Search

In the Italian university context, almost all universities offer blended degree courses, digital environments and e-learning systems. In many cases, dedicated centres also provide technical support and, less frequently, teaching and methodological support. The development of digital learning environments often does not correspond to the spread of an effective culture of educational innovation in university courses. The research examined the experiences of instructional design among 44 university teachers of blended degree courses at the University of Modena and Reggio Emilia. The analysis focused on teachers' level of satisfaction and the processes of teaching innovation linked to the blended methodology, investigating also the possible criticalities and strengths related to the process of (re)design and innovation. The activities of training and design for teachers contributed to the dissemination of good teaching practices and professional development. In line with what emerged from the research on blended learning, it seems necessary to build a framework for the adoption and implementation of 'blended learning' strategies at the institutional level, starting from the construction of a concrete agenda setting shared between the actors of the innovation process.

Introduction: recently the Italian Society of Clinical Biochemistry and Clinical Molecular Biology (SIBioC) Big Data and Artificial Intelligence Working Group (BAI-WG) conducted a survey to examine technological and information technology readiness for developing BAI applications and to investigate laboratory professionals' knowledge and skills. This article examines specific survey questions related to supporting data. These data are visible only by laboratory professionals for the purpose of supporting all laboratory processes and, ultimately for the validation of results and they can be generally ascribed under the quality management system (QMS). Methods: the questionnaire, designed by the BAI-WG, was sent to 1351 SIBioC members. The responses were evaluated using Survey-Monkey software and Google Sheets. Results: over 90% of respondents work in laboratories with a QMS in place. The participants consider digitisation of QMS as highly advantageous (93%). Nevertheless, computerisation of the QMS is actually often incomplete, and the connection between QMS and Laboratory Information Systems (LIS) is usually lacking or missing. Consequently, alternative systems, separate from the LIS, have been developed to record various QMS data essential for monitoring processes. Discussion: the integration of medical data sources is crucial for developing BAI applications. The issue of integration is relevant and strongly linked to digitisation. However, algorithms may often consider only reported data, but integration should also be extended to supporting data, which could be correlated with clinical and process outcomes. Current LIS lack the necessary features for BAI applications and QMS digitisation is still too far behind to allow for real-time control. Software vendors should move towards the total integration of health data. [ABSTRACT FROM AUTHOR]

The advent, in 2004, of online universities in Italy followed the global trend of open and distance learning institutions. Within a brief span, the authorization of as many as 11 online universities was granted. Despite ongoing regulatory revisions and a prevailing skepticism regarding the actual quality of education these institutions furnish, student enrollment continues to rise annually. This study explores enrollment trends in Italian online universities from their inception through 2021, considering concurrent enrollment trends in traditional public and private universities. Additionally, we examine in detail the gender composition, subject areas of degree programs, and, most importantly, the geographic origins of enrolled students. [ABSTRACT FROM AUTHOR]

A 82-year-old man suffering from pemphigus and Parkinson's disease presented at the emergency department of the Misericordia Hospital in Grosseto (Tuscany, Italy) with subconjunctival bleeding. Laboratory blood tests showed a prolonged activated thromboplastin time (aPTT), with normal prothrombin time (PT) and slightly reduced haemoglobin. The negative family and personal history of haemorrhagic disease rose the suspicion of the presence of an acquired inhibitor. The patient was referred for further diagnostics to the University Hospital of Siena where second level tests were performed at the Coagulation Unit. The aPTT mixture test revealed a non-correction both at room temperature and, more markedly, after incubation at 37°C for 2h, confirming the presence of an intrinsic pathway inhibitor. Among the measured factors, only the activity level of factor VIII was extremely low. The titration of FVIII inhibitor confirmed the diagnosis of Acquired Haemophilia A (AHA). [ABSTRACT FROM AUTHOR]

Objectives: the Italian Society of Clinical Biochemistry and Clinical Molecular Biology (SIBioC) Big Data and Artificial Intelligence (BAI) Working Group promoted a survey to frame the knowledge, skills, and technological predisposition in Clinical Laboratories. Methods: questionnaire, focusing on digitization, IT infrastructures, data accessibility, and BAI projects underway was sent to 1351 SIBioC participants. The responses were evaluated using SurveyMonkey software and Google Sheets. Results: the 227 respondents (17%) from all over Italy (47% of 484 labs), mainly biologists, laboratory physicians and managers, mostly from laboratories of public hospitals, revealed lack of hardware, software and corporate Wi-Fi, and dearth of PCs. Only 25% work daily on clouds, while 65%-including Laboratory Directors-cannot acquire health data from sources other than laboratories. Only 50% of those with access can review a clinical patient’s health record, while the other access only to laboratory information. The integration of laboratory data with other health data is mostly incomplete, which limits BAI-type analysis. Many are unaware of integration platforms. Over 90% report pulling data from the LIS, with varying degrees of autonomy. Very few have already undertaken BAI projects, frequently relying on IT partnerships. The majority consider BAI as crucial in helping professional judgments, indicating a growing interest. Conclusions: the questionnaire received relevant feedback from SIBioC participants. It highlighted the level of expertise and interest in BAI applications. None of the obstacles stands out more than the others, emphasising the need to allaround work: IT infrastructures, data warehouses, BAI analysis software acquisition, data accessibility and training. [ABSTRACT FROM AUTHOR]

