Healthcare System Digital Transformation across Four European Countries: A Multiple-Case Study

Multiple-case study; Digital transformation; Digital healthEstudio de casos múltiples; Transformación digital; Salud digitalEstudi de casos múltiples; Transformació digital; Salut digitalDigitization 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

Machine Learning Models to Predict Clinical Deterioration of Patients in In-Hospital and Out-of-Hospital Settings: A Systematic Review

reservedIntroduzione: I pazienti possono andare incontro ad un improvviso deterioramento clinico in qualsiasi momento, presentando eventi pericolosi per la salvaguardia vitale che devono essere riconosciuti prontamente al fine di poter attivare interventi di emergenza appropriati. Il riconoscimento del deterioramento clinico è una delle componenti chiave della sorveglianza infermieristica e, negli ultimi anni, diversi modelli di machine learning sono stati proposti a tale scopo. Obiettivo: Valutare l’efficacia dei modelli di machine learning nel predire il deterioramento clinico dei pazienti in ambito ospedaliero ed extraospedaliero. Metodi: È stata condotta una revisione sistematica della letteratura secondo le reporting guidelines Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Sono stati considerati eleggibili gli studi su pazienti di qualsiasi genere, di età ≥ 18 anni, sia in ambito ospedaliero che extraospedaliero, finalizzati allo sviluppo e/o alla validazione di modelli di machine learning per prevedere e riconoscere il deterioramento clinico. La ricerca è stata condotta sulle banche dati PubMed/MEDLINE, CINAHL, Scopus, Embase, Web of Science, ACM Digital Library, arXiv, IEEE Xplore, ClinicalTrials.gov e ICTRP in luglio 2023. Per la selezione e l’estrazione dei dati è stata utilizzata la piattaforma Covidence. La valutazione del risk of bias è stata condotta attraverso lo strumento Prediction model Risk of Bias Assessment Tool (PROBAST), di cui i risultati sono stati visualizzati utilizzando il pacchetto Risk-of-bias VISualization di R. La sintesi dei dati è stata redatta secondo le linee guida Synthesis without meta-analysis (SWiM). Risultati: Su 7464 risultati delle ricerche, 579 studi sono stati valutati per l’ammissibilità in full-text e 30 studi, pubblicati tra il 2016 e il 2023, sono stati inclusi. La maggior parte degli studi era riferito al contesto ospedaliero (n = 27). Complessivamente, sono state sperimentate 33 tecniche di machine learning. Tutti i modelli di machine learning si sono dimostrati superiori ai modelli tradizionali di riconoscimento del deterioramento clinico in termini assoluti di AUROC, con valori compresi tra 0.72 e 0.958. Nessun modello di machine learning ha considerato tutti gli indicatori utilizzati dagli infermieri per identificare il deterioramento clinico. Il risk of bias complessivo degli studi era alto/incerto. Conclusioni: Nonostante i risultati promettenti e le potenzialità dei modelli di machine learning per il riconoscimento del deterioramento clinico, sono emerse diverse riflessioni che devono essere profondamente considerate e dibattute in ulteriori studi prospettici prima che l’implementazione di questi modelli possa essere raccomandata ampiamente nella pratica clinica. Numero di registrazione PROSPERO: CRD42023450450.Introduction: Patients can experience clinical deterioration at any time, presenting life-threatening events that must be recognized immediately to provide timely emergency care. Detecting clinical deterioration is one of the key components of nursing surveillance and several machine learning models have been proposed for this scope. However, their effectiveness and perspectives on the nursing practice are still unclear. Objectives: To assess the reported effectiveness of machine learning methods in predicting clinical deterioration in both in-hospital and out-of-hospital settings. Methods: A systematic review was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies with patients of any gender, ≥ 18 years old in both in-hospital and out-of-hospital settings were considered eligible when aimed at developing, evaluating, or testing machine learning models to predict clinical deterioration. PubMed/MEDLINE, CINAHL, Scopus, Embase, Web of Science, ACM Digital Library, arXiv, IEEE Xplore, ClinicalTrials.gov, and the ICTRP were searched in July 2023. The Covidence platform was employed for the study selection and data extraction processes. Risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST) tool and visualized through the Risk-of-bias VISualization R package tool. Data synthesis was performed according to the Synthesis without meta-analysis (SWiM) guidelines. Results: Out of 7464 search results, 579 articles were assessed for full-text eligibility and 30 studies published between 2016 and 2023 were included. The majority was based in the in-hospital setting (n = 27). Overall, there were adopted 33 machine learning techniques. All machine learning models outperformed traditional early warning systems in terms of absolute AUROC, ranging between 0.72 and 0.958. Compared to indicators of nurses’ worry about deteriorating patients, machine learning models failed to account for all the indicators. The overall risk of bias was rated as high/unclear. Conclusions: Despite showing potential and promising results as instruments to predict clinical deterioration, several reflections emerged that need to be profoundly considered and discussed in further prospective studies before these models start being widely implemented into clinical practice. PROSPERO registration number: CRD42023450450

