Abstract BackgroundHealth information technology (HIT) interventions are complex, context-dependent, and often insufficiently theorized, which can hinder their design, implementation, and evaluation. Program theory approaches such as logic models and theory of change (ToC) are well-established in public health and implementation science for articulating causal assumptions. Their use in medical informatics, however, appears inconsistent. A systematic overview of how logic models and ToC have been applied to HIT interventions is therefore needed to support theory-informed development and cumulative learning in the field. ObjectiveWe aimed to map how logic models, ToC, and related program theory approaches have been conceptualized, constructed, and applied in HIT-related interventions across disciplines. A secondary objective was to identify implications for medical informatics research and practice. MethodsFollowing PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews), searches were conducted in PubMed, Web of Science, Academic Search Elite, APA PsycArticles, and CINAHL. Eligible publications used a logic model, ToC, or related construct within an HIT intervention in any health care or social-care setting. Next, 2 reviewers independently screened records and extracted data on study characteristics, type and purpose of HIT, model structure, theoretical foundations, and reported benefits and challenges. ResultsA total of 69 publications (2012‐2025) met the criteria. Use of program theory increased markedly after 2020 and spanned medical informatics, public health, health services research, and implementation science. Logic models were most frequently applied to patient-facing and self-management technologies, particularly mobile health, telehealth, and home-based remote monitoring. Most models were used to support HIT development or evaluation. Of the total, 60 (87%) studies provided a logic model visualization, although structures varied considerably. Out of 69, 50 (72%) studies cited guidelines for model development, most commonly UK Medical Research Council guidance, realist evaluation, or the Kellogg Logic Model. Out of 69 studies, 28 (41%) used behavioral or implementation frameworks, such as Capability, Opportunity, Motivation–Behavior model (COM-B), Consolidated Framework for Implementation Research (CFIR), Expert Recommendations for Implementing Change (ERIC), Fit between Individuals, Task, and Technology (FITT), or Non-adoption, Abandonment, and challenges to the Scale-up, Spread, and Sustainability (NASSS) to populate model content. Only 3 (4%) studies reused an existing model. Reported benefits concerned improved theorization, structured evaluation, and stakeholder engagement; challenges included limited empirical evidence, high resource demands, and tensions between specificity and generalizability. ConclusionsProgram theory approaches are increasingly used to conceptualize and evaluate HIT interventions; yet, their application in medical informatics remains fragmented. More systematic and theory-informed use of logic models could enhance conceptual clarity, methodological rigor, and cumulative learning. Future work should promote model reuse, establish repositories, strengthen reporting standards, and integrate program theory in HIT education and research to support coherent development, evaluation, and scaling of digital health interventions.

Routinely collected nursing real-world data (RWD) offer opportunities for nursing research to generate new knowledge and to improve quality care. Furthermore, advanced analytics with nursing routine data opens the development of predictive models that support risk detection and decision support. However, using RWD in nursing remains challenging due to insufficient data standardization, variable data quality, and difficulty for organizations implementing a targeted data strategy for secondary use of nursing data. To address this gap, we developed the NuDaK nursing data strategy framework as a practical guide to enable and optimize the secondary use of routinely collected nursing documentation data. To evaluate the completeness and applicability of the NuDaK framework for secondary use of nursing routine data in a clinical nursing setting. We conducted a formative feasibility study in collaboration with a large acute care hospital in Austria. The NuDaK framework was piloted on a surgical ward and evaluated across four implementation phases. Data were collected via phase-specific workshops and project meetings, continuous self-evaluation protocols by the project lead, and a final online focus group interview. Data were analyzed using deductive qualitative content analysis regarding completeness and applicability of NuDaK. Evaluation results were aggregated and used to update NuDaK. The four core NuDaK aspects (“Purpose & Benefits,” “Data Set & Nursing Documentation,” “Data Modelling & Software Requirements,” and “Data Integration & Data Quality”) were confirmed as conceptually complete. Based on the evaluation results, NuDaK was now updated: (i) adopting a circular structure with four temporal phases, (ii) defining interdisciplinary stakeholder involvement across phases, (iii) specifying professional roles and responsibilities across phases, and (iv) integrating an iterative nursing information model spanning dataset development, specification, and refinement. Further NuDaK additions comprise a data quality concept with operationalized dimensions and validation procedures. The NuDaK Nursing Strategy Framework was refined through real-world piloting. It now provides for organisations and research a nursing-specific, end-to-end nursing data strategy to enable systematic RWD use. While its feasibility was demonstrated in a real-world nursing setting, further implementation studies should assess transferability across heterogeneous institutions with different documentation systems, governance structures, and levels of digital maturity.

