3D-printed activated charcoal inlet filters for oxygen concentrators : a circular economy approach

As of May 2021, the current COVID-19 pandemic is still plaguing the world, challenging all the countries and their health systems, globally. In this context, conditions typical of low-resource settings surfaced also in high-resource ones (e.g., the lack of essential medical equipment, of resources etc.), while exacerbating in the already resource-scarce settings, because of COVID-19. This is the case of oxygen concentrators that are one of the first-line medical devices for treating COVID-19 patients. Since the beginning of 2020, their demand has been rapidly growing worldwide, aggravating the situation for low-resource settings, where the availability of devices providing oxygen-enriched air was already scarce. In fact, due to their delicacy, the lack of spare parts and of an appropriate health technology management system, oxygen concentrators can often be found broken or not working properly in these settings. The underlying problems have deep roots. The current regulatory frameworks and standards, which are set by high-income countries, are too stringent, and do not take into account the limited resources of poorer settings. Thus, they are often inapplicable in such settings. One of the main issues affecting the oxygen concentrators, is that related to the filters, which are designed to filter out dust, particles, bacteria, and to be used in medical locations complying with international standards (e.g., the air filtration level in a surgical theatre in Italy is at 99.97%). When used in low-resource settings, which do not comply with these standards and face several challenges (e.g., dust), these filters have a much-reduced lifespan. For these reasons, this paper aims to present the redesign of the inlet filter of an oxygen concentrator, which is used to prevent gross particles to enter the device. The redesign is based on a reverse engineering approach, and on the use of 3D-printing along with activated charcoal. After testing the filtration efficiency with a particle counter, the filter design has been refined through several iterations. The final prototype performs particularly well when filtering particles above 1 μm (with a filtration efficiency of 64.2%), and still has a satisfactory performance with any particle size over 0.3 μm (with a filtration efficiency of 38.8%). Following the United Nations Sustainable Development Goals, this project aims to empower local communities, and start a positive trend of self-sustained supply chain of simple spare parts for medical devices, leveraging on frugal engineering, 3D-printing, locally produced activated charcoal, and circular economy

Case studies on the use of sentiment analysis to assess the effectiveness and safety of health technologies : a scoping review

A health technology assessment (HTA) is commonly defined as a multidisciplinary approach used to evaluate medical, social, economic, and ethical issues related to the use of a health technology in a systematic, transparent, unbiased, robust manner. To help inform HTA recommendations, the surveillance of social media platforms can provide important insights to the clinical community and to decision makers on the effectiveness and safety of the use of health technologies on a patient. A scoping review of the published literature was performed to gain some insight on the accuracy and automation of sentiment analysis (SA) used to assess public opinion on the use of health technologies. A literature search of major databases was conducted. The main search concepts were SA, social media, and patient perspective. Among the 1,776 unique citations identified, 12 studies that described the use of SA methods to evaluate public opinion on or experiences with the use of health technologies as posted on social media platforms were included. The SA methods used were either lexicon-or machine learning-based. Two studies focused on medical devices, three examined HPV vaccination, and the remaining studies targeted drug therapies. Due to the limitations and inherent differences among SA tools, the outcomes of these applications should be considered exploratory. The results of our study can initiate discussions on how the automation of algorithms to interpret public opinion of health technologies should be further developed to optimize the use of data available on social media

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

The use of smart environments and robots for infection prevention control : a systematic literature review

Infection prevention and control (IPC) is essential to prevent nosocomial infections. The implementation of automation technologies can aid outbreak response. This manuscript aims at investigating the current use and role of robots and smart environments on IPC systems in nosocomial settings. The systematic literature review was performed following the PRISMA statement. Literature was searched for articles published in the period January 2016 to October 2022. Two authors determined the eligibility of the papers, with conflicting decisions being mitigated by a third. Relevant data was then extracted using an ad-hoc extraction table to facilitate the analysis and narrative synthesis. The quality of the included studies was appraised by two authors. The search strategy returned 1520 citations and 17 papers were included in this review. This review identified three main areas of interest: hand hygiene and personal protective equipment compliance, automatic infection cluster detection and environments cleaning (i.e., air quality control, sterilization). This review demonstrates that IPC practices within hospitals mostly do not rely on automation and robotic technology, and few advancements have been made in this field. Increasing the awareness of health care workers on these technologies, through training and involving them in the design process, is essential to accomplish the Health 4.0 transformation. Research priorities should also be considering how to implement similar or more contextualized alternatives for low-income countries

Scoping Meta-Review of Methods Used to Assess Artificial Intelligence-Based Medical Devices for Heart Failure

Artificial intelligence and machine learning (AI/ML) are playing increasingly important roles, permeating the field of medical devices (MDs). This rapid progress has not yet been matched by the Health Technology Assessment (HTA) process, which still needs to define a common methodology for assessing AI/ML-based MDs. To collect existing evidence from the literature about the methods used to assess AI-based MDs, with a specific focus on those used for the management of heart failure (HF), the International Federation of Medical and Biological Engineering (IFMBE) conducted a scoping meta-review. This manuscript presents the results of this search, which covered the period from January 1974 to October 2022. After careful independent screening, 21 reviews, mainly conducted in North America and Europe, were retained and included. Among the findings were that deep learning is the most commonly utilised method and that electronic health records and registries are among the most prevalent sources of data for AI/ML algorithms. Out of the 21 included reviews, 19 focused on risk prediction and/or the early diagnosis of HF. Furthermore, 10 reviews provided evidence of the impact on the incidence/progression of HF, and 13 on the length of stay. From an HTA perspective, the main areas requiring improvement are the quality assessment of studies on AI/ML (included in 11 out of 21 reviews) and their data sources, as well as the definition of the criteria used to assess the selection of the most appropriate AI/ML algorithm. © 2023 by the authors

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