Governance and Innovation in Building Smart Healthy Cities: Multi-city evidence from China

Governance and Innovation in Building Smart Healthy Cities: Multi-city evidence from China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Governance and Innovation in Building Smart Healthy Cities: Multi-city evidence from China Ying Zhang, Xi Wang, Ayan Mao, Pei Dong, Shuai Du, Minjie Zhao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7491675/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As digital infrastructure increasingly reshapes urban governance, Chinese cities are exploring diverse pathways to integrate health systems with smart city technologies. This study selected four typical cities in China, Hangzhou, Shenzhen, Chengdu and Shijiazhuang, which are constructing smart healthy cities through distinct governance models. Using case studies, interviews, and policy analysis, three emergent types of digital health governance are identified: platform-led integrated systems, institution-anchored service ecosystems, and government-enabled multi-stakeholder collaborations. Hangzhou exemplifies a platform-centered model, leveraging city-wide digital infrastructure for cross-sectoral coordination. Shenzhen demonstrates a hospital-led strategy that enhances clinical efficiency through integrated digital platforms. Chengdu adopts a government-facilitated, equity-oriented approach linking industrial innovation and community-based services. Shijiazhuang illustrates a technocratic model prioritizing standardized digital rollout in second-tier contexts. Despite differences in institutional design, population profile, and digital maturity, all cities aim to enhance service accessibility, data interoperability, and system responsiveness. Rather than converging on a single model, cities navigate context-specific trajectories shaped by local governance capacity, policy priorities, and demographic demands. The study argues for an adaptive, modular approach to policy transfer, focusing not on wholesale replication but on identifying transferable components such as data standards, inter-agency platforms, and inclusive digital tools. This multi-case analysis contributes to the understanding of digital health integration in urban settings and provides conceptual and practical insights for shaping smart health city development in China and comparable international contexts. Smart healthy city Healthy city Smart city Digital governance urban planning 1. Introduction Smart city and healthy city agendas has attracted increasing academic attention over the past decade, reflecting a shift from sectoral interventions toward more integrated and technology-enabled urban governance. Smart cities are typically associated with digital infrastructure, data analytics, and platform-based management systems(Neirotti et al., 2014 )(Schaffers et al., 2011 )(Townsend, 2013 ), while healthy cities focus on environmental sustainability, public health equity, and social determinants of health(Danielli et al., 2023 )(Chuchkova, 1994 ). Recent literature has begun to explore how these two paradigms converge(Pietra & Venco, 2024 ), especially in contexts where digital tools are deployed to support health service delivery, disease surveillance, and population-level wellness promotion(Jeong & Chung, 2024 )(Allam & Jones, 2020 ). China’ s national strategies such as Healthy China 2030 and Digital China has accelerated digital technologies into urban health governance(Tan et al., 2017 )(Liu, 2021 ). This development has given rise to a growing interest in the transformation of health service systems through smart infrastructure, digital platforms, and data-driven decision-making at the city level(Abernethy et al., 2022 )(Borges do Nascimento et al., 2023 ). As urban populations continue to grow and diversify, cities are increasingly expected to deliver more equitable, efficient, and accessible health services(Richardson et al., 2022 ). However, the pathways to achieving such integration vary significantly across regions, particularly in a large and diverse country like China. Globally, studies have examined various forms of smart health interventions, ranging from mobile health (mHealth) systems to AI-driven health monitoring(Oyekunle et al., 2024 )(Rahman et al., 2024 ). However, much of the existing research is either narrowly focused on technical applications or lacks a holistic view of urban governance structures. In the Chinese context, emerging studies highlight local government initiatives in areas such as digital medical records, health platforms, and AI-assisted diagnosis. Yet few studies provide comparative analyses across cities to understand the diverse development trajectories and governance models. This study investigates how different Chinese cities are incorporating digital health strategies into their urban governance frameworks. Specifically, it asks: What are the practical models adopted by cities to integrate health and digital systems? Are there identifiable pathways or typologies that could be scaled or adapted elsewhere? By comparing four cities, Hangzhou, Shenzhen, Chengdu, and Shijiazhuang, this study seeks to examine how different governance models are operationalized in practice, and how local conditions shape the integration of digital technologies into urban health systems. 2. Methods 2.1 Research Design This study adopts a multiple case study approach to explore how different Chinese cities are integrating digital infrastructure with urban health governance. By selecting four cities, Hangzhou, Shenzhen, Shijiazhuang, and Chengdu, this research captures a range of development patterns across diverse regions (eastern/coastal vs. inland), city tiers (new first-tier vs. second-tier), and governance orientations (technology-driven vs. public sector-led). The aim is to identify both common challenges and context-specific pathways in the evolution of urban health-related digital systems. 2.2 Case Selection The four cities were chosen using theoretical sampling to reflect heterogeneity in geographic location, economic development level, and institutional arrangements. Each city represents a distinct model of digital-health integration. Table 1 Characteristics of the Selected Case Cities City Feature Population (approx., million) Chengdu A policy-driven city emphasizing health equity and public service expansion. 21.47 Hangzhou A technology-led city with strong government-enterprise partnerships. 12.62 Shenzhen A frontier of smart technologies and entrepreneurial health platforms. 17.99 Shijiazhuang A second-tier inland city undergoing public health infrastructure modernization. 11.25 Note : Population figures are approximate and derived from official projections following the Seventh National Population Census of China (2020), adjusted using recent municipal statistical yearbooks and government-released estimates. 2.3 Data Collection and Analysis Field data were collected between October and December 2023 through a combination of focus group interviews, on-site observation, and policy document analysis, guided by a standardized research protocol. Four site visits were conducted in Hangzhou, Shenzhen (Luohu District), Chengdu (Wuhou District), and Shijiazhuang, covering municipalities with varying geographic, economic, and institutional profiles. These visits were conducted in cooperation with municipal health commissions, hospital groups, and digital health industrial parks. A semi-structured interview protocol was designed based on four dimensions: (1) local policies and enabling mechanisms for smart health development; (2) institutional strategies and organizational design; (3) digital infrastructure and platform application; and (4) implementation outcomes and governance challenges. The protocol was adapted slightly for each site to reflect local contexts, for example, in Chengdu, special emphasis was placed on industry-health integration, while in Shenzhen, interviews focused on intra-hospital digital transformation. Interviewees included city-level health administrators, hospital group leaders, digital platform operators, community health center managers, and technology solution providers, with a total of approximately 30 participants involved across all cities. A total of 4 focus group interviews and 8 field observations were conducted. All interviews were audio-recorded, transcribed verbatim, and anonymized prior to analysis. Policy documents and strategic planning materials were collected and coded in tandem with field data. All data were analyzed using NVivo 14.0 through thematic coding, structured by a grounded theory-informed approach. Coding was initially open and later axial, with emerging themes clustered around governance structure, digital platform functionality, coordination dynamics, and population equity outcomes. A cross-case comparative matrix was constructed to synthesize key patterns and divergences. Table 2 Overview of Field Research in Four Smart Health Cities (Oct–Dec 2023) City Site & Partner Institutions Key Participants Methods Focus Topics Hangzhou City Brain & Health Brain teams, Municipal Health Bureau City-level planners, platform architects, IT vendors Focus groups, document review Digital infrastructure, vertical-horizontal integration Shenzhen Luohu Hospital Group Digital director, hospital/CHC heads, tech staff Interviews, site visits Hospital-led integration, HIS/LIS/CDR architecture Chengdu Wuhou Health Bureau, Shuanghua Industrial Park Health bureau leads, platform managers (Huawei, West China) Interviews, roundtable Public–private collaboration, digital family doctor model Shijiazhuang Municipal Health Commission, Digital Health Platform Health IT officers, public health service managers Interviews, site observation Platform standardization, maternal-child health integration All interviews and field observations were conducted in accordance with ethical research guidelines. As the study involved institutional-level stakeholders (e.g., health administrators, hospital directors, platform designers) providing information in their professional capacity, no personal or sensitive individual data were collected. Interview transcripts were anonymized and stored securely. Due to confidentiality agreements with participating institutions, the full dataset is not publicly available; however, selected de-identified materials may be made available by the corresponding author upon reasonable request. 3. Results The empirical investigation across Hangzhou, Shenzhen (Luohu), Chengdu (Wuhou), and Shijiazhuang reveals distinct governance logics and innovation trajectories in the construction of smart healthy cities, reflecting varying degrees of institutional integration, technological maturity, and equity orientation. 