Background: Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios. Objective: This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care. Methods: Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria. Results: The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail. Conclusions: The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.

The existence, usage and benefits of digital technologies in nursing care are relevant topics in the light of the current discussion on technologies as possible solutions to problems such as the shortage of skilled workers and the increasing demand for long-term care. A lack of good empirical overviews of existing technologies in the present literature prompted us to conduct this review. Its purpose was to map the field of digital technologies for informal and formal care that have already been explored in terms of acceptance, effectiveness and efficiency (AEE), and to show the scope of the used methods, target settings, target groups and fields of support. A systematic literature search was conducted using Medline, Scopus, CINAHL, Cochrane Library, ACM Digital Library, IEEE Xplore, the Collection of Computer Science Bibliographies, GeroLit and CareLit. In addition, project websites were manually screened for relevant publications. Seven hundred fifteen papers were included in the review. Effectiveness studies have been most frequently performed for ICT, robots and sensors. Acceptance studies often focussed on ICT, robots and EHR/EMR. Efficiency studies were generally rare. Many studies were found to have a low level of evidence. Experimental designs with small numbers and without control groups were the most common methods used to evaluate acceptance and effectiveness. Study designs with high evidence levels were most commonly found for ICT, robots and e-learning. Technologies evaluated for informal caregivers and children or indicated for formal care at home or in cross-sectoral care were rare. We recommend producing high-quality evaluations on existing digital technologies for AEE in real-life settings rather than systematic reviews with low-quality studies. More focus should be placed on research into efficiency. Future research should be devoted to a closer examination of the applied AEE evaluation methods. Policymakers should provide funding to enable large-scale, long-term evaluations of technologies in the practice of care, filling the research gaps for technologies, target settings and target groups identified in this review.

Background: The field of digital technologies being developed or applied to support nursing care is extensive. The aim of this scoping review is to provide an overview on technologies for which results on positive or negative effects on persons in need of care, caregivers or care institutions are available and to appraise the reliability of these results. Methods: A scoping review design has been used to identify studies focussing on the effectiveness of digital technologies in nursing care for persons in need of care, caregivers or care institutions. The screening process included 19,510 scientific publications from 9 databases. Results: A total of 123 single studies and 31 reviews were subjected to the analysis. The included technologies comprise nursing and health information technologies, such as assistive devices, information and communication technologies, sensors and robotics. The results show that there are many studies that demonstrate positive effects, but the level of evidence is mostly low and study sizes are often small. Hardly any technology has been researched intensively enough to produce conclusive results. Studies on a high level of evidence (RCTs) lack for most technological areas. Heterogeneous results in some areas indicate that effects may depend strongly on the mode and specific context into which the technologies are introduced. Conclusion: Due to the limited evidence on effectiveness of digital technologies in nursing care, it is not surprising that care institutions are reluctant to put innovative technologies into practice. The scoping review indicates technology areas that should be subject to future research with higher quality studies. Research on outpatient, informal and cross-sectoral care should be intensified to further exploit the potential of digital technologies with a view to improving independence of care-recipients and unburdening formal and informal carers.

Objectives: Quality of life (QoL) is a major outcome parameter in dementia care. Self-ratings are considered the best way to evaluate QoL, but staff-ratings also provide valid results. In particular, the discrepancies between self-ratings and staff-ratings are underrepresented. The aim was to identify characteristics of people with dementia that improve the probability of completing a self rating QoL instrument on the ‘Quality of Life – Alzheimers’ Disease’ (QoL-AD). Additionally, a level of agreement was set between self-rated and staff-rated QoL-AD and possible influencing factors. Method: A cross-sectional study was conducted in 2010 in Berlin. Using the instrument QoL-AD, the self- and staff-rated QoL of people with dementia was assessed. Results: 104 residents (73.1% female, mean age: 79.0 years, mean cognitive function (MMSE): 11.5) were included in this research project. 49 (47.1%) residents were able to complete the QoL-AD questionnaire. A predictor to complete the QoL-AD was the MMSE-part ‘language’. Residents rated their QoL as significantly higher than the nursing staff did. If the primary nurse rated the QoL, a significantly better agreement was identified. Conclusion: The study generated new findings concerning a better understanding of QoL measurements. The results suggest the usefulness of performing self-ratings whenever possible. If proxy-ratings have to be used, these should be performed by primary nurses only in order to get reliable results.

