shown from 2010 to 2015, and an example in which these values are also displayed is shown. In this image, each year is represented in the Christian era for convenience of explanation.

This embodiment has the following features.
(1) The medical demand forecasting apparatus 10 of the present embodiment is a medical practice that stores the medical practice occurrence rates R1 _{a to di / 0} for each medical practice by gender, outpatient / hospitalization, and age group in the base year. Incidence rate storage unit 32, base year and future population data, including population data by gender, year, age group, and population data past the reference year, nationwide, by prefecture, and by municipalities And a population data storage unit 28 for storing population data by age group and gender nationwide. In addition, the medical demand forecasting apparatus 10 calculates a first adjustment coefficient K4 _{jn (n ≧ 1) indicating} a demand tendency of a main classification item in medical treatment medical expenses, and a second adjustment coefficient indicating a regional tendency of medical care actions for each prefecture. An adjustment coefficient storage unit 34 for storing (outpatient K2 _{en (n = 0)} , hospitalization K2 _{fn (n = 0)} ) is provided. Further, the medical demand prediction device 10 includes an input device 22 (a survey municipal designation unit) for designating a municipality to be surveyed. In addition, the medical demand forecasting apparatus 10 multiplies the estimated population data by gender, year, and age group in the designated municipalities to be surveyed, the nationwide medical treatment occurrence rate, and the adjustment coefficient, respectively. A medical treatment frequency demand forecasting calculation unit is provided for calculating a first medical practice frequency demand forecast for each future gender, outpatient / hospitalization, and age class medical practice. As a result, the medical demand forecasting device 10 of the present embodiment can perform demand forecasts in municipalities and detailed medical demand forecasts in both hospitals and clinics.

(2) The medical demand forecasting apparatus 10 of the present embodiment collects the total number of medical treatments performed by gender, outpatient / hospitalization, and age group in the base year, and the gender and age group in the base year by country. It has a CPU 12 (medical treatment occurrence rate calculation unit) that calculates medical treatment occurrence rates R1 _{a to di / 0} for each gender, outpatient / hospitalization, and age group in the base year based on the population. Then, the medical care action occurrence rate storage unit 32 of the external storage device 26 stores the calculated result. As a result, the medical treatment incidence rates R1 _{a to di / 0} for each gender, outpatient / hospitalization, and age group in the base year are preliminarily calculated and stored in the medical treatment incidence storage unit 32. It is possible to reduce the time for calculating the demand forecast of the number of medical treatments performed for each medical treatment performed by future gender, outpatient / hospitalization, and age group in municipalities.

(3) In the present embodiment, the total value of the actual number of medical treatments by gender, outpatient / hospitalization, and age group in the base year is the first actual number of medical treatments. The medical demand forecasting apparatus 10 calculates the number of medical treatments performed by the municipalities, genders, outpatients / hospitalizations, and age groups in the base year in medical institutions (male / outpatient: J _{ai / n (n = 0)} , Outpatient: J _{bi / n (n = 0)} , male / hospital: J _{ci / n (n = 0)} , female / outpatient: J _{di / n (n = 0)} 36 (second medical treatment act result storage unit).

  Further, the medical demand forecasting apparatus 10 includes, as a market share calculating unit, a CPU 12 for calculating the market share of the medical institution in the base year by dividing the medical practice count results by the medical practice count demand forecast in the reference year. .

  As a result, according to the present embodiment, the future market share of the medical institution to be surveyed can be easily obtained.

(4) The medical demand forecasting apparatus 10 of the present embodiment multiplies the first medical treatment demand demand forecast (Q _{a ~ di / n (n ≧ 1)} ) by the market share, and determines gender, outpatient / hospitalization. CPU 12 (a demand calculation unit ₎ that calculates another demand and further calculates a second medical practice frequency demand forecast S _{n (n ≧ 1)} for each medical practice that can be obtained by the medical institution based on the sum of these demands. ).

  As a result, according to the present embodiment, it is possible to easily obtain the second medical treatment act number demand prediction for each medical treatment that the medical institution can acquire.

(5) In the medical demand forecasting apparatus 10 of the present embodiment, the adjustment coefficient storage unit further stores a future medical cost reduction adjustment coefficient as a third adjustment coefficient K3 _{n (n ≧ 1)} , and executes the CPU 12 (demand calculation). When calculating the second medical treatment frequency demand forecast, the unit multiplies the value by multiplying the third adjustment coefficient, and then converts the value.

  As a result, according to the present embodiment, it is possible to easily obtain, in monetary terms, the second medical treatment act number demand forecast for each medical treatment that the medical institution can acquire.

(6) The medical treatment frequency demand forecasting program according to this embodiment causes a computer to function as a medical treatment occurrence rate storage unit 32 that stores the medical treatment occurrence rates R1 _{a to di / 0} nationwide in the base year. In addition, the program is a computer that stores data on gender, year, and age group by country, by prefecture, and by municipalities, including base year population data, past population data older than the base year, and future data. Function as a population data storage unit 28 for storing the estimated population data. Further, the program stores the computer with a first adjustment coefficient K4 _{jn (n ≧ 1)} representing a demand tendency of a main classification item in medical treatment and medical expenses, and a second adjustment coefficient representing a regional tendency of medical treatment by prefecture. It functions as an adjustment coefficient storage unit 34 for storing the outpatient K2 _{en (n = 0)} and the hospitalization K2 _{fn (n = 0)} . Further, the program causes the input device 22 of the computer to function as a surveying municipalities specifying unit that specifies a municipalities to be investigated. In addition, the above-mentioned program stores the computer in the future estimated population data by sex, year, and age group in the designated municipalities to be surveyed, the medical treatment activity occurrence rates R1 _{a to di / 0,} the first adjustment coefficient, and the second adjustment coefficient. By multiplying by the adjustment coefficient, it is made to function as a medical treatment frequency demand prediction calculation unit that calculates the first medical treatment frequency demand prediction in the municipalities to be surveyed.

