Exploring the Value Realization and Management of Healthcare Data Elements in the Context of New Quality Productive Forces

ABSTRACT

This review centers on the core role of medical data in new quality productive forces. It systematically examines the current landscape of the rapid expansion of these data and explores their value in depth. Drawing on value chain theory, the paper proposes a “value flow” analytical framework, and conducts an in‐depth exploration of the full dynamic process through which medical data are converted into data assets. We defined “value flow” as a process in which data value undergoes directional transmission, integration, amplification, and ultimate release along a chain‐like pathway spanning data collection, governance, analysis, application, and circulation. We argue that a focus solely on the static components of the “value chain” cannot fully capture the holistic landscape of the transformation. Instead, it is essential to integrate dynamic value flow to systematically resolve the core challenges inherent in this transformation. The tracking and optimization of value flow direction supports objectives including: (1) Eliminating data silos and process breakpoints, and guaranteeing the seamless flow of value across clinical practice, scientific research, administrative management and other application scenarios; (2) Defining the contributions and rights allocation of stakeholders at each stage, providing a robust basis for the development of a compliant and sustainable profit‐sharing mechanism; (3) Charting the operational orientation of data as assets, facilitating the precise allocation of data assets to high‐demand scenarios and maximizing the realization of their inherent value. This paper therefore focuses on the assetization of medical data, and incorporates value flow to suggest a sustainable value release mechanism and core components of asset management after assetization. It therefore establishes a factor‐oriented management framework for data assets rooted in value chain theory and guided by value flow direction. This framework offers medical institutions practical guidance for integrating theoretical underpinnings and operational feasibility of data assetization, supporting the management of data across its full life‐cycle, including value monetization and strategic planning of data assets.

With the rapid advancement of information technology, data has become a key driver of social progress, particularly in the medical field. The accumulation and innovative application of medical data have not only reshaped the structure of medical production factors but also provided a strong impetus for the development of new productivity. This paper highlights the central role of medical data in the emerging landscape of quality productivity, systematically analyzing the current trends of its rapid growth and deep value extraction, and exploring the entire process of transforming medical data into data assets. The paper delves into the core aspects of data asset definition, value assessment systems, classification and grading methods, and asset assessment models, offering both theoretical foundations and practical guidance for managing data assets in healthcare institutions. In response to the current challenges faced by medical institutions in data management, it proposes practical strategies, including promoting data standardization, establishing a robust data governance and quality control system, strengthening data security management, and clarifying data rights mechanisms. Looking ahead, medical data assets are poised to unlock significant potential across various domains, including smart hospital construction, big data applications, and artificial intelligence. These assets will play a pivotal role in optimizing the allocation of medical resources, enhancing the intelligence of diagnosis and treatment processes, advancing innovative research such as disease prediction and precision medicine, assisting in clinical decision‐making, and improving the overall efficiency of medical services. The purpose of this paper is to provide systematic solutions for the management and application of medical data, driving the high‐quality development of the healthcare industry toward digitization and intelligence, and ultimately leading to a comprehensive improvement in the effectiveness of medical services.