3.1.1 Hangzhou’s “Digital Brain”: Platform-Oriented Integrated Governance in Digital Health Hangzhou represents a prototypical case of platform-driven, integrated governance in the development of smart health cities. Its governance model is characterized by a centralized digital infrastructure—anchored by the City Brain and Health Brain systems—which facilitates cross-departmental data exchange and service coordination. The core logic of this model lies in two interrelated strategies: the use of digital platforms as governance enablers, and the deliberate pursuit of vertical–horizontal integration across administrative and service layers. “Platform leadership” in Hangzhou entails more than technical facilitation; it positions digital platforms as structuring agents that shape inter-organizational behaviors and institutional interactions. This is operationalized through the enforcement of unified data principles such as “one data, one source, one standard”, and the redesign of service processes to conform to standardized workflows. The governance role of platforms is thus embedded not only in their technical capacity, but also in their capacity to orchestrate multi-stakeholder ecosystems. The strategy of vertical-horizontal integration complements this logic. Vertically, the system connects provincial-level resources (e.g., Zhejiang Provincial Health Brain) with primary-level institutions such as community health centers. Horizontally, it promotes data interoperability and joint service delivery across sectors such as health, civil affairs, and social services. These integrations aim to dismantle traditional bureaucratic silos and support the construction of a more responsive, holistic urban health ecosystem. Hangzhou’s City Brain has evolved from an early-stage system (1.0) focused on traffic and emergency response to a more sophisticated governance engine (3.0) designed to support megacity management through real-time analytics and digital twinning. The Health Brain, developed in coordination with provincial-level authorities, follows a “1 + 3 + N” configuration: one integrated brain coordinating three domains—smart clinical care, public health surveillance, and digital health management—across multiple application scenarios. The synergy between the City Brain and Health Brain illustrates the city’s attempt to implement a “Health in All Policies” approach through digital means. Rather than treating health as a sectoral domain, health priorities are embedded within the broader digital governance architecture. This enables integrated planning and response across urban domains, including public safety, emergency preparedness, and infrastructure deployment. The emphasis lies not on specific technologies per se, but on the institutional effects and governance capacities that result from digital convergence. Several functional applications reflect this governance capacity. For instance, the Yi Jian Hu Ren (Mutual Recognition of Medical Tests) initiative—implemented across 251 public hospitals and encompassing 427 medical test items—has not only reduced redundant testing but also demonstrated an ability to enforce cross-institutional data standardization. As of August 2024, the initiative had more than 25 million registered users and saved an estimated 126 million RMB in medical insurance funds. Another example is Hang Xiao Yu, an AI-powered parenting assistant designed for families with children aged 0–6, providing personalized health education and behavioral nudges. 3.1.2 Shenzhen (Luohu): Digital Integration through Hospital-Led Smart Health Ecosystem The Luohu Hospital Group exemplifies a hospital-led, platform-based governance model in the development of an integrated smart health service system. Since its establishment in 2015, the Group has not only unified its five major hospitals and 45 community health centers under a single administrative umbrella but also advanced a comprehensive digital health architecture, anchored in a unified HIS/LIS/PACS infrastructure and a centralized Clinical Data Repository (CDR). A core innovation lies in its deployment of 5G-enabled applications—ranging from remote ultrasound, mobile diagnostic vans, to automated nucleic acid sampling robots and drone-based specimen delivery systems—designed to alleviate service bottlenecks and optimize patient flow. These applications are not merely technological upgrades but strategic responses to service coordination across institutional boundaries. As the Group’s digital director noted, “The hospital is no longer just a care provider; it is becoming a platform operator, a data hub, and a technology incubator” (Interview, Digital Director, Luohu Hospital Group) . The governance configuration allows for an agile yet centralized decision-making mechanism, enabling the scaling of real-time data sharing, role-based workstations (doctor, nurse, pharmacist, technician), and group-wide smart service management. However, challenges remain in ensuring service equity and interoperability across different tiers of providers, especially in peripheral communities. 3.1.3 Chengdu: Government-Enabled Industrial–Medical Integration with Public Service Orientation Chengdu’s approach, centered in the Shuanghua Digital Health Industrial Park, reflects a government-enabled, equity-driven model of industrial-medical integration. Strongly supported by Wuhou District authorities, the development of digital health infrastructure is grounded in a “dual chain-leader” model, combining Huawei’s indigenous digital infrastructure with West China Hospital’s medical expertise. Key platforms, such as the Digital Health Empowerment Center, Medical Engineering Transformation Hub, and the Wuhou Family Doctor digital platform, are designed to foster vertical and horizontal integration across public hospitals, primary care institutions, and digital health enterprises. This model foregrounds service inclusivity and local capacity building. As one district health official explained, “We didn’t just build a zone; we built a system where startups, public health, and hospitals co-evolve—policy leads, industry follows” (Interview, Health Bureau of Wuhou District) . At the application level, the Wuhou Family Doctor (武侯家医) platform demonstrates how digital interfaces can operationalize the People-Centered Integrated Care (PCIC) model, providing personalized care plans, remote monitoring, and corporate-based employee health packages, supported by a regional digital infrastructure. Despite these innovations, the sustainability of pilot programs and the strain on inter-agency coordination present ongoing governance dilemmas. Unlike Shenzhen’s hospital-led integration, Chengdu’s model involved broader stakeholder coalitions from the beginning. 3.1.4 Shijiazhuang: Infrastructure Modernization in a Second-Tier Context Shijiazhuang presents a third trajectory, infrastructure-led, health commission-directed smart health expansion, grounded in strong policy alignment with national and provincial digital health plans. The city’s dual-level health information platform has enabled city-wide interoperability across 45 second-tier and tertiary hospitals, linking electronic health records, e-prescription flows, and an emergency medical record system utilized in all 43 emergency sites. A notable focus has been the integration of women’s and children’s health services through the Women and Children Medical Consortium platform, which facilitates bidirectional referrals and patient data sharing across primary and specialist facilities. Importantly, public-facing services such as “Healthy Shijiazhuang” and the city’s health portal expand access to digital booking, consultation, and maternal services. According to a senior official at the city’s Health Commission, “Our goal is simple—equal access. Digital tools help us cover rural and urban patients with the same continuity of care” (Interview, Deputy Director, Shijiazhuang Health Commission) . Governance here is more centralized, with strong emphasis on technical standardization, data security (including SM2/SM4 encryption protocols), and inter-sectoral data exchange (e.g., mental health data shared with public security). Yet, limitations persist in bidirectional data integration with third-party systems and the interoperability of newly added service modules. Unlike Chengdu’s inclusive governance networks or Shenzhen’s entrepreneurial experimentation, Shijiazhuang’s strength lies in its systematic rollout of standardized digital platforms across public hospitals and community-level institutions. Its focus is less on cutting-edge innovation and more on ensuring that no node in the system remains disconnected. 3.2 Cross-case Synthesis: Divergent Pathways, Shared Goals Across the four cities examined, it is clear that digital health has become a central part of local urban governance agendas, but how it is understood and implemented varies widely. All cases share a common ambition: to improve the reach, efficiency, and responsiveness of health services by integrating digital tools. Yet, the institutional choices and operational logics they adopt differ in ways that are shaped by local capacities, governance structures, and developmental priorities. Hangzhou and Shenzhen both reflect the influence of broader smart city infrastructure, but they do so in different ways. Hangzhou is more data-centric, focusing on city-wide integration and real-time analytics, while Shenzhen’s model grows out of its hospital system, emphasizing internal coordination and clinical efficiency. Chengdu and Shijiazhuang, meanwhile, offer more state-centered approaches. Chengdu places strong emphasis on equity and service inclusivity, building a digital ecosystem that supports both community-level care and industrial development. Shijiazhuang’s model is more infrastructural and technocratic in tone, prioritizing standardized roll-out and ensuring connectivity across public institutions, especially in second-tier contexts where foundational systems are still being built. What links these cities is not a shared model, but a shared orientation: digital health is not a side project, it is increasingly embedded into how cities think about managing population health. The diversity lies in the routes they take to get there. A comparative snapshot of these pathways is provided in Table 3 , which summarizes each city’s core focus, governance approach, and notable innovations. Table 3 Comparative Overview of Governance Models in Smart Health Cities City Governance Model Innovation Highlights Challenges Hangzhou Platform-led, hybrid-integrated governance via “Digital Brain” AI-enabled smart agents (e.g., Hang Xiaoyu), city-wide data integration (City Brain & Health Brain), cross-sector applications for elderly/childcare, cloud-based hospitalization, mutual test recognition, and TCM digitalization through Smart Hangzhou TCM Data standardization across systems; sustaining inter-agency coordination Shenzhen Hospital-centered platform governance 5G-enabled remote care, centralized data infrastructure Service equity; inter-department integration Chengdu Government-enabled industrial–medical alliance Industrial incubation, integrated care through digital family doctor Cross-sector collaboration strain; pilot sustainability Shijiazhuang Health commission-led standardization Full-spectrum digital service for city-county users Data interoperability, new system linkage gaps 4. Discussion 4.1 Main Findings Despite the differences in local contexts, all four cities in this study share a common strategic ambition. To enhance primary care delivery, achieve cross-sectoral data integration, and improve health system responsiveness through digital innovation. However, the governance pathways they have adopted diverge significantly. These divergences reflect variations in institutional capacity, administrative structures, and developmental priorities. For instance, Hangzhou has developed a platform-led governance model characterized by centralized data infrastructure and top-down coordination, while Shenzhen relies on a hospital-led model built around its clinical network and IT systems. Chengdu emphasizes a government-enabled, equity-driven approach that integrates health and industrial development, whereas Shijiazhuang focuses on standardized infrastructure rollout under the direction of the municipal health commission. These cities differ not only in governance orientation and geographic location but also in population size and demographic composition, factors that significantly shape their digital health strategies. Megacities like Chengdu and Shenzhen, each with populations exceeding 15 million, face considerable challenges in scaling digital health services across highly mobile and socially diverse communities. Their governance approaches often emphasize interoperability, real-time analytics, and service coordination at scale. In contrast, mid-sized cities such as Hangzhou and Shijiazhuang, with populations closer to 10–12 million, focus on building foundational infrastructure and ensuring service equity across fragmented urban–rural landscapes. These differences are not merely managerial in nature. They reflect deeper structural dynamics, such as urban–rural population ratios, internal migration intensity, and aging population profiles, that influence local policy priorities. For example, Chengdu’s higher proportion of elderly residents and its strong emphasis on public service inclusivity have prompted investments in integrated, community-oriented digital care. Meanwhile, Shenzhen’s large non-registered migrant population has led to hospital-centered innovations aimed at improving throughput and clinical efficiency. These demographic and systemic conditions help explain why different cities adopt distinct governance and innovation pathways, even when pursuing shared national agendas like Healthy China 2030 and Digital China. Such variation aligns with existing literature that emphasizes the context-dependent nature of digital health implementation (Greenhalgh et al., 2012 )(Wherton et al., 2012 ). Governance structures, population profiles, and institutional legacies condition what is possible, for whom, and at what speed. Therefore, while all four cities pursue agendas, their implementation logics, from platformization to hospital-centrism to infrastructural standardization, diverge in response to both structural constraints and local opportunities. These findings support calls in the urban health governance literature for more adaptive, differentiated strategies that account for local capacities and demands(Webb et al., 2019 ). Rather than converging on a single digital governance model, Chinese cities are navigating multiple, context-sensitive trajectories that reflect a broader pattern of institutional experimentation under central policy guidance. 