目的:远程医疗,作为一个基于信息的工具,被广泛认为是弥补我国医疗资源配置不平衡的有效解决方案。本研究旨在分析远程医疗功能对公立医院运营效率的影响,特别关注它们对不同级别医院的异质性影响。
方法:基于2022年卫生信息化统计调查数据,采用横断面研究设计,以8944所公立医院为研究对象,分析远程医疗对医院收入和业务能力的影响。多元线性模型,倾向得分匹配(PSM),采用分组回归方法评估远程医疗对医院收入的影响,磋商次数,和放电的数量。
结果:描述性结果显示,在35.51%的公立医院中可以使用远程医疗。分析还表明,各种因素,比如医院级别,学术类别,医院的区域,管理水平和床位数量都对医院的运作有重大影响。此外,回归结果表明,开放远程医疗可以使医院收入增加0.140(P<0.01),医院会诊增加0.136(P<0.01),放电次数为0.316(P<0.01)。在使用倾向评分匹配校正内生性后,结果表明,开放远程医疗对医院收入的影响,协商,放电次数为0.191(P<0.01),0.216(P<0.01),0.353(P<0.01),分别。进一步进行异质性分析,以探讨远程医疗对不同级别医院的差异影响。分组回归显示,远程医疗对二级医院的收入有正向影响,系数为0.088(P<0.05),它对二级医院的医院咨询产生了更显著的积极影响,系数为0.127(P<0.01)。对基层医院的出院人数影响更大,系数为1.203(P<0.01)。远程医疗,另一方面,对三级医院的整体收入和运营能力没有显著的正向影响。
结论:远程医疗对医院收入有显著的促进作用,医院咨询和出院次数,这种影响在不同级别的医院之间是有区别的。通过远程医疗的建设,基层医院能够显著提高业务能力和收入,对改善基层公立医院的运行起到了积极的作用。
OBJECTIVE: Telemedicine, as an information-based tool, is widely recognized as an effective solution for compensating for the imbalanced allocation of medical resources in China. This study specifi-cally aimed to analyze the impact of telemedicine functions on the operational efficiency of public hospitals, with a particular focus on their heterogeneous effects on hospitals of different levels.
METHODS: A cross-sectional research design was used based on the 2022 Health Informatization Statistical Survey data, and 8 944 public hospitals were used as research objects to analyze the impact of telemedicine on hospital revenues and business capacity. Multivariate linear model, propensity score matching (PSM), and grouped regression methods were employed to evaluate the impact of telemedicine on hospital revenues, number of consultations, and the number of discharges.
RESULTS: The descriptive results showed that telemedicine was available in 35.51% of public hospitals. The analysis also demonstrated that various factors, such as hospital level, academic category, area of the hospital, administrational level and number of beds all had a significant influence on the operation of the hospital. Moreover, the regression results showed that opening telemedicine could increase hospital revenues by 0.140 (P < 0.01), hospital consultations by 0.136 (P < 0.01), and the number of discharges by 0.316 (P < 0.01). After correcting for endogeneity using the propensity score matching, the results showed that the effect of opening telemedicine on hospital revenues, consultations, and the number of discharges was 0.191 (P < 0.01), 0.216 (P < 0.01), and 0.353 (P < 0.01), respectively. Further heterogeneity analysis was conducted to explore the differential effects of telemedicine on hospitals of different levels. Grouped regression showed that telemedicine had a positive impact on the income of secondary hospitals, with a coefficient of 0.088 (P < 0.05), and it had a more significant positive impact on hospital consultations in secondary hospitals, with a coefficient of 0.127 (P < 0.01). An even greater impact on the number of discharges in primary hospitals, with a coefficient of 1.203 (P < 0.01). Telemedicine, on the other hand, did not have a significant positive impact on the overall revenue and operational capacity of tertiary hospitals.
CONCLUSIONS: Telemedicine had a significant promoting effect on hospital revenues, hospital consultations and the number of discharges, and this effect was differentiated between hospitals of different levels. Through the construction of telemedicine, primary hospitals were able to significantly improve their business capacity and revenue, which played a positive role in improving the operation of primary public hospitals.