1. Background

In 2024, China underscored the need to “promptly apply sci‐tech innovation achievements to specific industries and industrial chains, transform and upgrade traditional industries, cultivate and strengthen emerging industries, make plans for the development of future industries, and improve the modern industrial system” [1]. In the late 20th century, traditional productive forces were robust, bolstered by the twin pillars of informatization and automation. Since the start of the 21st century, emerging industries rooted in digital technologies have sustained their growth and expansion, and traditional industries have successively undergone in‐depth digital reshaping and holistic renovation. Digital technologies have emerged as the core driver of productive forces [2]. Leveraging the enabling effects of big data and artificial intelligence (AI), the widespread deployment of data resources has fostered the emergence of the digital economy, and elevated data resources to the status of a new production factor underpinning socioeconomic development. This has brought about profound changes to modes of production and organizational structures. During this process, information technology is a critical cornerstone for modern social development and also underpins the generation of data elements. The accumulation of large‐volume, high‐quality data sets is the core prerequisite for transforming data into production factors. A data element is typically defined as a data resource that is processed and refined to provide usability, be capable of participating in social production and operation activities, and yield economic value. Data elements are also capable of virtualization, low‐cost replication, subject plurality, non‐competition, and potential non‐exclusivity [3]. These characteristics are reflected in various aspects of social production, such as banking, insurance, healthcare, government, business, personal information, and industrial production. In the healthcare industry, information technology is essential for progression. It is surpassed only by the core operational value of healthcare services, and has forged a novel ecosystem in which healthcare information technology and its derivative products underpin development [4]. The completion of full‐scale informatization initiatives has led to the accumulation of an enormous volume of medical data. These data are typically in multi‐modal formats, with the majority derived from heterogeneous data sources. This structure is characterized by diverse data categories, extensive provenance, a high volume of missing values, and unstructured attributes, and imposes considerable challenges on the practical deployment of medical data. Heterogeneous data are typically characterized by non‐unified formats and standards, and inconsistent semantics and models. This hinders direct interconnection and integration across systems and facilitates the emergence of data silos. Excessive missing values can disrupt the continuity of data timelines and compromise the precision of data analysis. These two categories of critical issues directly impede the efficacy of data‐driven decision‐making and intelligent transformation. Medical data are also characterized by huge growth momentum. In an “average” medium‐sized Grade A tertiary hospital in a provincial capital city, the imaging data accumulated over a decade amount to nearly 200 terabytes, and structured data such as electronic medical records averages approximately 6–7 terabytes, depending on service throughput and system infrastructure. These data also maintain sustained growth year‐on‐year.

Large‐scale medical data have emerged as critical strategic assets for healthcare organizations. These data facilitate core operations and comprehensive competitiveness, and expand social outreach. The tremendous latent value embodied in these data is a key constituent of new quality productive forces. Meanwhile, AI technology has evolved from a “tool for mining data value” to a “core architect”. Empowered by technologies such as intelligent processing and privacy computing, it can now fully unlock the potential of data, and reshape the rules governing data circulation. This transforms the process of data elements evolving into productive forces into the integration of high‐quality data with high‐level AI capabilities. In‐depth exploration and analysis of the potential value of medical data is an effective approach to drive the future intellectualization, precision and efficiency of medical services [5]. Two key phases, creating data elements and creating data assets (assetization) are essential for data to be converted into tangible value and then evolve into new quality productive forces. For example, in the pharmaceutical field, AI‐based computer‐aided drug design has greatly shortened the drug R&D cycle and reduced R&D costs, with ordinary patients being the ultimate beneficiaries. Huaxin et al. studied the treatment of complex diseases, including mental health problems (e.g., adolescent internalizing disorders, schizophrenia, and anxiety‐impulsive subtype disorders). They found that the pathological mechanisms of these diseases show core challenges such as high heterogeneity, a disconnect between clinical phenotypes and neural mechanisms, and the lack of individualized basis for diagnosis and treatment regimens [6]. They pointed out that, unlike traditional diagnosis and treatment models that rely on empirical judgments, data‐driven technological innovations (including multi‐omics data analysis, the mining of neuroimaging biomarkers, and standardized governance of clinical data) are emerging as a key approach to break through the bottlenecks. Nanyu Kuang et al. took data‐driven approaches as the core, and explored the optimization of diagnosis and treatment regimens for mental health problems based on the standardized governance of multi‐dimensional clinical data and an evidence‐based analytical framework [7]. Through data standardization management and evidence‐based modeling, they transformed fragmented clinical data into reusable data assets. This improved the evidence‐based level of diagnosis and treatment regimens, and reduced the data analysis costs of subsequent scientific research, highlighting the role of data governance technologies in breaking down the transformation barriers between clinical practice and scientific research [8, 9]. Yuchao Jiang et al. defined the neurophysiological subtypes and disease trajectories of schizophrenia using extensive data sets and algorithms, including radiomics data and biomarker mining algorithms [10]. They extracted features from large‐scale neuroimaging data sets and defined disease subtypes to identify biomarkers that are strongly correlated with disease trajectories. This provided potential to move from symptom‐ to mechanism‐based diagnosis. This work provided precise targets for early disease intervention and new drug development, and verified the core value of advanced data mining technologies in unraveling the “black box” of complex disease mechanisms.

