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

Duan, J.; Jiao, F.; Zhang, Q.; Lin, Z.

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.

Citation

Duan, J., Jiao, F., Zhang, Q., & Lin, Z. (2017). Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation. International Journal of Environmental Research and Public Health, 14(8), Article 883. https://doi.org/10.3390/ijerph14080883

• Journal Article Type: Article
• Acceptance Date: Aug 5, 2017
• Online Publication Date: Aug 6, 2017
• Publication Date: Aug 6, 2017
• Deposit Date: Sep 22, 2018
• Publicly Available Date: Oct 10, 2018
• Journal: International Journal of Environmental Research and Public Health
• Print ISSN: 1661-7827
• Electronic ISSN: 1660-4601
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 8
• Article Number: 883
• DOI: https://doi.org/10.3390/ijerph14080883
• Keywords: Medical services demand, Grey Markov chain, Taylor Approximation, prediction
• Public URL: https://durham-repository.worktribe.com/output/1319088
• Related Public URLs: http://nrl.northumbria.ac.uk/id/eprint/31634