Artificial intelligence (AI) is increasingly embedded in digital health applications and shapes how patients access information, experience care, and participate in health decisions. However, concerns are growing about whether medical AI systems are fair, transparent, accountable, and inclusive. Biases in patient-facing AI tools may exclude certain groups and undermine core dimensions of patient empowerment, such as autonomy, trust, and control. However, existing research rarely examines the direct impact of AI-related bias on patient empowerment, instead inferring effects from proxy measures such as access, system performance, or information quality. This scoping review aims to systematically map and synthesize scientific literature on bias in medical AI in health care, with a specific focus on its implications for patient empowerment and digital inclusion. The review integrates technical, social, and structural dimensions of bias to provide a conceptual overview of how these factors shape equitable AI implementation. This scoping review was conducted in accordance with the Joanna Briggs Institute methodology and reported following the PRISMA-ScR guidelines. PubMed/MEDLINE and EBSCOHost databases were searched in August 2025, with Google Scholar used for supplementary searches. Peer-reviewed studies published in English or German from 2015 onward were included if they addressed bias in medical AI applications in health care in relation to patient empowerment or the digital divide. Eligibility criteria were structured using the Population–Concept–Context framework, encompassing patient populations, technical and structural forms of bias, and AI applications across the patient journey. Study selection was performed according to predefined criteria, and findings were synthesized descriptively. The search identified 497 records, of which 23 studies met the inclusion criteria. Most studies (22/23, 95%) reported at least one form of bias, and 18/23 (78%) addressed multiple bias categories (mean 2.9 categories per study). Social bias was most frequently described (21/23, 91%), followed by algorithmic or technical bias (16/23, 70%), structural bias (14/23, 61%), and design bias (12/23, 52%). Bias was predominantly conceptualized as a multidimensional sociotechnical phenomenon rather than a purely technical issue and was particularly concentrated in patient-facing AI applications. Although many studies proposed strategies such as transparency, participatory design, or inclusive data practices, these approaches were rarely implemented or empirically evaluated. Overall, the findings reveal a gap between the recognition of bias in medical AI and the operationalization of empowerment-oriented mitigation strategies. This scoping review shows that bias in medical AI is widely recognized as a multidimensional sociotechnical issue, yet its implications for patient empowerment are rarely examined in a conceptually explicit or operationalized manner. While risks related to digital inequality are frequently acknowledged, empowerment-oriented mitigation strategies remain largely underdeveloped and unevaluated. Future research should integrate intersectional perspectives and systematically assess how design, data practices, and governance structures influence empowerment outcomes in patient-facing AI applications.

BACKGROUND:Artificial Intelligence (AI) is increasingly applied in healthcare and is often linked to patient empowerment. However, biases in data, algorithms, and design may hinder empowerment by reinforcing inequalities and limiting autonomy. OBJECTIVES:This scoping review examines how bias in healthcare AI impacts patient empowerment. METHODS:We searched PubMed and multiple databases via EBSCOHost. Studies were included if they addressed bias in healthcare AI in connection with patients, focusing on empowerment, participation or related concepts. AI with patient-centered focus was defined as AI applications directly involving patients (e.g., chatbots, decision aids). After screening 488 records, 13 studies met the inclusion criteria. RESULTS:Various social, structural, and technical biases were reported. While patient involvement in AI applications was addressed (e.g., engagement in remote monitoring or chatbot use), explicit reference to empowerment, understood as autonomy, control, and decision-making, was rare. Most studies equated empowerment with patient engagement. CONCLUSION:Bias in healthcare AI can limit the empowering potential of digital health tools, and research explicitly linking bias to empowerment outcomes is still at an early stage.