  As a result, according to the present embodiment, the effect of the above (1) can be easily realized by causing the computer to function as the medical demand prediction device 10.

  Note that the embodiment of the present invention is not limited to the above embodiment, and may be modified as follows.

  In the above-described embodiment, the system is configured by the medical demand prediction device 10, but is not limited to this configuration. For example, the external storage device 26 may be provided in a server connected to the Internet. Alternatively, the input device 22 may be an input device provided in a server connected to the Internet.

10. Medical demand forecasting device (medical demand forecasting system)
12... CPU (medical treatment frequency demand prediction computing unit, medical treatment occurrence rate computing unit, market share computing unit, demand computing unit, and third adjustment coefficient computing unit),
13 ... system bus, 14 ... ROM, 16 ... RAM,
18 display, 20 output device,
22 input device (survey municipalities designation section),
24 communication control unit, 26 external storage device, 28 population data storage unit
30: number of medical treatment actions storage unit, 32: medical treatment occurrence rate storage unit,
34: Adjustment coefficient storage unit, 36: Medical institution medical care act number storage unit.

Claims (6)

A medical treatment incidence storage unit that stores the incidence of each medical treatment by gender, outpatient / hospitalization, and age group throughout the base year (hereinafter referred to as medical treatment incidence);
Base year and future population data, nationwide, prefectural, and municipalities by gender, year, and age group, and population data past the base year, nationwide age group A population data storage unit for storing different and gender population data;
An adjustment coefficient storage unit that stores at least one of a first adjustment coefficient representing a demand tendency of a main classification item in medical treatment medical expenses and a second adjustment coefficient representing a regional tendency of medical care actions for each prefecture;
A surveying municipalities designation section that designates the municipalities to be surveyed;
Multiplying the estimated population data by gender, year, and age group in the municipalities to be surveyed designated by the surveying municipalities designation unit, the medical treatment occurrence rate, and the at least one adjustment coefficient, A medical demand forecasting system comprising a medical treatment frequency demand prediction operation unit for computing a first medical practice frequency demand forecast for each medical practice by gender, outpatient / hospitalization, and age group in the target municipalities. Gender in the base year, outpatient / hospitalization, total number of medical treatments by age group, and gender in the base year, gender in the base year, and gender in the base year , A medical treatment incidence rate calculation unit that calculates the medical treatment incidence rate by outpatient / hospitalization, age group,
The medical demand prediction system according to claim 1, wherein the medical treatment occurrence rate storage unit stores a result calculated by the medical treatment occurrence ratio calculation unit. Gender, outpatient / hospitalization, and total number of medical treatments by age group in the base year as the first medical treatment results,
A second medical treatment frequency record storage unit that stores the second medical treatment frequency record for each gender, outpatient / hospitalization, and age group at the medical institution to be surveyed,
The market share calculation part which calculates the market share of the reference year of the medical institution by dividing the second medical practice number actual result by the first medical practice number demand forecast of the reference year. 3. The medical demand forecasting system according to 2.   The market demand is multiplied by the market share to calculate the demand by gender, outpatient / hospitalization, and the number of medical treatments that can be obtained by the medical institution based on the sum of the demands. The medical demand forecasting system according to claim 3, further comprising a demand calculation unit configured to calculate a demand forecast for the second number of medical treatment times for each medical treatment. The adjustment coefficient storage unit further stores a future medical cost reduction adjustment coefficient as a third adjustment coefficient,
5. The medical demand forecasting system according to claim 4, wherein when calculating the demand forecast for the number of times of the second medical treatment, the demand calculation unit calculates the demand multiplied by the third adjustment coefficient and converts the value into a value. Computer
A medical treatment incidence storage unit that stores the incidence of each medical treatment by gender, outpatient / hospitalization, and age group throughout the base year (hereinafter referred to as medical treatment incidence);
Population data by gender, year, and age group for the whole country, prefecture, and municipalities.Base data for reference year population data, past population data older than the reference year, and population data for future estimated population data. A storage unit,
An adjustment coefficient storage unit that stores at least one of a first adjustment coefficient representing a demand tendency of a main classification item in medical treatment medical expenses and a second adjustment coefficient representing a regional tendency of medical care actions for each prefecture;
A surveying municipalities designation section that designates the municipalities to be surveyed;
Multiplying the estimated population data by gender, year, and age group in the municipalities to be surveyed designated by the surveying municipalities designation unit, the medical treatment occurrence rate, and the at least one adjustment coefficient, A medical demand forecasting program for functioning as a medical practice frequency demand forecasting calculation unit that calculates a first medical practice frequency demand forecast for each future medical practice by gender, outpatient / hospitalization, and age group in the target municipalities.