Background: Digital nursing technologies (DNT) comprise an expanding, highly diverse field of research, explored using a wide variety of methods and tools. Study results are therefore difficult to compare, which raises the question how effectiveness of DNT can be adequately measured. Methods currently used might not be sufficient for certain specific nursing contexts. A comprehensive outcome framework that shows the multitude of possible outcome areas could be useful to generate more comparable results. The aim of the present study is to develop an outcome framework for DNT and to indicate which outcome areas have been most frequently evaluated in previous studies and how this has been done. Methods: We combined an inductive and deductive approach to develop the framework. The numerical analysis is based on a scoping review focussing on the effectiveness of DNT for persons in need of care, formal or informal caregivers or care institutions. Nine databases were included in the screening: Medline, Scopus, CINAHL, Cochrane Library, ACM Digital Library, IEEE Xplore, the Collection of Computer Science Bibliographies, GeroLit and CareLit. Additional literature searches and expert interviews were included. Results: The developed framework comprises four outcome target groups and 47 outcome areas. There are considerable differences in the researched outcome areas for the individual outcome target groups. Persons in need of care were by far the most frequently surveyed, particularly with respect to their psychological health. There are much fewer studies on formal and informal caregivers, and it is particularly noticeable that the quality of life of both groups has rarely been investigated. Care process quality was most frequently researched for organisations. Conclusion: We were able to provide a comprehensive DNT outcome framework, thereby identifying the outcome tools used and the less researched outcome areas. We recommend a detailed investigation of all areas and tools in future research projects with a view to initiating a discussion on the differing importance of existing outcome areas and on a standardisation of outcome tools. We also recommend the development of outcome areas for the macro level of effectiveness assessment.

Zusammenfassung: Einleitung: Das Asperger-Syndrom (AS) ist eine Störung von unsicherer nosologischer Validität, deren Abgrenzung zum sogenannten High-functioning Autismus sehr schwierig ist. Diese Störungen werden bis heute leider erst sehr spät erkannt, oft werden zunächst einige andere Diagnosen gestellt. Ziel der vorliegenden Untersuchung ist es, ein Instrument (MBAS) vorzustellen, welches für die Erfassung dieser Störungen sensibel ist. Methodik: Der Fragebogen wurde insgesamt 91 Probanden (44 mit einer autistischen Diagnose, 47 mit nicht-autistischen Diagnosen) vorgelegt. Ergebnisse: Die Items der MBAS erweisen sich überwiegend als mittelschwer und überwiegend gut trennscharf. Die interne Konsistenz der gesamten Skala ist als sehr hoch zu bewerten (Cronbach’s alpha = .91). Es liegt eine konvergente Validität der Ergebnisse aus der MBAS und dem ADI-R vor (r = 0.61, p = .001) Vergleicht man die gebildeten Gruppen «autistisch» vs. «nicht autistisch» in Bezug auf den Gesamtscore miteinander, so zeigt sich, dass der Fragebogen sehr gut zwischen den beiden Vergleichsgruppen unterscheidet. Bei einer Sensitivität von 95.5% und einer Spezifität von 95.7% kommt es nur zu wenigen Fehlklassifikationen, falsch negative Einschätzungen kamen in unserer Stichprobe lediglich bei sehr jungen Probanden vor. Schlussfolgerungen: Die «MBAS» erweist sich als reliabel und diagnostisch valide. Zum Zwecke der Präselektion oder Generierung von Verdachtsdiagnosen des Autismus auf hohem Funktionsniveau ist dieser Fragebogen gut geeignet.