Background: Health care systems have become increasingly more reliant on patients’ ability to navigate the digital world. However, little research has been conducted on why some communities are less able or less likely to successfully engage with digital health technologies (DHTs), particularly among culturally and linguistically diverse (CaLD) populations. Objective: This systematic review aimed to determine the barriers to and facilitators of interacting with DHTs from the perspectives of CaLD population groups, including racial or ethnic minority groups, immigrants and refugees, and Indigenous or First Nations people. Methods: A systematic review and thematic synthesis of qualitative studies was conducted. Peer-reviewed literature published between January 2011 and June 2022 was searched across 3 electronic databases. Terms for digital health were combined with terms for cultural or linguistic diversity, ethnic minority groups, or Indigenous and First Nations people and terms related to barriers to accessing digital technologies. A qualitative thematic synthesis was conducted to identify descriptive and analytical themes of barriers to and facilitators of interacting with DHTs. Quality appraisal was performed using the Mixed Methods Appraisal Tool. Results: Of the 1418 studies identified in the electronic search, a total of 34 (2.4%) were included in this review. Half of the included studies (17/34, 50%) were conducted in the United States. There was considerable variation in terms of the CaLD backgrounds of the participants. In total, 26% (9/34) of the studies focused on Indigenous or First Nations communities, 41% (14/34) were conducted among ethnic minority populations, 15% (5/34) of the studies were conducted among immigrants, and 18% (6/34) were conducted in refugee communities. Of the 34 studies, 21 (62%) described the development or evaluation of a digital health intervention, whereas 13 (38%) studies did not include an intervention but instead focused on elucidating participants’ views and behaviors in relation to digital health. From the 34 studies analyzed, 18 descriptive themes were identified, each describing barriers to and facilitators of interacting with DHTs, which were grouped into 7 overarching analytical themes: using technology, design components, language, culture, health and medical, trustworthiness, and interaction with others. Conclusions: This study identified several analytic and descriptive themes influencing access to and uptake of DHTs among CaLD populations, including Indigenous and First Nations groups. We found that cultural factors affected all identified themes to some degree and that cultural and linguistic perspectives should be considered in the design and delivery of DHTs, with this best served through the inclusion of the target communities at all stages of development. This may improve the potential of DHTs to be more acceptable, appropriate, and accessible to population groups currently at risk of not obtaining the full benefits of digital health.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Summary: The terms ‘telemedicine’, ‘telehealth’ and ‘e-health’ are often used interchangeably. We examined the occurrence of these terms in the Scopus database. A total of 11,644 documents contained one of the three terms in the title or Telemedicine was the most common term, with 8028 documents referring to it, followed by e-health ( n = 2573) and then telehealth ( n = 1679). Telemedicine was referred to in documents from 126 countries; the terms telehealth and e-health were found in publications from 55 and 99 countries, respectively. Documents with telemedicine in their title or abstract first appeared in 1972, and continued to appear at a low rate until 1994 when they started to increase rapidly; telehealth showed a similar pattern, but with the growth beginning about five years later. Although articles containing the term e-health appeared later than the other two terms, the rate of increase was higher. Articles (journal papers) were the most common type for the three key terms, followed by conference papers and review articles. Publication rates for telemedicine or telehealth or e-health were compared with two other relatively new fields of study: Minimally Invasive Surgery (MIS) and Highly Active Antiretroviral Therapy (HAART). Publications concerning HAART seem to have reached a peak and are now declining, but those with the three key terms and those concerning MIS are both growing. The variation in the level of adoption for the three terms suggests ambiguity in their definition and a lack of clarity in the concepts they refer to.

The secondary use of health data for training Artificial Intelligence (AI) models holds immense potential for advancing medical research and healthcare delivery. However, ensuring patient consent for such utilization is paramount to uphold ethical standards and data privacy. Patient informed consent means patients are fully informed about how their data will be collected, used, and protected, and they voluntarily agree to allow their data to be used for AI models. In addition to formal consent frameworks, establishing a social license is critical to foster public trust and societal acceptance for the secondary use of health data in AI systems. This study examines patient consent practices in this domain. In this scoping review, we searched Web of Science, PubMed, and Scopus. We included studies in English that addressed the core issues of interest, namely, privacy, security, legal, and ethical issues related to the secondary use of health data in AI models. Articles not addressing the core issues, as well as systematic reviews, meta-analyses, books, letters, conference abstracts, and study protocols were excluded. Two authors independently screened titles, abstracts, and full texts, resolving disagreements with a third author. Data was extracted using a data extraction form. After screening 774 articles, a total of 38 articles were ultimately included in the review. Across these studies, a total of 178 barriers and 193 facilitators were identified. We consolidated similar codes and extracted 65 barriers and 101 facilitators, which we then categorized into four themes: "Structure," "People," "Physical system," and "Task." We identified notable emphasis on "Legal and Ethical Challenges" and "Interoperability and Data Governance." Key barriers included concerns over privacy and security breaches, inadequacies in informed consent processes, and unauthorized data sharing. Critical facilitators included enhancing patient consent procedures, improving data privacy through anonymization, and promoting ethical standards for data usage. Our study underscores the complexity of patient consent for the secondary use of health data in AI models, highlighting significant barriers and facilitators within legal, ethical, and technological domains. We recommend the development of specific guidelines and actionable strategies for policymakers, practitioners, and researchers to improve informed consent, ensuring privacy, trust, and ethical use of data, thereby facilitating the responsible advancement of AI in healthcare.