The Chinese government also attaches great importance to this potential. In December 2022, Opinions of the Central Committee of the Communist Party of China and the State Council on Improving the Basic Systems for Data to Better Play the Role of Data Elements [11] (referred to as the Twenty Guidelines on Data) clearly defined the principles and requirements for establishing basic systems for data element creation. These included four systems for data elements, covering property rights, trading and circulation, income distribution, and governance. In September 2023, the China Appraisal Society released the “Guiding Opinions on Data Asset Valuation”, which clarified the methods and requirements for evaluating the value of data assets and consolidated the foundation for their circulation. These initiatives have provided strong policy support for unlocking the latent value of the rapidly expanding volume of medical data. Concurrently, the evaluation systems promoted by the National Health Commission, including the Classification and Evaluation of Electronic Medical Record System Application Levels and the National Healthcare Information Interconnection Standardization Maturity Assessment, have further standardized the development of medical informatization. A standardized informatization development model and data model can break down the barriers between information systems, reduce the difficulty in forming and applying medical data elements, and lay the foundation for the development of medical data elements [3, 4, 5]. This paper explores several aspects of this process. It starts from the requirement to unlock the value of medical data elements, and both defines value flow direction and elaborates on the urgent demand to unlock the value of medical data. Drawing on value chain theory, it then explores the path for unlocking data value by converting medical data into data assets through four links: creating data resources, turning those into products, making assets from data products and then valorizing the data assets. It proposes the theory of data value flow, aiming to develop a universal model for medical data elements. Third, it discusses the essential conditions and technical requirements for data assetization, clarifying the problems to be solved at each stage, and proposing a framework to enable medical institutions to transform data assetization. Finally, across medical institutions, research institutes and enterprises, it explores the social contributions and influence of the value release of data elements after their formation, against the background of new quality productive forces.

2. Unlocking the Value of Medical Data

The value of medical data can only be unlocked through a complete process of transforming raw, fragmented medical data resources into measurable, data production factors and then data assets that can be applied to specific scenarios and generate economic or social benefits, through systematic and compliant technical and management approaches. This process drives the morphological transformation of data from a “resource” to an “element” and then to a form of capital. This enables the conversion of inherent potential into actual productive forces. Data value chain theory provides the most accurate interpretation of this process. Miller et al. [12] conducted a systematic study on the application of big data across various industries and concluded that data do not automatically convert into useful information, regardless of the source, volume or modality. All industries have the challenge of systematically transforming raw data into high‐quality decisions. Medical data have enormous potential, and a distinctive value‐unlocking path due to the industry's particularities and stringent regulatory requirements. Non‐circulation of data beyond trusted domains is a principle that is strictly observed in the management and use of medical data, and fundamentally restricts the physical flow of raw data. To a certain extent, physical flow limits the delivery of data value. Mou et al. [13] therefore proposed that the value of medical data must be unlocked through the mutual cooperation and coordination of medical institutions and government departments.

The logical chain for unlocking the value of medical data is carried out around particular points (Figure 1).

Figure 1.

Schematic diagram of the data value chain.

Value unlocking is a logical chain and different value forms are generated by each link. The process includes the embodiment of value forms ranging from raw data resources to data capital. Traditional theoretical studies are all based on the forward chain from data resources to data elements, but neglect the value flow chain. The flow of value unlocking also highlights the importance of value chain theory in the process of data element formation. We suggest that medical data value flow has two elements: intra‐domain and extra‐domain value flow. This framework aims to address a core question: when data are confined within specific security boundaries (such as hospitals, regional health platforms, or trusted alliances), how should economic and social value be effectively stimulated, aggregated, and directed toward ultimate value realization?

Intra‐domain value flow is the value created and circulated through direct processing, analysis and application of medical data within the original institutions where the data were generated and owned, or within trusted alliances protected by strict agreements (e.g., regional medical institution alliances). This value flow strictly adheres to the principle of “data stays within domain, computing and knowledge move across domains”. Value‐unlocking activities do not involve the physical transmission of raw data to external entities. The processes of value generation, capture, and reinvestment within the intra‐domain value flow are primarily completed inside the institution or alliance, forming a positive feedback loop that enhances its own service capacity, efficiency, and competitiveness. Value creation is closely centered on the core businesses of the data holders, such as clinical diagnosis and treatment optimization, hospital management, internal scientific research analysis, and medical quality control. Data sovereignty, access permissions and usage purposes are fully controlled by intra‐domain entities, with relatively controllable security and privacy risks (Figure 2).

