Il rischio tecnologico in sanità

An overall risk management process in healthcare should involve the evaluating of potential risk related to medical devices and technology. The importance of this analysis is undelined by many legislative measures of both the Italian Country and the European Community. This article describes the importance of a medical devices vigilance system and the analysis that can be conducted to establish the baseline hazards associated with a device, evaluating its potential hazards. To improve patients safety, the risk analysis should include any risks associated with the manufacture and delivery of the device

Reliability analysis of GEM® Premier™ technology: a multicenter study

This paper describes how specific blood gas analyzer characteristics can support the emerging clinical and organizational user's needs while ensuring patient safety. A oneyear data set from two Italian hospitals was analyzed from 10 different blood gas analyzers. Performance measurements in terms of mean down time (MDT) were calculated to show how technical characteristics declared by the manufacturer compare to the analyzer availability in real clinical settings. Results show a high level of reliability for the analyzed technology, associated with very low MDT of each sensor integrated in the cartridge. Moreover, results show a high level of efficiency in cartridge usage. Such results are consistent with the specification of the GEM® Premier™ maintenance- free technology and are particularly relevant in the point-of-care testing setting.</p

Enabling technologies for future learning scenarios: the semantic grid for human learning

In this paper, starting from the limitations and constrains of traditional human learning approaches, we outline new suitable approaches to education and training in future knowledge based society. In our vision, learning and teaching are no longer standalone activities but complex, conversational and experiential-based processes implying collaboration, direct experience, mutual trust and shared interests. We identify characteristics of the environments suitable for these processes, and we compare different enabling technology infrastructures in order to justify why the Semantic Grid for Human Learning, that is a particular enhanced instance of the traditional Semantic Grid, is the most appropriate infrastructure to build our vision on. Finally, we present a realistic learning scenario as a case study, proving the effectiveness of our innovative learning approachesforfuture Education and Training

Health technology assessment methods guidelines for medical devices : how can we address the gaps? The International Federation of Medical and Biological Engineering perspective

Objectives Current health technology assessment (HTA) methods guidelines for medical devices may benefit from contributions by biomedical and clinical engineers. Our study aims to: (i) review and identify gaps in the current HTA guidelines on medical devices, (ii) propose recommendations to optimize the impact of HTA for medical devices, and (iii) reach a consensus among biomedical engineers on these recommendations. Methods A gray literature search of HTA agency Web sites for assessment methods guidelines on devices was conducted. The International Federation of Medical and Biological Engineers (IFMBE) then convened a structured focus group, with experts from different fields, to identify potential gaps in the current HTA guidelines, and to develop recommendations to fill these perceived gaps. The thirty recommendations generated from the focus group were circulated in a Delphi survey to eighty-five biomedical and clinical engineers. Results Thirty-two panelists, from seventeen countries, participated in the Delphi survey. The responses showed a strong agreement on twenty-seven of thirty recommendations. Some uncertainties remain about the methods to accurately assess the effectiveness and safety, and interoperability of a medical device with other devices or within the clinical setting. Conclusions As medical devices differ from drug therapies, current HTA methods may not accurately reflect the conclusions of their assessment. Recommendations informed by the focus group discussions and Delphi survey responses aimed to address the perceived gaps, and to provide a more integrated approach in medical device assessments in combining engineering with other perspectives, such as clinical, economic, patient, human factors, ethical, and environmental

COVIDIAGNOSTIX : health technology assessment of serological tests for SARS-CoV-2 infection.

Abstract Objective In vitro diagnostic tests for SARS-COV-2, also known as serological tests, have rapidly spread. However, to date, mostly single-center technical and diagnostic performance's assessments have been carried out without an intralaboratory validation process and a health technology assessment (HTA) systematic approach. Therefore, the rapid HTA for evaluating antibody tests for SARS-COV-2 was applied. Methods The use of rapid HTA is an opportunity to test innovative technology. Unlike traditional HTA (which evaluates the benefits of new technologies after being tested in clinical trials or have been applied in practice for some time), the rapid HTA is performed during the early stages of developing new technology. A multidisciplinary team conducted the rapid HTA following the HTA Core Model® (version 3.0) developed by the European Network for Health Technology Assessment. Results The three methodological and analytical steps used in the HTA applied to the evaluation of antibody tests for SARS-COV-2 are reported: the selection of the tests to be evaluated; the research and collection of information to support the adoption and appropriateness of the technology; and the preparation of the final reports and their dissemination. Finally, the rapid HTA of serological tests for SARS-CoV-2 is summarized in a report that allows its dissemination and communication. Conclusions The rapid-HTA evaluation method, in addition to highlighting the characteristics that differentiate the tests from each other, guarantees a timely and appropriate evaluation, becoming a tool to create a direct link between science and health management

Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population : study protocol

Purpose Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic management through frequent blood glucose monitoring with finger prick or Continuous Glucose Monitoring (CGM) systems. However, several non-invasive techniques have been proposed aiming at exploiting changes in physiological parameters based on glucose levels. The overall objective of this study is to validate an artificial intelligence (AI) based algorithm to detect glycaemic events using ECG signals collected through non-invasive device. Methods This study will enrol T1D paediatric participants who already use CGM. Participants will wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glycaemic measurements driven through ECG variables are the main outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep learning (DL) AI algorithm, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices. Results Data collection is expected to be completed approximately by June 2023. It is expected that sufficient data will be collected to develop and validate the AI algorithm. Conclusion This is a validation study that will perform additional tests on a larger diabetes sample population to validate the previous pilot results that were based on four healthy adults, providing evidence on the reliability of the AI algorithm in detecting glycaemic events in paediatric diabetic patients in free-living conditions

Health technology assessment of intensive care ventilators for pediatric patients

This paper is aimed at addressing all the critical aspects linked to the implementation of intensive care ventilators in a pediatric setting, highlighting the most relevant technical features and describing the methodology to conduct health technology assessment (HTA) for supporting the decision-making process. Four ventilator models were included in the assessment process. A decision-making support tool (DoHTA method) was applied. Twenty-eight Key Performance Indicators (KPIs) were identified, defining the safety, clinical effectiveness, organizational, technical, and economic aspects. The Performance scores of each ventilator have been measured with respect to KPIs integrated with the total cost of ownership analysis, leading to a final rank of the four possible technological solutions. The final technologies’ performance scores reflected a deliver valued, contextualized, and shared outputs, detecting the most performant technological solution for the specific hospital context. HTA results had informed and supported the pediatric hospital decision-making process. This study, critically identifying the pros and cons of innovative features of ventilators and the evaluation criteria and aspects to be taken into account during HTA, can be considered as a valuable proof of evidence as well as a reliable and transferable method for conducting decision-making processes in a hospital context

ELeGI as enabling architecture for e-learning 2.0

Web 2.0 is supposed to be the second generation of internet-based services – such as social networking sites, wikis, communication tools and folksonomies – that let people collaborate and share information online in a previously unavailable way. Currently, many leading enterprises have a strong interest in Web 2.0 and on the impact that it can have on traditional web-based applications such as e-learning, namely, e-learning 2.0. This paper analyses what the European Learning Grid Infrastructure (ELeGI) project could provide to e-learning 2.0, in terms of its vision of learning, processes, methodologies and technologies. Our preliminary investigations have raised up which results obtained in the ELeGI project can give support to e-learning 2.0 and improve some of its typical aspects and processes