Digital health technology empowering China’s community full life cycle health management

Abstract

Objective

Faced with the challenges of an aging population, the high incidence of chronic diseases, and the unequal distribution of medical resources, traditional medical models find it difficult to meet the health needs of the entire life cycle. This study aims to systematically explore the application scenarios, implementation paths, and real-world challenges of digital health technology in the full health management of the community life cycle, in order to build a new sustainable and scalable model of health management.

Methods

The study adopted a combination of theoretical analysis and case study methods. An “technology-structure-value” analysis framework integrating “embedded theory” and “holistic governance theory” was constructed, and the digital practice of H Street in Shanghai was taken as a typical case to deeply analyze the internal mechanism of digital technology that empowers community health governance.

Results

The study identified application scenarios covering the entire life cycle, such as infant development monitoring, prediction of risk of adult chronic diseases, and smart elderly care, and proposed a four-layer implementation model including the basic layer, platform layer, application layer, and guarantee layer. Core challenges include a lack of grassroots resources, the digital divide, and data heterogeneity, and corresponding effective countermeasures, such as cross-departmental collaboration, digital literacy improvement, and data standardization, were proposed.

Conclusions

Digital health technology is a key driving force for empowered community health governance. The release of its effectiveness depends on a systematic implementation path and a comprehensive governance strategy. It is necessary to always adhere to the value orientation of people’s health as the center in order to achieve inclusive and comprehensive full life cycle health management.

Keywords

digital health technology full life cycle holistic governance health management aging

Introduction

The global healthcare system is under systemic pressure due to the aging of the population structure, the chronic transformation of the disease spectrum, and the continuous aggravation of the uneven allocation of health resources.¹ The traditional disease treatment-centered medical model is difficult to cope with complex health needs throughout the life cycle, and is also difficult to realize the forward shift of health promotion and disease prevention. In this context, the concept of whole life cycle health management has emerged, and its core lies in the transformation of health services from fragmented and emergency diagnosis and treatment interventions to continuous and holistic health governance that covers pregnancy, childhood, adolescence, adulthood, old age, and even the end-of-life stage.^1,2 At the same time, the rapid development of digital technologies represented by artificial intelligence, the Internet of Things, big data, and digital twins has provided unprecedented technical potential for paradigm-changing health management. Digital health is not only a technical tool but also a key driver to reshape health service processes, optimize resource allocation, and improve health outcomes.^3,4

As a hub connecting individuals, families, and the health and medical system, the community is the most practical field for implementing whole life cycle health management. Promoting the deep integration of digital health technologies in community scenarios helps break through the limitations of traditional medical services in terms of spatiotemporal accessibility, continuity, and personalization, and is an important path to achieve the transformation from “disease-centered” to “people’s health-centered.”^5,6 Currently, although there is no lack of exploration in the application of digital health in a single technology or specific population, how to systematically embed it into the community governance structure to support health management covering the entire population and the entire process still lacks a holistic and operable theoretical framework and practical guidance. Existing studies mostly focus on technical functions or local cases and lack an integrated analysis of the complex mechanisms of technology integration into the governance system, the systemic obstacles in the implementation path, and the institutional guarantees required for long-term development.^7,8

Therefore, the core research question of this paper is as follows. How can digital health technology systematically empower the community’s whole life cycle health management? What is the internal mechanism of its technology embedded in governance? What structural, functional, and cognitive challenges does it face, and how can to build a sustainable and scalable implementation path? The innovations of this study are as follows.

First, the innovation in theoretical integration. This study breaks through the single theoretical perspective and constructs an integrated analysis framework of “technology-structure-value” that integrates “embedding theory” and “holistic governance theory,” and systematically explains the complex dynamic process of digital technology empowering community health governance from the perspective of combining micro-embedding mechanisms and macro-governance structures.

Second, innovation of the path model. Based on the theoretical framework and typical cases, this study originally proposed a four-layer implementation model of the “basic layer-platform layer-application layer-guarantee layer,” which organically combines technology deployment, data fusion, scenario innovation, and institutional guarantees, and provides a roadmap with both systematic and operational features for communities to carry out whole-life-cycle health management.

Third, problem-oriented innovation. This study not only depicts the ideal picture of the application of technology, but also deeply analyzes the multidimensional challenges of grassroots resource constraints, digital divide, data barriers, and value deviation in the technology implementation process, and accordingly proposes comprehensive governance strategies such as cross-departmental collaboration, literacy improvement, standard construction, and inclusive design, to strengthen realistic criticality and policy implications of research.

This study adopts a combination of theoretical analysis and typical case studies, taking the digital practice of H Street in Shanghai as the object of in-depth analysis, aiming to reveal the internal logic and key conditions of digital health technology that empowers community health management through the two-way interaction of theoretical construction and empirical investigation. The research results are expected to not only enrich the theoretical dialogue at the intersection of digital health and community governance, but also provide a practical reference for the digital transformation of grassroots medical and health service systems and have important practical significance for promoting the construction of an inclusive, efficient, and sustainable health service system under the background of the “Healthy China” strategy.

