| 线上线下融合的政府限价决策与医院容量分配 |
| Government limited price decision and hospital capacity allocation based on online-offline integration |
| 摘要点击 16 全文点击 0 投稿时间：2022-10-24 修订日期：2024-04-04 |
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| 中文关键词 线上线下融合；政府限价；医院容量分配；患者转诊 |
| 英文关键词 integration of online and offline; government limited price decision; hospital capacity allocation; patient referral |
| 基金项目 国家社会科学基金重点资助项目; 河北省教育厅人文社会科学研究重大课题攻关项目；国家自然科学基金青年项目 |
| 作者 | 单位 | 邮编 | | 吴晓丹 | 河北工业大学 | 300401 | | 李瑞莹 | 河北工业大学 | | | 刘国伟 | 河北工业大学 | 300401 |
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| 中文摘要 |
| 互联网医院快速发展, 2021年用户规模较2020年增长38.6%, 医疗资源有限前提下如何合理分配服务容量成为医院高效运营的关键. 本文通过构建三阶段博弈模型, 研究线上线下融合下的政府限价决策与医院容量分配问题, 分析政府、医院以及患者之间决策的交互作用. 直观上，由于医院可以从转诊患者获得线上线下两部分收益, 因此医院收益随转诊率增加而增加. 然而, 研究发现, 较高的转诊率反而会降低医院利润. 这是由于政府限定线上价格, 从而影响医院容量分配所致. 另外, 随着转诊率的增加, 政府收紧线上平台服务价格. 在线下医疗服务可及性处于较高 (低) 水平时, 随着转诊率增加, 线上平台容量分配越高 (低) . 进一步将模型拓展到竞争情形, 结果表明医疗服务可及性较低时, 随着转诊率的增加, 线上平台容量分配比例先增加后减少. |
| 英文摘要 |
| With the rapid development of internet-based hospitals, in 2022, the number of users increased by 21.7% compared with that in 2021. Under the premise of limited medical resources, reasonable allocation of capacity is the key to operate hospitals effectively. In this paper, a three-stage game model is constructed to study the government limited price decision and hospital capacity allocation under online and offline integration, and to analyze the interactions among patients, hospital and the government. Intuitively, the hospital benefits from a higher referral rate, since it benefits from the referred patients. However, our study shows that a higher referral rate may reduce hospital profit, which is affected by the government's limited price. Moreover, as the referral rate increases, the government tightens the price of the online platform, and the hospital raises (lowers) the capacity of the online platform when the accessibility of offline resources is at a high (low) level. Taking competition into account, when the accessibility of offline resources is at a high level, the capacity of the online platform first increases and then decreases with the referral rate. |
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