Employee medical benefit fund expenditure balancing algorithm and model

A technology for medical insurance and employees, applied in the field of data processing, can solve problems such as the inability to calculate the future trend of medical insurance funds

Inactive Publication Date: 2017-06-27
上海源烁信息科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method cannot measure the future trend of the medical insurance fund
[0004] Therefore, there is an urgent need for a medical insurance fund algorithm that can pred...

Method used

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  • Employee medical benefit fund expenditure balancing algorithm and model
  • Employee medical benefit fund expenditure balancing algorithm and model
  • Employee medical benefit fund expenditure balancing algorithm and model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] An employee medical insurance fund expenditure balance algorithm includes an expenditure algorithm and an income algorithm, and the steps of the expenditure algorithm are:

[0047] S11: Count the number of insured persons;

[0048] S12: Calculate the index of the number of insured persons;

[0049] S13: Calculate the index growth rate;

[0050] S14: calculate the total expenditure;

[0051] S15: Calculate medical pooling fund expenditure;

[0052] S16: Calculate the total expenditure of the medical pooling fund;

[0053] The steps of the income algorithm described are:

[0054] S21: Count the number of insured persons;

[0055] S22: Statistics per capita payment base;

[0056] S23: calculating various indicators;

[0057] S24: Calculate the total payment amount;

[0058] S25: Calculate the individual payment amount;

[0059] S26: Calculate medical pooling fund income;

[0060] S27: Calculate the total income of the medical pooling fund;

[0061]Wherein, the s...

Embodiment 2

[0071] Before using the general employee medical insurance fund expenditure balance algorithm of the present invention, it is necessary to classify and collect statistics on various indicators and summarize them. The basic data that needs to be counted include: number of insured persons, average cost per time, reimbursement rate, hospitalization rate, etc.

[0072] First, classify the average cost, reimbursement rate, and hospitalization rate according to age (1-100 years old), gender (male, female), and employment status (active, retired, and resigned). image 3 Describes the statistical situation of the hospitalization rate in 2010, and statistics the hospitalization rate according to the year, age, gender, and hospital level.

[0073] Second, fill in the missing parts of the data, such as age, gender, etc., so that each set of data includes data from age 1 to 100. Figure 4 is to complete image 3 The full data plot of the missing age distribution in , including statistic...

Embodiment 3

[0077] After the data is summarized and counted, the data is calculated to predict the expenditure of the medical insurance fund.

[0078] First, classify the data:

[0079] (1) Average cost per time: regression algorithm;

[0080] (2) Reimbursement ratio: geometric mean, maximum value, minimum value, using the mean square error method to remove singular points;

[0081] (3) Hospitalization rate: harmonic mean, maximum value, and minimum value, using the mean square error method to remove singular points;

[0082] (4) Overall result: Arima time-series analysis, using the smoothing index method to smooth the forecast data.

[0083] For the reimbursement rate and hospitalization rate, the maximum value and the minimum value are taken because these rates do not change much, and the channel shape made by taking the large and small values ​​can be used for scenario analysis for decision-making.

[0084] As for the average cost per visit, since the average cost per visit in diffe...

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Abstract

The invention relates to a medical benefit fund expenditure balancing algorithm and a model. The algorithm comprises an expenditure algorithm and an earning algorithm. The expenditure algorithm comprises the steps that S11, the number of insured people is counted; S12, indicators of the number of insured people is calculated; S13, the growth rate of the indicators is calculated; S14, total expenditure is calculated; S15, medical pooling fund expenditure is calculated; and S16, total medical pooling fund expenditure is calculated. The earning algorithm comprises the steps that S21, the number of insured people is counted; S22, the payment base per capita is counted; S23, all indicators are calculated; S24, total payment is calculated; S25, single payment is calculated; S26, medical pooling fund earning is calculated; and S27, total medical pooling fund earning is calculated. The algorithm has the advantages that medical expenditure of urban employees in 1-5 years in the future can be calculated according to current policies or future policy planning, and the change process of the influence of all the indicators on medical pooling fund expenditure is obtained; all factors influencing fund profit and loss are considered comprehensively, and fund profit and loss in the future are calculated.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to an employee medical insurance expenditure balance algorithm. Background technique [0002] With the continuous development of China's social insurance system and the continuous increase of the number of insured persons, the data of the urban employee medical insurance information system are also increasingly perfect. Nowadays, the policies of medical insurance for urban employees are constantly being adjusted from time to time. For decision makers who pay attention to the formulation of medical policies for urban employees, in addition to grasping the general direction policy of "balanced income and expenditure, with a slight surplus", they also pay attention to the future medical insurance policy. The development and changes of fund income and expenditure can better guide the policy formulation of employee medical insurance. In the reference materials of the 2016 Nation...

Claims

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Application Information

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IPC IPC(8): G06Q40/08
CPCG06Q40/08
Inventor 张晔朱忠池谢成超倪英杰高峰徐海鹏江延龙竺斌
Owner 上海源烁信息科技有限公司
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