WO2020119110A1

WO2020119110A1 - Big data-based method, device, and equipment for insurance pricing, and readable storage medium

Info

Publication number: WO2020119110A1
Application number: PCT/CN2019/095599
Authority: WO
Inventors: 李云峰
Original assignee: 平安医疗健康管理股份有限公司
Priority date: 2018-12-13
Filing date: 2019-07-11
Publication date: 2020-06-18
Also published as: CN109658263A

Abstract

A big data-based method, equipment, and device for insurance pricing, and a readable storage medium. When a pricing request is received, a payment plan and the average annual fees per capita for a specialty drug are acquired, an estimated annual payment per capita is acquired on the basis of the average annual fees per capita for the specialty drug and of the payment plan; a competing product apportionment factor of the specialty drug is acquired on the basis of preset competitive product apportionment rule; the incidence rate of an indication of the specialty drug is acquired, an annual risk premium for the specialty drug is acquired on the basis of the estimated annual payment per capita, of the incidence rate of the indication, and of the competing product apportionment factor; and, an annual gross premium per capita of an insurance program for the specialty drug is acquired on the basis of the annual risk premium and of a preset operation plan. The method, equipment, and device and the readable storage medium combine multiple influencing factors, namely fees for using the specialty drug, the incidence rate of a disease, the market situation of a competing product, and operating costs, in calculating the gross premium for the insurance program for the specialty drug, pricing of the insurance program for the specialty drug is performed by means of big data analysis and processing, thus comprehensively taking into consideration the price influencing factors of the insurance program for the specialty drug, and increasing the rationality and accuracy of pricing.

Description

Insurance pricing method, device, equipment and readable storage medium based on big data

This application requires the priority of the Chinese patent application submitted to the China Patent Office on December 13, 2018, with the application number 201811524280.0 and the invention titled "Big Data-based Insurance Pricing Methods, Devices, Equipment, and Readable Storage Media". The entire contents are incorporated by reference in this application.

Technical field

The present application relates to the field of big data technology, and in particular, to a insurance pricing method, device, device, and readable storage medium based on big data.

Background technique

With the development of medical technology, special medicines on the market (special treatment for a certain disease, medicines with high scientific content and high technical difficulty) are gradually increasing, such as erlotinib hydrochloride tablets (trocaine), lucote Ni tablets (Jieweiwei), etc.; but the special drugs on the market are more expensive. In order to reduce the burden on patients, some insurance agencies have proposed to include the medical expenses of special drugs into the insurance reimbursement scope; while the current pricing of special drug insurance plans is currently mainly conducted by experts to manually analyze and determine, and this pricing human factors Large, often do not consider the impact of market competition factors, which reduces the accuracy of special drug insurance plan pricing.

Summary of the invention

The main purpose of this application is to provide an insurance pricing method, device, equipment and readable storage medium based on big data, aiming to improve the accuracy of special medicine insurance pricing.

To achieve the above objective, the present application provides a big data-based insurance pricing method. The big data-based insurance pricing method includes:

When receiving the pricing request of the special medicine insurance plan, obtain the compensation plan of the special medicine insurance plan and the average annual cost of the special medicine corresponding to the special medicine of the special medicine insurance plan, and according to the average annual cost of the special medicine and The compensation plan obtains the estimated annual per capita compensation for the special medicine;

Obtain the special medicine market information of the special medicine and the competitive medicine market information of the special medicine, and obtain the competition of the special medicine based on the preset competitive medicine allocation rule, the special medicine market information and the competitive medicine market information Product sharing factor;

Obtain the incidence rate of applicability of the specialty medicine, and obtain the annual risk premium of the specialty medicine according to the estimated annual per capita compensation, the incidence of the applicability disease and the competitive product allocation coefficient;

Obtain the annual gross premiums per capita of the special medicine insurance plan according to the annual risk premium and the preset operating plan.

In addition, in order to achieve the above object, the present application also provides an insurance pricing device based on big data. The insurance pricing device based on big data includes:

The annual compensation acquisition module is used to obtain the payment plan of the special medicine insurance plan and the average annual per capita cost of the special medicine corresponding to the special medicine insurance plan when receiving the pricing request of the special medicine insurance plan, and according to the The annual per capita cost of the special medicine and the estimated annual compensation per capita for obtaining the special medicine in the compensation scheme;

The coefficient obtaining module is used to obtain the special medicine market information of the special medicine and the competitive product market information of the special medicine, and obtain based on a preset competitive product allocation rule, the special medicine market information and the competitive product market information Competitive product allocation coefficient of the special medicine;

A risk premium acquisition module, which is used to obtain the incidence rate of the applicability of the specialty medicine, and obtain the annual risk premium of the specialty medicine according to the estimated annual per capita payment, the incidence of the applicability disease and the competing product allocation coefficient;

The gross premium acquisition module is used to acquire the annual gross premiums per capita of the special medicine insurance plan according to the annual risk premium and the preset operation plan.

In addition, in order to achieve the above object, the present application also provides an insurance pricing device based on big data. The insurance pricing device based on big data includes a processor, a memory, and is stored on the memory and can be used by the processor Executed computer-readable instructions, where the computer-readable instructions are executed by the processor to implement the steps of the big data-based insurance pricing method as described above.

In addition, in order to achieve the above object, the present application also provides a readable storage medium on which computer-readable instructions are stored, wherein when the computer-readable instructions are executed by a processor, the Steps of big data insurance pricing method.

