Exploration and practice of data-driven high-quality development in smart hospitals

Area of Science:

• Healthcare Management
• Health Informatics
• Digital Health

Background:

• Addresses challenges in healthcare systems like data silos and delayed decision-making.
• Focuses on the high-quality development of public hospitals in China.
• Aims to intelligently integrate healthcare delivery, service provision, and management.

Purpose of the Study:

• To explore a data-driven approach for smart hospital construction.
• To facilitate intelligent integration of hospital operations.
• To achieve synergistic advancement in hospital delivery, service, and management.

Main Methods:

• Employed a phased action research method from 2016-2024.
• Included infrastructure construction, intelligent empowerment, and ecological expansion stages.
• Core pathways involved data integration, governance, closed-loop management, decision support, and internet hospital integration.

Main Results:

• Significantly improved data quality (standardization, completeness, consistency).
• Enhanced medical quality with AI diagnosis accuracy >90% and reduced antibiotic use.
• Optimized service efficiency, decreasing average length of stay by 42.06% and increasing bed turnover by 128.8%.

Conclusions:

• A data-driven smart hospital framework improves data quality, clinical performance, efficiency, and regional collaboration.
• Provides a replicable model for public hospital development in China.
• Recommends multi-center validation and continuous improvement of data governance and regulatory systems.