Literature DB >> 26297984

Transformation of potential medical demand in China: A system dynamics simulation model.

Wenya Yu1, Meina Li1, Yang Ge1, Ling Li1, Yi Zhang1, Yuan Liu1, Lulu Zhang2.   

Abstract

OBJECTIVES: The increasing of potential medical demand in China has threatened the health of the population, the medical equity, accessibility to medical services, and has impeded the development of Chinese health delivery system. This study aims to understand the mechanism of the increasing potential medical demand and find some solutions.
METHODS: We constructed a system dynamics model to analyze and simulate this problem, to predict the influences of health policies on the actual percentage of patients not seeking medical care (adjusting the quantity structure of hospitals and community health systems (CHSs), adjusting outpatient prices, and adjusting the level of health insurance).
RESULTS: Decreasing the number of hospitals, increasing the number of CHSs, and raising the proportion of health insurance compensation would effectively increase the transformation of potential medical demand. But currently, changes of the outpatient prices didn't play a role in the transformation of potential medical demand.
CONCLUSIONS: Combined with validation analysis and model simulation, we suggest some possible solutions. The main factors causing potential medical demand are accessibility to medical services and proportion of health insurance compensation. Thus, adjusting the number of hospitals and CHSs and increasing the proportion of health insurance compensation should decrease the actual percentage of patients not seeking medical care and accelerate the transformation of potential medical demand, which deserved being concerned in policymaking.
Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Health policy; Potential medical demand; System dynamics model

Mesh:

Year:  2015        PMID: 26297984     DOI: 10.1016/j.jbi.2015.08.015

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  8 in total

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