This application calculates the gross premium of the special drug insurance plan by combining multiple factors such as special drug use fee, disease incidence, market competition, operating cost and other influencing factors, and pricing the special drug insurance plan by means of big data analysis and processing. Therefore, the price influencing factors of the special drug insurance plan are more comprehensively considered, so that the pricing results can be more in line with the actual operating conditions of the insurance institution and the market demand for special drugs, which improves the rationality and accuracy of the pricing, and is also conducive to reducing the cost of the insurance plan. Operating costs.

BRIEF DESCRIPTION

FIG. 1 is a schematic diagram of a hardware structure of insurance pricing equipment based on big data involved in an embodiment of the present application;

2 is a schematic flowchart of a first embodiment of an insurance pricing method based on big data of this application;

FIG. 3 is a schematic diagram of functional modules of a first embodiment of an insurance pricing device based on big data in this application.

The implementation, functional characteristics and advantages of the present application will be further described in conjunction with the embodiments and with reference to the drawings.

detailed description

It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

The insurance pricing method based on big data involved in the embodiments of the present application is mainly applied to insurance pricing equipment based on big data. The insurance pricing equipment based on big data may be a personal computer (PC), a notebook computer, a server, etc. Data processing function equipment.

Referring to FIG. 1, FIG. 1 is a schematic diagram of a hardware structure of an insurance pricing device based on big data involved in an embodiment of the present application. In the embodiment of the present application, the insurance pricing device based on big data may include a processor 1001 (for example, a central processing unit (CPU)), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components; the user interface 1003 may include a display (Display), an input unit such as a keyboard (Keyboard); the network interface 1004 may optionally include a standard wired interface, a wireless interface (Such as wireless fidelity WIreless-FIdelity, WI-FI interface); the memory 1005 can be a high-speed random access memory (random access memory, RAM), or a stable memory (non-volatile memory), such as disk memory, memory 1005 may optionally be a storage device independent of the foregoing processor 1001. Those skilled in the art may understand that the hardware structure shown in FIG. 1 does not constitute a limitation on the present application, and may include more or less components than those illustrated, or combine certain components, or arrange different components.

With continued reference to FIG. 1, the memory 1005 in FIG. 1 as a computer-readable storage medium may include an operating system, a network communication module, and computer-readable instructions. In FIG. 1, the network communication module can be used to connect the pricing terminal and perform data communication with the pricing terminal; and the processor 1001 can call computer-readable instructions stored in the memory 1005 and execute the insurance based on big data provided by the embodiments of the present application. Pricing method.

The embodiments of the present application provide an insurance pricing method based on big data.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of a first embodiment of an insurance pricing method based on big data of the present application.

In this embodiment, the insurance pricing method based on big data includes the following steps:

Step S10: When receiving the pricing request of the special medicine insurance plan, obtain the compensation plan of the special medicine insurance plan and the annual average per capita cost of the special medicine corresponding to the special medicine insurance plan, and according to the average per capita of the special medicine The annual fee and the estimated annual compensation per capita for obtaining the special medicine by the compensation plan;

With the development of medical technology, special medicines on the market (special treatment for a certain disease, medicines with high scientific content and high technical difficulty) are gradually increasing, such as erlotinib hydrochloride tablets (trocaine), lucote Ni tablets (Jieweiwei), etc.; but the special drugs on the market are more expensive. In order to reduce the burden on patients, some insurance agencies have proposed to include the medical expenses of special drugs into the insurance reimbursement scope; while the current pricing of special drug insurance plans is currently mainly conducted by experts to manually analyze and determine, and this pricing human factors Large, often do not consider the impact of market competition factors, which reduces the accuracy of special drug insurance plan pricing. In this regard, this embodiment proposes a big data-based insurance pricing method, which combines the use cost of special drugs, disease incidence, market competition, operating costs and other influencing factors to calculate the gross premiums of special drug insurance plans to The way of big data analysis and processing has priced the special medicine insurance plan, thus more comprehensively considering the factors affecting the price of the special medicine insurance plan, so that the pricing results can be more in line with the actual operating conditions of insurance institutions and the market demand for special medicines, which has improved The rationality and accuracy of pricing is conducive to reducing the operating cost of insurance plans.

The insurance pricing method based on big data in this implementation column is implemented by insurance pricing equipment based on big data. The insurance pricing equipment based on big data is explained using a pricing server as an example; and for special medicine insurance plans that need to be priced The target of the special medicine is erlotinib hydrochloride tablet (Terokee); among them, erlotinib hydrochloride tablet (Terokee) is a test method approved by the SFDA. Drugs (tablets) for locally advanced or metastatic non-small cell lung cancer (NSCLC) positive for degenerative lymphoma kinase (ALK). In this implementation column, when the pricing personnel of an insurance institution need to price a special medicine insurance plan, they can perform credit operations on the pricing terminal (such as a personal computer PC, laptop, mobile phone, tablet, etc.), and the pricing terminal is based on the pricing The operation of the personnel sends the pricing request of the corresponding special medicine insurance plan to the pricing server. When the pricing server receives the pricing request, it needs to determine the type of special medicine targeted by the pricing process. For the special medicine category targeted by the pricing process, the pricing server may send inquiry information to the pricing terminal when receiving the pricing request, so that the pricing person manually enters the corresponding special medicine category response in the pricing terminal according to the inquiry information (or It is by selecting the menu option) and sent to the pricing server; of course, the pricing staff can manually enter the special medicine type directly when operating through the pricing terminal, and the pricing terminal adds the special medicine type to the pricing request and sends it to Pricing server.

