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Background
Digital health technologies enable patients to make a personal contribution to the improvement of their health by enabling them to manage their health. In order to exploit the potential of digital health technologies, Internet-based networking between patients and health care providers is required. However, this networking and access to digital health technologies are less prevalent in sociodemographically deprived cohorts. The paper explores how the use of digital health technologies, which connect patients with health care providers and health insurers has changed during the COVID-19 pandemic.
Methods
The data from a German-based cross-sectional online study conducted between April 29 and May 8, 2020, were used for this purpose. A total of 1.570 participants were included in the study. Accordingly, the influence of sociodemographic determinants, subjective perceptions, and personal competencies will affect the use of online booking of medical appointments and medications, video consultations with providers, and the data transmission to health insurers via an app.
Results
The highest level of education (OR 1.806) and the presence of a chronic illness (OR 1.706) particularly increased the likelihood of using online booking. With regard to data transmission via an app to a health insurance company, the strongest increase in the probability of use was shown by belonging to the highest subjective social status (OR 1.757) and generation Y (OR 2.303). Furthermore, the results show that the higher the subjectively perceived restriction of the subjects' life situation was due to the COVID-19 pandemic, the higher the relative probability of using online booking (OR 1.103) as well as data transmission via an app to a health insurance company (OR 1.113). In addition, higher digital literacy contributes to the use of online booking (OR 1.033) and data transmission via an app to the health insurer (OR 1.034).
Conclusions
Socially determined differences can be identified for the likelihood of using digital technologies in health care, which persist even under restrictive conditions during the COVID-19 pandemic. Thus, the results indicate a digital divide with regard to the technologies investigated in this study.
Access to digital technologies depends on the availability of technical infrastructure, but this access is unequally distributed among social groups and newly summarized under the term digital divide. The aim is to analyze the perception of a tracing app to contain Covid-19 in Germany. The results showed that participants with the highest level of formal education rate the app as beneficial and were the most likely to use the app.
The acceptance and use of digital technologies depend on the trustworthiness attributed to them. Experts were interviewed about how they assign trust to digital technologies or AI (N=12). The data were analyzed applying the focused qualitative content analysis. All of the experts have experience with digital technologies, but only seven with AI. The majority of experts generally trust digital technologies, but only five experts expressed a general trust in AI. Similar reasons contributing to trust building were given for digital technologies and AI. The results show the complexity of the trust building process and the construct of trust itself. The development of explainable AI and professional training are prerequisites to support a critical and safe use of these technologies.
Artificial intelligence (AI) tends to emerge as a relevant component of medical care, previously reserved for medical experts. A key factor for the utilization of AI is the user’s trust in the AI itself, respectively the AIt’s decision process, but AI-models are lacking information about this process, the so-called Black Box, potentially affecting usert’s trust in AI. This analysis’ objective is the description of trust-related research regarding AI-models and the relevance of trust in comparison to other AI-related research topics in healthcare. For this purpose, a bibliometric analysis relying on 12985 article abstracts was conducted to derive a co-occurrence network which can be used to show former and current scientific endeavors in the field of healthcare based AI research and to provide insight into underrepresented research fields. Our results indicate that perceptual factors such as “trust” are still underrepresented in the scientific literature compared to other research fields.