BACKGROUND:The Austrian Electronic Health Record (ELGA) aims to enhance healthcare coordination and patient empowerment, yet public uptake remains limited. OBJECTIVE:This study explored citizens' motivations and barriers toward ELGA use and reflected on the potential of science communication events to foster dialogue on digital health. METHODS:During the Long Night of Science at UMIT TIROL, 35 participants anonymously shared on a whiteboard their reasons for using or not using ELGA. Statements were thematically analyzed using the Context-Mechanism-Outcome (CMO) framework. RESULTS:Major barriers were concerns about data security and privacy, login complexity, and perceived lack of necessity. Facilitators included fast access to medical data, reduced paperwork, and improved continuity of care. CONCLUSION:Participants balanced digital convenience with privacy concerns. Public events such as the Long Night of Science provide valuable opportunities not only to inform citizens about digital health but to let them actively participate in science, exchange perspectives, and learn from their lived experiences.

Background:In 2018, the "TIGER International Recommendation Framework of Core Competencies in Health Informatics for Nurses" was published and used worldwide as a global guidance for educators and students to navigate educational requirements in an increasing digital working environment. Objective:Recent developments in digital health point to the need to revise these recommendations. Methods:This update followed the three-step logic of the 2018 framework development process, comprising (1) a global survey capturing the relevance of 43 core competency areas based on the ratings of 117 experts from all continents, (2) four regional expert workshops, and (3) 21 educational case studies. To ensure comparability with the 2018 framework, the same role-based structure was retained, covering Clinical Nursing, Nursing Management, Quality Management in Nursing, Coordination of Interprofessional Care, and Information Technology IT-Management in Nursing. Results:These recommendations underpin the high relevance level of health informatics for all nursing roles, characterizing each role by an informatics pattern: in Clinical Nursing patient issues, personal skills of working properly in a digital environment and a thorough understanding of the legal and ethical background of one's own work dominate the pattern. In Nursing Management, a similar composition prevails extended by management knowledge. The skill mix for Quality Management emerged as a blend of Clinical Nursing and Nursing Management, whereas Coordination of Interprofessional Care strongly emphasized coordination and teamwork capacities. IT Management in Nursing required basic and specialized computer skills incorporating problem solving, communication, and teamwork. These recommendations strongly suggest artificial intelligence (AI) topics should be addressed in all domains and for all roles of nurses. Conclusion:This updated recommendation framework provides guidance for focusing on areas that gained further priority such as patient safety, electronic health records, making use of its data and AI while retaining a core of invariant components of health informatics education for nurses.

BACKGROUND:Nursing diagnoses and interventions are essential components of clinical documentation and patient-centered care, yet nursing data in German-speaking healthcare settings are commonly documented using local terminologies. OBJECTIVES:This paper aims to analyze translation- and modeling-related challenges when mapping German nursing diagnoses to SNOMED CT. METHODS:Nursing diagnoses from the DiZiMa® catalog were translated and mapped to SNOMED CT using a structured semantic mapping approach. Two large language models (ChatGPT and Microsoft Copilot) were used in parallel to support translation, guided by established principles of scientific translation. RESULTS:While 98.6% of the diagnoses could be mapped to SNOMED CT, 27.2% showed semantic precision loss, particularly for risk diagnoses and context-dependent nursing concepts, often requiring postcoordination, or remaining unmapped (1.4%). CONCLUSION:Semantic interoperability of nursing data requires more than direct translation or simple 1:1 mapping, highlighting the need for nursing-specific modeling strategies.

