目的: 基于医院视角分析DRG支付方式改革下影响医保结算率的因素,为提高医院服务质量与效率提供实证依据。方法: 以S市同批实施DRG支付方式改革的12家医院为研究样本,通过医保结算系统采集2022年DRG支付病例医保结算清单数据,包括患者个人信息、疾病及诊治信息、DRG分组及住院费用信息等。构建“病组–医院”面板数据集,以医院单个病组的医保结算率为因变量,从费用特征、医院特征、病例类型特征三个维度选取自变量,以患者年龄、病组调整系数为控制变量,区分居民和职工两种医保类型,运用病组间固定效应模型分析医保结算率的影响因素。结果: 在费用特征维度,次均住院费用（β_居民=-51.70,P<0.01;β_职工=-39.18,P<0.01）、药占比（β_居民=-0.77,P<0.01;β_职工=-0.43,P<0.01）与结算率呈显著负向关联,政策范围内费用占比（β_居民=1.12,P<0.01;β_职工=0.71,P<0.01）与结算率呈显著正向关联;在医院特征维度,三级医院较二级医院（β_居民=7.79,P<0.01;β_职工=9.33,P<0.01）、民营医院较公立医院（β_居民=22.16,P<0.01;β_职工=13.83,P<0.01）结算率更高;在病例类型特征维度,低倍率病例占比（β_居民=0.09,P<0.10;β_职工=0.10,P<0.05）、住院大于60天病例占比（β_居民=1.30,P<0.01;β_职工=0.88,P<0.01）与结算率呈正向关联,而高倍率病例占比（β_居民=-0.21,P<0.10;β_职工=-0.29,P<0.01）与结算率呈负向关联。结论: 降低医疗费用水平、优化医疗费用结构（如提升政策范围内费用占比、降低药占比）有利于增加结算率。二级医院、公立医院应通过提升医疗效率、医院内部管理效能等方式,提高DRG结算率。另外,医保部门需进一步优化不同类型病例的结算规则,引导医院诊疗行为更加合理和高效。

Objective: From the perspective of hospitals, this paper analyzes the factors affecting the settlement rate of medical insurance under the DRG payment method reform and provides an empirical basis for improving the service quality and efficiency of hospitals. Methods: Taking the 12 hospitals in City S that have implemented the DRG payment method reform as research samples, the medical insurance settlement list data of DRG payment cases from 2022 were collected through the medical insurance settlement system, including patients' personal information, disease diagnosis and treatment information, grouping and hospitalization cost information. A patient-hospital panel dataset was constructed, which takes the medical insurance settlement rate of a single patient group in a hospital as the dependent variable, selects independent variables from the three dimensions of cost characteristics, hospital characteristics and case type characteristics, and takes patient age and the adjustment coefficient of the patient group as the control variables, distinguishes the two types of medical insurance, i.e. residents and employees, and analyzes the influencing factors of the medical insurance settlement rate by the fixed effect model between patient groups. Results: In the cost characteristic dimension, the average inpatient cost (β_residents=-51.70, P<0.01; β_employee=-39.18, P<0.01), the proportion of drug costs (β_residents=-0.77, P<0.01;β_employee=-0.43, P<0.01) was negatively correlated with the settlement rate, and the proportion of expenses within the policy scope (β_residents=1.12, P<0.01; β_employee=0.71, P<0.01) was positively correlated with settlement rate. In the characteristic dimension of hospital, the tertiary hospital was higher than the secondary hospital (β_residents=7.79, P<0.01; β_employee=9.33, P<0.01), private hospitals was higher than public hospitals (β_residents=22.16, P<0.01; β_employee=13.83, P<0.01) the settlement rate was higher. In the characteristic dimension of case type, the proportion of extremely low-cost cases (β_residents=0.09, P<0.10; β_employee=0.10, P<0.05), the proportion of patients hospitalized for more than 60 days (β_residents=1.30, P<0.01; β_employee=0.88, P<0.01) were positively correlated with the settlement rate, while the proportion of extremely high-cost cases (β_residents=-0.21, P<0.10; β_employee=-0.29, P<0.01) was negatively correlated with settlement rate. Conclusion: Reducing the medical expenses and optimizing the cost structure, such as increasing the proportion of expenses within the policy scope and reducing the proportion of drug expenses, are conducive to increasing the settlement rate. Secondary hospitals and public hospitals should improve medical efficiency and internal management efficiency of hospitals to improve DRG settlement rate. In addition, the medical insurance departments need to further optimize the settlement rules of different types of cases, and guide the diagnosis and treatment behavior of hospitals to be more reasonable and efficient.