4.2 Emerging Governance Typologies in China’s Smart Health Cities As the comparative analysis demonstrates, Chinese cities are experimenting with divergent governance pathways in their pursuit of smart health integration. These efforts reveal not only institutional variation but also evolving typologies of governance logic. We identify three emerging models: platform-driven integrated governance, institution-anchored service ecosystems, and government-enabled multi-stakeholder collaboration, each exemplified by a distinct city case. These typologies reflect differences in administrative capacity, digital maturity, and policy priorities, offering a conceptual lens to analyze smart health governance beyond technological deployment. 4.2.1 Type I: Platform-Driven Integrated Governance The platform-driven model, exemplified by Hangzhou, places a powerful, centralized digital platform at the core of governance. Rather than serving merely as a technical tool, the platform functions as a coordinating architecture that structures cross-sectoral interactions and institutional behavior. This model is characterized by strong top-down coordination, unified data standards, and real-time data exchange capabilities. In Hangzhou, this is operationalized through the “City Brain” and “Health Brain” infrastructures, which support not only inter-agency data flows but also vertical-horizontal integration across administrative levels and service domains. Government actors play a leading role in this model, not only initiating digital infrastructure but also formulating technical standards and overseeing coordination among various stakeholders. Platform governance thus becomes both an enabler and a shaper of institutional interaction. This approach is particularly suited to cities with high digital readiness, robust local tech industries, and political commitment to centralized orchestration. Its core strengths lie in its capacity to achieve comprehensive data integration, enable complex decision-making, and embed health objectives within broader urban governance agendas. Notably, this aligns with the “Health in All Policies” paradigm, implemented through a digital backbone. However, the model also faces substantial challenges. It demands significant up-front investment and ongoing maintenance costs. Failure to enforce interoperability standards risks creating new digital silos. Moreover, excessive reliance on technology can lead to techno-determinism, where efficiency is prioritized over equity or user experience. Sustaining long-term cross-sectoral coordination is difficult, and the centralization of data raises concerns around privacy, security, and surveillance. Ultimately, the success of this model depends not only on technological sophistication but on institutional mechanisms that address governance, ethical, and user-centric concerns. 4.2.2 Type II: Institution-Anchored Service Ecosystems The second governance type, observed in Shenzhen (Luohu) and Shijiazhuang, is defined by the anchoring role of established public institutions—typically large hospital groups or municipal health commissions—in spearheading digital health integration. Unlike the platform-led model that seeks to restructure governance city-wide, this institution-anchored approach is more incremental, focusing on optimizing internal workflows, enhancing clinical efficiency, and extending services through existing institutional networks. In Shenzhen’s Luohu District, the hospital group leads the integration of digital tools, deploying technologies such as 5G-enabled remote diagnostics and centralized clinical data repositories to improve service coordination across hospitals and community health centers. Similarly, Shijiazhuang’s health commission oversees a city-wide rollout of digital platforms designed to standardize services across secondary and tertiary facilities, particularly in maternal and child health. In both cases, digital governance remains embedded within pre-existing institutional structures, with innovation efforts tailored to internal capacity-building rather than systemic transformation. This model is particularly suited to cities where public health institutions possess strong administrative authority, adequate resources, and the capacity to lead innovation. Its advantages include leveraging institutional credibility, clinical expertise, and established patient networks to enhance service quality within defined ecosystems. Moreover, targeted digital upgrades can yield rapid efficiency gains and improve patient experiences without requiring wholesale structural reform. Nonetheless, several limitations constrain the broader transformative potential of this model. The most prominent challenge is the risk of data silos emerging between institutions, especially when interoperability with external systems is not prioritized from the outset. Additionally, services may remain largely confined within institutional boundaries, raising equity concerns for populations outside these networks. The model may also lack the agility needed to address more complex, cross-sectoral health issues that require broader systemic coordination. Furthermore, the direction of digital innovation may be shaped primarily by the institutional priorities and resources of leading actors, potentially limiting the diversity and inclusivity of solutions. Overall, while the institution-anchored model offers a pragmatic pathway for strengthening service ecosystems, particularly in cities with mature healthcare institutions, it must consciously build outward interoperability and equity considerations into its design to avoid entrenching institutional fragmentation. 4.2.3 Type III: Government-Enabled Multi-Stakeholder Collaboration The third governance model, as exemplified by Chengdu (Wuhou District), is characterized by the government acting not as a direct controller or platform operator, but as an enabler and orchestrator of a collaborative innovation ecosystem. This model prioritizes public value creation, health equity, and industrial development through the facilitation of partnerships across government agencies, public health institutions, digital technology firms, and academic research organizations. At the heart of Chengdu’s strategy is a deliberate state-enabled “dual chain-leader” mechanism, where public sector institutions, such as West China Hospital, collaborate with major technology companies like Huawei to co-lead digital health development. Anchored in the Shuanghua Digital Health Industrial Park, this model integrates policy support, shared infrastructure investment, and institutional alignment to foster a vibrant innovation ecosystem. Core platforms, such as the Wuhou Family Doctor system, exemplify how digital tools can be designed not merely for efficiency, but for equitable access, community-based care, and inclusive service delivery. This governance mode reflects a shift toward networked, participatory urban health governance. Rather than focusing exclusively on technological advancement, the model centers on enabling conditions for multi-stakeholder co-creation. Digital interventions are framed as tools for achieving broader societal goals, including reducing health disparities, strengthening local innovation capacity, and embedding person-centered approaches into health system design. It aligns with people-centered care (PCC) and the social determinants of health (SDoH) paradigms, using digital tools to operationalize these frameworks in ways that are tailored to local contexts. The advantages of this model are manifold. It allows for the pooling of diverse expertise across sectors, fostering cross-boundary innovation and contextualized solutions. It has the potential to enhance local economic development through the incubation of health-tech startups while simultaneously improving the inclusiveness and responsiveness of public health services. Moreover, by grounding digital health initiatives in collaborative governance, it builds systemic resilience and adaptive capacity. However, the model also faces critical challenges. Managing complex partnerships requires substantial coordination effort, and sustaining the momentum of collaboration over time demands consistent political leadership and institutional commitment. Pilot projects may struggle to scale beyond demonstration zones without structural mechanisms for long-term integration. Conflicts may arise between public interest goals and private sector profit imperatives, particularly when governance frameworks for data ownership, transparency, and accountability are underdeveloped. Ultimately, the success of this model hinges on the state’s ability to maintain a strategic steering role—empowering stakeholders without abdicating oversight—and on the existence of governance mechanisms that align divergent interests toward shared public health objectives. This government-enabled collaborative model is particularly suitable for cities with the institutional maturity and political will to coordinate across sectors, and where smart health is envisioned not only as a technological project, but as a vehicle for public value creation and inclusive development. Table 4 Comparative Typologies of Smart Health City Governance in China Governance Type Core Logic & Mechanism Representative Case(s) Typical Context Key Strengths Key Limitations / Challenges Platform-Driven Integrated Governance Centralized digital platforms serve as core infrastructures for data integration, analytics, and service coordination. Emphasizes top-down control, standardization, and cross-sector interoperability. The platform itself acts as a governance instrument. Hangzhou Cities with strong digital infrastructure, advanced technological capacity, and high willingness and ability for centralized coordination. High potential for integrated data systems and complex decision-making; boosts systemic efficiency; enables “Health in All Policies” implementation. High upfront and maintenance costs; risk of data silos; potential for techno-determinism; difficulty in sustaining cross-agency coordination; concerns over data privacy and surveillance. Institution-Anchored Service Ecosystems Led by major public institutions (e.g., hospital groups, health commissions), this model focuses on optimizing internal clinical processes and service coordination, with digital tools serving institutional modernization and targeted expansion. Shenzhen (Luohu), Shijiazhuang Cities with capable, resource-rich public health institutions acting as innovation anchors; focused on upgrading existing health service systems. Leverages institutional strengths and networks; enables rapid efficiency gains within existing ecosystems; high feasibility due to narrower scope. Risk of institutional data silos; equity challenges if benefits are confined to institutional networks; limited agility for addressing cross-sectoral issues; innovation may be constrained by internal priorities and resources. Government-Enabled Multi-Stakeholder Collaboration Government acts as coordinator and enabler, fostering partnerships across public institutions, industry, and academia to co-develop digital health solutions. Emphasizes public value, health equity, and inclusive innovation. Chengdu (Wuhou) Cities aiming to promote both health equity and digital health industry development; capable of managing complex partnerships and stakeholder dynamics. Catalyzes cross-sector innovation; supports local digital health industry growth; generates context-specific, inclusive solutions; builds more resilient and adaptive ecosystems. High coordination burden; challenges in sustaining pilot projects and scaling; potential conflict between public interest and private incentives; depends on strong and sustained governmental leadership. 