The scenario technique is a method for future research and for strategic planning. Today, it includes both qualitative and quantitative elements. The aims of this scoping review are to give an overview of the application of the scenario method in the fields of health care and to make suggestions for better reporting in future scenario projects. Between January 2013 and October 2013 we conducted a systematic search in the databases Medline, Embase, PsycInfo, Eric, The Cochrane Library, Scopus, Web of Science, and Cinahl since inception for the term ‘scenario(s)’ in combination with other terms, e.g. method, model, and technique. Our search was not restricted by date or language. In addition, we screened the reference lists of the included articles. A total of 576 bibliographical records were screened. After removing duplicates and three rounds of screening, 41 articles covering 38 different scenario projects were included for the final analysis. Nine of the included articles addressed disease related issues, led by mental health and dementia (n = 4), and followed by cancer (n = 3). Five scenario projects focused on public health issues at an organizational level and five focused on the labor market for different health care professionals. In addition, four projects dealt with health care ‘in general’, four with the field of biotechnology and personalized medicine, and additional four with other technology developments. Some of the scenario projects suffered from poor reporting of methodological aspects. Despite its potential, use of the scenario method seems to be published rarely in comparison to other methods such as the Delphi-technique, at least in the field of health care. This might be due to the complexity of the methodological approach. Individual project methods and activities vary widely and are poorly reported. Improved criteria are required for reporting of scenario project methods. With improved standards and greater transparency, the scenario method will be a good tool for scientific health care planning and strategic decision-making in public health.

Background: Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. Objective: In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. Methods: Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. Results: From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI’s application in the realm of medicine. Conclusions: The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.

Zusammenfassung: Mehr als die Hälfte der deutschen Bevölkerung hat Schwierigkeiten im Umgang mit Gesundheitsinformationen. Es ist eine wichtige Aufgabe der Versorgungsforschung zu untersuchen, wie sich die Professionen und Organisationen des Gesundheitssystems dieser Herausforderung stellen können. Das DNVF Memorandums Gesundheitskompetenz (Teil 1) nimmt Begriffsbestimmungen der individuellen und organisationalen Gesundheitskompetenz vor, präsentiert den nationalen und internationalen Forschungsstand und stellt ethische Aspekte der versorgungsbezogenen Gesundheitskompetenzforschung dar. Weiterhin wird die Relevanz der Gesundheitskompetenzforschung in verschiedenen Lebensphasen, bei unterschiedlichen Personengruppen sowie in verschiedenen Kontexten der Gesundheits- und Krankenversorgung erarbeitet. Vor diesem Hintergrund werden zentrale Forschungsgegenstände und zukünftige Forschungsdesiderata abgeleitet.

Influenza and pneumococcal vaccination can prevent disease and potentially life-threatening complications like sepsis. Elderly people have an increased risk of severe disease and therefore constitute a major target group for vaccination. To increase vaccination coverage, targeted interventions are needed that take theory-based specific determinants of vaccination behaviour into account. Moreover, message and campaign design should consider specific age-related characteristics (e.g., information processing, media use). The aim of this study is (i) to identify the specific informational and interventional needs of this risk group, (ii) to design and implement a targeted intervention aiming to decrease vaccine hesitancy, increase vaccine uptake and decrease the health and economic burden due to the respective diseases, and (iii) to measure the effect of this evidence-informed intervention on various levels. Prospective, multi-methods intervention study targeting individuals aged ≥60 years in a model region in Germany (federal state of Thuringia, 500,000 inhabitants ≥60 years old). The development of the intervention follows theory-based and evidence-informed principles: Data from a cross-sectional representative study provide insights into specific determinants of the target group’s vaccination behaviour. Additionally, media use is analysed to identify adequate communication channels for specific subgroups. In pilot studies, the intervention materials are adapted to the specific cognitive requirements of the target group. For development and implementation of the intervention, an interdisciplinary and trans-sectoral approach is used, including psychology, communication science, design, medical science, epidemiology and various public health players. The intervention will be implemented in autumn and winter 2017/18 and 2018/19 and adjusted in between. Evaluation of the intervention includes: awareness, use and recall of intervention materials, effects on changes in determinants of vaccination behaviour, self-reported vaccine uptake, and vaccination coverage in the intervention area (primary outcomes), as well as disease incidences (secondary outcomes) and the economic burden of influenza, pneumonia, invasive pneumococcal disease and sepsis for the healthcare system (tertiary outcomes). The data will add to the body of evidence on the effectiveness of evidence-informed vaccination campaign development as well as on the clinical and economic effects of pneumococcal and influenza vaccination. The effect of the intervention will teach valuable lessons about the principles of campaign development and evaluation, and can motivate a subsequent nationwide intervention. DRKS00012653 . Registered 24.11.2017. Retrospectively registered.

Background: Electronic health records (EHRs) are a promising approach to document and map (complex) health information gathered in health care worldwide. However, possible unintended consequences during use, which can occur owing to low usability or the lack of adaption to existing workflows (eg, high cognitive load), may pose a challenge. To prevent this, the involvement of users in the development of EHRs is crucial and growing. Overall, involvement is designed to be very multifaceted, for example, in terms of the timing, frequency, or even methods used to capture user preferences.Objective: Setting, users and their needs, and the context and practice of health care must be considered in the design and subsequent implementation of EHRs. Many different approaches to user involvement exist, each requiring a variety of methodological choices. The aim of the study was to provide an overview of the existing forms of user involvement and the circumstances they need and to provide support for the planning of new involvement processes.Methods: We conducted a scoping review to provide a database for future projects on which design of inclusion is worthwhile and to show the diversity of reporting. Using a very broad search string, we searched the PubMed, CINAHL, and Scopus databases. In addition, we searched Google Scholar. Hits were screened according to scoping review methodology and then examined, focusing on methods and materials, participants, frequency and design of the development, and competencies of the researchers involved.Results: In total, 70 articles were included in the final analysis. There was a wide range of methods of involvement. Physicians and nurses were the most frequently included groups and, in most cases, were involved only once in the process. The approach of involvement (eg, co-design) was not specified in most of the studies (44/70, 63%). Further qualitative deficiencies in the reporting were evident in the presentation of the competences of members of the research and development teams. Think-aloud sessions, interviews, and prototypes were frequently used.Conclusions: This review provides insights into the diversity of health care professionals’ involvement in the development of EHRs. It provides an overview of the different approaches in various fields of health care. However, it also shows the necessity of considering quality standards in the development of EHRs together with future users and the need for reporting this in future studies.