The Impact of Smart Wards Construction on Nursing Efficiency and Patient Safety: An Empirical Study Based on SEM

Authors

  • Jingjuan Wang The First Affiliated Hospital of Henan University of Science and Technology, Luoyang 471000, Henan, P.R.China
  • Muqing Niu The First Affiliated Hospital of Henan University of Science and Technology, Luoyang 471000, Henan, P.R.China & Business School, Henan University of Science and Technology, Luoyang 471000, Henan, P.R.China

Keywords:

Smart Wards, SEM, Nursing Efficiency, Patient Safety, Technology Acceptance

Abstract

Amid the ongoing digital transformation of healthcare, the emergence of smart wards is reshaping conventional approaches to nursing practice through the integration of technologies including the Internet of Things, big data analytics, and artificial intelligence. Against this backdrop, this study investigates whether the development of smart wards contributes to greater nursing work efficiency and improved patient safety, while examining healthcare professionals’ technology acceptance and interaction with these systems as a mediating mechanism from a Human-Computer Interaction (HCI) perspective. Data were collected from 669 healthcare professionals working across several large tertiary hospitals in China. Structural equation modelling (SEM) was subsequently employed to assess the relationships between smart ward development (SW), perceived nursing work efficiency (EFF), perceived patient safety (SAFE), and technology acceptance (TAPV). The empirical results demonstrate that SW has a significant positive influence on EFF and contributes substantially to improved perceptions of patient safety. The analysis further establishes a significant positive association between EFF and SAFE, suggesting that more efficient nursing practices are closely linked with enhanced patient safety. In addition, TAPV fully mediates the association between SW and EFF, whereas its mediating effect on the relationship between SW and SAFE is partial. These findings emphasise that healthcare professionals’ willingness to accept and engage with emerging technologies is a critical determinant of the benefits achieved through smart ward implementation. The study therefore offers theoretical and empirical evidence supporting the advancement and practical deployment of smart wards, particularly in relation to improving nursing performance and strengthening patient safety. On the basis of these findings, practical recommendations are developed to support the effective adoption and broader implementation of smart ward technologies across healthcare environments worldwide.

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References

[1] Gopal, G., Suter-Crazzolara, C., Toldo, L., & Eberhardt, W. (2019). Digital transformation in healthcare – architectures of present and future information technologies. Clinical Chemistry and Laboratory Medicine (CCLM), 57(3), 328-335. https://doi.org/10.1515/cclm-2018-0658

[2] Kumar, A., Masud, M., Alsharif, M. H., Gaur, N., & Nanthaamornphong, A. (2025). Integrating 6G technology in smart hospitals: Challenges and opportunities for enhanced healthcare services. Frontiers in Medicine, 12, 1534551. https://doi.org/10.3389/fmed.2025.1534551

[3] Baig, M. M., GholamHosseini, H., Moqeem, A. A., Mirza, F., & Lindén, M. (2019). Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges. Health Informatics Journal, 25(3), 1091-1104. https://doi.org/10.1177/1460458217740722

[4] Albahri, O. S., Zaidan, A. A., Zaidan, B. B., Hashim, M., Albahri, A. S., & Alsalem, M. A. (2018). Real-time remote health-monitoring systems in a medical centre: A review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects. Journal of Medical Systems, 42(9), 164. https://doi.org/10.1007/s10916-018-1006-6

[5] Zhou, L., Jiang, M., Duan, R., Zuo, F., Li, Z., & Xu, S. (2024). Barriers and implications of 5G technology adoption for hospitals in western China: Integrated interpretive structural modeling and decision-making trial and evaluation laboratory analysis. JMIR mHealth and uHealth, 12, e48842. https://doi.org/10.2196/48842

[6] Cai, X., & Pan, J. (2022). Toward a brain-computer interface- and internet of things-based smart ward collaborative system using hybrid signals. Journal of Healthcare Engineering, 2022(1), 6894392. https://doi.org/10.1155/2022/6894392

