The diversity of healthcare information systems, the variety of multi-channel data sources and the multi-point statistical standards result in inconsistent data formats. This prevents real-time data monitoring, hinders the full realization of big data’s benefits and consequently poses challenges for big data analytics (26). To meet the demands of hospital management for data utilization, hospital management and information technology assessment standards, disciplinary development and scientific research management needs, as well as regional data interoperability requirements, hospitals urgently need to establish an efficient digital management center. Hospital management has recognized the importance of the comprehensive quality management model. Decision-makers have adopted innovative approaches to integrate resources and coordinate cross-departmental efforts, actively advancing the establishment of an digital management center. This center employs appropriate analytical methods to conduct precise analysis of hospital-wide big data, establishing an efficient and unified quality management data decision-making repository.

The hospital digital management center serves hospital administration and is applied in clinical settings to meet the diverse service needs of medical personnel at various levels. The data management center must ensure comprehensive coordination, controlling the timeliness, accuracy and continuity of data. It efficiently utilizes data resources to assist departments and decision-makers in formulating management strategies, continuously optimizing the operational mechanisms of the quality management data center (27). The hospital digital management center leverages the hospital-wide network system platform to integrate dispersed functional department business systems. These include the outpatient and emergency information center, outpatient appointment center, clinical data center, five centers (chest pain center, trauma center, stroke center, critical maternal care center, critical pediatric and neonatal care center), performance center, operations center, medical quality management center, research data center and logistics management center. The system supports real-time querying and monitoring of relevant data indicators across the entire hospital. It enables precise analysis through dimensions such as hospital tiered evaluation metrics, national or provincial quality control indicators and hospital “15th Five-Year Plan” development targets. This facilitates data-driven digital management, highlighting the critical importance of real-time data monitoring.

During the 14th Five-Year Plan period, China’s digital economy has entered a new phase characterized by inclusive sharing and other functions. The development of data-driven healthcare faces new demands and expectations. Utilizing digital technologies to accelerate the integration of regional healthcare services is the future trend of China’s digital healthcare development (28). Under the backdrop of hospitals entering an era of big data and high-quality development, institutions should establish digital management centers to integrate internal data sources, unlock data value and transform latent data resources into an internal driving force for hospital advancement. This initiative aims to pursue high-quality, high efficiency hospital development, achieve data sharing capabilities and lay the foundation for building a digital hospital (29). By establishing a digital management center, hospitals can integrate clinical, operational, research data centers to systematically organize and consolidate data spanning clinical diagnosis and treatment, operational management, performance evaluation, personnel management, quality control and research projects, thereby implementing quality control management. This enables the full utilization of big data resources to meet the needs of hospital administration, clinical research, performance management and talent development. Functional departments can leverage the data-sharing capabilities of the digital management center to achieve benefits such as clinical decision support and the generation of business analysis reports, thereby driving reform and development in the precise application of big data.

The core talent bottleneck in the construction of county hospital digital management centers primarily manifests as dual deficiencies in technical capabilities and team structure. Due to the diversity and heterogeneity of information systems, IT departments often lack the technical capabilities required to maintain complex systems, making it difficult to ensure data integration and system stability. At the same time, operational departments lack the ability to apply data analytics tools, hindering their capacity to fully unlock the value of healthcare data. The information department lacks a dedicated operations and maintenance team, while quality management positions lack professionals with both medical knowledge and data analysis capabilities, making it difficult to implement data-driven quality control management. The dual constraints of technological gaps and shortages of specialized talent directly hinder the development of digital management centers. This makes standardized governance of cross-system data difficult to achieve and further impedes the support of digital applications such as clinical decision-making and performance management. The fundamental contradiction lies in the mismatch between resource endowment of county medical institutions and high technical demands of digital development. This issue urgently requires resolution through multiple channels, including targeted training, external recruitment and industry-academia-research collaboration.