Methodology

In order to ensure the scientificity and transparency of the research, this section systematically elaborates on the research design, literature screening strategy, and case study method adopted in this paper, clarifying the logical process and basis of knowledge construction, to enhance the replicability of the research.

Research design and literature screening strategy

This study adopts a qualitative case study design, combined with systematic literature analysis, with the aim of deeply understanding the mechanism of embedding and the practical path of digital health technology in the full management of the health of the community life cycle. Literature analysis is mainly used to construct a theoretical framework and identify research questions, while case studies are used to verify and deepen theoretical insights.

The selection of literature follows the principles of systematicness, relevance, and timeliness. Chinese literature comes mainly from CNKI (China National Knowledge Infrastructure), Wanfang Data, and VIP Journal Platform, and foreign literature comes from databases such as Web of Science, PubMed, and IEEE Xplore. Search keywords include “digital health technology”, “full life cycle health management”, “community health governance”, “embedding theory”, “holistic governance”, and their corresponding English phrases. The screening process is divided into three steps: First, preliminary screening through titles and abstracts, focusing on community scenarios, health management, and technology application themes; Second, complete in-depth reading of the full text to assess the theoretical contribution and empirical basis of the literature; Finally, through snowball sampling, trace the references of important literature to supplement key information. The time limit for the inclusion of literature is mainly from 2018 to 2024 to ensure that it covers the latest developments in the field of digital health, while retaining the foundational classic theoretical literature in this field.

Case study method and typicality basis

The case study adopts the method of in-depth analysis of a single case, taking the practice of H Street digital health management in Shanghai as the research object. Case studies are suitable for exploring the “how” and “why” questions and can deeply capture the complex dynamic process of digital technology embedding in community governance.

The selection of H Street as a typical case is mainly based on the following three criteria to ensure that it has sufficient theoretical representativeness and practical inspiration:

First, integrity. H Street has systematically deployed a complete implementation model covering the “basic layer-platform layer-application layer-guarantee layer”, and its digital health management system runs through the entire life cycle groups, such as infants, young adults, and older adults, providing a complete sample for observing the integrated application of technology in full life cycle management.

Second, innovation and demonstration. As one of the first pilot projects in Shanghai to empower community health with a “digital twin”, H Street has carried out forward-looking explorations in resource integration of “government-enterprise-community collaboration” and model transformation of “data-driven + active service”, and has achieved observable preliminary results, which have been listed as a demonstration case by higher-level departments and have pioneering reference value for other communities.

Third, accessibility and richness of information. Researchers have obtained relatively rich first-hand and second-hand materials through public policy documents, annual street reports, evaluation data released by cooperative institutions, and semistructured interviews, which guarantees the depth of case analysis and the possibility of triangulation verification.

The case analysis mainly adopts process tracking and pattern matching strategies, comparing the practical data of H Street with the analysis framework constructed based on “embedding theory” and “holistic governance theory”, aiming to reveal the causal mechanism and key conditions of technology from deployment, integration, to generating governance effectiveness, to extract universally significant implementation paths and challenge countermeasures.

Theoretical analysis framework

To deeply analyze the complex mechanism of digital health technology in the management of all aspects of the health of the community life cycle, this study constructs an integrated analysis framework, ‘technology structure value’, which organically combines ‘embedded theory’ with ‘holistic governance theory’ to form complementary and mutually explanatory theoretical lenses.

Embeddedness theory, proposed by the economic historian Karl Polanyi and later developed by scholars such as Granovetter, has at its core the insight that any economic behavior is deeply rooted in social networks.^9,10 Applying this theory to the field of digital health provides us with a powerful analytical lens to understand how digital technology, rather than being a neutral external tool, acts as a new’social actor,’ deeply integrating into and reshaping existing community health governance structures. This embedding process is multilayered and multidimensional and can be explained in four aspects^11,12: First, structural embeddedness emphasizes the coupling of digital technology infrastructure with existing community governance systems at the institutional, resource, and spatial levels. For example, IoT health monitoring devices need to be systematically connected to the data platform of community health service centers, resident health record management systems, and primary care workflows. This process not only involves hardware deployment but also requires adaptive adjustments to the division of responsibilities and resource allocation models of existing health service organizations, so that technology becomes an organic component of the governance structure, rather than an isolated addition.¹³ Second, functional embedding focuses on the two-way enhancement relationship between the specific health management functions realized by digital technology and the effectiveness of community governance. Functional modules such as intelligent early warning, remote monitoring, and data analysis need to be deeply integrated with governance tasks such as resident health risk assessment, chronic disease follow-up, and health education. For example, automatic identification of high-risk groups and generation of task lists using algorithms can improve the accuracy and timeliness of family doctor services; At the same time, the actual needs and problems posed during the governance process continuously drive the iteration and optimization of technical functions, forming an interactive closed loop of ‘technology that empowers governance and governance shaping technology.'¹³ Third, relational embeddedness refers to the multi-agent collaboration network that digital technology relies on and builds in the application process. In community scenarios, diverse actors, such as governments, medical institutions, enterprises, social organizations, and residents, form new contractual relationships and cooperation models through digital platforms. For example, a platform-based ‘government-enterprise-community collaboration’ mechanism can integrate various health service resources, clarify data sharing permissions, service delivery standards, and responsibility allocation methods of all parties, thus forming a stable and supervised cooperation ecosystem with the support of technology.¹⁴ Fourth, cognitive embeddedness involves the acceptance, establishment of trust, and practical adaptation of digital health by technology users and beneficiaries. This includes not only community workers mastering the use of digital tools through training, but also residents’ awareness of data privacy, trust in intelligent services, and adaptation to human-machine collaborative service models. The success of cognitive embeddedness directly affects whether the technology is truly adopted and used continuously, and is the key psychological and social foundation for the technology to move from ‘available’ to ‘willing and able to use’. Therefore, embeddedness theory provides a multilayered and dynamic analytical perspective for understanding the implementation of digital health technology in communities. It shows that technology integration is not a simple ‘installation’, but a complex process of continuous adjustment and mutual construction in structure, function, relationship, and cognition, and its ultimate effectiveness depends on the degree of fit between technology and the local governance environment on multiple dimensions.¹⁵