When the pricing server determines the type of special medicine (erlotinib hydrochloride tablets), it is also necessary to determine the cost of the special medicine, that is, the cost of buying erlotinib hydrochloride tablets; for this cost situation, the special medicine treatment course and use Methods and other factors are characterized in terms of per capita annual cost in this example, that is, each patient of the applicable erlotinib hydrochloride tablet spends annually on the purchase of erlotinib hydrochloride tablets. The per capita annual cost of erlotinib hydrochloride tablets can be calculated from the instruction information of erlotinib hydrochloride tablets. The instruction information includes the method of use (administration method) and the unit price of the instructions; for the convenience of description, the instructions The annual cost per person calculated from the information can be referred to as the "per capita annual cost of the manual". Specifically, the pricing server will first obtain the instruction information of erlotinib hydrochloride tablets. The instruction information includes the unit price of the instructions, the dosage of the treatment course (box), the duration of the treatment course (week), etc., such as the price per box of erlotinib hydrochloride tablets It is 3150 yuan, 1 box for each course of treatment, 3 weeks for each course of treatment; 52 weeks a year, then the per capita annual cost of the instructions for erlotinib hydrochloride tablets is

Annual cost per capita of the instruction manual = unit price of the instruction manual * (treatment dosage * 52/treatment duration) = 54600 yuan

The per capita annual cost of the erlotinib hydrochloride tablet can also be determined based on the historical drug cost (historical data) of the special drug; for the convenience of description, the per capita annual cost determined according to the historical drug cost can be called "historical per capita year cost". Specifically, the server will obtain the historical drug cost (historical data) of the reference city; the historical drug cost may be obtained by connecting the pricing server to the hospital system (or medical system) in the reference city, and the historical drug cost includes the reference The historical annual total cost and the number of users of erlotinib hydrochloride tablets in a certain year (or the most recent year) in the city; based on the total annual cost and the number of drug users in this historical year, the history of erlotinib hydrochloride tablets in the reference city can be calculated Annual cost per capita, that is:

Annual per capita historical cost = total historical annual cost / number of users.

Of course, the historical annual per capita cost derived from the above historical data and the annual per capita cost of the manual obtained from the instruction information belong to the data obtained in the experience category and/or forecast category, so they can be collectively referred to as the predicted annual per capita cost; To make the subsequent calculation results conform to the actual situation, you can also introduce a preset additional risk factor (expense additional risk factor is greater than or equal to zero), time trend factor (time trend factor is greater than or equal to zero), to characterize the next insurance cycle may bring The risk situation (such as characterizing currency inflation, drug price changes, etc.), so as to adjust (or modify) the predicted annual per capita cost, so as to obtain the annual per capita cost; specifically, according to the above forecast annual per capita cost (historical per capita annual cost or The annual cost per capita of the manual), as well as additional risk factors and time trend factors, calculate the annual cost per capita of erlotinib hydrochloride tablets, namely:

Annual cost per capita = predicted annual cost per capita * (1 + additional risk factor of cost) * (1 + time trend factor).

For example, when it is predicted that the per capita annual cost is 54600 yuan, the additional risk factor for the cost is 0.15, and the time trend factor is 0, the per capita annual cost of erlotinib hydrochloride is

Annual cost per capita = 54600*(1+15%)*(1+0)=62790 yuan.

In this embodiment, when the pricing server receives the pricing request, it will also obtain the payment plan of the special medicine insurance plan, which is the payment standard and description (or plan, amount, etc.) of the insurance institution when the payment event occurs. For example, the compensation plan includes a compensation ratio table, as shown in Table 1 below

Table 1 Payout ratio table

支出金额(万)Amount of expenditure (ten thousand)	赔付比例Payout ratio
不超过1万的Not more than 10,000	95％95%
超过1万至3万的部分More than 10,000 to 30,000	95％95%
超过3万至5万的部分More than 30,000 to 50,000	95％95%
超过5万的部分More than 50,000	95％95%

When it is determined that the insured's event expenditure (expenditure for purchasing special medicines) when the compensation event occurs, the pricing server can calculate the payment amount according to the expenditure and the above-mentioned compensation ratio table. Of course, in specific implementation, the compensation plan can be expressed in other ways. The compensation plan may be entered and sent to the pricing server by the pricing personnel when the pricing request is triggered through the pricing terminal, or may be stored in the pricing server in advance.

In this embodiment, when the pricing server obtains the per capita annual cost and compensation plan of erlotinib hydrochloride tablets, it can obtain the estimated per capita annual compensation of erlotinib hydrochloride tablets (that is, insured) according to the per capita annual cost and compensation plan The annual expenditure per person is the amount that the insurance institution needs to pay when the annual expenses per capita). For example, for the compensation plan, the compensation ratio table shown in Table 1 above may be included, and the per capita annual cost of erlotinib hydrochloride tablets is 62790 yuan, then the estimated annual per capita compensation of erlotinib hydrochloride tablets

Estimated annual compensation per capita = (10000-0)*0.95+(30000-10000)*0.95+(50000-30000)*0.95+(62790-50000)*0.95=141185.2 (yuan).

