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Int J Environ Res Public Health. 2017 Aug 6;14(8). pii: E883. doi: 10.3390/ijerph14080883.

Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation.

Author information

1
College of Pharmacy, Fujian University of Traditional Chinese Medicine, No. 1 Qiuyang Road, Fuzhou 350122, Fujian, China. ywjoy@126.com.
2
School of Economics and Management, Fuzhou University, No. 2 Xueyuan Road, Fuzhou 350108, Fujian, China. ywjoy@126.com.
3
Newcastle University Business School, Newcastle University, 5 Barrack Road, Newcastle upon Tyne NE1 4SE, UK. f.jiao@newcastle.ac.uk.
4
School of Economics and Management, Fuzhou University, No. 2 Xueyuan Road, Fuzhou 350108, Fujian, China. zhangqs@fzu.edu.cn.
5
Newcastle Business School, Northumbria University, Newcastle upon Tyne NE1 8ST, UK. Zhibin.lin@northumbria.ac.uk.

Abstract

The sharp increase of the aging population has raised the pressure on the current limited medical resources in China. To better allocate resources, a more accurate prediction on medical service demand is very urgently needed. This study aims to improve the prediction on medical services demand in China. To achieve this aim, the study combines Taylor Approximation into the Grey Markov Chain model, and develops a new model named Taylor-Markov Chain GM (1,1) (T-MCGM (1,1)). The new model has been tested by adopting the historical data, which includes the medical service on treatment of diabetes, heart disease, and cerebrovascular disease from 1997 to 2015 in China. The model provides a predication on medical service demand of these three types of disease up to 2022. The results reveal an enormous growth of urban medical service demand in the future. The findings provide practical implications for the Health Administrative Department to allocate medical resources, and help hospitals to manage investments on medical facilities.

KEYWORDS:

Grey Markov chain; Taylor Approximation; medical services demand; prediction

PMID:
28783088
PMCID:
PMC5580587
DOI:
10.3390/ijerph14080883
[Indexed for MEDLINE]
Free PMC Article

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