AIMS public health · 2026
Exploration and practice of data-driven high-quality development in smart hospitals.
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Li Lijuan, Yang Chuanzi, Zhang Zhiyan, Wu Qiang, Cao Xiaojun
Аннотация
BACKGROUND: In this study, we explored a data-driven approach to smart hospital construction, addressing challenges such as data silos, fragmented processes, and delayed decision-making within healthcare systems. The proposed framework supports the high-quality development of public hospitals in China and facilitates intelligent integration among healthcare delivery, service provision, and management functions, achieving synergistic advancement of the "trinity" of hospital operations.
METHOD: A phased action research method was employed, comprising three stages: (1) Infrastructure construction period (2016-2018): Achieving system interconnection and data sharing based on enterprise service bus, standardized interface transformation, and the establishment of an information security system. (2) Intelligent empowerment period (2019-2021): Implementing intelligent healthcare processes, patient services, hospital management, and closed-loop management. (3) Ecological expansion period (2022-2024): Constructing a regional pediatric alliance data collaboration network and enabling internet hospital service continuity. The core pathways include global data integration, data governance standardization, closed-loop management enhancement, intelligent decision support development, and internet hospital integration.
RESULTS: The study demonstrated four outcomes: (1) Improved data quality: Standardization, completeness, and consistency were significantly improved (p < 0.001). (2) Enhanced medical quality: The accuracy rate of AI-assisted diagnosis exceeded 90%, the completion rate of clinical pathways reached 96.03%, and the intensity of antibiotic use (defined daily doses) decreased from 28.27 to 17.14. (3) Optimized service efficiency: The appointment completion rate was 98.15%, the average length of stay in a hospital decreased by 42.06% (interrupted time series analysis showed that the post-intervention level was significantly reduced, β 2 = -0.701, p = 0.002), and the number of bed turnover increased by 128.8% (β 2 = 14.558, p < 0.001). (4) Significant regional collaboration: Established collaborations with 134 hospitals, with more than 2629 cases of remote consultation and transfer in the year 2024 and 88,900 times of mutual recognition of test results.
CONCLUSION: The construction of a data-driven smart hospital, through systematic integration and intelligent empowerment, demonstrated positive outcomes in improving data quality, clinical performance, operational efficiency, and regional collaboration. This provides a replicable implementation framework for the high-quality development of public hospitals in China. Since this was a single-center exploratory study, subsequent multi-center validation is necessary. Continuous improvement of data governance and comprehensive regulatory systems is required to further advance medical services toward enhanced precision, collaboration, and intelligence.
METHOD: A phased action research method was employed, comprising three stages: (1) Infrastructure construction period (2016-2018): Achieving system interconnection and data sharing based on enterprise service bus, standardized interface transformation, and the establishment of an information security system. (2) Intelligent empowerment period (2019-2021): Implementing intelligent healthcare processes, patient services, hospital management, and closed-loop management. (3) Ecological expansion period (2022-2024): Constructing a regional pediatric alliance data collaboration network and enabling internet hospital service continuity. The core pathways include global data integration, data governance standardization, closed-loop management enhancement, intelligent decision support development, and internet hospital integration.
RESULTS: The study demonstrated four outcomes: (1) Improved data quality: Standardization, completeness, and consistency were significantly improved (p < 0.001). (2) Enhanced medical quality: The accuracy rate of AI-assisted diagnosis exceeded 90%, the completion rate of clinical pathways reached 96.03%, and the intensity of antibiotic use (defined daily doses) decreased from 28.27 to 17.14. (3) Optimized service efficiency: The appointment completion rate was 98.15%, the average length of stay in a hospital decreased by 42.06% (interrupted time series analysis showed that the post-intervention level was significantly reduced, β 2 = -0.701, p = 0.002), and the number of bed turnover increased by 128.8% (β 2 = 14.558, p < 0.001). (4) Significant regional collaboration: Established collaborations with 134 hospitals, with more than 2629 cases of remote consultation and transfer in the year 2024 and 88,900 times of mutual recognition of test results.
CONCLUSION: The construction of a data-driven smart hospital, through systematic integration and intelligent empowerment, demonstrated positive outcomes in improving data quality, clinical performance, operational efficiency, and regional collaboration. This provides a replicable implementation framework for the high-quality development of public hospitals in China. Since this was a single-center exploratory study, subsequent multi-center validation is necessary. Continuous improvement of data governance and comprehensive regulatory systems is required to further advance medical services toward enhanced precision, collaboration, and intelligence.