4.3 Implications for Policy Adaptation This study highlights that smart health city development in China is not characterized by convergence toward a single model, but by diverse, context-sensitive trajectories. Cities often exhibit hybrid features or transition from one governance type to another in response to shifting priorities and capacities. This dynamic and experimental nature of governance reflects ongoing adaptation to both local contextual realities and evolving national policy mandates. The typologies identified: platform-based, institution-anchored, and government-enabled collaborative models, demonstrate that there is no one-size-fits-all solution. Instead of attempting to replicate “successful” cities wholesale, policymakers should focus on aligning governance strategies with local administrative capacity, demographic structure, health needs, and levels of digital maturity. For example, Shenzhen’s entrepreneurial, hospital-led logic may suit cities with robust healthcare institutions and IT infrastructure, while Chengdu’s equity-oriented, government-facilitated model may resonate in regions prioritizing public service delivery and community-based care. However, in the absence of effective inter-agency coordination and shared data standards, even well-funded interventions risk entrenching fragmentation rather than promoting integration. Contextual fit remains a key determinant of the success or failure of digital health reforms. Given the deeply embedded nature of health systems in sociopolitical, institutional, and economic structures, transferring governance models across cities or countries faces numerous challenges, including compatibility with legacy infrastructure, return-on-investment uncertainties, and institutional inertia. China’s centrally coordinated yet locally adaptive approach, shaped by its state-led innovation ecosystem, creates a distinctive institutional environment where even intra-national model replication is far from straightforward. The development of smart health cities is not a one-time transition but a continuous process of experimentation, adaptation, and institutional learning. Effective policy adaptation requires the creation of feedback loops and iterative mechanisms to evaluate, revise, and recalibrate strategies. This points to the need for performance measurement frameworks, such as digital health maturity indices or governance capacity assessments—that can support evidence-based evaluation and peer learning across cities. Cities should critically assess and contextualize external experiences rather than adopt models uncritically. The goal is not to locate a perfect governance template ex ante, but to cultivate the capacity to evolve, absorb lessons, and respond flexibly to emerging challenges. This aligns with recent scholarship emphasizing adaptive governance as a pathway to institutional resilience and long-term effectiveness(Chaffin et al., 2014 ). 4.4 Limitations and Future Research This study is limited by its reliance on official documents and elite interviews, potentially overlooking the lived experiences of patients, frontline workers, and marginalized communities. It focuses on city-level, formal governance structures, without fully capturing informal innovation or user-led practices. Future research should adopt bottom-up approaches to incorporate citizen perspectives and assess real-world system usability and equity impacts. Comparative international studies are also needed to explore how political, economic, and cultural contexts shape smart health governance trajectories. As digital health tools increasingly integrate AI, future inquiries must also address emerging ethical concerns. Ultimately, smart health cities are not merely technical systems, but socio-political projects whose long-term success hinges on their ability to deliver inclusive, scalable, and context-sensitive improvements in population health. Declarations CRediT authorship contribution statement Yin Zhang: Writing – original draft; Xi WANG: Writing – original draft, review & editing, Methodology; Ayan MAO, Pei DONG, Yueli MENG, Shuai DU, Minjie ZHAO : Investigation & Fieldwork; Wuqi QIU, Meng WANG, Jintao LI: Funding acquisition, Resources, Supervision. Funding 1.Commissioned research project by Huawei Technology Co., Ltd.: Research on the Technical Framework and Applications of Smart Healthy Cities; 2. Commissioned research project by Office of Healthy Hangzhou Construction Leading Group / Hangzhou Municipal Health Commission: Research on the Current Situation and Development Strategies of Smart Health City Construction in Hangzhou Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. 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The healthy cities project of the world health organization and the approach to its realization. Problemi Na Khigienata , 19 , 3–9. Danielli, S., Ashrafian, H., & Darzi, A. (2023). Healthy city: Global systematic scoping review of city initiatives to improve health with policy recommendations. BMC Public Health , 23 (1), 1277. Greenhalgh, T., Procter, R., Wherton, J., Sugarhood, P., & Shaw, S. (2012). The organising vision for telehealth and telecare: Discourse analysis. BMJ Open , 2 (4), e001574. https://doi.org/10.1136/bmjopen-2012-001574 Jeong, H. S., & Chung, H. (2024). Bridging smart technologies and healthy cities: A scoping review using WHO’s 6P framework. Sustainable Cities and Society , 105888. Liu, L. (2021). The rise of data politics: Digital China and the world. Studies in Comparative International Development , 56 (1), 45–67. Neirotti, P., De Marco, A., Cagliano, A. C., Mangano, G., & Scorrano, F. (2014). Current trends in Smart City initiatives: Some stylised facts. Cities , 38 , 25–36. https://doi.org/10.1016/j.cities.2013.12.010 Oyekunle, D., Matthew, U. O., Preston, D., & Boohene, D. (2024). Trust beyond Technology Algorithms: A Theoretical Exploration of Consumer Trust and Behavior in Technological Consumption and AI Projects. Journal of Computer and Communications , 12 (6), Article 6. https://doi.org/10.4236/jcc.2024.126006 Pietra, C., & Venco, E. M. (2024). Integrating Health and Smartness—New Sustainable Paradigms for the Urban Environment: A Case Study in Lianshi Town (China). Land , 13 (4), 405. Rahman, A., Debnath, T., Kundu, D., Khan, M. S. I., Aishi, A. A., Sazzad, S., Sayduzzaman, M., & Band, S. S. (2024). Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities. AIMS Public Health , 11 (1), 58. Richardson, S., Lawrence, K., Schoenthaler, A. M., & Mann, D. (2022). A framework for digital health equity. NPJ Digital Medicine , 5 (1), 119. Schaffers, H., Komninos, N., Pallot, M., Trousse, B., Nilsson, M., & Oliveira, A. (2011). Smart cities and the future internet: Towards cooperation frameworks for open innovation . Springer Berlin Heidelberg. Tan, X., Liu, X., & Shao, H. (2017). Healthy China 2030: A vision for health care. Value in Health Regional Issues , 12 , 112–114. Townsend, A. M. (2013). Smart cities: Big data, civic hackers, and the quest for a new utopia . WW Norton & Company. Webb, R., Rissik, D., Petheram, L., Beh, J.-L., & Stafford Smith, M. (2019). Co-designing adaptation decision support: Meeting common and differentiated needs. Climatic Change , 153 , 569–585. Wherton, J., Sugarhood, P., Procter, R., Rouncefield, M., Dewsbury, G., Hinder, S., & Greenhalgh, T. (2012). Designing assisted living technologies ‘in the wild’: Preliminary experiences with cultural probe methodology. BMC Medical Research Methodology , 12 (1), 188. https://doi.org/10.1186/1471-2288-12-188 Supplementary Files AppendixAandB.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7491675","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509766374,"identity":"0e81777d-9bed-49f1-8da3-27f3acd166ce","order_by":0,"name":"Ying 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College","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Du","suffix":""},{"id":509766379,"identity":"d07d70dc-f545-42bc-be2f-9481a819acc6","order_by":5,"name":"Minjie Zhao","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Minjie","middleName":"","lastName":"Zhao","suffix":""},{"id":509766380,"identity":"131894fb-2c05-45f7-8407-59b14f2f75da","order_by":6,"name":"Yueli Meng","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yueli","middleName":"","lastName":"Meng","suffix":""},{"id":509766381,"identity":"38d81763-86f5-4754-b4b7-1b30c7aa2c64","order_by":7,"name":"Jintao Li","email":"","orcid":"","institution":"Hangzhou Healthy City Guidence Center","correspondingAuthor":false,"prefix":"","firstName":"Jintao","middleName":"","lastName":"Li","suffix":""},{"id":509766382,"identity":"f91897a9-9b1b-4ebd-a94d-a31bb7fd66a6","order_by":8,"name":"Wuqi Qiu","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Wuqi","middleName":"","lastName":"Qiu","suffix":""},{"id":509766383,"identity":"b7afdfe5-db48-4022-9e9b-bd3ab89894df","order_by":9,"name":"Meng Wang","email":"","orcid":"","institution":"Hangzhou Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-29 22:07:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7491675/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7491675/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92612369,"identity":"ccccc34b-3bd9-4719-812c-c4e550974239","added_by":"auto","created_at":"2025-10-01 16:43:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":957174,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7491675/v1/032a6f89-41a1-4b92-817f-5e7c36278d6f.pdf"},{"id":90989754,"identity":"65fcc250-b8a9-42de-a365-5313f650f568","added_by":"auto","created_at":"2025-09-10 10:54:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18665,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixAandB.docx","url":"https://assets-eu.researchsquare.com/files/rs-7491675/v1/e6cf5745fd26cf38ca47e589.docx"}],"financialInterests":"","formattedTitle":"Governance and Innovation in Building Smart Healthy Cities: Multi-city evidence from China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSmart city and healthy city agendas has attracted increasing academic attention over the past decade, reflecting a shift from sectoral interventions toward more integrated and technology-enabled urban governance. Smart cities are typically associated with digital infrastructure, data analytics, and platform-based management systems(Neirotti et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)(Schaffers et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)(Townsend, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), while healthy cities focus on environmental sustainability, public health equity, and social determinants of health(Danielli et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)(Chuchkova, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Recent literature has begun to explore how these two paradigms converge(Pietra \u0026amp; Venco, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), especially in contexts where digital tools are deployed to support health service delivery, disease surveillance, and population-level wellness promotion(Jeong \u0026amp; Chung, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)(Allam \u0026amp; Jones, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eChina\u0026rsquo; s national strategies such as Healthy China 2030 and Digital China has accelerated digital technologies into urban health governance(Tan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)(Liu, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This development has given rise to a growing interest in the transformation of health service systems through smart infrastructure, digital platforms, and data-driven decision-making at the city level(Abernethy et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)(Borges do Nascimento et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As urban populations continue to grow and diversify, cities are increasingly expected to deliver more equitable, efficient, and accessible health services(Richardson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the pathways to achieving such integration vary significantly across regions, particularly in a large and diverse country like China.