Journal of Medical Internet Research

Technology-enabled interventions for chronic disease management, such as telehealth systems for hypertension self-monitoring, have demonstrated effectiveness but face challenges with sustained usage and high attrition rates. Understanding the factors associated with continued engagement is crucial for enhancing intervention design and sustainability.

Green nephrology has emerged as a crucial strategy to address the health care sector’s role in the climate crisis, particularly due to the high carbon intensity of dialysis-related services. Aligned with global net-zero commitments, sustainable kidney care can reduce environmental impact while maintaining high standards of patient care. This viewpoint paper proposes a net-zero carbon emissions kidney care center model to address global climate change challenges and advance health care sustainability goals. Based on the United Nations Sustainable Development Goals, we developed a 4D framework: digital transformation, low-carbon health care, circular economy, and preventive medicine. The digital transformation dimension features a precision kidney health system integrating acute and chronic kidney injury digital care models. The low-carbon health care dimension focuses on increasing the rates of kidney transplantation and choosing optimal dialysis modality. The circular economy dimension involves dialysis wastewater recycling, repurposing of medical materials, and integration of renewable energy into facility operations. The preventive medicine dimension incorporates telehealth education, behavioral interventions, and health inequality improvements. This net-zero carbon emissions kidney care model represents an environmental, social, and governance approach to ensuring implementation and continual improvement. It also provides actionable steps for implementing sustainable kidney care and serves as a reference model for net-zero emissions health care systems.

During the last 25 years, the healthcare sector has undergone a digital transformation; health issues and medical conditions are increasingly managed with the support of digital health technology. The Internet has transformed the boundaries around physicians’ work, which raises questions about how technological artifacts are transforming the boundaries that have traditionally existed between the health professions and patients regarding information and knowledge. This viewpoint paper analyzes how digital health technologies can transform the boundaries of physicians’ work by examining three examples of technology aimed at patients or citizens: Open Notes, PatientsLikeMe, and Apple Watch. Traditionally, the physician profession drew the boundaries that separated itself from other professions and patients. They did this in order to define and protect their jurisdiction and professional knowledge. However, in the three artifacts analyzed, technology changes the boundaries between laypeople and physicians. Therefore, health technologies aimed at citizens impact healthcare and its professions, and the materiality of artifacts can change the boundaries between physicians and citizens. Thus, the initiators and developers of technology aimed at patients or citizens may have the power to transform the field of knowledge in healthcare.

As the rapid advancement of digital health technologies has significantly improved health management, digital health literacy (DHL) has emerged as a crucial determinant of health outcomes.

While the relationship between Internet use and depression, and the mediating role of social participation in this association, has garnered significant attention, the COVID-19 pandemic has disrupted traditional forms of social participation. The extent to which this disruption has altered the association remains underexplored.

Large Language Models (LLMs) such as ChatGPT and Gemini are increasingly explored for their potential in medical diagnostics, including neurology. Their real-world applicability remains inadequately assessed, particularly in clinical workflows where nuanced decision-making is required.

Early prediction of treatment outcomes for patients with multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) undergoing extended therapy is crucial for enhancing clinical prognoses and preventing the transmission of this deadly disease. However, the absence of validated predictive models remains a significant challenge.

Hospital in the home (HITH) provides home-based care as an alternative to traditional hospitalization. In response to the COVID-19 Omicron wave, a public hospital in the rural Western portion of Southeast Queensland implemented a virtual HITH service to support adults, maternity patients, and children with moderate COVID-19 symptoms and additional health concerns. Although the pandemic accelerated the uptake of virtual care within HITH models, existing literature has focused on clinical outcomes, with limited evidence on key implementation outcomes.

The exponential growth of publications regarding the application of machine learning (ML) tools in medicine highlights the significant potential for ML to revolutionize the field. Despite the multitude of literature surrounding this topic, there are limited publications addressing the implementation and feasibility of ML models in clinical practice. Currently, Machine Learning Operations (MLOps), a set of practices designed to deploy and maintain ML models in production, is used in various information technology and industrial settings. However, the MLOps pipeline is not well researched in medical settings, where there are multiple barriers to implementing ML pipelines into practice.

Recent years have witnessed a transformative shift in the way patient-reported outcomes are captured. The increasing adoption of digital health technologies offers exciting possibilities for more efficient, engaging, and insightful data collection electronically. Regulators recommend the source data captured electronically should adhere to ALCOA principles to ensure data quality, integrity and be compliant with regulatory requirements. Traditionally paper diaries are used to collect safety data in clinical trials, however electronic form of these paper diaries represent a paramount resource that could improve data quality, reduce costs, and limit the burden on clinical staff and trial participants. eDiaries offer significant advantages but specific measures must be taken to ensure their optimal use. In this paper, we provide our reflections on key measures such as programming the eDiary platform, training the trial staff and participants, and real-time monitoring of participant compliance, to leverage eDiaries for optimal data collection. By implementing the measures discussed in this paper, eDiaries can offer significant advantages for both trial participants and clinical investigators by ensuring quality and integrity of the data collected

Syndromic surveillance now forms an integral part of the surveillance for a wide range of hazards in many countries. Establishing syndromic surveillance systems can be difficult due to the many different sources of data which can be used, cost pressures, the importance of data security and different (and rapidly evolving) technologies. Here we describe major points in the development of the UK Health Security Agency (UKHSA) English real-time syndromic surveillance service over its first two decades (1998 to 2018). We identify the key wider themes that we believe are important in ensuring a sustainable and useful syndromic surveillance service. We conducted semi-structured interviews with current members of the UKHSA syndromic surveillance team who were involved from the earliest stages and previous senior colleagues who were supportive of the syndromic surveillance work during the early phases. For this study, we partitioned the development of syndromic surveillance in England into three time periods: 1998 to 2005 (‘the beginnings’); 2006 to 2011 (‘the growth phase’); and 2012 to 2018 (‘mainstream’). We asked the interviewees for their views about the development of syndromic surveillance, and in particular the main drivers and events, the team and system, and outputs and uses. The results from the interviews highlighted some key themes including: the integration of syndromic surveillance into the public health system; creativity; good collaboration and teamwork; leadership and determination to persevere; and agility and the ability to adapt to new threats. Using the results of the discussions and our personal experience of running the syndromic surveillance service from inception and over decades, we constructed a set of recommendations for establishing and running sustainable syndromic surveillance systems. In this age of increased automation, with the ability to transfer data in real-time and to utilise machine learning and artificial intelligence, we are approaching a ‘new age of syndromic surveillance’. We consider the focus on the public health questions, relationships and collaboration, leadership and true teamwork should not be underestimated in the success of and usefulness of real-time syndromic surveillance systems.

The European Health Data Space (EHDS) aspires to enable secure, interoperable, and decentralized health data usage across Europe. This paper explores legal and technical challenges in implementing EHDS goals, particularly for secondary data use. It highlights federated and swarm learning as promising yet complex solutions, requiring robust infrastructure, standardization, and regulatory clarity. We emphasize the need for coordinated legislative and technological advances to realize EHDS ambitions.