Outcomes of patient education in adult oncologic patients receiving oral anticancer agents: a systematic review protocol

Abstract Background A large variety of oral anticancer agents have become available and while at first glance these therapies appear to provide only benefits, patients have expressed their need for educational interventions and raised safety issues. Although both patients and providers have recognized patient education’s importance, and an interplay with safety has been acknowledged, no systematic reviews of the literature that summarize all of the current evidence related to patient education’s outcomes for patients who receive oral anticancer agents have been performed to date. Accordingly, this systematic review will attempt to fill the gap in the literature as well as to map (1) contents, (2) methodologies, (3) settings, (4) timing/duration, and (5) healthcare professionals involved. Methods This protocol is being reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic review will be performed. Studies that targeted eligible adult patients (≥ 18 years old) in hospital, outpatient, and home settings, and reported patient education’s outcomes for those taking oral anticancer agents will be included. Searches will be conducted in PubMed/MEDLINE, CINAHL, Embase, and Scopus, and gray literature will be also sought. Two researchers will screen the search results independently and blindly in two phases: (1) title/abstract screening and (2) full-text screening using the Rayyan AI platform. An electronic data extraction form will be implemented and piloted, and then, two trained data extractors will extract the data cooperatively. Thereafter, a quality appraisal will be conducted using the Critical Appraisal Tools from The Joanna Briggs Institute. The results will be analyzed, grouped, clustered into categories, and discussed until a consensus is reached. Emerging evidence will be synthesized narratively and reported in accordance with the synthesis without meta-analysis guidelines. Discussion The systematic review’s results will be relevant to (1) policymakers and management at an institutional level, and (2) for clinical practice, in an evidence-based paradigm, potentially leading to a quality improvement with respect to safety and patient satisfaction. Systematic review registration PROSPERO CRD4202234179

Quantitative determination of In clustering in In-rich In x Ga 1− x N thin films

International audienc

Quantitative determination of In clustering in In-rich In x Ga 1− x N thin films

International audienc

Using Metaphors to Understand Suffering in COVID-19 Survivors: A Two Time-Point Observational Follow-Up Study