Step S20: Obtain the special medicine market information of the special medicine and the competitive product market information of the special medicine, and obtain the special medicine based on a preset competitive product allocation rule, the special medicine market information and the competitive product market information Competitive product allocation coefficient of medicines;

In this embodiment, when the pricing server obtains the estimated annual per capita compensation of erlotinib hydrochloride tablets, it can calculate the annual risk premium of the special drug insurance plan based on the estimated annual per capita compensation of erlotinib hydrochloride tablets; for the annual risk premium, Refers to the payment of compensation, so to calculate the annual risk premium, you also need to first obtain the probability of the occurrence of the compensation event, that is, the probability of the insured person using erlotinib hydrochloride tablets. In practice, for patients with ALK or NSCLC (applicability of erlotinib hydrochloride tablets), whether they will use erlotinib hydrochloride tablets may be affected by other therapeutic drugs currently on the market, that is, The probability of use of erlotinib hydrochloride tablets will be affected by market competition factors; based on the above considerations, in this example, the special drug market information of special drugs will be obtained, and the current application of erlotinib hydrochloride tablets to determine the current Other special medicine competing products on the market, and obtain the competing product market information of the special medicine competing products, and then analyze the special medicine market information and the competing product market information through certain competing product allocation rules (models) to obtain the special medicine competing products Market competitiveness, and converted into the corresponding competing product allocation factor to calculate the estimated annual per capita compensation of erlotinib hydrochloride tablets; where the competing product allocation factor can be understood as the positive competitiveness of erlotinib hydrochloride tablets Characterization, the stronger the competitiveness of erlotinib hydrochloride tablets, the higher the allocation factor of competing products.

In this embodiment, the competitiveness of erlotinib hydrochloride tablets is often affected by the price and reflected in business recognition, and business recognition is characterized by market share; therefore, the Competitive product allocation coefficient can be calculated from the market share and price. Specifically, the pricing server will obtain the market information of special medicines of erlotinib hydrochloride tablets and the market information of competing products of special medicine competing products, in which the market information of special medicines includes the special medicines of erlotinib hydrochloride tablets within a preset statistical period Drug sales and per capita annual cost of special drugs (that is, obtained in step S10), the market information of competing products includes the sales volume of special drugs and competing products and the annual per capita cost of competing products in the same preset statistical period. The annual fee may be obtained in a similar manner in step S10, and will not be repeated here.

When obtaining the special medicine market information and the proportion of competing product sales, the pricing server will calculate the sales proportion of each medicine according to the special medicine sales and the competing product sales respectively, so as to obtain the special medicine sales proportion and the competing product sales proportion (that is, calculate The sales volume of each drug accounts for the proportion of the total drug sales). For example, the sales volume of erlotinib hydrochloride tablets in 2016 was 40,000 boxes, and the sales volume of competing product A in 2016 was 10,000 boxes, then the proportion of sales of erlotinib hydrochloride tablets was 40,000/(40000+10000) = 0.8, competitive product A The proportion of sales volume is 10000/(40000+10000)=0.2.

At the same time, the pricing server will also substitute the per capita annual cost of special drugs and the per capita annual cost of competing products into the preset price scoring formula to calculate the special drug price score and competing product price score respectively. The preset price scoring formula is:

Where, P _i is the special medicine price score or competitive product price score, 0<P _i ≤100;

k1 is lower than the average price coefficient, k2 is higher than the average price coefficient, 0<k1<k2≤1;

v _i is the per capita annual cost of the special drug or the per capita annual cost of the competitive product;

The average cost of the special drug per capita annual cost and the competitive product per capita annual cost.

When the pricing server obtains the two indicators of the sales proportion of each drug and the price score, it performs a weighted operation (or other calculation) on the two indicators to obtain the competition score of each drug (special drug competition score and competitive product competition score) ,E.g

C _i = k3*X _i +k4*P _i

Where, C _i is the special medicine competition score or competitive product competition score, C _i >0;

k3 is the preset sales weight coefficient, k4 is the preset price weight coefficient, and k3 and k4 are both greater than 0;

X _i is the proportion of the sales volume of the special medicine or the sales volume of competing products, 0<X _i ≤1;

P _i is the special medicine price score or competitive product price score, 0<P _i ≦100.

It is worth noting that in this embodiment, the proportion of sales volume and price scores are not necessarily the same, so consider standardizing the two indicator scores first, so that the index values between different metrics are comparable and comparable Computational, that is, the two indicators are linearly changed, the values are mapped to the same score range, and then the weighted operation is performed to obtain the corresponding competition score. For example, for the above-mentioned sales ratio and price score, the sales ratio is in the range of 0 to 1, and the price score is in the range of 0 to 100, so when standardizing, for convenience of calculation, the price score can be mapped to The range of 0 with 1 (interval), so that the value range of the two indicators can be in the same score range through only one mapping, and the corresponding mapping function is

Among them, x is the score after mapping, x ₀ is the score before mapping, max is the upper limit of the original score range, min is the lower limit of the original score range.

When obtaining the competition score of each drug, the competition score of each drug can be added to obtain the total competition score, and then the ratio of the special drug competition score of erlotinib hydrochloride tablets to the total competition score is used as the competition product allocation coefficient , That is

Among them, U is the allocation factor of competitive products, 0<U≤1;

C _special for the special drug competition score, C _special> 0;

C _i is the competition score of each drug (including special medicine competition score and competitive product competition score), and C _i >0.

Step S30: Obtain the incidence rate of applicability of the specialty medicine, and obtain the annual risk premium of the specialty medicine according to the estimated annual compensation per capita, the incidence of the applicability disease and the competitive product allocation coefficient;

In this embodiment, when the pricing server obtains the competitive product allocation coefficient of erlotinib hydrochloride tablets, it will also obtain the incidence of the applicability of erlotinib hydrochloride tablets. The incidence of the applicable disease can also be obtained based on the historical data of the reference city (such as disease records), which includes the incidence of ALK and NSCLC; or the pricing server uses reptile technology (or by other means) Crawl (or query) from sites such as related drug store websites, disease encyclopedia websites, etc. Then the pricing server will obtain the annual risk premium of the special drug insurance plan of erlotinib hydrochloride tablets based on the estimated per capita annual compensation of the erlotinib hydrochloride tablets obtained in step S10, the incidence of applicable symptoms and the competitive product allocation coefficient obtained in step S20. ,which is:

Annual risk premium = estimated annual compensation per capita *applicable disease incidence*

Step S40: Acquire the per capita annual gross premium of the special medicine insurance plan according to the annual risk premium and the preset operation plan.