Figure 2.

Intra‐domain value flow showing (a) the value flow among intra‐domain institutions and (b) the value flow of intra‐domain medical alliances through trusted data platforms.

Inter‐domain value flow is the value created in broader socioeconomic scenarios by securely delivering the knowledge, insights, and models contained in medical data, or data derivatives processed through desensitization, aggregation and anonymization, to external entities. These include pharmaceutical companies, insurance firms, public health authorities, and research institutions and the value may flow via specific technical means and compliance mechanisms, abiding by the principle of non‐circulation of data beyond trusted domains. Extra‐domain value flow relies on the integration of medical data with external resources, demands, and application scenarios, serving public or commercial objectives that go beyond the missions of the original data‐generating institutions. Its materialization needs to be facilitated by well‐defined ownership agreements, pricing mechanisms, transaction rules, and market ecosystems, involving complex benefit distribution and compliance governance. It is highly dependent on privacy‐enhancing technologies such as federated learning, secure multi‐party computation, blockchain, and trusted execution environments. These technologies ensure that collaborative computing, model training, or controllable data services can be accomplished without data leaving the secure domain (Figure 3).

Figure 3.

Inter‐domain value flow realized on the basis of intra‑domain flow. (a) shows the value flow among intra‑domain institutions, and (b) shows the value flow of intra‑domain medical alliances through trusted data platforms. Building on this basis, (c) shows the data value flows across domains through trusted data transaction platforms under the supervision of relevant government departments.

The constraint of data not circulating beyond trusted domains does not block the release of value. Instead, it reshapes the path and driving force to unlock value. The elaborately designed intra‐domain and inter‐domain value flows act as efficient value transmission pipelines and value exchange networks for data, and directly determine the intensity, scope, and sustainability of value unlocking. Intra‐domain and inter‐domain value flows are not isolated from each other, but act as an organic whole to promote each other and evolve in a coordinated way. By clearly defining and elaborately designing these two value flows, it is possible to map out a clear flow landscape for medical data value. Internally, data value drives the digital transformation and refined operation of medical institutions and at the boundary, it enables the lossless and secure spillover of value by virtue of cutting‐edge technologies. Externally, data integrate into broader socioeconomic systems to stimulate innovation and enhance efficiency. The design of value flow is essentially a comprehensive arrangement of the property rights structure, technical pathways, business models and governance rules for data elements. A balanced, unimpeded and incentive‐compatible dual‐flow system can maximize the value of medical data as a critical production factor, while meeting the highest safety and ethical standards.

3. Process of Transforming Medical Data Into Data Assets

Ailing Pan et al. proposed that the unlocking of enterprise data value followed basic value chain theory [14]. Data value must be unlocked through activities such as initial data governance, data resource right confirmation, data resource on‐balance‐sheet booking, data asset evaluation, and data transaction management. Medical data also follow this theoretical system, but the path framework is slightly different from enterprise data. There are inherent differences between medical data and enterprise data, including in data sources, value objectives, standardization levels, sensitivity degrees, and key stakeholders. However, the two share the same core logic and architecture for value creation. Both follow a linear value‐adding path from raw data to intelligent decision‐making, and their complete chains include data discovery, integration, analysis, visualization, and decision support. This commonality reveals a fundamental principle: regardless of their sources, data must undergo systematic processing, integration, and interpretation to be transformed from disordered “raw materials” into high‐value “products” that can drive organizational development. The implementation logic of medical and enterprise data are also consistent. Whether clinicians formulate treatment plans based on integrated medical records or enterprise managers optimize operations with the help of business insights, their decision‐making processes have become more scientific and traceable because of the embedding of the data value chain. When discussing the process by which medical data are transformed into data assets and ultimately data elements, the core question is: what substantive problems does this process aim to solve? The answer focuses on key challenges such as data silos, lack of standardization, the balance between privacy, security and value utilization, and the absence of value evaluation and distribution mechanisms. This paper combines the previously proposed path framework [15] with the dynamic data value flow theory, to construct a systematic process for the formation of medical data assets (Figure 4).

Figure 4.