Accumulating evidence indicates that the COVID-19 pandemic carries risks to psychological health and represents a collective traumatic experience with consequences at the social, economic, and health levels. The primary aim of this study was to collect ongoing COVID-19 survivors’ pandemic-related experiences as expressed through the use of metaphors; the secondary aim was to explore socio-demographic variables associated with the metaphor orientation as negative, positive or neutral. An observational follow-up survey was conducted and reported according to the STROBE guidelines. Patients ≥ 18 years, who were treated for COVID-19 during the first wave (March/April 2020) and who were willing to participate in a telephone interview were involved and asked to summarize their COVID-19 experience as lived up to 6 and 12 months in a metaphor. A total of 339 patients participated in the first (6 months) and second (12 months) data collection. Patients were mainly female (51.9%), with an average age of 52.9 years (confidence interval, CI 95% 51.2–54.6). At 6 months, most participants (214; 63.1%) used a negative-oriented metaphor, further increasing at 12 months (266; 78.5%), when they used fewer neutral-/positive-oriented metaphors (p < 0.001). At the 6-month follow-up, only three individual variables (female gender, education, and experiencing symptoms at the COVID-19 onset) were significantly different across the possible metaphor orientation; at 12 months, no individual variables were significantly associated. This study suggests increasingly negative lived experiences over time and the need for personalized healthcare pathways to face the long-term traumatic consequences of COVID-19

Lessons learnt while designing and conducting a longitudinal study from the first Italian COVID-19 pandemic wave up to 3 years

Abstract Background Several scientific contributions have summarized the “lessons learnt” during the coronavirus disease 2019 (COVID-19) pandemic, but only a few authors have discussed what we have learnt on how to design and conduct research during a pandemic. The main intent of this study was to summarize the lessons learnt by an Italian multidisciplinary research group that developed and conducted a longitudinal study on COVID-19 patients infected during the first wave in March 2020 and followed-up for 3 years. Methods A qualitative research approach embedded into the primary CORonavirus MOnitoRing study (CORMOR) study was developed, according to the the consolidated criteria for reporting qualitative research. Multiple data collection strategies were performed: each member was invited to report the main lessons learnt according to his/her perspective and experience from the study design throughout its conduction. The narratives collected were summarized and discussed in face-to-face rounds. The narratives were then thematically analysed according to their main topic in a list that was resent to all members to check the content and their organization. The list of the final “lessons learnt” has been agreed by all members, as described in a detailed fashion. Results Several lessons were learnt while designing and conducting a longitudinal study during the COVID-19 pandemic and summarised into ten main themes: some are methodological, while others concern how to conduct research in pandemics/epidemics/infectious disease emergencies. Conclusions The multidisciplinary approach, which also included patients’ perspective, helped us to protect the consistency and quality of the research provided in pandemic times. The lesson learnt suggest that our research approach may benefit from changes in education, clinical practice and policies

Transitare la formazione infermieristica italiana nel periodo post pandemico: le priorità alla luce delle lezioni apprese

Introduzione. Con il ritorno alla normalità, molte attività didattiche sono state ripristinate senza una analisi approfondita di quali trasformazioni attivate nel periodo pandemico dovrebbero essere mantenute e valorizzate. Obiettivo. Individuare le priorità per transitare efficacemente la formazione infermieristica nel periodo post pandemico. Metodo. È stato adottato un disegno qualitativo descrittivo. Un network di nove università ha coinvolto 37 docenti, 28 infermieri tutor/guide di tirocinio e 65 studenti/neolaureati. La raccolta dati è stata effettuata con una scheda semistrutturata; le principali priorità emerse in ogni singola università sono state poi combinate in una visione di insieme. Risultati. Sono emerse nove priorità, tra le quali l’esigenza di: 1. riflettere sulla didattica a distanza per valorizzarne il ruolo complementare a quella in presenza (lezioni/laboratori); 2. ripensare l’apprendimento clinico rifocalizzandone gli obiettivi, la durata, le rotazioni, e le sedi da privilegiare; 3. comprendere come integrare gli spazi di apprendimento virtuale e quelli in presenza nella programmazione didattica; 4. proseguire nelle scelte inclusive e sostenibili. Considerato che la formazione infermieristica è essenziale per il Paese, è prioritario elaborare un piano educativo pandemico capace di garantirne la continuità in ogni circostanza. Conclusioni. Sono emersi nove ambiti prioritari accomunati dal ruolo sempre più importante della didattica digitale; le lezioni apprese, tuttavia, indicano l’esigenza di attivare una fase intermedia capace di guidare verso la completa transizione della formazione nel post pandemia