The Italian Society of Clinical Biochemistry and Clinical Molecular Biology (SIBioC) Big Data and Artificial Intelligence (BAI) Working Group promoted a survey to frame the knowledge, skills and technological predisposition in clinical laboratories. A questionnaire, focussing on digitization, information technology (IT) infrastructures, data accessibility, and BAI projects underway was sent to 1,351 SIBioC participants. The responses were evaluated using SurveyMonkey software and Google Sheets. The 227 respondents (17%) from all over Italy (47% of 484 labs), mainly biologists, laboratory physicians and managers, mostly from laboratories of public hospitals, revealed lack of hardware, software and corporate Wi-Fi, and dearth of PCs. Only 25% work daily on clouds, while 65%—including Laboratory Directors—cannot acquire health data from sources other than laboratories. Only 50% of those with access can review a clinical patient's health record, while the other access only to laboratory information. The integration of laboratory data with other health data is mostly incomplete, which limits BAI-type analysis. Many are unaware of integration platforms. Over 90% report pulling data from the Laboratory Information System, with varying degrees of autonomy. Very few have already undertaken BAI projects, frequently relying on IT partnerships. The majority consider BAI as crucial in helping professional judgements, indicating a growing interest. The questionnaire received relevant feedback from SIBioC participants. It highlighted the level of expertise and interest in BAI applications. None of the obstacles stands out more than the others, emphasising the need to all-around work: IT infrastructures, data warehouses, BAI analysis software acquisition, data accessibility and training. [ABSTRACT FROM AUTHOR]

Objective Sex-/age-differentiated cutoffs and the magnitude of serial changes in high-sensitivity cardiac troponins (hs-cTn) for acute coronary syndrome (ACS) diagnosis algorithms are still under discussion. This study presents a methodology to evaluate decision-making limits and to assess whether sex-specific cutoffs could improve diagnostic accuracy. Methods A high-sensitivity cardiac troponin T (hs-cTnT) 0-/3-hour protocol was adopted, applying the 2015 European Society of Cardiology Guidelines. Decision-making limits (99th percentile: 14 ng/L; delta change ≥ 30%) were agreed upon with the emergency department (ED) at the University Hospital of Siena in Siena, Italy. One-year requests (5177) for hs-cTnT serial determination were compared with the final International Classification of Diseases, 9th revision, clinical modifications diagnosis (contingency tables; receiver operating characteristic curves). Results The algorithm's capability to exclude or confirm ACS was verified by remarkable negative predictive value (97%) and high areas under the curve for the first troponin sampling (0.712), troponin sampling at 3 hours (0.789), and delta (0.744). The clinical utility for the general population—even those with comorbidities—accessing the ED was verified. Our data did not support a sex-differentiated cutoff utility because it would not have affected patient management. Conclusion This methodology allowed us to confirm the effectiveness of our decision-making limits. [ABSTRACT FROM AUTHOR]

Background: As defined by ISO 15189 competence is the "demonstrated ability to apply knowledge and skills" thus, its assessment is fundamental for ensuring the quality of the total testing process in order to reduce the risk for the patient. We have developed a functional software for the measurement of professional competences in order to standardize the procedure and to collect all the data in a single platform, avoiding redundancy and dispersion. Methods: Our model objectively assesses the skills, as they become measurable and comparable with appropriate standards and involves both managers and operators, to increase their active engagement. The assessment concerns everyone, but the standards to be met (numerical values) can vary according to the responsibilities. Several subjective and objective criteria are evaluated: each parameter can contribute in a variable proportion to the total skills measured according to the needs of the organization. Results: The data are automatically analyzed and can be easily monitored in real time in the form of indicators, thanks to dashboards. The comparison between the skills required and those measured allows highlighting the gap useful for planning personalized training paths. Conclusions: Our tool is reliable and highly adaptable to laboratories about competences to track criteria, standards and monitored indicators. The computerized management is a strategic action as it fulfills the requirements of registration, traceability, communication, data analysis and indicators development, which are the tenets of continuous improvement, and allows planning to be made on the basis of the actual training needs. [ABSTRACT FROM AUTHOR]

