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摘要： 推进医保支付方式改革是医保高质量发展的需要，更是减轻人民群众就医负担、增进民生福祉的需要。近年来，我国多元复合式医保支付方式改革取得积极进展，同时也对医保基金管理和医疗机构管理提出了更高要求。在医疗健康大数据背景下，临床预测模型被成熟应用于药物试验等领域，在量化评估患者所患疾病风险程度、医疗资源消耗强度等方面展现出较好的能力。本文旨在借鉴医保风险调整机制和风险预测模型相关国际经验，研究临床预测模型应用于医保支付方式改革下疾病诊断相关分组和医保偿付风险调整的可行性，为进一步提高医疗机构服务质量，提升医保基金使用效能提供参考。 Abstract: Advancing the reform of medical insurance payment methods is not only a necessity for the high-quality development of medical insurance, but also a requirement for alleviating the burden of people’s medical expenses and enhancing their well-being. In recent years, China has made positive progress in the reform of diversified and composite medical insurance payment methods, which has also raised higher requirements for the management of medical insurance funds and medical institutions. Against the backdrop of health big data, clinical prediction models have been maturely applied in areas such as drug trials, demonstrating good capabilities in quantitatively assessing the risk level of patients’ diseases and the intensity of medical resource consumption. This paper aims to draw lessons from international experiences in medical insurance risk adjustment mechanisms and risk prediction models, explore the feasibility of applying clinical prediction models to diagnosis related groups and medical insurance reimbursement risk adjustment under the reform of medical insurance payment methods, and provide corresponding references for further improving the quality of medical institution services and enhancing the efficiency of medical insurance fund utilization.

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