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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]