\u003c/p\u003e\u003cp\u003eGlobally, studies have examined various forms of smart health interventions, ranging from mobile health (mHealth) systems to AI-driven health monitoring(Oyekunle et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)(Rahman et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, much of the existing research is either narrowly focused on technical applications or lacks a holistic view of urban governance structures. In the Chinese context, emerging studies highlight local government initiatives in areas such as digital medical records, health platforms, and AI-assisted diagnosis. Yet few studies provide comparative analyses across cities to understand the diverse development trajectories and governance models.\u003c/p\u003e\u003cp\u003eThis study investigates how different Chinese cities are incorporating digital health strategies into their urban governance frameworks. Specifically, it asks: What are the practical models adopted by cities to integrate health and digital systems? Are there identifiable pathways or typologies that could be scaled or adapted elsewhere? By comparing four cities, Hangzhou, Shenzhen, Chengdu, and Shijiazhuang, this study seeks to examine how different governance models are operationalized in practice, and how local conditions shape the integration of digital technologies into urban health systems.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Research Design\u003c/h2\u003e\u003cp\u003eThis study adopts a multiple case study approach to explore how different Chinese cities are integrating digital infrastructure with urban health governance. By selecting four cities, Hangzhou, Shenzhen, Shijiazhuang, and Chengdu, this research captures a range of development patterns across diverse regions (eastern/coastal vs. inland), city tiers (new first-tier vs. second-tier), and governance orientations (technology-driven vs. public sector-led). The aim is to identify both common challenges and context-specific pathways in the evolution of urban health-related digital systems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Case Selection\u003c/h2\u003e\u003cp\u003eThe four cities were chosen using theoretical sampling to reflect heterogeneity in geographic location, economic development level, and institutional arrangements. Each city represents a distinct model of digital-health integration.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the Selected Case Cities\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePopulation (approx., million)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChengdu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA policy-driven city emphasizing health equity and public service expansion.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHangzhou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA technology-led city with strong government-enterprise partnerships.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShenzhen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA frontier of smart technologies and entrepreneurial health platforms.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShijiazhuang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA second-tier inland city undergoing public health infrastructure modernization.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eNote\u003c/b\u003e: Population figures are approximate and derived from official projections following the Seventh National Population Census of China (2020), adjusted using recent municipal statistical yearbooks and government-released estimates.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data Collection and Analysis\u003c/h2\u003e\u003cp\u003eField data were collected between October and December 2023 through a combination of focus group interviews, on-site observation, and policy document analysis, guided by a standardized research protocol. Four site visits were conducted in Hangzhou, Shenzhen (Luohu District), Chengdu (Wuhou District), and Shijiazhuang, covering municipalities with varying geographic, economic, and institutional profiles. These visits were conducted in cooperation with municipal health commissions, hospital groups, and digital health industrial parks.\u003c/p\u003e\u003cp\u003eA semi-structured interview protocol was designed based on four dimensions: (1) local policies and enabling mechanisms for smart health development; (2) institutional strategies and organizational design; (3) digital infrastructure and platform application; and (4) implementation outcomes and governance challenges. The protocol was adapted slightly for each site to reflect local contexts, for example, in Chengdu, special emphasis was placed on industry-health integration, while in Shenzhen, interviews focused on intra-hospital digital transformation. Interviewees included city-level health administrators, hospital group leaders, digital platform operators, community health center managers, and technology solution providers, with a total of approximately 30 participants involved across all cities. A total of 4 focus group interviews and 8 field observations were conducted. All interviews were audio-recorded, transcribed verbatim, and anonymized prior to analysis. Policy documents and strategic planning materials were collected and coded in tandem with field data.\u003c/p\u003e\u003cp\u003eAll data were analyzed using NVivo 14.0 through thematic coding, structured by a grounded theory-informed approach. Coding was initially open and later axial, with emerging themes clustered around governance structure, digital platform functionality, coordination dynamics, and population equity outcomes. A cross-case comparative matrix was constructed to synthesize key patterns and divergences.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverview of Field Research in Four Smart Health Cities (Oct\u0026ndash;Dec 2023)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSite \u0026amp; Partner Institutions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKey Participants\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMethods\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFocus Topics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHangzhou\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCity Brain \u0026amp; Health Brain teams, Municipal Health Bureau\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCity-level planners, platform architects, IT vendors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFocus groups, document review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDigital infrastructure, vertical-horizontal integration\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eShenzhen\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLuohu Hospital Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDigital director, hospital/CHC heads, tech staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterviews, site visits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHospital-led integration, HIS/LIS/CDR architecture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChengdu\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWuhou Health Bureau, Shuanghua Industrial Park\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHealth bureau leads, platform managers (Huawei, West China)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterviews, roundtable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePublic\u0026ndash;private collaboration, digital family doctor model\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eShijiazhuang\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMunicipal Health Commission, Digital Health Platform\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHealth IT officers, public health service managers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterviews, site observation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePlatform standardization, maternal-child health integration\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAll interviews and field observations were conducted in accordance with ethical research guidelines. As the study involved institutional-level stakeholders (e.g., health administrators, hospital directors, platform designers) providing information in their professional capacity, no personal or sensitive individual data were collected. Interview transcripts were anonymized and stored securely. Due to confidentiality agreements with participating institutions, the full dataset is not publicly available; however, selected de-identified materials may be made available by the corresponding author upon reasonable request.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe empirical investigation across Hangzhou, Shenzhen (Luohu), Chengdu (Wuhou), and Shijiazhuang reveals distinct governance logics and innovation trajectories in the construction of smart healthy cities, reflecting varying degrees of institutional integration, technological maturity, and equity orientation.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e3.1.1 Hangzhou\u0026rsquo;s \u0026ldquo;Digital Brain\u0026rdquo;: Platform-Oriented Integrated Governance in Digital Health\u003c/div\u003e\u003cp\u003eHangzhou represents a prototypical case of platform-driven, integrated governance in the development of smart health cities. Its governance model is characterized by a centralized digital infrastructure\u0026mdash;anchored by the City Brain and Health Brain systems\u0026mdash;which facilitates cross-departmental data exchange and service coordination. The core logic of this model lies in two interrelated strategies: the use of digital platforms as governance enablers, and the deliberate pursuit of vertical\u0026ndash;horizontal integration across administrative and service layers. \u0026ldquo;Platform leadership\u0026rdquo; in Hangzhou entails more than technical facilitation; it positions digital platforms as structuring agents that shape inter-organizational behaviors and institutional interactions. This is operationalized through the enforcement of unified data principles such as \u0026ldquo;one data, one source, one standard\u0026rdquo;, and the redesign of service processes to conform to standardized workflows. The governance role of platforms is thus embedded not only in their technical capacity, but also in their capacity to orchestrate multi-stakeholder ecosystems. The strategy of vertical-horizontal integration complements this logic. Vertically, the system connects provincial-level resources (e.g., Zhejiang Provincial Health Brain) with primary-level institutions such as community health centers. Horizontally, it promotes data interoperability and joint service delivery across sectors such as health, civil affairs, and social services. These integrations aim to dismantle traditional bureaucratic silos and support the construction of a more responsive, holistic urban health ecosystem.\u003c/p\u003e\u003cp\u003eHangzhou\u0026rsquo;s City Brain has evolved from an early-stage system (1.0) focused on traffic and emergency response to a more sophisticated governance engine (3.0) designed to support megacity management through real-time analytics and digital twinning. The Health Brain, developed in coordination with provincial-level authorities, follows a \u0026ldquo;1\u0026thinsp;+\u0026thinsp;3\u0026thinsp;+\u0026thinsp;N\u0026rdquo; configuration: one integrated brain coordinating three domains\u0026mdash;smart clinical care, public health surveillance, and digital health management\u0026mdash;across multiple application scenarios. The synergy between the City Brain and Health Brain illustrates the city\u0026rsquo;s attempt to implement a \u0026ldquo;Health in All Policies\u0026rdquo; approach through digital means. Rather than treating health as a sectoral domain, health priorities are embedded within the broader digital governance architecture. This enables integrated planning and response across urban domains, including public safety, emergency preparedness, and infrastructure deployment. The emphasis lies not on specific technologies per se, but on the institutional effects and governance capacities that result from digital convergence. Several functional applications reflect this governance capacity. For instance, the Yi Jian Hu Ren (Mutual Recognition of Medical Tests) initiative\u0026mdash;implemented across 251 public hospitals and encompassing 427 medical test items\u0026mdash;has not only reduced redundant testing but also demonstrated an ability to enforce cross-institutional data standardization. As of August 2024, the initiative had more than 25\u0026nbsp;million registered users and saved an estimated 126\u0026nbsp;million RMB in medical insurance funds. Another example is Hang Xiao Yu, an AI-powered parenting assistant designed for families with children aged 0\u0026ndash;6, providing personalized health education and behavioral nudges.