Empirical Study on the Impact of Intelligent Maintenance Technology for Ophthalmic Medical Equipment on the Quality of Primary Healthcare Services in China

With the deepening of China’s healthcare reform, the operational efficiency of equipment and the quality of services in primary healthcare institutions have attracted increasing attention. As common specialized diagnostic tools at the grassroots level, ophthalmic medical devices suffer from downtime due to faults and delayed maintenance, which not only impair diagnostic efficiency but also hinder the equitable allocation of medical resources. Drawing on both theory and practice of intelligent maintenance technology, this study selects county hospitals and township health centers in three provinces—representing eastern, central, and western China—as samples. Through questionnaires, in-depth interviews, and device log data, we construct multiple regression models and a satisfaction assessment framework to systematically empirically analyze the effects of intelligent maintenance technology. The results show that, after introducing an intelligent maintenance system based on IoT sensing, cloud-platform monitoring, and AI-driven fault diagnosis, average device availability increased by 15.8% compared to traditional maintenance, mean time to repair (MTTR) decreased by 42.3%, and patient satisfaction rose by 12.5 percentage points. Further path analysis indicates that improved equipment reliability and reduced maintenance costs are the primary mediating factors driving service quality improvements. Finally, in light of the current development of primary healthcare in China, we propose policy recommendations to promote the widespread adoption of intelligent maintenance technology—such as standardizing data protocols and interoperability, strengthening maintenance personnel training, and encouraging equipment manufacturers to offer end-to-end services—providing feasible pathways and decision-making references for enhancing the quality of grassroots ophthalmic care in China.