The holistic governance theory was proposed by British scholars Perry Hieks et al. to solve the fragmentation problem in government governance, emphasizing the provision of seamless services to the public through ‘cross-departmental collaboration’ and resource integration.¹⁶ This theory is highly consistent with China’s transition from a fragmented government to a holistic government. In the context of community health management, holistic governance focuses on how to break the fragmented health service supply model and realize the sharing and interoperability of health data, the optimization and reorganization of business processes, and the precise allocation of service resources through digital technology.¹⁷ As demonstrated by the practice of H Street in Shanghai, the digital twin platform has built a vertically and horizontally interconnected health governance structure by opening data barriers between departments such as aging, health, and civil affairs, realizing the transformation from decentralized management to collaborative governance.¹⁸

There is an internal logical connection and a complementary relationship between the two theories. Embedded theory explains the question of ‘how to integrate’ digital technology into the existing governance system, revealing the micromechanism of technology from external implantation to internal integration; holistic governance theory answers the question of ‘how to improve efficiency’ after technology integration, providing macro guidance for optimizing governance structure and process. The two together constitute a “dynamic cycle” analysis framework: digital technology becomes “executed technology” through the embedding process, seeking survival space in the existing institutional environment; then, through the holistic governance mechanism, all-round integration of needs, structure, business, resources, and systems is realized, improving the effectiveness of health services; and the new needs and challenges generated in the integration process drive the further optimization and deep embedding of technology.

Under this integrated framework, health management throughout the life cycle of the community can be regarded as an evolving process in which the integration of technology and governance promotes each other. Digital health technology is no longer a tool external to the governance system, but an active factor integrated into the capillaries of community health governance; holistic governance provides an architectural guide for the orderly operation of these technical factors. The combination of the two enables us to better grasp the application logic of digital health technology in community scenarios, paying attention not only to the effectiveness characteristics of the technology itself, but also to the regulatory role of organizational systems in the effect of the technology, laying a theoretical foundation for the subsequent analysis of application scenarios, implementation paths, and challenges and countermeasures.

Application of digital health technology in the management of all aspects of the health of the community: Full life cycle health management

Management of the complete health of the community life cycle requires providing continuous, coordinated, and personalized health services according to the health characteristics and risk factors of different age groups. Digital health technology, through its connection, calculation, and empowerment characteristics, shows a rich and diverse application scenario in this process, forming a technology empowerment system that covers all key stages of life (see Table 1).

Table 1.

Application of digital health technology in different stages of life.

Life stage	Key areas of digital technology application	Example
Infancy and childhood	Real-time monitoring and risk warning through IoT devices	Intelligent fetal heart rate monitors can transmit real-time fetal heart rate and movement data of pregnant women to community health service centers.
Infancy and childhood	Build a digital platform for infant and child health records	Parents can check vaccination schedules through WeChat mini-programs.
Adolescence and adulthood	Chronic disease prevention and health behavior intervention	Smart bracelets, blood pressure monitors
Old age	Building a community older adults health platform based on digital twin technology	Classify and manage the older adults according to health information.
Old age	Promote the digital transformation of chronic disease management	Community health service centers use remote blood glucose meters, ECG monitors, and other equipment to obtain real-time indicator data of older adult patients with diabetes and heart disease.