In this embodiment, when the annual risk premium is calculated, the expenditure data of the insurance special drug plan in the insured's compensation is obtained, and for the pricing of the special drug insurance plan, the operating expenses and profits of the insurance institution need to be considered. . Specifically, the pricing server also needs to obtain the preset operating plan, and obtain the annual gross premiums per capita of the special medicine insurance plan according to the preset operating plan and the annual risk premium; the preset operating plan may be preset in the financial system or business system The data can also be entered and sent to the pricing server by the pricing personnel when sending pricing requests through the pricing terminal. Among them, the preset operating plan may include preset per capita operating cost, preset tax rate, and preset profit margin; per capita operating cost is the operating cost (including system cost, labor cost, etc.) when each insured provides insurance services ); The default tax rate is the tax rate of premiums; the default profit rate represents the expected profit of insurance institutions. When the annual risk premium, preset operating cost per capita, preset tax rate and preset profit margin are obtained, the pricing server can bring it into the preset gross premium formula to calculate the annual gross premium per capita for the special medicine insurance plan , The default gross premium formula is:

Among them, F is the per capita annual gross premium;

R is the annual risk premium, R>0;

C is the preset operating cost per capita, C>0;

T is the preset tax rate, 0<T<1;

pr is the preset profit rate, 0<pr<1.

In this implementation column, when the pricing server calculates the per capita annual gross premium of the erlotinib hydrochloride insurance plan, it can feed back the per capita annual gross premium to the corresponding pricing terminal (or insured terminal), so that the pricing personnel can The gross premium is quoted (or the insured is paid).

Further, after obtaining the per capita annual gross premium of the special medicine insurance plan, the pricing server can also generate a corresponding pricing report for relevant personnel to view. Specifically, the pricing server pre-stores a report template; the pricing server will record the relevant pricing data during the pricing process; the pricing data includes input data (such as special medicine types, compensation plans), and data sources (such as historical data , Manual information), intermediate data (such as per capita annual expenses, estimated per capita annual compensation) and output data (such as per capita annual gross premiums) are recorded; when pricing is completed, the pricing server will extract the report template and fill these data into Get the pricing report in the report template, and store the pricing report, or send the pricing report to the pricing terminal, or send the pricing report to the relevant business security terminal, for the relevant business security personnel to Business security is monitored.

In this embodiment, when receiving the pricing request of the special medicine insurance plan, the compensation plan of the special medicine insurance plan and the average annual per capita cost of the special medicine corresponding to the special medicine insurance plan are obtained, and according to the special medicine The annual per capita cost of the medicine and the compensation plan are to obtain the estimated annual per capita payment of the special medicine; to obtain the special medicine market information of the special medicine and the competitive product market information of the special medicine, based on the preset competitive product allocation rules, The special drug market information and the competing product market information obtain the competing product allocation coefficient of the special drug; obtain the applicability incidence rate of the special drug, and according to the estimated annual per capita compensation, the applicability disease incidence rate Obtain the annual risk premium of the special drug with the competitive product allocation coefficient; obtain the annual gross premiums per capita of the special drug insurance plan according to the annual risk premium and the preset operating plan. In the above manner, this embodiment calculates the gross premium of the special drug insurance plan by combining multiple factors such as the use cost of special drugs, disease incidence, market competition, and operating costs, and analyzes the special drug insurance plan by means of big data analysis and processing. Pricing has been carried out, so that the price influencing factors of the special medicine insurance plan have been more fully considered, so that the pricing results can be more in line with the actual operating conditions of insurance institutions and the market demand for special medicines, which improves the rationality and accuracy of pricing, and is also conducive to Reduce the operating costs of insurance plans.

In addition, the embodiments of the present application also provide an insurance pricing device based on big data.

Referring to FIG. 3, FIG. 3 is a schematic diagram of functional modules of a first embodiment of an insurance pricing device based on big data of the present application.

In this embodiment, the insurance pricing device based on big data includes:

The annual compensation acquisition module 10 is used to obtain the payment plan of the special medicine insurance plan and the average annual per capita cost of the special medicine corresponding to the special medicine insurance plan when receiving the pricing request of the special medicine insurance plan, and according to The per capita annual cost of the special medicine and the estimated annual per capita compensation for obtaining the special medicine by the compensation scheme;

The coefficient obtaining module 20 is used to obtain the special medicine market information of the special medicine and the competitive product market information of the special medicine, and is based on a preset competitive product allocation rule, the special medicine market information and the competitive product market information Obtain the competitive product allocation coefficient of the special medicine;

The risk premium obtaining module 30 is used to obtain the incidence rate of the applicability of the special medicine, and obtain the annual risk premium of the special medicine according to the estimated annual per capita payment, the incidence of the applicable disease and the distribution factor of the competing products ;

The gross premium obtaining module 40 is used to obtain the average annual gross premium of the special medicine insurance plan according to the annual risk premium and the preset operation plan.

Wherein, each virtual function module of the pricing device of the special drug insurance plan is stored in the memory 1005 of the pricing device of the special drug insurance plan shown in FIG. 1, and is used to implement all functions of computer-readable instructions; each module is used by the processor 1001 When executed, the smart pricing function based on big data can be realized.

