A Smart RFID Device for Drugs Administration

In this work a device is described that can be used in hospitals to lower the risk of Adverse Drug Events (ADE) due to the incorrect link between patients and drugs. The device is a container that can be handled by the patient himself of by the caregivers. It is provided with an internal lock that prevents any openings when there is a mismatch between the patient and the drugs inside the container. It is based on passive RFID (Radio Frequency Identification) technology and is designed to give a visual alert if a wrong drug (tagged with RFID) is accidentally inserted, also preventing its administration by locking the top aperture. The internal RFID reader is designed to read both the patient?s wristband and the drugs inserted into the device (provided that they are tagged with RFID): the device will not open if some un-prescribed drug is found inside the container, alerting the patient/operator using the on-board display. Every single event is logged in an internal memory and can be transferred to a remote information system for analyses, statistics and risk management. The device is also provided with two status LEDs, a green one and a red one, and with a text display that can show information about the patient?s identity, and therapy. We are now working on the mechanical design and on the development of management software in order to link the device with CPOE (Computerized Physician Order Entry) systems and with automated drug storage systems

Clinical engineering online courses for Africa

In this paper is described one of the most interesting new projects of the Clinical Engineering Division of the International Federation for Medical and Biological Engineering (IFMBE/CED). This project is denominated “Electronic courses (E-courses) for developing countries”. The aim of this project is to train people living in developing countries on the activities involving medical equipment maintenance and management, using a virtual learning system for the lectures. The main outcome is the development of a strategy that can support training of Clinical Engineers as well as Biomedical Equipment Technicians. A system that can, at low price, develop training courses in several parts of the world, using distant and local expertise, not limited to language barriers. The E-Course project design began in late 2015. The initial focus has been put on three African countries: Gambia, Zambia and Mozambique. Today the project has designed two main streams for 2017 and 2018. The challenge is now keeping involving the tar-get countries to be sure to fit and fulfil their needs. A desirable goal is the creation of a sort of “experts bank”, involving many of the IFMBE structures such as divisions and committees, to have a dynamic list of professionals that can be consulted or asked to travel to specific destinations. This will be an important outcome that will facilitate the passage from this pilot experience to a project addressed to other developing countries all over the world

The inadequacy of regulatory frameworks in time of crisis and in low-resource settings : personal protective equipment and COVID-19

COVID-19 pandemic is plaguing the world and representing the most significant stress test for many national healthcare systems and services, since their foundation. The supply-chain disruption and the unprecedented request for intensive care unit (ICU) beds have created in Europe conditions typical of low-resources settings. This generated a remarkable race to find solutions for the prevention, treatment and management of this disease which is involving a large amount of people. Every day, new Do-It-Yourself (DIY) solutions regarding personal protective equipment and medical devices populate social media feeds. Many companies (e.g., automotive or textile) are converting their traditional production to manufacture the most needed equipment (e.g., respirators, face shields, ventilators etc.). In this chaotic scenario, policy makers, international and national standards bodies, along with the World Health Organization (WHO) and scientific societies are making a joint effort to increase global awareness and knowledge about the importance of respecting the relevant requirements to guarantee appropriate quality and safety for patients and healthcare workers. Nonetheless, ordinary procedures for testing and certification are currently questioned and empowered with fast-track pathways in order to speed-up the deployment of new solutions for COVID-19. This paper shares critical reflections on the current regulatory framework for the certification of personal protective equipment. We hope that these reflections may help readers in navigating the framework of regulations, norms and international standards relevant for key personal protective equipment, sharing a subset of tests that should be deemed essential even in a period of crisis

RFId Technologies for the Hospital. How to Choose the Right One and Plan the Right Solution?