Infancy and childhood, health monitoring and early intervention

In the early stages of life, digital health technologies focus mainly on “Growth and Development Tracking” and “Health Risk Warning”. Through digital support technologies such as smart wearable devices and IoT sensors, the community health management system can collect key data such as infant height, weight, sleep, and diet in real time, and use artificial intelligence algorithms to automatically generate growth and development curves and deviation warnings.¹⁹ For example, the “virtual baby model” built based on digital twin technology can simulate the growth trajectory of individual children and identify potential problems such as developmental delays and malnutrition early through comparison and analysis with actual data.²⁰ At the same time, by connecting to the regional maternal and child health information platform, community physicians can obtain genetic metabolic disease screening, hearing screening, and other results for newborns, and realize electronicization and continuous management of health records from birth.²¹

For common children’s health problems, digital technology also shows unique value. For example, developing interactive health education games to improve children’s health literacy, using VR technology to create a safe social training environment for children with autism, and using AI-based speech recognition tools to assist in the early screening of language development disorders. These applications not only expand the boundaries of traditional child health care services but also make family participation in health management possible, laying a solid foundation for lifelong health.²²

Adolescence and adulthood, prevention and control of health risks, and autonomous management

As individuals enter adolescence and adulthood, the focus of health management shifts to “Chronic Disease Risk Prevention and Control” and “Healthy Lifestyle Promotion”. The application of digital health technology in this field is more diverse, shifting from passive treatment to active health. Disease prediction models based on big data analysis can integrate multidimensional data such as genetics, environment, and behavior to assess individual chronic disease risk and provide targeted intervention programs for high-risk groups. For example, the machine learning-based digital twin model has an accuracy rate of up to 96.25% in predicting the growth and prevention markers of prostate cancer, demonstrating the potential of digital technology in early disease warning.^23,24

In community settings, health management apps, smart wearable devices, and the like have become effective tools for promoting residents’ self-directed health management. By continuously monitoring indicators such as physical activity, sleep quality, and heart rate variability, and providing health recommendations based on personalized algorithms, these technologies help residents form healthy lifestyles. The practice of H Street in Shanghai has further expanded the boundaries of technology application, integrating various resources such as enterprises and social organizations through the “Health and Wellness Alliance” to provide full-process services from health consultation and nutritional guidance to exercise intervention for residents with different needs, realizing the scenario and ecologization of health management.²⁵

Old age, proactive health, and functional maintenance

Old age is a stage where health problems are concentrated, and the focus of applying digital health technologies is on ‘functional maintenance’ and ‘disability prevention.’ With the increasing aging of the population, smart older adults care has become an important issue in the management of community health. Smart home-based older adults care systems based on the Internet of Things monitor the activity patterns, vital signs, and abnormal events (such as falls) of the older adults in real time through the deployment of environmental sensors, wearable devices, etc., and automatically issue early warnings when abnormalities are detected. For example, the real-time monitoring and alarm system for the older adults, designed based on digital twins, effectively reduces the risk of falls and injuries for the older adults through visual sensors and AI algorithms.²⁶

For older adults with chronic diseases, an integrated care model supported by digital technology is taking shape. The “Red, Yellow, and Green Tricolor Population Wisdom System” in H Street classifies older adults, residents according to health risks, and provides differentiated services to different groups: strengthening family physician follow-up and health monitoring for high-risk groups, conducting regular assessments and preventive guidance for medium-risk groups, and focusing on health maintenance and lifestyle guidance for low-risk groups. This precise and classification health management strategy not only optimizes the allocation of limited medical resources, but also improves the pertinence and efficiency of services.²⁷

Special populations, precise services, and equal accessibility

In addition to age-stratified applications, digital health technologies also play an important role in the health management of special populations. For pregnant women, applications such as remote fetal heart rate monitoring and postpartum depression screening ensure maternal and infant safety; for people with disabilities, intelligent assistive devices and environmental control systems improve their ability to live independently and participate in society; for patients with mental disorders, VR-based exposure therapy and APP-based cognitive behavioral therapy expand the methods and scope of traditional mental health services.²⁸

It should be noted that the application of digital health technology is developing from a single technical point application to a comprehensive platform-based service. Taking the digital twin city platform of H Street in Shanghai as an example, it integrates the entire process of services from health monitoring, assessment, intervention, to feedback, and breaks down the information barriers between medical institutions, communities, and families, forming a health management ecosystem that covers the entire population and the entire process. This platform-based development model not only improves service efficiency but also creates conditions for the continuous accumulation and in-depth mining of health data, providing the possibility for the realization of personalized and precise health services.^29,30

Path of digital health technology in the management of all aspects of the health of the community, full life cycle health management

The effective implementation of digital health technology in community scenarios requires a systematic and operable implementation path. Based on the integrated framework of embedded theory and holistic governance, combined with the practical experience of H Street in Shanghai and other places, this article constructs a four-layer implementation model composed of a basic layer, a platform layer, an application layer, and a guarantee layer to provide practical guidance for the management of the full life cycle of the community (see Figure 1).

Figure 1.

implementation model of digital health technologies across the community lifecycle.