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e3.1.2 Shenzhen (Luohu): Digital Integration through Hospital-Led Smart Health Ecosystem\u003c/div\u003e\u003cp\u003eThe Luohu Hospital Group exemplifies a hospital-led, platform-based governance model in the development of an integrated smart health service system. Since its establishment in 2015, the Group has not only unified its five major hospitals and 45 community health centers under a single administrative umbrella but also advanced a comprehensive digital health architecture, anchored in a unified HIS/LIS/PACS infrastructure and a centralized Clinical Data Repository (CDR). A core innovation lies in its deployment of 5G-enabled applications\u0026mdash;ranging from remote ultrasound, mobile diagnostic vans, to automated nucleic acid sampling robots and drone-based specimen delivery systems\u0026mdash;designed to alleviate service bottlenecks and optimize patient flow. These applications are not merely technological upgrades but strategic responses to service coordination across institutional boundaries. As the Group\u0026rsquo;s digital director noted, \u003cem\u003e\u0026ldquo;The hospital is no longer just a care provider; it is becoming a platform operator, a data hub, and a technology incubator\u0026rdquo; (Interview, Digital Director, Luohu Hospital Group)\u003c/em\u003e. The governance configuration allows for an agile yet centralized decision-making mechanism, enabling the scaling of real-time data sharing, role-based workstations (doctor, nurse, pharmacist, technician), and group-wide smart service management. However, challenges remain in ensuring service equity and interoperability across different tiers of providers, especially in peripheral communities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e3.1.3 Chengdu: Government-Enabled Industrial\u0026ndash;Medical Integration with Public Service Orientation\u003c/div\u003e\u003cp\u003eChengdu\u0026rsquo;s approach, centered in the Shuanghua Digital Health Industrial Park, reflects a government-enabled, equity-driven model of industrial-medical integration. Strongly supported by Wuhou District authorities, the development of digital health infrastructure is grounded in a \u0026ldquo;dual chain-leader\u0026rdquo; model, combining Huawei\u0026rsquo;s indigenous digital infrastructure with West China Hospital\u0026rsquo;s medical expertise. Key platforms, such as the Digital Health Empowerment Center, Medical Engineering Transformation Hub, and the Wuhou Family Doctor digital platform, are designed to foster vertical and horizontal integration across public hospitals, primary care institutions, and digital health enterprises. This model foregrounds service inclusivity and local capacity building. As one district health official explained, \u003cem\u003e\u0026ldquo;We didn\u0026rsquo;t just build a zone; we built a system where startups, public health, and hospitals co-evolve\u0026mdash;policy leads, industry follows\u0026rdquo; (Interview, Health Bureau of Wuhou District)\u003c/em\u003e. At the application level, the Wuhou Family Doctor (武侯家医) platform demonstrates how digital interfaces can operationalize the People-Centered Integrated Care (PCIC) model, providing personalized care plans, remote monitoring, and corporate-based employee health packages, supported by a regional digital infrastructure. Despite these innovations, the sustainability of pilot programs and the strain on inter-agency coordination present ongoing governance dilemmas. Unlike Shenzhen\u0026rsquo;s hospital-led integration, Chengdu\u0026rsquo;s model involved broader stakeholder coalitions from the beginning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e3.1.4 Shijiazhuang: Infrastructure Modernization in a Second-Tier Context\u003c/div\u003e\u003cp\u003eShijiazhuang presents a third trajectory, infrastructure-led, health commission-directed smart health expansion, grounded in strong policy alignment with national and provincial digital health plans. The city\u0026rsquo;s dual-level health information platform has enabled city-wide interoperability across 45 second-tier and tertiary hospitals, linking electronic health records, e-prescription flows, and an emergency medical record system utilized in all 43 emergency sites. A notable focus has been the integration of women\u0026rsquo;s and children\u0026rsquo;s health services through the Women and Children Medical Consortium platform, which facilitates bidirectional referrals and patient data sharing across primary and specialist facilities. Importantly, public-facing services such as \u0026ldquo;Healthy Shijiazhuang\u0026rdquo; and the city\u0026rsquo;s health portal expand access to digital booking, consultation, and maternal services. According to a senior official at the city\u0026rsquo;s Health Commission, \u003cem\u003e\u0026ldquo;Our goal is simple\u0026mdash;equal access. Digital tools help us cover rural and urban patients with the same continuity of care\u0026rdquo; (Interview, Deputy Director, Shijiazhuang Health Commission)\u003c/em\u003e. Governance here is more centralized, with strong emphasis on technical standardization, data security (including SM2/SM4 encryption protocols), and inter-sectoral data exchange (e.g., mental health data shared with public security). Yet, limitations persist in bidirectional data integration with third-party systems and the interoperability of newly added service modules. Unlike Chengdu\u0026rsquo;s inclusive governance networks or Shenzhen\u0026rsquo;s entrepreneurial experimentation, Shijiazhuang\u0026rsquo;s strength lies in its systematic rollout of standardized digital platforms across public hospitals and community-level institutions. Its focus is less on cutting-edge innovation and more on ensuring that no node in the system remains disconnected.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Cross-case Synthesis: Divergent Pathways, Shared Goals\u003c/h2\u003e\u003cp\u003eAcross the four cities examined, it is clear that digital health has become a central part of local urban governance agendas, but how it is understood and implemented varies widely. All cases share a common ambition: to improve the reach, efficiency, and responsiveness of health services by integrating digital tools. Yet, the institutional choices and operational logics they adopt differ in ways that are shaped by local capacities, governance structures, and developmental priorities. Hangzhou and Shenzhen both reflect the influence of broader smart city infrastructure, but they do so in different ways. Hangzhou is more data-centric, focusing on city-wide integration and real-time analytics, while Shenzhen\u0026rsquo;s model grows out of its hospital system, emphasizing internal coordination and clinical efficiency. Chengdu and Shijiazhuang, meanwhile, offer more state-centered approaches. Chengdu places strong emphasis on equity and service inclusivity, building a digital ecosystem that supports both community-level care and industrial development. Shijiazhuang\u0026rsquo;s model is more infrastructural and technocratic in tone, prioritizing standardized roll-out and ensuring connectivity across public institutions, especially in second-tier contexts where foundational systems are still being built.\u003c/p\u003e\u003cp\u003eWhat links these cities is not a shared model, but a shared orientation: digital health is not a side project, it is increasingly embedded into how cities think about managing population health. The diversity lies in the routes they take to get there. A comparative snapshot of these pathways is provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which summarizes each city\u0026rsquo;s core focus, governance approach, and notable innovations.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative Overview of Governance Models in Smart Health Cities\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGovernance Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInnovation Highlights\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChallenges\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHangzhou\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlatform-led, hybrid-integrated governance via \u0026ldquo;Digital Brain\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAI-enabled smart agents (e.g., Hang Xiaoyu), city-wide data integration (City Brain \u0026amp; Health Brain), cross-sector applications for elderly/childcare, cloud-based hospitalization, mutual test recognition, and TCM digitalization through Smart Hangzhou TCM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData standardization across systems; sustaining inter-agency coordination\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eShenzhen\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHospital-centered platform governance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5G-enabled remote care, centralized data infrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eService equity; inter-department integration\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChengdu\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGovernment-enabled industrial\u0026ndash;medical alliance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndustrial incubation, integrated care through digital family doctor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCross-sector collaboration strain; pilot sustainability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eShijiazhuang\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealth commission-led standardization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFull-spectrum digital service for city-county users\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData interoperability, new system linkage gaps\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Main Findings\u003c/h2\u003e\u003cp\u003eDespite the differences in local contexts, all four cities in this study share a common strategic ambition. To enhance primary care delivery, achieve cross-sectoral data integration, and improve health system responsiveness through digital innovation. However, the governance pathways they have adopted diverge significantly. These divergences reflect variations in institutional capacity, administrative structures, and developmental priorities. For instance, Hangzhou has developed a platform-led governance model characterized by centralized data infrastructure and top-down coordination, while Shenzhen relies on a hospital-led model built around its clinical network and IT systems. Chengdu emphasizes a government-enabled, equity-driven approach that integrates health and industrial development, whereas Shijiazhuang focuses on standardized infrastructure rollout under the direction of the municipal health commission.\u003c/p\u003e\u003cp\u003eThese cities differ not only in governance orientation and geographic location but also in population size and demographic composition, factors that significantly shape their digital health strategies. Megacities like Chengdu and Shenzhen, each with populations exceeding 15\u0026nbsp;million, face considerable challenges in scaling digital health services across highly mobile and socially diverse communities. Their governance approaches often emphasize interoperability, real-time analytics, and service coordination at scale. In contrast, mid-sized cities such as Hangzhou and Shijiazhuang, with populations closer to 10\u0026ndash;12\u0026nbsp;million, focus on building foundational infrastructure and ensuring service equity across fragmented urban\u0026ndash;rural landscapes.