Moving forward the Italian nursing education into the post-pandemic era: findings from a national qualitative research study

Abstract Background During the CoronaVIrus-19 (COVID-19) pandemic, nursing education has been dramatically transformed and shaped according to the restrictions imposed by national rules. Restoring educational activities as delivered in the pre-pandemic era without making a critical evaluation of the transformations implemented, may sacrifice the extraordinary learning opportunity that this event has offered. The aim of this study was to identify a set of recommendations that can guide the Italian nursing education to move forward in the post-pandemic era. Methods A qualitative descriptive design was undertaken in 2022–2023 and reported here according to the COnsolidated criteria for REporting Qualitative research guidelines. A network was established of nine Italian universities offering a bachelor’s degree in nursing for a total of 6135 students. A purposeful sample of 37 Faculty Members, 28 Clinical Nurse Educators and 65 Students/new graduates were involved. A data collection was conducted with a form including open-ended questions concerning which transformations in nursing education had been implemented during the pandemic, which of these should be maintained and valued, and what recommendations should address the transition of nursing education in the post-pandemic era. Results Nine main recommendations embodying 18 specific recommendations have emerged, all transversally influenced by the role of the digital transformation, as a complementary and strengthening strategy for face-to-face teaching. The findings also suggest the need to rethink clinical rotations and their supervision models, to refocus the clinical learning aims, to pay attention towards the student community and its social needs, and to define a pandemic educational plan to be ready for unexpected, but possible, future events. Conclusions A multidimensional set of recommendations emerged, shaping a strategic map of action, where the main message is the need to rethink the whole nursing education, where digitalization is embodied. Preparing and moving nursing education forward by following the emerged recommendations may promote common standards of education and create the basis on for how to deal with future pandemic/catastrophic events by making ready and prepared the educational systems

Reduced incidence of cardiovascular events in hyper-Lp(a) patients on lipoprotein apheresis. The G.I.L.A. (Gruppo Interdisciplinare Aferesi Lipoproteica) pilot study

Lipoprotein apheresis (LA) is the elective therapy for homozygous and other forms of Familial Hypercholesterolemia, Familial Combined Hypercholesterolemia, resistant/intolerant to lipid lowering drugs, and hyper-lipoproteinemia(a). Lipoprotein(a) [Lp(a)] has been classified as the most prevalent genetic risk factor for coronary artery disease and aortic valve stenosis. Our multicenter retrospective study has the aim to analyze the incidence of adverse cardiovascular events (ACVE) before and during the LA treatment, in subjects with elevated level of Lp(a) (>60\u2009mg/dL) [hyper-Lp(a)] and chronic ischemic heart disease. We collected data of 23 patients (mean age 63\u2009\ub1\u20099 years, male 77%; from hospital of Pisa 11/23, Pistoia 7/23, Verona 2/23, Padova 2/23 and Ferrara 1/23), with hyper-Lp(a), pre-apheresis LDL-cholesterol <100\u2009mg/dL, cardiovascular disease, on maximally tolerated lipid lowering therapy and LA treatment (median 7 years, interquartile range 3-9 years). The LA treatment was performed by heparin-induced LDL precipitation apheresis (16/23), dextran-sulphate (4/23), cascade filtration (2/23) and immunoadsorption (1/23). The time lapse between first cardiovascular event and beginning of apheresis was 6 years (interquartile range 1-12 years). The recorded ACVE, before and after the LA treatment inception, were 40 and 10 respectively (p\u2009<\u20090.05), notably, the AVCE rates/year were 0.43 and 0.11 respectively (p\u2009<\u20090.05) with a 74% reduction of event occurrence. Our data confirm long-term efficacy and positive impact of LA on morbidity in patients with hyper-Lp(a) and chronic ischemic heart disease on maximally tolerated lipid lowering therap