Unlocking the Value of Health Data in Beijing: A Full-Scenario Practice Blueprint from an AI Healthcare Pioneer
Since data was designated as a factor of production in 2020, the modalities involved in health data governance in China have gradually diversified, and governance processes have become increasingly standardized. Over the past few years, many cities have accumulated a substantial portfolio of high-quality medical data assets.
However, high-quality data merely represents the initial step. Only by transforming data into practical applications and achieving widespread adoption in specific scenarios—such as clinical diagnosis and treatment, scientific research and innovation, and public health welfare—can healthcare data fully unleash its value as a factor of production, thereby becoming the core engine driving the development of the medical industry.
This is the key reason why many provincial and municipal health big data systems have failed to operate efficiently. Regions need not only platforms capable of governing and storing data, but also the ability to perceive the genuine needs of the healthcare system and transform data elements into actionable, perceptible, and quantifiable value-based healthcare applications.
How to Achieve the Efficient Release of Digital-Intelligence Value? Beijing’s Exploration May Offer a Reference Model.
Recently, Yin Li, Secretary of the Beijing Municipal Committee of the Communist Party of China, conducted research on the application of health and medical data, focusing on “digital intelligence empowering the construction of Healthy Beijing.” He made field visits to leading enterprises, Grade-A tertiary hospitals, data centers, and other institutions.
In his view, Beijing leads China in the scale, quality, and potential of its health and medical data. With a solid industrial foundation and a robust innovation ecosystem in fields such as artificial intelligence and biopharmaceuticals, the city is well-positioned to foster the synergistic development of data, technology, and industry.
However, fully unlocking the value of medical data in Beijing is no easy task. The city concentrates the nation’s densest cluster of high-quality medical resources, boasting the largest number of tertiary hospitals and National Medical Centers in China, with its count of Grade 3A hospitals ranking first nationwide. At the same time, it bears the healthcare demands of a permanent resident population exceeding 20 million. This means that, in addition to the massive volume of data generated by these 20 million residents, the system involves the coordinated operations of nearly one hundred tertiary hospitals, thousands of community healthcare institutions, and hundreds of thousands of physicians. Data standards vary across different institutions, and clinical, research, health insurance, and public health data are often fragmented, resulting in pronounced data silos.
Yi Li, the Party Secretary, conducted a research visit to Yidu Tech, a key explorer and practitioner in breaking down the barriers of health data silos in Beijing.
Over the past twelve years since its inception, Yidu Tech has been deeply engaged in the governance and value conversion of medical data, starting with data integration at a single hospital and gradually building service capabilities that cover multiple scenarios. Over the past decade, it has achieved solid results in empowering healthcare services in Beijing.
Ten years ago, Yidu Tech’s first data platform was deployed at a Grade A tertiary hospital in Beijing. The challenge it addressed at the time was straightforward: to integrate and clean up data scattered across disparate internal systems. This type of foundational data governance work, often regarded as the “dirty and tedious” yet essential tasks in healthcare digitalization, serves as a prerequisite for the implementation of all intelligent applications and has enabled the company to refine its mature, full-scenario AI Hospital solution.
The subsequent chapter of the story witnessed the implementation of Yidu Tech’s solutions across a growing number of hospitals—including Peking Union Medical College Hospital, Peking University Cancer Hospital, Beijing Friendship Hospital, and Tsinghua Changgung Hospital—spanning clinical care, scientific research, and hospital management. These institutions are not ordinary clients; they represent the apex of Beijing’s healthcare system. Their concurrent adoption of Yidu Tech’s solutions demonstrates that its offerings are not only technically viable but have also earned the trust of the local medical ecosystem.
Clinical research represents another crucial piece of Yidu Tech’s strategic puzzle. In Beijing, the company’s products—including its Clinical Trial Management System (CTMS), intelligent patient recruitment solutions, and research-oriented ward infrastructure—have been deployed at multiple municipal and ministry-affiliated hospitals, such as Beijing Cancer Hospital, Beijing Shijitan Hospital, and Beijing Huilongguan Hospital. More notably, Yidu Tech has participated in the establishment of Beijing’s National Artificial Intelligence Pilot Base for the medical sector. As a national-level platform for the industrialization of medical AI, the Pilot Base plays a pivotal role in technology validation, clinical evaluation, and standard setting. Participation in this initiative signifies that Yidu Tech has evolved beyond being a mere technology service provider to becoming a core participant in the formulation of national medical AI standards and the incubation of the industry.
On the medical insurance payment side, Yidu Tech has served as the primary operating platform for “Beijing Puhui Health Insurance” for five consecutive years since its launch, cumulatively serving Beijing residents over 19 million times. Its service scope covers the entire process, including product design, system construction, platform operations, intelligent customer service, and smart claims settlement. This end-to-end involvement essentially validates its data capabilities within the “healthcare-pharmaceuticals-insurance” closed loop, where precise pricing, intelligent underwriting, and rapid claims processing all rely on support from high-quality de-identified data.
