The growing use of technology for mathematics in higher education opens new pedagogical and technological challenges for teachers. The objective of this study was to analyze the teaching approaches and technology-related pedagogical competencies of 29 mathematics teachers (15 females and 14 males) from nine European countries. After conducting semi-structured interviews, the Approaches to Teaching Inventory (ATI-16) and the Technological Pedagogical Content Knowledge (TPACK) framework survey were applied. The results show large individual variations in teaching approaches, technological competencies, and institutional support. One-third of teachers apply a more student-centered approach, one-third a more teacher-centered approach, and one-third a mixed approach. Educating and supporting teachers in embracing educational technologies thus needs to be tailored strongly to individual needs and the available institutional support resources and infrastructure.

To explore the challenges of secondary use of routinely collected data for analyzing nursing-sensitive outcomes in Austrian acute care hospitals. A convergent parallel mixed methods design was performed. We conducted a quantitative representative survey with nursing managers from 32 Austrian general acute care hospitals and 11 qualitative semi-structured interviews with nursing quality management experts. Both results were first analyzed independently and afterward merged in the discussion. On average, 76% of nursing documentation is already electronically supported in the surveyed Austrian hospitals. However, existing nursing data is seldom used for secondary purposes such as nursing-sensitive outcome analyses. This is due to four major reasons: First, hospitals often do not have a data strategy for the secondary use of routine data. Second, hospitals partly lack the use of standardized and uniform nursing terminologies, especially for nursing evaluation. Third, routine nursing data is often not documented correctly and completely. Fourth, data on nursing-sensitive outcomes is usually collected in specific documentation forms not integrated into routine documentation. The awareness of the possibilities for secondary use of nursing data for nursing-sensitive outcome analyses in Austrian hospitals is still in its infancy. Therefore, nursing staff and nursing management must be trained to understand how to collect and process nursing data for nursing-sensitive outcome analyses. Further studies would be interesting in order to determine the factors that influence the decision-making processes for the secondary use of nursing data for outcome analyses.

A large amount of patient data is produced and documented in patient care. Health care professionals expect that this routinely collected patient data can also be used for secondary purposes such as measuring the quality of care or to gain new knowledge. Routine data needs to be documented in a standardized form, based on clinical terminologies, to allow this secondary use of data. In Austria, hospitals are currently moving from paper-based documentation to computer-based documentation, but parts of the documentation are still done in paper-based form or without using clinical terminologies, especially in nursing. This study aims to analyze the availability of standardized electronic patient data in nursing in Austria. We conducted an online survey of 32 senior nursing managers at 32 Austrian hospitals. The study showed that 79% of hospitals use electronic health records for nursing documentation, but only 29% of the nursing care plans are documented in a standardized way using standardized nursing classification systems such as NANDA-I.

Under the term "big data", the secondary use of available IT-based clinical routine data promises to easily support quality management and research. Available clinical data can, for example, be used to measure care quality, as this is an important aspect in the evaluation of health interventions. Different indicators are available for measuring care quality, e.g. nursing sensitive outcome indicators. However, a specific methodology for using clinical routine data to measure quality of nursing care is not available at the moment. We present first ideas for a model to use clinical routine data to derive nursing outcome indicators measuring quality of care. In particular, we are interested in the question of which care indicators can be extracted to predict care outcome.

BACKGROUND 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. OBJECTIVE To evaluate the completeness and applicability of the NuDaK framework for secondary use of nursing routine data in a clinical nursing setting. METHODS 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. RESULTS 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. CONCLUSIONS 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.