The operational challenges in establishing a digital management center in county hospitals are primarily manifested in systemic deficiencies in cross-departmental collaboration and data governance. From a management structure perspective, blurred boundaries between multiple functional departments have led to fragmented data management processes, resulting in a situation where data is managed by many parties yet no one is held accountable. At the data level, the absence of unified data standards and governance mechanisms has led to inconsistent data formats across systems. This has triggered issues such as redundant data collection and uneven data quality, significantly increasing the error rate in critical indicators like healthcare quality monitoring data. The phenomenon of data silos further exacerbates two core contradictions: operational departments struggle to access real-time, accurate integrated data, leading to delays in precision management tasks such as DRG cost accounting;quality management departments are unable to establish closed-loop management systems, prolonging the response cycle from issue identification to corrective action. The deeper issue lies in the fact that the current management model has yet to establish an integrated mechanism spanning “systems-data-applications, “making it difficult to implement the policy-mandated transformation toward intelligent quality control. These structural deficiencies place county hospitals at a strategic disadvantage when confronting policy changes such as medical insurance payment reforms and performance evaluations, due to insufficient data support.

The heterogeneity of hospital business system databases is particularly pronounced. The development of each business system has only addressed the needs of its respective department, lacking long-term, forward-looking and systematic planning. The current lack of unified technical and data standards, the inability to automatically transmit data, the absence of effective linkage and sharing mechanisms have led to the formation of numerous “information silos” within hospitals. Certain metrics are difficult to obtain, data suffers from non-standardization issues and there is a lack of support from a hospital-wide knowledge base (30–37). The core reason behind the information silo problem encountered lies in the lack of systematic planning and data governance during the construction of digital management centers in county-level hospitals. The core issue lies in the adoption of heterogeneous database architectures across various business systems (HIS, LIS, PACS, etc.). Their development has focused solely on departmental needs without hospital-wide coordination, resulting in inconsistent data standards and a lack of standardized interfaces. This fragmented construction model triggers a three fold chain reaction: (1) Data flow is obstructed, preventing the automatic exchange of critical operational data such as clinical diagnoses and medication inventory, necessitating manual re-entry. (2) Data value is compromised, as structured data like electronic medical records and diagnostic tests cannot be cross-system linked, hindering advanced applications like Diagnosis-Related Group (DRG) cost analysis. (3) The absence of a unified data middleware platform leads to inefficient collection of real-world data (RWD) required for research, thereby constraining disciplinary advancement. These issues highlight that county hospitals have yet to establish a collaborative mechanism encompassing “top-level design, standard formulation and system restructuring” during their digital transformation process. There is an urgent need to break down data silos through technical means such as Master Data Management (MDM).

The “National Health and Medical Big Data Standards, Security, and Service Management Measures (Trial)” [National Health Commission Planning, Development, and Informatization Department Document (2018) No. 23], “Notice on Conducting the ‘Public Medical Institutions Economic Management Year’ Activity” [National Health Commission Financial Affairs Department Letter (2020) No. 262] and other documents elevate the development of health and medical big data to a national strategy. Through strategic planning, these policies promote the construction of a national integrated data center collaborative innovation system, with a focus on regions such as Beijing-Tianjin-Hebei and Yangtze River Delta as demonstration areas. This strengthens the layout of county hospital digital management centers and their integration with regional core node functions. Special funds for public hospital reforms are being allocated to county digital platforms to support the construction of smart hospitals. This policy framework, guided by strategic planning and supported by fiscal resources, systematically addresses persistent challenges in county hospitals, such as data silos and inconsistent standards by advancing the establishment of digital management centers. It enhances the utilization of clinical data, positioning these centers as the core engine driving the digital transformation of county-level healthcare.