\u003c/p\u003e\u003cp\u003eThese differences are not merely managerial in nature. They reflect deeper structural dynamics, such as urban\u0026ndash;rural population ratios, internal migration intensity, and aging population profiles, that influence local policy priorities. For example, Chengdu\u0026rsquo;s higher proportion of elderly residents and its strong emphasis on public service inclusivity have prompted investments in integrated, community-oriented digital care. Meanwhile, Shenzhen\u0026rsquo;s large non-registered migrant population has led to hospital-centered innovations aimed at improving throughput and clinical efficiency. These demographic and systemic conditions help explain why different cities adopt distinct governance and innovation pathways, even when pursuing shared national agendas like Healthy China 2030 and Digital China.\u003c/p\u003e\u003cp\u003eSuch variation aligns with existing literature that emphasizes the context-dependent nature of digital health implementation (Greenhalgh et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)(Wherton et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Governance structures, population profiles, and institutional legacies condition what is possible, for whom, and at what speed. Therefore, while all four cities pursue agendas, their implementation logics, from platformization to hospital-centrism to infrastructural standardization, diverge in response to both structural constraints and local opportunities.\u003c/p\u003e\u003cp\u003eThese findings support calls in the urban health governance literature for more adaptive, differentiated strategies that account for local capacities and demands(Webb et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Rather than converging on a single digital governance model, Chinese cities are navigating multiple, context-sensitive trajectories that reflect a broader pattern of institutional experimentation under central policy guidance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Emerging Governance Typologies in China\u0026rsquo;s Smart Health Cities\u003c/h2\u003e\u003cp\u003eAs the comparative analysis demonstrates, Chinese cities are experimenting with divergent governance pathways in their pursuit of smart health integration. These efforts reveal not only institutional variation but also evolving typologies of governance logic. We identify three emerging models: platform-driven integrated governance, institution-anchored service ecosystems, and government-enabled multi-stakeholder collaboration, each exemplified by a distinct city case. These typologies reflect differences in administrative capacity, digital maturity, and policy priorities, offering a conceptual lens to analyze smart health governance beyond technological deployment.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Type I: Platform-Driven Integrated Governance\u003c/h2\u003e\u003cp\u003eThe platform-driven model, exemplified by Hangzhou, places a powerful, centralized digital platform at the core of governance. Rather than serving merely as a technical tool, the platform functions as a coordinating architecture that structures cross-sectoral interactions and institutional behavior. This model is characterized by strong top-down coordination, unified data standards, and real-time data exchange capabilities. In Hangzhou, this is operationalized through the \u0026ldquo;City Brain\u0026rdquo; and \u0026ldquo;Health Brain\u0026rdquo; infrastructures, which support not only inter-agency data flows but also vertical-horizontal integration across administrative levels and service domains.\u003c/p\u003e\u003cp\u003eGovernment actors play a leading role in this model, not only initiating digital infrastructure but also formulating technical standards and overseeing coordination among various stakeholders. Platform governance thus becomes both an enabler and a shaper of institutional interaction. This approach is particularly suited to cities with high digital readiness, robust local tech industries, and political commitment to centralized orchestration. Its core strengths lie in its capacity to achieve comprehensive data integration, enable complex decision-making, and embed health objectives within broader urban governance agendas. Notably, this aligns with the \u0026ldquo;Health in All Policies\u0026rdquo; paradigm, implemented through a digital backbone.\u003c/p\u003e\u003cp\u003eHowever, the model also faces substantial challenges. It demands significant up-front investment and ongoing maintenance costs. Failure to enforce interoperability standards risks creating new digital silos. Moreover, excessive reliance on technology can lead to techno-determinism, where efficiency is prioritized over equity or user experience. Sustaining long-term cross-sectoral coordination is difficult, and the centralization of data raises concerns around privacy, security, and surveillance. Ultimately, the success of this model depends not only on technological sophistication but on institutional mechanisms that address governance, ethical, and user-centric concerns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Type II: Institution-Anchored Service Ecosystems\u003c/h2\u003e\u003cp\u003eThe second governance type, observed in Shenzhen (Luohu) and Shijiazhuang, is defined by the anchoring role of established public institutions\u0026mdash;typically large hospital groups or municipal health commissions\u0026mdash;in spearheading digital health integration. Unlike the platform-led model that seeks to restructure governance city-wide, this institution-anchored approach is more incremental, focusing on optimizing internal workflows, enhancing clinical efficiency, and extending services through existing institutional networks. In Shenzhen\u0026rsquo;s Luohu District, the hospital group leads the integration of digital tools, deploying technologies such as 5G-enabled remote diagnostics and centralized clinical data repositories to improve service coordination across hospitals and community health centers. Similarly, Shijiazhuang\u0026rsquo;s health commission oversees a city-wide rollout of digital platforms designed to standardize services across secondary and tertiary facilities, particularly in maternal and child health. In both cases, digital governance remains embedded within pre-existing institutional structures, with innovation efforts tailored to internal capacity-building rather than systemic transformation.\u003c/p\u003e\u003cp\u003eThis model is particularly suited to cities where public health institutions possess strong administrative authority, adequate resources, and the capacity to lead innovation. Its advantages include leveraging institutional credibility, clinical expertise, and established patient networks to enhance service quality within defined ecosystems. Moreover, targeted digital upgrades can yield rapid efficiency gains and improve patient experiences without requiring wholesale structural reform. Nonetheless, several limitations constrain the broader transformative potential of this model. The most prominent challenge is the risk of data silos emerging between institutions, especially when interoperability with external systems is not prioritized from the outset. Additionally, services may remain largely confined within institutional boundaries, raising equity concerns for populations outside these networks. The model may also lack the agility needed to address more complex, cross-sectoral health issues that require broader systemic coordination. Furthermore, the direction of digital innovation may be shaped primarily by the institutional priorities and resources of leading actors, potentially limiting the diversity and inclusivity of solutions. Overall, while the institution-anchored model offers a pragmatic pathway for strengthening service ecosystems, particularly in cities with mature healthcare institutions, it must consciously build outward interoperability and equity considerations into its design to avoid entrenching institutional fragmentation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3 Type III: Government-Enabled Multi-Stakeholder Collaboration\u003c/h2\u003e\u003cp\u003eThe third governance model, as exemplified by Chengdu (Wuhou District), is characterized by the government acting not as a direct controller or platform operator, but as an enabler and orchestrator of a collaborative innovation ecosystem. This model prioritizes public value creation, health equity, and industrial development through the facilitation of partnerships across government agencies, public health institutions, digital technology firms, and academic research organizations. At the heart of Chengdu\u0026rsquo;s strategy is a deliberate state-enabled \u0026ldquo;dual chain-leader\u0026rdquo; mechanism, where public sector institutions, such as West China Hospital, collaborate with major technology companies like Huawei to co-lead digital health development. Anchored in the Shuanghua Digital Health Industrial Park, this model integrates policy support, shared infrastructure investment, and institutional alignment to foster a vibrant innovation ecosystem. Core platforms, such as the Wuhou Family Doctor system, exemplify how digital tools can be designed not merely for efficiency, but for equitable access, community-based care, and inclusive service delivery.\u003c/p\u003e\u003cp\u003eThis governance mode reflects a shift toward networked, participatory urban health governance. Rather than focusing exclusively on technological advancement, the model centers on enabling conditions for multi-stakeholder co-creation. Digital interventions are framed as tools for achieving broader societal goals, including reducing health disparities, strengthening local innovation capacity, and embedding person-centered approaches into health system design. It aligns with people-centered care (PCC) and the social determinants of health (SDoH) paradigms, using digital tools to operationalize these frameworks in ways that are tailored to local contexts. The advantages of this model are manifold. It allows for the pooling of diverse expertise across sectors, fostering cross-boundary innovation and contextualized solutions. It has the potential to enhance local economic development through the incubation of health-tech startups while simultaneously improving the inclusiveness and responsiveness of public health services. Moreover, by grounding digital health initiatives in collaborative governance, it builds systemic resilience and adaptive capacity. However, the model also faces critical challenges. Managing complex partnerships requires substantial coordination effort, and sustaining the momentum of collaboration over time demands consistent political leadership and institutional commitment. Pilot projects may struggle to scale beyond demonstration zones without structural mechanisms for long-term integration. Conflicts may arise between public interest goals and private sector profit imperatives, particularly when governance frameworks for data ownership, transparency, and accountability are underdeveloped. Ultimately, the success of this model hinges on the state\u0026rsquo;s ability to maintain a strategic steering role\u0026mdash;empowering stakeholders without abdicating oversight\u0026mdash;and on the existence of governance mechanisms that align divergent interests toward shared public health objectives. This government-enabled collaborative model is particularly suitable for cities with the institutional maturity and political will to coordinate across sectors, and where smart health is envisioned not only as a technological project, but as a vehicle for public value creation and inclusive development.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative Typologies of Smart Health City Governance in China\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernance Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCore Logic \u0026amp; Mechanism\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRepresentative Case(s)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTypical Context\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKey Strengths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKey Limitations / Challenges\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlatform-Driven Integrated Governance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCentralized digital platforms serve as core infrastructures for data integration, analytics, and service coordination. Emphasizes top-down control, standardization, and cross-sector interoperability. The platform itself acts as a governance instrument.