Upper-extremity injuries and diseases rarely have life-threatening consequences, but failure to manage them properly can result in severe dysfunction. This article presents the current state of using virtual reality to support the rehabilitation process of patients with injuries and diseases of the upper extremities and points out their effects on upper-extremity functions. A scoping review was conducted to provide a comprehensive overview of the field of virtual reality for upper-extremity rehabilitation. PubMed, Web of Science, and the Cochrane Library were searched by two independent researchers between April and May 2021 to identify relevant publications and were examined according to inclusion and exclusion criteria. As a result of the literature review, 11 studies of various target groups were identified. Virtual-reality technologies were categorized into multisensory high-end systems and game-based systems. With respect to functional recovery, technologies based on virtual reality were not inferior to traditional rehabilitation. In addition, the users were highly motivated and satisfied. The results emphasize the need for stronger evidence-based virtual-reality technologies for rehabilitation of injuries and diseases of upper extremities.

Background: Depression is a major cause for disability worldwide, and digital health interventions are expected to be an augmentative and effective treatment. According to the fast-growing field of information and communication technologies and its dissemination, there is a need for mapping the technological landscape and its benefits for users. Objective: The purpose of this scoping review was to give an overview of the digital health interventions used for depression. The main goal of this review was to provide a comprehensive review of the system landscape and its technological state and functions, as well as its evidence and benefits for users. Methods: A scoping review was conducted to provide a comprehensive overview of the field of digital health interventions for the treatment of depression. PubMed, PSYNDEX, and the Cochrane Library were searched by two independent researchers in October 2020 to identify relevant publications of the last 10 years, which were examined using the inclusion and exclusion criteria. To conduct the review, we used Rayyan, a freely available web tool. Results: In total, 65 studies were included in the qualitative synthesis. After categorizing the studies into the areas of prevention, early detection, therapy, and relapse prevention, we found dominant numbers of studies in the area of therapy (n=52). There was only one study for prevention, 5 studies for early detection, and 7 studies for relapse prevention. The most dominant therapy approaches were cognitive behavioral therapy, acceptance and commitment therapy, and problem-solving therapy. Most of the studies revealed significant effects of digital health interventions when cognitive behavioral therapy was applied. Cognitive behavioral therapy as the most dominant form was often provided through web-based systems. Combined interventions consisting of web-based and smartphone-based approaches are increasingly found. Conclusions: Digital health interventions for treating depression are quite comprehensive. There are different interventions focusing on different fields of care. While most interventions can be beneficial to achieve a better depression treatment, it can be difficult to determine which approaches are suitable. Cognitive behavioral therapy through digital health interventions has shown good effects in the treatment of depression, but treatment for depression still stays very individualistic.

Due to the coronavirus pandemic, telerehabilitation has become increasingly important worldwide. While the effectiveness of telerehabilitation is considered proven for many indications, there is comparatively little knowledge about the implementation conditions. Therefore, this scoping review summarises the current state of facilitating and inhibiting factors that may influence the uptake of telerehabilitation. The review follows the JBI methodology for scoping reviews. The article search was carried out in five databases (MEDLINE, EMBASE, Web of Science, Cochrane and Psyndex) in May 2022, with an update in October 2023. Two independent researchers identified relevant studies according to the inclusion and exclusion criteria. The Consolidated Framework for Implementation Research served as the theoretical basis for the categorisation of the facilitating and inhibiting criteria in the organisational context. A total of 28 studies (timespan 2012 to 2023) have been included. The most relevant barriers identified are technical issues and a lack of technical skills. The factors considered most favourable for implementation are patients’ motivation and the involvement of high-level leaders. The results provide clear indications of factors that inhibit and facilitate implementation, but also show that further research is needed.

Background: Antibiotic resistance is a major health threat. Inappropriate antibiotic use has been shown to be an important determinant of the emergence of antibiotic resistance. Decision support systems for antimicrobial management can support clinicians to optimize antibiotic prescription. Objective: The aim of this systematic review is to identify factors influencing the implementation of decision support systems for antibiotic prescription in hospitals. Methods: A systematic search of factors impeding or facilitating successful implementation of decision support systems for antibiotic prescription was performed in January 2022 in the databases PubMed, Web of Science and The Cochrane Library. Only studies were included which comprised decision support systems in hospitals for prescribing antibiotic therapy, published in English with a qualitative, quantitative or mixed-methods study design and between 2011 and 2021. Factors influencing the implementation were identified through text analysis by two reviewers. Results: A total of 14 publications were identified matching the inclusion criteria. The majority of factors relate to technological and organizational aspects of decision support system implementation. Some factors include the integration of the decision support systems into existing systems, system design, consideration of potential end-users as well as training and support for end-users. In addition, user-related factors, like user attitude towards the system, computer literacy and prior experience with the system seem to be important for successful implementation of decision support systems for antibiotic prescription in hospitals. Conclusion: The results indicate a broad spectrum of factors of decision support system implementation for antibiotic prescription and contributes to the literature by identifying important organizational as well as user-related factors. Wider organizational dimensions as well as the interaction between user and technology appear important for supporting implementation.

Background: Inappropriate antimicrobial use, such as antibiotic intake in viral infections, incorrect dosing and incorrect dosing cycles, has been shown to be an important determinant of the emergence of antimicrobial resistance. Artificial intelligence-based decision support systems represent a potential solution for improving antimicrobial prescribing and containing antimicrobial resistance by supporting clinical decision-making thus optimizing antibiotic use and improving patient outcomes. Objective: The aim of this research was to examine implementation factors of artificial intelligence-based decision support systems for antibiotic prescription in hospitals from the perspective of the hospital managers, who have decision-making authority for the organization. Methods: An online survey was conducted between December 2022 and May 2023 with managers of German hospitals on factors for decision support system implementation. Survey responses were analyzed from 118 respondents through descriptive statistics. Results: Survey participants reported openness towards the use of artificial intelligence-based decision support systems for antibiotic prescription in hospitals but little self-perceived knowledge in this field. Artificial intelligence-based decision support systems appear to be a promising opportunity to improve quality of care and increase treatment safety. Along with the Human-Organization-Technology-fit model attitudes were presented. In particular, user-friendliness of the system and compatibility with existing technical structures are considered to be important for implementation. The uptake of decision support systems also depends on the ability of an organization to create a facilitating environment that helps to address the lack of user knowledge as well as trust in and skepticism towards these systems. This includes the training of user groups and support of the management level. Besides, it has been assessed to be important that potential users are open towards change and perceive an added value of the use of artificial intelligence-based decision support systems. Conclusion: The survey has revealed the perspective of hospital managers on different factors that may help to address implementation challenges for artificial intelligence-based decision support systems in antibiotic prescribing. By combining factors of user perceptions about the systems´ perceived benefits with external factors of system design requirements and contextual conditions, the findings highlight the need for a holistic implementation framework of artificial intelligence-based decision support systems.

Digital health interventions may contribute to closing the treatment gap for depression by reaching large populations at relatively low costs. This article presents the results of a broad, multisided German survey in 2020 on the acceptance and use of digital health interventions in depression care from the perspective of patients, their relatives, and health professionals. A total of 97 patients and relatives and 229 health professionals participated. Survey participants reported openness towards the use of digital health interventions in depression care but little knowledge and experience in the field. Digital health interventions appear to be a promising opportunity for reducing depressive symptoms and shortening waiting time for depression treatment, especially in rural areas. Providing information and technical competencies may increase awareness and knowledge about digital health interventions and the benefits of depression care.