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Zu Risiken und Nebenwirkungen fragen Sie Ihre KI Künstliche Intelligenz durchdringt alle Bereiche des Lebens - auch das Gesundheitswesen. Walter Swoboda zeigt in seinem Buch, wie KI funktioniert, welche Varianten dieser neuen Technologie in Gesundheit und Pflege zum Einsatz kommen können und welche Einsatzgebiete sich kurz- und langfristig ergeben. Auf Chancen, Risiken und ethische Herausforderungen geht er ein. Auch experimentelle Verfahren der Zukunft berücksichtigt er. Das Buch richtet sich an Praktizierende, Forschende und Studierende in den Bereichen Gesundheitswesen, Gesundheitsmanagement, Gesundheitsinformatik, Medizin, Pflegewissenschaften sowie Medizinethik.

Background: One measure national governments took to react to the acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic was mobile applications (apps). This study aims to provide a high-level overview of published reviews of mobile apps used in association with coronavirus disease 19 (COVID-19), examine factors that contributed to the success of these apps, and provide data for further research into this topic. Methods: We conducted a systematic review of reviews (also referred to as an umbrella review) and searched two databases, Medline and Embase, for peer-reviewed reviews of COVID-19 mobile apps that were written in English and published between January 1st 2020 and April 25th 2022. Results: Out of the initial 17,611 studies, 24 studies were eligible for the analysis. Publication dates ranged from May 2020 to January 2022. In total, 54% (n = 13) of the studies were published in 2021, and 33% (n = 8) were published in 2020. Most reviews included in our review of reviews analyzed apps from the USA, the UK, and India. Apps from most of the African and Middle and South American countries were not analyzed in the reviews included in our study. Categorization resulted in four clusters (app overview, privacy and security, MARS rating, and miscellaneous). Conclusions: Our study provides a high-level overview of 24 reviews of apps for COVID-19, identifies factors that contributed to the success of these apps, and identifies a gap in the current literature. The study provides data for further analyses and further research.

In the face of demographic change and constantly increasing health care costs, health care system decision-makers face ever greater challenges. Mobile health applications (mHealth apps) have the potential to combat this trend. However, in order to integrate mHealth apps into care structures, an evaluation of such apps is needed. In this paper, we focus on the criteria and methods of evaluating mHealth apps for cardiovascular disease and the implications for developing a widely applicable evaluation framework for mHealth interventions. Our aim is to derive substantiated patterns and starting points for future research by conducting a quasi-systematic scoping review of relevant peer-reviewed literature published in English or German between 2000 and 2021. We screened 4066 articles and identified n = 38 studies that met our inclusion criteria. The results of the data derived from these studies show that usability, motivation, and user experience were evaluated primarily using standardized questionnaires. Usage protocols and clinical outcomes were assessed primarily via laboratory diagnostics and quality-of-life questionnaires, and cost effectiveness was tested primarily based on economic measures. Based on these findings, we propose important considerations and elements for the development of a common evaluation framework for professional mHealth apps, including study designs, data collection tools, and perspectives.

Background Technology can support healthy aging and empower older adults to live independently. However, technology adoption by older adults, particularly assistive technology (AT), is limited and little is known about the types of AT used among older adults. This study explored the use of key information and communication technologies (ICT) and AT among community-dwelling adults aged ≥ 65. Methods A cross-sectional study was conducted among community-dwelling adults aged ≥ 65 in southern Germany using a paper-based questionnaire. The questionnaire included questions on the three domains sociodemographic aspects, health status, and technology use. Technology use was considered separately for key ICT (smartphone, computer/laptop, and tablet) and a range of 31 different AT. Data were analyzed using descriptive statistics, univariate analyses, and Bernoulli Naïve Bayes modelling. Results The questionnaire was answered by 616 participants (response rate: 24.64%). ICT were used by 497 (80.68%) participants and were associated with lower age, higher level of education, living together with someone, availability of internet connection, higher interest in technology, and better health status ( p  < .05). No association was found with sex and size of the hometown. The most frequently owned AT were a landline phone, a body scale, and a blood pressure monitor. Several AT related to functionality, (instrumental) activities of daily living- (IADL), and morbidity were used more frequently among non-ICT users compared to ICT-users: senior mobile phone (19.33% vs. 3.22%), in-house emergency call (13.45% vs. 1.01%), hearing aid (26.89% vs. 16.7%), personal lift (7.56% vs. 1.61%), electronic stand-up aid (4.2% vs. 0%). Those with higher interest in technology reported higher levels of benefit from technology use. Conclusions Despite the benefits older adults can gain from technology, its use remains low, especially among those with multimorbidity. Particularly newer, more innovative and (I)ADL-related AT appear underutilized. Considering the potential challenges in providing adequate care in the future, it may be crucial to support the use of these specific AT among older and frailer populations. To focus scientific and societal work, AT with a high impact on autonomy ((I)ADL/disease-related) should be distinguished from devices with a low impact on autonomy (household-/ comfort-related).

