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Medical facility service bearing capacity evaluation method

A technology of carrying capacity and facilities, applied in the field of big data, can solve the problems that the research results cannot provide timely and effective guidance

Active Publication Date: 2020-02-18
SHANGHAI INSTITUTE OF TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Existing methods are mostly limited to descriptive statistics and field research, and the research results cannot provide timely and effective guidance in the planning and layout of medical facilities

Method used

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  • Medical facility service bearing capacity evaluation method
  • Medical facility service bearing capacity evaluation method
  • Medical facility service bearing capacity evaluation method

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0055] The present invention provides a method for calculating GDCL of medical facility service carrying capacity from the perspective of residents' choice behavior, comprising the following steps: S1: Obtain POI data of medical facilities and residential areas and regional health statistical yearbook; S2: Calculate residential area i according to the gravitational model formula Potential A to medical facility j ij ;S3: If A i = 0 (ie ∑ j A ij =0), then the residential point i chooses the nearest medical facility k; if A i ≠0, then incorporate this potential into the multinomial Logit (MNL) model, and calculate the probability P that residents at residential point i choose facility j ij ; S4: Randomly give the probability distribution of each resident's medical facility selection, if P ik If it falls within the interval of the probability distribution, the residents at the residential point i choose to choose the medical facility k; S5: Repeat the above steps, and all the ...

Embodiment 2

[0058] In this embodiment, on the basis of Embodiment 1, step S2 includes the following sub-steps: Calculate the potential A from the settlement i to the medical facility j according to the gravity model formula ij .

[0059] When this embodiment is implemented, it is necessary to reasonably set the service capacity M of medical facilities j , The grade scale S of the medical facilities ij and the resistance factors of residents' travel and other parameters. Among them, the service capacity of medical facilities can be comprehensively reflected by different indicators such as the number of beds, the number of health technicians, and the number of consultations. Different grades of medical facilities should have different grade scale coefficients, and at the same time, the attenuation effect of grade scale attractiveness should be produced with the increase of travel distance. The travel friction coefficient β reflects the sensitivity of residents to the travel distance, u...

Embodiment 3

[0061] In this embodiment, on the basis of Embodiment 1, step S4 includes the following sub-steps: S41: Calculate the calculated P based on the MNL model formula. ij Sorting from small to large; S42: Generate a random floating-point number p in the range of (0, 1), if P ik ≤pik+1 , then the residents at the settlement i choose to choose the medical facility k.

[0062] When this embodiment is implemented, it is necessary to P ij Sort from smallest to largest. For each competitive selection of medical facilities, the present invention makes a decision by generating a random number between 0 and 1. Residents choose a medical facility every time they compete at random, but with P ij is proportional to the size of P ij The larger the value is, the higher the probability of choosing medical facility j is.

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PUM

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Abstract

The invention provides a method for evaluating the service bearing capacity of medical facilities. The method comprises the following steps: acquiring POI data of the medical facilities and residential areas and regional health statistics yearns; determining the potential Aij from the residential point i to the medical facility j according to the POI data of the medical facility and the residential point as well as the regional health statistics annual recognition; if the potential Aij from the residential point i to the medical facility j is zero, asking to select the nearest medical facility; if the potential Aij from the residential point i to the medical facility j is not zero, obtaining the probability Pij of selecting the medical facility j by the residential point i; probability distribution randomly given for each resident medical facility selection is acquired, If Pik is within the interval range of the probability distribution, the resident at the resident point i selects a medical facility k; judging whether all the residence points complete medical facility selection or not, If yes, counting the total number of the residence points selecting the medical facility j to obtain the service bearing capacity of the medical facility j. Therefore, the medical facility service bearing capacity of each area can be accurately evaluated, and effective guidance is provided for residents to select to seek medical advice.

Description

technical field [0001] The present invention relates to the technical field of big data, in particular, to a method for evaluating the service carrying capacity of medical facilities. Background technique [0002] Judging whether medical facilities have the corresponding carrying capacity and guiding medical needs reasonably and effectively have become the key to improving the macro allocation efficiency of medical and health resources and the micro operation efficiency of medical facilities. At the same time, the service carrying capacity of medical facilities is affected by both the level of medical service supply of the facility itself and the choice of residents' medical facilities. [0003] The existing calculation methods for the service carrying capacity of medical facilities are too simple, usually only considering the level of medical service supply of the facility itself. Considerations about the selection of residents' medical facilities are still limited to the ...

Claims

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Application Information

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IPC IPC(8): G06F30/20G06F119/14
Inventor 唐春雷武田艳张若晨王旭陈震夏清涛
Owner SHANGHAI INSTITUTE OF TECHNOLOGY