Artificial Intelligence (AI) offers potential to support and empower nurses, yet its development depends on the availability of high-quality, standardized data. Nursing data are often fragmented, unstructured, and semantically inconsistent, hindering their secondary use. This work aims to harmonize heterogeneous nursing data from hospitals and nursing homes to create an AI-ready data foundation. Following a generic harmonization process, example datasets from two institutions and software systems were extracted and mapped to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). Using open-source tools, we performed dataset specification, vocabulary identification, coverage analysis, semantic and structural mapping, and initial ETL implementation. A core dataset was defined, covering key data elements such as demographics, vital signs, and medication. Initial test transfers demonstrated the feasibility of mapping and integrating nursing data, though complex nursing constructs such as care plans and assessments remain challenging. This study establishes a structured methodological approach for cross-institutional nursing data harmonization and lays the groundwork for the development of future AI applications in nursing.

BACKGROUND:Nurses face increasing workload and documentation burden. The N!CA (Digitalisation of Innovative Care Processes to Unburden and Empower Nurses) project develops digital innovations for nurses. METHODS:Using a Theory-of-Change approach, we developed logic models for key nursing use cases: handover, interdisciplinary rounds, medication management, and patient self-assessment. RESULTS:The logic models highlight mechanisms such as workflow standardisation and point-of-care digital support that may help to reduce burden. The logic models will guide the planned innovations. CONCLUSION:Structured logic modelling can bridge evidence, context, and practice.

INTRODUCTION:The rapid integration of digital health tools into clinical practice presents new opportunities for supporting clinical reasoning. But doctors and nurses must learn how they can use digital health tools such as AI and telehealth to support clinical reasoning (CR). Our objectives were to investigate how and which digital tools have been integrated in medical and nursing CR education. METHODOLOGY:Rapid review of 46 studies about integrating digital health tools in medical and nursing CR education. RESULTS:Most studies reported about Large Language Models, EHRs and Clinical Decision Support Systems integrated into CR education. DISCUSSION:Digital tools are increasingly integrated into medical and nursing clinical reasoning education, yet the focus is limited to certain types of tools.

The advent of generative AI (GenAI) tools in higher education challenges fundamental assumptions about academic authorship, students’ cognitive development and how writing cultivates critical thinking (CT). This systematic mapping study synthesized emerging research on how GenAI is reshaping the relationship between academic writing and CT in higher education. Drawing on 25 peer-reviewed studies published between 2023 and 2025, we analyzed conceptualizations of CT and academic writing, identified pedagogical approaches, and examined the use of GenAI tools across a range of disciplines, educational levels, and geographic contexts. Findings revealed that interest in the use of GenAI in academic writing is global and spans all disciplines and higher educational levels. However, only a minority of studies drew on robust theoretical frameworks. CT was conceptualized predominantly within a cognitive skills paradigm, while broader understandings linked to criticality and critical pedagogy were largely absent. Pedagogical models were rare and mostly untested, and educators’ perspectives were underrepresented. While GenAI was seen as supporting writing processes, its potential to foster or hinder CT remained contested. Future research should broaden conceptual foundations, include educator perspectives, and prioritize pedagogical design and evaluation. Rethinking academic writing as a transformative practice may require moving beyond the cognitive paradigm towards more participatory and ethically grounded approaches to CT.

INTRODUCTION:Integrating nursing informatics education into nursing is essential for advancing the digital transformation of nursing. The objective was to develop nursing informatics curricula tailored to the needs in Kosovo and Israel. METHODOLOGY:Reviewing 16 international guidelines, conducting interviews with 22 national stakeholders, and undertaking an international Delphi study. RESULTS:The study identified key challenges and recommendations for nursing informatics education. It ranked a list of 40 nursing informatics topics and developed two national curricula. DISCUSSION:Effective nursing informatics education on a national scale requires the integration of national stakeholders and international perspectives to address diverse needs and contexts and to advance the field.

