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Digital chronic disease care faces challenges in evidence quality, clinical integration, and health equityDigital tools face hurdles in managing chronic disease care

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Key Takeaway
Note that digital chronic disease care faces significant hurdles in evidence quality, equity, and clinical integration.

This systematic review explores the integration of digital chronic disease care, encompassing intelligent sensing, digital behavioral interventions, immersive technology, and artificial intelligence and big data. The review identifies four core technological systems currently utilized in the management of chronic conditions.

Beyond technology, the authors synthesize emerging care model trends, including nurse leadership, multi-dimensional collaboration, and caregiver empowerment. These elements are presented as essential components for evolving care delivery. However, the review highlights significant systemic challenges: a lack of high-quality evidence, a substantial digital divide, difficulties in clinical integration, and a lack of personalized design.

Clinical application is currently limited by these barriers. The authors suggest a shift from technology-driven models toward evidence-driven and demand-driven models. Future improvements in management efficiency and health equity depend on focusing on precision, localization, and integration. The review notes that current evidence is insufficient to support broad clinical conclusions for individual patients.

How this fits prior evidence

This systematic review addresses a gap in the current evidence base regarding the infrastructure of digital chronic disease care. While a previous meta-analysis found that mHealth interventions may improve eHealth literacy in chronic disease patients, this review highlights the broader systemic barriers to implementation, such as the digital divide and lack of personalized design, which may impact the scalability of such interventions.

Managing a chronic illness is a long journey that requires constant support. Researchers looked at how digital tools, such as artificial intelligence and immersive technology, can help patients manage these ongoing conditions. They identified four main systems: intelligent sensing, digital behavioral interventions, immersive technology, and AI combined with big data.

While these tools offer new ways to provide care, the research highlights some significant hurdles. Currently, there is not enough high-quality evidence to back many of these systems. There is also a digital divide, meaning not everyone has equal access to these technologies. Additionally, it can be hard to integrate these tools into everyday clinical practice or design them to fit the unique needs of each individual patient.

To make these tools truly effective, the focus needs to shift. Instead of just looking at what the technology can do, the goal should be to create evidence-based models. This means focusing on precision, local needs, and making sure everyone has equal access to care.

What this means for you:
Digital tools like AI show promise for chronic disease, but they need more evidence and personalized designs.

Common questions

What types of technology are being used for chronic disease?

The research identifies four main systems: intelligent sensing, digital behavioral intervention, immersive technology, and artificial intelligence (AI) combined with big data. These tools are intended to help manage and monitor various chronic conditions.

What are the main challenges with these digital tools?

There are several hurdles, including a lack of high-quality evidence, a digital divide where not everyone has equal access, and difficulties in making these tools work in a clinical setting. Additionally, many tools currently lack a personalized design for individual patients.

How can these digital tools be improved for patients?

The review suggests moving toward models that are driven by evidence and patient demand. This means focusing on precision, local needs, and health equity to make sure the technology actually improves management efficiency for everyone.

Study Details

Study typeSystematic review
EvidenceLevel 1
PublishedOct 2026
View Original Abstract ↓
The global prevalence of chronic diseases is becoming increasingly severe, and traditional care models are confronted with multiple challenges, including uneven distribution of medical resources, insufficient continuity of care, and inadequate patient self-management capacity. The rapid iteration of digital technologies and the catalyst of the COVID-19 pandemic have driven chronic disease care into a new phase of digital transformation. Based on relevant studies published from 2021 to 2026, this paper structures its analysis around a core logical framework: evidence-based evidence, care models, population adaptability and practical implementation. It systematically reviews the core technical systems and innovative characteristics of care models in digital chronic disease care, deeply analyzes the adaptive differences across diverse populations and disease types, and conducts critical reflections from the perspectives of evidence quality, health equity, clinical applicability, and homogeneity in intervention design. Finally, it puts forward future development directions centered on precision, localization, equity, and integration. The study reveals that digital chronic disease care has formed four core technological systems in application: intelligent sensing, digital behavioral intervention, immersive technology, and artificial intelligence (AI) and big data. Care models demonstrate innovative trends of nurse leadership, multi-dimensional collaboration, and caregiver empowerment. Nevertheless, prominent issues persist, such as insufficient high-quality evidence, a striking digital divide, difficulties in clinical integration, and a lack of personalized design. In the future, deep integration of technological innovation with clinical demands, enhanced targeted interventions for vulnerable populations, implementation of localized long-term studies, and improvement of policy and ethical guarantee systems are required. These efforts will promote the shift of digital chronic disease care from technology-driven to evidence-driven and demand-driven models, ultimately improving the overall efficiency of chronic disease management and health equity.
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