Framework and examples of medical data value unlocking. EMR, electronic medical record; HIS, hospital information system; LIS, laboratory information system.

3.1. Medical Data Initialization

Healthcare institutions use fully developed large‐scale medical information systems, such as hospital information systems, laboratory information systems, picture archiving and communication systems, and electronic medical records, to collect relevant business and management data (data resource initialization). These core systems support the normal operation of healthcare institutions. They may include large‐scale database systems and additional storage service systems, and complex data structures. These are interconnected through a unified integration platform. Other smaller‐scale specialized healthcare systems used as medical auxiliary organizations operate relatively independently and can easily create information silos within healthcare institutions. These systems collectively collect and organize medical data but do not further process or govern it, making it difficult to unlock its full value. The initial data set may also contain “dirty data” such as duplicate records, inaccurate entries, or missing information. These data are therefore underused and may also consume valuable storage resources, impeding effective business decision‐making support [15].

3.2. Assetization of Healthcare Data Resources

Data assets are the diverse data resources owned and used by enterprises and institutions, including customer information, product transaction records, institutional service‐related data, and other market‐oriented information. These assets exist in digital formats across various computing systems, including computer terminals, network servers, and database servers. Generally, data assets have eight characteristics, including being non‐physical and non‐consumptive, processability, diversity, dependency, volatility of value, multiple derivation, shareability, and zero‐cost replication. Medical data assets are the recorded data generated by medical behaviors in medical institutions over the years, including records of the medical process and patients' conditions, and the data resources generated by the process of operating and managing medical institutions. They cover all data modalities, including textual, numerical, graphical, imaging, signal waveform, and audio. For these data to qualify as actionable data assets, they require systematic processing and transformation to meet established usability criteria.

Medical institutions must therefore establish comprehensive data asset management systems, to govern and standardize data to become a data paradigm that is used efficiently. The principles of medical data governance need to clarify how the data will be used, determine the classification of the data, and how data will be cleaned to make them fit for purpose [16, 17, 18, 19]. Common applications for medical data include medical research, pharmaceutical companies, and medical insurance. For example, the data required for medical research should be in a fixed and effective data format, with sufficient data quality to minimize missing and incorrect values. The insurance industry needs data to be accurate and reliable, and the application requirements for management decision support require data to be accurate and have particular statistical characteristics. Different applications have different focuses on the demand for data, and data therefore need to be formed into data assets through governance, and then further formed into data elements to play the role of market and productivity. The assetization of medical data is therefore closely integrated with its governance, emphasizing the development of data standardization, data governance quality control norms, data standards and indicators, and governance processes, to ensure the safety and reliability of medical data assets.

3.3. Productization of Healthcare Data Assets

The core concept of medical data asset productization lies in systematically organizing data resources into structured data products to fit specific applications, establishing legally‐recognized ownership through formal identification processes. In practice, medical data originate from patients' personal diagnostic and therapeutic information, and contain sensitive elements such as personal information, classified data, and high intrinsic value. Healthcare institutions are responsible for data stewardship, and establishing clear data ownership is fundamental to ensuring the proper attribution of data assets. The principle of data classification is based on the degree of sensitivity of information and evaluating the potential impact on personal, organizational, or national security after any leakage. This approach primarily addresses information security compliance and data protection requirements, ensuring that data assets are safeguarded appropriately for their sensitivity levels. Data classification, however, can distinguish and categorize data using principles and methods based on their attributes or characteristics, and establish classification systems and sorting rules for better management and use of data.

Data in different domains have different dimensions, and the classification dimensions and principles of data classification are also different. Medical data involves multiple dimensions, and we classified medical data into five dimensions: professional and technical, business domain, data source, application goal, and security management and use. Data classification and grading enable medical institutions to catalog, standardize, authenticate, and manage medical data assets more effectively. They also support organizing multiple medical institutions and medical management agencies to discuss the establishment of classification and grading guidelines and structures, promote the formation of standards, and ultimately productize data assets.

