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      Digitale Technologien in der Pflege – Was können sie leisten? Translated title: Digital technologies in nursing—what can they achieve?

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          Abstract

          Digitale Pflegetechnologien gewinnen in der Langzeitpflege zunehmend an Bedeutung. Sie umfassen alle Technologien, die mittels Vernetzung und/oder Sensorik Prozesse und/oder Produkte verändern, und schließen künstliche Intelligenz, also Verfahren, Methoden und Algorithmen, um mittels Daten zu lernen und darauf aufbauend zielorientierte Handlungen zu ermöglichen, ein. Ihre Anwendung reicht von der Förderung professioneller Zusammenarbeit über Steuerung und Verwaltung, Wissenserwerb und -weitergabe, Interaktion und Beziehung bis zur körpernahen Pflege.

          Digitale Pflegetechnologien haben das Potenzial, gleichzeitig die Qualität der Pflege zu erhöhen und die Arbeitsbedingungen in der Pflege zu verbessern. Allerdings stehen dem Hemmnisse auf verschiedenen Ebenen entgegen: Die Entwicklung dieser Technologien wird häufig von den technischen Möglichkeiten getrieben, sodass Produkte entstehen, die im Pflegealltag keinen konkreten Nutzen entfalten. Bei der Implementation wird nur die Bedienung geschult; es erfolgt aber keine Organisationsentwicklung zur systematischen Integration der Technologien in den Arbeitsalltag. Zudem fehlen hochwertige Evaluationen, die den tatsächlichen Nutzen im Arbeitsalltag abbilden, um so potenzielle Anwender:innen für die Technologie zu gewinnen. Schließlich ist die nachhaltige Finanzierung, insbesondere der Unterhaltung dieser Technologien, nicht gesichert.

          Eine gelingende Digitalisierung in der Pflege setzt daher voraus, dass Technikentwickler:innen und -anwender:innen ebenso wie Politik und Wissenschaft gemeinsam diese Hemmnisse überwinden. Das impliziert, dass Pflegende von Anfang an in den Entwicklungsprozess einbezogen sind, aber auch dass Orte geschaffen werden, in denen die Wirkung digitaler Pflegetechnologien im tatsächlichen Versorgungsgeschehen evaluiert werden kann.

          Translated abstract

          Digital care technologies are becoming increasingly important in long-term care. They encompass all technologies that change processes and products by means of networking and/or sensor technology and include artificial intelligence, that is, processes, methods, and algorithms for learning by means of data and enabling meaningful decisions based on this. Their application ranges from the promotion of professional collaboration, control and management, knowledge acquisition and transfer, interaction and relationships to physical caregiving.

          Digital care technologies have the potential to simultaneously increase the quality of care and improve working conditions in care. However, there are obstacles to this at various levels: The development of these technologies is often driven by technical possibilities, resulting in products that do not provide any concrete benefits in routine nursing care. During implementation, only the operator is trained; however, there is no organizational development for the systematic integration of these technologies into routine work. In addition, there is a lack of high-quality evaluations showing evidence of the actual benefits to routine work in order to attract potential users to these technologies. Finally, there is no sustainable financing, especially for the maintenance of these technologies.

          Successful digitization in long-term care therefore requires that technology developers and users, as well as policymakers and scientists, jointly overcome these obstacles. This implies that caregivers are involved in the development process from the outset (co-creation) but also that spaces are created where the effect of digital care technologies can be evaluated in routine caregiving.

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          Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?

          The field of artificial intelligence (AI) has evolved considerably in the last 60 years. While there are now many AI applications that have been deployed in high-income country contexts, use in resource-poor settings remains relatively nascent. With a few notable exceptions, there are limited examples of AI being used in such settings. However, there are signs that this is changing. Several high-profile meetings have been convened in recent years to discuss the development and deployment of AI applications to reduce poverty and deliver a broad range of critical public services. We provide a general overview of AI and how it can be used to improve health outcomes in resource-poor settings. We also describe some of the current ethical debates around patient safety and privacy. Despite current challenges, AI holds tremendous promise for transforming the provision of healthcare services in resource-poor settings. Many health system hurdles in such settings could be overcome with the use of AI and other complementary emerging technologies. Further research and investments in the development of AI tools tailored to resource-poor settings will accelerate realising of the full potential of AI for improving global health.
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            Digital technology and nursing care: a scoping review on acceptance, effectiveness and efficiency studies of informal and formal care technologies

