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.
Highlights
The population aged 65 and over has increased almost twice as fast as the younger population in the past two decades [1]
This study aims to address the above limitations in the current medical demand prediction literature by developing a new model which is based on the traditional GM (1,1) model and combines the Markov Chain (MC) model and Taylor Approximation algorithm
The test focuses on two criteria, including absolute mean which include auto regressive moving average (ARMA), propagation neural network (BP), and to compare with T-MCGM
Summary
The population aged 65 and over has increased almost twice as fast as the younger population in the past two decades [1] This sharp increase of the aging population has brought enormous pressure to the current medical services system. Developing an accurate and flexible forecasting system for predicting medical services demand becomes imperative [3,4]. Later studies by Hupert et al [6], McCarthy et al [7], Lowthian et al [8], and Hagihara et al [9] developed different models based on multiple regression analysis [6,7,8,9,10,11,12,13,14,15]. There are other forecasting models for predicting medical services demand
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