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Method for predicting hospital service capacity

A forecasting method and technology of service volume, applied in forecasting, instruments, data processing applications, etc., can solve problems such as difficult to control change factors, inability to accurately process complex data, etc., to achieve the effect of improving the quality of medical services

Inactive Publication Date: 2017-07-04
北京北青厚泽数据科技有限公司
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AI Technical Summary

Problems solved by technology

[0004] The present invention provides a prediction method of hospital service volume to solve the problem that the prior art cannot accurately process complex data and uncontrollable change factors by using a static causal result model for analysis and prediction

Method used

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  • Method for predicting hospital service capacity
  • Method for predicting hospital service capacity
  • Method for predicting hospital service capacity

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Embodiment Construction

[0025] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways defined and covered by the claims.

[0026] Hospital service volume indicators: There are two main aspects, macro and micro. The macro service volume of a hospital includes the number of outpatient and emergency visits, the number of emergency rescue visits, the actual bed days occupied by patients in hospital, the number of discharged patients, etc. The macro service volume is usually determined by social needs. The microscopic service volume refers to the internal service volume of each department and department, such as the amount of medicines, sanitary materials, and test specimens ordered by the hospital, the amount of radiographs taken by the radiology department, and the reasonable arrangement of personnel, equipment and beds, etc. (hereinafter referred to as the service volume i...

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Abstract

The invention provides a method for predicting hospital service capacity. The method includes the following steps: 1. reading historical data of a hospital service capacity index; 2. examining whether the data sequence in a training set is stable, and if the data sequence is stable, turning to step 5, and if the data sequence is unstable, turning to 4; conducting stability converstion on the data sequence in the data set, and implementing 5; 5. selecting an appropriate ARIMA model to knitting the data sequence in the data set; 6. estimating parameters of the ARIMA model; 7. examining the ARIMA model, if the examination is successful, turning to 9, and if unsuccessful, turning to 8; 8. repeating 5-7 examining ; 9. the ARIMA model which is subject to inspection being output; and10. using the ARIMA model to predict future trends of the prediction of ARIMA model. According to the invention, the method can predict service capacity of each hospital in a scientific manner on the basis of changes and fluctuation of the trend of service capacity.

Description

technical field [0001] The invention relates to the technical field of prediction, in particular to a method for predicting hospital service volume. Background technique [0002] Scientifically and accurately predicting hospital service volume has increasingly become an important basis for hospitals at all levels to handle daily work and plan for future development. In hospital information management, statistical forecasting has become an indispensable tool, which can provide objective basis for hospital management decision-making. Scientifically and accurately analyzing the dynamic changes of hospital service volume, fitting reasonable statistical models, and predicting service volume trends are of great significance for hospitals to rationally allocate human, financial, and material resources and formulate scientific hospital development plans. [0003] However, the number of outpatient visits is affected by many factors such as social medical policies, hospital treatment...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/22
CPCG06Q10/04G06Q50/22
Inventor 夏一粟刘红跃
Owner 北京北青厚泽数据科技有限公司
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