3.4. Medical Data Asset Product Factorization

Factorization of healthcare data products means unlocking the elemental value of healthcare data assets in practical applications. This is the end link in the data value chain theory. Data asset assessment is the principal task of factorization. It is a key means of measuring the value of healthcare data assets through the act of assessing and estimating their value and issuing asset assessment reports, following relevant laws, administrative regulations, and guidelines. How data assets are assessed requires consideration of multiple factors. Like traditional enterprise data assessment, there are four main factors to be considered: cost, scenario, market, and quality factors. Cost factors are the upfront, direct, indirect and opportunity costs and taxes involved in the formation of healthcare data assets. Scenario factors are derived from the scope of use, applications, business models, market prospects, financial forecasts and application risks of the data assets. Market factors are concerned with the main trading market, market activity, market participants and market supply and demand of the data assets. Quality factors are concerned with the assessment of assets and include data accuracy, consistency, completeness, standardization, timeliness, and accessibility.

Unlike traditional businesses, healthcare organizations collect data in different ways, from different sources, and for different purposes. Healthcare data can serve both for‐profit and non‐profit objectives. Non‐profit objectives supported by medical data assets include government decision‐making, disease control monitoring, and medical institution development research. From a profit perspective, these assets serve related enterprises and research institutions, such as medical research institutions, pharmaceutical companies, and insurance companies.

4. Medical Data Asset Management: A Novel Framework

Managing large‐scale medical data assets is a highly complex and meticulous task that has emerged alongside the widespread adoption of data. During the early healthcare informatics phase, the government established a three‐tier management system comprising medical institutions, governments at all levels, and national functional departments. Medical institutions operate dedicated data centers to manage internal systems and data. Regional governments, as primary data aggregators, selectively consolidate institutional data. National agencies collect decision‐support data from lower‐tier governments. Upgrading the legacy system is required for data asset tradability and circulation. This paper proposes a novel management model based on the data value chain, spanning data collection to the release of basic value. Core components include data standardization, governance, quality control, security, and rights confirmation [20, 21, 22, 23].

4.1. Data Standardization Management: A Critical Enabler of Healthcare Data Assetization

Data standardization management is pivotal for transforming data into assets. In China's healthcare sector, there are numerous informatization vendors, all adhering to national regulations. This has led to fragmented data standards across medical institutions, complicating data governance. Standardized data are urgently needed for asset circulation and application. A viable solution is building big data platforms. Their complexity arises from diverse institutional systems, data sources, formats, and structures. The core challenge in data asset management is integrating multi‐modal data. Big data platforms address this by aggregating data and performing secondary governance tasks, such as cleaning dirty data. For example, clinical data repositories have become successful data integration frameworks [24]. They have been widely adopted by research‐focused medical institutions and endorsed nationally, and integrate patient‐centric data, including visit records, electronic health records, imaging, lab results, and specialized departmental data. Some institutions also incorporate standalone physical examination data via integration platforms. These repositories are not perfect, but offer an effective approach to standardized data integration. They use a patient master index and defined data structures to organize homogeneous records by visit history for easy retrieval. When querying multiple patients, they ensure standardized field structures in the results. Variants of the model enhance its adaptability.

4.2. Data Governance and Quality Control: A Pivotal Factor in Medical Data Asset Quality

Data governance and quality control management are crucial for ensuring the quality of medical data assets. In healthcare, there are guidelines for data collection, but the absence of strict standards often leads to issues such as missing data, inaccuracies, and errors. Robust quality control is therefore essential for effective medical data governance. Two main methods should be adopted for data collection. First, collection systems should be enhanced using informatization technologies to accurately collect medical data in real time, including medical records, test results, and operation records. Processes should also be standardized to avoid data omissions and errors. Second, data analysis tools should be used to detect and verify abnormal data, establish data correction rules, and make corrections. For example, statistical methods can be applied to fill in missing information. Healthcare institutions' information management departments need to build secure and reliable data storage systems to protect data security and integrity, preventing data loss or damage. Besides the traditional manual quality control systems and operations, the application of large AI models means that automated quality control approaches combining knowledge bases and AI are gradually being integrated into data quality control systems [25, 26] (Figure 5).

Figure 5.

Medical data asset management: a novel framework. EMR, electronic medical record; HIS, hospital information system; LIS, laboratory information system; PACS, picture archiving and communication system.