Further, the special medicine market information includes the sales volume of the special medicine within a preset statistical period and the average annual cost of the special medicine, and the competitive market information includes the special medicine sales within the preset statistical period Of competing product sales and per capita annual cost,

The coefficient acquisition module 20 includes:

A specific gravity obtaining unit, configured to obtain the specific drug sales proportion and the competitive product sales proportion according to the special drug sales quantity and the competitive product sales quantity;

The score calculation unit is used to calculate the special medicine price score and the competitive product price score based on the special medicine per capita annual cost, the competitive product per capita annual cost and a preset price scoring formula;

A coefficient obtaining unit, configured to obtain a corresponding special medicine competition score based on the special medicine sales proportion and the special medicine price score, and obtain a corresponding competitive medicine competition score according to the competitive product sales proportion and the competitive product price score, According to the special medicine competition score and the competitive product competition score, the competitive medicine allocation coefficient of the special medicine is obtained.

Further, the preset price scoring formula is:

Further, the coefficient acquisition unit is specifically configured to map the proportion of the sales volume of the special medicine, the proportion of the sales volume of the competitive product, the price score of the special medicine and the price score of the competitive product to the same value interval, and then according to the mapping After the sales volume of the special medicine and the price score of the special medicine are obtained, the corresponding competition score of the special medicine is obtained, and the corresponding competition score of the competition is obtained according to the mapped sales weight of the competition and the price score of the competition.

Further, the coefficient acquisition unit is specifically configured to calculate a total competition score based on the special medicine competition score and the competitive product competition score, and then obtain a result based on a ratio of the special medicine competition score and the total competition score Describe the competing factors of special drugs.

Further, the preset operation plan includes a preset operating cost per capita, a preset tax ratio and a preset profit margin,

The gross premium obtaining module 40 is specifically configured to calculate the annual risk premium, the preset operating cost per capita, the preset tax rate, the preset profit rate and the preset gross premium formula Annual gross premiums per capita for special medicine insurance plans, where the preset gross premium formula is:

Among them, F is the per capita annual gross premium;

R is the annual risk premium, R>0;

C is the preset operating cost per capita, C>0;

T is the preset tax rate, 0<T<1;

pr is the preset profit rate, 0<pr<1.

Further, the insurance pricing device based on big data further includes:

The report generation module is used to obtain a preset report template and generate a corresponding pricing report according to the pricing data in the pricing process and the preset report template.

Wherein, the function realization of each module in the insurance pricing device based on big data corresponds to the steps in the embodiment of the insurance pricing method based on big data, and its function and implementation process will not be repeated here one by one.

In addition, the embodiments of the present application further provide a readable storage medium, and the storage medium may be a non-volatile readable storage medium.

Computer readable instructions are stored on the readable storage medium of the present application, wherein when the computer readable instructions are executed by a processor, the steps of the insurance pricing method based on big data as described above are implemented.

For the method implemented when the computer-readable instructions are executed, reference may be made to various embodiments of the insurance pricing method based on big data in this application, and details are not described herein again.

The sequence numbers of the above embodiments of the present application are for description only, and do not represent the advantages and disadvantages of the embodiments. The above embodiments do not limit the patent scope of this application. Any equivalent structure or equivalent process transformation made by the description and drawings of this application, or directly or indirectly used in other related technical fields, are equally included in this application. Within the scope of patent protection.

Claims

An insurance pricing method based on big data, characterized in that the insurance pricing method based on big data includes:

When receiving the pricing request of the special medicine insurance plan, obtain the compensation plan of the special medicine insurance plan and the average annual cost of the special medicine corresponding to the special medicine of the special medicine insurance plan, and according to the average annual cost of the special medicine and The compensation plan obtains the estimated annual per capita compensation for the special medicine;

Obtain the special medicine market information of the special medicine and the competitive medicine market information of the special medicine, and obtain the competition of the special medicine based on the preset competitive medicine allocation rule, the special medicine market information and the competitive medicine market information Product sharing factor;

Obtain the incidence rate of applicability of the specialty medicine, and obtain the annual risk premium of the specialty medicine according to the estimated annual per capita compensation, the incidence of the applicability disease and the competitive product allocation coefficient;

Obtain the annual gross premiums per capita of the special medicine insurance plan according to the annual risk premium and the preset operating plan.
The insurance pricing method based on big data according to claim 1, characterized in that the special medicine market information includes sales volume of special medicines and a per capita annual cost of the special medicines within a preset statistical period. The product market information includes the sales volume of the special drug competing products and the per capita annual cost of the competing products in the preset statistical period,

The step of obtaining the competitive drug allocation coefficient of the special drug based on the preset competitive drug allocation rule, the special drug market information, and the competitive drug market information includes:

Obtaining the proportion of the sales volume of the special medicine and the proportion of the sales volume of the competitive medicine according to the sales volume of the special medicine and the sales volume of the competitive product;

Calculating the special medicine price score and the competitive product price score according to the special medicine annual cost per capita, the competitive product annual cost per capita and the preset price scoring formula;

Obtaining a corresponding special medicine competition score according to the special medicine sales proportion and the special medicine price score, obtaining a corresponding competition medicine competition score according to the competition product sales proportion and the competition medicine price score, and according to the special medicine The competition score and the competitive product competition score obtain the competitive product allocation coefficient of the special medicine.
The insurance pricing method based on big data according to claim 2, wherein the preset price scoring formula is:

Where, P i is the special medicine price score or competitive product price score, 0<P i ≤100;

k1 is lower than the average price coefficient, k2 is higher than the average price coefficient, 0<k1<k2≤1;

v i is the per capita annual cost of the special drug or the per capita annual cost of the competitive product;

