Background: Despite Catalonia being an advanced region in terms of digital health adoption, the “Forum for Professional Dialogue” identified the need to improve information and communication technology (ICT) competences as one of the present and future challenges for health care professionals (HPs). Objective: We aimed to validate the digital competence test developed ad hoc for this study and to measure the digital competence level of Catalan HPs to establish their current level as the baseline for designing a strategic training plan. Methods: An exploratory observational study was conducted based on a voluntary survey where sociodemographic, professional and digital tool knowledge, digital tool use, and training needs data were collected and based on the score obtained from a digital competence test developed ad hoc. The digital competence test consisted of 2 “real-life scenarios” with 7 and 11 questions. Results: In total, 803 HPs, of whom 612 (76.2%) were women, completed the survey between June 28 and July 16, 2021. Most participants self-rated their digital competence level as either intermediate (384/803, 47.8%) or basic (357/803, 44.5%). The mean score in the digital competence test was 22.6 (SD 4.3). Therefore, most participants displayed a basic level of digital competence. The internal consistency of the digital competence test was 0.66, and the discrimination index of all questions was ≥0.2 for all items except for 1 question. Conclusions: This exploratory study highlights the need to improve the digital competence of HPs working in Catalonia, with special effort being made to provide training according to the specific needs of the different HP profiles. The results have informed the Health Plan for Catalonia 2021-2025 and lay the foundations for the development and deployment of a framework program for the digital competences of HPs. The developed digital competence test shows acceptable consistency for the objective pursued, although improvements are needed to fine-tune its accuracy.

Type 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its progression, associated complications and response to therapies. The advances in deep learning (DL) techniques and the availability of a large amount of healthcare data allow us to investigate T2DM characteristics and evolution with a completely new approach, studying common disease trajectories rather than cross sectional values. We used an Kernelized-AutoEncoder algorithm to map 5 years of data of 11,028 subjects diagnosed with T2DM in a latent space that embedded similarities and differences between patients in terms of the evolution of the disease. Once we obtained the latent space, we used classical clustering algorithms to create longitudinal clusters representing different evolutions of the diabetic disease. Our unsupervised DL clustering algorithm suggested seven different longitudinal clusters. Different mean ages were observed among the clusters (ranging from 65.3±11.6 to 72.8±9.4). Subjects in clusters B (Hypercholesteraemic) and E (Hypertensive) had shorter diabetes duration (9.2±3.9 and 9.5±3.9 years respectively). Subjects in Cluster G (Metabolic) had the poorest glycaemic control (mean glycated hemoglobin 7.99±1.42%), while cluster E had the best one (mean glycated hemoglobin 7.04±1.11%). Obesity was observed mainly in clusters A (Neuropathic), C (Multiple Complications), F (Retinopathy) and G. A dashboard is available at dm2.b2slab.upc.edu to visualize the different trajectories corresponding to the 7 clusters.

Digitization has become involved in every aspect of life, including the healthcare sector with its healthcare professionals (HCPs), citizens (patients and their families), and services. This complex process is supported by policies: however, to date, no policy analysis on healthcare digitalization has been conducted in European countries to identify the main goals of digital transformation and its practical implementation. This research aimed to describe and compare the digital health policies across four European countries; namely, their priorities, their implementation in practice, and the digital competencies expected by HCPs. A multiple-case study was performed. Participants were the members of the Digital EducationaL programme invoLVing hEalth profEssionals (DELIVER), a project funded by the European Union under the Erasmus+ programme, involving three countries (Denmark, Italy, and Slovenia) and one autonomous region (Catalonia—Spain). Data were collected using two approaches: (a) a written interview with open-ended questions involving the members of the DELIVER project as key informants; and (b) a policy-document analysis. Interviews were analysed using the textual narrative synthesis and the word cloud policy analysis was conducted according to the Ready, Extract, Analyse and Distil approach. Results showed that all countries had established recent policies at the national level to address the development of digital health and specific governmental bodies were addressing the implementation of the digital transformation with specific ramifications at the regional and local levels. The words “health” and “care” characterized the policy documents of Denmark and Italy (309 and 56 times, 114 and 24 times, respectively), while “development” and “digital” (497 and 478 times, respectively) were common in the Slovenia document. The most used words in the Catalonia policy document were “data” and “system” (570 and 523 times, respectively). The HCP competencies expected are not clearly delineated among countries, and there is no formal plan for their development at the undergraduate, postgraduate, and continuing educational levels. Mutual understanding and exchange of good practices between countries may facilitate the digitalization processes; moreover, concrete actions in the context of HCP migration across Europe for employment purposes, as well as in the context of citizens’ migration for healthcare-seeking purposes are needed to consider the differences emerged across the countries.