Objective: Our scope is to provide methodological elements on how to manage effectively the preanalytical phase in the laboratory testing process, by objectively measuring the risk connected to the phases handled by man with respect to those managed by machines.Background: Preanalytical errors account for most of the mistakes related to laboratory testing and can affect patient care. Hence, it is necessary to manage the risk connected to the preanalytical phase, as required by certification and accreditation bodies. The risk assessment discloses the steps at greater risk and gives indications to make decisions.Method: We have reviewed the state of art in the automation of the preanalytical phase, addressing needs and problems. We have used the proactive risk assessment methodology FMECA (Failure Mode, Effects, and Criticality Analysis) to identify the most critical phases in our preanalytical process and have calculated the risk associated.Results: The most critical phases were the human controlled ones. In particular, the highest risk indexes were associated to manual acceptance of test orders, identification of the patients, tube labeling, and sample collection.Conclusion: Automation in the preanalytical phase is fundamental to replace, support, or extend the human contribution. Nevertheless each organization is different about workloads and competencies, so the most suitable management must be tailor-made in each context.Application: We present a method by which each organization is able to find its best balance between automation and human contribution in the control of the preanalytical phase. [ABSTRACT FROM AUTHOR]

Online teaching environments acquire extremely high granularity of data, both on users’ personal profiles and on their behaviour and results. Learning Analytics (LA) is open to numerous possible research scenarios thanks to the development of technology and the speed of data collection. One characteristic element is that the data are not anonymous, but they reproduce a personalization and identification of the profiles. Identifiability of the student is implicit in the teaching process, but access to Analytics techniques reveals a fundamental question: “What is the limit?” The answer to this question should be preliminary to any use of data by students, teachers, instructors and managers of the online learning environments. In the present day, we are also experiencing a particular moment of change: the effects of the European General Data Protection Regulation (GDPR) 679/2016, the general regulation on the protection of personal data that aims to standardize all national legislation and adapt it to the new needs dictated by the evolving technological context. The objective of this work is to propose a three-point checklist of the questions connected to the management and limits of teachers’ use of data in Learning Analytics and students’ right of transparency in the context of Higher Digital Education, to take into account before conducting research. To this end, the paper contains an examination of the literature on privacy and ethical debates in LA. Work continues with legislative review, particularly the Italian path, and the discussion about online data management in our current universities’ two contexts: technology and legislation. [ABSTRACT FROM AUTHOR]

In this paper we introduce an approach for selecting a linear model to estimate, in a predictive way, the completion rate of massive open online courses (MOOCs). Data are derived from LMS analytics and nominal surveys. The sample comprises 722 observations (users) carried out in seven courses on EduOpen, the Italian MOOCs platform. We used 24 independent variables (predictors), categorised into four groups (User Profile, User Engagement, User Behaviour, Course Profile). As response variables we examined both the course completion status and the completion rate of the learning activities. A first analysis concerned the correlation between the predictors within each group and between the different groups, as well as that between all the dependent variables and the two response variables. The linear regression analysis was conducted by means of a stepwise approach for model selection using the asymptotic information criterion (AIC). For each of the response variables we estimated predictive models using the different groups of predictors both separately and in combination. The models were validated using the usual statistical tests. The main results suggest a high degree of dependence of course completion and completion rate on variables measuring the user’s behavioural profile in the course and a weak degree of dependence on the user’s profile, motivation and course pattern. In addition, residual analysis indicates the potential occurrence of interaction effects among variables and non-linear dynamics. [ABSTRACT FROM AUTHOR]

In the last few years, artificial intelligence (AI) is gaining attention in several medical disciplines, including laboratory medicine (LM). The raised interest on AI has been fueled not only by the huge amounts of information daily generated, but also by the special natural context offered by laboratories, where digitalization have already occupied an important part of the routine workflow of patients’ data. Motivated by these topics and under the auspices of SIBioC, a conference on AI and big data was organized in May 2022 in Bologna, Italy. This conference covered several topics of AI and big data, including but not limited to the current and future perspectives, comprising ethical challenges and the role of laboratory specialists, including young professionals, the productive integration of AI with information technologies and with other digital infrastructure, such as the LOINC and the block chain. Furthermore, some examples of real application of AI in LM were reported, including diagnosis and monitoring of familiar hypercholesterolemia, management of insulin treatments for diabetes, reference intervals identification and verification by indirect methods, COVID-19 diagnosis and the monitoring of outpatients monoclonal gammopathy treatment by digital healthcare. [ABSTRACT FROM AUTHOR]