Background: The evolution of eHealth development has shifted from standalone tools to comprehensive digital health environments, fostering data exchange among diverse stakeholders and systems. Nevertheless, existing research and implementation frameworks have primarily emphasized technological and organizational aspects of eHealth implementation, overlooking the intricate legal, ethical, and financial considerations. It is essential to discover what legal, ethical, financial, and technological challenges should be considered to ensure successful and sustainable implementation of eHealth.
Objective: This review aims to provide insights into barriers and facilitators of legal, ethical, financial, and technological aspects for successful implementation of complex eHealth technologies, which impacts multiple levels and multiple stakeholders.
Methods: A scoping review was conducted by querying PubMed, Scopus, Web of Science, and ACM Digital Library (2018-2023) for studies describing the implementation process of eHealth technologies that facilitate data exchange. Studies solely reporting clinical outcomes or conducted outside Europe were excluded. Two independent reviewers selected the studies. A conceptual framework was constructed through axial and inductive coding, extracting data from literature on legal, ethical, financial, and technological aspects of eHealth implementation. This framework guided systematic extraction and interpretation.
Results: The search resulted in 7.308 studies that were screened for eligibility, of which 35 (0.48%) were included. Legal barriers revolve around data confidentiality and security, necessitating clear regulatory guidelines. Ethical barriers span consent, responsibility, liability, and validation complexities, necessitating robust frameworks. Financial barriers stem from inadequate funding, requiring (commercial) partnerships and business models. Technological issues include interoperability, integration, and malfunctioning, necessitating strategies for enhancing data reliability, improving accessibility, and aligning eHealth technology with existing systems for smoother integration.
Conclusions: This research highlights the multifaceted nature of eHealth implementation, encompassing legal, ethical, financial, and technological considerations. Collaborative stakeholder engagement is paramount for effective decision-making and aligns with the transition from standalone eHealth tools to integrated digital health environments. Identifying suitable stakeholders and recognizing their stakes and values enriches implementation strategies with expertise and guidance across all aspects. Future research should explore the timing of these considerations and practical solutions for regulatory compliance, funding, navigation of responsibility and liability, and business models for reimbursement strategies.
A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific-and practical-medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.
With advancements in artificial intelligence (AI) dominating the headlines, diagnostic imaging radiology is no exception to the accelerating role that AI is playing in today's technology landscape. The number of AI-driven radiology diagnostic imaging applications (digital diagnostics) that are both commercially available and in-development is rapidly expanding as are the potential benefits these tools can deliver for patients and providers alike. Healthcare providers seeking to harness the potential benefits of digital diagnostics may consider evaluating these tools and their corresponding use cases in a systematic and structured manner to ensure optimal capital deployment, resource utilization, and, ultimately, patient outcomes-or clinical utility. We propose several guiding themes when using clinical utility to curate digital diagnostics.
Introduction: Today, modern technology is used to diagnose and treat cardiovascular disease. These medical devices provide exact measures and raw data such as imaging data or biosignals. So far, the Broad Integration of These Health Data into Hospital Information Technology Structures-Especially in Germany-is Lacking, and if data integration takes place, only non-Evaluable Findings are Usually Integrated into the Hospital Information Technology Structures. A Comprehensive Integration of raw Data and Structured Medical Information has not yet Been Established. The aim of this project was to design and implement an interoperable database (cardio-vascular-information-system, CVIS) for the automated integration of al medical device data (parameters and raw data) in cardio-vascular medicine.
Methods: The CVIS serves as a data integration and preparation system at the interface between the various devices and the hospital IT infrastructure. In our project, we were able to establish a database with integration of proprietary device interfaces, which could be integrated into the electronic health record (EHR) with various HL7 and web interfaces.
Results: In the period between 1.7.2020 and 30.6.2022, the data integrated into this database were evaluated. During this time, 114,858 patients were automatically included in the database and medical data of 50,295 of them were entered. For technical examinations, more than 4.5 million readings (an average of 28.5 per examination) and 684,696 image data and raw signals (28,935 ECG files, 655,761 structured reports, 91,113 x-ray objects, 559,648 ultrasound objects in 54 different examination types, 5,000 endoscopy objects) were integrated into the database. Over 10.2 million bidirectional HL7 messages (approximately 14,000/day) were successfully processed. 98,458 documents were transferred to the central document management system, 55,154 materials (average 7.77 per order) were recorded and stored in the database, 21,196 diagnoses and 50,353 services/OPS were recorded and transferred. On average, 3.3 examinations per patient were recorded; in addition, there are an average of 13 laboratory examinations.
Discussion: Fully automated data integration from medical devices including the raw data is feasible and already creates a comprehensive database for multimodal modern analysis approaches in a short time. This is the basis for national and international projects by extracting research data using FHIR.
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