背景:由于公共部门的等待时间,许多拥有全民医疗保健的国家都有平行的私人医疗保健部门。人们购买个人健康保险来支付私人服务。过去关于公共部门的等待时间与医疗保险需求之间关系的研究有两个局限性:没有考虑私营部门的能力,随后,省略反馈回路。这些限制也存在于香港的健康保险政策讨论中,公共部门过度紧张。缺乏对市场动态的了解可能会导致对公共政策的不切实际的期望。这项研究强调了这些局限性,并试图回答研究问题:是否可以定量解释部门间负担失衡与香港健康保险需求之间的历史动态。
方法:基于负反馈回路创建了系统动力学模型。该模型的初始输入是2009年有医疗保险的人口比例,并一直模拟到2019年。将2015年至2019年的结果与实际数据进行比较,以检验模型的解释力。进行多变量敏感性分析。
结果:随着初始波动,2015年至2019年,模拟结果趋于稳定,误差在可接受范围内。平均绝对百分比误差(MAPE)为0.94%。截至2019年底,医疗保险人口的模拟百分比为36.6%,而“实际价值”为36.7%。模拟的患者入院率和入住率也接近现实。灵敏度分析证明了模型的鲁棒性。
结论:我们可以定量解释卫生系统负担与医疗保险需求之间的反馈回路。使用局部参数化,该模型应可转移到其他全民卫生系统,以便更好地了解系统动态和制定更明智的政策。
Many countries with universal healthcare have a parallel private healthcare sector due to the waiting time in the public sector. People purchase individual health insurance to pay for private services. Past studies on the relationship between the public sector\'s waiting time and the demand for health insurance have two limitations: not considering the capacity of the private sector, and subsequently, the omission of a feedback loop. These limitations are also present in the health insurance policy discussion in Hong Kong, where the public sector is overstretched. A lack of understanding of market dynamics might lead to unrealistic expectations of public policy. This study highlights these limitations, and tries to answer the research question: whether the historical dynamics between the intersectoral imbalance of burden and the demand for health insurance in Hong Kong could be quantitatively explained.
A system dynamics model was created based on a negative feedback loop. The model\'s initial input was the percentage of population with health insurance in 2009, and to simulate the percentage continuously until 2019. Results from 2015 to 2019 were compared with actual figures to examine the model\'s explanatory power. Multivariable sensitivity analysis was performed.
With initial fluctuation, the simulated result stabilized and was within the acceptable error range from 2015 to 2019. The mean absolute percentage error (MAPE) was 0.94%. At the end of 2019, the simulated percentage of population with health insurance is 36.6% versus the \"real value\" of 36.7%. Simulated patient admissions and occupancy rates also approximate the reality. Sensitivity analysis demonstrates the robustness of the model.
We can quantitatively explain the feedback loop between health system burden and demand for health insurance. With local parameterization, this model should be transferable to other universal health systems for a better understanding of the system dynamics and more informed policy-making.