Digital technologies are increasingly being introduced into the healthcare system and in settings like hospitals and geriatric long-term care (LTC) facilities, offering potential benefits such as improved care quality, reduced workload or enhanced documentation processes. However, the success of these technologies depends also on the acceptance by the primary users, the nursing staff. This review synthesizes empirical studies that have explored the acceptance of digital technologies by nursing staff in geriatric LTC settings, building upon the foundational work by Yu et al. (2009). The goal is to identify influencing factors, assess the extent of existing evidence and highlight research gaps in this care setting. A systematic literature review was conducted following PRISMA 2020 guidelines. The SPIDER framework was used for eligibility criteria. Databases searched included PubMed, ACM Digital Library, Web of Science and the Health Administration Database ProQuest. Studies were included if they empirically examined the acceptance of digital technologies by nursing staff in geriatric LTC settings. Two reviewers independently screened the studies, extracted data and assessed methodological quality using the CASP (Critical Appraisal Skills Programme) checklist. A total of three studies met the criteria, highlighting a gap in research on this topic. The studies applied cross-sectional quantitative designs, highlighted critical determinants of technology acceptance, including perceived usefulness, ease of use, digital competence and organizational support. The studies involved a total of n=1,019 participants from Germany, Australia and The Netherlands. Barriers included lack of user involvement, lack of training, poor system design and demographic differences in digital affinity. This review shows that the acceptance of digital technologies by nursing staff in geriatric LTC settings is shaped by a constellation of individual factors, such as digital competence and perceived relevance of technology, as well as organizational factors like access to training and involvement of staff in the implementation process. Despite these insights, the limited number of empirical studies highlights a research gap in this care setting. To ensure sustainable digital transformation in geriatric LTC, future research should prioritize rigorous and participatory approaches, using longitudinal, intervention-based or multilevel study designs.

Geriatric co-management is known to improve treatment of older adults in various clinical settings, however, widespread application of the concept is limited due to restricted resources. Digitalization may offer options to overcome these shortages by providing structured, relevant information and decision support tools for medical professionals. We present the SURGE-Ahead project (Supporting SURgery with GEriatric co-management and Artificial Intelligence) addressing this challenge. A digital application with a dashboard-style user interface will be developed, displaying 1) evidence-based recommendations for geriatric co-management and 2) artificial intelligence-enhanced suggestions for continuity of care (COC) decisions. The development and implementation of the SURGE-Ahead application (SAA) will follow the Medical research council framework for complex medical interventions. In the development phase a minimum geriatric data set (MGDS) will be defined that combines parametrized information from the hospital information system with a concise assessment battery and sensor data. Two literature reviews will be conducted to create an evidence base for co-management and COC suggestions that will be used to display guideline-compliant recommendations. Principles of machine learning will be used for further data processing and COC proposals for the postoperative course. In an observational and AI-development study, data will be collected in three surgical departments of a University Hospital (trauma surgery, general and visceral surgery, urology) for AI-training, feasibility testing of the MGDS and identification of co-management needs. Usability will be tested in a workshop with potential users. During a subsequent project phase, the SAA will be tested and evaluated in clinical routine, allowing its further improvement through an iterative process. The outline offers insights into a novel and comprehensive project that combines geriatric co-management with digital support tools to improve inpatient surgical care and continuity of care of older adults. German clinical trials registry (Deutsches Register für klinische Studien, DRKS00030684), registered on 21st November 2022.

The use of assistive technologies (ATs) to support older people has been fueled by the demographic change and technological progress in many countries. These devices are designed to assist seniors, enable independent living at home or in residential facilities, and improve quality of life by addressing age-related difficulties. We aimed to evaluate the effectiveness of ATs on relevant outcomes with a focus on frail older adults. A systematic literature review of randomized controlled trials evaluating ATs was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The Ovid Medline, PsycINFO, SocIndex, CINAHL (Cumulative Index to Nursing and Allied Health Literature), CENTRAL (Cochrane Central Register of Controlled Trials), and IEEEXplore databases were searched from January 1, 2009, to March 15, 2019. ATs were included when aiming to support the domains autonomy, communication, or safety of older people with a mean age ≥65 years. Trials performed within a laboratory setting were excluded. Studies were retrospectively categorized according to the physical frailty status of participants. A total of 19 trials with a high level of heterogeneity were included in the analysis. Six device categories were identified: mobility, personal disease management, medication, mental support, hearing, and vision. Eight trials showed significant effectiveness in all or some of the primary outcome measures. Personal disease management devices seem to be the most effective, with four out of five studies showing significant improvement of disease-related outcomes. Frailty could only be assessed for seven trials. Studies including participants with significant or severe impairment showed no effectiveness. Different ATs show some promising results in well-functioning but not in frail older adults, suggesting that the evaluated ATs might not (yet) be suitable for this subgroup. The uncertainty of the effectiveness of ATs and the lack of high-quality research for many promising supportive devices were confirmed in this systematic review. Large studies, also including frail older adults, and clear standards are needed in the future to guide professionals, older users, and their relatives. PROSPERO CRD42019130249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=130249.

