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Patient satisfaction in the digital health era: Digital literacy and digital inclusion perspective under the Donabedian framework

Thu, 01 Jan 2026 00:00:00 GMT

Título próprio: Patient satisfaction in the digital health era: Digital literacy and digital inclusion perspective under the Donabedian framework Autoria: Geada, N.; Alturas, B. Resumo: The digital transformation of healthcare services is redefining how information is accessed and evaluated by citizens. While organizational progress is often measured by technical maturity, this study shifts the focus to the user’s perspective. Grounded in the Donabedian framework (Structure-Process-Outcome), we investigate how Digital Maturity (Structure) and Information Literacy/Inclusion (Process) culminate in Patient Satisfaction (Outcome). Using Structural Equation Modelling (SEM) with a sample of 212 participants, the results reveal that maturity acts as a catalyst for literacy, but satisfaction is strictly dependent on effective digital inclusion. This paper contributes to ‘Healthcare for Information’ by highlighting that technological infrastructure alone is insufficient without a robust healthcare strategy for health information users.

Innovative adoption model for digital health technologies among elderly with chronic diseases: Integrating Unified Theory of Acceptance and Use of Technology and Knowledge-Attitude-Practice model in a survey of 1222 patients in Shanghai

Thu, 01 Jan 2026 00:00:00 GMT

Título próprio: Innovative adoption model for digital health technologies among elderly with chronic diseases: Integrating Unified Theory of Acceptance and Use of Technology and Knowledge-Attitude-Practice model in a survey of 1222 patients in Shanghai Autoria: Chen, Y.; Yuan, J.; Li, C.; Wang, H.; Shi, L.; Zhao, S.; Oliveira, A.; Zhao, L. Resumo: Objective To propose and test an innovative model by integrating the Unified Theory of Acceptance and Use of Technology and Knowledge-Attitude-Practice model to explain the mechanisms influencing the adoption of digital health technologies by elderly patients with chronic diseases from the perspective of both internal and external factors, promoting the acceptance and utilisation of digital health technologies among elderly chronically ill patients. Study design A face-to-face questionnaire survey was conducted from July to September 2023. Study setting The study was conducted in 12 medical institutions in Shanghai, including 6 tertiary hospitals, 3 secondary hospitals and 3 community hospitals. Participants 1222 participants aged 60 years or more, diagnosed with one or more of the following chronic diseases: essential hypertension, type 2 diabetes, coronary atherosclerotic heart disease, stroke and chronic obstructive pulmonary disease, were involved in the study using convenience sampling. Critically ill emergency patients and those who were involved in medical disputes were excluded. Outcome measure The behavioural intention and usage behaviour of older patients with chronic diseases to use digital health technologies. Results The explanatory power of the proposed model for behavioural intention was 72.9%. There is a significant negative association between technology anxiety and the intention to use digital health technologies among older patients with chronic diseases (?=−0.224, p<0.001); effort expectancy (?=0.530, p<0.001) and performance expectancy (?=0.193, p<0.001) were also significantly associated with intention to use digital health technologies. Men (?=−0.104, p=0.016), relatively younger (?=−0.061, p=0.005), with experience in using digital health technologies (?=−0.452, p<0.001) were more likely to translate behavioural intention into use behaviour. Conclusions Acceptance of digital health technologies among older patients with chronic diseases was associated with a combination of internal and external factors, with the former playing a dominant role. These valuable findings provided insights and inspiration for improving digital health technologies acceptance and utilisation among older patients with chronic diseases.

Digital transformation in managing outgoing student applications: Enhancing administrative efficiency in higher education institutions

Wed, 01 Jan 2025 00:00:00 GMT

Título próprio: Digital transformation in managing outgoing student applications: Enhancing administrative efficiency in higher education institutions Autoria: Santos, E.; Trigo, A. Resumo: Digital transformation is essential for improving the operational processes of organisations and, consequently, their performance. This work presents the prototype of a computer application to support the management of outgoing students' applications in a higher education institution. The key outcomes of this work include the systematisation of the process, the establishment of key performance indicators, and the real-time monitoring and traceability of students' applications. From a managerial perspective, this work provides insights for higher education institutions aiming to digitalise and control their processes. Moreover, it offers a practical framework that can be adapted by any industry seeking to implement controlled processes, enabling the collection of data from activities to feed the key performance indicators.

Differentiable neural search architecture with zero-cost metrics for insulator fault prediction

Thu, 01 Jan 2026 00:00:00 GMT

Título próprio: Differentiable neural search architecture with zero-cost metrics for insulator fault prediction Autoria: Seman, L. O.; Buratto, W. G.; Villarrubia Gonzalez, G.; Leithardt, V. R. Q.; Nied, A.; Stefenon, S. F. Resumo: Reliable monitoring of high-voltage insulators is critical for maintaining the stability of electrical power systems, particularly under environmental contamination that can lead to flashover. Traditional inspection techniques struggle to anticipate degradation dynamics, while data-driven models often rely on fixed neural architectures that inadequately capture the complex temporal patterns in leakage current signals. This work proposes a Differentiable Neural Architecture Search (DARTS) framework, based on zero-cost metrics, tailored for time series forecasting in insulator monitoring. The method based on DARTS integrates a mixed encoder-decoder design with learnable selection over long short-term memory, gated recurrent units, and transformer components, coupled with a cross-attention bridge featuring temporal bias and gating mechanisms. To ensure efficient architecture exploration, the search leverages metrics such as SynFlow and Jacobian covariance for early candidate screening, followed by a bilevel optimization stage with entropy and diversity regularization. Experiments on real-world leakage current data demonstrate that the discovered architectures outperform manually designed baselines, offering improved forecasting performance.