As people’s demands for healthcare service quality continue to rise, hospitals need to enhance operational efficiency and social service capabilities through digital hospital construction to better meet the needs of patients. The maturity and development of technologies such as big data, artificial intelligence and Internet of Things provide technical support for hospital operations management and intelligence hospital construction, enabling more efficient data processing, more precise decision-making support and more intelligent equipment management. Through intelligence upgrades, hospitals can optimize resource allocation and achieve more refined management, thereby reducing costs related to human resources, materials, finances and improving both economic and social benefits. Additionally, hospital electronic health records are evolving toward intelligent and structured formats, not only recording patient diagnostic information but also automatically generating treatment recommendations, warning of medical risks and supporting the extraction of clinical research data, thereby serving as intelligent assistants throughout the entire patient care process. Patients can conveniently access medical information, test reports, and consultation services through mobile APP, WeChat and other mobile platforms, enabling them to book appointments, make payments and consult online, thereby enhancing their medical experience and satisfaction. Digital transformation will enable hospitals to break through traditional spatial and temporal constraints, establish an integrated online and offline service ecosystem, provide patients with more efficient, convenient and personalized medical services.

Hospital Information Systems (HIS), Laboratory Information Management Systems (LIS) and Picture Archiving & Communication Systems (PACS) will accelerate their migration to the cloud, enabling centralized data storage, real-time sharing and efficient management. Leveraging the powerful computing power and elastic scalability of cloud computing, hospitals can quickly process massive amounts of medical data, providing strong support for clinical decision-making and research innovation. The widespread adoption of medical IoT enables various medical devices, wearable devices and sensors to collect patients’ vital signs and diagnostic data, creating a “digital twin” of the patient. This enables comprehensive and dynamic monitoring of patients’ health conditions, laying the foundation for personalized and precision medicine. The deep integration of big data and artificial intelligence technologies unlocks the latent value of medical data, supporting hospitals in achieving refined management. Through in-depth analysis of clinical data, operational data and medical insurance data, hospitals can optimize resource allocation, reasonably arrange treatment processes, precisely control medical costs and improve operational efficiency. Artificial intelligence-assisted diagnosis systems can quickly and accurately identify disease imaging characteristics and assist doctors in formulating treatment plans, thereby improving diagnostic accuracy and treatment outcomes. Artificial intelligence empowers hospital logistics management, pharmaceutical management and equipment maintenance, achieving intelligent operations and maintenance, reducing labor costs and enhancing the overall operational efficiency of hospitals.

Common issues in China’s hospital quality management include inconsistent quality control standards, arbitrary performance evaluations, ineffective implementation of regulations, insufficient training intensity and a shortage of dedicated quality control personnel. These challenges are driving the transformation and upgrading of healthcare quality management (38). Weak data governance foundations lead to non-standardized data collection, missing key fields and frequent logical errors, significantly reducing data usability. Inadequate resource integration capabilities have led to a proliferation of heterogeneous systems within county institutions. Due to the absence of comprehensive design, imaging and laboratory data are stored in isolation, creating information silos. Technical and talent shortages hinder the advancement of intelligent systems, budget constraints limit the deployment of smart quality control tools, data cleansing relies heavily on manual operations and the severe shortage of professionals with expertise in both healthcare and information technology significantly restricts the depth of data value extraction. Heightened security risks manifest in unclear permission hierarchies and the absence of log auditing mechanisms. Limited security investments at county hospitals struggle to address legal disputes arising from data breaches or compliance failures. These issues collectively impede the development of digital management centers, severely undermining the operational effectiveness of hospital digital hubs.

The data sharing challenges encountered primarily manifest in three aspects during the construction of a digital management center for county hospitals: (1)Due to the lack of unified planning in information system development, critical data such as laboratory test results, imaging materials and medical records are scattered across different platforms, creating “information silos.” (2)Inconsistent data collection standards, with different departments employing varying quality control metrics and data entry specifications, result in poor data compatibility and inefficient sharing. (3)The absence of effective data integration mechanisms hinders cross-departmental data exchange and operational collaboration, compromising both the timeliness and accuracy of clinical decision-making while obstructing the establishment of a closed-loop quality management system. These issues directly result in diminished operational efficiency and increased management costs, severely limiting the value realization of the digital management center. To address these challenges, it is imperative to establish unified data standards and a shared platform to dismantle information silos, enabling seamless data interoperability and efficient utilization.