4.3. Data Security

Data security is a critical concern in determining whether healthcare institutional data qualify as public data resources. Public data are defined as information collected, organized, and managed by governments, social groups, or other entities for public access, and have characteristics such as publicness, shareability, openness, and non‐rivalry. As a vital production factor in the digital economy, public data facilitate value creation but demand strict security protocols because of their sensitive nature, including personal privacy, trade secrets, and public safety information [27]. The healthcare sector, as a core public service domain, has unique challenges in relation to patient privacy. Medical data share some attributes with public data, but the sensitive nature and associated security requirements preclude unrestricted disclosure. This dichotomy has resulted in fragmented data silos, preventing healthcare providers from fully leveraging data for innovation and population health improvement. However, medical data can be classified as public resources under specific conditions: if it is possible to allow simultaneous access by multiple users or organizations while upholding privacy regulations, there is shareability and openness, and utility is maintained regardless of the number of users, a characteristic known as non‐rivalry. The security of medical data is a key concern both domestically and internationally. China officially enacted the Personal Information Protection Law in 2021, emphasizing the rules governing the processing of personal information within data elements. The implementation of this regulation is similar to the EU's General Data Protection Regulation and the US's Health Insurance Portability and Accountability Act. The General Data Protection Regulation prioritizes individual data rights as fundamental human rights, enforcing strict protection across all regions. In contrast, the Health Insurance Portability and Accountability Act focuses more on balancing medical privacy, healthcare continuity, and industry efficiency. China's Personal Information Protection Law places greater emphasis on alignment with domestic realities, respecting individual privacy rights while also ensuring comprehensive protection of corporate information throughout the entire process, and strictly adhering to national security policies.

4.4. Research on Determining the Ownership of Medical Data

Determining the ownership of medical data involves clarifying data rights and ownership relationships, identifying data owners, and establishing data attribution [28, 29]. This process defines rights and responsibilities across all stages of data management, including collection, use, governance, processing, and disposal. It therefore ensures that medical data are used rationally and managed in compliance with regulations throughout the data life‐cycle. In the digital economy, the value of data, as a production factor, is realized primarily through circulation. China has made initial explorations in promoting data‐element trading, with the establishment of multiple trading platforms facilitating data flow and value creation in sectors such as energy and public services. Determining the ownership of medical data safeguards data rights and content during transactions and protects the interests of data subjects. However, this determination presents significant challenges. Disputes can arise over the ownership of subject matter: the data are collected by healthcare institutions, but contain sensitive personal identity and health details. The ownership is therefore fragmented across multiple parties, including individuals, hospitals, and government agencies. Patient data can be stored across various entities, replicated, updated, and lack traceability. Current laws, such as the Cybersecurity Law and the Personal Information Protection Law, do not provide clear definitions on this matter, posing security risks in data transactions.

The scarcity of practical cases for determining medical data ownership means that research has centered on identifying appropriate ownership‐determination methods and technical approaches. As new technologies and methods emerge, one secure and suitable approach for ownership determination is the adoption of blockchain models. Blockchain technology has the advantages of decentralization and traceability, and can be a fundamental platform for ownership determination. Patients, medical data owners, and data users can sign smart contracts through a blockchain platform to determine data ownership, ensuring the authenticity and validity of the data. Blockchain technology can also prevent users from tampering with, copying, and leaking patients' private information [30].

5. Medical Data Assets: Catalysts for Healthcare Development

Since 2011, the Chinese government has actively promoted smart hospital initiatives through targeted policies and assessment standards. Key programs such as the Electronic Medical Record System Grading Evaluation and National Medical Information Interoperability Maturity Assessment have accelerated hospitals' digital transformation. This transition from basic informatization to full‐scale digitalization is shaping the modern smart hospital landscape. Concurrently, AI has experienced its third major global advance. The shift from cloud‐based services to data‐driven AI systems is revolutionizing industries worldwide. The healthcare sector cannot remain insulated from these technological disruptions, requiring rapid adaptation to leverage emerging AI capabilities [31].