[7] Liao, C.-F., Yen, Y.-C., Huang, Y.-C., & Fu, L.-C. (2016). An empirical study on engineering a real-world smart ward using pervasive technologies. IEEE Systems Journal, 12(1), 240-249. https://doi.org/10.1109/jsyst.2016.2606129

[8] Yang, W., Lu, J., Si, S.-C., Wang, W.-H., Li, J., Ma, Y.-X., Zhao, H., & Liu, J. (2025). Digital health technologies/interventions in smart ward development for elderly patients with diabetes: A perspective from China and beyond. World Journal of Diabetes, 16(4), 103002. https://doi.org/10.4239/wjd.v16.i4.103002

[9] Choi, H., Tak, S. H., Song, Y. A., & Park, J. (2025). Nurses’ perspectives on the adoption of new smart technologies for patient care: Focus group interviews. BMC Health Services Research, 25(1), 391. https://doi.org/10.1186/s12913-025-12578-z

[10] Wen, M.-H., Bai, D., Lin, S., Chu, C.-J., & Hsu, Y.-L. (2022). Implementation and experience of an innovative smart patient care system: A cross-sectional study. BMC Health Services Research, 22(1), 126. https://doi.org/10.1186/s12913-022-07511-7

[11] Yesmin, T., Carter, M. W., & Gladman, A. S. (2022). Internet of things in healthcare for patient safety: An empirical study. BMC Health Services Research, 22(1), 278. https://doi.org/10.1186/s12913-022-07620-3

[12] Jian, W.-S., Wang, J.-Y., Rahmanti, A. R., Chien, S.-C., Hsu, C.-K., Chien, C.-H., Li, Y.-C., Chen, C.-Y., Chin, Y.-P., & Huang, C.-L. (2022). Voice-based control system for smart hospital wards: A pilot study of patient acceptance. BMC Health Services Research, 22(1), 287. https://doi.org/10.1186/s12913-022-07668-1

[13] Fang, J., Zheng, G., Feng, J., Cai, L., Zhang, J., & Wang, Q. (2025). Operational optimization in radiology: An intelligent information management platform significantly enhances workflow efficiency and user satisfaction. Journal of Radiation Research and Applied Sciences, 18(3), 101792. https://doi.org/10.1016/j.jrras.2025.101792

[14] Wen, M.-H., Chen, P.-Y., Lin, S., Lien, C.-W., Tu, S.-H., Chueh, C.-Y., Wu, Y.-F., Tan Cheng Kian, K., Hsu, Y.-L., & Bai, D. (2024). Enhancing patient safety through an integrated internet of things patient care system: Large Quasi-experimental study on fall prevention. Journal of Medical Internet Research, 26, e58380. https://doi.org/10.2196/58380

[15] Kirwan, M., Matthews, A., & Scott, P. A. (2013). The impact of the work environment of nurses on patient safety outcomes: A multi-level modelling approach. International Journal of Nursing Studies, 50(2), 253-263. https://doi.org/10.1016/j.ijnurstu.2012.08.020

[16] Hussain, A., Zhiqiang, M., Li, M., Jameel, A., Kanwel, S., Ahmad, S., & Ge, B. (2025). The mediating effects of perceived usefulness and perceived ease of use on nurses’ intentions to adopt advanced technology. BMC Nursing, 24(1), 33. https://doi.org/10.1186/s12912-024-02648-8

[17] Lee, D. (2018). Strategies for technology-driven service encounters for patient experience satisfaction in hospitals. Technological Forecasting and Social Change, 137, 118-127. https://doi.org/10.1016/j.techfore.2018.06.050

[18] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008

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Published

2026-06-30

How to Cite

Jingjuan Wang, & Muqing Niu. (2026). The Impact of Smart Wards Construction on Nursing Efficiency and Patient Safety: An Empirical Study Based on SEM. Decision Making: Applications in Management and Engineering, 9(1), 363–378. Retrieved from https://www.dmame-journal.org/index.php/dmame/article/view/1814