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双边市场视角下实体医院型互联网诊疗平台服务定价策略研究

Pricing strategies of entity hospital based online diagnosis and treatment platform from the perspective of two-sided markets
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摘要 目的:探究实体医院型互联网诊疗服务最优定价策略,并为政府制定配套措施、促进互联网诊疗服务发展提供政策建议。方法:基于双边市场理论构建互联网诊疗平台上的医患交互模型,并以北京市为例,采用主体建模方法进行仿真验证。结果:互联网诊疗服务的社会福利随价格呈先上升后下降趋势,适当提升定价将有利于互联网诊疗社会福利的提升;此外,在现有定价水平下,提高医保报销比例,提升医生补贴力度,增加网络外部性均有利于社会福利的提升。建议:为同时兼顾互联网诊疗市场中医、患、平台三方效用,政府应适当上调目前的互联网诊疗服务定价。在新的价格政策出台前,政府应该注重社会层面患者需求的培养,提高医保报销比例;同时平台也应重视线上服务质量,适当增加对线上医生的补贴力度,促进社会福利最大化的实现。 Objective:To explore the optimal pricing strategy for entity hospital based online diagnosis and treatment services and provide policy recommendations for the government to promote their development.Methods:A doctor-patient interaction model for an online diagnosis and treatment platform was constructed based on the theory of two-sided markets.Using Beijing as an example,a simulation was conducted using the agent based model.Results:The social welfare of online diagnosis and treatment services exhibited an upward and then doumward trend.Properly increasing pricing will help improve social welfare of online diagnosis and treatment.Moreover,increasing medical insurance reimbursement rates,improving doctor subsidies,and enhancing network externalities can improve social welfare at the current pricing level.Suggestions:To balance the benefits of doctors,patients,and platforms in the online diagnosis and treatment market,the government should adjust the current pricing level appropriately.Before introducing new pricing policies,the government should focus on cultivating patient demand and increasing reimbursement rates.The platform should also prioritize online service quality and increase subsidies for online doctors to maximize social welfare.
作者 刘昊鹏 马骋宇 LIU Hao-peng;MA Cheng-yu(School of Public Health,Capital Medical University,Beijing 100069,China)
出处 《中国卫生政策研究》 CSCD 北大核心 2024年第3期40-47,共8页 Chinese Journal of Health Policy
基金 北京市自然科学基金面上项目(9222003)。
关键词 互联网诊疗 双边市场 定价 多智能体 仿真 Online diagnosis and treatment Two-sided market Pricing Multi-agent Simulation
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