            Background 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. Methods 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. Results 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. Conclusion 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. Electronic supplementary material The online version of this article (10.1186/s12913-019-4238-3) contains supplementary material, which is available to authorized users.
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              The NASSS-CAT Tools for Understanding, Guiding, Monitoring, and Researching Technology Implementation Projects in Health and Social Care: Protocol for an Evaluation Study in Real-World Settings

              Background Projects to implement health care and social care innovations involving technologies are typically ambitious and complex. Many projects fail. Greenhalgh et al’s nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework was developed to analyze the varied outcomes of such projects. Objective We sought to extend the NASSS framework to produce practical tools for understanding, guiding, monitoring, and researching technology projects in health care or social care settings. Methods Building on NASSS and a complexity assessment tool (CAT), the NASSS-CAT tools were developed (in various formats) in seven co-design workshops involving 50 stakeholders (industry executives, technical designers, policymakers, managers, clinicians, and patients). Using action research, they were and are being tested prospectively on a sample of case studies selected for variety in conditions, technologies, settings, scope and scale, policy context, and project goals. Results The co-design process resulted in four tools, available as free downloads. NASSS-CAT SHORT is a taster to introduce the instrument and gauge interest. NASSS-CAT LONG is intended to support reflection, due diligence, and preliminary planning. It maps complexity through stakeholder discussion across six domains, using free-text open questions (designed to generate a rich narrative and surface uncertainties and interdependencies) and a closed-question checklist; this version includes an action planning section. NASSS-CAT PROJECT is a 35-item instrument for monitoring how subjective complexity in a technology implementation project changes over time. NASSS-CAT INTERVIEW is a set of prompts for conducting semistructured research or evaluation interviews. Preliminary data from empirical case studies suggest that the NASSS-CAT tools can potentially identify, but cannot always help reconcile, contradictions and conflicts that block projects’ progress. Conclusions The NASSS-CAT tools are a useful addition to existing implementation tools and frameworks. Further support of the implementation projects is ongoing. We are currently producing digital versions of the tools, and plan (subject to further funding) to establish an online community of practice for people interested in using and improving the tools, and hold workshops for building cross-project collaborations. International Registered Report Identifier (IRRID) DERR1-10.2196/16861
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                Author and article information

                Contributors
                wolf-ostermann@uni-bremen.de
                Journal
                Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz
                Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz
                Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1436-9990
                1437-1588
                7 February 2024
                7 February 2024
                2024
                : 67
                : 3
                : 324-331
                Affiliations
                [1 ]Institut für Public Health und Pflegeforschung, Universität Bremen, ( https://ror.org/04ers2y35) Grazer Str. 4, 28359 Bremen, Deutschland
                [2 ]Leibniz-WissenschaftsCampus Digital Public Health, Bremen, Deutschland
                [3 ]SOCIUM Forschungszentrum Ungleichheit und Sozialpolitik, Universität Bremen, ( https://ror.org/04ers2y35) Bremen, Deutschland
                Article
                3843
                10.1007/s00103-024-03843-3
                10927896
                38326568
                78e698c2-687c-49ce-bd27-3d2e755d643d
                © The Author(s) 2024

                Open Access Dieser Artikel wird unter der Creative Commons Namensnennung 4.0 International Lizenz veröffentlicht, welche die Nutzung, Vervielfältigung, Bearbeitung, Verbreitung und Wiedergabe in jeglichem Medium und Format erlaubt, sofern Sie den/die ursprünglichen Autor(en) und die Quelle ordnungsgemäß nennen, einen Link zur Creative Commons Lizenz beifügen und angeben, ob Änderungen vorgenommen wurden.

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                History
                : 1 September 2023
                : 26 January 2024
                Funding
                Funded by: Universität Bremen (1013)
                Categories
                Leitthema
                Custom metadata
                © Robert Koch-Institut 2024

                digitalisierung,digitale pflegetechnologien,künstliche intelligenz (ki),pflege,technik,digitalization,digital nursing technologies,artificial intelligence (ai),nursing,technology

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