From the perspective of urban governance, Yidu Tech has participated in the construction of Beijing’s Population Health Information Platform and the “Jingzhi” Three-Medical Linkage Platform. The former aggregates health records and clinical data of residents across the city, establishing a city-level foundational health data infrastructure; the latter facilitates compliant data circulation and sharing among healthcare services, medical insurance, and pharmaceuticals, laying a solid data foundation for coordinated regional health administration. Within the public health prevention and control system, the company has successively built the National Center for Disease Control and Prevention’s Overseas Epidemic Data Analysis Platform and municipal-level intelligent infectious disease monitoring and early warning platforms, reshaping traditional emergency response models. By leveraging real-time analysis and computational assessment of multi-source data to evaluate risks, it captures abnormal signals in advance, enabling a shift from reactive response to proactive early warning.
Furthermore, Yidu Tech also undertook the support services for the 2022 Beijing Winter Olympics and Paralympics. Against the backdrop of extremely complex cross-border and cross-regional population movements, ensuring “zero errors” in epidemic prevention and control as well as medical security served as the ultimate test of the stability and reliability of the entire data infrastructure.
Examining its comprehensive implementation pathway in Beijing, Yidu Tech’s differentiated advantage lies not in a breakthrough in a single product or isolated technology, but in achieving the integrated deployment across several scenarios that is rare in the industry: in-hospital clinical care, clinical research, health insurance and public welfare, and urban governance. Unlike most vendors capable of digital transformation only at a single stage, Yidu Tech starts with data governance at the foundational level and extends upward layer by layer, ultimately forming a comprehensive service system covering residents, hospitals, research institutions, and government departments.
A natural question arises: Why Yidu Tech? In an era where the medical AI sector is crowded with players and fragmented scenario-based implementation has become the norm, how has a startup managed to achieve full-chain, large-scale, and deep penetration within Beijing’s healthcare system—a megacity framework characterized by the highest standards, deepest barriers, and strictest compliance requirements?
The answer lies not in breakthroughs from any single product or project, but in the long-term accumulation and resulting path dependence of its underlying technological engine, YiduCore. Essentially, YiduCore is an industrialized system for medical data governance, with its core function being the transformation of fragmented, heterogeneous, and non-standard raw medical data into computable, traceable, structured assets. As of September 2025, the system had cumulatively processed nearly 7 billion authorized medical records, covering more than 1.3 billion patient visits, while its disease knowledge graph basically encompasses all known diseases. This scale of data itself constitutes a temporal barrier—competitors cannot replicate equivalent real-world data processing experience in the short term.
The operational logic of YiduCore can be summarized as a self-reinforcing cycle: with each hospital or application scenario integrated, the system acquires more real-world clinical data to optimize disease models and knowledge graphs. Enhanced model capabilities improve the accuracy and stability of clinical decision support, research recruitment, and health insurance risk control, thereby attracting more institutions to adopt the platform. The deployment in additional scenarios feeds back more data, further strengthening the foundational infrastructure. This “data-algorithm-scenario” flywheel effect means that as coverage expands, Yidu Tech’s technological leadership does not grow linearly but accelerates.
If YiduCore is the core and engine of medical intelligence, Yidu Tech’s greater ambition is to build upon it a unified, open, and scalable AI-powered healthcare operating system—much like Windows in the PC era or iOS in the mobile era. In the future, smart hospitals, regional healthcare systems, and even population-level health governance will operate on this unified intelligent foundation. This system features three key characteristics: end-to-end integration, forming a closed loop from data governance to real-world application; multi-tier adaptability, serving both top-tier tertiary hospitals and supporting primary care operations, supplemental commercial insurance (such as Huiminbao) management, and public health early warning systems; and ecosystem openness, providing standardized interfaces and reusable “capability building blocks” that enable various institutions to rapidly develop their own applications on top of the foundation.
Unlike the approach of applying general-purpose large models to medical scenarios, Yidu Tech starts from the most fundamental layer of medical evidence, transforming data into trustworthy evidence, converting evidence into actionable intelligence, and turning intelligence into deployable, full-scenario capabilities, ultimately embedding AI truly into every decision-making step in healthcare.
On this basis, regulatory compliance trust and a localized ecosystem constitute the entry barriers for high-barrier scenarios. With over a decade of deep cultivation in Beijing, Yidu Tech has established a trust network covering the Health Commission, Healthcare Security Administration, Centers for Disease Control and Prevention, and multiple top-tier Grade A tertiary hospitals. It has also formed a full-process compliance system encompassing data de-identification, encryption, authorization, and traceability, achieving the principle of “data available but not visible, controllable and traceable.”
This composite barrier—comprising a technological foundation, the flywheel effect, an operating-system-level architecture, regulatory trust, and a local ecosystem—collectively explains why, in today’s medical AI landscape characterized by “isolated successes but widespread systemic challenges,” only Yidu Tech has achieved end-to-end integration from single-hospital digitalization to a city-wide health ecosystem in Beijing, thereby providing a replicable and scalable benchmark model for the digital and intelligent transformation of healthcare in China’s megacities.
Beijing’s experience undoubtedly offers a reference model, but its true value lies not in “whether the solution itself is replicable,” but in the transferability of its underlying logic.
In this case, Yidu Tech, guided by application scenarios, fueled by high-quality data, and powered by AI capabilities, has indeed built a data governance and application system tailored to local needs.
Of course, the Beijing initiative is merely a beginning. Only when this framework takes root in more regions and drives more intelligent healthcare applications from concept to reality will the data element be fully unleashed, becoming the core engine propelling the development of the healthcare industry.