Patients with chronic obstructive pulmonary disease (COPD) suffer from voice changes with respect to the healthy population. However, two issues remain to be studied: how long-term speech elements such as prosody are affected; and whether physical effort and medication also affect the speech of patients with COPD, and if so, how an automatic speech-based detection system of COPD measurements can be influenced by these changes. The aim of the current study is to address both issues. To this end, long read speech from COPD and control groups was recorded, and the following experiments were performed: (a) a statistical analysis over the study and control groups to analyse the effects of physical effort and medication on speech; and (b) an automatic classification experiment to analyse how different recording conditions can affect the performance of a COPD detection system. The results obtained show that speech—especially prosodic features—is affected by physical effort and inhaled medication in both groups, though in opposite ways; and that the recording condition has a relevant role when designing an automatic COPD detection system. The current work takes a step forward in the understanding of speech in patients with COPD, and in turn, in the research on its automatic detection to help professionals supervising patient status.

Background: Although digital health is essential for improving health care, its adoption remains slow due to the lack of literacy in this area. Therefore, it is crucial for health professionals to acquire digital skills and for a digital competence assessment and accreditation model to be implemented to make advances in this field. Objective: This study had two objectives: (1) to create a specific map of digital competences for health professionals and (2) to define and test a digital competence assessment and accreditation model for health professionals. Methods: We took an iterative mixed methods approach, which included a review of the gray literature and consultation with local experts. We used the arithmetic mean and SD in descriptive statistics, P values in hypothesis testing and subgroup comparisons, the greatest lower bound in test diagnosis, and the discrimination index in study instrument analysis. Results: The assessment model designed in accordance with the competence content defined in the map of digital competences and based on scenarios had excellent internal consistency overall (greatest lower bound=0.91). Although most study participants (110/122, 90.2%) reported an intermediate self-perceived digital competence level, we found that the vast majority would not attain a level-2 Accreditation of Competence in Information and Communication Technologies. Conclusions: Knowing the digital competence level of health professionals based on a defined competence framework should enable such professionals to be trained and updated to meet real needs in their specific professional contexts and, consequently, take full advantage of the potential of digital technologies. These results have informed the Health Plan for Catalonia 2021-2025, thus laying the foundations for creating and offering specific training to assess and certify the digital competence of such professionals.

BACKGROUND In the healthcare field the digital revolution has changed the way in which healthcare is provided through the prevention, diagnosis and monitoring of people's health. In Catalonia, work has been conducted to evaluate the digital competence (DC) of healthcare professionals using a validated test based on the ACTIC (accreditation of competence in information and communication technologies) model. It is therefore important OBJECTIVE To determine the DC level of nursing students as future healthcare professionals. METHODS The aim of this study is to analyze the current DC level of students taking the Degree in Nursing at the Terres de l’Ebre Campus, Universitat Rovira i Virgili (URV), Catalonia, Spain. We have used a questionnaire to evaluate the DC level of these students. RESULTS Our sample comprised 147 students from various modules of the Degree in Nursing. Our results show differences in levels of self-perception and DC but also differences in the competence of students across the various academic years. CONCLUSIONS Evaluating the level of competence of nursing degree students is fundamental for practising and enhancing the most important digital competencies required in the field of professional nursing