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

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.

[1]  Masatake Nagai,et al.  A GM(1,1)–Markov chain combined model with an application to predict the number of Chinese international airlines , 2007 .

[2]  Xiaohua Li,et al.  Predictors of medical and mental health care use in patients with irritable bowel syndrome in the United States. , 2017, Behaviour research and therapy.

[3]  Qingyun Du,et al.  Spatio-Temporal Variation and Prediction of Ischemic Heart Disease Hospitalizations in Shenzhen, China , 2014, International journal of environmental research and public health.

[4]  Jing Wang,et al.  [Comparison of predictive effect between the single auto regressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) combination model on the incidence of scarlet fever]. , 2009, Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi.

[5]  Yulia R. Gel,et al.  Forecasting demand for health services: Development of a publicly available toolbox , 2015 .

[6]  Peter A Cameron,et al.  Demand at the emergency department front door: 10‐year trends in presentations , 2012, The Medical journal of Australia.

[7]  Hamidreza Mostafaei,et al.  The Application of Fourier Residual Grey Verhulst and Grey Markov Model in Analyzing the Global ICT Development , 2014 .

[8]  Yi Zhang,et al.  Transformation of potential medical demand in China: A system dynamics simulation model , 2015, J. Biomed. Informatics.

[9]  S. Prückner,et al.  The demographic impact on the demand for emergency medical services in the urban and rural regions of Bavaria, 2012–2032 , 2015, Journal of Public Health.

[10]  Raffaele Argiento,et al.  A Bayesian framework for describing and predicting the stochastic demand of home care patients , 2014, Flexible Services and Manufacturing Journal.

[11]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[12]  D. Umberson,et al.  Social Relationships and Health: A Flashpoint for Health Policy , 2010, Journal of health and social behavior.

[13]  Marc S. Goldstein,et al.  Workforce Projections 2010–2020: Annual Supply and Demand Forecasting Models for Physical Therapists Across the United States , 2015, Physical Therapy.

[14]  N. Hupert,et al.  Anticipating demand for emergency health services due to medication-related adverse events after rapid mass prophylaxis campaigns. , 2007, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[15]  P. Deb,et al.  Demand for Medical Care by the Elderly: A Finite Mixture Approach , 1997 .

[16]  T. Akita,et al.  Predicting future blood supply and demand in Japan with a Markov model: application to the sex‐ and age‐specific probability of blood donation , 2016, Transfusion.

[17]  Li-Hui Chen,et al.  Forecasting financial crises for an enterprise by using the Grey Markov forecasting model , 2011 .

[18]  Yi Lin,et al.  A historical introduction to grey systems theory , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[19]  S. Riedel-Heller,et al.  Review: Health Care Utilization and Costs of Elderly Persons With Multiple Chronic Conditions , 2011, Medical care research and review : MCRR.

[20]  Akihito Hagihara,et al.  The aging population and future demand for emergency ambulances in Japan , 2013, Internal and Emergency Medicine.

[21]  Yue Zhang,et al.  Patient choice analysis and demand prediction for a health care diagnostics company , 2016, European Journal of Operational Research.

[22]  M. Grossman.  On the Concept of Health Capital and the Demand for Health , 1972, Journal of Political Economy.

[23]  Mats Thorslund,et al.  Health trends in the elderly population: getting better and getting worse. , 2007, The Gerontologist.

[24]  Lei-Chuan Lin,et al.  Analyzing Taiwan IC Assembly Industry by Grey-Markov Forecasting Model , 2013 .

[25]  Kun Chen,et al.  Forecasting and Analyzing the Disease Burden of Aged Population in China, Based on the 2010 Global Burden of Disease Study , 2015, International journal of environmental research and public health.

[26]  Sunday Ayoola Oke,et al.  A modified grey-Markov fire accident model based on information turbulence indices and restricted residuals , 2016 .

[27]  S. Zeger,et al.  The challenge of predicting demand for emergency department services. , 2008, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[28]  Stefan Nickel,et al.  Modeling the demand for long-term care services under uncertain information , 2012, Health Care Management Science.

[29]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[30]  R. Scheffler,et al.  Global Health Workforce Labor Market Projections for 2030 , 2016, Human Resources for Health.

[31]  F. Schwendicke,et al.  Estimating future dental services' demand and supply: a model for Northern Germany. , 2016, Community dentistry and oral epidemiology.

[32]  Ujjwal Kumar,et al.  Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India , 2010 .