Non

Sentiment Analysis for Performance Evaluation of Maintenance in Healthcare

This paper presents a framework which makes use of Sentiment Analysis techniques for retrieving Real World Data (RWD) starting from scheduled and corrective maintenance data. The scope of the analysis is to automatically extract features from maintenance work orders, in order to calculate Key Performance Indicators of maintenance operations on medical devices, for Health Technologies Assessment purposes. Data are extracted from Computerized Maintenance Management System reports of healthcare facilities

Radio Frequency Identification (RFID) in health care: where are we? A scoping review

Purpose (RFID) is a technology that uses radio waves for data collection and transfer, so data is captured efficiently, automatically and in real time without human intervention. This technology, alone or in addition to other technologies has been considered as a possible solution to reduce problems that endanger public health or to improve its management. This scoping review aims to provide readers with an up-to-date picture of the use of this technology in health care settings. Methods This scoping review examines the state of RFID technology in the healthcare area for the period 2017-2022, specifically addressing RFID versatility and investigating how this technology can contribute to radically change the management of public health. The guidelines of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) have been followed. Literature reviews or surveys were excluded. Only articles describing technologies implemented on a real environment or on prototypes were included. Results The search returned 366 results. After screening, based on title and abstract, 58 articles were considered suitable for this work. 11 articles were reviewed because they met the qualifying requirements. The study of the selected articles highlighted six matters that can be profitably impacted by this technology Conclusion The selected papers show that this technology can improve patient safety by reducing medical errors, that can occur within operating rooms. It can also be the solution to overcome the problem of the black market in counterfeiting drugs, or as a prevention tool. Further research is needed, especially on data management, security, and privacy, given the sensitive nature of medical information

Creation of a system for the coding of medical devices

Medical devices have different nomenclatures for their classification. Some of the most significant nomenclatures are the Universal Medical Device Nomenclature System (UMDNS) and the Global Medical Device Nomenclature (GMDN) by the Emergency Care Research Institute (ECRI). In Italy the main are CIVAB and “Classificazione Nazionale Dispositivi Medici” (National Classification for Medical Devices - CND). The aim of this study is to create a system to automatically decode several device models from CIVAB to UMDNS code. All medical devices are coded with a table which is based on their definitions presented in these nomenclatures. The coding is lastly applied to a list of models of medical devices, developed by different companies

A convolutional neural network approach to detect congestive heart failure

Congestive Heart Failure (CHF) is a severe pathophysiological condition associated with high prevalence, high mortality rates, and sustained healthcare costs, therefore demanding efficient methods for its detection. Despite recent research has provided methods focused on advanced signal processing and machine learning, the potential of applying Convolutional Neural Network (CNN) approaches to the automatic detection of CHF has been largely overlooked thus far. This study addresses this important gap by presenting a CNN model that accurately identifies CHF on the basis of one raw electrocardiogram (ECG) heartbeat only, also juxtaposing existing methods typically grounded on Heart Rate Variability. We trained and tested the model on publicly available ECG datasets, comprising a total of 490,505 heartbeats, to achieve 100% CHF detection accuracy. Importantly, the model also identifies those heartbeat sequences and ECG’s morphological characteristics which are class-discriminative and thus prominent for CHF detection. Overall, our contribution substantially advances the current methodology for detecting CHF and caters to clinical practitioners’ needs by providing an accurate and fully transparent tool to support decisions concerning CHF detection

Case study: IBM Watson Analytics cloud platform as Analytics-as-a-Service system for heart failure early detection

In the recent years the progress in technology and the increasing availability of fast connections have produced a migration of functionalities in Information Technologies services, from static servers to distributed technologies. This article describes the main tools available on the market to perform Analytics as a Service (AaaS) using a cloud platform. It is also described a use case of IBM Watson Analytics, a cloud system for data analytics, applied to the following research scope: detecting the presence or absence of Heart Failure disease using nothing more than the electrocardiographic signal, in particular through the analysis of Heart Rate Variability. The obtained results are comparable with those coming from the literature, in terms of accuracy and predictive power. Advantages and drawbacks of cloud versus static approaches are discussed in the last sections