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHangzhou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCities with strong digital infrastructure, advanced technological capacity, and high willingness and ability for centralized coordination.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh potential for integrated data systems and complex decision-making; boosts systemic efficiency; enables \u0026ldquo;Health in All Policies\u0026rdquo; implementation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh upfront and maintenance costs; risk of data silos; potential for techno-determinism; difficulty in sustaining cross-agency coordination; concerns over data privacy and surveillance.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInstitution-Anchored Service Ecosystems\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLed by major public institutions (e.g., hospital groups, health commissions), this model focuses on optimizing internal clinical processes and service coordination, with digital tools serving institutional modernization and targeted expansion.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShenzhen (Luohu), Shijiazhuang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCities with capable, resource-rich public health institutions acting as innovation anchors; focused on upgrading existing health service systems.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLeverages institutional strengths and networks; enables rapid efficiency gains within existing ecosystems; high feasibility due to narrower scope.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRisk of institutional data silos; equity challenges if benefits are confined to institutional networks; limited agility for addressing cross-sectoral issues; innovation may be constrained by internal priorities and resources.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGovernment-Enabled Multi-Stakeholder Collaboration\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGovernment acts as coordinator and enabler, fostering partnerships across public institutions, industry, and academia to co-develop digital health solutions. Emphasizes public value, health equity, and inclusive innovation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChengdu (Wuhou)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCities aiming to promote both health equity and digital health industry development; capable of managing complex partnerships and stakeholder dynamics.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCatalyzes cross-sector innovation; supports local digital health industry growth; generates context-specific, inclusive solutions; builds more resilient and adaptive ecosystems.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh coordination burden; challenges in sustaining pilot projects and scaling; potential conflict between public interest and private incentives; depends on strong and sustained governmental leadership.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Implications for Policy Adaptation\u003c/h2\u003e\u003cp\u003eThis study highlights that smart health city development in China is not characterized by convergence toward a single model, but by diverse, context-sensitive trajectories. Cities often exhibit hybrid features or transition from one governance type to another in response to shifting priorities and capacities. This dynamic and experimental nature of governance reflects ongoing adaptation to both local contextual realities and evolving national policy mandates. The typologies identified: platform-based, institution-anchored, and government-enabled collaborative models, demonstrate that there is no one-size-fits-all solution. Instead of attempting to replicate \u0026ldquo;successful\u0026rdquo; cities wholesale, policymakers should focus on aligning governance strategies with local administrative capacity, demographic structure, health needs, and levels of digital maturity. For example, Shenzhen\u0026rsquo;s entrepreneurial, hospital-led logic may suit cities with robust healthcare institutions and IT infrastructure, while Chengdu\u0026rsquo;s equity-oriented, government-facilitated model may resonate in regions prioritizing public service delivery and community-based care. However, in the absence of effective inter-agency coordination and shared data standards, even well-funded interventions risk entrenching fragmentation rather than promoting integration.\u003c/p\u003e\u003cp\u003eContextual fit remains a key determinant of the success or failure of digital health reforms. Given the deeply embedded nature of health systems in sociopolitical, institutional, and economic structures, transferring governance models across cities or countries faces numerous challenges, including compatibility with legacy infrastructure, return-on-investment uncertainties, and institutional inertia. China\u0026rsquo;s centrally coordinated yet locally adaptive approach, shaped by its state-led innovation ecosystem, creates a distinctive institutional environment where even intra-national model replication is far from straightforward.\u003c/p\u003e\u003cp\u003eThe development of smart health cities is not a one-time transition but a continuous process of experimentation, adaptation, and institutional learning. Effective policy adaptation requires the creation of feedback loops and iterative mechanisms to evaluate, revise, and recalibrate strategies. This points to the need for performance measurement frameworks, such as digital health maturity indices or governance capacity assessments\u0026mdash;that can support evidence-based evaluation and peer learning across cities.\u003c/p\u003e\u003cp\u003eCities should critically assess and contextualize external experiences rather than adopt models uncritically. The goal is not to locate a perfect governance template ex ante, but to cultivate the capacity to evolve, absorb lessons, and respond flexibly to emerging challenges. This aligns with recent scholarship emphasizing adaptive governance as a pathway to institutional resilience and long-term effectiveness(Chaffin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Limitations and Future Research\u003c/h2\u003e\u003cp\u003eThis study is limited by its reliance on official documents and elite interviews, potentially overlooking the lived experiences of patients, frontline workers, and marginalized communities. It focuses on city-level, formal governance structures, without fully capturing informal innovation or user-led practices. Future research should adopt bottom-up approaches to incorporate citizen perspectives and assess real-world system usability and equity impacts. Comparative international studies are also needed to explore how political, economic, and cultural contexts shape smart health governance trajectories. As digital health tools increasingly integrate AI, future inquiries must also address emerging ethical concerns. Ultimately, smart health cities are not merely technical systems, but socio-political projects whose long-term success hinges on their ability to deliver inclusive, scalable, and context-sensitive improvements in population health.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYin Zhang:\u003c/strong\u003e Writing \u0026ndash; original draft; \u003cstrong\u003eXi WANG:\u003c/strong\u003e Writing \u0026ndash; original draft, review \u0026amp; editing,\u0026nbsp;Methodology;\u0026nbsp;\u003cstrong\u003eAyan MAO, Pei DONG, Yueli MENG, Shuai DU, Minjie ZHAO\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eInvestigation \u0026amp; Fieldwork; \u003cstrong\u003eWuqi QIU, Meng WANG, Jintao LI:\u003c/strong\u003e Funding acquisition, Resources, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.Commissioned research project by Huawei Technology Co., Ltd.: Research on the Technical Framework and Applications of Smart Healthy Cities; 2. Commissioned research project by Office of Healthy Hangzhou Construction Leading Group / Hangzhou Municipal Health Commission: Research on the Current Situation and Development Strategies of Smart Health City Construction in Hangzhou\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbernethy, A., Adams, L., Barrett, M., Bechtel, C., Brennan, P., Butte, A., Faulkner, J., Fontaine, E., Friedhoff, S., \u0026amp; Halamka, J. (2022). 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Healthy China 2030: A vision for health care. \u003cem\u003eValue in Health Regional Issues\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 112\u0026ndash;114.\u003c/li\u003e\n \u003cli\u003eTownsend, A. M. (2013). \u003cem\u003eSmart cities: Big data, civic hackers, and the quest for a new utopia\u003c/em\u003e. WW Norton \u0026amp; Company.\u003c/li\u003e\n \u003cli\u003eWebb, R., Rissik, D., Petheram, L., Beh, J.-L., \u0026amp; Stafford Smith, M. (2019). Co-designing adaptation decision support: Meeting common and differentiated needs. \u003cem\u003eClimatic Change\u003c/em\u003e, \u003cem\u003e153\u003c/em\u003e, 569\u0026ndash;585.\u003c/li\u003e\n \u003cli\u003eWherton, J., Sugarhood, P., Procter, R., Rouncefield, M., Dewsbury, G., Hinder, S., \u0026amp; Greenhalgh, T. (2012). Designing assisted living technologies \u0026lsquo;in the wild\u0026rsquo;: Preliminary experiences with cultural probe methodology. \u003cem\u003eBMC Medical Research Methodology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 188. https://doi.org/10.1186/1471-2288-12-188\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Smart healthy city, Healthy city, Smart city, Digital governance, urban planning","lastPublishedDoi":"10.21203/rs.3.rs-7491675/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7491675/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs digital infrastructure increasingly reshapes urban governance, Chinese cities are exploring diverse pathways to integrate health systems with smart city technologies. This study selected four typical cities in China, Hangzhou, Shenzhen, Chengdu and Shijiazhuang, which are constructing smart healthy cities through distinct governance models. Using case studies, interviews, and policy analysis, three emergent types of digital health governance are identified: platform-led integrated systems, institution-anchored service ecosystems, and government-enabled multi-stakeholder collaborations. Hangzhou exemplifies a platform-centered model, leveraging city-wide digital infrastructure for cross-sectoral coordination. Shenzhen demonstrates a hospital-led strategy that enhances clinical efficiency through integrated digital platforms. Chengdu adopts a government-facilitated, equity-oriented approach linking industrial innovation and community-based services. Shijiazhuang illustrates a technocratic model prioritizing standardized digital rollout in second-tier contexts. Despite differences in institutional design, population profile, and digital maturity, all cities aim to enhance service accessibility, data interoperability, and system responsiveness. Rather than converging on a single model, cities navigate context-specific trajectories shaped by local governance capacity, policy priorities, and demographic demands. The study argues for an adaptive, modular approach to policy transfer, focusing not on wholesale replication but on identifying transferable components such as data standards, inter-agency platforms, and inclusive digital tools. This multi-case analysis contributes to the understanding of digital health integration in urban settings and provides conceptual and practical insights for shaping smart health city development in China and comparable international contexts.\u003c/p\u003e","manuscriptTitle":"Governance and Innovation in Building Smart Healthy Cities: Multi-city evidence from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-10 10:54:27","doi":"10.21203/rs.3.rs-7491675/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09cb9610-5122-49cb-8d8d-38b5d7c64e3d","owner":[],"postedDate":"September 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-01T16:35:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-10 10:54:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7491675","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7491675","identity":"rs-7491675","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}