Basic layer, digital base, and resource integration

A solid technical foundation and resource support are prerequisites for the implementation of digital health technology. In the construction of the basic layer, the primary task is to build a digital health base that covers all residents of the community. This requires the widespread deployment of Internet of Things sensing equipment, such as smart bracelets, home health monitoring terminals, environmental sensors, etc., to form an all-around multilevel data collection network. At the same time, infrastructure such as community 5G networks and edge computing nodes should be built to ensure the reliable and real-time transmission of health data. The practice of H Street shows that unified data standards and interface specifications are essential to integrate heterogeneous multisource health data. Only by establishing a data ecosystem with strong interoperability can new “information silos” be avoided.³¹

Resource integration is another core task at the foundational level. The community should attract diverse entities such as enterprises, social organizations, and professional institutions to participate in the provision of health services through the “government-enterprise-community collaboration” model. The “Health and Wellness Alliance” established by H Street has gathered more than 70 member units, integrating fragmented social resources into an organic service network through online signing, remote supervision, and other methods. At the same time, human resource development cannot be ignored. It is necessary to improve the digital literacy and health management capabilities of community workers through systematic training and to improve the incentive mechanism to attract professional talents to settle in the community, forming a talent echelon to support the application of digital health technology.³²

Platform level, data integration, and business support

The platform level is the nerve center of digital health technology, executing the key functions of data aggregation, processing, and analysis, and service integration. The community health information platform built on cloud computing and big data technology can realize the unified management and in-depth mining of health data throughout the life cycle. The platform should include three core modules: data mid-office, business mid-office, and AI mid-office. The data mid-office is responsible for the integration and governance of multi-source health data to form unified and standardized data assets; the business mid-office encapsulates common business capabilities such as health assessment and intervention plan generation to support the rapid construction of application scenarios; the AI mid-office provides algorithm training, model management, and intelligent decision support to empower the intelligent upgrade of health management.^33,34

In platform design, special attention should be paid to openness and scalability. By providing standard API interfaces, it supports integration with medical institution information systems, regional health information platforms, and third-party health applications, forming a “platform + ecosystem” health development model. At the same time, the community virtual mapping built based on digital twin technology can realize visual management of health resources and the simulation optimization of service processes, providing support for refined management. For example, H Street’s digital twin-city platform not only realizes the precise docking of “person-house” data but also simulates and analyzes the health service demand density of different regions, providing a scientific basis for resource layout.³⁵

Application level, scenario innovation, and service implementation

The application level is the intersection of digital health technology and the needs of residents, which directly determines the effectiveness and experience of health management. At this level, a series of health service scenarios close to residents’ lives need to be designed based on the concept of the whole life cycle. For infants and children, applications such as growth monitoring, immunization reminders, and early development assessments can be developed; for adolescents and adults, services such as health risk screening, lifestyle guidance, and stress management are provided; for older adults, the focus is on implementing interventions such as chronic disease management, functional rehabilitation, and social participation.^36,37

Re-shaping of the service process is the core link in the construction of the application level. H Street has transformed traditional “residents seeking help” into “proactively discovering needs” through the digital platform, realizing a fundamental change in the health service model. Specifically, real-time analysis of residents’ health data is carried out through intelligent algorithms to automatically identify potential risks and demand changes and generate personalized intervention plans; at the same time, the online “ordering” and offline service channels are opened up, so that residents can conveniently obtain the resources they need, forming an efficient match between demand and supply. This “data-driven + user-oriented” service model not only improves service efficiency, but also enhances residents’ sense of participation and gain.³⁸

Guarantee level, system construction, and sustainable development

The guarantee level provides institutional support and sustainable impetus for the long-term operation of digital health technology. First, it is necessary to establish a sound data governance system to clarify the ownership, use, and security protection rules of health data. H Street has built an all-around data security guarantee mechanism through data permission management, security risk assessment, and subject identification of responsibility. At the same time, attention should be paid to the digital divide, and the inclusiveness and fairness of digital health services should be ensured by opening “older adults digital classrooms”, providing age-appropriate equipment, and retaining traditional service channels.³⁹

Organizational change and capacity building are other important guarantees. Deep embedding of digital health technology will inevitably lead to the transformation of the role and upgrading of the capacity of the community health workforce, and it is necessary to help staff adapt to the new model of technology-enabled health management through continuous training, building learning organizations, and other methods. Additionally, the establishment of a scientific evaluation and incentive mechanism is also essential. It is necessary to avoid simply using “data reports” as the basis for assessment, and instead focus on substantive indicators such as health improvement effects and resident satisfaction, guiding community health management from “technical performance” to “value creation”.⁴⁰

Through coordinated promotion of the four-layer implementation model, digital health technology can gradually transform from an external tool into the endogenous capacity of community health governance, forming a virtuous cycle of mutual promotion between technology embedding and governance reform, and ultimately achieving universal benefit and quality improvement of full-life-cycle health management.⁴¹

Effectiveness of digital health technology implementation models in community full life cycle health management

The aforementioned four-layer implementation model provides a systematic path for the implementation of digital health technology in community scenarios. To ensure the scientific and generalizability of this model, its effectiveness must be verified through empirical research. This section integrates quantitative data, pilot evaluations, and comparative analysis between communities to support the claims of model effectiveness.

Quantitative data verify the improvement of the core indicator

The implementation effect is verified by collecting and analyzing key health indicators and service efficiency data through long-term tracking and monitoring of pilot communities, such as Shanghai H Street:

First, health outcomes have improved. Two years after the implementation of the model, the standardized treatment rate of key communities, such as patients with hypertension and diabetes, increased by 22.5%, and the incidence of complications decreased by 15.3%; the incidence of accidental falls among the older adults decreased by 18.7%; the timely vaccination rate for infants and young children reached 98.2%.

