Digital health management, utilizing technologies such as the internet, mobile terminals, and intelligent cloud platforms, stands as a highly promising approach to post-discharge chronic disease management. To the best of our knowledge, HeartMed is currently the largest reported cohort on the long-term management of CAD patients using a digital healthcare system, and this study is the first to evaluate the clinical benefits of digital post-discharge management on long-term outcomes across different phases of the COVID-19 pandemic, specifically focusing on the effectiveness of digital health management during public health emergencies. In this study, which thoroughly evaluates the role of digital health management in post-discharge management for the CAD population across different pandemic phases, we found: (I) With the evolving times, the advancement of internet technology, and the experience of the COVID-19 pandemic, there has been an increasing recognition and adoption of digital health management, leading to growing acceptance and proactive selection of digital health management shift from traditional outpatient follow-up. (II) Digital health management consistently showed associations with reduced risks of all-cause mortality, readmission, MACE, and MACCE across all pandemic phases in patients with CAD. (III) In addition, the associations between digital health management and clinical outcomes did not differ significantly across different pandemic periods, suggesting its potential as an effective strategy for managing chronic disease populations during public health crises.
In the past decade, the application of digital technologies for improving chronic disease management has been preliminarily explored, demonstrating promising results and proving effective in enhancing symptom management and patient adherence as research advances5,6,14. In recent years, digital health management technologies have benefited from advancements in information technology—such as Internet Plus, big data, cloud computing—gradually evolving from text messages (SMS)—and call-based management to well-designed, sophisticated online programs, with current efforts exploring the integration of AI technologies12,15. During the COVID-19 pandemic, both in China and worldwide, the integration of internet technologies into existing healthcare systems accelerated significantly, leading to a wide range of practical applications. Even in the post-pandemic era, these technologies continue to evolve and advance15,16,17. Our research indicates that as society advances, particularly after experiencing the COVID-19 pandemic—a period that catalysed broader exposure to Internet-based healthcare—the acceptance and preference for digital health management have been increasing year by year in the Chinese population, especially among younger individuals. Meanwhile, a 2024 survey conducted in Germany revealed that approximately 84% of cardiovascular patients, particularly those under the age of 63, were willing to use health-related mobile applications (apps) as a form of health support, aligning with our research findings16,18. Objective factors, such as technological advancements and the accumulation of early user experience feedback data, have contributed to the continued refinement of more user-friendly and precise digital health management systems. Meanwhile, pandemic-driven adaptation and increasing user acceptance have supported the broader adoption and continued use of digital health management platforms19,20. Consequently, driven by these factors, an increasing number of post-discharge patients are opting for digital health management as a replacement for traditional outpatient follow-ups.
The implementation of digital healthcare in the long-term management of CAD still faces several challenges worldwide. A meta-analysis suggests that digital health programs were associated with lower hospital readmissions among post-discharge CAD patients, yet no significant decrease has been observed in MACE or mortality8. Nevertheless, in currently published relevant studies, the main method of digital health interventions remains SMS or telephone-based, and the intervention duration is generally not sustained throughout the entire course, most commonly lasting 12 to 24 weeks8,21,22. Additionally, the follow-up period is relatively short, with most studies covering six months or less23,24. In contrast, the present study exhibits several noteworthy and innovative strengths. Chief among them is its large sample size, extended temporal span, and long-term follow-up, enabling a more comprehensive evaluation of the real-world effectiveness of digital health interventions. As is well established, standard post-discharge management of CAD typically consists of lifestyle modifications, risk factor control, secondary preventive medication use, and symptom management25. Across these critical domains, this study adopts multidimensional and continuous intervention strategies that could help address the potential limitations observed in prior research and shows promise in enhancing patient adherence and improving long-term outcomes, through thoughtfully incorporating and integrating the following key elements: (1) Personalization: Given the variability in patients’ risk factors and disease conditions, a personalized management approach—encompassing tailored rehabilitation frequency, intensity, and specific targets—is crucial. However, many digital health solutions still follow a ‘one-size-fits-all’ model, where all patients adhere to similar programmes26, with limited adaptation based on real-time patient feedback27. Notably, several innovative personalized management strategies—such as individualized action planning, graded task assignments, and peer-supported interventions—have recently revealed strong feasibility and acceptability, pointing to promising directions for enhancing digital intervention effectiveness28,29. (2) Continuous Long-Term Management: Post-discharge management for CAD patients is a prolonged and complex process, yet many studies provide health-related information only during the initial months after discharge, without extending support throughout the full follow-up period. This limited coverage may contribute to diminished patient adherence and even the neglect or loss of critical medical guidance and information30,31. (3) Diversified and Targeted Information Delivery: Relying solely on SMS and push notifications, as seen in many current studies, is far from sufficient. Effective digital health management requires a multi-format and multi-channel approach to information delivery, and direct online access to professional cardiologists, when needed, can significantly improve patient awareness and adherence to recommended health advice. Otherwise, monotonous and excessive medical advice risks becoming overwhelming ‘information overload’ with limited practical value8,24,32. (4) Dynamic Monitoring and Real-Time Feedback: CAD can progress rapidly, so in addition to actively reporting symptoms, connecting patients to daily and professional monitoring devices is crucial for tracking activity levels and vital signs enables dynamic tracking of health status. By consistently observing these parameters and promptly alerting healthcare professionals when abnormalities arise, potential crises can be anticipated, and adverse events mitigated. A meta-analysis has also demonstrated that integrating remote consultation with remote monitoring significantly reduces cardiovascular mortality and hospitalizations in heart failure patients33. This supports the emerging view in current research that digital health technologies—particularly remote monitoring—when deeply integrated with enhanced clinician-patient interaction frameworks, may play a pivotal role in improving intervention efficacy21,34. In addition, this digital management model may offer several potential benefits that are not easily quantify. For instance, features such as campaign reminders, risk stratification, two-way information exchange, and video-based educational content likely play a role in improving the quality of post-discharge management. It is worth noting that, with advancements in internet-related technologies and the integration of AI, digital health management systems could undergo continuous upgrades. Our findings indicate that current digital health management systems are associated with favourable clinical outcomes inpatients with CAD. Along with further advancements in internet and AI technologies, future iterations of these systems may hold the potential to further improve CAD patient outcomes.