Objective Having public trust in national electronic health record systems (NEHRs) is crucial for the successful implementation and participation of NEHRs within a nations healthcare system. Yet, a lack of conceptual clarity precludes healthcare policymakers from incorporating trust to the fullest extent possible. In response, this study seeks to validate an existing framework for public trust in the healthcare system, which will help provide a clearer understanding of what constitutes public trust in NEHRs across members of the public in different countries, cultures, and contexts. Methods Twenty-four focus groups were conducted in Austria, Germany, France, Italy, the Netherlands, and Switzerland with residents of each respective country to discuss their viewpoints on our public trust in NEHRs framework in order to validate said framework. Results Frameworks describing the causes and effects of public trust in NEHRs were created for each country studied. Across all countries, the frameworks remained similar to our base framework, highlighting our frameworks’ robustness. Data security, privacy, and autonomy were consistently described as the most important aspects of public trust in NEHRs. Concurrently, health system actors, such as doctors, were found to have significant influence on NEHR implementation. Their influence, however, can either be beneficial or detrimental to public trust in NEHRs, depending on their actions and how the public perceives those actions. Additional results detail contextual insights into country-specific viewpoints and the role of healthcare stakeholders in public trust in NEHRs. The results showcase the differences and similarities in which different populations across Europe view trust in NEHRs in the context of our framework. Conclusions These findings present public trust frameworks in the context of NEHRs for the study countries. These frameworks can assist stakeholders in obtaining a comprehensive understanding of the complexity of public trust in implementing and promoting their NEHRs, including measurements of public trust.

BACKGROUND:The use of Logic Models provides a structured approach to understanding and visualizing the impact of interventions in complex systems, such as Ambient or Active Assisted Living (AAL). OBJECTIVES:To apply a Logic Model to represent and categorise findings from a study on using a robo-coach for training younger seniors in an AAL app. METHODS:A Logic Model was developed to represent the effects that were observed in the interaction between 34 younger seniors and the robo-coach, focusing on how the training impacted their perceptions on the robo-coach. RESULTS:The Logic Model maps how specific training activities directly enhance user confidence, shaping perceptions of the robo-coach's usability and role in AAL adoption. CONCLUSION:The Logic Model proved to be an effective tool for retroactively analyzing the impact of the robo-coach intervention.

The human-induced climate crisis endangers health and the foundations of life for us humans. The professional society GMDS – German Assocation for Medical Informatics, Biometry and Epidemiology – considers itself responsible for the health in society. What does this mean in times of the climate crisis? During a workshop entitled “Climate change, digitalization and health“ at GMDS2023 in Heilbronn, action areas for GMDS as a professional society and for its members were discussed. On the micro level, for example, the necessity, the anticipated benefits for human health, and the climate consequences of planned projects should be reflected upon. On the meso level, GMDS-organized events could be made climate-neutral, for example. On the macro level, GMDS could, for example, influence funding bodies to consider climate consequences in decisions regarding research proposals. We invite everyone interested to actively participate in the planned further discussions. The establishment of a GMDS project group or working group on this topic is planned. Now is the time to take responsibility for the climate. This applies at the professional level for us personally as well as for the professional society GMDS as an institution.

BACKGROUND:Logic models graphically present the socio-technical components of a variety of 'programs' such as educational programs. They show the underlying logic and assumptions of how a program is supposed to work. We suggest that they can be used to describe the mechanisms of complex socio-technical health IT interventions. OBJECTIVE:To assess the suitability of logic models to describe cause-effect chains of health IT. RESULTS:We are currently conducting an integrative review of the impact of patient portals on patient outcomes. We extracted the following elements of logic models from the found publications: resources, activities, output, outcome, and impact. These factors are then used to populate the logic model and form a structured graphical representation of the evidence. Until now, all the evidence we found could be fit into the logic model. The logic model was able to accommodate diverse types of evidence. CONCLUSION:Logic models seem to be suitable for representing evidence on the impact of health IT.