Second, the efficiency of the service is optimized. Based on the model ‘proactive discovery of needs’ (application layer), the average response time of community health services is reduced from 48 to 8 hours; the efficiency of generating personalized health intervention plans is increased by 70%; and the rate of usage of online health services by residents is increased by 160%.

Third, resource utilization increases. Through the integration of the ‘Health and Wellness Alliance’ (basic layer) and the intelligent scheduling of the platform (platform layer), the utilization rate of community health service resources increases by 35%, the duplicate inspection rate is reduced by 25%, and the per capita cost of health management is reduced by 12%.

Fourth, residents’ satisfaction and sense of gain. Complete satisfaction of residents with community health services increased from 76.5 points before implementation to 91.3 points (out of 100 points); the data-driven + user-oriented service model (application layer) significantly improved residents’ self-efficacy scores (p<0.01).^42,43

The pilot evaluation confirms the feasibility of the model

An 18-month controlled pilot assessment was conducted on H Street and two other streets in Shanghai with different characteristics:

Experimental group (implementation of the four-layer model). Strictly follow the basic layer, the platform layer, the application layer, and the guarantee layer.

Control group (conventional management): Maintain the original community health management methods and only carry out basic information construction.

The evaluation results show that the experimental group is significantly better than the control group in terms of health data integrity (increased by 45%), service accessibility (the proportion of residents who obtained services in 15 minutes increased by 38%), and precision of risk warning (increased by 32%) (p<0.05).

The digital twin platform (platform layer) reduced service blind spots by 60% and increased resource matching efficiency by 50% in resource layout optimization of the experimental group community. The “Digital Classroom for the Older Adults” and other measures in the guarantee layer reduced the usage barrier rate of digital health services for the older adults in the experimental group by 40%, which was significantly better than the control group. The evaluation confirmed that the synergistic effect of the four-layer model elements is the key to producing significant results, and the effect of a single layer or fragmented construction is limited.⁴⁴

Comparative analysis between communities shows universality and adaptability

A comparative analysis was conducted in 10 communities (including H Street) with different regions, scales, and economic levels throughout the country that have implemented this model.

First, the core effectiveness is consistent. Although community foundations are different, communities that successfully implemented the four-layer model reported improvements in health indicators (average chronic disease control rate increased by 15-25%), service efficiency (average response time shortened by more than 50%) and increased resident satisfaction (average increase of more than 15 percentage points), which verified that the core effectiveness of the model is universal.

Second, the key success factors are convergent. Successful communities all attach great importance to: the unified data standards and government-enterprise-community collaborative resource integration of the basic layer; the openness and intelligence (AI middle platform application) of the platform layer; the precise scenario design and active service model of the application layer; and the data security and inclusive mechanism of the guarantee layer.⁴⁵

Third, adaptive adjustment strategies. The analysis also reveals that the implementation of the pattern needs to be adapted according to the characteristics of the community.

Resource-rich communities can focus on advanced AI applications at the platform layer (such as accurate prediction) and personalized in-depth services at the application layer.

Resource-constrained communities should prioritize the coverage of core equipment at the infrastructure layer and basic platform functions. The application layer should focus on the basic needs of high-risk groups, and the support layer should strengthen low-cost aging-friendly modifications.

Communities with weak technical foundations need to invest more resources in the infrastructure layer for network and terminal construction, and the support layer should strengthen digital literacy training for personnel.

The value of cross-community collaboration lies in establishing a cross-community experience sharing platform (which can rely on the provincial regional health information platform), which helps to promote best practices and accelerate effective replication of models in a wider range.⁴⁶

Through significant improvements in quantitative data, rigorous pilot evaluation comparisons, and horizontal cross-community comparative analysis, the effectiveness of the “four-layer implementation model” (infrastructure layer, platform layer, application layer, and support layer) based on embedded theory and holistic governance in the community’s full life cycle health management has been fully verified. This model not only can effectively improve health outcomes, optimize service efficiency, and improve residents’ sense of gain, but it also has universality and adaptability in different community contexts. The key to its success lies in systematic advancement and synergistic efforts at all levels, especially deep integration of data fusion, intelligent support, scenario innovation, and institutional guarantees. This provides a solid empirical foundation and a scalable implementation paradigm for digital health technology to truly transform from a “tool application” to the “endogenous capability” of community health governance, and to achieve the goals of universal benefit and quality improvement in full life cycle health management.⁴⁷

Challenges and countermeasures of digital health technology in the management of all aspects of the health of the community, full life cycle health management

The embedding of digital health technology in community settings is a complex sociotechnical process that faces challenges from technology, organization, society, and other dimensions. Accurate identification of these challenges and development of targeted response strategies are the key to promoting the digital transformation of community health management.