Mobile health (mHealth) apps enhance clinical nursing by improving access to resources and patient care. Further benefits include reduced errors, time savings, better communication, cost reduction, and training. Understanding factors driving nurses' continued mHealth adoption is key to its sustained success. This study extends the expectation-confirmation model (ECM) to explore the determinants of Iranian nurses' continuance intention to use mHealth apps in their daily clinical routines. A cross-sectional, descriptive-analytical study was conducted among 315 nurses from hospitals affiliated with Kashan University of Medical Sciences. The Nurses' Mobile Health Device Acceptance Scale (NMHDA-Scale) was developed by the authors in 2022. The Intention to Continue Using Mobile Health Applications for Nurses questionnaire assesses nurses' future willingness to use mHealth apps in their practice. This questionnaire was designed based on the ECM and the approach by Waltz et al. Its primary aim is to identify the factors that influence mHealth device acceptance, specifically among clinical nurses, as previous studies have not focused on this group and have shown inconsistent relationships between various factors. Participants completed structured questionnaires measuring perceived usefulness, perceived ease of use, social influence, habits, and technology anxiety. Data were analyzed using structural equation modeling in AMOS (version 26). The model tested relationships among confirmation, perceived usefulness, social influence, technology anxiety, and mHealth continuance behavior. The analyzed sample (n=315) consisted primarily of female (252/315, 80%) and married (243/315, 77.1%) nurses, with a mean age of 35.67 (SD 1.24) years. The analysis revealed that perceived usefulness was significantly influenced by both confirmation (P<.001) and social influence (P<.001). Perceived ease of use was negatively impacted by new technology anxiety (P<.001), indicating that higher anxiety levels reduced perceived ease of use. Additionally, mHealth continuance behavior was positively associated with habits (P=.002), social influence (P<.001), and perceived security risks (P=.008). Contrary to expectations, perceived usefulness did not directly influence mHealth continuance (P=.15), suggesting that other factors, such as habits and social influence, play a more significant role in long-term use. Sustained mHealth app use by nurses hinges more on social influence and confirmed expectations than perceived usefulness. Although new technology anxiety remains a barrier, habits and social influence are key to long-term adoption. Hospital leaders should prioritize strategies that foster positive social reinforcement, minimize security concerns, and reduce anxiety through training and support when integrating mHealth into nursing workflows. These findings offer critical insights for improving digital health implementation, ultimately enhancing patient care and clinical efficiency.

The use of mobile tools in nursing care is indispensable. Given the importance of nurses' acceptance of these tools in delivering effective care, this issue requires greater attention. This study aims to design the Mobile Health Tool Acceptance Scale for Nurses based on the Expectation-Confirmation Theory and to evaluate it psychometrically. Using a Waltz-based approach grounded in existing tools and the constructs of the Expectation-Confirmation Theory, the initial version of the scale was designed and evaluated for face and content validity. Construct validity was examined through exploratory factor analysis, concurrent validity, and known-group comparison. Reliability was assessed using measures of internal consistency and stability. The initial version of the scale consisted of 33 items. During the qualitative and quantitative content validity stage, 1 item was added and 1 item was removed. Exploratory factor analysis, retaining 33 items, identified 5 factors that explained 70.53% of the variance. A significant positive correlation was found between the scores of the designed tool and nurses' attitudes toward using mobile-based apps in nursing care (r=0.655, P<.001). The intraclass correlation coefficient, Cronbach α, and ω coefficient were 0.938, 0.953, and 0.907, respectively. The 33-item scale developed is a valid and reliable instrument for measuring nurses' acceptance of mobile health tools.

The present study investigates the feasibility and usability of a sensor-based eHealth treatment in psychotherapy for pediatric obsessive-compulsive disorder (OCD), and explores the promises and pitfalls of this novel approach. With eHealth interventions, therapy can be delivered in a patient's home environment, leading to a more ecologically valid symptom assessment and access to experts even in rural areas. Furthermore, sensors can help indicate a patient's emotional and physical state during treatment. Finally, using sensors during exposure with response prevention (E/RP) can help individualize therapy and prevent avoidance behavior. In this study, we developed and subsequently evaluated a multimodal sensor-based eHealth intervention during 14 video sessions of cognitive-behavioral therapy (CBT) in 20 patients with OCD aged 12-18. During E/RP, we recorded eye movements and gaze direction via eye trackers, and an ECG chest strap captured heart rate (HR) to identify stress responses. Additionally, motion sensors detected approach and avoidance behavior. The results indicate a promising application of sensor-supported therapy for pediatric OCD, such that the technology was well-accepted by the participants, and the therapeutic relationship was successfully established in the context of internet-based treatment. Patients, their parents, and the therapists all showed high levels of satisfaction with this form of therapy and rated the wearable approach in the home environment as helpful, with fewer OCD symptoms perceived at the end of the treatment. The goal of this study was to gain a better understanding of the psychological and physiological processes that occur in pediatric patients during exposure-based online treatment. In addition, 10 key considerations in preparing and conducting sensor-supported CBT for children and adolescents with OCD are explored at the end of the article. This approach has the potential to overcome limitations in eHealth interventions by allowing the real-time transmission of objective data to therapists, once challenges regarding technical support and hardware and software usability are addressed. www.ClinicalTrials.gov, identifier (NCT05291611).