The average cost of the special drug per capita annual cost and the competitive product per capita annual cost.
The insurance pricing method based on big data according to claim 2, characterized in that, the corresponding special medicine competition score is obtained according to the special medicine sales proportion and the special medicine price score, and according to the competition product sales proportion The steps for obtaining the competitive product competition score corresponding to the competitive product price score include:

Map the proportion of special medicine sales, the proportion of competing products sales, the price score of the special medicine and the price score of the competition products to the same numerical range, and then obtain according to the mapped proportion of sales of special medicine and price of special medicine Corresponding special medicine competition scores, the corresponding competitive product competition scores are obtained according to the mapped competitive product sales proportion and competitive product price scores.
The insurance pricing method based on big data according to claim 2, wherein the step of obtaining the competitive drug allocation coefficient of the special drug according to the special drug competition score and the competitive drug competition score includes:

The total competition score is calculated according to the special medicine competition score and the competitive product competition score, and then the competitive medicine allocation coefficient of the special medicine is obtained according to the ratio of the special medicine competition score and the total competition score.
The insurance pricing method based on big data according to claim 1, wherein the preset operation plan includes a preset operating cost per capita, a preset tax rate and a preset profit margin,

The step of obtaining the annual gross premiums per capita of the special medicine insurance plan according to the annual risk premium and the preset operating plan includes:

The per capita annual gross premium of the special medicine insurance plan is calculated according to the annual risk premium, the preset per capita operating cost, the preset tax rate, the preset profit margin and the preset gross premium formula, where The preset gross premium formula is:

Among them, F is the per capita annual gross premium;

R is the annual risk premium, R>0;

C is the preset operating cost per capita, C>0;

T is the preset tax rate, 0<T<1;

pr is the preset profit rate, 0<pr<1.
The insurance pricing method based on big data according to claim 1, characterized in that after the step of obtaining the annual gross premiums per capita of the special medicine insurance plan according to the annual risk premiums and the preset operating plan, the method further includes :

Obtain a preset report template, and generate a corresponding pricing report according to the pricing data in the pricing process and the preset report template.
An insurance pricing device based on big data, characterized in that the insurance pricing device based on big data includes:

The annual compensation acquisition module is used to obtain the payment plan of the special medicine insurance plan and the average annual per capita cost of the special medicine corresponding to the special medicine insurance plan when receiving the pricing request of the special medicine insurance plan, and according to the The annual per capita cost of the special medicine and the estimated annual compensation per capita for obtaining the special medicine in the compensation scheme;

The coefficient obtaining module is used to obtain the special medicine market information of the special medicine and the competitive product market information of the special medicine, and obtain based on a preset competitive product allocation rule, the special medicine market information and the competitive product market information Competitive product allocation coefficient of the special medicine;

A risk premium acquisition module, which is used to obtain the incidence rate of the applicability of the specialty medicine, and obtain the annual risk premium of the specialty medicine according to the estimated annual per capita payment, the incidence of the applicability disease and the competing product allocation coefficient;

The gross premium acquisition module is used to acquire the annual gross premiums per capita of the special medicine insurance plan according to the annual risk premium and the preset operation plan.
The insurance pricing device according to claim 8, wherein the special medicine market information includes special medicine sales volume and annual per capita annual cost of the special medicine within a preset statistical period, and the competitive market information includes The sales volume of the special drug competing products and the annual per capita cost of the competing products within the preset statistical period,

The coefficient acquisition module includes:

A specific gravity obtaining unit, configured to obtain the specific drug sales proportion and the competitive product sales proportion according to the special drug sales quantity and the competitive product sales quantity;

The score calculation unit is used to calculate the special medicine price score and the competitive product price score based on the special medicine per capita annual cost, the competitive product per capita annual cost and a preset price scoring formula;

A coefficient obtaining unit, configured to obtain a corresponding special medicine competition score based on the special medicine sales proportion and the special medicine price score, and obtain a corresponding competitive medicine competition score according to the competitive product sales proportion and the competitive product price score, According to the special medicine competition score and the competitive product competition score, the competitive medicine allocation coefficient of the special medicine is obtained.
The insurance pricing device according to claim 9, wherein the preset price scoring formula is:

Where, P i is the special medicine price score or competitive product price score, 0<P i ≤100;

k1 is lower than the average price coefficient, k2 is higher than the average price coefficient, 0<k1<k2≤1;

v i is the per capita annual cost of the special drug or the per capita annual cost of the competitive product;

The average cost of the special drug per capita annual cost and the competitive product per capita annual cost.
The insurance pricing device according to claim 9, wherein the coefficient acquisition unit is specifically configured to compare the sales volume of the special drug, the sales volume of the competing product, the price score of the special drug and the competing product The price scores are mapped to the same numerical range, and then the corresponding special medicine competition scores are obtained according to the mapped special medicine sales proportion and the special medicine price scores, and the corresponding competition competition is obtained according to the mapped competition product sales proportions and competition product price scores score.
The insurance pricing device according to claim 9, wherein the coefficient acquisition unit is specifically configured to calculate a total competition score based on the special medicine competition score and the competitive product competition score, and then according to the special medicine The ratio of the competition score and the total competition score results in the competitive drug allocation coefficient of the special medicine.
The insurance pricing device according to claim 8, wherein the preset operation plan includes a preset operating cost per capita, a preset tax rate and a preset profit margin,

The gross premium acquisition module is specifically configured to calculate the specialty based on the annual risk premium, the preset per capita operating cost, the preset tax rate, the preset profit rate and the preset gross premium formula Per capita annual gross premiums for pharmaceutical insurance plans, where the preset gross premium formula is:

