Empirical Study on the Impact of Intelligent Maintenance Technology for Ophthalmic Medical Equipment on the Quality of Primary Healthcare Services in China
DOI: 10.23977/jpim.2025.050104 | Downloads: 5 | Views: 175
Author(s)
Xue Shiping 1
Affiliation(s)
1 Wuhan Tianbo Medical Equipment Technology Co., LTD., Hubei, Wuhan, China
Corresponding Author
Xue ShipingABSTRACT
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.
KEYWORDS
Ophthalmic Medical Equipment; Intelligent Maintenance; Primary Healthcare; Service Quality; Empirical StudyCITE THIS PAPER
Xue Shiping, Empirical Study on the Impact of Intelligent Maintenance Technology for Ophthalmic Medical Equipment on the Quality of Primary Healthcare Services in China. Journal of Precision Instrument and Machinery (2025) Vol. 5: 26-33. DOI: http://dx.doi.org/10.23977/jpim.2025.050104.
REFERENCES
[1] Cao, Hengkui, et al. "Barriers and enablers to the implementation of intelligent guidance systems for patients in Chinese tertiary transfer hospitals: usability evaluation." IEEE Transactions on Engineering Management 70.8 (2021): 2634-2643.
[2] Lee, DonHee, and Seong No Yoon. "Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges." International journal of environmental research and public health 18.1 (2021): 271.
[3] Luo, Le, et al. "Large hospitals' outpatient diversion system in China: Following individual intention and referral." The International Journal of Health Planning and Management 37.4 (2022):1973-1989.
[4] Kaium, Md Abdul, et al. "Understanding continuance usage intention of mHealth in a developing country: An empirical investigation." International Journal of Pharmaceutical and Healthcare Marketing 14.2 (2020): 251-272.
[5] Alam, Mohammad Zahedul, et al. "Adoption intention and usage behavior of mHealth services in Bangladesh and China: A cross-country analysis." International Journal of Pharmaceutical and Healthcare Marketing 14.1 (2020): 37-60.
[6] Wu, Qun, Lan Huang, and Jiecong Zong. "User Interface Characteristics Influencing Medical Self-Service Terminals Behavioral Intention and Acceptance by Chinese Elderly: An Empirical Examination Based on an Extended UTAUT Model." Sustainability 15.19 (2023): 14252.
[7] Zhang, Chenchen, et al. "Applications of artificial intelligence in myopia: current and future directions." Frontiers in Medicine 9 (2022): 840498.
[8] Kelly, Sage, Sherrie-Anne Kaye, and Oscar Oviedo-Trespalacios. "What factors contribute to the acceptance of artificial intelligence? A systematic review." Telematics and Informatics 77 (2023): 101925.
[9] Hameed, BM Zeeshan, et al. "Breaking barriers: Unveiling factors influencing the adoption of artificial intelligence by healthcare providers." Big Data and Cognitive Computing 7.2 (2023): 105.
[10] Sarkar, Soumodip, and Sara Mateus. "Doing more with less-How frugal innovations can contribute to improving healthcare systems." Social Science & Medicine 306 (2022): 115127.
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