Journal of Medical Internet Research

Digitalization is steadily advancing on a global scale, exerting a profound influence on health care systems. To facilitate acceptance of the digital transformation, guiding principles emphasize the need for digital health structures to be person-centered and promote high-quality care. This paper examines the implementation challenges within the German health care system, with a particular focus on how change initiatives engage with existing infrastructures and organizational modes of health care delivery. This approach provides a framework for analyzing how established infrastructure determines new developments while also highlighting the procedural dynamics of change and the integration of innovations within existing information infrastructures. These established infrastructures are referred to as the installed base.

Intraoperative neurophysiological monitoring (IONM) guides the surgeon in ensuring motor pathway integrity during high-risk neurosurgical and orthopedic procedures. Although motor-evoked potentials (MEPs) are valuable for predicting motor outcomes, the key features of predictive signals are not well understood, and standardized warning criteria are lacking. Developing a muscle identification prediction model could increase patient safety while allowing the exploration of relevant features for the task.

Digital mental health interventions (DMHIs) offer unique strengths as emerging services with practical applications for adolescents and young adults (AYAs) experiencing depression, anxiety, and stress. Although promising, acceptance and participation in DMHIs vary across interventions, participants, and contexts. It is essential to delineate and synthesize the factors that promote or hinder DMHI use.

As health care demands rise and resources remain constrained, optimizing health care systems has become critical. Information-driven technologies, such as data analytics and artificial intelligence (AI), offer significant potential to inform and enhance health care delivery at various levels. However, a persistent gap exists between the promise of these technologies and their implementation in routine practice. In this paper, we propose that fragmentation of the innovation ecosystem is behind the failure of new information-driven technologies to be taken up into practice and that these goals can be achieved by increasing the cohesion of the ecosystem. Drawing on our experiences and published literature, we explore five challenges that underlie current ecosystem fragmentation: (1) technology developers often focus narrowly on perfecting the technical specifications of products without sufficiently considering the broader ecosystem in which these innovations will operate; (2) lessons from academic studies on technology implementation are underused, and existing knowledge is not being built upon; (3) the perspectives of healthcare professionals and organizations are frequently overlooked, resulting in misalignment between technology developments and health care needs; (4) ecosystem members lack incentives to collaborate, leading to strong individual efforts but collective ecosystem failure; and (5) investment in enhancing cohesion between ecosystem members is insufficient, with limited recognition of the time and effort required to build effective collaborations. To address these challenges, we propose a series of recommendations: adopting a wide-lens perspective on the ecosystem; developing a shared-value proposition; fostering ecosystem leadership; and promoting local ownership of ecosystem investigation and enhancement. We conclude by proposing practical steps for ecosystem members to self-assess, diagnose, and improve collaboration and knowledge sharing. The recommendations presented in this paper are intended to be broadly applicable across various types of innovation and improvement efforts in diverse ecosystems.

Emerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent.

The adoption of the European Health Data Space (EHDS) regulation has made integrating health data critical for both primary and secondary applications. Primary use cases include patient diagnosis, prognosis, and treatment, while secondary applications support research, innovation, and regulatory decision-making. Additionally, leveraging large datasets improves training quality for artificial intelligence (AI) models, particularly in cancer prevention, prediction, and treatment personalization. The European Union (EU) has recently funded multiple projects under Europe’s Beating Cancer Plan. However, these projects face challenges related to fragmentation and the lack of standardization in metadata, data storage, access, and processing. This paper examines interoperability standards used in six EU-funded cancer-related projects: IDERHA (Integration of Heterogeneous Data and Evidence Towards Regulatory and Health Technology Assessments Acceptance), EUCAIM (European Cancer Imaging Initiative), ASCAPE (Artificial Intelligence Supporting Cancer Patients Across Europe), iHelp, BigPicture, and the HealthData@EU pilot. These initiatives aim to enhance the analysis of heterogeneous health data while aligning with EHDS implementation, specifically for the EHDS for the secondary use of data (EHDS2). Between October 2023 and July 2024, we organized meetings and workshops among these projects to assess how they adopt health standards and apply Internet of Things (IoT) semantic interoperability. The discussions focused on interoperability standards for health data, knowledge graphs, the data quality framework, patient-generated health data, AI reasoning, federated approaches, security, and privacy. Based on our findings, we developed a template for designing the EHDS2 interoperability framework in alignment with the new European Interoperability Framework (EIF) and EHDS governance standards. This template maps EHDS2-recommended standards to the EIF model and principles, linking the proposed EHDS2 data quality framework to relevant International Organization for Standardization (ISO) standards. Using this template, we analyzed and compared how the recommended EHDS2 standards were implemented across the studied projects. During workshops, project teams shared insights on overcoming interoperability challenges and their innovative approaches to bridging gaps in standardization. With support from HSbooster.eu, we facilitated collaboration among these projects to exchange knowledge on standards, legal implementation, project sustainability, and harmonization with EHDS2. The findings from this work, including the created template and lessons learned, will be compiled into an interactive toolkit for the EHDS2 interoperability framework. This toolkit will help existing and future projects align with EHDS2 technical and legal requirements, serving as a foundation for a common EHDS2 interoperability framework. Additionally, standardization efforts include participation in the development of ISO/IEC 21823-3:2021—Semantic Interoperability for IoT Systems. Since no ISO standard currently exists for digital pathology and AI-based image analysis for medical diagnostics, the BigPicture project is contributing to ISO/PWI 24051-2, which focuses on digital pathology and AI-based, whole-slide image analysis. Integrating these efforts with ongoing ISO initiatives can enhance global standardization and facilitate widespread adoption across health care systems.

Primary care physicians’ (PCP) referral rates to digital health programs are highly variable. This study explores whether knowledge of the digital remote blood pressure monitoring (RBPM) program and information on referral patterns influence PCPs’ intention to refer patients.

The COVID-19 public health emergency catalyzed widespread adoption of both video- and audio-only telemedicine visits. This proliferation highlighted inequities in use by age, race and ethnicity, and preferred language. Few studies have investigated how differences in health system telemedicine implementation affected these inequities.

In-person nutrition and exercise interventions improve physical function in chronic diseases, yet the acceptability and effectiveness of web-based delivery, especially with different levels of personnel support, require further investigation.

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Clinical reasoning (CR) is an essential skill; yet, physicians often receive limited feedback. Artificial intelligence holds promise to fill this gap.

Despite the rapid growth of research in artificial intelligence/machine learning (AI/ML), little is known about how often study results are disclosed years after study completion.