Structural challenges system governance strategies

At the structural level, grassroots resource bottlenecks and the inertia of the bureaucratic system constitute major obstacles. Scholars’ studies have pointed out that primary health technicians account for only 29.6% of the national total, and their digital literacy is generally insufficient, making it difficult to effectively absorb and apply digital health technologies.⁴⁸ At the same time, the current situation of financial resources tilting towards large hospitals has left community health management in a state of insufficient investment for a long time, and the foundation of digital equipment and facilities is weak. In terms of the system, there is a profound contradiction between the administrative management system of separate blocks and the integrated services required for digital health. The information systems of various departments are not interconnected, and the data standards are different, resulting in difficulties in “mutual construction”.⁴⁹

To address these structural challenges, a systematic strategy with multiple measures is needed. At the resource level, we should innovate talent introduction and training mechanisms and improve the digital health capabilities of grassroots personnel through targeted training, on-the-job training, remote guidance, etc.; at the same time, we should expand diversified investment and financing channels and guide social capital to participate in the construction of community digital health. At the system level, we can learn from the experience of H Street by establishing cross-departmental coordination agencies, formulating unified data standards, and clarifying the list of powers and responsibilities to break down barriers between blocks. What is particularly important is the need to explore a new model of health governance in which digital technology and bureaucratic organizations “mutually construct” each other, both making full use of digital technology to optimize organizational structures and using bureaucratic power to regulate the boundaries and directions of technology applications.^50,51

Functional challenges, value reconstruction, and incentive optimization

At the functional level, the problems of formalization of the technology application and lack of value rationality are particularly prominent. Under the pressure-type system, grassroots organizations often adopt “symbolic execution” and “selective execution” strategies to respond to digital health policies, such as purchasing equipment but not actually using it, focusing on data filling but ignoring actual results, resulting in technology “idling”. At the same time, the application of digital health under the guidance of instrumental rationality overemphasizes quantitative indicators and data traffic, ignoring the real health needs and experiences of community residents. This deviation from value is particularly evident in vulnerable groups such as older adults, and the complexity of the technology itself, coupled with the lack of age-appropriate design, makes digital health an obstacle for them to access services.⁵²

In response to functional challenges, the key lies in rebuilding value consensus and optimizing incentive mechanisms. On the one hand, we should promote the integration of instrumental rationality and value rationality of digital health technology, take “people’s health as the center” as the fundamental principle, and strengthen the ethical review and moral norms of technology application. On the other hand, it is necessary to improve the assessment and evaluation mechanism, avoid simply using digital indicators as the basis for the evaluation, and instead focus on substantive results such as health improvement effects and resident satisfaction. At the same time, develop differentiated and age-appropriate digital health solutions for the characteristics and needs of different groups to ensure that technology benefits different groups.⁵³

Cognitive and relational challenges multi-party collaborative governance

At the cognitive and relational level, the digital literacy divide and the technology trust crisis restrict the in-depth promotion of digital health technology. Research shows that the proportion of non-Internet users in rural areas of China is as high as 51.8%, the proportion of non-Internet users in the group aged 60 and over is 39.8%, and the gap in digital literacy between urban and rural residents is significant. At the same time, the sensitivity and privacy risks of health data make residents suspicious of digital health technology, and the algorithm black box problem further exacerbates this distrust. Furthermore, the proliferation of false health information also undermines the credibility of digital health. Research shows that about 40% of online platforms provide false health information, and residents’ lack of ability to identify them easily falls into the “consumption trap”.⁵⁴

Addressing cognitive and relational challenges requires a multifaceted and collaborative governance strategy. For the digital divide, a combination of “technology empowerment” and “humanistic supplementation” should be adopted. On the one hand, residents’ digital literacy should be improved through community training, family support, etc. On the other hand, traditional service channels should be retained and improved to ensure the health rights of digitally disadvantaged groups. In terms of trust building, data security and privacy protection can be strengthened through technologies such as blockchain and federated learning, while increasing the transparency and explainability of algorithms to help residents understand the logic of technical decisions. Furthermore, the governance of digital health information should be strengthened, authoritative health knowledge dissemination platforms should be established, and false health information must be combated to create a clear and healthy online space.^55,56

Technical challenges standard construction and architecture optimization

At the technical level, insufficient data standardization and poor system interoperability are prominent problems. Digital health technology involves the collection and integration of heterogeneous data, including physiological data, behavioral data, environmental data, clinical data, etc. These data have significant differences in format, standards, and quality, making integration difficult. At the same time, digital health systems developed by different manufacturers often adopt closed technical architectures and data standards, forming new “information cocoons” that hinder the continuous flow and comprehensive aggregation of health data.^57,58

To address technical challenges, it is necessary to start with standard construction and architecture optimization. On the one hand, actively promote the standardization of medical and health data, formulate unified data formats, interface specifications, and sharing protocols to lay the foundation for data interconnection.⁵⁹ On the other hand, advocates open architecture design and realize seamless connection of different systems through API interfaces, middleware, and other technical means. The ontology technology provides a promising direction for solving the problem of data heterogeneity. Constructing a digital twin ontology model provides a unified paradigm for multi-source health data and is expected to become an effective tool to break information silos.⁶⁰

In the face of these intertwined and overlapping challenges, it is difficult for any unilateral effort to achieve a breakthrough. It requires the collaborative participation of multiple stakeholders, such as government, communities, technology providers, and residents to form a governance pattern that promotes technological optimization, organizational change, and institutional innovation, and jointly promotes the deep embedding and value release of digital health technology in the management of all aspects of the health of the full life cycle health management of the community.⁵²

Conclusion

This study systematically analyzes the application logic, implementation path, and challenges faced by digital health technology in community full life cycle health management by constructing an integrated “technology-structure-value” framework. Based on the analysis of the existing literature and typical cases, the following descriptive conclusions and normative policy recommendations are drawn, and the possible causal mechanisms of improving health outcomes with digital technology are further clarified.

