Life-long learning is a necessity associated with the requirements of the fourth industrial revolution. Although distance online education played a major role in the evolution of the modern education system, this share grew dramatically because of the COVID-19 pandemic outbreak and the social distancing measures that were imposed. However, the quick and extensive adoption of online learning tools also highlighted the multidimensional weaknesses of online education and the needs that arise when considering such practices. To this end, the ease of collecting digital data, as well as the overall evolution of data analytics, enables researchers, and by extension educators, to systematically evaluate the pros and cons of such systems. For instance, advanced data mining methods can be used to find potential areas of concern or to confirm elements of excellence. In this work, we used text analysis methods on data that have emerged from participants’ feedback in online lifelong learning programmes for professional development. We analysed 1890 Greek text-based answers of participants to open evaluation questions using standard text analysis processes. We finally produced 7-gram tokens from the words in the texts, from which we constructed meaningful sentences and characterized them as positive or negative. We introduced a new metric, called acceptance grade, to quantitatively evaluate them as far as their positive or negative content for the online courses is concerned. We finally based our evaluation on the top 10 sentences of each category (positive, negative). Validation of the results via two external experts and data triangulation showed an accuracy of 80%.

Background: The biomedical and health informatics (BMHI) fields have been advancing rapidly, a trend particularly emphasised during the recent COVID-19 pandemic, introducing innovations in BMHI. Over nearly 50 years since its establishment as a scientific discipline, BMHI has encountered several challenges, such as mishaps, delays, failures, and moments of enthusiastic expectations and notable successes. This paper focuses on reviewing the progress made in the BMHI discipline, evaluating key milestones, and discussing future challenges. Methods: To, Structured, step-by-step qualitative methodology was developed and applied, centred on gathering expert opinions and analysing trends from the literature to provide a comprehensive assessment. Experts and pioneers in the BMHI field were assigned thematic tasks based on the research question, providing critical inputs for the thematic analysis. This led to the identification of five key dimensions used to present the findings in the paper: informatics in biomedicine and healthcare, health data in Informatics, nurses in informatics, education and accreditation in health informatics, and ethical, legal, social, and security issues. Results: Each dimension is examined through recently emerging innovations, linking them directly to the future of healthcare, like the role of artificial intelligence, innovative digital health tools, the expansion of telemedicine, and the use of mobile health apps and wearable devices. The new approach of BMHI covers newly introduced clinical needs and approaches like patient-centric, remote monitoring, and precision medicine clinical approaches. Conclusions: These insights offer clear recommendations for improving education and developing experts to advance future innovations. Notably, this narrative review presents a body of knowledge essential for a deep understanding of the BMHI field from a human-centric perspective and, as such, could serve as a reference point for prospective analysis and innovation development.

The purpose of this study is to investigate the awareness of nursing personnel on processing patients' personal data according to the General Data Protection Regulation (GDPR) (Regulation EU 2016/679 of the European Parliament and of the Council). A cross-sectional study was conducted between May and June 2019 on a sample of 229 nurses working in the health care sector. A self-filled electronic questionnaire was constructed, evaluated for its validity and reliability, and used as a measuring tool. The participants were invited from nursing professional pages on social media. The study indicated that the nursing staff who participated were not fully informed about the GDPR and its implementation. The level of awareness of the participants was found to be related to the age, the employment in the private health sector, the education level, the position of responsibility and the years of experience.

Research on digital games explicitly designed for medical or health-related purposes has grown rapidly over the past years. Serious games have been used to convey health-related information to children, to engage them in health decision making, to promote healthy behaviours, and to influence health outcomes. In this process, parents represent an important, yet often neglected, target audience. Studies increasingly stress the importance of engaging parents and caregivers as agents to support their children, and of involving them directly in some part of gameplay. The present work reviews the latest research on games for children with chronic diseases from the perspective of parents and caregivers, concluding that further research is needed, in order to create evidence-based games that are tailored to the specific challenges faced by both paediatric patients and their parents.

In late 2019, the COVID-19 disease emerged, caused by the SARS-CoV-2 virus, and has since spread worldwide, becoming a global pandemic and resulting in almost seven million deaths to date. In addressing this global crisis, artificial intelligence has played a crucial role, particularly through the development of predictive models using machine learning algorithms, which have been successfully applied to solving a multitude of problems across multiple scientific fields. The purpose of this paper is to identify the model, or models, with the highest accuracy in predicting a COVID-19 patient’s mortality outcome by comparing their performance metrics. Different ML methods employed in model development include logistic regression, decision trees, random forest, eXtreme gradient boosting (XGBoost), multi-layer perceptrons, and the k-nearest neighbors. The metrics used for the comparison of these models were accuracy, precision-recall, F1 score, area under the receiver operating characteristic curve (AUC-ROC), and runtime. The data used comprised the clinical characteristics and histories of 12,425,179 individuals who attended health facilities in Mexico. Following a comprehensive evaluation, the XGBoost model achieved the highest overall score across all metrics. It scored 93.76% in precision, 95.47% in recall, 91.13% in F1-score, 97.86% in AUC-ROC, and had a runtime of 6.67306 s. Therefore, XGBoost was determined to be the preferred method for predicting the mortality outcome of COVID-19 patients.

Telemedicine improved accessibility to healthcare services, and during the COVID-19 pandemic, clinicians worldwide enhanced telemedicine services. Greek geomorphology includes remote areas, and telemedicine could be a valuable tool in reducing healthcare inequalities. This paper addresses scientific evidence regarding telemedicine’s opportunities and challenges in Greece. Researchers investigate critical fields regarding telemedicine’s clinical effectiveness, safety, efficiency, and legal framework. Data included telemedicine use in both remote areas and for older adults. Stakeholders’ attitudes and telemedicine challenges. Current evidence supports the complementary role of telemedicine in traditional healthcare services. However, it is highlighted that more methodologically reliable studies should be performed, especially in terms of efficiency.