智慧化投入视角下我国养老机构运营效率评价

Evaluation of the Operational Efficiency of Elderly Care Institutions in China from the Perspective of Smart Investment

摘要: 随着人口老龄化程度不断加深，养老机构在满足老年人照护需求的同时，也面临资源配置效率与运营质量提升的挑战。智慧养老的发展为提升养老服务供给能力提供了新的技术路径，但相关投入能否有效转化为运营效率提升仍有待检验。本文以2024年全国31个省级行政区养老机构为研究对象，在投入–产出指标体系中将智慧养老院规模纳入投入变量，并选取机构数量、职工人数、床位数量和固定资产原价作为投入指标，以营业收入、年在院总人天数和年末在院人数作为产出指标。基于CCR模型与SBM模型测算养老机构运营效率，并分析规模报酬特征及投入冗余与产出不足情况。研究结果表明，我国养老机构整体运营效率较高但尚未达到普遍有效水平，综合效率、纯技术效率和规模效率均值分别为0.92、0.95和0.97。效率差异主要来源于机构规模配置与服务供给之间的不匹配，不同地区分别呈现规模报酬递增或递减特征。松弛变量分析显示，部分地区在智慧养老建设、人力与床位投入方面存在一定冗余，同时服务利用强度和经营收益仍有提升空间。总体来看，应进一步优化资源配置结构，提升服务利用效率，推动智慧技术应用与机构运营能力协同提升，以促进养老服务供给与实际需求之间形成更加有效的匹配。

Abstract: With the deepening of population aging, elderly care institutions face increasing challenges in improving both resource allocation efficiency and operational quality while meeting the growing care needs of older adults. The development of smart elderly care provides a new technological pathway to enhance the supply capacity of elderly care services; however, whether such investments can effectively translate into improved operational efficiency remains to be examined. Using data from elderly care institutions across 31 provincial-level administrative regions in China in 2024, this study incorporates the scale of smart elderly care facilities as an input variable within an input-output indicator system. The number of institutions, number of employees, number of beds, and original value of fixed assets are selected as input indicators, while operating revenue, annual total resident-days, and the number of residents at year-end are used as output indicators. The CCR model and the SBM model are applied to measure the operational efficiency of elderly care institutions, and further analyses are conducted on returns to scale as well as input redundancy and output insufficiency. The results show that the overall operational efficiency of elderly care institutions in China is relatively high but has not yet reached a fully efficient level, with mean values of 0.92 for overall efficiency, 0.95 for pure technical efficiency, and 0.97 for scale efficiency. Efficiency differences mainly stem from mismatches between institutional scale and service supply, with regions exhibiting either increasing or decreasing returns to scale. Slack analysis further indicates that some regions experience redundancy in smart care infrastructure, human resources, and bed capacity, while service utilization intensity and operating revenue still have room for improvement. Overall, optimizing the structure of resource allocation, improving service utilization efficiency, and promoting the coordinated development of smart technology application and institutional management capacity are essential for achieving a better match between the supply of elderly care services and actual demand.