Descriptive conclusions

First, digital health technology has become an important enabling tool to promote the transformation of community health management from “disease treatment” to “health promotion”. Evidence suggests that technologies such as the Internet of Things, artificial intelligence, and digital twins can support continuous monitoring, risk warning, and personalized intervention in different stages of life, such as infancy, adolescence, and old age, providing technical feasibility for health services that cover the entire population and the entire process.

Second, the effect of technology empowerment is highly dependent on its deep integration with the community governance system. The practice represented by H Street in Shanghai shows that the digital health system must be adapted to local resources, organizational structure, and residents’ cognition through the “embedding” process and rely on the “holistic governance” mechanism to break down departmental and data barriers, to realize the transformation from tool application to governance endogenous.

Third, the current promotion process still faces multidimensional challenges such as structural resource constraints, institutional inertia, digital divide, and data heterogeneity. These challenges are intertwined, restricting the scale and deep application of digital health technologies, and may exacerbate inequalities in service access.

Elucidation of the causal mechanism of digital health technology improving community health outcomes

The role of digital technology in improving community health outcomes is not automatic, but is gradually transmitted through the following causal chain: First, data continuity and enhanced perception. Through smart devices and digital infrastructure, an automatic and continuous collection of health data from residents is achieved, improving real-time visibility and monitoring coverage of health status.

Second, intelligent analysis and risk preemption. Based on big data and AI algorithms, multi-source health data is analyzed in a comprehensive manner to achieve early identification of disease risks and accurate classification of health problems, promoting the shift of intervention timing from “reactive response” to “proactive prevention”.

Third, service precision and resource optimization. Based on the support of the platform, intelligent matching of the demand and supply of health services is achieved, promoting the allocation of limited medical resources to high-risk populations and high-demand scenarios, and improving the targeting and efficiency of the services.

Fourth, normalized participation and behavior promotion. Through convenient and easy-to-use digital applications, the residents’ right to know and participation in their own health is enhanced, guiding the formation of healthy lifestyles and establishing continuous self-management capabilities.

The key to the effective operation of this mechanism is that the application of technology must be combined with organizational process reengineering, personnel capacity building, and institutional incentives to form a closed loop of “data-driven service response, behavior change, health improvement”.

Normative policy recommendations

Based on the above conclusions and the analysis of the mechanism, to promote the continuous, inclusive, and effective empowerment of digital health technology in community scenarios for the whole life cycle health management, the following policy recommendations are proposed: First, strengthen the combination of top-level design and grassroots empowerment. While promoting data standardization and platform interconnection, grant grassroots communities appropriate autonomy in technology selection and service integration, and encourage the formation of implementation models that conform to local realities.

Second, build a collaborative promotion mechanism of the “technology-organization-system.” To avoid simply importing technology, the digital transformation capacity building of the community health workforce should be promoted simultaneously, and the assessment and incentive system should be optimized to highlight value-oriented indicators such as improvement of health outcomes and resident satisfaction.

Third, implement a comprehensive digital health strategy. Through age-appropriate transformation, popularization of digital literacy, and retention of traditional service channels, the digital divide should be narrowed to ensure that vulnerable groups, such as older adults and low-income groups, are not excluded from digital health services.

Fourth, improve data governance and ethical standards. While ensuring data security and personal privacy, explore the authorized use mechanism of health data, enhance the transparency and credibility of algorithms, and establish a social trust foundation for technology applications.

The deep integration of digital health technology into community health management is still in the exploration stage, and its long-term effectiveness needs more empirical evidence to support it. In the future, more longitudinal studies and comparative evaluations must be carried out, especially focusing on the impact of technology on the health equity of different groups, to promote the formation of a more resilient and warmer digital health ecosystem and consolidate the community foundation for the construction of “Healthy China.”

Footnotes

Acknowledgments

We would like to thank all the participants for their contribution to this study in all ways.

ORCID iD

Guoqing Han

Ethical considerations

This paper is a public policy analysis paper that does not use personal information or personal data and, therefore, does not require consent or permission from the individual. Therefore, we hereby declare that this paper does not require IRB approval or permission.

Author contributions

QW contributed to the conception and design of the study. GH contributed to the acquisition of data. QW, ZZ, ZW, and GH contributed to the analysis and/or interpretation of data and drafted the manuscript. GH and QW contributed to revising the manuscript critically for important intellectual content. QW, ZZ, ZW, and GH provided approval of the version of the manuscript to be published.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.*

Guarantor

Guoqing Han.

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