The integration of the Internet of Things and 5G+ communication technologies provides an interconnected medical service environment. Organizations can use informatization to integrate medical digital resources, thereby optimizing healthcare service processes, standardizing diagnosis and treatment, and boosting clinical efficiency. The aim is to create an innovative hospital model that offers patient‐centered convenience and refined management. Its core is leveraging digital tools to enhance service quality and efficiency while adopting smart interconnection to develop a hospital ecosystem with full‐life‐cycle integration, ubiquitous access, borderless collaboration, process optimization, and research focus. Full ecological integration ensures seamless data consolidation in healthcare institutions, removing information silos and establishing a patient‐centered, intelligent one‐stop service system for the entire health life‐cycle [32]. All healthcare organizations need to deploy ubiquitous network infrastructure for “5 A” communication, enabling smooth collaboration anytime, anywhere, for anyone, on any device, and through any network. Patients can access top hospital specialists via telemedicine platforms, solving the problem of unequal medical resource distribution. Organizing operational workflows streamlines hospital processes, improving response to patient needs and overall efficiency. Research‐oriented hospitals focus on integrating clinical practice and scientific research, constantly generating, applying, and spreading new medical knowledge and technologies [33, 34].

Second, big data and AI drive advanced hospital research, operational decision‐making, and smart healthcare expansion. Machine learning and deep learning are key AI techniques that enable precise analysis and prediction across diverse applications. In medical image recognition, they enhance diagnostic accuracy. For standardized data analysis, they uncover insights for clinical decision‐making. In pharmacological research, they map complex drug‐disease relationships. These capabilities support auxiliary disease diagnosis and drug development, infusing innovation into precision medicine and intelligent healthcare services.

The application of “AI + healthcare” has also broadened the scope of decision‐support systems in medical institutions. It optimizes process management and enhances service quality by streamlining workflows and improving resource allocation [35, 36, 37]. AI‐enabled advances in healthcare range from the development of automated medical devices to sophisticated data analytics. These innovations accelerate medical research, offering novel approaches to studying complex and rare diseases. Finally, data sharing and collaboration among regional medical institutions, exemplified by national health information platforms, significantly elevate the overall quality of regional healthcare services. These platforms integrate clinical data to facilitate evidence‐based decision‐making, enabling seamless patient referrals, and promoting coordinated care, contributing to better population health outcomes [38, 39, 40].

6. Conclusions

This article has examined the pivotal role of medical data in fostering new quality productive forces, focusing on data elements and asset management applications. It has outlined the process and methodologies for transforming medical data into valuable assets. As a unique production factor, medical data exhibit distinct traits: virtuality, low‐cost replicability, and multi‐stakeholder involvement. They therefore present complex management and application challenges. We suggest that healthcare institutions can optimize medical data assets through four key strategies: standardization, governance with quality control, security enhancement, and rights confirmation. Applying these assets to smart hospital development, medical research, and operational management can significantly propel the medical industry forward.

Looking ahead, technological advancements will bring new opportunities and challenges. Future breakthroughs are expected in data rights confirmation and security, streamlining data asset transactions and circulation. Medical data assets will also expand into precision medicine, personalized treatment, medical robotics, and telemedicine, driving industry evolution. Future studies should prioritize the technical implementation of data rights confirmation, robust security measures, and innovative ways to leverage data assets for medical advancements.

Author Contributions

Zhixuan Yu: project administration (equal), writing – original draft (equal), writing – review and editing (equal). Jiaxuan Wei: investigation (equal), resources (equal), validation (equal). Jianlin Wang: conceptualization (equal), data curation (equal), formal analysis (equal), supervision (equal). Jie Lu: conceptualization (equal), formal analysis (equal). Pengfei Cao: conceptualization (equal), formal analysis (equal), methodology (equal), resources (equal), supervision (equal), writing – original draft (equal).

Ethics Statement

The authors have nothing to report.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

1.

Data elements: Data resources that have undergone standardization, organization and capitalization processing, and can therefore participate in social production and business activities, bring economic benefits to users or owners, and be invested as production factors.

Data assets: Data resources that are legally owned or controlled by specific entities, can be measured and managed, and can generate current or future economic benefits.

Assetization: The process of “data capitalization”, or transforming raw or preliminarily processed data resources into standardized data assets with identifiable owners, which can be measured, traded and generate economic benefits, through a series of legal, technical, managerial and economic measures.

New quality productive forces: “Advanced productivity that is freed from traditional economic growth mode and productivity development paths, features high‐tech, high efficiency and high quality, and comes in line with the new development philosophy” [41].

Data Availability Statement

Data sharing is not applicable to this article as no datasets were generated or analyzed.