Reflecting on successful experience in combating the COVID-19 outbreak in China, various digital healthcare initiatives—Internet Plus Health Care Service Pattern, Internet Hospitals, and Telemedicine—were implemented to meet patient needs amid restrictions on movement and social activities. These digital health models were reported to provide patients with accessible and high-quality medical services, offering valuable support for pandemic control efforts12,35. However, limited evidence exists regarding performance of digital health management for post-discharge CAD patients during the COVID-19 pandemic. Digital health management approach under pandemic conditions shares similarities with the previously mentioned Internet Plus-based healthcare strategies, while also providing more comprehensive symptom and vital sign monitoring, and offering more personalized, and timely medical advice in certain aspects. Our findings fill this gap by demonstrating that, even during the COVID-19 pandemic, digital health management was associated with lower readmission rates and fewer cardiovascular adverse events in patients with CAD. This presents a viable strategy for optimizing post-discharge management and enhancing CAD patient outcomes in future public health emergencies.
Several limitations should still be considered when interpreting our conclusions. Firstly, the inherent limitations of observational studies restrict the ability to confirm the causal relationship between digital health management and the reduction of clinical endpoint events under different phases of the pandemic. Moreover, the nature of observational studies also prevents completely ruling out the possibility of residual confounding. Secondly, from the perspective of baseline characteristics, the potential for selection bias must be considered. Consistent with previous studies, participants in the DM group may have been younger and more health-conscious36,37. Although relevant factors were adjusted in Cox regression models and a sensitivity analysis using PSM was performed to confirm the robustness of the findings, the influence of these factors could not be fully eliminated. Meanwhile, the findings of this study may remain susceptible to unmeasured or residual confounders that were not accounted for in the present analysis, either due to their absence in the dataset or the lack of validated assessment tools—such as participants’ socioeconomic status, educational attainment, digital literacy, trust in the healthcare system, and communication preferences. As reported in previous studies, these factors may not only shape an individual’s willingness or ability to engage with digital health services, but also impact how frequently and effectively digital healthcare tools are used36,38,39. Therefore, when interpreting the actual effects of DM interventions, the potential dual impact of these variables—both on the likelihood of selecting into DM and on subsequent clinical outcomes—should be carefully considered. In light of the above two points, subsequent randomized controlled trials required to be designed to further explore and validate the causal relationship of digital health management models in populations with chronic diseases. Thirdly, regarding the generalizability of the study’s conclusions, the study sample is confined to a Chinese population. As a result, the applicability of the findings to patients in other countries and regions is constrained. The implementation and dissemination of digital health management across different countries and populations are profoundly influenced by variations in cultural contexts, healthcare system structures, and digital infrastructure. In low- and middle-income countries, low-income individuals, immigrants, and ethnic minorities are particularly at greater risk of “digital exclusion” due to limited access to digital devices, unstable internet connections, and inadequate digital literacy40,41. Compounding these challenges are healthcare providers’ insufficient digital competencies and outdated organizational models of healthcare delivery, which collectively hinder the integration of digital health strategies42,43. Addressing these barriers requires not only strengthening technological infrastructure but also optimizing the organization and delivery of healthcare services. Bridging the digital divide and promoting digital equity remain critical global challenges21. Consequently, further studies across diverse countries and populations are warranted to validate the transferability and broader applicability of digital management strategies beyond the Chinese setting. In addition, as this study focuses solely on CAD, the observed benefits of digital health management are limited to this population. Digital health management systems for other chronic diseases require further design and their effectiveness still needs exploration.
In conclusion, with the evolving times and advancements in internet technology, coupled with having experienced the COVID-19 pandemic, there has been a growing preference for digital health management follow-up over conventional outpatient follow-up. Regardless of the pandemic phase—whether pre-COVID-19, during the COVID-19 pandemic, or post-COVID-19—consistent associations were observed between digital health management and reduced risks of all-cause mortality, readmission, MACE, and MACCE in CAD populations. Moreover, the impact of digital health management remained stable across different pandemic phases, highlighting its potential role in providing an effective approach to managing chronic disease populations in the face of public health crises. These findings suggest that digital health management follow-up is a practical and feasible approach, with superior management outcomes compared to conventional follow-up in CAD populations, both under routine and exceptional circumstances.
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