Among them, F is the per capita annual gross premium;

R is the annual risk premium, R>0;

C is the preset operating cost per capita, C>0;

T is the preset tax rate, 0<T<1;

pr is the preset profit rate, 0<pr<1.
The insurance pricing device according to claim 8, wherein the insurance pricing device based on big data further comprises:

The report generation module is used to obtain a preset report template and generate a corresponding pricing report according to the pricing data in the pricing process and the preset report template.
An insurance pricing device based on big data, characterized in that the insurance pricing device includes a processor, a memory, and computer-readable instructions stored on the memory and executable by the processor, wherein the computer When the readable instructions are executed by the processor, the following steps are implemented:

When receiving the pricing request of the special medicine insurance plan, obtain the compensation plan of the special medicine insurance plan and the average annual cost of the special medicine corresponding to the special medicine of the special medicine insurance plan, and according to the average annual cost of the special medicine and The compensation plan obtains the estimated annual per capita compensation for the special medicine;

Obtain the special medicine market information of the special medicine and the competitive medicine market information of the special medicine, and obtain the competition of the special medicine based on the preset competitive medicine allocation rule, the special medicine market information and the competitive medicine market information Product sharing factor;

Obtain the incidence rate of applicability of the specialty medicine, and obtain the annual risk premium of the specialty medicine according to the estimated annual per capita compensation, the incidence of the applicability disease and the competitive product allocation coefficient;

Obtain the annual gross premiums per capita of the special medicine insurance plan according to the annual risk premium and the preset operating plan.
The insurance pricing device based on big data according to claim 15, characterized in that the special medicine market information includes the sales volume of the special medicine within a preset statistical period and the per capita annual cost of the special medicine. The product market information includes the sales volume of the special drug competing products and the per capita annual cost of the competing products in the preset statistical period,

The step of obtaining the competitive drug allocation coefficient of the special drug based on the preset competitive drug allocation rule, the special drug market information, and the competitive drug market information includes:

Obtaining the proportion of the sales volume of the special medicine and the proportion of the sales volume of the competitive medicine according to the sales volume of the special medicine and the sales volume of the competitive product;

Calculating the special medicine price score and the competitive product price score according to the special medicine annual cost per capita, the competitive product annual cost per capita and the preset price scoring formula;

Obtaining a corresponding special medicine competition score according to the special medicine sales proportion and the special medicine price score, obtaining a corresponding competition medicine competition score according to the competition product sales proportion and the competition medicine price score, and according to the special medicine The competition score and the competitive product competition score obtain the competitive product allocation coefficient of the special medicine.
The insurance pricing device based on big data according to claim 16, wherein the preset price scoring formula is:

Where, P i is the special medicine price score or competitive product price score, 0<P i ≤100;

k1 is lower than the average price coefficient, k2 is higher than the average price coefficient, 0<k1<k2≤1;

v i is the per capita annual cost of the special drug or the per capita annual cost of the competitive product;

The average cost of the special drug per capita annual cost and the competitive product per capita annual cost.
A readable storage medium, characterized in that computer readable instructions are stored on the readable storage medium, wherein when the computer readable instructions are executed by a processor, the following steps are implemented:

When receiving the pricing request of the special medicine insurance plan, obtain the compensation plan of the special medicine insurance plan and the average annual cost of the special medicine corresponding to the special medicine of the special medicine insurance plan, and according to the average annual cost of the special medicine and The compensation plan obtains the estimated annual per capita compensation for the special medicine;

Obtain the special medicine market information of the special medicine and the competitive medicine market information of the special medicine, and obtain the competition of the special medicine based on the preset competitive medicine allocation rule, the special medicine market information and the competitive medicine market information Product sharing factor;

Obtain the incidence rate of applicability of the specialty medicine, and obtain the annual risk premium of the specialty medicine according to the estimated annual per capita compensation, the incidence of the applicability disease and the competitive product allocation coefficient;

Obtain the annual gross premiums per capita of the special medicine insurance plan according to the annual risk premium and the preset operating plan.
The readable storage medium according to claim 18, wherein the special medicine market information includes special medicine sales volume of the special medicine within a preset statistical period and per capita annual cost of the special medicine, and the competing product market information Including the sales volume of the special drug competing products and the per capita annual cost of the competing products in the preset statistical period,

The step of obtaining the competitive drug allocation coefficient of the special drug based on the preset competitive drug allocation rule, the special drug market information, and the competitive drug market information includes:

Obtaining the proportion of the sales volume of the special medicine and the proportion of the sales volume of the competitive medicine according to the sales volume of the special medicine and the sales volume of the competitive product;

Calculating the special medicine price score and the competitive product price score according to the special medicine annual cost per capita, the competitive product annual cost per capita and the preset price scoring formula;

Obtaining a corresponding special medicine competition score according to the special medicine sales proportion and the special medicine price score, obtaining a corresponding competition medicine competition score according to the competition product sales proportion and the competition medicine price score, and according to the special medicine The competition score and the competitive product competition score obtain the competitive product allocation coefficient of the special medicine.
The readable storage medium of claim 19, wherein the preset price scoring formula is:

Where, P i is the special medicine price score or competitive product price score, 0<P i ≤100;

k1 is lower than the average price coefficient, k2 is higher than the average price coefficient, 0<k1<k2≤1;

v i is the per capita annual cost of the special drug or the per capita annual cost of the competitive product;

The average cost of the special drug per capita annual cost and the competitive product per capita annual cost.